From ff2befa70144127de597adeb807f770ac91bbd6e Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Fri, 7 Nov 2025 14:03:11 +0100 Subject: [PATCH 01/31] Update: call def and atk mechanisms from `config.yaml` files --- experiments/cn_privacy/config.yaml | 8 +++- experiments/cn_vs_noisybn/config.yaml | 8 +++- src/attacks.py | 39 ++++++++++++++++ src/defenses.py | 11 +++++ src/{membership_attack.py => mia.py} | 66 ++++++++------------------- src/run_exp.py | 2 +- test/cn_privacy/config.yaml | 8 +++- test/cn_vs_noisybn/config.yaml | 8 +++- 8 files changed, 97 insertions(+), 53 deletions(-) create mode 100644 src/attacks.py create mode 100644 src/defenses.py rename src/{membership_attack.py => mia.py} (85%) diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index af7e1ef..800176d 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -18,11 +18,17 @@ rpop_prop: 0.5 # Sample size of reference p pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop # MIA +def_mec: 'def_idm' # Defense mechanisms to consider +atk_mec: 'atk_mle' # Attack mechanisms to consider n_samples: 20 # Number of data samples -n_bns: 500 # Number of BNs to sample within the CN error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector + +# DEF-IDM ess_vec: '[1, 10, 50, 100, 1000]' # List of ESS +# ATK-MLE +n_bns: 500 # Number of BNs to sample within the CN + # Other seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 73a3cbe..2a5354a 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -19,10 +19,13 @@ rpop_prop: 0.5 # Sample size of reference p pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop # MIA +def_mec: 'def_idm' # Defense mechanisms to consider +atk_mec: 'atk_mle' # Attack mechanisms to consider n_samples: 30 # Number of data samples -n_bns: 50 # Number of BNs to sample within the CN tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector + +# DEF-IDM ess_dict: # Eps list to evaluate for each ess 1: 'np.arange(0.1, 10, 0.1)' 10: 'np.arange(0.1, 10, 0.1)' @@ -31,6 +34,9 @@ ess_dict: # Eps list to evaluate for e 40: 'np.arange(5e-6, 1e-2, 5e-6)' 50: 'np.arange(5e-7, 5e-4, 5e-7)' +# ATK-MLE +n_bns: 50 # Number of BNs to sample within the CN + # Inferences n_infer: 1000 # Number of inferences to perform diff --git a/src/attacks.py b/src/attacks.py new file mode 100644 index 0000000..6253e6a --- /dev/null +++ b/src/attacks.py @@ -0,0 +1,39 @@ +from src.utils import get_ll, sample_from_cn +import numpy as np +import pyagrum as gum + +# Get the maximum likelihood BN inside a CN +def atk_mle(cn, data, exp, config): + + # Sample from the CN ... + n_bns = config["n_bns"] + bns_sample = sample_from_cn(cn, exp, n_bns) + + # ... and take the MLE one + bn = mle_bn(bns_sample, data) + + return bn + +# Get the maximum likelihood BN within a set +def mle_bn(bns_sample, data): + """ + Given a list `bns_sample` of BNs, + find argmax_{BN in bns_sample} ll(BN | data), + where ll is the log-likelihood function. + """ + + mle_bn = None + mle = -np.inf + + for bn in bns_sample: + + # Estimate the likelihood of data + bn_ie = gum.LazyPropagation(bn) + llr_im = data.apply(lambda x: get_ll(x.to_dict(), bn_ie), axis=1).dropna() + llr = np.sum(llr_im) + + if llr > mle: + mle_bn = bn + mle = llr + + return mle_bn \ No newline at end of file diff --git a/src/defenses.py b/src/defenses.py new file mode 100644 index 0000000..0cf8b6a --- /dev/null +++ b/src/defenses.py @@ -0,0 +1,11 @@ +import pyagrum as gum +from src.utils import add_counts_to_bn + +# Estimate a CN from data by local IDM +def def_idm(bn, ess, data): + bn_counts = gum.BayesNet(bn) + add_counts_to_bn(bn_counts, data) + cn = gum.CredalNet(bn_counts) + cn.idmLearning(ess) + + return cn \ No newline at end of file diff --git a/src/membership_attack.py b/src/mia.py similarity index 85% rename from src/membership_attack.py rename to src/mia.py index 472c65c..52f1ad3 100644 --- a/src/membership_attack.py +++ b/src/mia.py @@ -8,8 +8,10 @@ from sklearn import metrics from src.config import get_base_path, set_global_seed -from src.utils import (add_counts_to_bn, get_ll, get_llr, noisy_bn, - safe_assert, sample_from_cn) +from src.utils import (get_llr, noisy_bn, + safe_assert) +from src.attacks import * +from src.defenses import * # Get the attack power related to a fixed error @@ -53,29 +55,7 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): return power_vec, auc -# Get the maximum likelihood BN -def get_maxll_bn(bns_sample, rpop): - """ - Given a list `bns_sample` of BNs, - find argmax_{BN in bns_sample} ll(BN | rpop), - where ll is the log-likelihood function. - """ - maxll_bn = None - maxll = -np.inf - - for bn in bns_sample: - - # Estimate the likelihood of rpop - bn_ie = gum.LazyPropagation(bn) - llr_im = rpop.apply(lambda x: get_ll(x.to_dict(), bn_ie), axis=1).dropna() - llr = np.sum(llr_im) - - if llr > maxll: - maxll_bn = bn - maxll = llr - - return maxll_bn # Find eps s.t. |AUC(eps) - AUC(CN)| < tol @@ -91,9 +71,10 @@ def get_eps(exp, ess, config): eps_vec = eval(config["ess_dict"][ess]) results_path = base_path / config["results_path"] n_samples = config["n_samples"] - n_bns = config["n_bns"] error = eval(config["error"]) tol = config["tol"] + def_mec = eval(config["def_mec"]) + atk_mec = eval(config["atk_mec"]) # Read data gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv') @@ -130,11 +111,8 @@ def get_eps(exp, ess, config): learner.useSmoothingPrior(1e-5) bn_theta_hat_vec.append(learner.learnParameters(bn.dag())) - # ... and estimate CN from pool (by local IDM) - bn_counts = gum.BayesNet(bn) - add_counts_to_bn(bn_counts, pool) - cn = gum.CredalNet(bn_counts) - cn.idmLearning(ess) + # ... and run Defense mechanism: estimate the CN + cn = def_mec(bn, ess, pool) cn_vec.append(cn) # Debug @@ -156,12 +134,9 @@ def get_eps(exp, ess, config): cn = cn_vec[sample] bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample]) - # Extract random subset within simplex - bns_sample = sample_from_cn(cn, exp, n_bns) - - # Get the maximum likelihood BN - best_bn = get_maxll_bn(bns_sample, rpop) - bn_ie = gum.LazyPropagation(best_bn) + # Attack mechanism: extract a BN from the CN + ext_bn = atk_mec(cn, rpop, exp, config) + bn_ie = gum.LazyPropagation(ext_bn) # MIA try: @@ -246,8 +221,9 @@ def attack_cn_bn(exp, ess, config): # Init hyperp. results_path = base_path / config["results_path"] n_samples = config["n_samples"] - n_bns = config["n_bns"] error = eval(config["error"]) + def_mec = eval(config["def_mec"]) + atk_mec = eval(config["atk_mec"]) # Read data gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv') @@ -284,11 +260,8 @@ def attack_cn_bn(exp, ess, config): learner.useSmoothingPrior(1e-5) bn_theta_hat_vec.append(learner.learnParameters(bn.dag())) - # ... and estimate CN from pool (by local IDM) - bn_counts = gum.BayesNet(bn) - add_counts_to_bn(bn_counts, pool) - cn = gum.CredalNet(bn_counts) - cn.idmLearning(ess) + # ... and run Defense mechanism: estimate the CN from BN + cn = def_mec(bn, ess, pool) cn_vec.append(cn) # Debug @@ -321,12 +294,9 @@ def attack_cn_bn(exp, ess, config): cn = cn_vec[sample] bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample]) - # Extract random subset within simplex - bns_sample = sample_from_cn(cn, exp, n_bns) - - # Get the maximum likelihood BN - best_bn = get_maxll_bn(bns_sample, rpop) - bn_ie = gum.LazyPropagation(best_bn) + # Attack mechanism: extract a BN from the CN + ext_bn = atk_mec(cn, rpop, exp, config) + bn_ie = gum.LazyPropagation(ext_bn) # MIA try: diff --git a/src/run_exp.py b/src/run_exp.py index ec51f00..1fcb6e5 100644 --- a/src/run_exp.py +++ b/src/run_exp.py @@ -6,7 +6,7 @@ from src.config import get_base_path from src.inference import run_inferences -from src.membership_attack import attack_cn_bn, get_eps +from src.mia import attack_cn_bn, get_eps def run_cn_vs_noisybn(config): diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index d05ae95..3035334 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -18,11 +18,17 @@ rpop_prop: 0.5 # Sample size of reference p pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop # MIA +def_mec: 'def_idm' # Defense mechanisms to consider +atk_mec: 'atk_mle' # Attack mechanisms to consider n_samples: 5 # Number of data samples -n_bns: 5 # Number of BNs to sample within the CN error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector + +# DEF-IDM ess_vec: '[1, 100]' # List of ESS +# ATK-MLE +n_bns: 5 # Number of BNs to sample within the CN + # Other seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index 0876424..e4eb9f9 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -19,14 +19,20 @@ rpop_prop: 0.5 # Sample size of reference p pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop # MIA +def_mec: 'def_idm' # Defense mechanisms to consider +atk_mec: 'atk_mle' # Attack mechanisms to consider n_samples: 5 # Number of data samples -n_bns: 5 # Number of BNs to sample within the CN tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector + +# DEF-IDM ess_dict: # Eps list to evaluate for each ess 1: 'np.arange(0.1, 10, 0.5)' 50: 'np.arange(5e-7, 5e-4, 1e-6)' +# ATK-MLE +n_bns: 5 # Number of BNs to sample within the CN + # Inferences n_infer: 5 # Number of inferences to perform From 9fe0c15082b0980c44801b1ec69e68adf69f7f0a Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Mon, 10 Nov 2025 12:39:33 +0100 Subject: [PATCH 02/31] Change: data generation and running of exps are now independend and modular --- README.md | 50 +++++++++++-------- .../config.yaml => configs/cn_privacy.yaml | 0 .../config.yaml => configs/cn_vs_noisybn.yaml | 0 .../tests/cn_privacy.yaml | 0 .../tests/cn_vs_noisybn.yaml | 0 experiments/__init__.py | 0 experiments/cn_privacy/Plot_results.ipynb | 37 +++++++++++--- experiments/cn_privacy/__init__.py | 0 experiments/cn_privacy/data.py | 15 ++++++ experiments/cn_privacy/exp.py | 39 +++++++++++++++ experiments/cn_privacy/main.py | 14 ------ experiments/cn_vs_noisybn/Plot_results.ipynb | 27 +++++++--- experiments/cn_vs_noisybn/__init__.py | 0 experiments/cn_vs_noisybn/data.py | 15 ++++++ .../cn_vs_noisybn/exp.py | 31 +++--------- experiments/cn_vs_noisybn/main.py | 14 ------ src/attacks.py | 7 ++- src/config.py | 13 +++-- src/data.py | 30 ++++++++++- src/defenses.py | 4 +- src/mia.py | 18 ++----- test/cn_privacy/test_integration.py | 11 ++-- test/cn_vs_noisybn/test_integration.py | 11 ++-- test/conftest.py | 8 +++ 24 files changed, 221 insertions(+), 123 deletions(-) rename experiments/cn_privacy/config.yaml => configs/cn_privacy.yaml (100%) rename experiments/cn_vs_noisybn/config.yaml => configs/cn_vs_noisybn.yaml (100%) rename test/cn_privacy/config.yaml => configs/tests/cn_privacy.yaml (100%) rename test/cn_vs_noisybn/config.yaml => configs/tests/cn_vs_noisybn.yaml (100%) create mode 100644 experiments/__init__.py create mode 100644 experiments/cn_privacy/__init__.py create mode 100644 experiments/cn_privacy/data.py create mode 100644 experiments/cn_privacy/exp.py delete mode 100644 experiments/cn_privacy/main.py create mode 100644 experiments/cn_vs_noisybn/__init__.py create mode 100644 experiments/cn_vs_noisybn/data.py rename src/run_exp.py => experiments/cn_vs_noisybn/exp.py (57%) delete mode 100644 experiments/cn_vs_noisybn/main.py create mode 100644 test/conftest.py diff --git a/README.md b/README.md index 8437bd8..681356d 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ Code for paper ["Towards Privacy-Aware Bayesian Networks: A Credal Approach"](https://doi.org/10.3233/FAIA251419) presented at [ECAI 2025](https://ecai2025.org/). -## Set up Python environment +## Setting up Python environment Create and activate a Python virtual environment with: @@ -11,21 +11,19 @@ python3 -m venv venv source venv/bin/activate[.fish] # use `.fish` suffix if using fish shell ``` -Install all dependencies with: +Install dependencies with: ```bash pip install -r requirements.txt ``` -Upgrade all Python packages with: +Upgrade dependencies with: ```bash pip install --upgrade $(pip freeze | cut -d '=' -f 1) pip freeze > requirements.txt ``` -This updates the requirements file with the upgraded packages. - ## Experiments `` is the name of the experiment to run. It can be one of the following. @@ -34,47 +32,55 @@ This updates the requirements file with the upgraded packages. 2. `cn_vs_noisybn`: additional experiment, not reported in the paper. It compares two privacy techniques, namely the CN and a noisy version of BN. All models are naive Bayes with target variable T. First, the CN and noisy BN hyperparameters are fine-tuned so that they achieve the same privacy level; then, their accuracy is computed in terms of most probable explanation (MPE) on variable T. -## Run code - -### With Docker (recommended) +### Running code -1. Build the Docker image: +1. Generate ground-truth models and data: ```bash -docker build . -t bnp:2025 +python -m experiments..data ``` -2. Run the experiment: +*Notice:* this will delete any existing ground-truth model, data, and result. + +2. Run the experiment: ```bash -docker run [-d] [--rm] -v bnp:/workspace bnp:2025 python -m experiments..main +python -m experiments..exp ``` 3. Results available at: -`/var/lib/docker/volumes/bnp/_data/experiments//output/`. +`experiments//output/`. -### Without Docker +### Using Docker (recommended) #FIXME: check -1. Run the experiment: +1. Build the Docker image: ```bash -python -m experiments..main +docker build . -t bnp:2025 ``` -2. Results available at: +2. Run the Docker container: -`experiments//output/`. +```bash +docker run [-d] [--rm] -v bnp:/workspace bnp:2025 +``` + +where `` can be the data generation or the run of an experiment, or both (see above). + +3. Results available at: + +`/var/lib/docker/volumes/bnp/_data/experiments//output/`. -## Test code +## Testing code -Run tests with: +Run integration tests with: ```bash pytest [--cov=src] [--cov-report=term-missing] [--capture=no] ``` -Test results are available at: +Results are available at: `test//output/`. @@ -100,6 +106,6 @@ Analyze code by running: pylint $(git ls-files '*.py') ``` -## Plot results +## Plotting results Use the `Plot_results.ipynb` notebook available for each experiment. Plots will be saved at: `experiments//output/plots`. diff --git a/experiments/cn_privacy/config.yaml b/configs/cn_privacy.yaml similarity index 100% rename from experiments/cn_privacy/config.yaml rename to configs/cn_privacy.yaml diff --git a/experiments/cn_vs_noisybn/config.yaml b/configs/cn_vs_noisybn.yaml similarity index 100% rename from experiments/cn_vs_noisybn/config.yaml rename to configs/cn_vs_noisybn.yaml diff --git a/test/cn_privacy/config.yaml b/configs/tests/cn_privacy.yaml similarity index 100% rename from test/cn_privacy/config.yaml rename to configs/tests/cn_privacy.yaml diff --git a/test/cn_vs_noisybn/config.yaml b/configs/tests/cn_vs_noisybn.yaml similarity index 100% rename from test/cn_vs_noisybn/config.yaml rename to configs/tests/cn_vs_noisybn.yaml diff --git a/experiments/__init__.py b/experiments/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index 6492fc0..fb21897 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -17,18 +17,18 @@ "from pathlib import Path\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", - "from src.config import *" + "from src.config import * # noqa" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ce91ef75", "metadata": {}, "outputs": [], "source": [ "# Choose config file\n", - "config = get_config(\"config.yaml\")\n", + "config = load_config(\"cn_privacy\")\n", "\n", "# Get results path\n", "res_path = get_base_path(config) / config[\"results_path\"]\n", @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "52eff632", "metadata": {}, "outputs": [], @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "3bc7788b", "metadata": {}, "outputs": [], @@ -164,10 +164,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ade37b54", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mIndexError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 23\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m# Loop over subplots\u001b[39;00m\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(axes.flat):\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m \u001b[43mplot_bn_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mexps\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 24\u001b[39m plot_cn(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexps[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, ax, ess=ess[\u001b[32m0\u001b[39m], color=CN_color, \u001b[38;5;28mtype\u001b[39m=\u001b[33m\"\u001b[39m\u001b[33m-\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 25\u001b[39m \u001b[38;5;66;03m# plot_cn(f\"{exps[i]}\", ax, ess=ess[1], color=CN_color, type=\"--\")\u001b[39;00m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36mplot_bn_bound\u001b[39m\u001b[34m(exp, ax)\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mplot_bn_bound\u001b[39m(exp: \u001b[38;5;28mstr\u001b[39m, ax):\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Import results\u001b[39;00m\n\u001b[32m 5\u001b[39m results = os.listdir(res_path)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m r_path = \u001b[43m[\u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mexp\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m-ess1.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 7\u001b[39m result = pd.read_csv(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mres_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mr_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 8\u001b[39m error = result[\u001b[33m\"\u001b[39m\u001b[33merror\u001b[39m\u001b[33m\"\u001b[39m]\n", + "\u001b[31mIndexError\u001b[39m: list index out of range" + ] + }, + { + "data": { + "image/png": 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7uef3S5al0tI0mySMjEi/+6nquzxuLjoilHqmIc/MRJ4B8ou5h2Jh7qFYmHu5ceqkNH+eFFVvldCidw7rg+d+mfDn1hNH1bRsrnTfuhyOsPRUQ56RyDTxkGmA/GLuoViYeygW5l5u3bghvfEL6Xfu0YyivVRZJoJMUzlKumhPkrq7u3X33XfLMAzZ7Xb19PTo05/+dLGHFVchJszFixfN216vV93d3XHv12q1avv27Wpra1Nra6scDofZqj3fjh8/ntZ6b775pj7+8Y+by7W1tWX7Bl/OY0d5Y+6hWJh7KBbm3uxdv27o4vuStS79IrzaOVJtrVRTZVV7c+Kd5paFcsk05JlJ5BmgcJh7KBbmHoqFuZedS5cMnXxvsmAvnUP033jpf6Vcp/Z7/1/J/vuzH1wJq7Y8I5FpIsg0QOEw91AszD0UC3Nv9s6dNTRvnjQ3TrVWOlkmgkxTGUr+EdpsNvMspZGRETkcDj355JN6+eWXdfz4cV26dCnpV75Ft1LP9KysbALkihUrYpadTmfCdaN/1tbWlvF9AQAAoPKcOS3Vzk2/YG/RO69o2bFX8juoClfKmYY8AwAAgFI2Omro7bekxUvSK9hb9M4rWvTuq6lXHDwsvUHOSUcp5xmJTAMAAIDyce2aobNnpSVLZ/4s7SwTQaapCCXdaW/Tpk36xS9+EfM9wzDU2dmpzs7OlNvX1NRobGwsX8OTlHmoi27VvjydXv5J7s9utyddN/qsrZ6enozvCwAAAJXl+nVD585OdtlL1+3/8veyjkn60oN5G1clK/VMQ54BAABAqTIMQwG/ZExICxakt83t//L36d/BP/299C1yTjKlnmckMg0AAADKx+mT0rx58ZsqZJRlIsg0Za+ki/YCgYD8fr95Ga7oy3EZhlGsYcVoaGgwb0eHvXRkcxZXdIjMNFCGQqGquOYzAAAA4jtzerLleiZd9iJndhlvvCJ94vfyOLrKVOqZhjwDAACAUnXmjDQ8PHlZ3HRk3ZniPj7kSqTU84xEpgEAAEB5uHrF0PnzUl2cQ8iMs0wEmabslXTRXl1d3awCTE26n0bOwtq1a83b6bReDwQCkmLPsMqEw+HIajsAAABUtxs3DJ07J1ks6W8Tc2bXd/+eor0slHqmIc8AAACgFF2+bOj4sVnkl3TRmSKpUs8zEpkGAAAA5eHkSWnBbTnsshdBpilrJV2019fXV+whpBTd/tzv96dcPxIasw12VqtVNptNgUDADJeZbAsAAIDqdOa0NLdWmjMnvfWnn9lVM/gqZ2xlodQzDXkGAAAApebWLUNv/UpavFiqrU1vGzpT5Eep5xmJTAMAAIDSd/myoYsXpOUr4v/8xN/0SJJGjVt68+a7OvB8vd55fZEkqXauoWf+8V01/9ZdWlg7v1BDRoGk+ZEdknE6nZKkI0eOJF1vYGDAvN3a2pr1/UW2DQQCSc8ciw6MkTECAACg+ty4YejsWWnxkvS3iXtm1z/N4mwvlCzyDAAAAEqFYRg6FpAmJqQFC9LfbtadKVDWyDQAAAAoVYZh6L2gdNvC1OueG7ugM/5FZsGeJP3ho5dV98GxPI4QxUTRXg5s375d0mTgS3Zm1d69eyVNnvkVffZXtFAopNbWVrW1tSXcj8vlMs/I8nq9Cdfr6ekxb7e3tydcDwAAAJVttl32TJEuFKgo5BkAAACUinNnpQvvS8uWZbbdib/p0a/+r5M6svOkNtw8qQdDk19bP3pSxs9OSn2nEn99qyf1HaCkkWkAAABQqi5fkkbC0qJFyde7MXFT58eG9eoPP2h+b8HCCX1+y+U8jxDFRNFeDtjtdrndbkmJz84KBALq6OiQ1WrVwYMHE+5rw4YN8ng86ujoSBgKrVar9uzZI0lqa2uLeyZXKBTSrl27JE22sLfZbJk8JAAAAFSIm7nqshdBF4qKQ54BAABAKbh6ZbLLnsWa/T56X5SuX59a3vY3Uk1NzazHhtJGpgEAAEApMgxDwaC0KI0ue2fG39d7Q8t08p3bzO89tPmSllom8jhCFFtZFe29/vrr2r59ux566CF97GMf0+233z5jna9+9avavHmzXn755YKOrb29XW63W16vV83NzTFnc/X09KixsVE2m00HDx40z8CKJzrcJTsjzOl0qru7W4FAQI2NjebZXIFAQD09PVq1apVCoZC6u7vlcDhm/fgAAABQns6ckWpz0WUvgm57s1KqmYY8AwAAgGIaGzP09tvSwkWT+SUbFy5IL0c1PVv3oPSJ+ynYy6VSzTMSmQYAAAClJxSSrlyWFqYo2rs6cV3v3wrp8I+muuwttY6r2XklvwNE0c0t9gDS8frrr2vr1q0aGBiQNFmNKsU/Q+7ixYvq6enRL37xC7399tsFHWd7e7s2b96szs5ONTc3a3h4WJJks9m0fft280yvZDo7O80zwVK1S3c6nRoZGVFbW5taW1sVCARktVq1du1auVwubd++PWn4BAAAQGW7ecPQmTOSxZL+Nkm77EX8099L33ow+4FVoXLINOQZAAAAFINhTHbYGx3NLLtM95MfSmNjk7draqQnn87N+FAeeUYi0wAAAKB0TEwYCh6XFi1Ovp5hGDo9dl7H/2uF3j813/z+xifCum2hkd9BouhKvmjv4MGD+uxnPytpcrI6HA7Z7XZ1dHTEXf9v//Zv1dPTI7/fr5dfflmf+cxnCjlc2e12dXZ2Zr29w+GQ3+9Pe32r1Tqr+wMAAEDlOns2x132IiLd9u6jcC8d5ZRpyDMAAAAotPfPS+fPSctXZL+Pk+9Jr/zH1PJDn5Ma7qbLXi6UU56RyDQAAAAoDSPD0rVr0vLlyde7bFzT8I2rOvyTj5jfW/GhW1r/+at5HiFKQUlfHjccDqulpUWGYchqtcrn8+lnP/uZeZZTPHa7XTabTZLU3d1dqKECAAAAJeXmDUOnT0tLlqS/TVpd9iL+KYN1qxiZBgAAAEjs6lVDfr9ksU52x8vWD7qlXzd/09y50ta/zsnwqh55BgAAAMjc+Lih48dTfz5jGIZO3Tqrd/79AwpfnOq59sW/uqS58/I7RpSGki7a27Vrl0KhkGpqanTw4EHdf//9aW3ncDhkGIYCgUCeRwgAAACUprNnpdo5eeiyFxHptoekyDQAAABAfGNjht55S7rttslCu2y9/Zb0xutTy4+1SB/+MF32coE8AwAAAGTu4kVp9KY0f37y9ULjlzVy/aZe/eepdnwfWTWqBz59Lc8jRKko6aK9np4e1dTUyOFw6BOf+ETa2zU0NEiSjhw5kqeRAQAAAKXr5k1Dp89IS5amv01GXfYi6LaXEpkGAAAAiC94XLoxKi1cmP0+DEPq3ju1vGiR9Bd/Neuh4dfIMwAAAEBmxsYMnQim/nxm3JjQybGz+u++D+na5Vrz+49tDafdjAHlr6R/1ZGzsJqbm7PaPhQK5XA0AAAAQHk4d1aqrcljl70Iuu2lRKYBAAAAZjp/3tDZs5Jl2ez28/qA5H93annLn0sWK132coU8AwAAAGTmwvvS2C1pXorL246MhxW+NKEjP7Oa37t79Q19/JM38jtAlJSSLtqzWq2SpIsXL2a03WuvvRazPQAAAFAtbt40dOp0AbrsRdBtLykyDQAAABDr2jVD/ncli0WqmUV93fi41NM9tVy3XNr8p7MfH6aQZwAAAID0jY0ZOnEi9eczY8a4To2d0xv/8mGN3pwq23K6wrPKSCg/JV20Z7PZZBiGvF5vRtt5vV7V1NRo7dq1eRoZAAAAUJrOnZPmZNBlT5JO/E2PfvV/ndSRnSf16esn9WBo8mvrb/1Kb/7gsPpf6JPxs5NS36mZX9/qyd+DqQBkGgAAAGDK2Jihd96SFiyQ5s6d3b5e+XfpzOmp5a+4pIUL+YQrl8gzAAAAQPrOnZUmxlNnnQvjIxq5UKtf/NtU6/H7Hrymht8ZzfMIUWpKumhv06ZNkqSBgQH94z/+Y1rbbN++3Wy53tLSkq+hAQAAACXn5k1Dp09JSzPoshftZy9JN29OLf/xn1/JzcCqGJkGAAAAmHLihHTjhrRo0ez2Mzoq/fiHU8sf+aj0yBdmt0/MRJ4BAAAA0jM6aujUSWnpsuTr3TJu6czY+/K9+GFNjE+edFRTY+ixr4QLMEqUmpIu2mttbTXbp7tcrpShcM+ePWpvb1dNTY1sNpu+8pWvFGCUAAAAQGk4f37y8lKZdNmLuHZVOtg3tfzbjdd012/dyt3gqhSZBgAAAJh08YKhM6elZZbZ7+tgnzQyMrX81aekuXPpspdr5BkAAAAgPefOSROSamtTrDd2URdOLdCbry42v7fus1f14fqx/A4QJamki/YsFov27dsnwzAkTYbCT37yk/J4POY6wWBQ3/nOd/TAAw/or//6r83vd3d3F3y8AAAAQLGYZ3Fl2WXP2yddvz61/IU/v5ybgVU5Mg0AAAAgXb9u6J13JItl8kSj2bh6Vfpp79Tyb/229OnPzG6fiI88AwAAAKR286ahkyelZSk+n7lpjOr8+EX9148+JBmTwWjuPENf+ItLBRglSlGKKykXn8Ph0L59++RyuRQKheTz+eTz+VTz62Rvs9nMdSPBcd++ffrEJz5RjOECAAAARXHunKQsu+xdvy71vTS1/LH7ruvue0dzNrZqR6YBAABANRsfN/TO29L8edLcHHwi8dPeycK9iG1/I/PYGrlHngEAAACSO3tGqk3j85nTY+/r7DtL5B9cZH7vD79wWct/YzzhNj84fVgfvq1Oa61352q4KCEl3Wkvwul06tixY9q6dasMw0j4Zbfb5fP59NhjjxV7yAAAAEDBjI4aOp3GWVyJ/OvB2A+9vvBndNnLNTINAAAAqtV7JybzxqLFqddNZWRY8v5savmBT0prH6BgL9/IMwAAAEB8N24YOn1aWpLi85lrE9d1cSykV3/4QfN7ty2a0Oe/lPjzmOPXzuuF0/36+8B+bRvsVPDa+7kaNkpEyXfai7BYLOrs7NTf/u3fyuv1yufzaXh4WJL0wAMPyOFw6P777y/yKAEAAIDCO39uspN6Nl32bt6UXvqXqWXbvTf0m/fdzN3gYCLTAAAAoNoMXzR06pS0fHlu9veTH0m3bk0tP/k3udkvUiPPAAAAADOdOjnZUTydLnsnB6066b/N/N5Dmy9piWUi4TbfP3VIxq9v+0J+zZ9TNiVeSFNJ/0a/+c1vyul06q677jK/t2rVKm3dulVbt24t3sAAAACAEnHrlqFTJ6WlWXbZ+/m/SpejTuR69M8uiStL5Q6ZBgAAANXqxo3Jy+IuW6acZIwzp6X+Q1PLjs9Kv/VbqXdsGIbGjQnNnVM7+0FUGfIMAAAAkNi1a4bOnZPq6pKvd3niqobHLunwj37T/N7SunE1P3Yl4TZvXj6hgXDAXP7SR9frw7eluCOUnZK+PK7b7VZDQ4M+9rGPafv27Xr99deLPSQAAACgpJw7KxmSarP4/OnWqPQvP51arv/Nm/qdRrrs5RKZBgAAANVofNzQO+9Ic+dJ8+blZp8/6JaMX7eZmDtXav1qetu9d/2Czt4cyc0gqgx5BgAAAEjs5Elp/rzkJykZhqFTY+d0/D8/oPdPzze//8ifhbVgoZFwm66TPzeXl9Tepta7HsrZuFE6SrpoT5qcjIFAQB0dHWpsbNSKFSv01a9+VS+//HKxhwYAAAAU1a1bhk6fyr7LXv8hKRyaWn4kTpe9I6F3NWaMZz1GkGkAAABQfU6+J129LC1enJv9+d+VBnxTy1/4ovSRj6busnd17KbevXpGY0biS04hOfIMAAAAMNOVK4YunJcWL0m+XnjiskI3b+iVn6wwv3f7h26p6Y+uJtzmP0Pv6J2rZ8zlr9Q3yzovR+EKJaWki/b+4R/+QQ6HQ4ZhmF8jIyPyeDxqbm5WbW2tHn/8cf3whz/UpUuXij1cAAAAoKDOn5MmjOy67I2NST/tnVr+aMNNrfnUjZh1fnXllL7+7g/1f/7q+/rB6cO6NUHxXqbINAAAAKg2w8OGTr4nLbPkZn+GIXXvnVpeuFD6y6+ks52hd6+e1gQFe1kjzwAAAADxvXdCWnBb8i57E8aETo6d0zs//w1dGp5rfv+LXw5r7tz424wbE/r+qUPm8u3zl+nP7vx0roaNElPSRXsul0s/+9nPNDExoe7ubjmdTlmt1piA2N3drZaWFtXV1emBBx7QN7/5TR0/frzYQwcAAADy6tatyQ/Csu2y98q/S8PDU8uPPHF5Rrjcd+rfJUkXRy+r/d0f6up4bFEfUiPTAAAAoJrcvGHo3belZcukOTn69GFoUHr7ranlP90iLV+eusve+ZshhW5d1eLa23IzkCpEngEAAABmunTJ0Mhw6s7iI+NhXbk2rld/Wmd+76MNo1r7h9cTbvPyhSGdvjH14c3Tq/5IC2vnJ1wf5a2ki/aiPfbYY9q3b5+Gh4fl8/nkdrtls9liwuHAwIDa2trU0NCgj33sY/q7v/u7Yg8bAAAAyIvz5yRD2XXZGx+X/jmqy96H60f1id+LDYlHL5/U4OWgufzEnX9I+/VZItMAAACgkk1MGHr3HWlOrTQvR58pTUxIPfumli0W6U//t9TbjU7ckv/aWVnmkmFyhTwDAAAATHb0fi8oLVyUfL1xY1wnx87rl30f1PUrUx/kOF2hhCc43Ry/pe7T/2Eu1y/8gP7kw+tyMWyUqLIp2ot2//336+tf/7reffddjYyMqLOzU06nUxaLxQyHfr9f7e3txR4qAAAAkHOz7bL36mHp/fNTyxu3XJoREvdFBcMltbfpz+/8THZ3hrjINAAAAKg0p05J4UvSkiW52+erh6WT700t/+VXpMWLU3fZO371fRmq0bw5WZzlhJTIMwAAAKhWl8KTuWfhwuTrXRgP6fIl6bU+i/m937zvhn6n8WbCbX563qfhW1fM5f9Pwxc0l0xT0cqyaC+axWLR1q1bzTO8Ojs7ZbPZij0sAAAAIG/On5cMI7suexMTUu/+qeXf+Mgtrf2D2C57/335PQ1Fddn7szv/UJZ5KU4bQ9bINAAAACh34ZChE0HJas3dPm/dkn70g6nlD31Y+uJjqbcL3bqqMzeGZaldqFsT47o6diN3g8IM5BkAAABUC8MwdPy4tCjFxyW3jFs6M3Zer//0w7p1c6osy+kKqybBOUiXx67rR2f/01z++NKVav7AfTkYNUrZ3GIPYLaOHz+unp4e7d27VwMDA8UeDgAAAJBXY2OTXfaWZNll77X/ks6dnVr+/JZLmn6iFl32CotMAwAAgHJ286aht9+e7ASe6DJP2fjXg9LFC1PLf/2kNG9e8i5748aE3rlyWkvm3qaamhodODegH5w5rD+/8zP66qqHVVtT9n0MSg55BgAAANViZES6ekVaviL5eufHRhS+ME+v/3zqg5z7f/+aVv32aMJtfnzmP3VtfKoL3zN3f1E1iSr8UDHKsmjv5ZdfVnd3t/bt26dQKCRpsqI1wm63a/PmzXI6nUUaIQAAAJAf589LxoQ0N4sj+YkJqfcnU8srPnRLv7vhWsw6b14+oV9ePmEu//mdn9YyuuzlHJkGAAAAlWBiwpD/3cnb8+fnbr/XrsV2CG+4W3J8NvV2p65f1I3xUS2fv1ShW1fVfeY/dG18VLuP/1THr5/TN+/9y9wNsoqRZwAAAFBtJiYMnTguLV6SfL2bxqjOjV/Qaz9ZqYnxyaK7mhpDX/xyOOE2F0Yv6afnfeby7y2/R5+s+1guho0SVxZFe5cuXZLX69XevXvV09Njfj86BDocDrW0tGjTpk2yWCzxdgMAAACUtbExQ++dyL7L3i980qlTU8uf/9LlGZfY3XtqWpe9lXTZywUyDQAAACrR6VNSOCTVLc/tfg/8VLpyZWp5299Ic+Yk7zJxdeymgtfOyzJvsSTp/z11SNfGpzpZbLrj93I7yCpCngEAAEC1G74oXb+eOvucHbugi+8t1H//51R134MPX9WHV44l3Gbf6f/QLWNcklQj6f9o+EIuhowyUNJFe9/85jdjWqpHB0Cr1apNmzappaVFGzZsKNYQAQAAgII5f36yW142XfYMQ3oxqlNF3QfGtO6zV2PW+eWloP77ynvm8l+u3KClcxdmO1yITAMAAIDKdSlsKBiU6upyu99wSPrZganl++3S734q+TaGYShw9Yzmz5mn2po58l89q5cvDJk/b/7Affpk3W/mdqBVgDwDAAAASOPjk9lnSYoue9cnbujC+Ij+60c283tz503o0T+/lHCb965f0L9d+KW5/EcfXKvfXvrRWY8Z5aGki/bcbrdqampkGIZsNpvsdruam5vlcDi0atWqYg8PAAAAKJixMUMn35OWZtll743XpRPBqeXP/eklzZ03tWwYhvaenuqyt3TuQv3ZnX+Y3Z3BRKYBAABAJbp509Dbb01+aDVnTm73vf/H0uhUgzxt+xuppiZ5l733b17S8K2run3+UhmGof/7xEFFysvmz5mrtrsfy+0gqwR5BgAAAJAuXpjMKIsXJ1/v9Nj7Ov/2Uvl/OdUM4dNfvKLlHxhPuM33Tx3SxK/Ty7yaWv0P2yM5GTPKQ47jdH7U1dWpoaFBn/zkJ/XAAw8QBgEAAFB13n9fGh+fRZe9n0wtL6sb0+9/blqXvcsndPTKSXP5L+/8jJbQZS9nyDQAAACoFBMThgJ+yZC0YEFu933urHTo51PLf/gZ6XfuTV6wNzpxS+9eOy3Lr/NL//B/662rp8yf/9VKhz6yMMfX760y5BkAAABUq7ExQyfS6LJ3ZeKaRsYv6T9++EHzewsXT+jzf3o54Ta/unxSr4XeNZc33/H7+sjCFbMeM8pHSRftbd26VRaLRSMjI+rr61NbW5saGxtVW1urhx56SN/5znd0/PjxYg8TAAAAyKuxMUPvnci+y96bv5SOBaaWH/7Ty5o3f2p5epe9ZXMX6gm67OUEmQYAAACV5sxpKTSSfT5J5oc9kycrSZMd/P76ydTbHL/6viYkzZszV9fHR9V1cqrq74MLLHLVfzb3A60S5BkAAABUu/ffl8bGpHnzEq9jGIZOjZ3TyTfqdDowdWbTw49f0uJlEwm36Tp1yFxeVLtAX131cM7GjfJQ0kV7nZ2dGh4els/n0zPPPKNVq1bJMAwZhqG+vj61traqoaFBH/vYx7R9+3a9/PLLxR4yAAAAkHPvvy9N5KjL3hLLuNZ/PrbL3tDloH4V1WXvr1ZuoMtejpBpAAAAUEkuXTIUDEoWa+73ffyY9Np/TS0/8gVpZX3yLnvhW9d05uawrLWLJEk/PPOqhm9dMX/uvvtPtLB2fqLNkQJ5BgAAANXs1q1fd9lLccLSZeOqLt26qld+9AHze8vqxrThT64k3MYX9s/4XGb5/DycGYWSVtJFexH333+/2tvb9e6778rv96uzs1MOh8MMh36/Xx0dHWpubtaKFSu0efNm/ehHPyr2sAEAAIBZGxszdOq91KEwkbd+Jb3z9tTyZ1suacFthrlsGIb2nprqsmeZu0hbPvqHWY4WiZBpAAAAUO5GRw29/Za0ePFkF7xc69k3dXvBAunLruTrjxsTevvqKS2pvU01NTU6e2NEL557zfy53WLT537DnvuBViHyDAAAAKrRubOTjRGSNVSYMCZ04tZZBf7zA7pwZqod3yN/HvtZTLRxY0LfOznVZW/5vCX6izs35GzcKB9lUbQXbdWqVdq6dat+9rOfaWJiQn19fXK73frEJz4hwzA0MjKinp4eOZ1Ozc2mFQkAAABQQi5emGy9nu2hbXSXvUVLxvXpL8R22Xvj0nG9dfWUufzllQ4tnntbdneGtJBpAAAAUG4Mw9CxgGRMTBbU5dqbv5T++82p5U2PS7ffnrzL3unrF3VjbFS3/bqT3v9z8l81ZkxeW3eOavTsb25STU3yfSBz5BkAAABUg9FRQ6dOSktTNFQIjV/W1dFRvfKTFeb3PnDHLf3+564m3ObQxTf13o0L5vK2VZ/T4rl5CFooeWVXtDfdhg0b9PWvf10+n09+v18ul0sWi8U8wwsAAAAoV2Njht47kX2XvXffkY7+99Syw3lZty2K7bK37/RUlz3r3MX60kf/INvhIktkGgAAAJS6s2elixelpctyv++Jidgue0uXSk/8RfJtro3f1PFr52WZt1iS9Eb4uF4LvWv+/LEPr9M9Sz+a+8FiBvIMAAAAKtHZs5JqpNraxOuMG+M6OXZWb//bB3V5ZOqElT/5SjhhI4bRiTHtPf3v5vJHb1uhljt+P0ejRrkp+9OcXn75ZXV3d8vr9SoQCBR7OAAAAEDOXLwg3bqVfdHe/h8bkiY7S9y2aEIbvngl5uevXzqmt6+eNpe/XO/gbK4iINMAAACglF2+PNllz2LJz/5f+y8peHxq+S/+SlqyJHGHPMMw5L9yRvPmzFVtzRyNTYzr//feQfPnS2pv0/9oeDQ/g8UM5BkAAABUmps3JrvspcpAF8fDunrN0Ks/tZrfu/Pum7I3XU+4zUvnf6ELo5fN5f/R8KjmzUlSGYiKVnZFe8ePH1dPT4/6+vrk9XrN708/Y8vpdGrz5s2FHh4AAACQE2Njht57L/tOFscC0i+Hpj7o+swXL2vRkiRd9uYt1pc+uj7r8SJ9ZBoAAACUi1u3DL39lrRoUfIOE9kaG5N+1DO1/Bu/If1JS/JtLoxe0vCtq7p9/uTZTS+9/wudvHHR/PnTts+rbv6S3A8WksgzAAAAqHynT0/mnzlJrl16yxjTmbHzerPvQ7p+dSosOV3hhNtdHbupH5w5bC7fs+Sjevg37s/VsFGGyqJo7/XXX9fevXvV09MTc6ZWdAi0Wq3atGmTWlpatGHDhmIMEwAAAMiZ4YvSrVFpSZafNe3/yVSXvQW3Taj5sdgue7+4dEzvXD1jLn9lZbMW1dJlL1/INAAAACg3hmHo2DFpfExavDg/9/Hzf5POn59adn1VWrAgcZe90YkxvXP1jCxzF0qSwreuaW/UyUirFn1Qf/oRTkbKNfIMAAAAqsX164bOnpEs1uTrvT82rMuhOXqtb6rzwm/ff12/03gz4TY/OfufujJ+w1z+P+7+Y82pSVIZiIpX0kV7mzdvltfrVSgUMr8XHQJtNpt5ttb991N9CgAAgMowPm7oxInsL4t74oT0xi+mPuj6g0evaIllwlw2DEP7Tv27uVw3b4n+9KNNWY8XiZFpAAAAUK7On5PePyetuD0/+79xQ3rxx1PLd62SHvpc8m1OXDuvCRmaN2fyo40XTvXr2vjUh2LP/mYLl5bKIfIMAAAAqs3pk1Lt3ORd9m4aozo7fkG/+OlHdWt0asXHtoYTbjM8elm954+Yy79r/U09uPy3czJmlK+SLtrr7u5WTU1NTAi02+3avHmznE6nVq1aVcTRAQAAAPlx8YI0dis3XfbmzZ/QQ5sux/x8IBzQu9fOmstb6bKXN2QaAAAAlKOrVwz5/ZK1Ln/38bMD0qVLU8vbnpZqaxN32QvfuqaTNy7q9nmTnSwC187Je+EN8+efvn01H3rlGHkGAAAA1eTqVUPnzkt1KXLQ+bELuvT+Ar3RP9V5wd50VXf91q2E23SfeUWjE2Pm8jN3//Fsh4sKUNJFe9LkWVsOh0MtLS3atGmTLBZLsYcEAAAA5E2ky97iLAv2Tp2SfjF1spaaNl7RsrppXfaiLh+1fN4SPU6Xvbwi0wAAAKCcjI0ZevttaeFCqTZPTesuXZL+5adTy6vXSA/+fuL1x40JvX31lJbULjSLyP7vEwcVKSWbVzNX2z/2WH4GW+XIMwAAAKgWJ9+T5s+XahKfS6TrEzd1fnxEr/3kLk2MT644Z46hL375UsJtTt24qIPvD5rLD33gft27bGXOxo3yVdJFe93d3XrsMYI2AAAAqsfFi9KtWXTZe3H/hAxjsh373HmGPrc5tsueL+yXP6rLnqv+s1pYOz/r8SI5Mg0AAADKiWEYOh6QRkelfNZm9e6Xbt6YWn7qf5dqknwydub6sK6PjWrF/MlOFv8x8iv96spJ8+d/sfIzunNhnq7jW8XIMwAAAKgWV64YGr6Yutv42fH3NXJisY7+12Lzew8+fEUfunMs4Tb/76l/18SvTzmqrZmj/9HwaE7GjPKX5CrMxRcvDH7nO9/RQw89pBUrVqi2tlYrVqzQAw88oG9961u6dClx5SoAAABQ6sbHDZ0IZl+wd+6s9NqrUx90PfjwFVlvT9xlb8W8pdr8kSTtLDBrZBoAAACUk/fPS+fOScuW5fE+3pf+9eDU8u81SavXJC7YuzZ+U8eunZd13uSHYjfGR/Xd9/7N/Pnt85eptf6hPI22upFnAAAAUC1OBKUFC5J32bs6cU0Xx8M6/KMPmt+bN39CX/jzxMfB71w5o1dH3jKXnR9+UPWLPpCTMaP8lXTRXrSXX35ZH/vYx9Ta2iqv16uRkREZhqGRkRENDAzI7Xarrq5O//iP/1jsoQIAAABZGR6e7LI3b1522+9/cVyGMZkoa2sN/dGfxnbZOxJ+V4Fr58xlV/1ndRtd9gqGTAMAAIBSdvWqIb9fsliTf1A1Wz/+gTQ+Pnm7Zo705FOJ1zUMQ4Gr5zRvTq1qayY/zvjx2f/UxVtTWaft7i9q8dwF+RswJJFnAAAAULkuhQ2NhKRFixOvYxiGTo2d0/lfWXTszYXm9z/zJ5djmidM36br1L+Zy7fNma9tq/4oR6NGJSiLor09e/aoublZgUBAhmHIMIyYn0e+ZxiGXC6X/u7v/q5IIwUAAACyMz5uKHhcWpIkFCZz4X3pP1+ZOrz/1GevasUHx83l6V32bp+/VJs+8nvZDhcZItMAAACglI2NGXrnLem226S5c/N3PydOSK8enlr+3B9Jq2yJKwQvjF7SxdHLWjp38kOx8zfD2n/2NfPn9y27S5//4Nq8jReTyDMAAACoVIZhKHhCWrQw+XqXjWsKj13TKz+a6pK3cPGEPvf45YTbvH7pmN68/J65/Bd3flofWJDHtuYoO3mM37lx8OBBtba2qqamRoZhyOFwqKWlRWvXrpXValUgENDAwID27t2rgYEBGYah9vZ2NTQ06Mtf/nKxhw8AAACkZXhYujWa/aVx9784ponxycP7OXMMff5Lse3YXwu9q2PXzpvLrvqH6LJXIGQaAAAAlLrgcenGqGS15Pd+frBPitR7zZ8vbW1NvO7oxJjeuXpGlrlTn57908l/1agxJkmqkfR//tZm1eSzLSDIMwAAAKho4bB0OSwtX5F4nQljQidvndWpN5br9LGpLt+f+1JYi5caCbYx9L2Th8xl69xF+nK9I2fjRmUo+U57LS0t5u3u7m797Gc/09atW3X//fdr1apV2rBhg5555hkdOXJEX//61yVNVsK63e5iDRkAAADIyPi4oRPB7Av2hoelw/9eay4/8Jmr+sAdU132JqZ12fvA/GXadAdd9gqFTAMAAIBS9v77hs6elSx5bvjwq6PS0ODU8mMt0gc/lLjg7sS18zIMQ/PmTJ6c9MtLQb068rb58y9+eJ3uXXpn3saLSeQZAAAAVKqJCUPBY9LiFJ/NhMcv6+rYDf3Hj243v2dZPqYNX7yacJv/GD6q49enGil89a7PacncFO38UHVKumhvz549CoVCqqmp0T/8wz/oscceS7q+2+3WM888I0kKhUL61re+VYhhAgAAALMyMiyNjkrzsmx8t7/3lsbHJj/sqvn/s3ffcVJU6f74P9VherpnprsnkAQEZgAVESSou5hWASNm0A2GDQLmDa5yvbv3e/d3d++64K5u3gU33I0qYMCEChjICgxBEZic80znXFXn90cz1dNM7J4883m/Xr5efU6fqnrGW96dZ+qp50gCy+6Ob8f+ibMwLjlcPeVamPTGpOOlnmNOQ0RERERDmd8vUFwE2GxAfzasEwLY9FJsnJYO3PeNzte7I35UB1tgM1gAAIpQ8ZfKHdr36fpUfC/v5v4Kl05jPkNEREREI5nTAfh8gMnU+RpFqKiU61Gybyya62LPVW7+ugsppo677EVUBS9U79LGE0yZ+PKky/osbho5hnTR3qZNmwAAubm5WLlyZY+OWbt2Lex2OwDgxRdf7K/QiIiIiIj6hKoKlJcD6WnJHe90COz9KNZlb8GVfoyfLMfOf0aXvbEpNqyYuCjpeCkxzGmIiIiIaKiSZYHCgug2tQZD/17r0EGgtCQ2vuc+wGrruEpQESoKfDVI06dqW9++13gEFYEmbc3D065HdkpGv8ZMzGeIiIiIaOTSns10k1a0KE4EwjL2vpGpzY2dGMGl1/k7PWZb4xE0hF3a+Dt5NyFFx0YK1N6QLto7ePAgJEnCkiWJ7eu8cOFCCCFQUlLS/WIiIiIiokHU0gyEQ73osvd2BHIk9mv9mV32PnYUoCLQqI0fmHotk8MBxJyGiIiIiIaqigog4Acslv69jqIAL2+KjbNzgLu+3Pn62qADASUMsz6aJHnkAF6q3q19P9U8Fl+b9KV+ipbaYj5DRERERCNVSzMQDEZfYuqMLBTUyA049eF4eByxN51uv98Fvb7jYwJKCJtr92njGWkTsGzcwr4Km0aYIV2053Q6AQB5eXkJHZebmxt3PBERERHRUKSqApWV0a2hkuFyqdj9QSxRvPBSPyZOi8TOLwQ21ca67I0z2XDHWV9MOl5KHHMaIiIiIhqKmpsEamsAm73/r7VrJ1BfFxvfvwowpXbcZS+ghFHqq9e2xQWAF6t3w6sEtfEPZi6HUdfJEzLqU8xniIiIiGgkUhSBsrLud0BqUloQ8EvY/7ZNmzt7ZgjzLw90eszrdQfglmNd+L4//VbopCFdmkWDaEjfGa0t1IuLixM6rvXtrdbjiYiIiIiGIkcLEAx0/SZXV954Jww5HPuV/qZ73HHf73ecittCavWU69hlb4AxpyEiIiKioSYQECgsBGw2QOq4dq7PhELAlldj48lnAzfe1PFaIQSKfXUw6vTQn36oVeZvwLbGI9qaK7PPx2XZs/oxYmqL+QwRERERjURNjUAk3PUOSGERQa3chE/fHYegP/bS0PJVrk7zKGfEhzfqD2jjBbY8XJ7F/IU6N6SL9lpbqG/fvj2h47Zv3w5JkrBwIVtMEhEREdHQpKoCFRXJd9lzexTs3hErwJt9SQBnzzijy17NXm083mRnl71BwJyGiIiIiIYSRREoLABSjIDB0P363tr+HuByxsYPPQIYDB0/4WoOe9AcdiPDYAYQLeL7a+UOqBAAAKOkx1Mzlvd3yNQG8xkiIiIiGmlkWaCiHMiwdr2uXm6C12XAgR2xhefND+C8eaFOj3m5dh+Cauw5zRPTb4PU329K0bA2pIv2Vq1aBSD6VtYvfvGLHh3z4IMPap9XrFjRL3EREREREfWWowUI9KbL3rthhIOxt7tuvje+y94+x0lUBmNd9h6Yeh1SdAPwVI7iMKchIiIioqGksgLw+QBLN9tA9QWvF3j7rdj4vPOBK77U8dqwKqPQVwtrm21x9zlO4binUhvfO/kqTLGM6adoqSPMZ4iIiIhopGloAGSl65eYgmoIjUoLjrw5Pm63oztWujo9pi7owHttuoQvzpmDubapfRAxjWRDumhv+fLlmDZtGgDgySef7DYpfPDBB7FhwwZIkgS73Y77779/IMIkIiIiIkqIqgpUVgDpyXbZ88vYvT1W7Xfe/ACmnRvWxopQz+iyl4nbJnwh6XgpecxpiIiIiGioaGkWqK4GBmrH0rffAAL+2PiRR9Fpl4nKQCNUoWovGoWUCP5e9aH2fU5KBh6cel1/hksdYD5DRERERCNJJCJQVQFkZHS9rlZpgqc+FUd2xR7iLLjShykzI50e82LNbihCBQDoIOHxvFv6JGYa2YZ8q41NmzZh4cKFkCQJTz75JF588UUsWbIEF110EXJzc3Hw4EEUFxdjw4YNcDqdEEJAkiRs2rRpsEMnIiIiIuqQ0wH4A0BWVnLHv/leECF/LFm86cwuey0nURVs1sYPTbueXfYGEXMaIiIiIhpswWB0W1yrFRiI3Zmam4C2O6pe8kVg3oKOL+yO+FEVaEa2MfbkbEv9J2gKx/Kcx/NuQZohtd/ipc4xnyEiIiKikaK+DlBF1132fGoAzYoDH2+ZBqFGcxidXuD2b7k7PabEV4fdLSe08W0TvoBpaeP6LG4auYb8k7v58+dj48aNuPPOOwEA+fn5yM/Pb7dOCKF9/uMf/4irr756wGIkIiIiIuopVRWoKAfSktyOyhUIY/e7sYdVM+YEMeOCM7rs1ca67J2VmoVbx1+SdLzUe8xpiIiIiGgwKYpAYSFgMAJG48Bcc8urgHy6CYUkAQ8/2vE6Vago9NUiTW/SuvA1htx4re5jbc3sjLNx8/iL+ztk6gTzGSIiIiIaCUIhgaqq6ItMXamRG+Asz8DJg7GHOJdd78XYiXKnx/yreqf22aQz4tHcG3sdL40OQ3p73FbLly/HwYMHMW/ePAghOvwHAOx2O7Zt24aVK1cOcsRERERERB1r7bJnMiV3/NvbAwj6Yu/e3HRP/Ntde1tOojrYoo0fmno9jDp9chejPsOchoiIiIgGS00N4HUn/+JQoqqrgD27Y+Ol1wLTZ3TcZa826IBPCcKsjyVI/6j6AGE19kDsv865EzppWDzKGLGYzxARERHRcFdXC+gkQNdFauFRfXCpHux7daw2ZzSp7XY7auuYuxxH3WXa+O5JV2Kcyd4HEdNoMOQ77bWaP38+Dh06hB07dmDTpk04ePAgnE4n7HY7cnNzcdddd+GOO+4Y7DCJiIiIiDrV6y57wRB2v2vRxtPOC+HceSFtfGaXvYmpWexIMYQwpyEiIiKigeZ2CVSWA/bMgbvmy5uA1qZrBgOw+sGO1wWUMEr89bAbYgnScU8F9jpOaeNbxl+MOdap/Rgt9RTzGSIiIiIaroJBgZoawGbrfI0QAlWROjSezETp52ZtfvHtHtiz1U6P+VfVR9o4w2DGqinX9FncNPINm6K9VosXL8bixYsHOwwiIiIiooS5XNEue1lZyR2/9X0/Au7Y07ab7nFDatOwYk/LCdSwy96Qx5yGiIiIiAZCJCJQUACkpXfdTaIvFZwCjhyOjW+7A5hwVvsue0IIlPjqYJT00J/uoqcIFX+teF9bY9Gb8Hjerf0dMiWI+QwRERERDTc11YBB33Ve5FQ88KlB7Hp5kjZnSVdw/Zc9nR6zz3EKxf46bbx6yrWwGi2dric6E3vKExERERENACEEKsqS77LnDPmx5510bXz2jBBmXxzUxopQsakm1mVvUmo2u+wREREREY1SQgiUlQKqAphM3a/vm2sCmzfGxmYz8I37O17bHPagMexGhiHWwWJ741GUBRq08YNTr8MYk7W/wiUiIiIiolHA7xeoqwPSMzpfowgV1Uodqg5no648lkDd8DU3LOmiw2NkVcG/q3dq43EmG+6edGWfxU2jw5DrtHfkyBG0tLQgKysLF1544WCHQ0RERETUJ5xOwOdPrsueEALvfuSHz5mjzZ3ZZW9X8+eoDTm08cPTroeBXfYGBXMaIiIiIhpsjY1AQz2QndP92r5y5DBQVBgbf+1ewG5v32Uvoioo8tXCZoh1oPDKQbxYs1sbTzbn4N7JV/VrvNQx5jNERERENJLUVAFGI+Kep5zJobgQiESw+9Vsbc6eLePqW72dHrOj6RjqQk5t/Ni0ZTDpjX0RMo0iQ6bT3lNPPYXs7GwsWLAAS5cuxYIFC6DX6/HlL38Zbrd7sMMjIiIiIkpar7vsRXzYuzXWYeKsqWHMXRTfZW9zbazL3mRzDpaNuyjZcClJzGmIiIiIaCgIBASKiwGbfeCuqarAK5ti48xM4Ctf63htZaARslCQoov1FHipZjc8ckAb/2DGirjvqf8xnyEiIiKikcbnFWhoANLTO18jCwXVcgNK9o1DS32s6O7mb7hgTOn4mKASxqY2z2RyLeNwy4RL+ipsGkUGvWjP5XJhxowZWLduHRwOB4SItpYUQkAIgY0bNyIrKwsffPDBIEfaMyUlJVi9ejUWLFiAzMxMZGZmYsGCBdiwYcOAxrFhwwZkZmYO6DWJiIiIqGMuF+DzJbctlSpUvLvLB09zLDu86d74Lns7m4/HvdH18NQb2GVvAI2knIb5DBEREdHwpigCRYWAyQgYBrDmbe8eoLo6Nv7WSsBsbt/KwiMHUBlohs0Qe6OpItCIdxsOa+PLss7DlTnn92u8FDOS8hmAOQ0RERERxVRUAKbUrrvsNSkOhEIq9rxu1+bGTYpg0TX+To95q/4QnBGfNn487xbopUEvv6JhaNDvmgULFqC4uDhurjUpBABJkqCqKpYsWYLy8vKBDi8h69atQ15eHkpKSvD888/D4XDA4XDgqaeewpo1a7Tv+ltrUup0OuF0Ovv9ekRERETUOSEEysuAtC7e5OpKc9iD/W/btfG4yRHMvzzWgUIRKl6u3aeNzzbn4MZxC5KMlpIxUnIa5jNEREREw191NeD1AJYku3wnIxIGXns5Nj5rInDzbe3XqUJFgbcGFn0KdKefmgkh8NeK96Ei+vuzQdLjP2cuH4iw6bSRks8AzGmIiIiIKMbjEXC0dL0DUkREUCs34sQH4+F1xd56un2lC/pO+iK4I368VvexNp5rnYqrci7oq7BplBnUor1nnnlGS5CEEFi/fj2Ki4uhqiocDgc2btyIadOmad+vWLFiMMPt0oYNG7BmzRosX74c27Ztw/z587Xvli9fjkOHDqGlpQULFizo9yRtKP97IiIiIhptXC7An2SXPUWoeH+fH+7G2MHL7nZD1+a3+I/O6LL3yLQb2WVvAI2UnIb5DBEREdHw53YJVFUM7La4ALBjB9DSEhs/+DBgMLRvZVEbdMCnBGHRx/KbT5yF+NQTKwS7e9KVmGYZ16/xUsxIyWcA5jREREREFCOEQGU5kGruel293IygX4eP37Fpc1PPDWHepYFOj3mlbj8CalgbPzH9NkhdtfIj6sKgFe25XC6sWbMGkiQhLy8PxcXFWLlypZYA2mw2LZFavHgxAODQoUN49dVXByvkTrW+NQUAmzZt6nBNbm4uVq1aBafTiZUrV/ZbLOvWrUN+fn6/nZ+IiIiIek4IgYpywGxJ7vjGiBOfvJ2ljXMmRHDRVbGW7LKqYHPNXm08xTwGN7DL3oAZKTkN8xkiIiKi4S8cFigoiHb41g3gX/39PuCt12PjmecAVy1uvy6ohFHqr4e9zba4YVXG3ypjW65mGdPx8LTr+zNcamOk5DMAcxoiIiIiiudxAw4XYOni2UxIhNGgNOPTd8Yj6I8lUctXuTrdTrcx5MI7DYe18ZXZ52OBPa+vwqZRaNCK9jZs2KB9Xrt2rZYInslms2H9+vXa+Kc//Wm/x5aotWvXAgCWLFnS5brWpHHz5s398iZXfn4+nn76aS0eIiIiIhpcLld0a6rU1MSPjQgZuz8JwFHXtsueJ64l+0fNx9EQdmnjR6bdAL00qM20R5WRktMwnyEiIiIa3oQQKCsFVDW5Dt+9sfVtwOeLjR95DNDp4p9wCSFQ7K+DHrq4fOWNugNx+cz38m5BuqGbVhjUZ0ZKPgMwpyEiIiKiGCEEyssBSzepRa3cCL/DhIM7MrS5WQsDOGduqNNjXqzZDVkoAAAdJHwv75Y+iZlGr0F7ordt2zYAwPz583H77bd3uTY3NxcrV66EEAL5+flwu90DEWKPtSa3ubm5Xa5r+33bhLivrFixAps2bYLdbu/zcxMRERFRYoQQqKwALGndr+1IY8SBj9/O1sZZY2VcsiT2NCyiKni5dp82nmYZi+vZZW9AjZSchvkMERER0fDW2AA0NQJW68Be1+EAtr0bGy+8CFh4cfuWFC1hD5pCHliNsTYXzWEPXqnbr41nZUzGbRMu6dd4Kd5IyWcA5jREREREFON0RpspmLso2vOrATQrTuS/OQ5yJFY2dcdKV6fHlPkbsLP5uDa+afxFmJl+Vl+ETKPYoBXtHTx4EJIkdfvmU6s777xT+1xSUtJfYSWsbZvzvLzu2162JmsvvfRSn8axZs0aLFmypMf/PomIiIiof7l70WUvLCLYmx9Ac1Xs4Ou/6obBEFvzYfNncV0pHmaXvQE3EnIa5jNEREREw5vfL1BcDFhtA3/t118DwuHY+OHH2q+JqAoKfbXIMMQnRv+o+hAhNaKN/2vmndAxnxlQIyGfAZjTEBEREVGMqgqUl3XfTKFGboS3zoKju9O1uYuu8uHs6ZFOj/l39U6I05+NkgGP5S7rfcA06hm6X9I/nE4nJEnCRRdd1KP1bd+Aamlp6a+wEnbw4EHtc3dvcbWuyc/Pj0skeys/Px+bN29GcXFxn52TiIiIiJInhEBFBZCa5M5OdZEmHHxrjDa2Z8u49NquuuyNw3Vj5ycdLyVnJOQ0zGeIiIiIhi9FESgqBEwpiHvBZyDU1gK7PoqNr14CnHNu+y57lYFGRISCDF0sOTrpqcLulhPaeNm4hbjQ1vHWrNR/RkI+AzCnISIiIqIYRwvg9wNZWZ2v8ap+uFQPPt4yDUJEcxidXuDWb3beZe9zTyXyXbEXV7466QqcldrFRYh6aNCK9lr1JIkCgGnTYkm70+nsp2gS15skzOl09kmb9NaW60PF1KlTe7Qu3PY1TACKokBRlH6IqH+0jXU4xU3DH+89Giy892iwDMd7z+UCXE4gKxtQ1cSODYoQDhwNoaE89lDrmrvc0BlUKKfP9X7TMTSFY9sRPTzlekAVUDA8/v0MF2oP/483nHMa5jPtMZ8h6l+892iw8N6jwdKf915lRbS7tz0z8byjt17ZJEFVow+49HqBlasFzvzxPHIA5b5GZBrToZwOUBUq/lyxQ1uTqjPie9Nu5n+X/WA05DMAc5qOMKch6l+892iw8N6jwTJc7j1FAUpKAIul8/xICIHKSA1aSjNw8pBFm7/sBg+yx0e0ZzBnHvOPqg+1cZo+FSsnLxnS/y5Gip7mNMPZoBftDXeJJqdZbUp6W1paep0Qrl69GsuXL8f8+UOns0p5eXlSxx06dGhIvaGXiL179w52CDRK8d6jwcJ7jwbLcLj3hAAqKzKgKBJSKhJPKGr1zdjz8kJtnJoeRMbE3Th8OHouRah4CbEue+ORAfNJD3ad2tX74ClOaWnpYIfQ75jPtMd8hmjg8N6jwcJ7jwZLX957fp8BFRVWpKeHIZX12Wl7pK7OgkMHz9PGCy+uQWlZEUrbxKEKgVLRDAUCJin2GOKoqEYp6rXxteo5OPXJMZwaiMBHmdGQzwDMaTrCnIZo4PDeo8HCe48Gy1C+91zOFNTWpiEjo/Mtbr26AGoNTTj84uXanMEoY8qFe3D4cKjDYwpEIwpRq40XK3n4dH/fdW2mzo2GnIZFe73UmwSmt2+jbd++Hdu3b2fLdSIiIqIhJBAwwO83IiMj3P3iMwSlMCqq0uCsiT1EuPDKIhiMseK/Y6iBG0FtfJN0PnRS+22oiHqC+QwRERHR8CPLEmqq02A2RzDQqYAQwP49k7Sx0ajg6qXtC4ScIoAAZGRIJm0uKCL4CLHf/bJhwWJpRv8GTCMecxoiIiIiUhSgsdEMi1nudI2AQLPBBXfpWWiszNbmL7i8GJaMjgv2VKHiIxRpYytMzGGoT7Forw/1RRv1nnI6nVixYgV27NjR/eIBNmXKlB6tC4fDqK2NVSQvWLAA559/fn+F1ecURdEqyRctWgS9Xj/IEdFowXuPBgvvPRosw+neEwL4/DgwbixgNne/Pv5YgeJIBWrfHqfNpVkVfGW1FSbzPABARJXx/PFPgNMviuVZxuPRhXdBJ+n66kegNgby9/uhgPlMFPMZov7Fe48GC+89Gix9fe8JARQWAOlpgNXaFxEm5rNjQHVVLP/46j063HDDF+LWBJUwDjqLMdVohkGK/bx/r/oA/oZY14sfnf9VXJVzQf8HPUqNtnwGYE7TijkNUf/ivUeDhfceDZbhcO/V1gJmE2DP7HxNs+yACKfiwEuxojtLhoJ7HzPDnDavw2Peb/oUzRV+bfydGbdg8VmX9lnc1LXRkNOwaK+X2rZST/StrN7cYCtXrsRTTz01pFqutyorK+vRuuPHj2P27NnaWK/XD8n/B98Twzl2Gt5479Fg4b1Hg2Wo33tul4DXC7T5FbHnx6o+nCwAqgss2tzS5R5Y0iQA0fYZ25qOozni0b5/LPdGGA3G3oZNndDpRn4xJPOZ9pjPEA0c3ns0WHjv0WDpi3uvoV7A0QJkZXe/tq+pKvDy5tjYagPuvkeCXh//e3O5vwlGgxEmfSxXqQo0453Gw9r4i5nnYPHYuZDYNbzfjIZ8BmBO0xHmNEQDh/ceDRbeezRYhuK9J8sCNdXR/KSzX4EVoaBWbUL1kTGor4h1A7/xbjfSM2LPYNoKqRFsqt2jjc8252DFxEuh1w2tn38kGw05zaAX7TkcDrjd7oSOKSkp6XHSMXXq1MSDSkCiSV3bVu1ZyTzNBbB582aUlJRg06ZNSR1PRERERH1PCIGKisQ77AHRFuvVkTocfnuCNmdOV3D1rV5tHFZlvFK7TxvPSJuAJWPm9ipm6hvDOadhPkNEREQ0fPj9AsXF0YdRg+Hj/UBlRWz8zfuBtPT4h1stIQ8aQi6MSYm1ARRC4K+VO6AIFQCgl3T4wcwVLNgbIoZzPgMwpyEiIiIa7errAFUBDF1UPzUpToRkBbtei/3+Z8+RcdXN3k6P2Vqfj5ZI7PvH826BgQV71McGvWhvyZIlCa0XQmDNmjVYs2ZNt2slSYIsd75ndV/Iy8vTPrdN9noimbe4nE4nVq5ciUOHDiV8LBERERH1H48HcLmB7CT+5u9SPCgpBso/j3XZW3y7B+Y0oY13NB2LSxAfzV3GbXGHiOGc0zCfISIiIhoeFEWgsAAwmbp+GNVfIhHg1TZd9saNB269/Yw1qoICXy2shvg3mQ66inDUXaaNvzLxcuSlje/HaCkRwzmfAZjTEBEREY1m4bBAVSWQYe18TUREUCs3oHjPWDgaYt3Ab/2GC8aUjo/xykG8WrdfG5+fcTaWjrmwj6Imihn0oj0hRPeLTmt98y6RY/rbwoULtc89ab1eUlICAMjNzU3qeitWrMCqVauQlZXV6fXazrdNUkfDfs9EREREg6WqAjCnJn6cIlRUyfU4/PZEbc5kVrHk9vgue6/WxhLEmWlnYUnOnF7FS31nOOc0zGeIiIiIhoeqSsDvBzIzB+f6H34ANDXFxg88BKSkxHfKqwo0QxYKMnSxxCiiyvhb5Qfa2G5Mw6PTbuz3eKnnhnM+AzCnISIiIhrN6usBIQFd7djbIDsQDgF73owlU+PPDuOLS/2dHvNq3X74lJA2fnL6rewUTv1iUIv2Ek3shlIi2Gr+/Pna5+Li4m7XtyZrib691mr79u3Yvn071q1b16P1bd8yW7VqFdavX5/UdYmIiIioc263gNMFJLOzjkNxobpcj5Jjadrc1bd5kJYR+913e+PRuC57j+UuY4I4RAz3nIb5DBEREdHQ53IKVFcB9kEq2AsEgDe2xMa5ecDSa+PXeOUAKgINyDJmxM2/WX8QdSGnNv5u7k2wGi2goWG45zMAcxoiIiKi0SoUEqiqAmxddNkLiTDqlSaceH8CfK5YZd8d97vQ2U63zWEPttbna+NFmefi4syZfRU2UZxBK9obSYnJ8uXLsXnzZhw8eLDLdfn5sf+wV69endS1epIUr1ixAps3R/cqcDgcfHuLiIiIqJ9VVQCpSXTZk4WCarkeR96erM2lpKpYujxWoBdSI3Ft2M9Jn4ircy7oVbzUN0ZKTsN8hoiIiGjoCoej2+KmZwA63eDE8O5WwOuJjR9+FNDpYi8RqUJFka8OZp0JujYvF7WEPXi5dp82Pjd9Iu44a9GAxEzdGyn5DMCchoiIiGg0qq0B9FLXeVK93ISQz4D979i0uWnnhTB3UbDTYzbW7EZYyAAACcAT02/to4iJ2hu0or2VK1cO1qX73FNPPYXNmzcjPz8fJSUlnbZVf+mllwBE3/xq+/ZXW06nE2vWrIHdbsfatWv7LWYiIiIi6hsej4DTCWRlJ35sk+JAQ5URBYdjXfauvMmLDJuqjbc3HoUj4tPGj027kV32hoiRktMwnyEiIiIamoQQKC0BhABSUgYnBpcrWrTX6sJ5wBfOqLtrCLngjviQnRLf4uKf1TsRVCPa+L9m3gm9NEiVh9TOSMlnAOY0RERERKNNMChQWwvYbJ2vCahBNCoOHNs6CaFALA9ZvsqJzh6xVAWa8UHTZ9r4+rELcG7GpL4Km6gdZsh9YP78+XjyyScBdP52VklJCdatWwe73Y4dO3Z0eq7Fixdjw4YNWLduHdasWdMv8RIRERFR36msAFLNiR8XFhHUyo04snW8Nmcwqrj2zlgLi2iXvY+18Xnpk3AVu+xRH2M+Q0RERDQ0NdQDzU1ARhfbPfW3N7YAoVBs/PBjiHuJKKiEUeSrhc2QFndcgbcGO5uPa+Mbxi7AfHseiPoDcxoiIiKi0aW6CtDru+6yVyM3IuRIxcH3YwnV7IsDmDkn3Okx/67eCRXRzsoGSY/v5t3UZzETdYRFe31k7dq1ePLJJ7F9+3YsXboUJSUl2nebN2/GggULkJubix07dnTZCt3pdGqf256jO06nEyUlJdi+fTu2b9+uzW/YsAElJSVx5yUiIiKivuHxCDgdgMWS+LENcjNa6lJw4mDs4Mtu8MGWFeuy917DETjbdNl7NJdd9qh/MJ8hIiIiGlp8PoGSEsBmH7wYGuqBjz6Ija/4EnD+7Ph8pNTfAJ2kh0Gn1+ZUIfCXitjvdKk6I7eUon7HnIaIiIhodPD7BerrgYyMztf4VD+cqhsH3hgHRY7mMJIkcMf9rk6POeWtxifOQm1811mXYpI5p8/iJuoIi/b60Nq1a3Ho0CHk5uZi6dKlyMzMRGZmJp5++mk89dRTKC4u7rTleqv169cjNzcXubm5CbVez8zMRF5eHlasWAEAsNvtsNvtePrpp5GXl4fMzEysW7euVz8fEREREcWrrARSUxM/LqiG0KA04+jWcYCIJox6g8D1X2nTZU+JYEvdJ9p4VsZkfCl7dq9jJuoM8xkiIiKioUGWBYoKo7mGXt/9+v7yysuAokQ/63TAgw/Hf+8Ie9EQcsKqj0+KPmz+DEX+Om28aso1GJ+a2d/hEjGnISIiIhoFqqqAFCM63eJWCIEquR6+2nQc2xPrCH7R1X5Myot0esw/qz7Sxha9CQ9Nu75P4ybqiGGwAxhp5s+fj/Xr1yd9/JIlS1BcXJzwcUKIpK9JRERERInzeAScLUBWduLH1ipN8DSm4vjHsYRx0bVeZI1RtPF7jUfglGNd9h6btoxd9qjfMZ8hIiIiGnzVVYDfD2QOYp1beRnwyf7Y+MabgClTY/mIrCo45a1Bht4cl6f4lRD+XbVTG5+VmoVvnr1kIEImAsCchoiIiGgk83oFmhqAzKzO13iED17Vh31bciFamyboBW79Rudd9vJdJTjhrdLG35h8NbJSumjlR9RH2GmPiIiIiCgJVUl22fOrAbQoThzZOhZCjSaMOr3ADV+NddkLKmG8VvexNp6dcTauyJ7V65iJiIiIiGhoczoFqioBm21w49i8MfY5xQTcvzr++6pAMyJChklvjD+uZm/cy0f/MeOOdmuIiIiIiIiSUVkBmFI777KnChUVkTo4Sm04lW/R5i9f5sWYCUqHxyhCxb+qY132Mo3p+AZfPKIBwqI9IiIiIqIEeb0CjhbAktb92jPVyI0Itpjx6b50be6SJT7kjI8ljO82HoFL9mvjx3LZZY+IiIiIaKQLhQQKC4AMa3Q72sFy/LPoP63u/DIwZkwsH/HJQVQEGmE3xCdENcEWvN1wSBtfbJ+BJTlz+j1eIiIiIiIa+dzu6HOZtC6eyzgVD4JqCLtfGaPNmVJV3HSPu9NjdjV/jopAkzZ+eOr1SDOY+iRmou6waI+IiIiIKEFVldG3uRLlUX1wqm4cfmcMVCX60EuSBJZ9Lb7L3pa6T7TxBRlTcFnWeb2OmYiIiIiIhi4hBEpLop9TUgYvDlWN77KXng7cc1+b74WKQl8tUnUp0J3xYtH/Vb4PWagAAB0k/HDmnXz5iIiIiIiIek0IgYpywGzpfI0iFFTJ9Wg4kYWKU7EHOEtXeGDNVDs8JqzKeLFmtzaelJqNFRMv7bO4ibrDoj0iIiIiogS0dtnr6m2ujgghUC3XQ3am4ejuWJe9i67yY+xEWRu/03AY7rguezfyQRcRERER0QhXXwe0NAMZGYMbx8EDQHlZbPz1bwEZGbF8pCHkgjvia9d5It9ZjHxXiTb+8sTLMSN9Qn+HS0REREREo0BzE+B2A2ZzF2sUFyKqjJ0vZ2tzaVYF16zwdHrMuw2H0RSOdeH7Tt5NSNEZ+iRmop5g0R4RERERUQKqq4CUJDqjOxUPvGoAh9/LhiK36bJ3dywhDChhvF4f67I3xzoFl7LLHhERERHRiObzCZSWAjb74MYhy8Arm2PjMWOAO1bExiElgiJfLWxnbIsbURX8X+X72thmsOCx3GX9HS4REREREY0CTY0CBacAq7XzNREho1ZuQMWhHDRUxlqXL7vbDXOa6PAYnxzCy7X7tPE56RNx/dj5fRY3UU+waI+IiIiIqId8XoHmpsS77ClCRbVSD3jScHhnrMvevMv8mDClbZe9fLjlgDZ+LHcZu+wREREREY1gsixQWACkpgJ6/eDGsusjoKE+Nl75AGAyxfKRMn89dJIOBl18oG83HEJNyKGNv517E2zGLvatIiIiIiIi6oHGRoFTpwCrDTB00QCvSXEgIqvY9VqmNpc5VsaVN3k7Peb1+k/gVYLa+Inpt0InsYSKBhbvOCIiIiKiHqpKusueCyERxuFtWZDDsV/Bb7on1pY9oITwev0BbTzXOhWLMs/tVbxERERERDS0VVUCwUDX2zwNhGAQ2PJabDx1KnD9jbGxI+JFbcgJqz4+UEfEi801e7XxjLQJuHPipf0bLBERERERjXgNDdEOe7ZuCvbCIoJauRFFu8fB2WjU5m/9hgvGlI6PcYS9eLP+oDa+2D6Dux7RoGDRHhERERFRD/i8Ai3NyXTZU1AlN0DvT8PBD2Jd9uZ80Y9JeRFtvLXhMDxtuux9O/cmdtkjIiIiIhrBHA6BmurB3xYXALa9C7hdsfGDjwB6fTQfkVUFBZ4aWPXmdjnKv6t2IaCGtfF/zbwLenanICIiIiKiXmioj3Yk765gDwDq5SbIIT32vGnX5iZMCeMLi/2dHrOpdi9Cauz5zBPTb+1lxETJYfZMRERERNQD1dWAMQVItI6uSXFCgYJD2zMRCcV+/b75Xrf22a+E8HrdJ9p4ni0XX8ic2euYiYiIiIhoaAqFBIoKgYyMxHOMvubxAFvfio1nXwBcdkVsXBVoRljIMOmNcccV+WrxQfOn2viaMRfioszp/R0uERERERGNYPV10YI9u737gr2AGkKD0oLj28fB59Zr83esdEGn7/iYmmALtjce1cbXjLkQs61T+iByosSxaI+IiIiIqBs+n0BzU+Jd9iIighq5AYZgOg7uyNDmZy0MYMrMNl326vPhVYLa+Nu5y9hlj4iIiIhohBJCoKQ4+rmz7ZoG0puvR7fHbfXIt6HlIz45iIpAI+yG+GRIFQJ/qdihjU06I9bMuH1A4iUiIiIiopGptjb6cpM9E9B3UnTXVp3SCNlrwsfvWrW53FlBzPlCsNNjXqjeBRUCAKCXdPhu3s29jpsoWSzaIyIiIiLqgqIIlJQAKabEO2A0yC2QJCD/fStCgY677PnkEN6oP6CN59tycbF9Rq/jJiIiIiKioamuDnA4ol32BltTI/BBrPYOiy4D5syNJj5CCBT5apGqM0J3RjK0q+VzFPhqtPH9Zy/BWalZAxIzERERERGNPDU10Zebelqw51MDaFFcOLx1LMLB2POXFatdnT7LKfLVYp/jlDZePuGLmGoZ29vQiZLGoj0iIiIiok4IIVBeDng9iXfZC4kw6pVmGEIWHNgWexp3zoVB5J0f1sZvNxxilz0iIiIiolHC5xUoLQFstsGOJOrVVwBZjn6WdMBDj8S+awg54Yz4kWZIjTsmoITwz6oPtfF4kx3fmrJ0AKIlIiIiIqKRqKYmmidl9rBgTwiBarkeoRYLDn0Qe/5ywSV+TJ8d7vSYf1Z9pI1TdUY8PO3GXsdO1Bss2iMiIiIi6kR9HVBbDdjtiR9bJzdBJ+lw+AMrAr5YlnlTXJe9IN5s02VvoS0PF2fO7E3IREREREQ0RMkyUFgImM09exDV3yorgP17Y+Prrwdy86IvEIWUCIr8dbAZLO2Oe7l2PxwRnzZeM+N2mPVDYJ9fIiIiIiIadqqrYgV7uh5WMHmEH27Fh0/eGANFjuYwkiRwx0p3p8ccdZfhM0+FNr5v8tUYY7J2up5oILBoj4iIiIioA253dFtce2bi2+IG1CCaFAdSwhZ8/F4s6cs7P4hz5oa08dsNh+BTYuPHcm/qddxERERERDQ0VVYAwUC0aG8oeHkTIET0s9EIrHwg9l2Zvx4SJBh18dWFtUEH3qw/qI0X2vJw7Zh5AxEuERERERGNMFWVAmVl3RfsWQr3wlIYfeNIFSqqInXw12Xg072xLZIuXuzDxGmRDo9Xz+iyZzNYcP+UJX3yMxD1hmGwAyAiIiIiGmpCQYFTJ6Nb4ibTAaNGbkSKZMCRnRnwe2InuPmMLntvtHnYdZF9Bi7KnN6ruImIiIiIaGjyeoyorQVycgY7kqhTJ4FjR2PjO1YA48ZH31ZyRLyoCzmRbcxod9zfKt+HLBQAgA4SfnjOnZASfcuJiIiIiIhGNSEEqqqAivKeddjL2fosAKBixiK4FA8CIog9r06AENFcRG8QuO2bnXfZ29tyAmWBBm384NTrkG4YIm9T0ajGoj0iIiIiojZkWaCgINpdz2RK/Hiv6odTdSNdzcD+d2IPuaaeE8J5C2Jd9d6sPwh/my57385d1qu4iYiIiIhoaIpEJNTWpmHypMS7ePcHIYBNL8XGaWnAfd+IfpZVBUXeWmToze2K8Y64SnHQVayNV5x1Kc5JnzgQIRMRERER0QghhEBlJVBZDmRmdV+wZyncC0vRfgCAqWAPqs4eA2eJHYVHLNqaK2/yIHuc0uHxEVXBCzW7tfF4Uya+Muny3v8gRH2A2+MSEREREZ0mhEBZKeD3AenpyR1fJdcjVUrBkV3p8Lpi78jcdK9be0DnlYN4qyHWZe8S+0wssOf1NnwiIiIiIhpiVBWor0uDJAkYUwYnhrZbSQFA/iGgJFZ7h7vvBWz2aLJSE2xBUInApDfGnUNWFfy1coc2zjCY8e08vnhEREREREQ911qwV1XRs4I9INZlDwAytz6DsJCx8+Vsbc5kVrHsbk+nx29vOor6kFMbfyf3JqTojJ2uJxpI7LRHRERERHRaXR3QUB9NFpPhVr3wqT5kCCv2bbVq85PywrjgkqA2frP+APxKWBs/lntj0jETEREREdHQVV8HeL0pyMgId7+4n7TdSkpRgFc2xb7LygLu+mr0s08OodzfAJsxrd05tjbkozrYoo2/PW0ZMo1JvOlERERERESjkhACFeVAVWXPC/badtkDAHvxQRh3lKCycJY2d82dbmTY1Q6PDyghbK6JvcA0PW08lo1fmPwPQdTH2GmPiIiIiAiA2yVQWgzY7MltWaUKFZVyPSw6M47tSYO7peMuex45gLfqD2nffSFzJuazyx4RERER0Yjj8wqUlwNpaYNXsNf6kMtStB+Wwr3YswuorY19f/9qIDVVghACRb4amHRG6KX4xwauiA+bavdo4+lp43HXxMsG6kcgIiIiIqJhTgiB8jKguqrnBXtAfJe9VrO2/1X7nG5TcM0Kb6fHv1F/EC7Zr42/n3dbu3yHaDDxbiQiIiKiUS8YFDh5AkjPAPT65M7hVDwIiRD0ihF737Zp8xOmhHHhooA2frP+IAJq2y573FKKiIiIiGikkWWBwgLAnNrzB1L9oe1Drqy3nsWWV2PfTZoMLLs5+rkh5IQz4kOaIbXdOf5dvSuuU/gPZ94Jgy7JxImIiIiIiEYVIQTKyoCamsQK9s7sstdqtvox5hmi3fOW3eNCqll0eLwr4sPrdZ9o4/m2XFyRPavDtUSDhUV7RERERDSqybLAqZOAwQCkpCR3DkUoqJLrkK4z4/jHaXA2temyd49bS0I9cgBvt+mytyjzHMyz5fYmfCIiIiIiGoIqKoBgCEg1D14MZz7kSi/Zj6me2NZQDz4CGAwSwmoExf462Aztt8Ut9tXh/aZj2nhJzhxckjmzfwMnIiIiIqIRQVUFSkuB2hogMzOxXY466rLX6lupzyJrnIwrl/k6XbO5dh+CakQbPzH9NkjJbLNE1I9YtEdEREREo5YQAqUlQCAIWNo/n+qxZsUFGQp0woA9b1m1+bGTIlhwRazL3ht1B87osndT8hclIiIiIqIhqaVZoLYGsNm6X9ufOnrI9a3U6Ny55wFfuio6V+ZrhIAE4xnd84QQ+GvlDrT2rUjRGfAfM+7oz5CJiIiIiGiEUFWBslKgvjbxgr3Ouuy1mm/Yjwev3Q6DsePv60NObGs8oo2vzrkAF9qm9TwAogHCoj0iIiIiGrVqqoHGBsDei4dpESGjRq5Hus6MEwcsaKmPZYk3fs2N1ude7ogfbzfEuuxdmnUe5tqmJn9hIiIiIiIackJBgaJCwGpN7KFUX+vsIdd8w37MM+zFI98GJEmCM+JDbbAFNn37loC7W07gpLdaG39z8mJMNGf3a9xERERERDT8qWq0YUJ9HWBPsGAP6LrLXqsri37X6XcvVO+CLFQAgA4SHs+7JbEAiAYIi/aIiIiIaFRyOATKy6IJY280yi0QENAJPfa8Geuylz0+gksW+7XxG/UH4lqxP5Z7Y+8uTEREREREQ4qqChQXA5IOMHbS8WGgdPWQ67tjnsX8BRJkVUGhtwbphtR220QFlTD+UfWhNh6bYsPKqdf0V7hERERERDRCqKpASTHQ0JBcwV53XfZapX9+GJbP8tvNl/jrsbvlhDa+dcIXkJs2PrEgiAYIi/aIiIiIaNQJBAQKTgIZGYCum9+ILYV7YSnc2+F3IRFGndKEdJ0Fp/LNaKxJ0b678ase6E932XNF/NjaEEseL8+ahTnWqb39MYiIiIiIaAipqwNczmieMZi6e8g1PbAfOLoXNcEWBJUwUvUp7da8WvcxWiJebfzkjNtg0Zv6JV4iIiIiIhoZFEWguOh0wZ49ue7jWVt/0eO1Yzf+qd3cv6t2ap9TdAY2UKAhjUV7RERERDSqyLLAqZOAMSX6T3dytj7baZeKerkJOkkHCTrsfjO2x27mGBlfvManjV+v/4Rd9oiIiIiIRjCvV6CsFLDZBzuSnm0lpfzt5yj3N8BmTGv3XX3IidfrPtHG82y5uGHsgj6NkYiIiIiIRhZFiXbYa2oEsrKSK9gzFOxEetHHPV6fdjy+296n7nIccZdq43smfQnjTPbEAyEaICzaIyIiIqJRQ4ho0hgKARZL9+tbO1RYiva367YXUENoVBxIl8woPGpGfWWsAvD6r7hhOL0dliviwzsNh7XvrsiahdnWKX3y8xARERER0eCTZYHCAsBs7r6Td3/r6VZS+k8/RvaJ49BL7QP+W+UHiAgFACAB+OHMFe22zyUiIiIiImrV2mGvuQnIzEruHCERhu3ttQkf19ptTwiBf1V9pM1nGMxYOeWa5IIhGiAs2iMiIiKiUaO6Kpo02mzdrwXiO1Sc2a2iRm6AUTIAkLD7Tas2b82Ucdn1sS57W+o+QSiuy96y5IInIiIiIqIhqaIcCIWjRXuDrSdd9lpNefnv7eaOucvxibNQG98xYRFmZUzuk9iIiIiIiGjkURSBokKguRmwZyZ3jpAIw3niNYwpOZrwsa3d9vY7ClDkr9PmV025BjZjD7o3EA0iFu0RERER0ajQ0iJQXtbz7arO7FDRttueT/XDobphkVJRcjwVtaUmbd11X/Fo2+66Ij680xjrsvel7Nk433p2b38UIiIiIiIaIlqaBWprAZu1+7X9radd9lqduZWUIlT8tWKHNk7Xp+K7eTf1aYxERERERDRytHYdd7QAmb0o2DsVKsOs9/6WdBxjXnoeL1Tv1MZjU2y4e9KVSZ+PaKAYBjsAIiIiIqL+5vcLnDoFWG09366qow4VOVufRfn0L6JarkeqlAJAwu43Yk/n0m0Krrgx1mXvtbpPEFZlbfxo7o1J/wxERERERDS0hILRjhJWKzAUdo9NpMteq7Eb/4Sy2b8HALzbcBiVwSbtu0en3YislIw+i4+IiIiIiEaO1oI9lzP5DntBNYSCcDl0EvDJw38AABzYno73XojtsavTCdz/wyZcdGWw0/O813gENeXvaeNHc29Eqj4luaCIBhA77RERERHRiBaJCJw6CaSaAKOxZ8d01qHCUrQfKHgfbsUPs86EilMmVBWlat9fc6cbplQBAHBEvHi3TZe9q3Iu4LZSREREREQjhKoKFBVFXwrqaZ7RnxLtsteqtdueO+LHSzW7tflplnH4yqQr+jJEIiIiIiIaIbSCPVfyBXsBNYSCcBl0kgSzLvqc5ZNtGfEFe3qBlf/VdcFeUAljU80ebTzNMha3jr8kuaCIBhiL9oiIiIhoxFJVgeIiIBIBzOaeH9dVh4qcrc/Boo9uh7v7DZs2b8lQcNXNsS57W87osvfYNHbZIyIiIiIaKWprALcLSB8ijehMGxPvstdq7MY/4cWa3fApIW3uBzOXw6jT90VoREREREQ0gshytFGCyw3Y7cmdo7VgTy/pYNZFn7d8/F4Gtr0YqwDU6QVW/VcTFl7RecEeALzVcAiOSOzZzON5t8LAXIaGCW6PS0REREQjVlUV4GgBMrO6X9uquw4VY0qOYELxZziGL6DsZKzL3pI7PEi1nO6yF/bivYYj2neLc+bg3IxJCcdPRERERERDj9crUF6e/AOqvrZ/H/Cnws1Q1djcfd8EVj0ASKf37a30N6LM39Dhdrdl/gZs//xv2viq7AtwadZ5/R43ERERERENL7IMFBUCXi9gt3W/viMBNYiCcBkMkh6prQV772Zg+8b4gr3V/68J8y/rumDPIwewpe5jbTzXOhVX51yQXGBEg4BFe0REREQ0IrU0C1RWAJkJtmbvqsteq5nv/Rm/C12rjVMtKhbf5tXGr9V9jLCIddl7NJdd9oiIiIiIRoLWbaAslujWuINt54fA3/4KCBGbe+Bh4N6vR4v1hBCoDrag2F+PLGN6u+OFEPhLxXaoiJ7AKBnw1Mw7BiJ0IiIiIiIaRmRZwqmTQDAI2HpdsGdAqi4FALD/nQzs2BR7kKPXC6z+7ybMu7Trgj0AeKV2H/xKWBt/f/qt2otLRMMBi/aIiIiIaMTx+QROnYomjok8SOuuy16r7OLDsHkPA1gEAFh8uweW9OhDrpawB+81HtHWLsmZg3PSJyYSPhERERERDVHlZUAonHxXib703jvAi/+On/veE8DyO6MPqSKqgiJfLRpCTmQZ06GX2idHex0n8bm3Sht//eyrMdmc069xExERERHR8CLLEmqq0pFmSbxRQiu/GkBBuAwpOiNMUrRgb9/WDLy/+YyCvR81Yd6i7gv2GkMubG04rI2vyJqFhfbpyQVHNEiGwLuARERERER9JxwWOHUSSE0FDAm+otKTLnutvpUaXWtKVbH0jrZd9j5BRCjamF32iIiIiIhGhuYmgbo6wGYd3DiEAN54Pb5gT5KAH/y/WMGeTw7iiLsELREPclKsHRbshZQI/lH1oTbOSbFi9ZRr260jIiIiIqLRKxIBqqvSEQrpk+6w51MDOHVGwd7et9sX7D3Yw4I9AHipZg/k089iJACPT781ueCIBhE77RERERHRiKGqAsVFgCIDGQk+SOtpl71W8w37Mc+wF2NvOR9pVhUA0Bz2YFubLnvXjJmLmeyyR0REREQ07AWDAkWFgNUaLZAbLEIAmzcCW9+Kzen1wI9+DCxeGg2sIejCKW81UvVG2A1pnZ7rtbqP0RT2aOMnp9+GNIOp32InIiIiIqLhJRwWOHECCIf1sKTJSZ3Dd7rDnqlNwd6et6z48BW7tkZviBbszf1izwr2yv2N+Kj5M21807iLMTP9rKTiIxpMLNojIiIiohGjsgJwOpNrz55Il71W95ufRXDFr7Txa3Ufa132JACPTFuWeCBERERERDSktL4cpDcARuNgxgH8+5/A+9tjc0Yj8NN1wKWXSVCEijJ/AyoDTbAb0mDU6Ts9V2PIhS11n2jjudapWDZuYX+GT0REREREw0g4LHDicyAcBCyWZAv2/CgIl8cV7O1+04qPXrVra/QGgYf/pwkXXNKzgj0A+Hf1TojTn42SAd/O47MYGp64PS4RERERjQiNjQJVVUiqPXuiXfZazdPvx/jqgwBau+wd1b67Zsw8zEifkHgwREREREQ0pNTWAG4XkJ4+eDEoCvDXP8cX7KWmAs/+OlqwF1TC+NRdjppAM3KMGV0W7AHA36o+RFhEH7xJAP5r5p2QBrOFIBERERERDRmhkMDnx6Nb4ya6q1Err+pHQaQMqbqUWMHeG/EFewajwCM/Tqxg74SnCodcxdr4q5Mux1mpWckFSTTIWLRHRERERMOezxvdqspmA3RJ/IabTJe9VmM3/gkA8Ertfshtu+zl3pD0OYmIiIiIaGjweATKywGbffBikGVg/R+APbtic5Y04Fe/AxYslOCM+JDvKkFQCSMrJaPb4rvP3OXY7ziljW8d/wWcbz27v8InIiIiIqJhJBSKdthT5ORfXPKoPhSEy2CWTEiRou3Kd71uxUev2bU1BqPAwz9uwuyLe16wJ4TAP6s+0sZpehNWT702uSCJhgBuj0tEREREw1o4LHDiBGA2A4Ykf7uteGwzACAiIiiL1MCtemHVpUPX5mFXS70Bf/zBBAgRnbvyZg/u/rYTANAUdmNH0zFt7XVj52N6GrvsERERERENZ7IsUFgAWCzJvRzUF8Jh4Pe/AY7FmnrDagV+9Xtg5kyg0t+IEn8DMgypMOm637tXESr+Uvm+Nk7Tm/D49Fv6I3QiIiIiIhpmQsFowZ4sA+kZyZ3Do/pQGC6DRZcK4+mCvZ1bbNj1emybJINRxSM/acL5C0MJnfuAswinfNXa+P6zlyLTOIgt0Yl6iUV7RERERDRsKUr0IZoQ0a2hesOn+lEcroSQALu+fTa65y2rVrCn1wvc8BWP9t2ZXfYensYue0REREREw11ZaXQ7KJut+7X9IRgEfv0ccPJEbC47G/jNH4Czpig44a1FY8iFTGM69FLPqgq3NR5FRaBRGz889QZkpyT5NI6IiIiIiEaMUFDg888BRUm+YM+t+lAULoNFZ4ZRMkCIaMHe7jfiC/Ye/UkTZiVYsKcIFf+u3qmNc1IycO/ZVyUXKNEQwaI9IiIiIhq2KisAtwvIzEr+HEIINCkOVMi1MOtMMEkp7dY4G/X4dF+aNv7iNT5kjY0W6TWG3Hi/TZe968cuQF7a+OQDIiIiIiKiQdfcJNDQAGRmDs71/T7guV8AxUWxuXHjgd/+AbCPD+GoqxIhISMnxdrjc3rkAF6sju2xO8U8Bl+bfGVfhk1ERERERMNQMCjw+fFog4Rkt8R1KR4URSqQ1kXBnjFFxaP/24Tz5idWsAcAHzZ/hqpgszZ+eOoNsOhNyQVLNESwaI+IiIiIhqWGBoHqaiCrFwV7ilBQKdehSXHAquu8O8XerVYINdplT6cTuOGrbu27V+r2QRZq9DtIeIRd9oiIiIiIhrVgUKCoMLoNrSQN/PXdbuDZdUBFRWxu0uRowZ6wuZDvrIFZb0SmIa3zk3Tgperd8CpBbfyDmSuQouMjAiIiIiKi0SwYFDj+WXQXobTEUgyNU3GjOFKJdJ0ZhtMFex+9asOet84o2PtpE86bl3jBXkiNYGP1Hm082ZyDO85alFywREMIM3IiIiIiGnY8nuhDNJst+YdoQTWE0kgVgiIEuy4DUicncrfocXR37NWyixf7MOas1i57LnzQ9Kn23fXjFmBa2rjkAiIiIiIiokGnqgLFhYDeABgG4a/njhbg5+uA2prYXG4u8NzvBFypdajytiDTkAaDTp/Qecv9jXiv8Yg2viJrFi7PntVHURMRERER0XAUCEQL9nQSYOlFwV5RpAIZOotWsPfhKzbsfTu+YO+xnzbh3CQK9gDgnYZ8NEc82vjxvFtgTDAnIhqKWLRHRERERMNKKCRw6kT0ja9kH6I5FTdKI9UwSnpY9V33et+31QpViRb0SZLAjV+LJYYv18Z32Xt42vXJBURERERERENCTTXg9gzOtrhNjcAza4HGhtjcuecBT/8ygmp9NTzBAHKMnb9w1BkhBP5auQMqBADAIOnx1MzlfRk6ERERERENM35/dEtcnQ6wWJI7h0N2o1iuQIYuDQZJ33HBnknFt3/ahHMuTK5gzysH8Wrtx9p4VsZkXDPmwuQCJhpiWLRHRERERMOGogBFBdHPJlPix6tCRa3chFqlQXvrqyuVhSk4vDNW1LfgSj/GT5YBAPUhJz5o/kz77sZxCzHNwi57RERERETDldstUFEO2AehYK+2Fvj52minvVZz5gL/9XMfitVK6BQdslK6fuGoMx87C/CZJ7bX7r2Tv4SplrG9DZmIiIiIiIYpv1/g88+iHcbN5uTO4ZBdKJGr4gr2Pthsx753rNqaFJOKb/+sCTPnJFewBwCv1u2HVwlq4yen35bwi0xEQxWL9oiIiIhoWBACqChPvutFWERQHqmBR/XCpsuArpukrrLQhBeeGwNFjq1bdo9b+/xK7T4oWpc9HR6edkPiQRERERER0ZAQiQgUFgBp6dFOEwOpsgL4xTrAHUs3cNElAo/8TzOK5HpkGMww6YxJnTukRvC3yg+0cbYxAw9NZYdwIiIiIqLRyueLdtgz9KJgr1l2ovSMgr33N9mx/90zC/YaMXNOOOlY36o/hC11n2jjRZnn4JLMmUmfj2ioYdEeEREREQ0LLmcKDAYgJzvxY32qH8XhSkACbPqMbtdXFJjw4i/HIBKKPa277isuTJzapsteU6zL3rJxCzHFMibxwIiIiIiIaNAJIVBeCsgykJY2sNcuKQae+zng88XmLr1cxdf+sxq1qhuZxnTopeSqCANKGL8pfQuN4Vg14ON5tyDNkNrbsImIiIiIaBjy+QSOfwYYjb0p2HOgVK6GVZcG/emCvR0b7fj4vTYFe6kqvvOzRsy4ILmCPSEEXqzZjZdr92lzOkj4/vTbkguaaIhi0R4RERERDXkBvwF1dWnIywMS6XouhECj4kCFXAOLLhUmKaXbY8pPmfDSr+IL9paucOP2b8UedL1cuw8qBIDWLnvsVEFERERENFw1NwH1DUBW1sBe99RJ4JfPAqHYTk+46hoZy75bCp9ORbbB2vnB3agPObG26FVUBBq1ufMzzsYtEy7uTchERERERDRM+bwCx48DKSlAapLv8TTJDlSotXEFe9tfsuOTbfEFe99d24jps5Mr2FOEiufL38P2pmPanF7S4afn3o3zMiYlFzjREMWiPSIiIiIa0kJBoKoqHampMvT6nh8nCwWVch2aFSesup51pyg/ebpgLxxbe81dLixf6daKBeuCDnzYpsvezeMvwtnsskdERERENCwFAgJFRYDNltgLQr312THgt78Gwm2eYy29KYSrHyiGUW+CWZ98N7zP3BX4RckWeOSANpeTYsW6WfdCl2TXPiIiIiIiGr68XoHPPwNSTMkX7Ll1PpTJ1bAbrNBLOggBbHvRjgPbYwV7JrOK76xtxPTzkyvYC6syflnyBj5xFmpzqTojfnXB/bgi+/zkAicawli0R0RERERDliwLFBYCOp2A0Sh6fFxADaEkUomwCMOuS4fUg6dvZacL9uQ2BXvXftmFO+53xz28a9tlTy/p8BC77BERERERDUuqGi3YMxoBwwD+pfzQQeCPvwMUJTZ37QovrvhGOezGNBh0CbytdIZ3Gw7jL5U7oAhVm5udcTZ+N2c1xppsvQmbiIiIiIiGIa9X4PinQKoZMJmSO4dL50WDwYELdZNjBXsvZOLAjgxtjckc7bCXl2TBnk8OYW3RK/jcW6nNWQ1mrJ/7EC60TUsucKIhjkV7RERERDQkCSFQXgb4fEBqqtLt+lYO2Y1SuQopkhFWfXqPjik7YcJLv44v2LvuKy7c/q34gr3aoAMfNR/XxjePuxiTzTk9jo2IiIiIiIaOmmrA4x7YbXH37QH+/DygxmrqcP09Lbjyq/XIMmb06IWjjkRUBX+p3I5tjUfj5m8edxH+59yvwqQ39iZsIiIiIiIahjwegeOfAeZeFOw1yC1oMDpgUU1awd57/87EwffPKNhb14i8WckV7DnCXvxv4WaUBRq0ubEpNvxl3qPISxufXOBEwwCL9oiIiIhoSKqrA+rrAJu9Z+sVoaJWbkCd0oQMnQUGqWe/6pZ+bsLGX4+BHIkV7N3wNRdu/Ya73fZYm2v3xnXZe5hd9oiIiIiIhiW3W6CiHLBnDtw1P/oQ+OffANGmifiN99djyQo30g09e+GoI66IDz8v3oIT3iptTgcJ359+K74++eqkCwGJiIiIiGj4crsFPj/eu4K9erkZFXItLGoqdJC0gr1DH8QK9lItKr73TCOmnZtcwV5tsAU/LtiEhrBLm5tmGYs/X/goJqQOYMJGNAhYtEdEREREQ47bJVBaHC3Y68nzpbCIoDxSDbfqg02XAV0PH0qVHE/Fpt/kxBXs3Xi3C7d8vX3BXk2wBbuaP9fGt46/BBPN2T26DhERERERDR2RiEDBKSAtHdDpul/fF47kj8WeXbGLSZLATQ/V4Npbw0jRpSZ93lJ/PdYWvYqmsFubS9en4rnZ38Rl2bN6FTMREREREQ1Pbne0w57FknzBXp3chKpIHWy6dDScLth7999ZOPxh3xXslfjq8JPCzXDLfm3ugowpWH/hg8g0Jv9iE9FwwaI9IiIiIhpSgkGBkyeA9AxAr4/fNqojXtWP4nAlJAmw6zO6XtxGyWep2PibMVDkWHXesnuduOU+T4frN9fEd9l7cCq77BERERERDTdCCJSVRvOMZB9eJXY94MDHE/DJ/rO0OUkSWP69aiy5XoWuhx3CO7K35SR+V7YVITWizU01j8Xv567GNMu4XsVNRERERETDk9slcPw4kJYGpKQkd446uRFVkQbY9OmAiBbsHdk+C6VHY89gzGkqvvdMA6aeE+niTJ371F2OdUWvIqDGCv4uzToXv75gJSz6AUjWiIYAFu0RERER0ZAhywKnTgJ6Q/fJpBACjYoDlXItLLpUpEjGHl+n+LNUbDqjYO+me524uZOCvepgM3a3nNDGt43/Aiaas3p8PSIiIiIiGhqamoDGBiBzAH6dFwLY/JIUV7Cn0wvc81Q1LrtKAEhu21pVCGys2Y3Ntfvi5i/PmoVfzP4GMgzm3oRNRERERETDlMsp8PnnQHoaYEyiYE8IgXqlCVWRetj06dBJOiiKwOFts1B2bLK2zpyu4PFnGjFlZnIFe/taTuFXpW9CFoo2t2zcQvz0vHtg1OmTOifRcMSiPSIiIiIaEoQQKC0BAkHAbut6rSwUVMq1aFacsOrSoZd6vqdV0aep2PzbMwr27nPi5ns7LtgDgM01+7QuewZJjwenXdfj6xERERER0dAQCAgUFwFWGyAlVy/XY6oK/PPvwIfvxy5kMKq4/0d1WPAFkfR5A0oIvyl9G584C+Pmvzl5Mb43/ZaEciMiIiIiIho5HA6BE58DGenJF+zVyo2oVRpg02dAJ0kQKrD1n1koO9amw14vC/bebTiMP1VsQ9us6L5JV+HJGbdBx3yGRhkW7RERERHRkFBbG+14kZXd9bqAGkJxpBIyIsjUWxO6RtGxVGz+XXzB3s1fd+Kmezov2KsONGNPmy57t0/4As5KZZc9IiIiIqLhRFEEiooAoxEw9PNfxRUF+MufgH17YnP6lAge/mkjZs9TOj+wG/UhJ9YWvYKKQJM2lyIZ8JPzvoabxl/Um5CJiIiIiGgY64uCvZrWgj1drGDvrb9n4eiudG2dOV3B93/eiLNnJF6wJ4TAxpo92FS7N27+8bxbcP+UpYkHTTQCsEy1j5WUlGD16tVYsGABMjMzkZmZiQULFmDDhg39cr3Nmzdj6dKlyMzMhCRJWLBgAVasWNFv1yMiIiLqDw6HQFkJYM/sZp3ixslIMSQIZOjSErpG4dH2BXu3frPrgj0A2FS7V+uyZ5T0eGAqu+zRyMV8hoiIiEaiSCTa1dvrBtISSyMSJsvAH38fX7BnTA3jppV7cN7c5DpRAMCn7nKs+fzvcQV7Y1Ks+NeC77Jgj6gN5jREREQ02rS0nC7Yy+hNwV4DapXGuIK9N/8vvmAvxRzG956pT6pgTxEqnq/YFlewp4OE/z33ayzYo1GNRXt9aN26dcjLy0NJSQmef/55OBwOOBwOPPXUU1izZo32XV9wOp1YunQpnn76aaxYsQKHDh3CoUOHcNddd2H79u1YvXo18vLykJ+f3yfXIyIiIuovgYBAwcloQqnr5LdTFSqa9E4URyphkcxI1ZkSukbBEXO7gr3bvuXAjV/rvGBPCIGXa/dhd1yXvS9iQmo3lYVEwxTzGSIiIhqJXE6Bo0eA5ubuXxLqrXAY+M0vgUMHYnMWq4xbHtiNcVOcSZ1TCIGtDfn4ccFGeJWgNj/HOgWbL1qD2dYpvQuaaARhTkNERESjTUuzwMkTpwv2jIkfL4RAtVx/umAvHTpJgnq6YO/YnljBnskcxs2rd2Py9MQL9iKqjOdK3sB7jUe0uRTJgN/OWYXbz/pi4kETjSDcHrePbNiwAWvWrMHy5cuxadOmuO+WL1+O+fPnY8GCBViwYAFKS0tht9t7db3Fixdj4cKF2LZtW9z8/PnzsWrVKixevBj5+flYsGABDh06hPnz5/fqekRERET9QZajBXvGlM7fAAuJMGqNTQhKYUzT5UEvJfbeScERM17+fQ5UJVawd/tKB67/srfTYyKqjPXl7+HD5s+0OaNkwANTr03o2kTDBfMZIiIiGmlkWaCiAqitBjKsQEoSHScSEQgAv34OOHUyNmfNkvG9Z+pR2+xO6pwRVcGfKrZhR9OxuPlbxl+M/zn3K0jRJfFUjmiEYk5DREREo01zk8Cpk4DVBhiSqPwRQqBKrke90gS7LgNSa8HeX7Pw6d5YwV6aVcH139qN7AmJ5zV+JYR1Ra/iM0+FNpeuT8X6uQ9ivj0v8aCJRhh22usDre3WAbRLBlvl5uZi1apVcDqdWLlyZa+ut2bNGgDA+vXrO/zebrfj+eef18YrVqzo1fWIiIiI+oMQAiXFQDAEWCwdr/GoPpwKlyIsybCIVEiS1PHCTpw63L5g745VXRfseeQA/qdg4xkFe3r8bNY9GM8uezQCMZ8hIiKikcbtEjhyGGhoALKy+79gz+cDfrEuvmAva1wE//HrBow/W07qnK6ID/9fwUtxBXs6SPiP6bfj6fPuYcEeURvMaYiIiGi0aWzsi4K9WjQozfEFe3/Jblew972f1ydVsOeM+PDfp16IK9jLSbHiXwu+x4I9otNYtNcH1q5dCwBYsmRJl+tak8bNmzfD6XQmfb0NGzYgNze3yzbu8+fP1+IpKSnBhg0bkr4eERERUX+orgKamgCbrf13QgjUy80oCJchRTIiVST+lO1kvhmv/CG+YG/5Aw5cd1fnBXvVwWY8deIfOOGt0uZsBgv+Ou8x3DBuQcIxEA0HzGeIiIhopJBlgdISgc8+jW4NZbcBCb73kzC3G1j3NFBSHJsbOymC//hVI8ZMUJI6Z4m/HmtO/B0n2+QlGQYzNlz4EO47++qEX2YiGumY0xAREdFo0tAgUHgq+YI9VainC/YcsOnStYK9N/6cjU/3pWnr0qwKnni2EROnJb4lbn3IiR+e/BdK/Q3a3BTzGLy44PuYmX5W4kETjVAs2usDrclWbm5ul+vafp9sguZ0OuF0OrF582bk5XVdfdy23Xpnb5cRERERDYaWFoGKcqCj3WgiQkapXIVKuRZWXRqMUuIdJE4eMuPVP8YX7K14wIFrV3ResPeZuxz/eeKfqAs5tbmp5rHYdNGTWMC3vmgEYz5DREREI4HbLXDsKFBfD2RmASZT/1/T0QKs/V+ByljjCEzMDWPNLxuQOSa5gr09LSfww5P/QlPYo81Ns4zFpoVP4tKs83obMtGIxJyGiIiIRouGBoHCgt4V7FXKdfEFewrw+p+y8dn+WMFeuk3BE881JFWwV+qvxw9O/ivuWcusjMl4YcHjmGjOSjxoohGMRXu9lJ+fr33uLkEDom3RAeCll15K6notLS1x467e5Lrooos6PY6IiIhosPj9AgWngAwroDvjt9GAGsSpcCncqheZeit0UuK/rp48aMYrZxTs3fmQA9d0UbC3o/EYfly4CT4lpM1dYp+JlxZ+H5PNOQnHQDRcMJ8hIiKi4U6WBcrLBD47Buj10ReDBqIRXWMj8PT/CtTWxi429dwQnni2AdZMNeHzqULghepdeK7kDYTV2Ja6V2afj40Ln8AUy5g+iZtopGFOQ0RERKNFQ71AUUF096JkC/Yq5Fo0nVGwt+VP2Tj+caxgL8Ou4IlnGzBxqtzF2Tr2mbsC/33qBTgjPm3ui5nn4O/zvoPMlPQujiQanVi010sHDx7UPnf3FlfbNW0TyUTk5uZi1apVsNvtWLVqVZfXbJssZmWxYpmIiIgGXyQiUHAy2vXCeEYDvWbZiRPhEkiQkKFL6/gE3ThxwIxX1udAqLEHZ3c93IKld3RcsKcKgX9Ufog/lL8DRcQerC2fsAjPX/gwrEZLUnEQDRfMZ4iIiGg4c7sFPj0G1NYA9syB6a4HRK/39E8EmhpjeceMOUE8/kwj0jJEwufzKyGsK34VL9fui5tfOeUa/H7OaqQbzL2OmWikYk5DREREI52qClRWChQWAjZ78gV75XItWhRnfMHe89n4/JP2BXtnJVGwt99RgP8t3AS/Etbmrh87H3+c+yDSDAOUrBENM0n850xtFRcXJ32s0+nU3upKxPr167F+/fpu1x04cED73LYNe3+bOnVqj9aFw+G4saIoUJTkto0YDG1jHU5x0/DHe48GC+896i1VBQoLgEAgmliqp2vkFKGiRm5Ag9qMdCkNBuihqqLNccoZn/Udnv/EAQu2/OnMgr1mXHWrF0oHjS6CShi/LduKA65CbU4C8HjuLbhv0lWQBO/10U5VE++QMtwwn2mP+QxR/+K9R4OF997IIsvRwrmqSsCSFt0aCojlGP2pohz4xc8Bnyf2PvyshX488KMmGE2iXe7RPp+JVxdy4pniV1EVbNbmTDoDfjzzq7hh3AIIVUAB71lK3GjIZwDmNB1hTkPUv3jv0WDhvTc6yTJQUgw0N8e6iif6a54iVFTINXAobtj0GRACkCMCr/8pBycPtS3Yk/H4L+oxdrIcl9d0l9MAwI6mY3i+YhsEYs92vnLW5Xhq+u3QCYn3LCVlNOQ0LNrrJafTmdD6tm9TtbS0JJUQ9oTT6cT27du18VNPPdUv1+lIeXl5UscdOnRo2LaI37t372CHQKMU7z0aLLz3KBlNjaloajQjwxrR5iKQUWdsQUgKwyJMkND1PlaFhYUdzleeHI+Db02GELHjL731KOxTSnH4cPv1HhHCZhxFPTzaXAr0+KZ0CfLKjNhdtjvBn45GotLS0sEOod8xn2mP+QzRwOG9R4OF997wFgjoUVeThkhEB0uaPCBb4baqrbXgjS3TEQnF2oZPm12NRbcfxPHPu++wd/TosbhxmWjBa/gUQcS6WNiQiofEpcgo8GNXwa6+C55GndGQzwDMaTrCnIZo4PDeo8HCe290CId1qKlOj+Y+FhnJ/E+8ChUNBgd8ugAsIhV1AFRFwoG35qC6IFawZ04P4ob7d6O22Yva5s7Pd2ZOI4TAXpRhF0ri5m/G+bi8diz21O1JPGii00ZDTsOivV7qTQKTaDKZiKefflo7/9q1a/st8SQiIiLqCY/HiKZGM9IzYgV7fimIOmMLdADSRGrS5648MR4H3p4DtCnYu+zWIzh/UVmH6+uEBy/jKDwIaXM2pOJh6TJMluxJx0E0HDGfISIiouFCVYGW5lQ0N5mRYlKQlp74dk29UVFlxtuvz4ASif1Jfcb8CnxpxWHo9IltiSuEwCFUYQcK4zpRTEMWHpAWwSolnx8RjTbMaYiIiGgkCvgNqKpKh04nYLEkl/u0Fux5dX6kCXN0TpHwyZtzUFM4Xltnzgjg5gd2wz7Gl9D5hRDYhgLko0qbkwB8FfNxmS43qZiJRhsW7fWhoZJ0lZSUYN26dQCA5cuX48knnxzQ60+ZMqVH68LhMGpra7XxggULcP755/dXWH1OURTtLYZFixZBr+94qz6ivsZ7jwYL7z1Klt8HfHoMmHI2YDBEE7kGpRmVch3O142DUTJ2ebyqKlqHvRkzZkCni917xz+24ODbOXEFe199rBlX3JQJILPduQ46i/BC2UcIqbEk99y0ifj9Basx1mTr5U9KI81Q+f1+oAyVn5f5zMDg/67TYOG9R4OF997w5vUCxYVAqgk491xAp+v+mL70yZEQ3n7dBCUSu/AVyzz48qMCOt2FXR6rqorWjWLu3DlQIPDnyu34oLkgbt2t4y7G/5t5F1J0/JM99Y2h8vv9QBoqPzNzmoHB/22nwcJ7jwYL773Ro7EBKC4CFi4ETKbkzqEIBWVyNXSKGVZ9RnROBl7bMAY1hRZtnS1Lxvd+0YJxk2Z2eq4zcxqdTo+IKuN35VuR74gV7BklPZ457z4sGTM3uaCJzjBUfr/vT/wLQC+1baWe6FtZ/XGDOZ1OLF26FEA0Gdy0aVOfX6M7ZWVlPVp3/PhxzJ49Wxvr9fph+8vFcI6dhjfeezRYeO9RT0UiAoWFgNkCpKQAESGjUqmFQ3Uj02CFTurJ07bYvabT6aHTRQv0Pt1nwRt/zo7bEvfu7zbjymV+APHnFULgjfoD+EfVh2jbA+PqnAvwzPlfh0WfZOZLI5puoJ8GDwLmM+0xnyEaOLz3aLDw3hs+FEWgtgaoqADMZqDNry4DQgiB9z/24oX1aVCV2O+G19zpxvJVLkg9ymdi3EoQz5a8jlO+am1OBwn/MeN23D3pS5AGcq9fGvFGQz4DMKfpCHMaooHDe48GC++9kUlVBSorgKoqwGaPNkFIhiIUVMi18Ao/7EZrdE4GXtuQg4LDbQr2smU8+Vwjxk5UceYzlc7odHqERQTPFL+GY57Yfr1p+lT8Yc4DuChzenJBE3VgNOQ0LNrrpUSTurat2rP64a9MixcvRklJCVatWoX169f3+fmJiIiIekpVBYoKo8lghhXwqwGURCohQ4X99JtdyTq2Nw1v/CUrrsPePd9rxhU3+tutlVUFf67Yjm1NR+Pmvzl5MR6ffksPCweJRibmM0RERDRU+bwCRUVAwA/Y7QPfXU8WCt7c6cIbf8mMe1Ho5q87sexuDxKtr6sTbjx/8p9ojni0OavBjF/Ovh9fzDqnr8ImGnWY0xAREdFIEIkIFBcBDgeQmZl8/qMIBSWRKnhVP6z69OicDLz8hxwUHokV7NlzZDzxXAPGnqUkdH53xI+fFb+CYn+dNpdtzMCf5z2Cc9InJhc00SjGor1eysvL0z63TfZ6oq/f4lq6dCny8/OxadMmLF++vE/PTURERJSoqkrA6YwmmM2yA+VyDUySCRm61F6d99ieNLzx1zYFe5LAvY+34PLr2xfseeUgflG8BZ+2eeNLL+nwo3O+jOVnLepVHEQjAfMZIiIiGmoURaCuFigvj3bXs2cOfAwBNYRXtrmw419j4+ZXPODANSu8CZ/vc1GHt3ECckTV5nIt4/CHOQ/gbMuYXsdLNJoxpyEiIqLhLhAQOHUSCIejz1OSJQsFpZEqeFWfVrAnR4BX/pCDwqOxgr3MMTKeeLYBYxIs2HOKAP5fwQuoDTm0uUmp2fjrvEcxyZyTfOBEoxiL9npp4cKF2ueetF4vKSkBAOTm5vZpHEuXLsXBgwdx6NAhzJ8/v0/PTURERJSoxkaBykrAaldRGalDvdwCqz4Neql3LfuP7UnDW3+LFexJksC932/BZde1L9irCzrwdNHLqA7G/mifYTDjNxesxCWZM3sVB9FIwXyGiIiIhhKfL9pdwucdnO56AOCQ3Xj5rQD2vjxOm5Mkga9+uwVfuql93tEVVQi8UL0Lr+N43PyXsmfj5+d/HWmG3r3QRETMaYiIiGh4czmjBXsGI2C1Jn+eaMFeJXwiEFew9/IfxqDoqFlblzkm2mFvzITECvYahBcbcRjeUFibOzd9Iv504SPITundzkpEoxn3AuultslXcXFxt+tbk8YlS5b0WQwrVqxASUlJp8lgfn4+VqxY0WfXIyIiIuqKzxvdFjfVGkaxUo5GxQm7PqPXBXtln07EW3/LjivYu++Jjgv2Tniq8NTJf8YV7E1KzcZLC7/Pgj2iNpjPEBER0VCgqgLVVQLHjgCyDGRmDXzBniJUVIbr8MIrwbiCPZ1O4Jv/0ZxwwZ5fCWFt0St4rf7juPnVU67F7+asYsEeUR9hTkNERETDVX2dwPHjQKoZsFi6X98ZWSgoiVTAJwLI0KVF5yLA5t+dUbA3NrmCvZPeKvwLh+BFrGDvYvsM/GP+d1iwR9RLLNrrA61tzg8ePNjluvz8fO3z6tWr++TabZPBzt4M2759O7KysvrkekRERERdCYcFTpwAIileFCrFiIgIbPp0SJLUq/OWfToR+e/OjivY+/qTLbj02vYPznY2H8f/V/ASPHJAm5tvy8XGi57ANMu4duuJRjvmM0RERDSY/P7og6qKCsBm793DqmSFRQTF4Qq88qIBB9+MbYmrNwis/u8mfGFJoIuj26sNtuA/T/wTh1yxAiIjdPj5eV/Hd/Jugk7in+WJ+hJzGiIiIhpOFEWgpCTaZdxmA1JSkj9XRMgoilTAL4LtCvaKP40V7GWNlfFkEgV7B5yF+EnhZoQga3PXjLkQz1/4ENIN5i6OJKKe4F8H+sBTTz0FIJrwtbZW78hLL70EIPrmV2ft0Z1OJ1avXo01a9Z0e90VK1bA6XTi0KFDsNvtna7btm0b8vLyuj0fERERUW8oikBBgYo6uREV+jKk6kyw6HrfPeLIzvRowd5pkiTwjTXNWHRNfMFe69ZTvy59C7KIJZ43j7sIf533GDKN6b2OhWgkYj5DREREg0FVBWpqBI4eASIhIDNzcLbD9ap+HA+U4M1/2HF0e442b0xR8chPmjD/smBC5zvqKsN/nPgHqoLN2pwdqXhCugrXjZ3XZ3ETUQxzGiIiIhouwmGBglNAfW20w7i+FxsURYSM4kglgiIUV7C36bdnFOyNk/HkLxuQMz6xgr0dTcfwTNFriIhYwd5dEy7Fs7O/iRSdMfnAiUhjGOwARoL58+fjySefxLp167B69Wps27at3ZqSkhKsW7cOdrsdO3bs6PRcixcvjnvba+3atR2uW716NTZv3owlS5Zg6dKlHa5paWmB0+lESUlJjxJMIiIiot4oKZfxqaMGaroHNl16n3SPyP8oDVv/ka2NJSm6NdWZnS5CagS/K92KvY6TcfPfzl2G1VOu7XWnP6KRjPkMERERDTS/X6C4GPC6Aautdw+qkiWEQKPiQHmoFh/+39k48XFsWyeTWcVjP23CzDmhhM73VsMh/L3yA6gQ2vxc61R81TMbVonb4RL1F+Y0RERENBz4/QKnTkYL6zJ72YQ3crpbeBARZOii7cojYQmbf5uDkuNnFOw914DscT0v2BNC4LW6j/Gv6p1x8zdiFn44YwX07BxO1GdYtNdHWhO3devWYenSpVi/fr3WCn3z5s1YuXIlcnNzsWnTpi7fuHI6ndrnzt4IW716NTZs2AAg2la9JxYuXNijdURERETJKKn1Y2dVJSwZKuz6jO4P6IFDH6bjnX/EMldJEvj6miZ8YUl8pwtnxId1Ra+iwFejzaVIBqyddS+uG9fxm/NEFI/5DBEREQ0EVRWorwfKSgFTSu8fVCVLFgoq5Vo0BF344E9TUXA4TfvOnK7gu2ubMO3ccI/PF1FlbCh/Dx80fxY3v3zCIvxg+h3Yv2dfn8VORB1jTkNERERDmcMRLdgzmYAMa+LHWwr3AgD8MxYhIiIoClcgDDmuYG/Tb3JQ+nmsYC97fARPPteIrLE9L9hThcDfKt/HWw2HtDkJwF2Yhyt1eWyQQNTHWALbh9auXYtDhw4hNzcXS5cuRWZmJjIzM/H000/jqaeeQnFxcact11u1JpK5ubkdvsGVn5+vJYOJ6CoJJSIiIuqNoqYWbCssQUaaDla9pU/OefD99gV7V3/5IC5ZHL8lbkWgEU+d+EdcwV6WMR3/WPAdFuwRJYj5DBEREfWnQEDgxHGgrASwWgFLWvfH9BVL4V7tIVdADeFkuBRNAS+2/SE3rmAv3abgyWcbEyrYc0S8+O9TL8YV7Okg4YczV+B/zv0KjDq+N080UJjTEBER0VAjhEBNTTQXSrMAZnP3x3QkZ+uzyNn6LMKnC/YikJGui54sEmpfsJczPoInf5lYwV5EVfCb0rfiCvaMkh6/OO8buFKXl1zgRNQl/sWgj82fPx/r169P+vglS5aguLi4y/MLITr9noiIiGigyKqCU8467CtoQbY5DWZj3+xpdfD9dLz7r1jBnk4ncNWXD2L6hdUAxmrzh10leLb4dQTU2AO16WnjsX7uQzgrdZBadhANc8xniIiIqK8JIdBQD5SUAinGwemul7P1WQDA0Wnno0yphgiY8NZvp6KiILZlrT1bxuO/aMT4yXKPz1vkq8W6olfREvFqczaDBb+64H5ckjmz734AIuox5jREREQ0VCiKQHkZUFcL2DMBXZIttSyFe2Ep2g8AcJx4DZG8OUhrU7C38TdjUHYiltvkTIh22Msc0/OCvaASxs+Lt+CIuzR2Xb0Jv5+zGgutedh1aldywRNRl1i0R0REREQJCyhhHHdV4GRpCFZkwGzqm5boB3ak471/xxfsffM/m6C3Vset29qQj79W7ICK2B/KL8+ahedmfxNphlQQEREREdHgCwYFSooBpzPaXc8wgH+NFkIgjAhSCnZrD7i8p96ENOEybP7VeNSWmrS1OeMjePwXjcgZ3/OHWruaP8cfyt5BWMSK/PIs4/HHuQ9gkjmn734QIiIiIiIadkIhgaICwO2JvrjUm11ls0+/hAQA5733f/A+/AcAQDgkYeOvx6D8ZOyZyJizInji2cQK9twRP35a9DKKfLXaXJYxHc9f+DBmZUyGovT8XESUGBbtEREREVFCWkIefO6pRGO9HvpAOtKsfXPeA9vT8d4L8QV7K3/YhHmX+3H4cHROESr+r+J9bG3Ijzv27olX4j9m3gG9lOSrakRERERE1GeEEGhsAIpLAKMByBqA7noREUFIRBBQg3CrPvhUHyJQsWTrz7U1c995EauaV6ChKkWbG392GI8/0wh7jtqj6yhCxb+rd2JL3Sdx81fnXIB1s76ONIOpkyOJiIiIiGg08HkFTpyIfs7MTP48slCgntqOtNMvIQHA2JKjyCrKR93kBdj4qzEoP9WmYG9iBE8+29Dj3AYAGkNu/LhwI2qCLdrcxNQs/OXCR3G2ZUzywRNRj7Boj4iIiIh6RBUqKvxNKAvUQ3Vb4G82wGbrm3N/si0D216MZa86fbRgb+EVQSin88uQkLGu+NW49uw6SPjPmcvxtUlX9k0gRERERETUK8GgQGkJ4GgBrLb+6a4nCwUhEUJQDcOteuFV/ZAhQyDawSIFRph1qZhcfATjSo5px40tP4yJ3oNowCIAwKS8ML73TCMybD17qOWTQ/hV6RvId5XEzT849To8Mu0G6PgSERERERHRqNbSLHDqFJCaCpjNyZ0jIiJoVlyok5tw1Tu/avf99Hf+jF/5r0NFm4K9sRMjeOK5Btize16wVxlowo8LNqIl4tXmZqadhT9d+AjGmPqoWwMRdYlFe0RERETULa8cQJm/ES0RD0zhDJRWS8jI6F1L91Yfv5eB7S/FF+yt/n9NmH9ZUJtziQA24yga3T5tLk1vwi9nfwuXZc/qfRBERERERNQrQgg0NgIlxYBBD2Rl9815FaEgJMIIqCF4hR8e1Y+wCEcL9ACkSEaYdEZYpNR2x85878/t5r6V+iwe8S7CtPNC+M7PGmFJFz2KoybYgrVFr6C6TQeKVJ0RPzvvXlw7bl6yPx4REREREY0AQghUVwEV5UCGFTAaEz9HSITRKLegUXEAEDi75GTcS0itxpQeRrY3HxWnX0YaOymCJ59rgC2r5wV7J73V+Fnhy/AqsecwC215+P3cB5BhSLLakIgSxqI9IiIiIuqQrCpwRLyoCjRDOrYPKZIBGed9AYXl0TfE9PreX2P/uxnYsTFWsKfXC6z+7ybMuzSWKBb6avF3HIQPYW1ugikT6+c+hBnpE3ofBBERERER9UooKFBaCrQ09667nipUhEQEIRGCT/XDLXwIqCHte6NkQIpkgFmX3u25sorykV18uN38fMN+3HzOh1j6zHSkmntWsHfEVYrnSl6HT4nFMt6UiT/MWY1zMyb16BxERERERDQyybJAWQnQ0ADYMwFdgg24A2oQ9UozmhUn9JIe6TozdJIO5277a6fHtL6MNG5yBE88m1jB3iFnMX5RsgVhVdbmFufMwS/O/wZM+iSqDYkoaSzaIyIiIqI4fiWEhqAL1cFmqFBh0Zlw7isvQAhgx31fiG43ldL76+x7JwPvbzqjYO9HTZi3KFawt7flJH5T+jYiiCWPc6xT8Ps5DyA7JaP3QRARERERUdKEEGhuAoqLAF2C3fWEEAgjgqAagk8NwCt88KkBtJbRGSQ9UiQjrLo0SAm2+A4FJIz79/91+v0jWb9Aufn3PYrxzfqD+EfVh1ARK/Cbb8vFby5YiSzmJEREREREo1ooKFBQCPi9QGZWz3cnEkLAJwKok5vgUj0wSgbYdOla7tPZS0it5hv2Y/FZO3HDs3mwJlCw92HTZ/h92da4/Gb5hEX40blfhl5KsNqQiHqNRXtEREREBFWocMl+VAea0RL2QqfTwWowQy/pYPksH2nHo8lhxuf5CMyZ3+vr7duagfc3xxfsPfijJsw9XbAnhMArtfvxQs2uuOOuHXMh1s66j297ERERERENslBIoKwUaGoEbPauu+u1FuiFRAR+NQCv6oNH9UOcflCkl3RIkYzISKJAr61wUMKBHRkIvvcZbjcc7HRd+onDsHyWD//sznObsCpjffm7+Kj5eNz8irMuxQ9nrkCKjn9aJyIiIiIazbxegZOfRz/b7D07RhUqvMKPGrkBXtWPVCkFdn37l4Fmvvfnbs/1/Uk/R1XW73oc75a6T/CPqg/j5h6Yci0ey13WqzyMiJLHvywQERERjWJhNYLGkBuVgWaE1AjMuhRkGWNvcwkIZP37T9r6ubv+jP29LNrb+7YVH7xs18Z6w+mCvS9GC/Yiqow/dvBw7Hqch3XnfR0GPX+FJSIiIiIaLEIItDRHu+tJOiA7p/2aiIgW6AXUINyqDz7VhwhUSAB0koQUyYh0nQW6PnowFA5JOPRBOvZvtcLv1eO36b/s9pixG/+Estkdd9trCXvwTPFrKPTVanN6SYcfzFiOr0y6ok9iJiIiIiKi4au5SaCgALBYAJOp+/WKUOFSPKhRGhESIZh1JmTqrR2u7a7LXitbQT5aunkZCQBUIfDPqg/xev0BbU4C8NSM5bhn8pe6D56I+g2feBIRERGNMkIIeOQAaoMO1IdckCQgQ5+KDENqbA0EfF4gsjsfs0/FksPsksPIKspHy/TkCvf2vGXFh6/YtbHeIPDw/zThgkuiBXvuiB/PFL+GE94qbY1B0uNuMR+X6KbwbS8iIiIiokEUCgmUl0W762VYAaMRkIWCkAghqIbhVr3wqn7IkCEQ3RoqBUaYdalI64etliIhCfkfpmPfO1b43HoAwDzDXsw37O/22LTjHXfbK/LVYl3Rq2iJeLU5m8GCX1+wEhdnzujbH4CIiIiIiIYVVRWorgYqygGbreuO40A0X3IoLtTKjZChwKIzwaxr31mvrenvdN9lr1VXLyMBgKwq+EP5O3FNEgySDmtn3Ycbxi3o8XWIqH+waI+IiIholIioChxhDyqCTfDLIaToDMg0prXrbuH1CdTVAj4fsHR7++Rw5nt/xv4kivZ2v2HFR6/ZtbHBKPDQ/xcr2KsONuPpwpdRF3Jqa2wGC34zeyV8x6oTvh4REREREfWdlmaBU4UKIgjDaI+gRvXCE/IjLMLRAj0AKZIRJp0RFim1u9P1ihwBDn+Ujj1v2+Bz6eO++1bqsz0+z5kPuHY2H8cfyt5BRCja3Iy0CfjDnAcw0Zzd+8CJiIiIiGjYkmWBkmKguQnIzAR0XbyXFBYRtChO1MnNUKEiTWdGmqTv/AAAigy0vHICY0q777LXqrOXkQAgpETwi5ItyHeVaHNmXQp+O2cVFmWd2+NrEFH/YdEeERER0Qjnk0OoDzlRE2yBKlSk61ORndL+TS5/IFqs53EDplRgWmN+h8lhdnHi3fZ2vWHFzjMK9h7+nybMvjhasPepuxw/L34NPiWkrZlqHosNFz6Es1IysQss2iMiIiIiGkiqUBFQInAHgzhZ7kd5sw96cwh6A4AIYJQMSJEMMOvSBywmOQIc2ZWOPW9Z4XW2/9P2DbkfYX5L9132WrU+4PKcfyH+VfVR3HZRALAkZw5+Nus+pBl6sN8VERERERGNWMGgwKmTQDAIZGZ1sU4NoUlxoEFpgQQgTWeBvpuu46oKfLY/DTu32PC/we8lXMXTUbc9jxzA04Uvo8BXo83ZjWl4fu5DmG2dktgFiKjfsGiPiIiIaARShApXxIfKQDOcER+Mkg5Wg7nD5DAQEKivB1xOwGQCbPbo/Mz3Om/Bnki3vZ1brNj1ul0bG4wqHvlJE85fGC3Q2954FM9XbIMiVG3NJfaZ+PUF98NqtEBRlDNPSUREREREfUgIgaAaQUAJwR0JwBnxwasE4PYI1FYDOuhhtxphlNIgndGpeyAoMnB0d7RYz93S/k/aU2aGcOs33Fj2xs+BlsTOnf3SBvznPYtxxF0aN//w1Ovx0LTroeuHbX2JiIiIiGj4cLujBXs6XXRL3I741QDqlRY4FCd0kh4Zuva7HJ1JCKDgsBkfvWpDY00K5hn2Yn56z19CanVmt73msAc/LtiIqmCztma8KRN/mfcIplnGJXx+Iuo/LNojIiIiGkGCShiNIReqgs2IqAos+hTkdNBVD4i+GdbQADgdgMEYK9YDgKyifGQXd96Cvafd9nZusWHX67Es1mBU8ehPmjBrYQiqEPhX1UfYUv9J3DErJizCf51zF4y6rlvFExERERFR4oQQCKkRBNQwvJEAXLIfzogfQghAAvTQwSAM8NRa4HBIyEmL5guDQZGBT/elYfcbNria2/8pe1JeGLd904ULLgki7Xg+0j7v+TZSrayfH4Xx0wxgyhgAQKouBetm3YulYy/sbfhERERERDTMNTQIFBUCaWnRpgdtCSHgFX7Uy01wqV4YJQOsuvQevehUdsKED16xo6YkdtJvpT6bdJyt3faqAs34SeFGNIU92nfT08bjzxc+grEme9LnJ6L+waI9IiIiomFOCAGX7EdNsAXNITckSUKG3gyDoeOit1BYoLEBaG4GjAYgwwqcmUN21WWv7ZrOuu0JES3Y2/3GGQV7/9uEWQtCCCph/Lr0LXziLNS+lwA8Mf02fH3y1YPSvYOIiIiIaCQKqxEElAh8cgCOiB9u2YeIUCBBgg4STDojbAaL1gXC5RYorwRUEe0iMRi/mqvK6WK9N61wNravGJw4LYxbv+HC3EVBLb6xG/+U9PUe2X0S900ZgwmmTPxx7gOYmT4x6XMREREREdHwp6oCVZVAZWU0LzK0qaxRhQqP6kON0gi/GoBJSoFd33HzhDPVlKbgw1dsKP3c3O67H6W/gFu/6cZFV/mhS6Lhd4G3Bj8t3AyvEtTmLrROw/q5D8JqtCR+QiLqdyzaIyIiIhqmwqqMlrAHlYEmBJQwTDojMo2dv8UVDgs0NkaL9XQ6wNpBsR7QfZe9Vp112xMC+Og1G/a8GSvYM6ZEC/bOmx9Cc9iDtUWvoMRfr32fqkvBs7O/gatyLujhT09ERERERGeKqAoCSggBJQxHxAtXxI+wGgEkCRIAk86INH0q9B1s+SrLAnV1QFMTkD5I3fVUFTj+sQW7XrfB0dA+gAlnh3HLN9yYd1mg3UOssv/5fY+uIYTA6/UH8K+qj6BCaPMLbXn49QUrkZmS3qufgYiIiIiIhrdIRKC4CHC0AJmZ0HIPRahwKm7UKo0IiwhSdT0v1muqMeCj1+w4eah98Zw1U8ZN97lx2fW+uOLARBx2leDnxVsQUiPa3JeyZ+O52d9Eqj4luZMSUb9j0R4RERHRMOOVA6gLOlEXckAIIN2QiuxOtsAFgIgs0NwENDREk8v0dHT5llZPuuy1Xdu2254QwIev2rD3rfiCvcd+2oRz54VQ4q/HzwpfRkvEq30/JsWK9XMfwnkZk3p8XSIiIiKi0U4R6ukCvQicES+csg9BJQKBaBdrk2RAqs6IdENqt+fyeAUqK6JFc4PRXU9VgRMHosV6zXXti/XGTY7glq+7sOCK9sV6iQirMv5Y9g52tnweN//lsy7Hf85cDqOu427lREREREQ0OgQCAqdOAuEwkJkVnYsIGQ7FjVq5AQoUWHRmmHWmrk90mqtZj51bbPh0bxqEiE+0zOkKrv+KG1ff6oMpVXRyhu7tbD6O35VthSJUbe7W8Zfgx+d+FQbmOERDGov2iIiIiIYBWVXgiHhRFWiGWw4gRdLDZkjTtrDq8BhZoLk5WqwnSd0X6wE977LXqm23PSGAD1+xYe/bbQr2TCq+/dMmnHNhCAechfhlyZtxb3qdlz4Jf5z7AMaa7D2+JhERERHRaKMKFQElgoAagjvihyPiQ0AJQQgAkkCKZIRJZ4DF2LMHR61kJdpdr7kRsKQBxgHuridU4OQhM3a+bkNTTfvuD2MmRnDLfW5c9CU/evusqTnswTNFr6LIX6fN6SUd/mvmnbhr4mW9OzkREREREQ17bpfAyRPRruNWKxAWETTJDtQrzQAELDozDFLPEhOfW4c9b1mR/2EGFDn+OY7RpGLJHR5cd5cHlvTki/UA4M36g/i/yvfj5u4/ewm+l3dLp7syEdHQwaI9IiIioiEsoIRRH3SiOtgMFSosOhNyuuiqB0QfvDkcQH1dtPNdWlr3xXqtEumy1/aYfXnz8cHLNuzbekbB3tNNmDkniNfrDuAfVR+ibfq5OGcO1p1/Hyz6xB4sEhERERGNZEIIBNUIAkoIHjkIR9gLrxKI/i4tBIw6A0w6I+yGtF49hPF6BSpOd9ezDnB3PSGAU4fN2LXFhoaq9sV6ORMiuPk+Ny6+2g99L4v1ZFXBMXcZ/lD+DhwRnzZvN6bhNxesxEL79N5dgIiIiIiIhr36OoHi4mjzA9UQQmWkBQ2KAzpJQprODL3Us4csQb+Ej9+z4pP3MhAOxR+j1wtcvsyLZXe7YctSOzlDzwgh8O/qnXi17uO4+f+YfjvuO/vqXp2biAYOi/aIiIiIhhhVqHDJftQEWtAc9kCn08Fq6D4plBUBpwOoq4t2rLCkIaEHXIl22WuVXXwY5X8uwL59S7S5FJOKb/+sCbmz/Vhfvg3bm47FHfPNyYvx+PRboOthoktERERENBIJIRBSIwioYXgjAbhkP5wRP4QQgATooUNqHxTotdXaXa+pEbBYgJT2NXP9Rgig8KgZO7fYUF/R/sJZY2XcfJ8bX1jq61WxniPsRb67BIddJTjmLoNfCcd9PzPtLPx+zgOYaM5K/iJERERERDTsKYpAZQVQXQ0YrQFUoxktYRcMkh5WXde7HbUVCUs49H469r5tRcAXn8xIksDFi3245etujJmg9D5moWJ9+bt4v+lTbU4v6fD0effgpvEX9fr8RDRwWLRHRERENESE1QgaQ25UBpoRFhGkSinIMqZ3+3BOUQVcTqCuFlAUwGwBDEn8lpdMl71WX/z0j/gXokV7KakqvvOzRkw4z43/LdyCTz3l2jq9pMOPzvkylp+1KOlrERERERENV2E1goASgU8OwBHxwy37IAsFgAQdJJh0RtgMlh4/GEqU1ytQWQnICmAbwO56QgDFn6Zi5xYbasvad9rOHCNj2T0uLLrWn1QuowgVRb5a5LuihXol/vpO114zZi6ennUvO34TEREREY1ykYhAYYFAtcOPQHoj3IoPJskAm6775zKtFBk4uicNu9+wweNon8zMXeTHbd9yYeJUuU9iDqkRPFfyBg46i7S5VJ0Rv75gJS7PntUn1yCigcOiPSIiIqJBJISARw6gNuhAQ9gFAMjQpyJDl9rtsaoQcLuAmhpAlqNdMpJ5wNVq/0O/S2i9EMCOjXZ8/J5Vm0tJVfHdtY1In16PH5x8GdXBFu27DIMZv71gJS7OnJl8kEREREREw0REVRBQQggoYTgjPjgjPoTVCCBJkCQJJsmANH1qj7dZSkY4IhAMAh434HYD4VD0JR+zud8uGUcIoOR4tFivpqR9kZw9W8aN97hx2XU+GIyJndsjB3DYVYJ8VwmOuErhVYJdrrcZLFg5ZSm+efaSPutaSEREREREw5PXp+DA517URBqBtCBSkYJMfUaPjxcq8PkBC3ZusaGlvn0yM3NuEHesdCH3vHAHRycZsxzEz4pewUlvlTZnM1iwfu5DmGub2mfXIaKBw6I9IiIiokEQURU4wh5UBJvgl0NI0RlgN/Ss1boqBDxuoLY2+tDNYon+M5CEALa/ZMcn22IFeyaziu+sbUT47GI8dfI1eOSA9t2k1GxsuPAhTLOMG9hAiYiIiIgGgCLU0wV6ETgjXjhlH4JKRPveJBmQqjMi3dD9yzm9IcvRIj2vF3A5gVAIgBR9uSclZWCL9cpOmLBzix1VRe2L9ayZMm64240rbvDB2MPteVUhUOavR/7pQr1CXw1EN8eclz4JX8qejStyzscF1in9WiBJRERERERDn6wqKG50YW9hI2CMwJZmgklKoFhPAMWfpeLDl+2or2yfzJw9I4Q7Vrowa0GoL8NGS9iDnxRuQkWgSZsbZ7LhLxc+ity08X16LSIaOCzaIyIiIhpAPjmE+pATNcEWqEJFuj4V2Sk9SwgFBDweoK4GCASjhXpWWz8H3FEcAtj2QiYO7IjFbTJHO+xVjT+MPxS8c3qLr6j5tlz8ds4qZBrTBz5YIiIiIqI+pgoVASWCgBqCO+KHI+KDXz7d5U0CUiQjTDoDLMb+335VVgSCAcDnA9yuaJ4AAHo9YDIB1v6tEexQ+SkTdr5mQ0VB+4tn2BXc8FU3rljmQ4qpu5K7aP70qacMh5wlOOwugTPi63J9mt6ERVnn4UvZ5+Py7PMxxmTtcj0REREREY0OYVVGQ9CJw9WNqK5RkJNmhjklsYSpsjAFH7xsR2Vh++PGTY7g9m+5MO+yAPqqsbcQAlXBZhx0FuHdxsNoCnu073It4/DnCx/B+NTMvrkYEQ0KFu0RERER9TNFqHBFfKgKNMMR8cEo6WA1mHvc5UFAwOcF6uoAvw9INZbw7JMAAQAASURBVAO2QSjWA6IFe++9kImDZxTsfXttAz6xv4+XS/fFrb95/MX48blfRYqOv3YSERER0fAjhEBQjSCghOCRg3CEvfAqgWiHNyFg1BmQojMg05g+IFuuKmq0k57fH+2kF/BH5yXd6SK9QaxRqyw04aPXbCg/2f4BVrpNwXVfduNLN/tgSu28WK/1oVS+qxj5rhKc9FZDEWqX1821jMNVORfgiuzzMc+WC6NO3+ufhYiIiIiIRoagEkZt0IlKfxPq6gVCzWZMtOmhS6AJd32lER++YkfRsfatyzPHyLjl6258YakP+j5IRSKqgs89lTjoKsIhZzEawq52a+ZYp2L93AdhN6b1/oJENKj49JT+f/b+Pb6x+7zvfb8LAO8cEuSMJGuk0UigJEuekWSBM7ItW7FjkWmaXuI2pLXbpEl2EpFtdvpqdndDVqf7NG1PdrTJ1Enas5NuUnUT57LbMZnYSS+nCTlpYjuyZA0pS9ZIsiViNPcLZwjwChKX9Tt/LAIDkAAJkiCun7de0IDAwsJvAQ8W8XA96/kBAIB9shqPaHZtXpdWbylix9XkrtWhHLvqJSwtG1276nTOqKsrTme9BGOkP/l/2jT1Z+kFe/9w+Ir+c91X9fLV76Yt//O+v6G+oz9QkIOXAAAAwF4ZYxSxY1qx17QUDWs+tqJQdEXGGBnLyCO36l018nqaCvYd1zZGa4kivXnnJB5bkstyprttPqC8dXHYrUvv1+prf9Sqc29vPoDVeMAp1vvsDy+rriFzsd5qPKK3Fi/o9fVpb2cjC1s+X72rRh9re1ifOXRcz7Qf0z0N7XnZDgAAAACVYzm2piurt3RtNSg7bil4qUHxsEtt3txzqLnrHn3tj1p19luNkkl/UHNrXH/tRxf06b+xpJrNs+TuyEJ0RdPzAZ2Zn9Eb8+cUtiNZl32m/SP6N4/9jBrce3xSACWBoj0AAIA8MsZoIRbW5dVburW2IMuy1OxuUItnZ6dYrYSNrl2TFueluvriddZLMLb033+/TdN/frtgr77R1vND5/Tb1im9F7yavL3W8mjoIz+uH7zLX4yhAgAAADmJ2FGF41Etx8IKRcOajy0pZuKSLLlkqc5Vo1ZPo1wFrIozcor0wqtOJ72lJcmOO530amulpubiF+klXA44xXqBtzYX6zU0x/UDn19U199aUn3j5mK9q6tBTc8H9Pp8QGcXLyhq4ls+1731B/WZQ8f1mYPHdcL7oOrcNXnbDgAAAACVYyG6oourN3VrbVE1llt1sSad/8BJonLtTL4YdOvr/7lFb3yjWXY8PQGrb7T1A59fUPePZM51cpE67e2Z0Iy+t3xZW62pxnLrKe9D+qt3deqHP/SUPHQXByoGRXsAAAB5ELFjmoss6mL4psLxiOpcNbuaIiu8anT9mnOArq5OavXuy3B3JFPBXkOTrb/zf76l34yd0s2UThgHaw7o3z3x9/VYy9FiDBUAAADIKmJiWjERfXfpshbjq4rYUcmyZMlSncujJne93NYO5kjKAyOjSMSZ5nZxUVqYl+K2U5hXUyM1NmpH0zYVwtXzNfraH3n1/hubi/USB7Ce/VtLamy+fdgpasf09uIlTc/P6PX5gK6sBbd8jhrLrRPeB/WZQ8f1fQeP6f7GO/O+HQAAAAAqgzFGodiyzq/Maj66rHpXrdprmrWwKAXOO7lVff326wkvufTy/69FZ043KxZNT8Q8Nba+/3NL+qG/s6jmVnvHY8xl2ttUbTXN+vTBY/rsHY/r6bZH1OSp2/FzAih9FO0BAADswVIsrOtrIV1ZDUpGavbU6+AOp8CVpNVVo9kb0tycVFPrTINb7A4as5dr9NarjXr71SaFbt7+2tjQHNdfe/EV/ebqH6a1aX+o6W7930/8Ax2uZ3oqAAAAFJ8xRivxNQWjy7q6ckvfM7OyZOmu6IoaPXVq9uRw1GYfRCJG4VVpcUFaWJDicckYqbZGaijBIr2Eaxdq9PU/atX3vt246b66BltdPYv6gZ7FZLHezciCptenvH1r4bxW7eiW67+rzqvvO3hM33/ouJ7yPsxBKQAAAABbihtbc5FFnQ/PaiW+pkZXrQ7VtsjIOd5y9YrTrdyzTVVMZNXStyYP6JX/3qK1cHpC5nIZPf2DS/qbP76otju27hC+0U6mvZWcYyyfPfS4vv/QY3qs5T65CnxSGYDCo2gPAABghxKJ4KXwLS3Ewqqx3PJ6mnY1bdZaxEkeb91yEsdiF+vN33Lr7KuNOvtqk25cqt10f0NzXB/7P/6bvrgyITulYfsz7R/Rrx3/KTUV6cAnAAAAIEm2sbUYW9WtyKJurM0rYqJyyaV6y6MDlvNdtclTL3cBK+OiMaPVVWlpUZqfl6Lrx2ncHmfK2+0OIBXbjUs1+voft+rdqQzFevW2Pvu3F/VXepdU1xzV95avaPqSU6h3ITy75XrdcumjrQ8ku+k91HT3jjuVAwAAAKg+MTuum5EFfbAyq4gdUbO7QQdrnGYKsbjR1SvS3C3pQMvWJ0XFotLrX2vWX/6XVi0vbJ5y9sRnlvW5/3lBd90by2lcu5n29qT3IX32jsf0mYOP6Z4GGiIA1abE/yQEAABQOsLxiG6szetS+KZs2Wpw1enQLrrqSU53jdlZp1jP7ZZaWopXrLey6NI7U406+0qjLr6Xveiu9Y413fPCf9JXVs+k3f5j935a//ShHyn4VGIAAACA5EwztBQLazYyr9m1BcVl5JFLTe46HXA532/j9s6nL9qtWNxoNSwtLUsLIWl1zbnd7Zbq6nKblqkU3Lzi0df/uFVvn2mUTHqyUlNn67OfW9THf+S63jPv6/+eDeiNmQ+0El/bcp3tNc36voPH9OlDx/R02yNqqdlcCAgAAAAAG9nG1qod1a21BV0I35RtbB3wNOhASiOBSNTownlpNbx1gwTblt76ZpO+9ketmr+1uWTmIyfC+pGfmdd9D23dLVxy8tF3li7qTGhGZ0LvM+0tgB2haA8AAGAbS7GwLodv6cbavCyXSy2ehl0XqEVjRrduSjdmJZclNTcXZ/qryKql7327QWdfbVLgbL3seObs1VNr6/GPhfVkV0iTd3xZf7kYSN7nkqV/9nCP/u69ny7UsAEAAABJ0lo8qvnoimYj85qLLklyuhQc2MN39d2KxZ1OeivL0vyCFA5LsiWXW6qvc07QKSe3rnn0jf/cqrde3Vys56mL6bH/6btq/uR39NrajP7TuWtbrsuSpeMH7tNnDh3Xpw8e06MH7mWKJwAAAABZReyYInZUa3ZM4diaFuOrWo6tqu47r8lImj/2hFrcDfK40jvjrYSNPjgnyUjNWXotGCN99/UG/cVXWnXzyuaZhnwfWdWPPD+vhx/fehpbpr0FkC8U7QEAAGRgjNFCLKyL4VndiiyqzlWjtprmXU/XFIsZ3bol3bjhnN3V3FT4Yr14TAqcrdfZV5v0vdcbFI1kHoBlGT3iX9XHu1b04Mfm9H78A3356su6sHgzuUyTu06/fvyn9amDHynU8AEAAFDlVuJrmo8u6/pqSIuxsCSp3l2rNk9TQadVjdtGa2tOkd7CgrS87NxuWc50ty27a8a979rfn5YkzT3oz3j/3HWPvvFfWvTWN5tkUor17IZl2Y++o7bPvqG5u76rP7PDUjD78xzwNOhT7Y/qM4eO61Ptj6p9l93JAQAAAFQm29iK2DGt2TGtxSNaiq86l9hqSpd0I7flUo3LoxrLI98f/r4k6YOPfnLT+kIhowsXnK7mtZtr8SRJ596u05//oVdXzm3ubHfPAxH97Z+Z12MfW83Yne/2tLczmpp/X99buiJ7i4lvmfYWQK4o2gMAAEhhG1uh6LLOr8xqMRZWvatGh2p33xojFjcKBqXrVyUjqbHRmRarUIwtXXyvTm+92qh3zzQqvJz9yR94dE0nuubVcvI9vW/O6asLH+jce9c3pZ5317Vp5Imf1UPNd+/v4AEAAFDVbGNrOb6mYGRJ19dCCsejcllSg6u2oIVgtnGK9MJhaT7kFOkZW7JcUk2N0z27gDWDu/bwn35RkvTKhqK90Kxb3/gvrXrz5SYZ25KRUfzuS4o+claxR95W7Og5yTJalKQssww/3HRY33/oMX360DE9duDopq4XAAAAAKpP1I6vd82LaiUe0VIsrKX4qsLxiIwxsixLRlKt5VaN5Vazu15uz+ZmA41vTavp7OvJ6yvHnZzGyOj6denaVScv82Sofrlyrlb/4w+8+uCd+k33Hbo7qs/91IJOfmZlU5MFpr0FUAgU7QEAAEiKG1u31hZ1PnxDK/GImtx1OriHA4GxuFEoKF27Jtm21NRUuGI9Y6TrF2t09tUmnX21UYvB7F/57rpvVR/+a+fkfvxdzSig3166pMjVWNblH285qt98/O/v6bUBAAAAsokbW4uxsOYiC7q+Nq+YHZfLcqnJXafG2sIc9DByivRWw850t4sLznd6y3KK9BobC981e6/a35/WwZnXk9fnHvRr/qZb3/ivrXrzL5sUd68p+uibij5yVtEPvy3TuvUBqUZ3nT7R9mF95tBxPXPwI7qrzluArQAAAABQaowx613znCltl2JhLa93zYuauJx2BpZcslRjeVTrcu+4W/qdX/73adc/OP6bisWNLl+W5oNSS8vmHG32ikd/8RWvvjvduGl9re0x/fUfX9Cn/upyWqHfbqe9/cyh43q85SjT3gLYMYr2AABAVYvacd1YC+lCeFZRE1ezu16HMhSkNb7lTCWVOIMrm7htNB9yivXiMamhMfPZXfsheMOjs6826uyrTbp5tSbrcs3339Tdf/VNxR/+nt63z+nd2Iq0sPW672s4pB+806+fvf+vqs6dfd0AAADATkXsmBZjYd1Ym9etyKJsY1TrcqspS5eFfRlDxCgclhYXpYV5KR53bq+pLc8ivY0SXfYkyfdf/4N+r/2zmnp3WZGHXlX0p95W7P4ZyRPfch0PNN6pTx88rs8cOq4nW32qdfGnZQAAAKBaxOy41uyYInZU4fiaFtcL88LxteRUsZYkj9yqcXnU6K6TOw9FbKld9iSp6ezrqvn2lN5r8mt1TWppTV9+/qZbX/vjVn3n5SYZk14Y2NAc1w/93QV99nPLqq0zMsboYphpbwEUD39ZAQAAVWktHtW1tZAuhm/KGKMDnga1bDGFU+JMrg+O/2bG+21jtDAvXb0qRaPOgT3P5hO48m5p3qV3zjTqrVeadCWQufOIqV2V6/h31fqps1q693u6aG7poiRtcZJYq6dRH2/7sD558FE93fYIyScAAADyKhyPaDG6ouuRkELRFRkZ1Vs1avU0ylWAuWYjUaPVVaeL3sKCFIs6t3tqpLr6wnXJLoTULnuSdNeFaS0//k80/1e37lxY56rRU96H9JlDx/V9Bz+iexsO7fdQAQAAABSRMUZRE9Paeue85diaM6VtbFURk5ihx8iSpVrLoxqXR94dds3bqdQuewne3/2iYj/j14Hm27ctL7j0l/+lVdN/0ax4LH08tfW2un5kUX/l84uqaYw5095eZ9pbAMVH0R4AAKgqK/E1XV2d05XVoFyy1OJp2PZsr9QzuRrfmk7rtmcbo8UFp1gvsuYU6zU07OsmaC1s6bvTjTr7aqPOvV2/6Wwx44orfu8F2Y++o5qPvqOFtguyLVtzkrKdJFZrefRk6wP65MGP6On2R/Ro8z20cgcAAEDeGGO0El9TMLqsa2tBrcTXZMlSg6t2x1Mj7UYsJq2G3Vpddet7310v0rMkt0uqrdv/7/CFFjUxXVy9qbev39Q/+MoXNt3/s29+S68+9sym2w/Xt+szB4/r04eO6SnvQ6p31xZiuAAAAAAKKG7s9elso1qLR7W4Xpi3HF+Tke2U5RnJbblU63Kr3lWjZld9wce5sctewp0fvK57Lk9r7kG/VlcsvfInLfrWxAFF19KPabjdRs/89SV95u9c03vW+/p312f0xsI5rcS3n/b2+w89pu8/9BjT3gLYVxTtAQCAqrAYC+ty+JZurM3L43LL62nKuYNH6plcd3753+uD478pI6OlJenqZSkcdqbB3diGPZ9iUen97zTo7CtNeu+NhrQzxYyM7EOzij70rmIPvyv7ofcUr1nddp0PNx3WJ9sf1SfbH5Hf26EGDsgBAAAgj2xjazG2qrnIom6szWvNROWSS43uWh2sObCvzx2LO530lpecTnrLy9L1641yu5wuepVUpLdmIroam9X51Rt6LzSrK/HrWmi8Iblsnbw1q8evndv0mKcu3NTJ87OaPvohnfB26DOHjuvTB4/p/sY7972AEgAAAEBhROyoIrbTOW8ltqrF2JqW42GtxiPS+vd+y0g1Lo9qXO6CdT7fcsxRo8iaFF6VHvvS5i57CQ/99y/q1x/8tL7531oUXt7QKt2ydeyHz+mOv/JtvWO/pz86z7S3AEoTRXsAAKBiGWM0H1vRhZVZBaPLqnN51F7TvKODUBvP5Go6+7qs16b0fptfK8tSfYPU6t2HwUuyben8u3U6+2qT3p1q1Fr49tlcduOSYg9+V9GHvqvYQ+/Kbgtuu747a1v1dPsj+lT7o/pY+8M6VNuyPwMHAABA1YracS3FwpqNzGt2bUFxGXnkUqO7bl87M8Rtp0hvZUWaD0nhFafJtMsl1dVJLS1SY6MznZOnjP8iumpHdDl+XZdj13Vx7YY+WL2uoPuWc6RNkjYUI/7cN97Nuq5/8/q8av/el9TkKXzHDAAAAAD5YRs7OZ3tWjyqpVhYi+td82xjS5YkI7ktyynOszxqrC2NKV5jMaPVNWltVVpack62ikWdesI7z0/rYGBzl72EQ+deV/A77ygce1qSZNwxxR54X63PflurD57VN+yQtJD9uZn2FkApKOM/UQEAAGRmG1vB6LI+WLmhpdiqGly1OlS7u04eqV32Eg7+xy/qnT7/vnTWM0a6+kGtzr7aqLPfatLyvHOGmPFEFXvwe8kivfg9l7ZdV6O7Tie9D+pT7Y/q6fZH9EDjXXTNAAAAQN6txaOaj65oNjKvueiSjDGqdXl0wNMg9z5NI2Qbo7X1Ir2FBaejni3JZUm1tVLzgWTjCGd5e1+Gsa9W7FVdjjkFepdi13Upel03zYaTdbb46+7J87N66sLNrPe3vfMd6ey09MTTeRoxAAAAgP1iG1urJqao4rocvqUVE9FSfFWr8YhMoomc5XSNq7U8avE0FL1rXqpY3CgSkdbWnPxtaUmKrM9S63I5J1fV1UmNjc5tT3z9i9uu86cbf0XPP/hPFX30rOxH3lG8dlVLkpMcZsC0twBKDUV7AACgYsTsuOYiizq3ckOrdlTN7rpdF+tJUt2bU2ld9hLuOv+6Dl+a1tyD/r0MN82tax699UqTzr7aqOCNGhnLVvxDVxR74l2nUO+BGakmuuU63HLpeMt9ySK9x1ruV43LveVjAAAAgN1Yia9pPrqs66shLcbCkqR6d63aPE37cqKIkVOkF151OuktLUl2XLJcUk2N1NScXqRXbpbtFacwb71I73Lsum7Z8ztbSbRG7qv3qOHGveqovU+/PP3L2z/md35V+gJFewAAAEApMcZo1Y4qHF/TYmxVoeiy5iPLmjGzkiRveFb1nlrVWG559ykH24u4vV6gt+p0z1takiJrTjd0y5JqPJKnRqrP0vS7/f1pHZzJ3mUvwe96Tcc+/mt67egdGe9n2lsApY6iPQAAUPYidkw31uZ1YWVWMcV1wN2g5l1O8RSJGq0sS8Gg9NRvZz+T6+E//aJe2WPR3kLQrXe+1ai3Xm3UtfN1sluDij50RtFn31Xswe/JNC9tu477Gu7QJ9sf0SfbH9VTbQ/pgKdh28cAAAAAO2UbW8vxNQUjS7q+FlI4HpVlSY2uWrXv4USZbIycgzzhFWlxUVqYl+Lr3RJqa53uC64ybYqwYC8li/MS/4bsxZ2tZK1W7iv3ynP5iJrnjujxtiP6vkfu0olPuuXrkFzf+ab0X7c/yKU3vym98TLd9gAAAIAiWotHtWKvaTm2plB0SfPRsOImLsuS3HKrzuVRq6dRzZZz3KO1pknuEkmIbHO7g154RVpcktbCzqxCRk4Hvdoa6UDLFuuwpdnLNbocqNXlmTr9L+/8bs7P/3PfeFc/kVK0l5j29vsPPaZPtj+ipl0eKwKAQqBoDwAAlK3VeETXVkO6tHpTRtIBd8OOO8slOnYsLTmFeuEVSZZ0+NK07jyf/SDXwZnX1f7+zrvthZctvTvVqLOvNumDD2xFH5hxuun1vCv7zhvbPt7radIn2j+sT7Y/qk+0f1iH6zkzDAAAAPsjbmwtxsKaiyzo+tq8YnZcLsulJnedGmvr8v58kYhReFVaXHCmvI3FnNtra6T6BsldZk2kjTGatxfTO+jFr2vBXt7Zilbr5bl8RO7L98p9+Yi8C0f01NE7deKESx/9u9IDPsnl2tBZ43d+Nff1020PAAAAKJiIHVM4HtFyLKxQNKyF2LKiJiZjJLflUp3LowOeerk3TN0at7PM+VpAiZOrIhFpZdnporeysl6gZ0tuj3OS1Xad0JfmXbo8U6cr55wivSsf1CoSkew7ruuJg1/VY/arOY/pqQs39beu27rjqR9k2lsAZYeiPQAAUHaWY2u6snpL11ZDslyWWjyNmxLYrdjGaHX9YGAwKEUjkiyprk5qaXWWOf7n2bvsJeTabS+6Zum9Nxv0nVfr9N3QDUU6vqPoZ76r+H0fSO6tE+1ay6MnW3361EFnyttHmu8h4QQAAMC+idgxLcbCurE2r1uRRdnGqNblVpO7Xm5Pfr+HRmPO9/KlRWl+fv17uW4f6GlszOvT7StjjObs+bTpbS/FbmjZrOxoPdZyo9xXjsh96Yg8V47IffmI2uMH5e90yd8p+X9cuv8BbT391RsvOx30ckW3PQAAAGBfRO24Vu2IVmJrCsWWFYosK2LHZCwjl5wCvUZ3ndxWac6gE4kaRdak8HretrzsFOfJcorycumCHotK187X6nKgLtlJb37OLbvtluJHZhTruKD4p88rdu9FqW5NP/P7X9/xOH/5zA3p7/zN3W8oABQJRXsAAKBsLMbCuhi+qZtrC/JYbnlrmuTa6mBViljcaDUsheal+aAztZbLJdXXO5dU7e9P6+DM9lNJbdVtz45LgbfrdOZsWN9d/UCrD3xP0c+9J9WvbrveDzffo0+1O0V6/laf6t21OW0jAAAAsBur8YgWoiu6HgkpFF2RkVG9VaNWT2PO37dzkfhOvrwszYek1TXndrfbOYFm4/fyUmUbo1t2cNMUt2GztqP1WIsH5L58JKWL3n1yhdrU3m7pSb/U+UPSk53S0aPbFOlttJMue6mPodseAAAAsGtxY2s1HtFKPKL56LJCsWWtxNdkyZKRVG95VOeqUXOJTtcaixmtrsmZmWhZWl5yCu4sS7JczjS32xXoGSOFZj3J4rzL52p1/UKtYg2Lih05r/i9FxT73HnF77ko07y06fEnz8/qqQs3dz54TkQCUKYo2gMAACXNGKNQbFkXVmYVii2r3qpVe01zTgetojGjlRUpFJIW5yXbdhLL7abWevhPt++yl7psotueMdJ7gahe/uCqAuYDhe//nkxXcNt13FXn1dPtj+iT7Y/oE20fVnvtgZyfHwAAANgpY4xW4msKRpd1bS2YPJDU4KpVm6dpZwViW4jFjdbWnIM9CwvOtEkyksst1ddJLS15eZp9ZRtbN+JzacV5l+M3tGYiO1qPNe+VZ31620ShnrXQKkuW2g9Kfr/k/5vSk37pvp0W6aXaaZe9BA5yAQAAADmzja1wPKpV2ynQm4+uaCkWlixJslRjuVXvqtHBmtL8W38s7kxzm8jXlpacKW+t9Q56Ho9zYtV23c9XVyxdOed00LsSqNOVQK2WYmtOcd69FxT/1HnF7r0g4w3lNK6f+8a7u98oTkQCUIYo2gMAACXJNrbmIkv6IHxDK7E1Nbhrdahm+6N6axGj5WUpOOd08LAk1dRKjU1bnwGWkGuXvYSDM6/rxqt/ovHaFl2qPafIXZelJ7Z+TINVp4+3P6RPrk95e3/DnXk7MAoAAAAYYxQ1ccVMXFHb+TdiRxWOR7Qaj2kxtqI1E5VLLjW6a/N2IMk2znS3K8tOkd7ysnN7YtqkAwec66UqbuK6Hr+VNsXt5disooruaD2uuXa5r9wr96X75LniFOq5lm7nMocOSU9+Qs50t53SvUf2UKS30W667KU+loNcAAAAQBpjjFbtqNOhPBZWKLqsxdiKbBlZRvJYbtW5a9SWY7OBQovb6wV6q06OtrQkRdYkIyc/q/FInprtO5/bcWn2So3TQW+9SG/2plH88CWni95HLij2A+dl3zG7o/Edrm/XYweO6vHWo9K//kdaPnBETSXajRAA8o2iPQAAUFJidlw3Iws6vzKrNTuqJnedDm7Rec7ImWJraUmam3MST1nOWWC7OSi4ky57Ccde+Q0N/+gzWe+3jEsP1x5V1z2P6JMHH9VjB47K49qi1R8AAACQRcyOJwvynOuxZIeHcCyiNTuqqInJGK13eZCs9eseyy235VK9q0bNrr0fBLGN00kvHJYW5p3v5MZ2pk6qqZGam0urSO/IlfWuDR9+RDET07X4zbQpbq/GZhVTfEfrdN08dHuK2ytOFz3XSlPaMofukPyfkjpPONPd3nNPHov0NvrC+P6sFwAAAKgSa/GoVuw1LcfWFIouaT4aVtzEZVmSW27VuTxq9TTJVYRk567z33OuPPlkxvttc7uDXnhFWlyS1sLOLEFGTge92hrpQA5dzxdDLl0JOAV6lwN1unLBrdW2a4ofmXG66J04r/hd1yS3nfP4D9Yc0PGW+/R4y/16rOWojh+4T221zTk/HgAqDUV7AACgJETsqG6szev8yqzixtYBT4Oas5xNFbeNwmFpadEp1IvFnC569XVSS+vux7DTLnsJT124qZPnZ/Xa0TuSt7Ws3KmPeR/RDz/0iD528CE1exp2PzAAAABUvLixFUt0xTMxxWxba+vdHFbtSLKzg5Ek4xTDmfXHuuWSx3LLk8eCvEyMnCK91bA0vyAtLki2vd6docaZOimX7taFtmKHdTk6qx/89ilFrbh+3r+g6/Gbiiv3g0syllyzdzrFeZfvlfvyfXJfuUeu1c3zRd151+0uek/6pcP7WaQHAAAAYNcidkzheETLsbBC0bAWYsvJk6Dclkt1Lo8OeOrltkoj0fno1/+bJOnG556TkVOgF4k43c6Xl6WVlfUCPVtye5yO5005nEwVjVi6fuF2F71LgRqFPHOK3RtwprrtuqD43ZekmljOYz3gadCxA0f0RMv9Or5eoHdXnZfcCABSULQHAABy5j3/tnPlmexd5XZqNR7RldWgrqzekpHU4m7I2IUuFjdaWZHmQ9L8vHNw0OVyWrY3bj5OtisP/slLu37sz/3Fe/pn3X9Fnz38qH7sxId15EB7fgYFAACAspaYqjYxTe3GqWrX7IjCdkRxO+50xjPWeoc8I7dccq93x/NYbnk9TQU/wBGJOCfMLC463fTicadYsLa29Ir0oiamG/Fbuhqb1dX4TV1b/3fBXtLJ87P6+SsXJEn3BM7qSsoJN5sYS3W3PiRz/ojcl47Ic+WI3FfulRWpy7j4XR+6XaTn75TuPsxBKAAAAKDURO24Vu2IVmJrCsWWFYosK2LHZCwjl5wCvUZ3ndxW6Z2AH4sb1X57Wh+68L4kaWZyWpcOd8rYkiynKC/XHM0YKXjDk5zi9lKgRlcXlxQ9fFGxe88r7r+g2N+4KNWv5jy+eleNHm2+93YHvZajuq/hEAV6ALANivYAAEDO7v/aHzpXfqx/z+tajq3qcnhO19eCslwutXgaN52tFokarSxLwaDTVc8ofx08wvaaLsduaGbxhj5YuaFruq7Fz3dIli+3FURrVHOuQw+sPaK/8fCH9dwzhzXRzJS3AAAA1SQfU9V6LLea3fVye0qj+i0SNVpddbroLSxI0YhzAMhTI9XVS+4S+MprG6M5O6RrsZu6Gp/V1dhNXYvP6mY8KDvZfzDdz33j3bTrP7FetOcyLjUv3i3rwhFF3lsv0Lt6j6xobdbnv/vw7QK9j/qlu+/mQBQAAABQSuLG1mo8opV4RPPRZYViywrHI8n76yyP6lw1WWf7KQYjo2hUziUira5K4bBzicWkz/7+f0gu+/Cf/AfN/S+dOR0nWV2xnGluz9Xq8kydLt5Y1VL7JaeD3tELin/ygkzzUs7j9FhuPdR0tx5vOarHWu7XYy33ydf4oYzNGAAAW6NoDwAA5OaNb8p74R1JUvyNb0r+T+1qNQvRFV0M39StyKJqLLe8Nc1yrZ9tZWS0tiotLTmFeuEVSetniDUf2L6FezbL8bC+F7yp9xdmdTl2XbdqryrcfOv2ArmcOBf1yH31HnlmHtJDsUf0I0/69AOfr1F7OwfoAAAAKk05TFWbL7GYU6S3tOR0tI6sSbIkt0uqrZMaitxkYsle0dXYrK7FbyY76F2P3VRE0ZzXcfL8rJ66cDP581MXbuqv/OFjevnSD8p97W5Z8RpJUrZ36vA9zjS3nSecf+/6EDkAAAAAUCpsYydPnpqPrmg+uqylWDjZybzG5Va9q0btNc3FHqokKW47xXmxqLS2tl6YtyqtrSrZOc8Y54Qpj0eqq5PuvTKtu86/nlzHHR+8rkOBac096E9btx2XZq/cnub24mVbNzxXFLv3HcWPnFfsr1+Q8YZyHqslSw803qnHWo4mLx9uukd17po8vRoAUN0o2gMAADlx/d6vpV/fQdGeMUah6LLOh2e1EF1R3XqCbFmWbGO0EjZaXHAK9aIRSZaTiLa07myMxpauzK3qu7du6nz4hq67rmm++YqiLUFngZYcVxSpkfvqvfJcOqKGm0d01LpXxw7erYc73Hrm70uH7+EgHQCgMsRilsJhye3O3JkKlSVubNnGyJbzr7J0JNvXMcRtLUZsSdLc0prc7uJ0lzNS2lS1TjFeTGsmotV4RDFjy9qwvMuynO5460V59Vbj7al+MryURlJMUqwIr/N2YjFpecnppBcOSzKSyy3V10sHcv3OnGdRE9X1+C1djd1cL9JzOugtmuWdr8xYali4Q7Wzh2VdPax/9Nq/3bTIj5/9U31rqS/jw++51+milyjSu+NOvv8DAACguGxjK26MjBL/ll6eUSjGSBE7qoVYWKHoshZjK+snVBl5LLfq3DVqWz/+ULQxyigWy9I1L3o7hbQspzDP49l6hqGH//SLGW+bOHRClwN1ujJTp4sXpIuRG1r70IzTRe8T52XfMbujcR+ua9djLUf1eOtRHT9wVMcOHFFTCXUjBIBKQ9EeAADY3hsvy/rOK8kfre+8Ir3xsvTE01s+LG5szUUW9UH4hsLxiBpdtTpYe0CxuNHyshSaN5oPSnHbSUbr653LdowthW65dP7amgKLs7psX9Otumtaab8suzUkHdzBtq3Wy3PlXtXfuFeHY0f0SNMRPXH3XfI94pLvh6Q77lRRk3sAAPbT7Gyjvj3t/HEYpcesH4ix1/+7fT39dtvYiimuuGKyZStmbMUVl634+u3x9Uc5EtOzFuMQjzG2Pri2Ikm68saMLKs4RXuWnO231i8uOcV4zv/r5SrSuArFyPn+XVcntRS4SM82tm7Z87oWm90wtW1oVwcerYUWua8ddi5X1/+9cZesmDO97ZOel9XZ/J1Nj/N7XtGTnpf1euxpHTki+U9IT3auF+ndwfd/AAAA7I0xRraM4saWMcbJytZPpIpr/TZjFDdxxey4ousnFcWMrbiJK2o7mVyiA7iUyOGMLFlVXLKXYOSWS3WuGrV6mpKz+RRa3F4vzotIkagze1A47BTpGVsy68mny+X87aWmZufdzNvfn9bBmdc33X5w5nX92W//V7368AHFP3xe8e+7Jrnt3NfrOaDHWu/T4y3367GWozp+4D611ZZGN0IAqBb8WR4AAGzvd341821fyFy0F7XjuhmZ1/mVm4qaqJpc9WqxmrWyLN0IGS3OS7btJKn1DU6b90xsW5q/6dGNKx5dvLmiC2vXdd19TYstlxW9+5LM3YvS3blvhrXSoNrrR3QofEQdniN6vP2IOu87pI4TLh26g+I8AED1sWOWGhulxqZij6Ry2Ca9uC7Z2S5RdGfs9QM36wV1Zv1AjLldXhezE2V2zh/bnQMyZtP1BMtybnfJJZcseWSpxrKSP7lUI0tWSXzXsW2jxTrnz1GHWw7I5Sr+mLB/Fu1lXY3N6kr0pi6t3tTV2E3dcs0qZsV2vrK1WrmvH5b72t23i/OuHZZrZeuDSj9dnyGXWfdLj/6q4sNP69Ah4hAAAABaL6RL5HLpne0SuVw0FtUte1m2jM6tXFfcMk7hlmKK2nHZxlbUOLmek7pZSrbxTpw55DyZjGXJZZyu3i7L5eR1liXXev7mkVu1bk/yZ0iNb01LklaO+7dZMr9iMWdK20jUmcY20TUvMXOQMU5unpjSdquueZnYtrQYdGv+lkfzN90K3nLrViimW6sr+sXzI1kf9zPWv9PXn3pm2/U3uRp0vOWInmi9X8dbjuqxA/fprjovcQUARUbRHgAA2NobL0tvfnPz7W9+c1O3vYgd1fXVkC6Eb8o2tmrteilcp0tz0vKy8/eImlqnMCA1YbVtKTTr0eyVGs1e8ejS/KKu2NcVbLys6IcuK37PRZl7dzYtlmulSS2hIzpijujR5iP62N1HdPKxgzp0iD9wAACAdPGUQrvbhXW3D9Qk/o3J6XAQTxbbOaV1UWMrbmLJx0i3O7ll47Ks9UK7lP+vH5ypc9U4JXh8Z8E+aH/fOcg19+DeDnIZWwqvuLQ871ZwPqbLa3NOxzz3rObrr2up+ZpiDRu+w2c5WSdN3CXXzTud4rxrKcV5wXZZZuujXq2tUvtBqb1dOnhIelwvy//KK1mXb7v4inT5m9KhrTuIAwAAoHRlK66LGzstn4vYccWNrZgdU2z9ZKmovX4yle0U2iVPkEq2Kb/9j5GRjCXbtnVNi3LJ0o21eXlc7tv5neWSx/KozlUjd4V37y6WO7/87yVJHxz/zbyv2zZGsfXCvGhUWl0vzAuvOLMFJTJ0y3V7SttcZg6SJDvuFOWFbnk0d8vo2kJYt1aXFYwua9EsasWzqPiBeZmWednt87Lvn5dqIzp5flYffXdz5/CEpy7c1Mnzs3rt6B3J22pVo0ea79VHvUedDnotR3W04Q6OiwBACaJoDwCAnXjjZeffbaaFrSiZuuyl3veFpxWOR3R1dU6Xw3NaWzOyVhu1MOfS6qoky5l268AB58BecNajD67U6OaVGs1ecevKyrxma64qevclxQ9fVPzxSzIN4R0NsSZ8QHeuHtFDdffpowfv1TP3H9Ejd7XJtZNT2QAAqEK33PN6K/o91a9W5x9ujZxpibaSnEY12c0u0dPOORHAJUsey1KNVUf3A5SFh//0i5KkVzIU7RkjRVYtLS+4tTTv1vK8S0sLbi3Pu7W04NbSgjSnoBYarmu59Zpid15V/ENXZB+6Jbl2MbXtvHdT5zz37F2yYjXJZRobpbZ26eARpxDv4CHp4EHn0taeft3j2fD5+99+bftBbNFBHAAAlLaYsfWBmVNN8Hty83fAqpR52thEO7tENmec6Umt1DzOlTxpymO5VOv2qHH9RKrtxG1bzVadJKnZ00DsFVDjW9NqOvt68vpuu+3F4k7XvGhUWlu7PaVtNLIeS+shlOia15BD17x4TJoPWro6F9H1hRXdDK8oGF3Sgr2kZfei1uoWZLfMy26Zl3k49wYFP/eNd7dd5hdePqfxT3xOj7U4RXodjR+Sx5XLGVMAgGKjaA8AgJ1IFLBVy0GdbF32Et78ps7/5R/pnSMdCi+7FQk1yI67ZIy0uuBR8HqNZq+uF+hddelGfE6RD11U/J5Lit9zQbEnL0t1azsaUuOaV/fpiI61HNHH7zmip+6+T3fWte5xQwEAqE4RKya35VKDq7bYQymaUpk2FtXHmPWL7fxr21b69fX7bNuSjNOd2rnNyno967rWrx++MqWDM85Brsu/867etD6RUqDn1vKCS9GIS0ZG5sCC4h+6cvviu6r4XdekmujON3a1Pq1zXt3Nw7ojcrfuaGrSwUPSoUNSe6fTJe/gwfR/6+t3+fncLpdJyNBBHAAAlAdbRmuKqsldx8m7VYoTp6pLoste4vpW3faMjKIRp2teLCqtpkxpG4tL1vo5R6ld82qbnRP2Nq3LGC1FI7oSDOva/LJmwysKRpa1YC9pybWo1doFxRoXZB9YkA7a0sH8bO/J87N66sLNbZd77IPLemztqHSYnAYAyg1FewAA5Cr1oE+1HNTZqsveOtfIqL711G8peL1G87M1unWtRjdnLUUPXlf8nvcVv+eiYicuKn73Zal2Zwf4vPF2PVR3RP47j+jEHffp0QNHdLD2wG63BgAAZJCYwgfV53ZR13rBV6brmYrBUorJktfN+jK2ZNav24mCNNtKXrfj0uVLd0q2JXuhUZKVocjs9hg2PU/KdWPWC9ZyKGazba0Xvll6aPlVyZbeqf/45vFnGHP68+T+nNsV4xlT+IOL/1fzF5N/DTwxNarfXvoBmdq19aK8K4p/6GqySM805d79ISnuUsP8XWpdPqy7Yod1n/uwOhoOy9fWpkNPWGr7rFOM19ys/T+4mkMuk7ZstZyYBQBABXJZLqYiRVVpfGtaknbdaa4cpXbZk6Sms6+r8a1pLTz6pGJRKRqT1lbXi/NWpNU1pU1z7HZJnhpnVqDGlCZ0MRPTvL2kYGRJ1+bCurm8rFtry5q3l7RsLSpcu6how4JMbURySWpbv+RRQ6xZXqtVd9a16t5mr+5radWHGrz6ga/+Uu4rIacBgLJE0R4AYGeqcXrYhNSDPhWUAMXicUXjRjHbdtrCx23FbVt6/RXdnUNniiNXv60rf/KXeu3oIcWOXlL8ExcVv/uK5IntaBx3uQ7peMsRPXnwqD5y4IgePXCvvDVNu90sAACwhYsXpDffuEOXglEtB5vVWFez/YMqTaLLWaaCsQ0FaJkLtjYXb21VALe54GyLYq5MBWObCsFSiuOyXN/8+NTnKWY3ijuL+NzS3292ujF8ZemzRR1HPhkZyWVLnqhMTVTyxGQ8UanG+dd4ovKbM/LPv5J8jN/zih7+sT69+ljdrp7Ta7fpPs/derjpHh1rP6wnDh1Wx4G7VOsqgT835tplL4FuewAAlCXbSPPztbp6RXK7zfYPAPIgHpdCc8536GLFXufvOh3nZn7+Nwr+3MWS2OZUzb/17/XqT6y/BpYzIXKia15jk9GylrVgL2s+vqhQdEk3QiuaW11WKL6kJWtJ4ZoFxepWbq/QJenA+iUP3NE6NUZbnYK8Wq8ON7bqfm+rHjjoFOXdWevVHXUtmXOoN16W3nkz9ycjpwGAslQCf0WrLIFAQENDQzpz5owCgYAkyefzqb+/X319fWX/fADWUbhWMQVrOdt40KeICZBt24rZRrH47X+dQjuTUnRntByOKxiytbgszc/bWliUVpakxSVpZcVSeMnSatjS2opb0VWXImG3IuH1f1dr9eu1v6m7c/ym8FN3/gv9xd9+JqdlLVm6r+4OPea9T8cO3KePHDiijxy4V82ehj28KgCAfCCfqR7felX6T7/3kWIPA0XypOdlySW9Hquy7/Rytt3veSV5PV+vgZGR3LGUIrlY1gI6Je6vSSyXcntNLHm/85jUdd2+b9O6PTHJtfXBwp/+/a9L8+m3/eyb39Krj239Pf6Ap0EPNd2tDzffo4ebD+vhpsN6qPmwDpTy9/eddNlLfUy15bkAUIHIaapLPG5pcaFOoZDE7LjV5Y5zTqe52QcK32nOtqWlZefEt2LE3h3nptX2vtNxzjM9XZTXoNBStznt9g9e1+qlP9Cb99+jUHRZt9aWFIota8laVNi9JOOy0x/gktSYhwHFXapZblVjtFWtatUdNV7d3diqo95WPXioVQ/e0aq7G9rU5Knf/XOQ0wBAVaBoL4+Gh4c1ODiorq4uvfTSS/L7nS9J4+Pjev755zU0NKSJiQn5fL6yfD5s5j3/tnPlmdwKVSpKNRetSRSuJa5X0/ufKUHaQQJk27YicVuxuK143CiaWnRn24rFbMWM0Vokrkjc1mospqUVW0uLRguL0tKS0fJ6sd3KskuRsEvRRJHdqjul4M6TLLyLxzb/tcC4YzL1YefSsH6pD8scWJG54/bPnStn5X/tlQxbktlTF27q5PlZvXb0jrTbXXKpo+kuHTtwn44dOKJjLffpw833qNG9u24eAID9Qz5TfZ70ON/pq7FwSyrO9hsZyVrviGYZybIlly1jGafgyrJv35+43bJv3+dyHm/WH5e6LrNpuQ3rSDyHy9ZPXfuXMpb0yt07mGpnn1iWkWVp/WJkuZTy8/rFtWEZS+nLuTLdt3k5lyX9g+l/KQWd5/4H9/1z/e/f9wuy3TEZdzTtX9sdk+2KyXZHnX9dMcVdMdmu6O3rlvNvfP3fUnby/KyeunBz0+2p3+M9llsPNN6ph5vv0Yeb79GH1wv07qrz7v9Utvm00y57CXSmAICyR05TfdovvKMHbq2q8YnjVVe01/6+U7Q292DlF2xl8thffFGS9Mqxwm2/MUZGRnHblrsuIiMjV0NElmVky7nPlu10Nneurd9mZIydvowk2yRuSXn8htsyLXPyf/zb5JiO/o9/qz/r+MeyU9Z/exxm02222bCMTPI2O/X/aY+1Myxze/x2cm3asJ3r/19fV+o2JteXst5M25oY5W9N/HnW9+UjE1/S0I+uH6d1Sard2/tsLTXLvdCqhkirWo1XB2tadbihVUdaW/XgIa8eOdyqB+5oksft3n5lu0VOAwBVg6K9PBkdHdXg4KB6eno0NjaWdl9PT4/8fr86OzvV2dmpc+fOyev1ltXzIbOjX/sD2TKaf+7H5LL38ctZCWr+0q9IkpaGfq/IIyk8z5uvqnn9y/LS1J8p9vjHCvr8djyuZRORJM1Hlwsae81f+pXkL47Yl36l4t9/2zYytuR641W1ZUqQ3vymzp/+U93s8Csai2stZmstFlNk/fryqtHKktHisrS2fLuTXXTVrbVER7uwW5HVlOvrt0dXXVmK7qK3C+0altavr8i0hmXuul2MZ28symtYkakPS7XRnLa9//e/vuPX6x9+41398rEndezAER1vcTroPdx0WPXuPWbJAIB9Rz5Tfb5b+5Z+8ug/kSR9/ZkfL/JoiuMnv/47kiV9/dM/6hS+uW8XwVmWkXHZslKK4MyGQrtEEZxJLbTT7eUSxXKJ+52fiz9l18nzs+r8f85Kko594gubTrqoZCfPz+p48O3kz8fn3lVL429VxWvwc994N+t9vzYd0lzvr+v+xjtV46qAv23spiNF6mOr7eQ8AKgQ5DTV5xff+bL+p7/8bZ20jf7lx8roBIM8+aX/ekqSNPTjzxV5JIV3/IOL+mszTte1v3z9V/Wd++9dLwpzislMWgHbxiIys+H+lCK69QKz9EelF74lJVKI4H8v6LafPD+r+899L/nzA+e+pw/O/l5l5DRZPsYnz8/qqYubT0BKyNZQYJO1OrnmW+VaaJVnqVUHbK8Oult1d2OrjrR41XGwVQ99qEX3Pe5RW7vkchVxv0JOAwBVg6K9PAgEAurv75ekTclZgs/nU19fn4aHh/X8889nXa4Unw9ZvPFNtV1w/uj97Vf/WIvHq+dsptazb+jx73xLknThm3+s+WNPFHlEhfXYesGi5BSufecXv1DQ57ftuM6ZOUlS8+J5uQp0UCX1fZckz3e+VXrvvzGyjdOe3tiSSVxf/9de74Qej0uxmHN7NCpFI5Yiq5Yia5aiEZciq5ZiEUvxqEuxmKUf/9NfU1uWpwz/m9/QF+78HUXD9SnFeC6thd2y4+lJnZFxprFKFtMtJovpjHdV5u6VDMV260V5DWGZ+lWpJreiu73I1oVj28dduKmv1H2f9CgJIQCUE/KZ6vSx5m/o5K0LkqSPNv55ZfyBfwdOnp+9vf11X6+q7U8t3vq5b7yrn6jSbU+9rRivQZ3Lo1pXjWpdHtWl/Fu36eea5O21KdfrXDWqdWdb3qOalJ9bzr6uOy58JetYDr77lg7OnJOeuLuAr8A++sJ4xpvj8bi+/nXn5KRnnnlG7v3sjAEAKChymup07c/+QscvXpEktQRerarv9CfPz+r4hUuSqm/bJen/9bXbJ5x/7mt/rq8cqZ7ZsEoppymUrU5ASi7zP97XT33/U3IttMo136qacKvaLa8+1NCiIy1ePdDeqvs/VK+7H5M+dLfU1qbS7iZOTgMAVYOivTwYGhqSJHV1dW25XH9/v4aHhzU+Pq5QKLTrM6sK/XzYzBgj+3f/tRJfhR74g9/XO4+dLOqYCun+P/jdtOtvV1HBYsvZb8v7zpvJn73vvKmDb39HC8c+WrAx2JYlj5wObDWWRy6rMH3/U9/31Nv2+v7bcaO1tUTBnKXIqhSNOj/Hok4xXTTi/ByJWIquaf1f5/ZoxFkuGrEUjUrxqCXbthSPWorHLMVizr/xqOUU60Ut2TFLsfjtZbbit17V/bWvZ73/kfAZHVj9A73W3pFeaJe4Xp9egCdPaU6b1eiu0wFPg1o8Dfqn33xt9yviLC4AKDvkM9Xp8T/+w+T1Sv8DfybVWri28eSMpy7c1Ccu3NKZo3cWdBy2cTpUuAp4kOTE+RtZp4f9oatrerfjvgwFch7VuWvWi+JSC+RuX89YTJcsoktZ3n17+RrLU9gDRKe+uP0yfI8HAJQxcprq9PfPnEler6bv9FL15jNS5pwmpy5rFSDbyfZPXbipp763oNfuuVuyXZKxJOOSZVvOddslGVfyupW8LeX+xO3GJdlWyvKWLONKW+/t26y09aatY/2xicdlXsfmsclYslKW61w+q6euZD8BKfkaXL2m3zEfV0P307r7sNTaWuJFeQAArKNoLw9GR0clOWdObSX1/tHRUQ0MDJTF82Gz+Ot/qZq3bhe1tL7zpn7zj/551SQFv/P2G8mfW95+o2q2XZK+lGHK0KXf/mX9zz9apDO53vxaQZ5m4/ueUJD3v379UiQ/+ftfly5svcxP1/0bfe1vFfdsvmZ3vQ54GtTsqVeLp1EtNQ3Ov55GHfA0JAvymtf/PVDT6PzraVCzu16e1I6NH/tnGZ+Ds7gAoDKRz1ShN17Wh757+yDPUxdu6ucXDynw0NbvSaXwvRfYdJDnnyzdpYsPPyiX5ZLLsuSSS+6U6y7LkstyyS1LluWS27LklkuWZa0v55JLllyJn7W+vOWSJSttXe71daUuc/u6JSt1mfXncW0aS+L2HT7PP+nd9Hr8h+9EpZ/8NwV7/Yv2nfJ/68l61xemg9KPvlSYcRTaGy9Lb35z++Xe/Kaz7BMU7gEAyg85TRV642WduHo1+eNTF27q6YmDOtNe+TnNibnN+Uy1bLsk/cPXNn9v/4f/7Yp+xv85p0gtrTAsUQS2ubjsdiFZemHb5kKy1AK4TOtIec6N69jwGGvDuJLFbxvWmfZ8Kev4+cbnsh7Z/0enlvVzS/9nTq+h2y15POkXt0eqqZE87pTrKfcnf67ZfJ/HI3nqbi/n3viYDJdcl2v/5S9IV3KLDf/ZX5N+6pO5LQwAQImgaG+Ppqenk9c7Ojq2Xd7r9SoUCunUqVO7StAK/XzIzPV7v7bptmo5m6kaW28nbHUWU6WfybVV+/FKfv9znSZ2rzFgydIBT72a3Q06UOMU0rV6GjcV16UW3x3w3L69yVMvd4E6LgIAKgv5TJX6nV/ddFP/196SfvhfFH4sxfDFzcVbP/0Xb0h/8/9dhMEU0BsvS2++svn2aijW2q5wrZJfgwyf9y2XpdseAKDMkNNUqQzfcX519i2d/ev/ovBjKbBjv7U5n6mWbW8597KOB89tuv1kKKDfP3KfFh4ozHdZ27Z19q2zkoyOHf+IXK79/9t8y7mXdfxLGfK5dX7PKzr1z19W9CNPpxXBZSrAK5sOdG+8LL2TfZs3qeS8DgBQsSja26MzKe23tzurKrHM9PR0WmJXys+3G/fff39Oy0UikbSf4/G44vH4Powoz974ptzf2fwlsRoKt6q5aE2icC2bSn7/t3rPN/pHf/k9DX74w05RnbtRB2qc4rpEgV2iE15LhuK7Rnft3qY5to3i2v/9Z+o+uiz216gYxB6KxbbtYg9h35HPbFYV+Uym4qU3v6n49DekJz5R+DEVUhVvv+tLX1C2QzPmS1+Q/SsfK8g4ivF7fattTyjka1Aw2eI9mwr/HPCdEsVC7KFYqiGfkchpMqnWnKb1/Cv6WMNfVux3GUnOtp/ffHyqKrZdkusPsp+Qcmz6V2X/nY8XZBzxeFyx+Jwk6amPxVWI5uFbbXvCvf/jV2X/0NavQTn9asglj9uoIvO6FHyvRLEQeyiWashpKNrbo5mZmV0/NhQKyev1lvTz7cb58+d39bi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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Layout 4x3\n", "fig, axes = plt.subplots(4, 3, figsize=(10, 9.5))\n", diff --git a/experiments/cn_privacy/__init__.py b/experiments/cn_privacy/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/cn_privacy/data.py b/experiments/cn_privacy/data.py new file mode 100644 index 0000000..ee5f68a --- /dev/null +++ b/experiments/cn_privacy/data.py @@ -0,0 +1,15 @@ +from src.config import load_config +from src.data import generate_randombn + + +def main(): + # Load config + config = load_config("cn_privacy") + + # Generate BNs and data + generate_randombn(config) + + +if __name__ == "__main__": + + main() diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py new file mode 100644 index 0000000..d3ed457 --- /dev/null +++ b/experiments/cn_privacy/exp.py @@ -0,0 +1,39 @@ +import gc +import multiprocessing # noqa: F401 # pylint: disable=unused-import +from itertools import product + +from joblib import Parallel, delayed + +from src.config import get_base_path, load_config +from src.mia import attack_cn_bn + + +def main(): + # Load config + config = load_config("cn_privacy") + + # Get base path + base_path = get_base_path(config) + + # Set number of threads for parallelization + num_cores = eval(config["num_cores"]) + + # For each ESS and each model ... + exp_vec = [ + f.stem for f in (base_path / config["data_path"]).iterdir() if f.is_file() + ] + ess_vec = eval(config["ess_vec"]) + + # ... run MIA attack on BN and CN + Parallel(n_jobs=num_cores)( + delayed(attack_cn_bn)(exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) + + # Clean + gc.collect() + + +if __name__ == "__main__": + + main() diff --git a/experiments/cn_privacy/main.py b/experiments/cn_privacy/main.py deleted file mode 100644 index edc5fb9..0000000 --- a/experiments/cn_privacy/main.py +++ /dev/null @@ -1,14 +0,0 @@ -from src.config import get_config -from src.data import generate_randombn -from src.run_exp import run_cn_privacy - -if __name__ == "__main__": - - # Load config - config = get_config("experiments/cn_privacy/config.yaml") - - # Generate BNs and data - generate_randombn(config) - - # Run experiment - run_cn_privacy(config) diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index f602c56..9e85d97 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -19,18 +19,18 @@ "from matplotlib.ticker import LogLocator\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", - "from src.config import *" + "from src.config import * # noqa" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7b61e02b", "metadata": {}, "outputs": [], "source": [ "# Choose config file\n", - "config = get_config(\"config.yaml\")\n", + "config = load_config(\"cn_vs_noisybn\")\n", "\n", "# Get results path\n", "res_path = get_base_path(config) / config[\"results_path\"]\n", @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "49a48f2f", "metadata": {}, "outputs": [], @@ -66,10 +66,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "38f1e202", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "No objects to concatenate", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m dirs:\n\u001b[32m 25\u001b[39m \n\u001b[32m 26\u001b[39m \u001b[38;5;66;03m# Get results\u001b[39;00m\n\u001b[32m 27\u001b[39m files = [f \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m os.listdir(\u001b[38;5;28mdir\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m.csv\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m f]\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m data = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m data.reset_index(inplace=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 30\u001b[39m data[\u001b[33m\"\u001b[39m\u001b[33mbn_noisy_probs_1\u001b[39m\u001b[33m\"\u001b[39m] = data.apply(\n\u001b[32m 31\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m row: (\n\u001b[32m 32\u001b[39m row[\u001b[33m\"\u001b[39m\u001b[33mbn_noisy_probs\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m (...)\u001b[39m\u001b[32m 36\u001b[39m axis=\u001b[32m1\u001b[39m,\n\u001b[32m 37\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/BN-Privacy/venv/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[39m, in \u001b[36mconcat\u001b[39m\u001b[34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[39m\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[32m 380\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m op = \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 386\u001b[39m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 387\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 388\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 390\u001b[39m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 391\u001b[39m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 392\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 393\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 395\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m op.get_result()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/BN-Privacy/venv/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[39m, in \u001b[36m_Concatenator.__init__\u001b[39m\u001b[34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[39m\n\u001b[32m 442\u001b[39m \u001b[38;5;28mself\u001b[39m.verify_integrity = verify_integrity\n\u001b[32m 443\u001b[39m \u001b[38;5;28mself\u001b[39m.copy = copy\n\u001b[32m--> \u001b[39m\u001b[32m445\u001b[39m objs, keys = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 447\u001b[39m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[32m 448\u001b[39m ndims = \u001b[38;5;28mself\u001b[39m._get_ndims(objs)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/BN-Privacy/venv/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[39m, in \u001b[36m_Concatenator._clean_keys_and_objs\u001b[39m\u001b[34m(self, objs, keys)\u001b[39m\n\u001b[32m 504\u001b[39m objs_list = \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[32m 506\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) == \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m507\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo objects to concatenate\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 509\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 510\u001b[39m objs_list = \u001b[38;5;28mlist\u001b[39m(com.not_none(*objs_list))\n", + "\u001b[31mValueError\u001b[39m: No objects to concatenate" + ] + } + ], "source": [ "res_path = get_base_path(config) / config[\"results_path\"]\n", "dirs = natsorted(\n", diff --git a/experiments/cn_vs_noisybn/__init__.py b/experiments/cn_vs_noisybn/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/cn_vs_noisybn/data.py b/experiments/cn_vs_noisybn/data.py new file mode 100644 index 0000000..b495094 --- /dev/null +++ b/experiments/cn_vs_noisybn/data.py @@ -0,0 +1,15 @@ +from src.config import load_config +from src.data import generate_naivebayes + + +def main(): + # Load config + config = load_config("cn_vs_noisybn") + + # Generate BNs and data + generate_naivebayes(config) + + +if __name__ == "__main__": + + main() diff --git a/src/run_exp.py b/experiments/cn_vs_noisybn/exp.py similarity index 57% rename from src/run_exp.py rename to experiments/cn_vs_noisybn/exp.py index 1fcb6e5..f11ca11 100644 --- a/src/run_exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -4,12 +4,14 @@ from joblib import Parallel, delayed -from src.config import get_base_path +from src.config import get_base_path, load_config from src.inference import run_inferences -from src.mia import attack_cn_bn, get_eps +from src.mia import get_eps -def run_cn_vs_noisybn(config): +def main(): + # Load config + config = load_config("cn_vs_noisybn") # Get base path base_path = get_base_path(config) @@ -37,25 +39,6 @@ def run_cn_vs_noisybn(config): gc.collect() -def run_cn_privacy(config): +if __name__ == "__main__": - # Get base path - base_path = get_base_path(config) - - # Set number of threads for parallelization - num_cores = eval(config["num_cores"]) - - # For each ESS and each model ... - exp_vec = [ - f.stem for f in (base_path / config["data_path"]).iterdir() if f.is_file() - ] - ess_vec = eval(config["ess_vec"]) - - # ... run MIA attack on BN and CN - Parallel(n_jobs=num_cores)( - delayed(attack_cn_bn)(exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) - ) - - # Clean - gc.collect() + main() diff --git a/experiments/cn_vs_noisybn/main.py b/experiments/cn_vs_noisybn/main.py deleted file mode 100644 index f0f9b7b..0000000 --- a/experiments/cn_vs_noisybn/main.py +++ /dev/null @@ -1,14 +0,0 @@ -from src.config import get_config -from src.data import generate_naivebayes -from src.run_exp import run_cn_vs_noisybn - -if __name__ == "__main__": - - # Load config - config = get_config("experiments/cn_vs_noisybn/config.yaml") - - # Generate BNs and data - generate_naivebayes(config) - - # Run experiment - run_cn_vs_noisybn(config) diff --git a/src/attacks.py b/src/attacks.py index 6253e6a..0177554 100644 --- a/src/attacks.py +++ b/src/attacks.py @@ -1,7 +1,9 @@ -from src.utils import get_ll, sample_from_cn import numpy as np import pyagrum as gum +from src.utils import get_ll, sample_from_cn + + # Get the maximum likelihood BN inside a CN def atk_mle(cn, data, exp, config): @@ -14,6 +16,7 @@ def atk_mle(cn, data, exp, config): return bn + # Get the maximum likelihood BN within a set def mle_bn(bns_sample, data): """ @@ -36,4 +39,4 @@ def mle_bn(bns_sample, data): mle_bn = bn mle = llr - return mle_bn \ No newline at end of file + return mle_bn diff --git a/src/config.py b/src/config.py index 016bb82..d04cd49 100644 --- a/src/config.py +++ b/src/config.py @@ -1,3 +1,4 @@ +import os import random import shutil from pathlib import Path @@ -7,16 +8,22 @@ # Read configuration for experiment -def get_config(path): +def load_config(name: str): - with open(path, "r") as f: + root = get_root_path() + + test_dir = "/tests" if os.getenv("USE_TEST_CONFIG") == "1" else "" + + config_path = root / f"configs{test_dir}" / f"{name}.yaml" + + with open(config_path, "r") as f: config = yaml.safe_load(f) return config # Set global seed -def set_global_seed(seed): +def set_global_seed(seed: int): random.seed(seed) gum.initRandom(seed) diff --git a/src/data.py b/src/data.py index 167b2ac..6db2c87 100644 --- a/src/data.py +++ b/src/data.py @@ -1,6 +1,7 @@ from itertools import product from pprint import pformat +import numpy as np import pyagrum as gum from numpy.random import randint @@ -24,8 +25,13 @@ def generate_naivebayes(config): create_clean_dir(data_path) create_clean_dir(results_path) - # Set BN (naive Bayes) structure + # Retrieve hyperparameters n_modmax = config["n_modmax"] + gpop_ss = config["gpop_ss"] + pool_ss = int(gpop_ss * config["pool_prop"]) + n_samples = config["n_samples"] + + # Set BN (naive Bayes) structure bn_str_gen = ( f'{config["target_var"]}->X{i}[{randint(2, n_modmax+1)}]' for i in range(config["n_nodes"] - 1) @@ -44,6 +50,15 @@ def generate_naivebayes(config): data_gen.drawSamples(config["gpop_ss"]) data_gen.setDiscretizedLabelModeRandom() gpop = data_gen.to_pandas() + + # For any data sample ... + for sample in range(n_samples): + + # ... sample pool and rpop + pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False) + gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + + # Save gpop gpop.to_csv(f"{data_path}/exp{i}.csv", index=False) # For each ESS ... @@ -78,9 +93,13 @@ def generate_randombn(config): create_clean_dir(data_path) create_clean_dir(results_path) + # Retrieve hyperparameters n_nodes_vec = eval(config["n_nodes_vec"]) edge_ratio_vec = eval(config["edge_ratio_vec"]) n_modmax = config["n_modmax"] + gpop_ss = config["gpop_ss"] + pool_ss = int(gpop_ss * config["pool_prop"]) + n_samples = config["n_samples"] # For each configuration ... for i, (n, r) in enumerate(product(n_nodes_vec, edge_ratio_vec)): @@ -100,4 +119,13 @@ def generate_randombn(config): data_gen.drawSamples(config["gpop_ss"]) data_gen.setDiscretizedLabelModeRandom() gpop = data_gen.to_pandas() + + # For any data sample ... + for sample in range(n_samples): + + # ... sample pool and rpop + pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False) + gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + + # Save gpop gpop.to_csv(f"{data_path}/exp{i}.csv", index=False) diff --git a/src/defenses.py b/src/defenses.py index 0cf8b6a..442483e 100644 --- a/src/defenses.py +++ b/src/defenses.py @@ -1,6 +1,8 @@ import pyagrum as gum + from src.utils import add_counts_to_bn + # Estimate a CN from data by local IDM def def_idm(bn, ess, data): bn_counts = gum.BayesNet(bn) @@ -8,4 +10,4 @@ def def_idm(bn, ess, data): cn = gum.CredalNet(bn_counts) cn.idmLearning(ess) - return cn \ No newline at end of file + return cn diff --git a/src/mia.py b/src/mia.py index 52f1ad3..8410c85 100644 --- a/src/mia.py +++ b/src/mia.py @@ -7,11 +7,10 @@ from scipy.stats import norm from sklearn import metrics +from src.attacks import * # noqa from src.config import get_base_path, set_global_seed -from src.utils import (get_llr, noisy_bn, - safe_assert) -from src.attacks import * -from src.defenses import * +from src.defenses import * # noqa +from src.utils import get_llr, noisy_bn, safe_assert # Get the attack power related to a fixed error @@ -55,9 +54,6 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): return power_vec, auc - - - # Find eps s.t. |AUC(eps) - AUC(CN)| < tol def get_eps(exp, ess, config): @@ -95,9 +91,7 @@ def get_eps(exp, ess, config): # For any data sample ... for sample in range(n_samples): - # ... sample pool and rpop, ... - pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False) - gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + # ... retrieve pool and rpop, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] rpop = gpop[~gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes].sample(rpop_ss) @@ -244,9 +238,7 @@ def attack_cn_bn(exp, ess, config): # For any data sample ... for sample in range(n_samples): - # ... sample pool and rpop, ... - pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False) - gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + # ... retrieve pool and rpop, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] rpop = gpop[~gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes].sample(rpop_ss) diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 4dca807..9e0806c 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,15 +1,10 @@ -from src.config import get_config -from src.data import generate_randombn -from src.run_exp import run_cn_privacy +from experiments.cn_privacy import data, exp def test_integration(): - # Load config - config = get_config("test/cn_privacy/config.yaml") - # Generate BNs and data - generate_randombn(config) + data.main() # Run experiment - run_cn_privacy(config) + exp.main() diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 1ef1eea..3abff5a 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,15 +1,10 @@ -from src.config import get_config -from src.data import generate_naivebayes -from src.run_exp import run_cn_vs_noisybn +from experiments.cn_vs_noisybn import data, exp def test_integration(): - # Load config - config = get_config("test/cn_vs_noisybn/config.yaml") - # Generate BNs and data - generate_naivebayes(config) + data.main() # Run experiment - run_cn_vs_noisybn(config) + exp.main() diff --git a/test/conftest.py b/test/conftest.py new file mode 100644 index 0000000..8948023 --- /dev/null +++ b/test/conftest.py @@ -0,0 +1,8 @@ +import os + +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def enable_test_config(): + os.environ["USE_TEST_CONFIG"] = "1" From 072a651ac2f14ae1834c90687b4757d76ee6b07c Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Mon, 10 Nov 2025 16:35:07 +0100 Subject: [PATCH 03/31] Update `Dockerfile`, revert some previous changes --- Dockerfile | 9 +++++++- configs/cn_privacy.yaml | 2 +- configs/cn_vs_noisybn.yaml | 2 +- configs/tests/cn_privacy.yaml | 2 +- configs/tests/cn_vs_noisybn.yaml | 2 +- experiments/cn_privacy/Plot_results.ipynb | 8 +++---- experiments/cn_privacy/data.py | 15 ------------- experiments/cn_privacy/exp.py | 14 ++++++++----- experiments/cn_vs_noisybn/Plot_results.ipynb | 12 +++++------ experiments/cn_vs_noisybn/data.py | 15 ------------- experiments/cn_vs_noisybn/exp.py | 13 ++++++++---- src/config.py | 6 +++--- src/data.py | 20 +++++++++--------- src/inference.py | 10 ++++----- src/mia.py | 22 ++++++++++---------- test/cn_privacy/test_integration.py | 5 +---- test/cn_vs_noisybn/test_integration.py | 5 +---- 17 files changed, 71 insertions(+), 91 deletions(-) delete mode 100644 experiments/cn_privacy/data.py delete mode 100644 experiments/cn_vs_noisybn/data.py diff --git a/Dockerfile b/Dockerfile index cd0c712..3b514b1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,11 @@ -FROM python:bookworm +FROM python:3.12-slim + +RUN apt-get update && apt-get install -y \ + build-essential \ + swig \ + libglpk-dev \ + python3-dev \ + && rm -rf /var/lib/apt/lists/* WORKDIR /workspace COPY . . diff --git a/configs/cn_privacy.yaml b/configs/cn_privacy.yaml index 800176d..21f8d6c 100644 --- a/configs/cn_privacy.yaml +++ b/configs/cn_privacy.yaml @@ -1,7 +1,7 @@ ## Configuration file # Paths -base_path: experiments/cn_privacy/output # Base path for output +out_path: experiments/cn_privacy/output # Output path bns_path: bns # Where to save ground-truth BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results diff --git a/configs/cn_vs_noisybn.yaml b/configs/cn_vs_noisybn.yaml index 6c6ac26..91073a8 100644 --- a/configs/cn_vs_noisybn.yaml +++ b/configs/cn_vs_noisybn.yaml @@ -1,7 +1,7 @@ ## Configuration file # Paths -base_path: experiments/cn_vs_noisybn/output # Base path for output +out_path: experiments/cn_vs_noisybn/output # Output path bns_path: bns # Where to save ground-truth BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results diff --git a/configs/tests/cn_privacy.yaml b/configs/tests/cn_privacy.yaml index 3035334..374379b 100644 --- a/configs/tests/cn_privacy.yaml +++ b/configs/tests/cn_privacy.yaml @@ -1,7 +1,7 @@ ## Configuration file # Paths -base_path: test/cn_privacy/output # Base path for output +out_path: test/cn_privacy/output # Output path bns_path: bns # Where to save ground-truth BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results diff --git a/configs/tests/cn_vs_noisybn.yaml b/configs/tests/cn_vs_noisybn.yaml index e4eb9f9..ea8b4e6 100644 --- a/configs/tests/cn_vs_noisybn.yaml +++ b/configs/tests/cn_vs_noisybn.yaml @@ -1,7 +1,7 @@ ## Configuration file # Paths -base_path: test/cn_vs_noisybn/output # Base path for output +out_path: test/cn_vs_noisybn/output # Output path bns_path: bns # Where to save ground-truth BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index fb21897..c3ffb32 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -17,12 +17,12 @@ "from pathlib import Path\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", - "from src.config import * # noqa" + "from src.config import * # noqa" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -31,10 +31,10 @@ "config = load_config(\"cn_privacy\")\n", "\n", "# Get results path\n", - "res_path = get_base_path(config) / config[\"results_path\"]\n", + "res_path = get_out_path(config) / config[\"results_path\"]\n", "\n", "# Choose where to save plots\n", - "plots_path = get_base_path(config) / \"plots\"\n", + "plots_path = get_out_path(config) / \"plots\"\n", "create_clean_dir(plots_path)" ] }, diff --git a/experiments/cn_privacy/data.py b/experiments/cn_privacy/data.py deleted file mode 100644 index ee5f68a..0000000 --- a/experiments/cn_privacy/data.py +++ /dev/null @@ -1,15 +0,0 @@ -from src.config import load_config -from src.data import generate_randombn - - -def main(): - # Load config - config = load_config("cn_privacy") - - # Generate BNs and data - generate_randombn(config) - - -if __name__ == "__main__": - - main() diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index d3ed457..b7f701c 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -4,23 +4,28 @@ from joblib import Parallel, delayed -from src.config import get_base_path, load_config +from src.config import get_out_path, load_config from src.mia import attack_cn_bn +from src.data import generate_randombn def main(): + # Load config config = load_config("cn_privacy") + # Generate BNs and data + generate_randombn(config) + # Get base path - base_path = get_base_path(config) + out_path = get_out_path(config) # Set number of threads for parallelization num_cores = eval(config["num_cores"]) # For each ESS and each model ... exp_vec = [ - f.stem for f in (base_path / config["data_path"]).iterdir() if f.is_file() + f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] ess_vec = eval(config["ess_vec"]) @@ -33,7 +38,6 @@ def main(): # Clean gc.collect() - if __name__ == "__main__": - main() + main() \ No newline at end of file diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 9e85d97..f151af2 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -19,12 +19,12 @@ "from matplotlib.ticker import LogLocator\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", - "from src.config import * # noqa" + "from src.config import * # noqa" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "7b61e02b", "metadata": {}, "outputs": [], @@ -33,10 +33,10 @@ "config = load_config(\"cn_vs_noisybn\")\n", "\n", "# Get results path\n", - "res_path = get_base_path(config) / config[\"results_path\"]\n", + "res_path = get_out_path(config) / config[\"results_path\"]\n", "\n", "# Choose where to save plots\n", - "plots_path = get_base_path(config) / \"plots\"\n", + "plots_path = get_out_path(config) / \"plots\"\n", "create_clean_dir(plots_path)" ] }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "38f1e202", "metadata": {}, "outputs": [ @@ -86,7 +86,7 @@ } ], "source": [ - "res_path = get_base_path(config) / config[\"results_path\"]\n", + "res_path = get_out_path(config) / config[\"results_path\"]\n", "dirs = natsorted(\n", " [f\"{res_path}/{dir}\" for dir in os.listdir(f\"{res_path}/\") if \"results_\" in dir]\n", ")\n", diff --git a/experiments/cn_vs_noisybn/data.py b/experiments/cn_vs_noisybn/data.py deleted file mode 100644 index b495094..0000000 --- a/experiments/cn_vs_noisybn/data.py +++ /dev/null @@ -1,15 +0,0 @@ -from src.config import load_config -from src.data import generate_naivebayes - - -def main(): - # Load config - config = load_config("cn_vs_noisybn") - - # Generate BNs and data - generate_naivebayes(config) - - -if __name__ == "__main__": - - main() diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index f11ca11..cf11626 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -4,24 +4,29 @@ from joblib import Parallel, delayed -from src.config import get_base_path, load_config +from src.config import get_out_path, load_config from src.inference import run_inferences from src.mia import get_eps +from src.data import generate_naivebayes def main(): + # Load config config = load_config("cn_vs_noisybn") + # Generate BNs and data + generate_naivebayes(config) + # Get base path - base_path = get_base_path(config) + out_path = get_out_path(config) # Set number of threads for parallelization num_cores = eval(config["num_cores"]) # For each ESS and each model ... exp_vec = [ - f.stem for f in (base_path / config["data_path"]).iterdir() if f.is_file() + f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] ess_vec = config["ess_dict"].keys() @@ -37,7 +42,7 @@ def main(): # Clean gc.collect() - + if __name__ == "__main__": diff --git a/src/config.py b/src/config.py index d04cd49..212b31c 100644 --- a/src/config.py +++ b/src/config.py @@ -35,12 +35,12 @@ def get_root_path(): # Get base path -def get_base_path(config): +def get_out_path(config): root_path = get_root_path() - base_path = config["base_path"] + out_path = config["out_path"] - return root_path / base_path + return root_path / out_path # Create an empty directory diff --git a/src/data.py b/src/data.py index 6db2c87..23ef5a9 100644 --- a/src/data.py +++ b/src/data.py @@ -5,7 +5,7 @@ import pyagrum as gum from numpy.random import randint -from src.config import create_clean_dir, get_base_path, set_global_seed +from src.config import create_clean_dir, get_out_path, set_global_seed from src.utils import compact_dict @@ -15,10 +15,10 @@ def generate_naivebayes(config): set_global_seed(config["seed"]) # Set paths - base_path = get_base_path(config) - bns_path = base_path / config["bns_path"] - data_path = base_path / config["data_path"] - results_path = base_path / config["results_path"] + out_path = get_out_path(config) + bns_path = out_path / config["bns_path"] + data_path = out_path / config["data_path"] + results_path = out_path / config["results_path"] # Create empty directories create_clean_dir(bns_path) @@ -66,7 +66,7 @@ def generate_naivebayes(config): # ... create results subdirectories and metadata files meta_file_path = ( - base_path + out_path / config["results_path"] / f'results_nodes{config["n_nodes"]}_ess{ess}' / config["meta_file"] @@ -83,10 +83,10 @@ def generate_randombn(config): set_global_seed(config["seed"]) # Set paths - base_path = get_base_path(config) - bns_path = base_path / config["bns_path"] - data_path = base_path / config["data_path"] - results_path = base_path / config["results_path"] + out_path = get_out_path(config) + bns_path = out_path / config["bns_path"] + data_path = out_path / config["data_path"] + results_path = out_path / config["results_path"] # Create empty directories create_clean_dir(bns_path) diff --git a/src/inference.py b/src/inference.py index 1a59c05..c138809 100644 --- a/src/inference.py +++ b/src/inference.py @@ -5,13 +5,13 @@ import pyagrum as gum from more_itertools import random_product -from src.config import get_base_path, set_global_seed +from src.config import get_out_path, set_global_seed from src.utils import add_counts_to_bn, get_min_max_bns, noisy_bn, safe_assert def run_inferences(exp, ess, eps, config): - base_path = get_base_path(config) + out_path = get_out_path(config) target = config["target_var"] # Set seed @@ -24,8 +24,8 @@ def run_inferences(exp, ess, eps, config): ] # Store ground-truth BN - gt = gum.loadBN(f'{base_path / config["bns_path"]}/{exp}.bif') - gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv') + gt = gum.loadBN(f'{out_path / config["bns_path"]}/{exp}.bif') + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') # Learn BN from gpop bn_learner = gum.BNLearner(gpop) @@ -63,7 +63,7 @@ def run_inferences(exp, ess, eps, config): ) res_path = ( - base_path + out_path / config["results_path"] / f'results_nodes{config["n_nodes"]}_ess{ess}' ) diff --git a/src/mia.py b/src/mia.py index 8410c85..18df490 100644 --- a/src/mia.py +++ b/src/mia.py @@ -7,9 +7,9 @@ from scipy.stats import norm from sklearn import metrics -from src.attacks import * # noqa -from src.config import get_base_path, set_global_seed -from src.defenses import * # noqa +from src.attacks import * # noqa +from src.config import get_out_path, set_global_seed +from src.defenses import * # noqa from src.utils import get_llr, noisy_bn, safe_assert @@ -58,14 +58,14 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): def get_eps(exp, ess, config): # Get base path - base_path = get_base_path(config) + out_path = get_out_path(config) # Set seed set_global_seed(config["seed"]) # Init hyperp. eps_vec = eval(config["ess_dict"][ess]) - results_path = base_path / config["results_path"] + results_path = out_path / config["results_path"] n_samples = config["n_samples"] error = eval(config["error"]) tol = config["tol"] @@ -73,8 +73,8 @@ def get_eps(exp, ess, config): atk_mec = eval(config["atk_mec"]) # Read data - gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv') - bn = gum.loadBN(f'{base_path / config["bns_path"]}/{exp}.bif') + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + bn = gum.loadBN(f'{out_path / config["bns_path"]}/{exp}.bif') n_nodes = config["n_nodes"] gpop_ss = config["gpop_ss"] rpop_ss = int(gpop_ss * config["rpop_prop"]) @@ -207,21 +207,21 @@ def get_eps(exp, ess, config): def attack_cn_bn(exp, ess, config): # Get base path - base_path = get_base_path(config) + out_path = get_out_path(config) # Set seed set_global_seed(config["seed"]) # Init hyperp. - results_path = base_path / config["results_path"] + results_path = out_path / config["results_path"] n_samples = config["n_samples"] error = eval(config["error"]) def_mec = eval(config["def_mec"]) atk_mec = eval(config["atk_mec"]) # Read data - gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv') - bn = gum.loadBN(f'{base_path / config["bns_path"]}/{exp}.bif') + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + bn = gum.loadBN(f'{out_path / config["bns_path"]}/{exp}.bif') n_nodes = len(bn.nodes()) gpop_ss = config["gpop_ss"] rpop_ss = int(gpop_ss * config["rpop_prop"]) diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 9e0806c..39542de 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,10 +1,7 @@ -from experiments.cn_privacy import data, exp +from experiments.cn_privacy import exp def test_integration(): - # Generate BNs and data - data.main() - # Run experiment exp.main() diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 3abff5a..a67e4bb 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,10 +1,7 @@ -from experiments.cn_vs_noisybn import data, exp +from experiments.cn_vs_noisybn import exp def test_integration(): - # Generate BNs and data - data.main() - # Run experiment exp.main() From d365427e3063d423f45b48ffa5a88b4dbd7b4b53 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Mon, 10 Nov 2025 16:38:54 +0100 Subject: [PATCH 04/31] Fix `README.md` --- README.md | 18 +++++------------- experiments/cn_privacy/exp.py | 7 ++++--- experiments/cn_vs_noisybn/exp.py | 6 +++--- src/inference.py | 4 +--- 4 files changed, 13 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 681356d..c5b4d4c 100644 --- a/README.md +++ b/README.md @@ -34,25 +34,19 @@ pip freeze > requirements.txt ### Running code -1. Generate ground-truth models and data: +1. Run the experiment: ```bash -python -m experiments..data +python -m experiments..exp ``` *Notice:* this will delete any existing ground-truth model, data, and result. -2. Run the experiment: - -```bash -python -m experiments..exp -``` - -3. Results available at: +2. Results available at: `experiments//output/`. -### Using Docker (recommended) #FIXME: check +### Using Docker (recommended) 1. Build the Docker image: @@ -63,11 +57,9 @@ docker build . -t bnp:2025 2. Run the Docker container: ```bash -docker run [-d] [--rm] -v bnp:/workspace bnp:2025 +docker run [-d] [--rm] -v bnp:/workspace bnp:2025 python -m experiments..exp ``` -where `` can be the data generation or the run of an experiment, or both (see above). - 3. Results available at: `/var/lib/docker/volumes/bnp/_data/experiments//output/`. diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index b7f701c..dd42172 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -5,12 +5,12 @@ from joblib import Parallel, delayed from src.config import get_out_path, load_config -from src.mia import attack_cn_bn from src.data import generate_randombn +from src.mia import attack_cn_bn def main(): - + # Load config config = load_config("cn_privacy") @@ -38,6 +38,7 @@ def main(): # Clean gc.collect() + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index cf11626..2fa13c5 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -5,13 +5,13 @@ from joblib import Parallel, delayed from src.config import get_out_path, load_config +from src.data import generate_naivebayes from src.inference import run_inferences from src.mia import get_eps -from src.data import generate_naivebayes def main(): - + # Load config config = load_config("cn_vs_noisybn") @@ -42,7 +42,7 @@ def main(): # Clean gc.collect() - + if __name__ == "__main__": diff --git a/src/inference.py b/src/inference.py index c138809..02ef6b3 100644 --- a/src/inference.py +++ b/src/inference.py @@ -63,9 +63,7 @@ def run_inferences(exp, ess, eps, config): ) res_path = ( - out_path - / config["results_path"] - / f'results_nodes{config["n_nodes"]}_ess{ess}' + out_path / config["results_path"] / f'results_nodes{config["n_nodes"]}_ess{ess}' ) results.to_csv(f"{res_path}/{exp}.csv", index=False) From 4193c517cc1e076fba2d859164db7137465266cf Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 11 Nov 2025 19:28:29 +0100 Subject: [PATCH 05/31] Change `cn_privacy` exps: update code modularity --- experiments/cn_privacy/exp.py | 70 ++++++--- experiments/cn_vs_noisybn/exp.py | 2 +- src/attacks.py | 4 +- src/config.py | 2 +- src/data.py | 25 ++- src/mia.py | 252 +++++++++++++++++++++---------- src/utils.py | 19 ++- 7 files changed, 261 insertions(+), 113 deletions(-) diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index dd42172..948d63a 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -1,39 +1,75 @@ import gc import multiprocessing # noqa: F401 # pylint: disable=unused-import from itertools import product +import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import get_out_path, load_config +from src.config import get_out_path, load_config, set_global_seed from src.data import generate_randombn -from src.mia import attack_cn_bn +from src.mia import phase_attack_mechanism, phase_defense_mechanism, phase_estimation, phase_mia_vs_bn, phase_mia_vs_cn, phase_theoretical_power def main(): - # Load config + # Init configs config = load_config("cn_privacy") - - # Generate BNs and data - generate_randombn(config) - - # Get base path out_path = get_out_path(config) + set_global_seed(config["seed"]) + results_path = out_path / config["results_path"] + n_samples = config["n_samples"] + error = eval(config["error"]) + def_mec = config["def_mec"] + atk_mec = config["atk_mec"] - # Set number of threads for parallelization - num_cores = eval(config["num_cores"]) - - # For each ESS and each model ... + # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] ess_vec = eval(config["ess_vec"]) - # ... run MIA attack on BN and CN - Parallel(n_jobs=num_cores)( - delayed(attack_cn_bn)(exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) - ) + # Generate BNs and data + print("#"*5, "Generate BNs and data", "#"*5) + generate_randombn(config) + + # Estimate BNs from rpop and pool + print("#"*5, "Estimate BNs from rpop and pool", "#"*5) + for exp in exp_vec: + phase_estimation(exp, config) + + # Defense mechanism + print("#"*5, "Defense mechanism", "#"*5) + for exp, ess in product(exp_vec, ess_vec): + phase_defense_mechanism(def_mec, exp, ess, config) + + # Attack mechanism + print("#"*5, "Attack mechanism", "#"*5) + for exp, ess in product(exp_vec, ess_vec): + phase_attack_mechanism(atk_mec, exp, ess, config) + + # MIA vs CN + print("#"*5, "MIA vs CN", "#"*5) + for exp, ess in product(exp_vec, ess_vec): + phase_mia_vs_cn(exp, ess, config) + + # MIA vs BN (for comparison) + print("#"*5, "MIA vs BN", "#"*5) + for exp in exp_vec: + phase_mia_vs_bn(exp, config) + + # Compute theoretical power + print("#"*5, "Compute theoretical power", "#"*5) + for exp in exp_vec: + phase_theoretical_power(exp, config) + + # ------------------------- + + # OLD! TODO: remove + # # ... run MIA attack on BN and CN + # Parallel(n_jobs=eval(config["num_cores"]))( + # delayed(attack_cn_bn)(exp, ess, config) + # for exp, ess in product(exp_vec, ess_vec) + # ) # Clean gc.collect() diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 2fa13c5..ae26aa5 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -18,7 +18,7 @@ def main(): # Generate BNs and data generate_naivebayes(config) - # Get base path + # Get output path out_path = get_out_path(config) # Set number of threads for parallelization diff --git a/src/attacks.py b/src/attacks.py index 0177554..79da39b 100644 --- a/src/attacks.py +++ b/src/attacks.py @@ -5,11 +5,11 @@ # Get the maximum likelihood BN inside a CN -def atk_mle(cn, data, exp, config): +def atk_mle(bn_min, bn_max, data, exp, config): # Sample from the CN ... n_bns = config["n_bns"] - bns_sample = sample_from_cn(cn, exp, n_bns) + bns_sample = sample_from_cn(bn_min, bn_max, exp, n_bns) # ... and take the MLE one bn = mle_bn(bns_sample, data) diff --git a/src/config.py b/src/config.py index 212b31c..90ccc48 100644 --- a/src/config.py +++ b/src/config.py @@ -34,7 +34,7 @@ def get_root_path(): return Path(__file__).resolve().parents[1] -# Get base path +# Get output path def get_out_path(config): root_path = get_root_path() diff --git a/src/data.py b/src/data.py index 23ef5a9..7e731eb 100644 --- a/src/data.py +++ b/src/data.py @@ -6,7 +6,7 @@ from numpy.random import randint from src.config import create_clean_dir, get_out_path, set_global_seed -from src.utils import compact_dict +from src.utils import compact_dict, safe_assert, save_bn def generate_naivebayes(config): @@ -43,7 +43,7 @@ def generate_naivebayes(config): # ... generate BN, ... bn = gum.fastBN(bn_str) - gum.saveBN(bn, f"{bns_path}/exp{i}.bif") + save_bn(bn, f"exp{i}", bns_path) # ... and generate gpop from BN data_gen = gum.BNDatabaseGenerator(bn) @@ -99,6 +99,7 @@ def generate_randombn(config): n_modmax = config["n_modmax"] gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) + rpop_ss = int(gpop_ss * config["rpop_prop"]) n_samples = config["n_samples"] # For each configuration ... @@ -107,14 +108,14 @@ def generate_randombn(config): # ... generate BN, ... bn_gen = gum.BNGenerator() bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=n_modmax) - gum.saveBN(bn, f"{bns_path}/exp{i}.bif") + save_bn(bn, f"exp{i}", bns_path) - with open(f'{results_path}/{config["meta_file"]}', "a") as m: + with open(f'{out_path}/{config["meta_file"]}', "a") as m: m.write( f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()} Max categories: {n_modmax}\n" ) - # ... and generate gpop from BN + # ... and generate gpop from BN #TODO: check if number of levels is coherent, otherwise: rejection sampling of data data_gen = gum.BNDatabaseGenerator(bn) data_gen.drawSamples(config["gpop_ss"]) data_gen.setDiscretizedLabelModeRandom() @@ -124,8 +125,20 @@ def generate_randombn(config): for sample in range(n_samples): # ... sample pool and rpop - pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False) + shuffled_idx = np.random.permutation(gpop.index) + + pool_idx = shuffled_idx[:pool_ss] + rpop_idx = shuffled_idx[pool_ss:pool_ss + rpop_ss] + gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) + + # Debug + safe_assert(pool_ss == len(pool_idx)) + safe_assert(rpop_ss == len(rpop_idx)) + safe_assert(sum(gpop[f"in-pool-{sample}"]) == pool_ss) + safe_assert(sum(gpop[f"in-rpop-{sample}"]) == rpop_ss) + # Save gpop gpop.to_csv(f"{data_path}/exp{i}.csv", index=False) diff --git a/src/mia.py b/src/mia.py index 18df490..7d083b9 100644 --- a/src/mia.py +++ b/src/mia.py @@ -7,10 +7,10 @@ from scipy.stats import norm from sklearn import metrics -from src.attacks import * # noqa +import src.attacks from src.config import get_out_path, set_global_seed -from src.defenses import * # noqa -from src.utils import get_llr, noisy_bn, safe_assert +import src.defenses +from src.utils import check_consistency, get_llr, get_min_max_bns, noisy_bn, safe_assert, save_bn # Get the attack power related to a fixed error @@ -57,7 +57,7 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): # Find eps s.t. |AUC(eps) - AUC(CN)| < tol def get_eps(exp, ess, config): - # Get base path + # Get output path out_path = get_out_path(config) # Set seed @@ -67,7 +67,7 @@ def get_eps(exp, ess, config): eps_vec = eval(config["ess_dict"][ess]) results_path = out_path / config["results_path"] n_samples = config["n_samples"] - error = eval(config["error"]) + error = eval(eval(config["error"])) tol = config["tol"] def_mec = eval(config["def_mec"]) atk_mec = eval(config["atk_mec"]) @@ -93,7 +93,7 @@ def get_eps(exp, ess, config): # ... retrieve pool and rpop, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] - rpop = gpop[~gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes].sample(rpop_ss) + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes] # ... estimate BN from rpop, ... learner = gum.BNLearner(rpop) @@ -202,22 +202,20 @@ def get_eps(exp, ess, config): return exp, ess, eps_best +# Learn BN parameters from a given DAG and data +def learn_bn_params(dag, data): -# Membership attack against CN and BN -def attack_cn_bn(exp, ess, config): + learner = gum.BNLearner(data) + learner.useSmoothingPrior(1e-5) + bn = learner.learnParameters(dag) - # Get base path - out_path = get_out_path(config) + return bn - # Set seed - set_global_seed(config["seed"]) +# Estimate BNs from rpop and pool +def phase_estimation(exp, config) -> None: - # Init hyperp. - results_path = out_path / config["results_path"] - n_samples = config["n_samples"] - error = eval(config["error"]) - def_mec = eval(config["def_mec"]) - atk_mec = eval(config["atk_mec"]) + # Get output path + out_path = get_out_path(config) # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') @@ -231,99 +229,187 @@ def attack_cn_bn(exp, ess, config): safe_assert(gpop_ss == gpop.shape[0]) safe_assert(n_nodes == gpop.shape[1]) - bn_theta_vec = [] - bn_theta_hat_vec = [] - cn_vec = [] - - # For any data sample ... - for sample in range(n_samples): + # For each data sample ... + for sample in range(config["n_samples"]): # ... retrieve pool and rpop, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] - rpop = gpop[~gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes].sample(rpop_ss) + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes] # ... estimate BN from rpop, ... - learner = gum.BNLearner(rpop) - learner.useSmoothingPrior(1e-5) - bn_theta_vec.append(learner.learnParameters(bn.dag())) + bn_learnt = learn_bn_params(bn.dag(), rpop) + save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config["rpop_path"]) # ... estimate BN from pool, ... - learner = gum.BNLearner(pool) - learner.useSmoothingPrior(1e-5) - bn_theta_hat_vec.append(learner.learnParameters(bn.dag())) - - # ... and run Defense mechanism: estimate the CN from BN - cn = def_mec(bn, ess, pool) - cn_vec.append(cn) + bn_learnt = learn_bn_params(bn.dag(), pool) + save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config["pool_path"]) # Debug safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) safe_assert(len(pool) == pool_ss) safe_assert(len(rpop) == rpop_ss) + + return - # Debug - safe_assert(len(bn_theta_vec) == n_samples) - safe_assert(len(bn_theta_hat_vec) == n_samples) - safe_assert(len(cn_vec) == n_samples) - # Compute theoretical bound - compl = bn.dim() - bound = math.sqrt(compl / pool_ss) +# Apply defense mechanism to a BN, namely, derive a CN from a BN +def phase_defense_mechanism(def_mec, exp, ess, config) -> None: - # Find power (beta) for any error (alpha) given theoretical bound - z_alpha = [norm.ppf(1 - i).item() for i in error] - z_one_minus_beta = [bound - i for i in z_alpha] - beta = [norm.cdf(i).item() for i in z_one_minus_beta] + # Get output path + out_path = get_out_path(config) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + + # For each data sample ... + for sample in range(config["n_samples"]): + + # ... read the related BN + bn = gum.loadBN(f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif") + + # ... retrieve pool, ... + pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :len(bn.nodes())] + + # ... and derive the CN + def_mec_fn = getattr(src.defenses, def_mec) + cn = def_mec_fn(bn, ess, pool) + # bn_min, bn_max = get_min_max_bns(cn, exp) + # save_bn(bn_min, f"bn_min_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") + # save_bn(bn_max, f"bn_max_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") + base_path = out_path / config["cns_path"] / f"ESS: {ess}" + cn.saveBNsMinMax(f"{base_path}/bn_min_{exp}_sample{sample}.bif", f"{base_path}/bn_max_{exp}_sample{sample}.bif") + + + return + +# Apply attack mechanism to a BN, namely, derive a BN from a CN +def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: + + # Get output path + out_path = get_out_path(config) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + + # For each data sample ... + for sample in range(config["n_samples"]): + + # ... read the related CN + bn_min = gum.loadBN(f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_min_{exp}_sample{sample}.bif") + bn_max = gum.loadBN(f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_max_{exp}_sample{sample}.bif") + + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :len(bn_min.nodes())] + + # ... and derive the BN + atk_mec_fn = getattr(src.attacks, atk_mec) + bn = atk_mec_fn(bn_min, bn_max, rpop, exp, config) + save_bn(bn, f"bn_{exp}_sample{sample}", out_path / config['atk_path'] / f"ESS: {ess}") + + return + +# MIA attack vs a BN +def phase_mia_vs_bn(exp, config) -> None: + + # Get output path + out_path = get_out_path(config) # Init results - results = pd.DataFrame({"error": error, "power_bound": beta}) + results = pd.DataFrame({"error": eval(config["error"])}) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - # Run MIA against CN - for sample in range(n_samples): + # For each data sample ... + for sample in range(config["n_samples"]): - # Retrieve sample-related info - y_true = gpop[f"in-pool-{sample}"] - cn = cn_vec[sample] - bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample]) + # ... read the BNs as estimated from rpop and pool, ... + bn_theta = gum.loadBN(f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif") + bn_theta_hat = gum.loadBN(f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif") - # Attack mechanism: extract a BN from the CN - ext_bn = atk_mec(cn, rpop, exp, config) - bn_ie = gum.LazyPropagation(ext_bn) + bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) + bn_theta_ie = gum.LazyPropagation(bn_theta) - # MIA - try: - power_vec, _ = run_mia(bn_ie, bn_theta_ie, rpop, gpop, y_true, error) - results[f"power_CN_sample{sample}"] = power_vec + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :len(bn_theta.nodes())] - except Exception: + # try: - # Debug - with open(f"{results_path}/log.txt", "a") as log: - log.write(f"{exp}: error with sample {sample} (CN).\n") - log.write(traceback.format_exc()) + # ... and perform membership inference on gpop + power_vec, _ = run_mia( + bn_theta_hat_ie, bn_theta_ie, rpop, gpop, gpop[f"in-pool-{sample}"], eval(config["error"]) + ) + results[f"power_BN_sample{sample}"] = power_vec - # Run MIA against BN - for sample in range(n_samples): + # except Exception: - # Retrieve sample-related info - y_true = gpop[f"in-pool-{sample}"] - bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat_vec[sample]) - bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample]) + # # Debug + # with open(f"{results_path}/log.txt", "a") as log: + # log.write(f"{exp}: error with sample {sample} (BN).\n") + # log.write(traceback.format_exc()) - try: + # Save results + results.to_csv(f'{out_path}/{config["results_path"]}/bn_{exp}.csv', index=False) - # MIA - power_vec, _ = run_mia( - bn_theta_hat_ie, bn_theta_ie, rpop, gpop, y_true, error - ) - results[f"power_BN_sample{sample}"] = power_vec +# MIA attack vs a CN +def phase_mia_vs_cn(exp, ess, config) -> None: - except Exception: + # Get output path + out_path = get_out_path(config) - # Debug - with open(f"{results_path}/log.txt", "a") as log: - log.write(f"{exp}: error with sample {sample} (BN).\n") - log.write(traceback.format_exc()) + # Init results + results = pd.DataFrame({"error": eval(config["error"])}) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + + # For each data sample ... + for sample in range(config["n_samples"]): + + # ... read the BN as inferred from the CN + bn_theta_hat = gum.loadBN(f'{out_path}/{config["atk_path"]}/ESS: {ess}/bn_{exp}_sample{sample}.bif') + + # ... read the BN as estimated from rpop, ... + bn_theta = gum.loadBN(f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif") + + bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) + bn_theta_ie = gum.LazyPropagation(bn_theta) + + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :len(bn_theta.nodes())] + + # try: + + # ... and perform membership inference on gpop + power_vec, _ = run_mia( + bn_theta_hat_ie, bn_theta_ie, rpop, gpop, gpop[f"in-pool-{sample}"], eval(config["error"]) + ) + results[f"power_CN_sample{sample}"] = power_vec + + # except Exception: + + # # Debug + # with open(f"{results_path}/log.txt", "a") as log: + # log.write(f"{exp}: error with sample {sample} (BN).\n") + # log.write(traceback.format_exc()) # Save results - results.to_csv(f"{results_path}/{exp}-ess{ess}.csv", index=False) + results.to_csv(f'{out_path}/{config["results_path"]}/cn_{exp}-ess{ess}.csv', index=False) + +# Get theoretical power +def phase_theoretical_power(exp, config): + + # Read BN + bn = gum.loadBN(f'{get_out_path(config) / config["bns_path"]}/{exp}.bif') + + # Compute bound + bound = math.sqrt(bn.dim() / int(config["gpop_ss"] * config["pool_prop"])) + + # Find power (beta) for any error (alpha) given theoretical bound + z_alpha = [norm.ppf(1 - i).item() for i in eval(config["error"])] + z_one_minus_beta = [bound - i for i in z_alpha] + beta = [norm.cdf(i).item() for i in z_one_minus_beta] + + return beta + + diff --git a/src/utils.py b/src/utils.py index 8dfad4a..7ae857e 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,6 +1,7 @@ import sys from math import prod from tempfile import TemporaryDirectory +import os import hopsy import numpy as np @@ -122,6 +123,19 @@ def safe_assert(condition): if IN_PYTEST: assert condition +# Open `path` for writing, creating any parent directories as needed. +def safe_open_dir(path): + + if not path.exists(): + path.mkdir(parents=True, exist_ok=True) + + return path + +# Save a BN, with its name, into `path` +def save_bn(bn, bn_name, path): + + with safe_open_dir(path) as dir: + gum.saveBN(bn, f"{dir}/{bn_name}.bif") # Extract BN min and BN max from a CN def get_min_max_bns(cn, exp: str): @@ -211,11 +225,10 @@ def sample_from_cpts(cpt_min, cpt_max, n_samples) -> list: # BNs sampler from a CN -def sample_from_cn(cn, exp: str, n_samples: int) -> list: +def sample_from_cn(bn_min, bn_max, exp: str, n_samples: int) -> list: # Get the DAG and extreme BNs - dag = gum.BayesNet(cn.current_bn()) - bn_min, bn_max = get_min_max_bns(cn, exp) + dag = gum.BayesNet(bn_min) # For each variable ... cpts_dict = {} From de6304f75fa00d1b7db9b1ef18f1da7d325656fd Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Wed, 12 Nov 2025 10:52:16 +0100 Subject: [PATCH 06/31] Fix `cn_privacy` test --- .gitignore | 2 +- configs/tests/cn_privacy.yaml | 6 +- experiments/cn_vs_noisybn/Plot_results.ipynb | 100 +++++++++++++------ src/mia.py | 23 +++-- src/utils.py | 2 +- 5 files changed, 90 insertions(+), 43 deletions(-) diff --git a/.gitignore b/.gitignore index 2e6e678..7d18de2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,5 @@ venv -output +output* bin __pycache__ .pytest_cache diff --git a/configs/tests/cn_privacy.yaml b/configs/tests/cn_privacy.yaml index 374379b..9e1c9df 100644 --- a/configs/tests/cn_privacy.yaml +++ b/configs/tests/cn_privacy.yaml @@ -2,7 +2,11 @@ # Paths out_path: test/cn_privacy/output # Output path -bns_path: bns # Where to save ground-truth BNs +bns_path: bns/gt # Where to save ground-truth BNs +rpop_path: bns/rpop # Where to save BNs learnt from rpop +pool_path: bns/pool # Where to save BNs learnt from pool +cns_path: cns # Where to save CNs learnt from pool +atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CNdata_path: data # Where to save data as generated from ground-truth BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results meta_file: exp_meta.txt # File of metadata diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index f151af2..831eab9 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7b61e02b", "metadata": {}, "outputs": [], @@ -66,25 +66,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "38f1e202", "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "No objects to concatenate", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m dirs:\n\u001b[32m 25\u001b[39m \n\u001b[32m 26\u001b[39m \u001b[38;5;66;03m# Get results\u001b[39;00m\n\u001b[32m 27\u001b[39m files = [f \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m os.listdir(\u001b[38;5;28mdir\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m.csv\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m f]\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m data = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m data.reset_index(inplace=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 30\u001b[39m data[\u001b[33m\"\u001b[39m\u001b[33mbn_noisy_probs_1\u001b[39m\u001b[33m\"\u001b[39m] = data.apply(\n\u001b[32m 31\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m row: (\n\u001b[32m 32\u001b[39m row[\u001b[33m\"\u001b[39m\u001b[33mbn_noisy_probs\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m (...)\u001b[39m\u001b[32m 36\u001b[39m axis=\u001b[32m1\u001b[39m,\n\u001b[32m 37\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/BN-Privacy/venv/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[39m, in \u001b[36mconcat\u001b[39m\u001b[34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[39m\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[32m 380\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m op = \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 386\u001b[39m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 387\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 388\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 390\u001b[39m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 391\u001b[39m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 392\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 393\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 395\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m op.get_result()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/BN-Privacy/venv/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[39m, in \u001b[36m_Concatenator.__init__\u001b[39m\u001b[34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[39m\n\u001b[32m 442\u001b[39m \u001b[38;5;28mself\u001b[39m.verify_integrity = verify_integrity\n\u001b[32m 443\u001b[39m \u001b[38;5;28mself\u001b[39m.copy = copy\n\u001b[32m--> \u001b[39m\u001b[32m445\u001b[39m objs, keys = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 447\u001b[39m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[32m 448\u001b[39m ndims = \u001b[38;5;28mself\u001b[39m._get_ndims(objs)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/BN-Privacy/venv/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[39m, in \u001b[36m_Concatenator._clean_keys_and_objs\u001b[39m\u001b[34m(self, objs, keys)\u001b[39m\n\u001b[32m 504\u001b[39m objs_list = \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[32m 506\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) == \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m507\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo objects to concatenate\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 509\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 510\u001b[39m objs_list = \u001b[38;5;28mlist\u001b[39m(com.not_none(*objs_list))\n", - "\u001b[31mValueError\u001b[39m: No objects to concatenate" - ] - } - ], + "outputs": [], "source": [ "res_path = get_out_path(config) / config[\"results_path\"]\n", "dirs = natsorted(\n", @@ -191,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "b6274696", "metadata": {}, "outputs": [], @@ -218,10 +203,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "0e9e8e36", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Ess vs eps\n", "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", @@ -250,10 +246,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "90bf5cc9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Ess vs CN certainty\n", "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", @@ -273,10 +280,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b5c19b7e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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v6L4L7tOqeavGqToAg/EJDwAAAADhRDgOREh82ntKW1ipbkfXCON2KmlqpeLTTpOUNT7FAdDbh9/WLX+/RV324a9Rt86+Tt36wq166KKHmEEOjDM+4QEAAAAgEliQE4gAd+g2UjDu1u3o0q0v3Kq3D78d4coAuN239b6gg3G3zr5O/aDuBxGqCIA/L+15SV/46xeGBOODuT/h8dKel8apMgD+NNgaVLalTD888EOt37de1//1eq1/bb0abA1GlwYAADAE4TgQAYRuQHRrsDWMGLQFsuXAFjXaGsNcEQB/3DebR2p95Ob+hAc3m4Hx9/bht/X5Zz+vK5+8Uo+894gauxu1r3fg0x1XPnmlPv/s57k+AQBAVKGtChBm4QjdrBZrmKuKLk6nU0455XA65JTT97G/bU6nHBrY5nT6bvdsG+64fvYbqQb3sR1Oh+TUsDW493Wof2z/NofTIUlB1eQzdlANg+vznKd/23DnGbxt2JoG1RfMa/fUMOi1j/rnPMLPPeD74u89HeHvRK+9N6S/w4VPFWpKwhSZZFKcKU5ms9n11TTw1f1npOe99zXLrDhzXND7DnnePPT5oL931yHfYwXab9iazKN7H0wmU0g/D0xeodxsfuiihyJUFYDBWMMDmFhYwwOIflyn44dwHAizTe9tCmn/r//t6zot87Thg9BhQshgwtigxgYIIYeEpMEE04O2ARNdj71HrfZWo8uYNDw3GfpDeX83HYYL2QN+bx4I+0dzA8Md2PsL/E0yDbkJMaobIqO8keLvJoZZ/m9CjHQTY6LdhOBmMzAxsIYHMHGwhgcQ/bhOxx/hOBBm21q3hbT/ruO7tOv4rjBVAwDRzymn+px9klOSw+hqJq/BNyH8fpIhiO+D+cTCaG4eBLoh8sq+V0J6veVbynXlyVd6biaYTKaBGwX95xv8nHed7hsKnhssg57ze8z+1+I91n1Txd9zE+2GBeAPn/AAJgY+4QFEP65TYxCOA2HW3ttudAkwmDvwMMssmSSzzJ4ARZInRHE/5w5NBm9zPx/U2P7zuffzDnS8axh8rOHO429bUGMH1RDovJ5tw50niBqGHRughsp/Var5WPOYf8bzp87XZ5d+Vg6nQ3aH3fXVOfDV6XT6PPb+6v7jeb5/f4cccjiGjh/ueEOedwxznhGO5/4kCSavWLsJ8c99/9Q/9/3T6DKG5f37y31TwPs5zycZhnnOJ3D3+t0f6nM+Nwo06Nxev1sHPzfkmF77B/uczzG93pMxHdPrZoj3a/P3nFlmz6czgrmZ4v50yuD/vsUSPuEBTAx8wgOIflynxiEcB8IsLSEtpP0tSRatnL0yuHAwQAgZcFskgtARQsjRBKHBBMSB3peAIbDXeT3/A+sOrYMIpgdv83k8KAB3P4fo987hd0IKx1fNXaVrT7s2jBUZz93+aHBoP1LY7vf5/pDeKaffmwdBH88xxpsEg286jOL1jHjTIcBNjGDfH25CwJv7mpNT6lOf0eUgDAaH/P5uMAz73KBPMYz06YNgbpAMvuky+LkRzz3MzZSX9r4U0vu14fUNuiz7Mr//Vhzu34Du93q4fy/6+7frcP9mDHiMAP9uHO7f3N71Df63Y6DzAZHEJzyA6Md1ahzCcSDMTs88XW+0vDHm/S9ZfInWnb0ujBUBGOzqU6/WH7b/Ycz7rzl1TRiriQ7e/6MuSYoztp7Jyr2grTtoH9XNA++bDqO4eRDRTyyEctOhf/9Ar2f38d3qtncb/SMDRsV9XbHESnBe2f+KXtkfWgulyWJIGD9SeD/ov9uBgn1/NwuGjB9hkozfCSjusYPGh3LjIdBkmECv1XOjYpiJOJ7xo3lvvV6jz3vl9X4FOsaQ+kzyf/wAN1cCjR/p5krAn73JpN3Hdof0CY+6g3VaOG2h5++p+z33eTzoec/fa69xgfb1N9b9ePDxB774P2+gOoBoxyexjEU4DoQZoRsQ/bIt2Voxa8WY/gGycvZK/uGBMXPPuoxz333gJkRA619bH9J/Tz912qe07ux1nsDevYi0d4gf6HnPc46BTx+4b2y4b1B4Pzea/b3HD3dM9w0F77H+ngt0TO/n/R0zqPN47e/vuUDnGfY5p8PzWr2POZrngMnIe7F7/pojmlz/7PVGlxCSkQJ978A92LDd37iAAX4QNxJGU6N73Ig3JoZ5LSPemBjmZsdINyb8HTPg2CBvdgR6Hf72DebnOdx5w3FDZlQ19o955/A7CsXG9zYyyTIEhONAmBG6ARPDrStu1Rf++oURFzvxlhKfoltyb4lgVQDcwnWz2btFFiY+dwuoQIH7iM/1h48+YX//WL/POew+xxnuZkqw+3vfDBnpOe/X4e+5EY8ZxA2W0dwg8X69TqdT+9v3h/QJD5Ncvdvl1MDrJBkGEEbu3ylOZ4DfLfzKwSSwvXW70SVMaITjQAQQugHR74wZZ+i+C+4LajVwyXWN3nfBfSx2AowTbjbDnyEtoGCoUD/hce1p1/qd6eZ9E8TpSs59Pokg+YbpTqfTJ7T3PKeB8YPHDtlfgY8x7PhBz/mrecjYQMccrubB9Tk9R/IdP0LNw72nQ+rzOp/39mHrG/yce7yf92PE+gIca/D7MfjmSsjvbYh/fwa/VgCItPbedqNLmNAIx4EIIHQDJoZV81bpoYse0g/qfqAtB7YEHLdy9krdknsL1ygwzrjZDES3SLUT5CYIJpNhbwQEuPHgL6z3/mSFz/gRbjzc9dJd2t429lmlp2SconVnrRsS9Ltrdz/v+er9fP8uw44JcMxAxw/mmAHHjlDzsGMHzTz3fn6444103hHfVz/vVVDvfYBjhvS+DvMeBDrmaN77QO9NMK9r1DWG8r76ea+CrjHA39P6lnod6zmmsUpLSBvzviAcByKG0A2YGM6YcYYeuughNdgatHH7Rr3W9Jq6Hd3KsmTp9Omna82pa5iBChiEm81AdOMTHsDIjL7ZkzMrJ6RwfMWsFVoxe0UYKwIwWKifxDot87QwVhN7TM6AjZeA8fHOO+/ojDMG/if27bff1rJlywysKPx2vPG8tj/4Y8W916yEHrscqUnqWTJPp33hRi350H8ZXR6Afna7XS+99JIkadWqVYqLY7VEIBq8ffhtbjYDUertw2+P6RMeD130ENcrMA4abA268skrx7z/k1c8yY0sIMJi/To1Ohdk5jgQQZ3//rdayu9R75YtyvbZ0ivt3K7emq+oeeVKZZXcrpTlyw2qEgCA6MYnPIDoxSc8gOjGJzyA6JdtydYKZ5K2mka/yPVKZxLXaYho4gZEyIkXX1TzdZ9Tx5bAs9wkqWPLFjVf9zmdePHFcaoMAICJKduSrdKVpbp59s2646Q79KuLfqV1Z6/jfwgAg7nbCa6cvXLYcStnr9RDFz2kVfNWjVNlACTXGh4p8Smj2oc1PIBx1LJdt+5rVorDMardUhwO3bKvWTr0XoQKiw3MHAcioPPf/9aeb9woZ1dXUOOdnZ3ac+NNWvjwb5hBDgAAgAmHT3gA0YtPeAAjcNilvm7J3i319bi+2nuHPuf52i3Zewa+en/v8zXQvr2+zx0/oDN6enRfy2HdmjVDneaR5zKnOBy6r+WwzujpkbY8KF1SPg5v1OREOA5EQEv5PUEH427Ozk613HOvFj78mwhVBQAAAESW+xMeL3WxhgcQTVbNW6WHPnS7fvB6mbYM07phpTNJt3zodp3BJzwQKf6C6OGC42FD554ggujBIbafbU670e+KJGlVZ5ce2t+iH2RatCUlOeC4lZ1duqXV5grGJenAv8apwsmJcBwIs+4dO0ZspRJIx+uvq7uhQUnZ2SMPBgAAAAAgGO/X6IzHv6GHejvUkBCvTVOnantSgtpNZqU5HTqtu1drjh+XtbdP2vsNKcEinZxvdNUI1YhB9BhnP/uEzMOF2H6OGyVBdLQ6o6dHDx1oGfk69dZ9wphiJwnCcSDM2h7dGNL+LT/8kaZ/8QsyJyXJ1P/H+3tTYqJMJlOYqgUAAAAATGp766SNn5X6W6pk9/ZpXWtb4PG9HdKm66Trn5Lm5o5TkZOAvW+UwbF3645gZz/3Dh9ED55VTRAdOeYEKT5Jiksc9DVJik90fY0bPMZrm+drklT/W+noLp/Dj3idekuaEoEXGDsIx4Ew63r33ZD2P1FbqxO1tcOOMSUmDgTniYkyJScPfO8J0hNlTuz/Pjlp4PukRFfY7v04Obn/ceJAEJ+YJHNS//GSkweOHc+vDQAAAACYMJ77licYD1pvh1TzbVdAHo3cQXTQwfHg0HlwAD3K2c/+jusc3WKKGIVggughgXSAIHrIMRKDO258/7Hj+scF0Rc8aO2Hpdcrxr7/7A+Er5YYRMoFhJmjvT3i53D29MjZ0yMdP65xvw8cFzckiPed5Z7oG7wnJQcO5ZOSZEpKHrQtkVnzMET3jh1qfeQRZbz6mkxdXdqVlaWUZcuUcc0aJS1ZYnR5AAAAwOi1bJea/zG2fZtekg69J2Vmh2/2c6Cez6M9LkF05MSNZtazn+B42CDavT0xiCDaa9/JngOs+EJo4fjKL4avlhhEOA6EmTktzegSIstul7OjQ/aODkNOz6x5hFvnv/+tlvJ7PGsFJPY/37V3r7reeENtv/udUleuVFbJ7UpZvty4QgEAAABvDofU2y71uP+ckHo6vL5vl978Q2jn+NlZ4akV/sX5C5sDBdD+guOEIIJo71nViQTR0SjrNGnhR8Z2I2vRKmnmqeGvKYaQ9ABhlrx0qTrr68e8f3phgWZ+9atydnfL0d0tZ3ePnN1dA9/3dPtu6+mWo6urf1y3HD3dnu9d21zjXdt6vL7vH9fV5ZqFPkEwax7hdOLFF7Xnxpvk7Bz+Y6YdW7ao+brPad6Pf6Qp558/TtUBAABgUnA6pd7OgdC6d1CA3dPh9f3gMcOMG22rlFg3bOgcTBA9ljYciYMCaYJoBPDR70m/vtR17QcrIVXK/27kaooRhONAmGVcs0Ztv/vdmPeffv31Spg9O4wVjczpcMjZ2zsQnHuH8O7gvcc7rB8UvPvb5gne+78fHMq7A/6uLteMh4kglmbNDz72JJw13/nvf2vPN250/R0MgrOzU3tuvEkLH/4NM8gBAAAmI6fT1cKjp33QH3+Btp8xPe3947zH9D+W0+hXN76Cmv08mjYco+wl7S/QJohGNJubK139sGsx3GAC8oRU13gWzQ3Z5Es7AIMlLVmi1JUrPS0aRiP1rLOUlJ0dgaqGZzKbZUpKkpKSxv3ckuTs6xs6s72r2+8s+YFQfWgo7xu8D8ymH3Ls7oHZ9MyaD9LgWfM+wfvEnDXfUn5P0MG4m7OzUy333KuFD/8m7PUAAABgFOx9I7cU6Wn3M6Z96DjvQNvRZ/QrC5/4ZNfrCeU1TT9ZWv3N0feSJogGRu/kfNciuDXfdvX8D2TRKteMcYLxsCAcByIgq+R2NV/3uRFbNXgzpaQo6/bbIlhV9DLFx8sUH29Iv3a/s+a7u4a2rukOELyPuG3QrPkur7CeWfNBC/esebvNNqYbWJLU8frr6m5oMORGFgAAwITjcAyddT1kdnUwM7UHPbZ3G/3KwsccLyVOkRLTvP70P05I9X085M8U/2MSUqW4eOnpktAW+sv+b2npFeF7rQCGNzfXFZC3bJdjy4M69p9/KL6vU2mZs2Sac6Zr8U16jIcV4TgQASnLl2vej38UVC9jyRWMz/vxj2jVYIComzXf1eXb1iZAb3m/s+a7hrauGXJsrxn0zJofu7ZHHtXs/7nL6DIAAIg+Ldtl2vKgPvTeS4qzd8rcMEua80FpxRdcC44hejmdUl9X4FnYQwLtQGMGz9w2ZnJFRJjMg8JodyDtL5z2F2T7C76nuGZcR8qKL4QWjq/8YvhqARC8rNPkvHiD3kxzzSBftWqV4uLiDC5qciIcByJkyvnna+HDv1HLPfeq4/XXA45LPessZd1+G8F4jIqWWfOO/oVZfVvPeM2a99PGxvdxl/++893d/dsGhfUTada8H7aqKvXu3q3ExYtdfxYtUuLiRYqfOZOFUwEAsWlvnfTct6Tmf8gsKd39fHuTtPs1Vzi38COuBcf4GHjo+nqCXNjRX1uRAAF2zwnJOXH/fTbE4AB7SKCd1h9qTwkwzs+Y+OSJ1y4k6zTXtdf8j9Hvu2gVM1QBTHqE40AEpSxfroUP/0bdO3ao9ZFH1PLaazJ1dWtaVpaSly5VxrXX0JoBhvGeNR83bdq4n98za96rB/zQWfODwvoRQvkhs+a9jxHGWfPO7m6deOEF6YUXfJ43p6X1B+WLlbh4kRIXLVJSf3huTk0N6ZwAAESt92uCW0Cs+R/Sry91LSB2cv741GY0h31sCzt6xgQIvh29Rr+y8IlL8hNG+5ldPdws7MGBdkKqZDYb/cqix0e/57r2RjOLPyHV1dMYACY5wnFgHCQtWaKsO+/Uey+5Pg6zjI/DAIbOmm/61KfUWf9G2I/raG9X1zvvqOudd4Zsi581yxOaJ3lmmy9WwkknycTvAwDARLW3Ttr4WakvyLV2ejtcQfr1T0XXDHKnc1BoPdLCjn4CbX+tR/pGt/h3VDPFSUlTAve4HjwLO2FQgB1oTByxRMTNzXXdlArmJpbk+vle/XB0XaMAECH8VwgAEHOSly4LKRxPXrZMCXPnqmfnTvU0Nwc1E73v4EH1HTyojldf9XnelJCghIUL+gPzxT4zz+MzMsZcIwAA4+K5bwUfjLv1dkg133YF5KPldEp93QHahfhpKxJwjJ8+2XKOvp6oZBrUBzvYWdgjjIlLnHgtRTDg5HzXNVfzbanppcDjFq1yzRgnGAcQIwjHAQAxJ+OaNWr73e/GvP9J5WWelkhOu129+w+4gvKdO9XT1KSepp3q3tmkvv37RzyWs7dXPTsa1LOjYci2uPT0IX3NkxYvVsLChTInRnDhJgAAgtGyfWx9jCVXOPdCuZQ0dYS2In4CbWc0LM8dJgmpw/e4DjgLe5gFIBNSCLHh39xcV0Desl2OLQ/q2H/+ofi+TqVlzpJpzpmuxTfpMQ4gxhCOAwBiTtKSJUpduVIdW7aMet/Us87yWSvAFBenxHlzlThvrrTqIz5jHZ2d6mluVs/Onep2B+c7m9Szc6ccJ06MeC770aPqfPNNdb75pu8Gs1kJJ500tLf54sWKnzWLRUEBAJHjdEodrdKJA9Lf/l9ox3r+++GpaTzEJfqZXT24rcgIfbAH/0lIlcy0VoMBsk6T8+INejPNNYN8FW0/AcQwwnEAQEzKKrldzdd9Ts7O4D8KbkpJUdbttwU93pySouTTTlPyaaf5PO90OmU/csQrNG8emHm+Z4/U1zf8gR0O9e7Zo949e9T+ku/HYk0pKf1h+aKBFi2LXCF63JQpQdcOAIgxDrvUfkg6fkA6cdDP1/3S8YOu76N5MUhT3Cj6YAeYhe2vrUhcgtGvDAAARADhOAAgJqUsX655P/6R9tx4U1ABuSklRfN+/COlLF8e8rlNJpPiZ8xQ/IwZSl250mebs7dXPXv2uGaYNzV5QvPu5ibZDx0e8djOzk51b9um7m3bhmyLmzlDSYP6micuWqTEefNkSuB/+gFgUurrcQXanqD7QH/I7f31gCsYdzrGtzaTWUqb6SeMHvzY3yzsAGPik2gpAgAAgkY4DgCIWVPOP18LH/6NWu65Vx2vvx5wXOpZZynr9tvCEoyPxJSQoKTFi5W0ePGQbfbjx/t7mjf5zjpvagoq4LcfOqyOQ4eHtpOJj1fi/Pk+oXlSf5/zuOnTadMCANGot9MVagcMvPu/dhwxutLA5p8tfeFZo6sAAAAxjHAcABDTUpYv18KHf6PuHTvU+sgjanntNZm6ujUtK0vJS5cq49prfHqMGylu6lSlLF8+JKR3OhzqO3hQPU1NrsDca9Z57969rv6ww+nr88xQ1/PP+2wyT53a35ploaeveeKiRUpcuFDmlJRwv0QAiG1Op9R9fFAbkwN+WpwclLqPRraW+BRp6ixpymxpav+fKbN8v75WIdX/ZuznmP2B8NULAAAwBoTjAADItUhn1p136r3+Ht7LJtDCRCazWQlz5ihhzhylnXuuzzZHd7drUVCvxUDdwbn96MjBiuP4cXX961/q+te/hmyLP2mOkhYt6u9pPtCuJeGkOTKZzWF7fQAw4TmdUmeb7yzv4/v99/bu7YhsLUnThobcU2f3h+CzBr4mTRu5Pck5N4QWjq/84tj3BQAACAPCcQAAJjFzUpKSTzlFyaecMmRbX1ubV2C+0zPzvLd5l5y9Iy+21rdvv/r27Vf7y6/4PG9KSlLiwoW+bVr6v49LTw/bawMAwznsUvvhwH283YH3iYOSvSeytaRk+p/dPWWWNHXOQPCdmBq+c2adJi38iNT8j9Hvu2iVNPPU8NUCAAAwBoTjAADEqPiMDMVnZCg150M+zzvtdvXu2zewGKjXrPO+gwdHPK6zu1vd//mPuv/znyHb4jIz+0Pzgb7miYsXK3H+fJkSE8P22gAgJPbegfYlftua9Lc8aT8kOe0RLMQkTckKEHjPHmh5MiXLtRClET76PenXl45uxntCqpT/3cjVBAAAECTCcQAA4MMUF+daoHP+fOn88322Odrb1dPc3L8YqFerlp075egYORixt7aqs7VVnfX1vhvMZiXMm9c/y9w129zdriU+ayaLggIID/cilv7amXgH35FexNIcPyjgnuX/a9pMKS7K/5dtbq509cPSpuuCC8gTUl3j5+ZGvjYAAIARRPm/tAAAQDQxp6UpeelSJS9d6vO80+lU36FDQ/qadzftVO+evZJ9hJmVDod6d+1S765dan/hRd9zpqZ6tWhZPDDzfNEimdPSwv0SAUxE3cd925h49/b2/toV6UUskwO3M/Fe3DIlU5pMazOcnC9d/5RU822p6aXA4xatcs0YJxgHAABRgnAcAACEzGQyKSErSwlZWUo7+yyfbc6eHvXs2eMJzbt37nSF6E1Nsh8ZeXamo6NDXe++q6533x2yLT4ry9PXPHFRf6uWxYuVcNJJMsXzzxxgQnMvYukOuwMF3scPSr3tka0lcar/2d1T5/iG4cnpIy9iOVnNzXUF5C3b5djyoI795x+K7+tUWuYsmeac6Vp8kx7jAAAgyvB/jQAAIKJMiYlKslqVZLUO2WY/etQ1y3xQaN7T1CRnd/eIx+5raVFfS4s6XnvN95wJCUpYsMBnMVD3nziLhTYtgJEcDqnjsO8sb0/wfcCr1/dByT7y74GQpGQEbmviHXwn8imVoGWdJufFG/RmmmsG+apVqxQXF2dwUQAAAP4RjgMAAMPEpacr5cwzlXLmmT7POx0O9e3fr253WN7f17ynqUm9+/e7ZpQOw9nbq56GBvU0NOjEoG3m9PSBwNwTnC9S4sKFMicZtKAdMBnYe6UTLUNndR/f79vT+0RL5BexTJvpv52J9yzvKbOkhOQI1gEAAIBoRzgOAACijslsVsLcuUqYO1f6yId9tjm6utTT3Nw/y3xnf29z1+KgjmPHRjy24+hRdb71ljrfemvQSU1KOOkk377m/d/Hz54t02TqDwyMRm9X4HYm7tnexw/0L2I5/I2rkJjjpbQsV7g9OOj2/pqWFf2LWAIAACAq8K9GAAAwoZiTk5V86qlKPtW3d63T6ZS9tXVgMdCdO9XT1Oyacb57t9TbO/yBnU717t2r3r171f6Pf/hsMiUnuwLzQaF54uLFips6NdwvERgf3cd9A27v2d2er/sjv4hlXNIwfby9Zn6nTp9ci1gCAADAcITjAABgUjCZTIqfPl3x06crNTfXZ5uzr0+9e/f69jXvb9XSd+jQiMd2dnWpe/t2dW/fPmRb3IwZSly0sD8wXzww83z+PJkSEsL2+oCgOJ1Sl81/O5PBX3sGNx0Ks8QpQ2d1Dw68p86Ski2xu4glAAAADEU4DgAAJj1TfLwSFy5U4sKF0n/5brOfODEww7y/r3l3k2vWubOjY8Rj2w8fVufhw+rcWue7IT5eifPm+fY1X+SadR43YwaLgmJ0HA5X2xKfPt4BWp1EehHLZMswbU3mDHyfNCWydQAAAAAhIhwHAAAxLW7KFKWcsUwpZyzzed7pdKqvpcU3NO+fed67d68rrBxOX59rhnpTk/T3v/tsMk+Z4hOaexYIXbhQ5tTU8L7AyaRlu0xbHtSH3ntJcfZOmRtmSXM+KK34gpR1mtHVjY29T2pv8Z3VffzA0MC7vUVy9EW2lrSZQ2d1+/vKIpYAAACYJAjHAQAA/DCZTEqYNUsJs2Yp7ZxzfLY5enrUu2tXf19z12Kg7gDd3tY24rEdJ06o6+231fX220O2xc+ZM6hNiys4T5gzR6a4uLC9vgllb5303Lek5n/ILCnd/Xx7k7T7Nen1CmnhR6SPfk+amxv4OOOprztAOxP3Apb9wXf7YUV0EUtTnDQly2tWd4DAe0qWFEcbIAAAAMQWwnEAAIBRMicmKmnJEiUtWTJkm91m810M1B2gNzfL2dMz4rH79u9X3/796njlVZ/nTYmJSly4wLevef/ioHEWS7heWvR5v0badJ3UO0KLm+Z/SL++VLr6Yenk/MjV031iUNA9uK1J/58uW+RqkKS4RK9we3Dw7dXqJHW6ZI7RmyoAAADACAjHAQAAwijOYlHqhz6k1A99yOd5p92u3v37vWaZ7/SE6H379494XGdPj7rf36Hu93f4PaerRYtvb/OEBQtkTkwM22sbd3vrpI2flfo6gxvf2+EK0q9/anQzyJ1OqevoMIG3V/Ad6UUsE9L8z+6eOse3t3dKBotYAgAAACEiHAcAABgHprg41wKd8+ZJqz7is83R0aGe5mafvuaudi075Tgxchhrt9nU+cYb6nzjDd8NZrMS5s71zDAf6HO+WPFZWdG/KOhz3wo+GHfr7ZBqvu0KyB0OqbN1UB9vf61ODkp9XZF5DW7J6QH6eM/2nfmdNDWydQAAAADwIBwHAAAwmDk1Vcmnn67k00/3ed7pdMp++LDf0Lxnzx6pb4QFGh0O9e7erd7du9X+4ks+m0ypqa7e5ot8Q/PERYsUNyUt3C9x9Fq2u1qljEXTS9I9J7uC8UgvYpk6feisbn9fE1IiWwcAAACAUSMcBwAAiFImk0nxM2cqfuZMpa5c6bPN2durnj17fBYD7dm5U91NTbIfPjzisZ0dHep+d5u63902ZFv8zJlefc1drVqSFi1Swrx5MsWP0z8ftz405Knuo/Fq25GqrrYEOfrMMsc7lJzRq4wlHUpKHxSCt7eM/dwms5SW5Tu72yfw7p/5nZYlxU/gtjUAAABAjCMcD7PGxkaVlZVp69atamxslCRZrVYVFxerqKgoYuerra1Va2ur53xr1qxRSUlJ2M8HAACigykhQUmLFytp8WJJF/pssx8/PhCWe4Jz16xzZ9fI7UP6Dh1S36FD6nj9dd8NCQlKnD+/Pzhf6GrV4p5tnpkZ3jYt+9/yfNt5JEEtb05Tx6GkIcM6Dyep7f0pSp3ZrawPHlPK9N7AxzQn+JnVPWdoq5O0GSxiCQAAAMQAwvEwKi8vV2lpqfLy8vTAAw8oJydHklRdXa21a9eqrKxMNTU1slqtYTlfaWmpysvLlZeXp6qqqiHnq6ioCOv5AADAxBA3dapSli9XyvLlPs87HQ71HTzoFZo3u2ad79yp3n37XAtTDqe3Vz2NjerpnwDgzTxtmmeGuSsw718cdOFCmZOTgy++p0PaUSMdcs1oP7EvSXv+mSGn3Tzsbh2HktT8t+ma9+E2TTmp2xV8533Ha6b3bBaxBAAAAOCDcDxMKisrVVpaqoKCAlVVVflsKygoUE5OjnJzc5Wbm6udO3fKYrGEdL78/HzV1tYGPJ/Vag3r+QAAwMRnMpuVMGeOEubMUdp55/lsc3R3uxYF9e5r3t+mxXH06IjHdhw7pq63/qWut/416KQmJcyZ49vXvD9Ej58zRyazWeppl/7zV+ndJ6X3n3MtqinXjPFggnE3p92sPf/M0ML/PqKUBVbpg58K7o0BAAAAEJMIx8OgsbFRxcXFkjQkqHazWq0qKipSeXm51q5dG3BcMCorK1VbWytJeuCBB/yOycnJUUFBgaqrq1VaWqqKiooxnw8AAEx+5qQkJZ9yipJPOWXItr62tv6wvEk9TTsHFgfdtUvqHaaNiSQ5nerdt0+9+/ap/eWXfTaZEhOUmBGvxKSjSpzSraSpfUqc1qfEqSbFJTrV8ua0oINxz+nsZrW8NU0LP/aBUe0HAAAAIPYQjodBWVmZJCkvL2/YccXFxSovL1d1dbVsNtuYZnPbbDZPEJ+XlzfsMVauXKnq6mpVVlaqrKyM2eMAAGBM4jMyFJ+RodT+Fm5uzr4+9e7b51kQ1BOaNzWp7+DBEY/r7OlV98FedStRku/CluYkydE9tno7WpLUPfOjGtqhHAAAAAAGEI6HQWVlpSSN2Nvbe3tlZeWYFsx0zxiXNGLY7R3Wj/V8AAAAgZji45W4YIESFyyQLrjAZ5ujvV3dTU3q+c+76qnbrJ7tb6ln3yH1HIuTo2/k2eBjDcbd2v76mmavHH7iAgAAAIDYRjgeovr6es/32dnZI463WCyy2WzauHHjmMLqRq8FsDIzM4cd6x3Gb9myZdTnAgAAGJOOVpn/87RS3nlCKY1/l+J7pTMkneFa87Ovy6yeY/HqOR6vnt6Z6rFnqbvNrt4DhyS7PSwldG3bFpbjAAAAAJi8CMdDtHXrVs/3I80cd4+pr6/3CdXHg/eMcwAAgLBrPyJtf8q1qObOFyRHn99hJpOUsPA0JSy9QmlLr5SyTvNsc/b0qGfPHvXs3KkD3/mu+g4dGnM5jvb2Me8LAAAAIDYQjoeooaFhzPuOpe+4dwDf2to67Fjv7TabbVTnGauWlhYdGuX/yO7YscPnsd1ulz1Ms8aiifdrmoyvD5gMuE6BUWo/JNP2v8i07Ump6R8yOQNfN85ZZ8h5+uVynn65NMNr0U/vay0uTvELF7r+zHswpHDcnJrKdQwYhP+eAtGP6xSIfrFynRr92gjHQzTa0Nm7FUpra+uow3HvPuIjnXu8Z6dL0v3336/vfve7IR2jrq5uxOB/onv55ZeNLgHACLhOAf8Su9s04/Crmnnon7LY3pFJjoBjj0/J1qGZ5+nQzHPVmTrX9eS2g5JGXqxzqiVdqSHU2ZqYoKaXXgrhCADCgf+eAtGP6xSIfpP5Ot25c6eh5yccD1EoIe5YZnNbLBaVlJSovLxctbW1w84+X79+/ZDzjTaMBwAASOw+opmHXIF4+tF3ZZIz4NhjU5fo0MwP69DMc9WVMmfM5+xYdb5Sn//7mPdP3rJVDotF7RdfLGdy8piPAwAAAGDyIhwPo/EKnsvKylRbW6v6+nqVlpaqoqJiyJjKykpPf/Pxri9Uubm5WrZsmdFlhJ3dbvfc6TvvvPMUFxdncEUABuM6Bbwc2yvTtj+7Wqbsfn3YQNw5N1fO06+Q8/TLlWZZoDRJi8JQwu5nnlbnlq0jD/TDZLcr7dm/alpdvWbecrOmXnqpTGZzGKoCMBL+ewpEP65TIPrFynXq3WXDCITjIfL+AY52JngoYfXmzZu1du1aVVZWSpJKS0tltVrV2NioiooK1dbWavPmzaqurh7zOcbihhtuUGFh4aj22bFjh6688krP47i4uEl7wbvFwmsEJjquU8Qk2y7p3T+5FtXc8/rwY+efLS29Qjr9cpks82WKQDmzSkrUfN3n5OzsDH6nuDifPub2Q4d0YN2dOvroRs26606lfOADEagUQCD89xSIflynQPSbzNep0a+LcDxEow24vduwhHJnxGKxqKqqSrW1tSorK1Nubq5sNpusVquKi4tVV1fnE9Z7L+QZSVlZWcrKyhqXcwEAgDBoa3KF4e8+Ke2tG2agSVpwrisQX3q5NO2kiJeWsny55v34R9pz401BBeSmlBTN+/GPFD9zpg5+/251bB2Ydd751ltqunqN0q+8UjNvuVkJ/HsFAAAAiHmE4yHKzs72fD/a/uPhaHOSl5fns0int8bGRp9xAAAAkqQjDQOB+P43A48zmaWFH+6fIX6ZNHX2uJXoNuX887Xw4d+o5Z571fF64NnsqWedpazbb1PK8uWSpAW/fVjH//pXHSwvV9++/Z5xR594Qsefe07Tv/JlZX7uczInJkb8NQAAAACIToTjIVqxYoXn+2DaqrgD6/GYyb3Va7bUaFudAACASebwDundJ1x/Dvw78DiTWVq0aiAQn2L8DOuU5cu18OHfqHvHDrU+8ohaXntNpq5uTcvKUvLSpcq49holeU1YkCSTyaRpF1+sKRdcoCMPPaQjD/xSzq4uSZKjo0OH7vuBbFXVmnVHqaZceKFMpkg0hgEAAAAQzQjHQ5STk+P5vqGhYcTx7gA91JncNpttxJnnNTU1klxBPDPHAQCIQYfec80Of+cJqeWdwONMcdLi86VlV0qnXSqlzRivCkclackSZd15p9576SVJ0rJVq0bsUWhOSdHMr35Vlk98Qi333qdjf/mLZ1vvrl3ac8NXlXbeeZp15zolLVkS0foBAAAARBez0QVMBgUFBZJ8Z2r7U19f7/m+uLh4zOerrq5WRkaGTCaTT+sUbzabzbMYZ1lZ2ZjPBQAAJhCnUzr4rvT8eulnZ0s/O0t6/vv+g3FzvLQkT7r8/6Tbd0jXPSHlXh+1wXioEubM0dz77tXC3/9OSUtP99nW/vLLarziSh34/t2yHz1qUIUAAAAAxhvheBisW7dOkiv8DhRWS9LGjRsluWabe88492az2VRcXKzS0tKAx3HPCJek2tpav2Pc+xcUFHjCewAAMAk5ndKBt6W//T9XGP7zc6UXNkiHtg8da06QTr5IuuJ+VyD+mceknOuk1LEvEj7RpObmanFVlWb/7/cU5704ut2utt/+Vg0XXay2Rx+V0243rkgAAAAA44K2KmGQk5OjkpISlZeXq7i42Ce8dmtsbFR5ebksFos2b94c8FirV6/2mWHub9Z3bm6uJFdrlquvvnrI9srKSlVWViovL09VVVVjeUkAACCaOZ3SgX+52qW8+6TUOkxrt7hE1wzxpVdIp1wspVjGq8qoZYqLU0ZhoaZdfLEO3/9ztf72t1JfnyTJbrPpwHe+q7ZHHtWsO+9U2tlnGVwtAAAAgEhh5niYlJWVqaSkRLW1tcrPz/eZQV5dXa3c3FxZrVZt3rx52F7h3ot6BpqFXlRUpJycHFksFp8x9fX1Ki4uVnFxsYqKivyG9AAAYIJyOqW99VLNt6WffEiqOF/6xw/8B+Pxya7e4Z/4pXR7g3TtI9KZ1xCMDxI3dapmlZbI+qc/Ke38VT7but97T7s+9zntufEm9ezZa1CFAAAAACKJmeNhVFZWpjVr1qiiokL5+flqbW2V5FoQc926dSopKRnxGBUVFZ5+5MP1Cq+rq1NlZaXWrl2rxsZG2Ww2z8KbdXV1Adu2AACACcTplPbWSe8+4ZohbtsVeGx8inRyvmtRzZM/KiVNHa8qJ7wk62ItqKzUiRde0MH1G9TT1OTZdvyvf9WJv/9d07/4BU3/0pdkTk01rlAAAAAAYUU4HmY5OTmqqKgY8/55eXlqaBjmo9FeioqKVFRUNOZzAQCAKORwSHu2uMLwd5+Uju0JPDYhVTrlImnpla5gPDFt3MqcjKZccIHSzj1Xrb//gw7/7GdynDghSXJ2d+vw/T+X7bE/Kuu22zTt0o/LZDIZXC0AAACAUBGOAwAAGM3hkHa/2h+I/0k6vi/w2MQprt7hS69w9RJPZCZzOJkSEzX989cr/bJLdejHP5at+jHXDH5JfQcPat/tt6vtD3/QrLvuUsoZywyuFgAAAEAoCMcBAACM4LBLu15xLaq57c/SiQOBxyZNk079mCsQz14tJSSPW5mxKn7GDM353/+VZc01Onj33er0WjC984031FRYqPRPfkJZN92k+BkzDKwUAAAAwFgRjgMAAIwXe5/U/E9XD/Ftf5baDwUem5Qunfbx/kD8Qik+adzKxICUM5Zp4e9/p2NPP62We+5V34H+mxhOp45WP6bjzzyrGTfcoMzPfkamxERjiwUAAAAwKoTjAAAAkWTvlZpecrVM2faU1HE48Nhki3T6pa4e4osvkOIJW6OByWRS+sc/rqkXXqgjv3xQRx58UM7ubkmSo71dLffcI9umTcpad4em/td/GVssAAAAgKARjgMAAISbvVdqfME1Q3z7X6TO1sBjUzK9AvHzpbiE8aoSo2ROTdXMb3xd6Z/4hFruvVfHn33Ws62nuVl7vvwVpZ2/SrPuuENJVquBlQIAAAAIBuE4AABAOPT1SI3Pu2aIb/+L1GULPDZ1hnT6ZdKyK6WFH5Hi+CfZRJI4b67m/eiHan/9Wh28e726t2/3bGt/8SU1vvyKMj/9ac346g2KmzbNwEoBAAAADIf/EwMAABir3i5XIP7OE9J7z0jdRwOPTcuSll7u6iG+8MOSOW7cykRkpJ11lhY/Vi1bVbUO/ehHsttsrg19fWr9zW909M9/1sybbpTlk5+UKY6fNwAAABBtCMcBAABGo7dT2rHZ1TLlvWelnuOBx06dI53eH4gvOIdAfBIyxcUp45o1mvaxi3XoZz9T2+//INntkiR7a6sOfOvbanv0Uc2+806lrlhhcLUAAAAAvBGOAwAAjKSnQ9pR42qZ8p+/Sj0nAo+dNtcVhi+9Qpp3lmQ2j1+dMExcerpm33mnMq6+WgfXb1D7P//p2db97jY1f+azmnbJx5R1221KOOkkAysFAAAA4EY4DgAA4E9PuysIf/dJ6f3npN6OwGPT5/cH4ldKc3MJxGNY0pIlmv/LB3Ti+b/r4IYN6t21y7Pt2NPP6Pjfntf0L31J07/4BZlTUgysFAAAAADhOAAAgFv38f5A/Anp/VqprzPwWMtCVyC+7ErppBzJZBqvKhHlTCaTpv73hUr7yIfV9vDDOnz/z+XocN1ccXZ16fBPfyrbHx/TrNtv19SLL5aJvzsAAACAIQjHAQBAbOs66grE33lC2lEr2bsDj81Y7ArDl14hzfkggTiGZU5M1PQvfUnpV1yhlh/+SEf/+EfPtr59+7X35luU8vvfa/addyp56VIDKwUAAABiE+E4AACIPZ026b1nXDPEG/4m2XsCj52+xNUuZekV0uzlBOIYtfiZM3XS3d9XxrXX6OD/+74633rLs61za512frJAloICzbz5JsVnZhpYKQAAABBbCMcBAEBs6GiV3nvaNUO88e+Sozfw2BmnDswQz1pKII6wSFm+XAsf+YOOPfWUWu69T30tLa4NTqdsVVU69uyzmvHVG5T56U/LlJBgbLEAAABADCAcBwAAk1f7EWn7U65FNXe+IDn6Ao/NWjqwqGbWaeNWImKLyWxW+uWXa+rq1Tr8wANqfehXcva4PrngOH5cLRvKZNtUpVnr7tCUVasMrhYAAACY3AjHAQDA5HLikLT9z/2B+EuS0x547Kzl/YH4FdLMU8avRsQ8c1qasm66SZZPflIt5ffoeE2NZ1tPY6N2ry3SlAsuUNYdpUpavNjASgEAAIDJi3AcAABMfMcPStv+5ArEm/8pOR2Bx845c2CG+PTscSsR8Cdx/nzN+7+fqP2VV3Tw7vXqfv99z7YTL7ygEy+/rMzPflYzbviK4qZMMbBSAAAAYPIhHAcAABPTsf3Stj+7FtVsflmSM/DYk3L6A/HLpUzreFUIBC3t3HO1+PE/qm3TJh3+8U9kP3rUtaG3V60PPaSjTz6prFtuVvpVV8lkNhtbLAAAADBJEI4DAICJ4+he1wzxd56Qdr+mYQPxuStci2qefrmUsXCcCgTGzhQfr8xPfUrpl1yiQ//3U7U9+qhkd7UFsh85ov13/Y/a/vCIZt11l1JzPmRwtQAAAMDERzgOAACim22X9G5/y5Q9rw8/dv7ZrnYpp18mWeaPS3lAuMVZLJr9zf+RZc3VOnj3enW8+qpnW9c776j5U5/StEsvVdZttyph9mwDKwUAAAAmNsJxAAAQfdqaXGH4u09Ke+uGGWiSFpw70DJl2knjVSEQccmnnKIFv3pIJzZv1sENZerds8ez7dhTT+n45s2aUbRWmZ//vMzJyQZWCgAAAExMhOMAACA6HGkYCMT3vxl4nMksLfywKxA//TJpKjNnMXmZTCZNzctT2qpVav31b3S4okLOjg5JkrOzU4d+/BPZqh9TVkmJpn40XyaTyeCKAQAAgImDcBwAABjn8A7XgprvPiEd+HfgcSaztGjVQCA+JWu8KgSigjkpSTOKi5R+5RU69IMf6OiTf/Js6927V3tvvFGpZ52lWXfdqeRTTzWwUgAAAGDiIBwHAADj69B7rtnh7zwhtbwTeJwpTrJe4ArET7tUSpsxbiUC0Sph1iydVFamjGuv1YG716vrX//ybOt4/XXtvOoTsqy5WjO/8Q3FZ2QYWCkAAAAQ/QjHAQBAZDmdUsu2/pYpT0iHtgcea46XrP/lWlTztI9LqZnjVCQwsaR88INa9OgjOvrkn9Tyg/tkP3TYtcHhkO2RR3XsL09r5te+poxrr5EpIcHYYgEAAIAoRTgOAADCz+mUDr7T3zLlSenwfwKPNSdI2f8tLbtSOvVjUgqzXYFgmMxmWa66UlPz83Wk4hdq/fVv5OztlSQ5jh3TwbvvVtumjZq1bp2mfPjDBlcLAAAARB/CcQAAEB5Op3TgX652Ke8+KbU2BB4blyQtWe1qmXLKxVKKZbyqBCaduClpyrr1VlkKCnSw/B6d2LzZs61nR4N2f/FLmrJ6tWaVlihxwQIDKwUAAACiC+E4AAAYO6dT2vdGf8uUJ6W2nYHHxidLS/JcLVNOuUhKnjZuZQKxIHHhQs3/2U914p//1MG716unYeAG1YnNm9X+4ovKvP5zml78ZcVNSTOwUgAAACA6EI4DAIDRcTqlvXUDLVNsuwKPjU+RTvmoa4b4yR+VkqaOW5lArJry4Q8r7YnH1fbIozr005/KceyYJMnZ26sjD/xStieeUNYttyr9istlMpsNrhYAAAAwDuE4AAAYmcMh7dkyMEP82J7AYxNSXTPDl14pnZwvJTJDFRhvpoQEZV73WU277FId+slPZNu4yXUdS7IfOqz969ap7ZFHNPuuO5Vy5pkGVwsAAAAYg3AcAAD453BIu1/tD8T/JB3fF3hs4hRX7/BlV0rZq6XE1HErE0Bg8RkZmvPtbytjzRod/P7d6tiyxbOt61//UtOaa5R+xeWaecutSpiVZWClAAAAwPgjHAcAAAMcdmnXK65FNbf9WTpxIPDYpGnSqR9ztUzJXi0lJI9bmQBGJ/m007Tg4d/o+F+fU0t5uXr3DdzsOvrkn3SsplYziouVef3nZE5KMrBSAAAAYPwQjgMAEOvsfVLzP109xLf9WWo/FHhscrp06sf7A/ELpXhCNGCiMJlMmnbxRZryXxeo9Ve/0uHKB+Ts7JQkOTs6dOiHP5StulqzSks0ZfVqmUwmgysGAAAAIotwHACAWGTvlZpecrVM2faU1HE48Nhki3T6pa4e4osvkOITx6tKABFgTk7WjK98RelXXaWWe+/Tsaee8mzr3b1be772daWee45mrVun5FNOMbBSAAAAILIIxwEAiBX2XqnxBdcM8e1/kTpbA49NyfQKxM+X4hLGq0oA4yRh9mzNvfceZXzqWh38f99X17vverZ1vPKqdl71CWVcc41mfv1rirNYjCsUAAAAiBDCcQAAJrO+HqnxedcM8e1/kbpsgcemzpBOv8y1qObCj0hx/DMBiAWpOTlaVF2lo48/rpYf/FD2I0dcG+x2tf3+9zr21FOaceM3lHH11TLF83sBAAAAkwf/ugUAYLLp7XIF4u88Ib33jNR9NPDYtCxp6eWuGeILz5PMceNVJYAoYjKbZfnkJzX1ox/V4Z//Qq2//a3U2ytJsh89qoPf+1/ZHnlUs+66U2nnnGNwtQAAAEB4EI4DADAZ9HZKOza7Wqa896zUczzw2KlzpNMvdy2queAcAnEAHnFTp2pWye2yFBaoZUOZTrzwgmdb9/vva9f1n9fU/HxllZYocd48AysFAAAAQkc4DgDARNXTIe2ocbVM+c9fpZ4TgcdOm+sKw5deIc07SzKbx69OABNO0uLFml/xC5148UUdXL9BPTt3erYdr6nRiRdeUOYXPq8Za9fKnJZmYKUAAADA2BGOAwAwkfS0u4Lwd5+U3n9O6u0IPDZ9wUDLlLm5BOIARm3K+ecr7dxz1fr73+vwT38mxwnXTThnT4+O/KJCR//4uLJuu1XTLrtMJpPJ4GoBAACA0SEcBwAg2nUf7w/En5Der5X6OgOPtSx0zQ5fdqV0Uo5EWAUgRKaEBE2//nqlX3aZDv3ox7JVV0tOpySpr6VF+0pK1faHRzTrrjuVsny5wdUCAAAAwSMcBwAgGnUddQXi7zwh7aiV7N2Bx2YsdoXhS6+U5pxJIA4gIuKnT9ec//2eMq69RgfuvludW+s82zrffFNNhVcr/aqrlHXLzYqfOdPASgEAAIDgEI4DABAtOm3Se8+4Zog3/E2y9wQeO32JKwxfeoU0ezmBOIBxk7x0qRb+9rc6/swzOnjPverbv9+z7ejjj+v4c89pxle+rIzrrpM5MdHASgEAAIDhEY4DAGCkjlbpvaddM8Qb/y45egOPnXFq/wzxK6SspQTiAAxjMpk07ZJLNOXCC3Xklw/qyC9/KWe36xMujvZ2tdx7n9qqqjSr9A5NufC/6EcOAACAqEQ4DgDAeGs/Im1/yrWo5s4XJEdf4LFZSwdmiGedNm4lAkAwzCkpmvn1r8nyyU+o5d57dezpZzzbept3ac8NNyjtwx/WrDvXKSk728BKAQAAgKEIxwEAGA8nDknb/9wfiL8kOe2Bx85aLi27Qjr9CmnmKeNXIwCMUcJJJ2nuD36gjGuv1YG716t72zbPtvZ//lONl1+hjE9/SjO/+lXFpacbWCkAAAAwgHAcAIBIOX5Q2vYnVyDe/E/J6Qg8ds6ZrtnhS6+UpjO7EsDElLpypRZXV8lW/ZgO/ehHsre1uTbY7Wp7+Lc69uenNPPGG2UpLJApLs7YYgEAABDzCMcBAJCklu0ybXlQH3rvJcXZO2VumCXN+aC04guja2dybL+07c+uRTWbX5bkDDz2pJz+QPwKKXNxiC8AAKKDKS5OGWuu1rSPXazDP7tfrb//vdTnah9lb2vTge98R22PPqpZd65T2llnGVwtAAAAYhnhOAAgtu2tk577ltT8D5kleT7s394k7X5Ner1CWvgR6aPfk+bm+j/G0b2uGeLvPOHaZ7hAfN5KVxh++uVSxsJwvhIAiCpx06Zp1ro7ZFlztQ6u36D2l17ybOvevl27rvucpl58sWbdfpsS5s41sFIAAADEKsJxAEDser9G2nSd1Nsx/Ljmf0i/vlS6+mHp5HzXc7Zd0rv9LVP2vD78/vPPdrVLOf0yyTI/LKUDwESRZLVqfmWFTrzwglrWb1BPc7Nn2/Fnn9WJ55/X9C9+UdO/9EWZU1MNrBQAAACxhnAcABCb9tZJGz8r9XUGN763Q9r4GSnnc9Lera79AzJJC86Vll3pCsSnnRSOigFgwjKZTJr6X/+lKeedp9bf/k6H779fjvZ2SZKzu1uH779ftj/+UVm336Zpl1wik8lkcMUAAACIBYTjAIDY9Ny3gg/G3fq6XG1W/DGZpYUf7m+Zcpk0dXboNQLAJGNKTNT0L35B6VdcrpYf/lBH//i45HS1ouo7cED7br1Nbb//g2bddadSli0zuFoAAABMdoTjAIDY07Ld1SolVCaztGjVQCA+JSv0YwJADIifMUMnff/7yrjmWh28+251vvGGZ1tnfb2aCgplKfikZt50k+KnTzewUgAAAExmhOMAgNiz9aHQ9k+fJ51/u3TapVLajPDUBAAxKGX5GVr4h9/r2FN/Ucu996rv4EHXBqdTtqpqHXvmWc244QZlfubTMiUmGlssAAAAJh2z0QUAADDu9r8V2v7p86Xc6wnGASAMTCaT0i+7VNnPPK3pX/myTwjuOHFCLeXlarz8Cp144QUDqwQAAMBkRDgOAIg9PSdC2787xP0BAEOYU1OVdeONsj79tKZ+9KM+23qamrS7+MvaVVys7sadBlUIAACAyYZwHAAQexKnhLZ/Uoj7AwACSpw3V/N+8mMt+PWvlXTKKT7b2l94UY2XX66DG8pkP37coAoBAAAwWRCOAwBiz5wzQ9t/9gfCUwcAIKC0c87W4j8+ptnf+bbiLJaBDX19av31r9Vw0cVqq6qS0243rEYAAABMbITjAIDYs+ILoe2/8ovhqQMAMCxTfLwyrrlG2c8+o4zPfEaKi/Nss7e26sA3v6WmwqvVUVdnYJUAAACYqAjHAQCxJ+s0aeFHxrbvolXSzFPDWw8AYFhxFotm/89dsj7xuNLOO9dnW9e776r505/R3ltuVe/+/QZVCAAAgImIcBwAEJs++j0pLml0+ySkSvnfjUw9AIARJZ18suY/+KDm3f8zJSxY4LPt2NNPq+Fjl+jQz34mR1eXQRUCAABgIiEcBwDEppmnSUlTgx+fkCpd/bA0NzdyNQEARmQymTT1v/9b1qf+rJm33iJzaqpnm7OrS4f/76dquOQSHXv2WTmdTgMrBQAAQLQjHAcAxKYX75E6Dgc3dtEq6fqnpJPzI1sTACBo5sREzVi7VtZnnlH6lVf6bOvbt197b7pZu677nLq2bzemQAAAAEQ9wnEAQOxp2S69/H8DjxecK33lFTlWFsmWvlQn0hbLOf8c6axi6auvu4JxZowDQFRKmJWlkzas16JNG5V85gd8tnVs2aKdn/ik9n/7O+prbTWoQgAAAESreKMLAABgXDmd0tO3SY4+12NTnPTx+6RZS+W8eIPeTHtJkrRq1SrFxcUZWCgAYDRSPvABLXrkER3785/Vcu996jt0yLXB4ZBt40Yde+YZzfzaV5Vx7bUyJSQYWywAAACiAjPHAQCx5V+bpKaXBh6fe4M0a5lx9QAAwsZkNiv9iitkfeYZTS8q8gnBHceO6eDd69V45VU68Y9/GlglAAAAogXhOAAgdnTapOfuGng89STpgjsMKwcAEBlxU9KUdcvNsj79F03JW+2zraehQbu/9CXt/soN6mlqMqZAAAAARAXCcQBA7Pjb/5PaDw08/tgGKWmKcfUAACIqcf58zf/pT7XgoQeVdPISn20nnn9eDZddrpZ775X9xAmDKgQAAICR6DkOAIgNe+ulLb8ceLwkTzr9cuPqAQCMm7TzztPixx9X26Mbdej//k+Oo0ddG3p7deSXD8r2xJPKuvlmpV91pUxm5g8BiE52u13Hjh3T8ePH1dnZKYfDMeZjOZ1OpaenS5Lee+89mUymcJUJIEwmwnUaFxenKVOmKCMjQykpKUaXMyaE4wCAyc9hl/5yiySn63FcknTJPVIU/uMCABAZpvh4ZX7m05r28Ut0+P9+qrZHH5X6gyX74cPaf9ddanvkEc26606lfuhDBlcLAL56enrU3Nys1NRUWSwWzZ07V2azecxhmdPp1In+T81MmTIlKkM3INZF+3XqdDrV19enY8eOac+ePZo1a5amTZtmdFmjxrQIAMDkV/crad8bA49X3SplWo2rBwBgmPiMDM3+1je1+PHHlXr22T7but5+W83Xfkp7S0rUe/CgQRUCgC+73a7m5mbNmDFDc+fO1bRp0xQXFxd1QRmA2GIymZSQkKDp06dr/vz5OnDggLq7u40ua9QIxwEAk9uJFqn2ewOPM63Sh280rh4AQFRIPvUULfj1rzT3Jz9Wwty5PtuO/enParj4Yzr8i1/IMQH/Jw/A5HLs2DGlpqYqIyPD6FIAwK/k5GSlp6fr2LFjRpcyaoTjAIDJ7blvSt1HBx5fcq+UkGxcPQCAqGEymTTtox+V9em/aOZNN8nk1SvT2dmpQz/6sRov+biOPfecnE6ngZUCiGXHjx/X1KlTjS4DAIY1ZcoUtbe3G13GqBGOAwAmr6Z/SP96dODxsqukJauNqwcAEJXMSUma8eViZT/7jKZddpnPtt69e7X3Gzdq1+e/oK73/mNQhQBiWWdnp9LS0owuAwCGlZycTFsVAACiRl+P9JdbBx4nTpEuutu4egAAUS9h1izNvadcC//wByWfcYbPto5XX9XOq67Sge99T31tbQZVCCAWORwOmc3ENwCim9lslqN/sfOJhN+uAIDJ6dWfSYe2Dzy+8C5p2knG1QMAmDBScz6kRZs2as7ddytuxoyBDQ6H2v7wiBou/phaf/d7Ofv6jCsSQExh8U0A0W6i/p4iHAcATD62XdIL5QOPZ50hnVVkXD0AgAnHZDbL8omrlP3sM5r+pS9KCQmebY6jR3Xw//0/7bzqKrW/8oqBVQIAACAUhOMAgMnn2XVSb8fA44//QIqLN64eAMCEFTdlirJuu03Zf/6Tplx4oc+27vd3aNfnv6DdX/uaenbtMqhCAAAAjBXhOABgcnnvWWn7UwOPP/RZacHZxtUDAJgUEhct0vyf36/5DzygxOxsn20najer8eOXquUHP5Sjvd2gCgEAADBahOMAgMmjp0N65vaBxymZUv73jKsHADDpTFn1EVmfeFyz7lwn89Spnuedvb06Ulmphos/pqNPPinnBFyQCgAAINYQjgMAJo+X7nP1G3fL/66UmmlcPQCAScmUkKDM665T9l+flWXNGslrAaq+Q4e0r/QONV17rTrfesvAKgEACE5paalKS0uNLiPmlJaWqry8fOSBiCjCcQDA5HD4femfPx54PO8s6YOfMa4eAMCkF5+ZqTnf/Y4W//Expa5Y4bOt661/qWnNNdp3xzr1trQYVCEAAMPLz89XeXm51qxZY3QpMSc/P1+lpaUqLCw0upSYRjgOAJj4nE7pL7dIjl7XY1OcdOkPJTP/mQMARF7y6adrwW8f1twf/VDxJ83x2Xb0iSfUePHHdPiBB+To6TGoQgBAOFVXV6uwsFDZ2dkymUwymUzKyMhQdna2iouLVV1dLZvNNmQ/m83mGe/+09jYOOL5iouLZTKZlJubG9bXUVxcrNraWlVVVSknJyesx54svH/G/n6mocjLy1NZWZmqq6uZuW8gUgMAwMT39mPSzhcHHp/9ZWn2GcbVAwCIOSaTSdMuvljZf/mLZnz9azIlJ3u2OTo6dOi+H6jx0st0fPNmOZ1OAysFgPB4/+BxfedP76jg5y/r4h+9qIKfv6zv/OkdvX/wuNGlRUx1dbUyMjJUWFio6upqn2DbZrOpsbFRlZWVKiws1Nq1a4M6plGhaHl5uSorK5WXl6eCggJDaoBUUlIiq9Wq8vJyVVdXG11OTCIcBwBMbF1Hpb/eOfB46hzpv+4wrh4AQEwzp6Ro5le/quxnnta0j3/cZ1vvrl3a89WvafcXv6TuHTsMqhAAQvPWbpvWVLyi/B++qF+/3KStzW3afuC4tja36dcvNyn/hy9qTcUremu3zehSw6q4uFiFhYWe2cNFRUWqq6tTW1ub2traVFdXp6qqKuXl5UmSrFbrsMdzb6+urlZ9fX1Eax/MZrN5QvmKiopxPfdEY7FYPH8ipaqqSpKCvqGC8CIcD7PGxkYVFxcrNzdXGRkZysjIUG5uriorKyN+vuzsbM/5ysvLw/5xDwCISn/7vnTi4MDji+6WkqcZVw8AAJIS5szR3Pvu1cLf/05JS0/32db+8stqvOJKHfj+3bIfPWpQhQAwes+/16JrKl/Vaztbhx332s5WXVP5qp5/b3KsuVBcXOzJdXJyctTQ0KCKigrl5OR4gtOcnBwVFBSopqZGDQ0NKi4uHvaYpaWlnsB1vGePr1+/XpKrrcdIIX40Ky8v97SGiRTvGyCRCshzcnKUk5Mjm83GAp0GIBwPo/LycmVnZ6uxsVEPPPCA5+JZt26dSktLPdvCxd20Pz8/X3V1dWpoaFBbW5seeOABbdmyRRkZGXwkA8Dktu9NacsDA4+z/1tadpVh5QAAMFhqbq4WV1Vp9v9+T3GZmQMb7Ha1/fa3arjoYrU98oicfX3GFQkAQXhrt01f+V2dOnvtQY3v7LXrht/VT/gZ5NXV1Z5g3Gq1avPmzUHNCh9pTGZmptatWydJqq2tjWjA6807gJ3ofa43btyoysrKcZ95HwnumynuGxcYP1Edjk+fPl333Xefjh07ZnQpI6qsrFRpaannLqH3QgYFBQWqq6tTa2urcnNzwzKju7KyUrW1taqrqxvSGyonJ0dVVVUqKSnx9MECgEnH4XAtwul0uB7HJUqX3CuZTMbWBQDAIKa4OGUUFir7r88q8/Ofl+LjPdvsNpsOfPd72vnJArW/9rqBVQLA8O5+epu6eh2j2qez1671z2yLUEXjw7vVRUVFRVhnD5eUlIz77PFNmzZ5vne3gIHxioqKJLluXpDjja+oDsfb2tpUUlKijIwMXXPNNXrzzTeNLskvd2sTaaBP0GBWq1VFRUWy2Wwh9xCy2WwqLi5WWVnZsOPKyspktVrpWQRgcqr/jbS3buDxR26WpmcbVw8AACOImzpVs0pLZP3Tn5R2/iqfbd3vvaddn/uc9tx4k3r27DWoQgDw7/2Dx0dspRLIq42t2tEyMRfprK6u9kxwLCgoiEiY7M526uvrxyUUrampkSQW4YxC7om27p8RxkdUh+OS5HQ65XQ6VVVVpdzcXK1cuVKPP/640WX5cP8iG+mXpDtA9/7lOhbuj9oE0xcqLy9PNpuN/uMAJpcTh6Ta7ww8zljsCscBAJgAkqyLtaCyUvMrfqHERYt8th3/61/VeMklavnxj+Xo6DCmQAATUlevXf85eDzoPzsOtWvHofagxv70+dAWEf6/v+0YVW2j+dMVZJuXsdi4caPn+5F6iI9VUVGRJ98Zj9nj7kxp5cqVQe/jnhSanZ0tk8kkk8mkjIwMFRYWDtvSpLa2Vvn5+crIyJDJZFJ2dvawr7G8vFwZGRk+73VlZaVnXT/3Y3cN7nOXlpZ6njOZTMrPzx9y7Pr6ep81Ak0mk2fNvuEUFxd7ah+stLTU8z4Eer2j6SG+YsUKz3EwfuJHHmKctrY2VVRUqLKy0tOru76+XgUFBbJYLCouLtYdd9yhadOMXXjNu/fUcLy3V1ZWqqSkZEznc78XZWVlI64q3NrqurMbyVV1AWDc1X5b6rINPL7kHikhxbByAAAYiykXXKC0c89V6+//oMM/+5kcJ05Ikpw9PTry81/o6B8fV9Ztt2napR+XibZhAEawq7VDH/3hi0aX4deTb+7Tk2/ui8ixn7v5fJ0ya2pEju0dUkayBUlFRYXy8/PV2NioyspKT4uNcKuvr/dMnvRuBzyc6upqT/jrzd3+o7q6WiUlJUO6G5SWlg4JhhsbG1VeXq7q6mrV1dUNyaqOHDkim82m1tZW2Ww2rV69ekj4npmZ6cnX3PmYxWJRpte6HoPzucG1uBdRra+vV319vSoqKvzWMxL3ZFSbzRbw9ZaWlmrLli0BO014y8/P92SgNptN6enpo6oHYxPVM8fT09NVUlKiHTt26LnnntPq1as9M8nb2tpUVlZmeMsV74vU312kwdwXmvfdx9FyX+SVlZXD3rm02Wyqra2lhxSAyaX5ZenN3w88Pv1y6eShMwMAAJgITImJmv7565X97DOyFBb4rJ3Rd/Cg9t1+u5o/9Wl1vv2OgVUCQGxyB8mRnnCYl5fnCasjOXvcPYFSCq4bQWVlpScYz8vLU01Njdra2uR0OtXQ0KCysjJZLJYheZh3UFxRUeHJ8hoaGlRQUKDGxka/gbtbfX29Fi9erPr6elmtVk94LbnawTQ0NKihocHzGtatW+d5rqGhwe9EUqvVqqqqKrW1tXn+uNuXNDY2htSSuLa2VuXl5T6vta6uzlNfdXW1J8gfjvfPJJjxCI+oDse9eV+Et99+uywWy5CWK6eccooefPDBca1r69atnu+D+cXiHhPKSrrumfOS6xdVdna2349cuH/RBHN3CgAmBHuv9JdbBx4npEkXs5o3AGDii58xQ3P+93+1qKpKKYNm83W+8YaaCgu176671HfokEEVAkDsGo9WtQ888IDnXKNpxTEa3oGr90xrf9zr3Umu1i81NTXKy8vz5FFWq1UlJSVqa2vzmenunh0uuXpne29zB9R5eXmqra0NmI25Z06XlZWpoaFBRUVFQc9098cdnnvnaZIra3TPeA+1BXJJSYnPa83JyfEJ6YNpleJdG+H4+Inqtir+pKenq6ysTGVlZdq8ebN+8Ytf6LHHHpMk7dixQ0VFRSopKVFxcbGKioq0aFAPv3BraGgY8742m23Mdx+rqqo8PZQaGxuVn5+vvLw8z4VXWFgom802po+FAEDUevXnUsu7A48vXCelzzOuHgAAwizljGVa+Pvf6djTT6vlnnvVd+CAa4PTqaOP/VHHn/2rZtxwgzI/+xmZEhONLRZAVFmQmarnbj4/qLFOp1Md/esapKamjti66WfP7wipLcoVHzxJX71wyZj3H86CzNSIHFdyhZXuwDSUDCcYOTk5ntC4tLRURUVFYT+fd/g70rHXrx+YhDS4Zcpw3GOHW8C0rKxMubm52rhxY8DQu6CgYMztiAcb7rV617h169Yxd1/w19nB3UNcCu4Gi/cNC+9Z/oisCReOe1u9erVWr14tSdq8ebNqamo8d57cAXp+fr5KS0t14YUXRqSG0d5VGvwXfay/6Nwz6d0huOS6C+X+KIu/fk/joaWlRYdGOZtlxw7fhT3sdrvs9sgtqGEU79c0GV8fEHFH98j89w1y/7PdOfN0OVaslcJ4PXGdAtGP6xSxYsrFFyv1/PPV+tBDanvoV3J2d0uSHO3tarnnHrVt2qSZpSVKO//8qOtHznUKhJf7U/MjSYo36+SsKUEfs73d9bsjLS1txN8jX/2v7JDC8a9duERLgqxtLIJ5f8ZixYoVnhm/W7ZsCbltrXed/n6uv/jFL7Rkiesmwt133+3JdQbvN1aHDx8O+jju152Xl6f09PSgz+vusFBdXT3i36vGxsaAr+2OO+4I+pzBXiP+ePf1dreMGXxsf98Pfrx48eIh272PHUyNw9USqb/j4eZ0Okf9336j/60wocNxt2PHjmnnzp1644039MYbb8hkMnn+0jz33HOqqanxrIj7xS9+MaznDuVOTqgfy8nLy9POnTu1ePHiIceqrKzUypUrVVBQENI5Ruv+++/Xd7/73ZCOUVdXN+nvkL388stGlwBMOMve3qCZve2ex2/OvU5HX341YufjOgWiH9cpYsKZZ8r8zW9q6h//qGSvj5/3Njdr3w1fVfeypTpeWCj77NkGFhkY1ykQuvT0dJ3oX7A3Etrb20ccMyfNpBUL0rV119FRH3/lwnTNTlVEX0OkXHbZZZ6Q+P7779c555wT0vG834Ourq4h70lWVpY+//nP61e/+pXuueceff3rX5fFYlFfX58kyeFwhPQ+9vb2+q3FH3dbj/nz54/qnO5WKYsWLdLixYuHHTtv3jyfY3vXd8opp4x4XofDIUnq6ekJqsadO3fqrbfe0vPPP6+mpia1tbWpqanJs93fz2S49969zWKxjHj+YGr0zvZSUlJ8rs1grtNo0N3drZdeemlU++zcuTNC1QRnwvQc9+dvf/ubLrroImVkZKi4uFi1tbVyOp2e1isNDQ3asGGDpk2b5mm5csopp+itt96KSD3j3b6ksrJSixcv1ooVKzy9n9xsNpsKCwuVn58/Lr2xACCSMo/UaebhVzyP989eraOWpQZWBADA+HHMmK6jRWvVesvN6p3n204s6Z13Nf17/6spm6pk6m+RAACRcOtqq1ISRhcjpSSYdct/j7w+W7T6/Oc/78l6nnjiCb355psRP6f3hMNvf/vbYT22d1g9UlbkblPsHR4Hw/1+3XzzzXryySeH/fO9731v2GOEyxNPPKEPfOADOvPMM3XdddfpV7/6ld544w1Jing75rHKyMgwuoSYMeFmjh87dkyVlZVav36950J2zxLPycnRunXr9MlPftIzvqSkRCUlJaqurlZpaal27NihnJwc1dbWhqXVineblNGG0KFc7MXFxaqsrFRFRYWn4b+7N1VxcbHnDl9tba1yc3ND6o0+3nJzc7Vs2TKjywg7u93umTlz3nnnKS4uzuCKgAmit1Pmihs9D53JFmV96ufKSpsR9lNxnQLRj+sUMW3VKjmvv15HH3tMh3/8Ezn6///D5HAo7W9/07Q33tD0b3xD6Z/8hEwGXhtcp0B4vffee5oyJbwtSVxtVVwzUYNpqyJJ55wyRfd/Ok43/P4NdfaO3AYhJSFO93/6QzrnlKyQ6zXShg0b9OUvf1mSdP311w9pDTsa7pnGkpScnOz35zplyhTdfvvtuueee/SrX/1Kd911l+LjXfGd2WwO6e/CnDlzPN/39PQMe6yVK1fqzTff1BtvvDGqc+bl5am6ulp//vOf9fWvf31U9SUkJHi+D+acZrPrZk1iYmLA8dXV1bruuuskSWvXrlVhYaHP5NLGxkZPKxt/P5Ph3nv3tmDqHa5Gt5aWFs/3s2fPVlpa2qivU6MlJSVp1apVo9pnpMVhI23ChONvvvmm1q9fr+rqas9z7lA8Ly9PpaWlnv7j/hQUFKigoECFhYV67LHHVFpaqtdffz3kukYbcHu3CxnrD7+0tFSVlZUqKyvzWQlXcr0XDQ0NKi8vV2lpqaSBlYLDtZDBcG644QYVFhaOap8dO3boyiuv9DyOi4ub9P+AjoXXCITNiz+R2po8D01531HctFkRPy3XKRD9uE4Rk+LiNP3aa2W55BId+tnP1Pb7P3jW37C3tanlu9/V0U2bNPvOdUpdudLgYrlOgXAwmUwRDcVGc/wLT5ulR4vO0fpntunVxsDtUM+xZmrdx07XmfMtYarSOMXFxaqvr1dlZaUaGxu1YsUKbd68edg8yGazqbKyckgO4/0+D/e+33nnnXrggQdks9n05S9/WVar1We/sfKejXz06NFhj3XHHXf41FBRURFwbGNjo6fGsrIyVVdXq7a2Vo899tiw7X4HL3I6+P0JVmNjY8DxmzZtkuTKyyorK4ds927p4e9nMlxNo6k3mOvMu5bs7Oyg/75EE5PJNOr/7hv974Sob6vyt7/9TStXrlRubq6qq6s9DeydTqcKCgrU0NCg5557bthg3Jv7bl9dXV1Y6nMvgCmNvv/4WGaO22w2lZeXy2q1Dht2l5SUqKGhwXMO71WGIykrK0vLli0b1R/3HToAGOLwDukfPxx4PHeFlPM54+oBACBKxKWna/add8r65BNK+/CHfbZ1b9um5s9epz0336zefWNfQA8A/DlzvkWPFp2rmpvP1/XnLdLKRRk6fc40rVyUoevPW6TaW87Xo0XnTopg3M37U/v19fXKyMhQaWmp6uvrPV0EbDab6uvrVVpa6tk+VhaLRevWrZPk6gjg7nseqhUrVni+dy+cGYjVavUsCFpZWan8/HzV1tbKZrN5Xmt5eblnjT9/+xUWFvp0N5AGJnBmZ2eHnFW5A/lNmzapsbFRNptNtbW1PpM23WNqa2tVWVnp0/XB3X0hWtR7rS3ifUMEkRXVM8dPPvlkzwXkvSprSUmJ1q1b57OKa7DC3X/b+xdLMMd2v56x/iV33/EKZqFNq9WqzZs3Kzc31/PLa7z7ogPAmDmd0tO3SvYe12OTWbr0B5I56u/rAgAwbpKWLNH8Xz6gE8//XQc3bFDvrl2ebcefeVYn/va8pn/pS5r+pS/KnJJiYKUAJpuTZ03Vdy6ffC1RA6moqFB+fr5KS0s9AW95eXnA8cHkNsMpKSnxtBT2DpdDYbFYlJOTo/r6etXU1AzpRuCvBovF4lnnL1BIn5OTM2Q/aaDzgb8Z25I0ffr0MbyKAYWFhZ7A3nvyqjQwm33dunWeULy4uHhIGB5NIfSWLVskyaftCyIvqhOGhoYGzyxxi8WisrIyORwObdiwYUzBuDTwFy1cf/m9fwEE09fbHaCP9S+6+xyDL/pAcnJyPIE4wTiACeWdx6XGvw88PqtImnOmYeUAABCtTCaTpv73hbI+9Wdl3XarzKmpnm3O7m4d/tnP1HDJx3Xs6ad9Jh0BAEbH3cGgqqpKBQUFPtmSO3h2f5K/qqoq5PO5Z2CHkzuP8p6lPJyioiI1NDSoqKjI83rdr7WoqEh1dXV+X6v7ffC3X0lJidra2kJu/1tUVKSioiJZLBafmmpqanzOWVdXp7y8PE8uZrVaVVBQoKqqKs+2vLw8w4Ny982H0bYrRmhMzij+15HZbPa7yGYo7rnnHh05ckQrV64M2zELCwtVXV2tnJycYdu11NfXKzc3V5KrrcvgO2vBqKysVHFxsYqKiobt9+QtIyNDmZmZUbso5zvvvKMzzjjD8/jtt9+etAtyvvTSS5KkVatWGd5TCYhqXcekn66UThxwPZ4yS/raFil5bDdGg8V1CkQ/rlNgZH2HDqnlhz/S0T/+cci2lBW5mn3nnUpeujRi5+c6BcJr27ZtOv3008N6TKfTqRMnTkhyLSQ4EXoZIzy8s6mGhgbDA2G4+Pu5TMTrdCy/r4zOBaN65nhNTY22bt0athBbkm6//XZt2LAhrMd094Gqr68f9qMuGzdulOSazR0oGHd/zCNQb6qrr75akqu9SjBtXNz9ryJxtxEAIubv6weCcUm66O6IB+MAAEwW8TNn6qS7v69FVZuUcqbvp646t9Zp5ycLtP+b31LfKNdMAgBMfDk5OZ7Z42RF0cM9ATYaZrDHmqgOx4NdZNNo7o+ESArYyN/dj8pisWjz5s0Bj7V69WpVVlaqvLzcb0BusVhUU1Mjm82m1atXDxuQNzY2avXq1SoqKgq51xUAjJv9/5Je+8XA48UXSGeE74YmAACxImX5ci185A86qbxM8VlZAxucTtmqqtRw0cU68utfy9nTY1yRAIBx5w7F3evawVg2m83Tlz3YLhEIn6gOxyXpl7/8pb7yla/ol7/85Yhjd+7cqZUrV+rkk0/Wgw8+OA7VDSgrK1NJSYlqa2uVn5/vM4O8urpaubm5ngUyh+v97R12B5qFnpeX5wnIFy9erPLyck+vKPeKwcXFxcrNzdW6deu4sABMHA6H9JdbJafD9dicIH38PmkCfHwMAIBoZDKblX755cp+5mlN/3KxTImJnm2O48fVsqFMjVdcqRP9bVAAAJOfe/a4zWYbdlFRjI/169dL0pA+9hgfUR+Ol5SUqLKyMqiFDBYvXiyHw6GGhgZt2LBhHKrzVVZWprq6OlmtVuXn5ysjI0MZGRlav3691q1bp4aGhhH7jFdUVMhqtcpqtQ778Za8vDw1NDTogQce0JYtW1RYWCiTyaTFixertLRUFotFO3fuDHlxAwAYV2/8Vtrz+sDjD98ozTjZuHoAAJgkzGlpyrrpJln/8pSm5uf7bOvZuVO71xZpd/GX1b1zp0EVAgDGU1VVlaxWq0pLS4dtEYzIcneasFqteuCBB4wuJybFG13AcB544AHZbDaZTKag+yCVlZXpox/9qBobG/X444/rqquuinCVvnJyckKaqe0OvYNVUFBAyxQAk0P7Ean22wOPLQul828zrh4AACahxPnzNe//fqL2V1/Vwe/fre733/dsO/HCCzrx8svK/OxnNeMrX1bc1KkGVgoAiCR3297c3FwVFhaqrq7O6JJiUn5+vudnMVynCUROVM8cd88Wt1qt+uAHPxjUPu5FBSTp0UcfjURZAIBIqP221Nk28Phj5VJCinH1AAAwiaWdc44WP/5HzfrWNxWX7rXodW+vWh96SA0Xf0y26mo5HQ7jigQARJS7/W9jY6PyB32qCJGXn5+v1tZWbd68mXYqBorqcHzr1q0ymUwjtiIZLCcnR06n09OHGwAQ5Xa96mqp4nbapdKpFxtXDwAAMcAUH6/MT31K2X99Vhmf/rQUF+fZZj9yRPv/55tqKrxaHfx/FQBMWjk5OWpra1NNTY3RpcScmpoatbW1jTr3RHhFdTjuXpwyMzNzVPu577bQMwkAJgB7n/TULQOPE1Kli8d/3QgAAGJVnMWi2d/8Hy1+/I9KPfccn21d77yj5k99Wntvu129Bw4YVCEAAEBkRHXP8bEabZgOADDQ6xVSyzsDjy8olSzzjasHAIAYlXzKKVrw0EM6sXmzDm4oU++ePZ5tx556Ssc3b9b0tV/S9C98QebkZL/H6N6xQ62PPKKMV1+TqatLu7KylLJsmTKuWaOkJUvG66UAAAAEJapnjo91BvjWrVsliUb2ABDtju2Tnr974PHM06RzbjCuHgAAYpzJZNLUvDxZ//KUZt58s0ypqZ5tzs5OHf7J/6nxko/r2LN/ldPp9Gzr/Pe/1fzZ69R46WWy/f4PSmxoUMLevep64w21/e53arz0MjV/9jp1/vvfRrwsAAAAv6I6HHf3Dq+trdXx48eD2ufo0aOqr6+XyWTSihUrIlwhACAkz66Tek4MPP74fVJ8onH1AAAASZI5KUkziouU/cwzSr/icp9tvfv2ae9NN2nX565X13vv6cSLL6r5us+pY8uWYY/ZsWWLmq/7nE68+GIkSwcAAAhaVIfjV199tef71atXB7VPYWGh5/vi4uKw1wQACJMdtdK7Tww8PvNaadFHDCsHAAAMlTArSyeVlWnRo48o+QMf8NnW8frr2nnlVdr9lRvk7OwM6njOzk7tufEmZpADAICoENXheEFBgT70oQ9Jkurq6nTxxRfrrbfe8ju2qalJF110kWpra2UymWS1WvWJT3xiPMsFAASrt0t6+vaBx8npUv73jKsHAAAMK+WDH9SiRx/RnPXrFTdzxsAGp1Oy20d1LGdnp1ruuTfMFQIAAIxe1C/IWVVVpSVLlshkMqmmpkY1NTXKycnRihUrlJ2drYaGBjU2Nqq2tnbIfgCAKPXPH0mtXutJrP6WNCXLsHIAAMDITGazLFddqan5+TpS8Qsd+dWvpb6+MR2r4/XX1d3QoKTs7PAWCQAAMApRH45brVZt3bpVq1ev1tGjRyVJ9fX1qq+v9xnnvRjMc889pw9+8IPjWSYAIFhHGqSXfjDw+KQcKffzxtUDAABGJW5KmrJuvVV9LS06+uSfxnyctkce1ez/uSuMlQEAAIxOVLdVccvJyVFTU5PWrl0ryRWED/4judqwNDQ0BN2fHAAwzpxOVzsVe3f/Eybp0h9I5jhDywIAAKPXs3tPSPt3bdsWpkoAAADGJupnjrulp6eroqJC5eXl2rRpk+rq6tTa2qrMzEzl5uYqLy9PixcvNrpMAMBwtv1Jatg88Hjll6STPmRcPQAAYMwc7e2G7g8AABCqCROOu6Wnp2vt2rWeWeQAgAmi+7j0zB0Dj9OypP/+H+PqAQAAITGnpRm6PwAAQKgmRFsVAMAk8PcN0vF9A48v+r6UYjGsHAAAEJrkpUtD2//008NUCQAAwNgQjgMAIu/gO9KrPx94vGiVtLzQuHoAAEDIMq5ZE9r+114TpkoAAADGZsK1VWlqalJ9fX1QY61Wqz74wQ9GtiAAwPAcDumpWySn3fXYnCB9/D7JZDK2LgAAEJKkJUuUunKlOrZsGfW+qWedpaTs7AhUBQAAELwJEY4fO3ZMa9euVXV19aj2Kyoq0s9//vORBwIAIuetP0i7Xx14fN7XpZmnGlcPAAAIm6yS29V83efk7OwMfqeEBGXdflvkigIAjEppaakkqayszOBKMJ5KS0s1ffp0lZSUGF2KoaI+HH/jjTeUl5cnm80mp9NpdDkAgNHoaJWe++bA4/QF0vm3G1cPAAAIq5TlyzXvxz/SnhtvCj4g7+1V74EDSlm+PLLFAQBGlJ+fr9raWtXV1RldCsZZfn6+8vPztWXLFlVVVRldjmGivud4YWGh2traPMG41WpVXl6e8vLyJEkmk8nz2Gq1ep5z/4ABAAba/F2ps3Xg8cfKpMRU4+oBAABhN+X887Xw4d8o9ayzhh/o1VJt3+0l6vzXvyJcGQBERnV1tQoLC5WdnS2TySSTyaSMjAxlZ2eruLhY1dXVstlsQ/az2Wye8e4/jY2NI56vuLhYJpNJubm5YX0dxcXFqq2tVVVVlXJycsJ6bITHBz7wAU2bNk1ms9nv36lQ5OXlqaysTNXV1Z5PD8SiqA7H77nnHjU2NnoC8IaGBu3YsUPPPfecnnvuOVksFknyPN6xY4c2bNggp9Mpi8WiT3ziE8a+AACIZbu3SHW/Hnh86iXSaZcYVg4AAIiclOXLtfDh38j61J9l+fSn1LMkW73z5iklJ0cZn/mMrH95SnP+3//zjHd2dWn3V25Qz549BlYNICQt26WnS6QHL5J+/mHX16dLXM9PUtXV1crIyFBhYaGqq6t9gm2bzabGxkZVVlaqsLBQa9euDeqYRoWS5eXlqqysVF5engoKCgypAcYrKSmR1WpVeXn5qNtZTxZRHY7X1NRIknJycvTcc89p8eLFPtszMzMluXqSu5WUlMhisai6ulpNTU3jVisAwIu9T/rLzQOP41OkizcYVw8AABgXSUuWKOvOO9V2221q/Z+7NP+3D2v2/9ylpOxsWT75CU3/ypc9Y+1Hjmh3UbHsR48aWDGAUdtbJ/3q49L9Z0uvV7jWFzr4tuvr6xWu53/1cde4SaS4uFiFhYWe2btFRUWqq6tTW1ub2traVFdXp6qqKk+nA3d3g0Dc26urq1VfXx/R2gez2WyeUL6iomJcz43RsVgsnj+R4m6pEuwNnckmqsPxrVu3ymQyac2aNX63u/9iDP4IytVXXy1JeuyxxyJaHwAggC2/lA78e+DxBbdLGQuNqwcAAESFmd/4hqZdeqnncU9jo/Z8/Rty9vQYWBWAoL1fI/36Uqn5H8OPa/6Ha9z7NeNTV4QVFxersrJSkmsCZ0NDgyoqKpSTk+MJLnNyclRQUKCamho1NDSouLh42GOWlpZ6cq3xnj2+fv16SfJpUYzRKy8v97SmiZQXX3xRu3btUmtra8QC8pycHOXk5Mhms6m8vDwi54hmUR2OuwX64bsv4MHheHZ2tpxOpx599NFIlwYAGOzYfulvAx+b1oxTpHO/blw9AAAgaphMJs25+/tKXbHC81zH669r/ze/6VlnCkCU2lsnbfys1NsR3PjeDmnTdRN+Bnl1dbUnGLdardq8eXNQs8JHGpOZmal169ZJkmprayMasHrzDkBjuc90OGzcuFGVlZXjPvM/Etw3c9w3TmJJVIfj7l8kgVbMtVqtcjqd2rJli9/twSxqAAAIs+fu0v9n797j26zr/o+/r7Zbd17anYBtwNKhIqCQbOiN90BZAgh63/5uUsATenu7hk0B5dBQ5CCgjIaBHASWDE/cHtgab7y9AYVmKE5R2BpEUECWjNEdYGNtdl7Xtdfvj5Ir6dZzk15p83o+Hn2Q75XrSt5t9y3tJ998vjq4Oz2+4C6pZLR9eQAAQF4pGj1as75/v0ZntM3c+b+/1rvff8DGVAB69fRN0qH9/bumdZ9Uf3Nu8gyRzFYToVAoq6t3U62BpaErVK9atcq6nWoBA1RVVUnqePGk0HqP531x3DTNbovcXq9Xko74pqWK5dnexRUA0Iv476RXMlpanXKRNOdM+/IAAIC8VOxwaHY4pOL39pGSpHcfeEDJx35lXygA3dv2Wu+tVLrz5hpp++vZzTNEIpGIVVvy+Xw5KSbX1tZKkmKx2JAUJVP7+7EJJw7ncrkkpf+NFIq8Lo7Pnz9fkrp9a8nChQsldawQf+mllyRJO3futH6Y5LJZPQDgMIdapCevSY9LJ0vnfKf78wEAQEEbPXu2Zj/4gIzSUuvY1htv1N6//MXGVMAw1npA2vZqnz+K3n1dRe++3rfz1ywbXLY/3NmvbP36aD2Qna9fF1auXGnd7q2H+EBVVVVZnROGYvV4qsaWqrn1xO/3yzAMlZWVdXuOYRgyDKPLXtWBQEBlZWWqrKy0ntvr9aqsrEyGYaiioqJPPa4TiYT8fr8qKiqs50s9bk8tTbp6vp6+xsFgUGVlZZ2+1+FwWG632/oahMNhK0PquQOBgHXMMAxrMW+mWCwmv99vPZZhGHK73b1+/ldeeaUmTZqkuXPnHnFftr6+KfPea3k2VC1+8kWJ3QF6UlVVZf2jveuuu3T11Vcfcc6FF16oX/7ylzr77LN10UUXWW8PMQyDt4cAwFD6033SjvXp8cIbpYkz7MsDAADy3thTT9UxwaA2f+MbkmlKhw5p0+VX6Phf/FylXRQCAPSgeYP04Ef7dKohaXxu03T2cl3HRy4s+Ys0/cScPHRmkTCXNaZQKCSv16tEIqFwOGy1uMi2WCxmrYRPrRLOpWQyaX0EAoEjCrWJREKBQEBr165VXV3X/z4ikYhV/D38sSORiCKRiKqrq60V+CndPV8wGFQkElFDQ8MRi2p37NihZDKppqYmJZNJLVy48Ijie3l5+RF7IDocDpVnvBPq8H7zh2dJbeIai8UUi8UUCoW6zNObbHx9M3m9XoXDYSUSCSWTyYJZdJzXK8cnT56sRYsWyTRNLV++XLt27TrinNQ//mQyqXA43KmVyuETAwCQI00bOq8mOfrD0ryv2JcHAAAMG5POPUfTr73WGrfv3q3GKr8Obd9uYyoASLfrzXWR0OPxWMXqXK4eb2pqsm73tmFoNkWjUQWDQYVCIZmmKdM01dDQYGWIRCJdtlQOh8NWYdzj8ai+vl7Nzc0yTVPxeFy1tbVyOByqqKjodF1moTjzOePxuHw+nxKJRJcF95RYLKY5c+YoFovJ6XRaxWupox1NPB5XPB638tfU1FjH4vG4QqHQEY/pdDpVV1en5uZm6yPVviSRSHTqbd9fA/36dpUxpZD2cczr4rjU8Y+4vb1db7zxhiZNmnTE/U6nU6tWrbK++aZpyuVyad26dTr++OOHPjAAFBrTlH5TLR1KvZ3RkC74nlRUbGssAAAwfJT/55fl+Owl1rh1yxY1Ll6i9n37bEwFAB2GYk+7FStWWM/Vn1YY/ZFZ8Mxc6TwUqqurO62Id7lcnYrIh7fySCaTVnuTqqoq1dfXy+PxWC9UOJ1OVVdXq7m5udPjplaHSx29szPvSxWoPR6PotFoty1ZUiuna2trFY/HVVVVNaiV9qniuc/n6/RCi8fjsRb2Zva3H4j+fn27kpmtkIrjed1Wpa98Pp+am5sVjUbldDp12mmn2R0JAArHa49LbzydHs/7T2mW2748AABg2DEMQ0d961tq3bJFe5/9gyTpwCuvaPO11Zp1370yinnRHehV2ZyOFiN9YJqm9r334tO4ceNkGEbPF6y5a3BtUU6plBYc2So3K8rm5OZx1VEsTBUsc91mwuVyWUXbQCCgqqqqrD9fZvF1qFtmdNWzPdXjWjryBYilS5dat/vTGSJ1bk8bqNbW1srtdmvlypXdFr19Pp+qq6v7/Lw96elrnZlx3bp1A27f09+vb1cyXzDJfJfBSDciiuNSRwuWCy+80O4YAFBYWvZIv7kuPR43VVp4k315AADAsGWUlGjW3XfrzS98US2vvipJ2rN6tbYFg5pRU2NzOmAYGDWm7723TVPte/Z03J4wQeqtOL7gmsEVx8+8Vpr2/oFfb5N58+ZZK24HU7jsq1AoZLUIWbp0adbbBe/YsSOrj9cfXbVx6alonPq6Z64W74t169ZJ6liJ3duLPj2tjq4Zov/vZH5ug1k53t+vby6zDDd5XRxP/UOsqKjQV7/6VZvTAACO8IegtGtTenzOd6Sx3e9kDgAA0JOi8eM1e/lDevPiS3To7bclSU0/eUSjZs5S+aVftDkdUMCmf0A67l+ljX/s/7XHLxiWhXFJqqystIq0oVAo58Vxp9OpqqoqhcNhBYNB1dTUjIhNEQfyOaQK1/3tjZ5qleJ0Onu9tqf7c7FhaSKRUCwWU319vRKJhJqamrLSviRb/0bsfGeBnfK6OF5XV6cNGzbI4XBQHAeAfLPtVenPD6THx31M+vAl3Z8PAADQB6NmzNDs0HJt/Nzn1b53ryTpnaVLNWrWTE08+2yb0wEF7JxbpR9/Smrtx14Ao8ZJ3ltylynHqqqqFAgElEwmFYlEFIvFclI0zVRbW6twOCypY2PJrjZ3HKjMjStz3SZmsJxOp2KxWL+Lx6lWOKnWNAOR7a9LJBJRIBDo9Lk4HA6rgN9d73M7DXVPejvl9YacLpdLpmkqmUxq165ddscBAKSYpvTE1VL7oY5xUYl0wV29vx0TAACgD8a8//2aee+9UqrXuGlq89XXaP/Lr9gbDChkM93SRY90FLz7YtS4jvNnDu/9iDJbm1RWVub8+RwOh9XrOhwOZ3VjxGz3lM5l641Uv+xUm5S+Sq3ur6sbRBugLIpEIqqsrFQikbA2FjVNU83NzWpoaMibnFLnfxP5/MJJtuV1cTyzmfwdd9xhYxIAQCcvPSpt/FN6/C9f63t/QwAAgD6Y8K8f01E3p/cyMffvV+PixWrdvNnGVECBO8ErffnxjlYpPTl+Qcd5J3iHJlcOVVVVWSuQE4mE3G53r0XhZDKpYDA44OfMbKfS1UaLA5XtntKpljO5EAgEJHXk7O1rkPkCQurFjGg0qkgk0uN12Srux+Pxbu9buXKlpI6ifVetebL54sdgZWbpbzub4Syvi+MLFy7UaaedJtM0VVtbq40bN9odCQCwv1l6+ob0eNIs6czs7OINAACQqeyiizQl423xbe++q7f8frXxzmLAPjPdHYXvJc9Lp/ulY/9FmnFKx39P90tfe6Hj/mG+YjxTKBSyCuSxWExlZWUKBAKKxWJWgTWZTCoWiykQCFj3D5TD4bD24YtGo1krQqdWY0t9W5GdasOSTCaPKOJGo1EtWrQoK7m64nQ6rUJ3OByW1+tVNBpVMpm0vtbBYFAVFRWdvtaZ11VWVsrv93fKnkgkrOuWLl066IyStGrVKiUSCSWTSUWj0U7vMEidE41GFQ6HOxXko9FoVl/8GKzM9i6FVBzP657jUsfbIObOnSvTNOVyuRSJRPSJT3zC7lgAULhW3yrtezc9/uQdUukE+/IAAIARbdo3rlTrpkbtevI3kqSD6+PadMWVOjYckjF6tM3pgAI2/QPS+QNfHT3chEIheb1eq3d0MBjscXW4z+cb1PNVV1dr6dKlXRamB8rhcMjlclmbQvbWkztzlXNFRYVcLpfKy8uVSCSUSCTk8Xhyunq8urpaDodDfr+/xxcJDu8Dn2pLEwgEFA6HrR7uh5syZcqg8qU2bE0mk536uUsdRXin06mamhqrKO73+48ohudTEXrt2rWSlPONZ/NNXq8clzr+kTQ0NMjhcKi5uVkej0dLlizRM888ozfffFO7du3q8QMAkEWbGqR1P0qPTzhX+sCn7MsDAABGPKOoSEcvXaqxGcWPfX/5i7be/G2ZpmljMgCFxufzKR6Pq66uTj6fr1NhM1V4rq6uts4ZrMx+59mSKnz2ZRNIl8ulUChkfZ6pDTI9Ho8aGhpUX18vn88nj8eTsyJvVVWV4vG4qqqqrOdIfa2rqqq67dud+j50dV11dbWam5utIvpgslVVVcnhcHTKVF9f3+k5Gxoa5PF4rLY2TqdTPp9PdXV11n25/Br2VerFh6HorZ9PDDOPf5u46KKL9OKLL0qSduzYYb31wOjjhm+GYejQoUO5iocs+fvf/66TTz7ZGr/yyis66aSTbEyUG21tbVqzZo0kacGCBSpObS4EDBftbdKKT0hbX+oYl4yRlvxFKp9jb64sYp4C+Y95CuS/XM3TQ83N2njJZ3Uwo93m1Csu17QlS7Ly+EC+evXVV3Xiidnd38c0Te3Zs0eSNGHChD7XWTD8xWIxud0dLW/i8bjtBVl0byjnabb+XQzk55XddcG8XjmeSCQUj8eVSCS0c+dOGYZh/UMwTbNPHwCALFn7g3RhXJIWXDOiCuMAACC/lZSVaXY4pOKMDeXeve9+7fz1r+0LBQDDjMvlslaP52JlOoanUCgkSXmxgn2o5XVxvKysTA6HQ5MnTz7iI/WWhd4+AABZsPsd6Znb0uMpc6WPXWFfHgAAUJBGH3ecZj34QKde41u+dYP2vvCCjakAYHhJFcVXrVplcxLkg2QyafVlTxXJC0leF8fr6+vV1NQ04I8dO3bY/SkAwMjw9A1SS8Y+Ducvk0pK7csDAAAK1jiXS8fU3pE+0NqqTZdfoZYsbVgHACNdavV4MpnscVNRFIalS5dK0hF99AtFXhfHAQB5YMMfpJczVhScfKFU8Qn78gAAgII36ZOf1LSrr7LG7Tt3qrHKr0MskAKAPqmrq5PT6VQgEFCCFxcLViKRUDAYlNPp1IoVK+yOYwuK4wCA7h06KD1xdXo8eqJ0znftywMAAPCeKV/9qhwXXWSNWzdtUuOSJWo/cMDGVAAwPDgcDtXX18vhcKiystLuOLCJ1+vt9G+hEFEcBwB078/3S+/+Mz0++wZp0tH25QEAAHiPYRg66qYbNf5f/9U6duClv2nLtdUy29ttTAYAw4PT6dTq1auVSCTk9XrtjoMh5vV61dTUpNWrVxdkO5UUiuMAgK41b5SevTM9PuoUaf5X7csDAABwGKOkRDPv+Z5K3/9+69ju+nptu3OZjakAYPhwuVxqbm5WfX293VEwxOrr69Xc3CyXy2V3FFtRHAcAdO03AenQ/vcGhnTB96TiElsjAQAAHK54wgTNDi1XyfTp1rGmH/1ITT//uY2pAADAcJDXVY5zzz13UNd7vV5dc801WUoDAAXktSelf/4mPXZ/SZo93748AAAAPRh11FGaHVqujZ//gtr37ZMkvfOd72rUMcdo4sc/bm84AACQt/K6OF5fXy/DMPp9nWmaMgyjoPvlAMCAHdzbsWo8ZdwUaeHN9uUBAADogzEnnqiZ93xPjYuXSG1tUnu7Nl91tY7770c09qST7I4HAADyUN63VTFNs18fmdcBAAbgD8uknW+lx95bpXHl9uUBAADoowlnnqmjbrzRGpv79mnTZYvVunWrjakAAEC+yuuV43V1dX0+d+3atYrFYopGozIMQ/X19ZozZ04O0wHACLT9dem5+9Pj2R+VPvw5+/IAAAD0U9klF+tg41tq+sEPJUmHtm9XY5Vfx/38ZyqeONHmdAAAIJ/kdXH8wgsv7Pe5wWBQ1113ncLhsB599NFcRQOAkcc0pSeultpbO8ZGsfSpu6WivH+TEQAAQCfTr75arZs2a/dTT0mSWt54Q5uv/IZmh5bLGDXK5nQAACBfjLiKR3V1tRYuXKi6ujrt2rXL7jgAMHy8XCe9uSY9/uhiaQb9OQEAwPBjFBXpmNo7NPbUU61je597Tm/feistOAEAgGXEFccl6aKLLpJpmlqxYoXdUQBgeNiflJ66Pj2eeIz08etsiwMAADBYRWPGaNaDD2jU7NnWsWRdRDvC/J0IAAA6jMji+Lx58yRJTz/9tM1JAGCYeOY70t7t6fEn75BK6ckJAACGt5Lycs0Oh1Q8ebJ1bPv3vqedjz9hYyoAAJAvRmRxPGXdunV2RwCA/LflRWntw+nxXI904r/ZlwcAACCLSufM0awHvt+p1/jWmhrta2iwMRUAAMgHI7I4niqKJ5NJe4MAQL5rb5Mev0rSe703i0ulTwYlw7A1FgAAQDaNmzdPR99+uzU2W1u1acnX1LJhg42pAACA3UrsDtCTgWyouW7dOtXW1uYgDQCMQA0/krbE0uMFV0tTKuzLAwAAkCOTP/0ptW7erO333CNJatu5U43+y3T8o79QSXm5veEAAIAt8ro47nA4ZAxw9aJhGHK5XFlOBAAjyJ5tUvTW9LjcKX3sSvvyAAAA5NgUf5UObmrUzsgvJUmtb72lTUu+pmN//CMVjRljczoAADDUhkVbFdM0+/0hiRXkANCT+puklp3p8fnLpFH8UQgAAEYuwzB09M03a/wZ/2Id2//Xv2rLdTUy29ttTAYA9gkEAgoEAnbHsFUgEFAwGLQ7BmyQ98XxVKG7r5xOpzwej55++mmdffbZOUoFAMPcm3+UXvpFevzBz0hzF9oWBwAAYKgYo0Zp5r33qvSEE6xju3/7W22/+24bUwGAPbxer4LBoC6++GK7o9jK6/UqEAiosrLS7igYYnldHG9vb+/3x/r16/X0009r4UKKPADQpUMHpSeuTo9HT5DOW2pfHgAAgCFWPHGiZoeWq3jaVOvYjod/oOZHV9qYCsBwEYlEVFlZqYqKChmGIcMwVFZWpoqKCvn9fkUiESWTySOuSyaT1vmpj0Qi0evz+f1+GYYht9ud1c/D7/crGo2qrq6uIFoTp76OFRVH7rPl8XhUW1urSCRS8KvoC01eF8cBADnwlwel7a+lx5+4Xpp0jH15AAAAbDDqmGM0e/lyGePGWcfevu027VmzxsZUAPJZJBJRWVmZKisrFYlEOhW2k8mkEomEwuGwKisrtWjRoj49pl2F2GAwqHA4LI/HI5/PZ0uGfFNdXS2n06lgMKhIJGJ3HAwRiuMAUEiSjdKzGfsxzDhZOt1vXx4AAAAbjT3pJM28a5lU9N6fxm1t2nzlN3Tgtdd6vhCA4sm4lj6/VJf+5lJd+OsLdelvLtXS55cqnozbHS0n/H6/KisrrRXhVVVVamhoUHNzs5qbm9XQ0KC6ujp5PB5JHW1/e5K6PxKJKBaL5TT74ZLJpFWUD4VCQ/rc+a6urk6S+vziBoY/iuMAUEh+e53Uui89vuBuqbjEvjwAAAA2m/iJT2jGt663xu379qnRf5la337bxlRA/nrl3Vf0n7/9T33mfz+jn7/2c7247UX9s/mfenHbi/r5az/XZ/73M/rP3/6nXnn3FbujZo3f71c4HJYkuVwuxeNxhUIhuVwuORwOORwOuVwu+Xw+1dfXKx6Py+/veRFSIBCQw+Gwbg+lpUs72mp6PJ5ei/jBYNBqv5JLQ/U8vXG5XHK5XEomk2zQWSDyvjj+8MMPa/HixXr44Yd7PXfDhg2aP3++TjjhBP3gBz8YgnQAMIy8/lvptcfT49O+KB37EfvyAAAA5Inyz39e5V/+sjU+9M47arxssdr27LUvFJCH1mxao6889RWte2ddj+ete2edvvLUV7Rm0/BvUxSJRKzCuNPp1OrVq/u0Kry3c8rLy1VTUyNJikajQ1YUziz69qUov3LlSoXD4Zyvbh+q5+mL1AsbqRcRMLLlfXG8urpa4XDYeltDT+bMmaP29nbF43HdcccdQ5AOAIaJg/uk31ybHo8tkzy32JcHAAAgz0yvvlYTvV5r3PLaa9r8zW/KPHTIxlRA/njl3Vd01e+v0v5D+/t0/v5D+3X1s1cP+xXkme01QqGQtdo7G6qrq4d89fiqVaus26kWMOisqqpKUscLCfQeH/nyuji+YsUKq5dTbW1tzye/J3VeIpHQY489lqtoADC8rLlLSr6VHntukcZPsS8PAABAnjGKinRMsFZjPvQh69jeNWv09q23yTRNG5MB+eGudXfpQNuBfl2z/9B+3d1wd44S5V4kErHqUj6fLyfF5FQdKxaLDUkhtr6+XpLYhLMXLpdLUvrrhZErr4vjqdXiTqdTp556ap+uyfxB9eijj+YiFgAML+++If3p3vR41ukdLVUAAADQSdHYsZr94AMaNWuWdSy5apWaaNuJAhdPxnttpdKdtW+vVSKZyHKiobFy5Urrdm89xAeqqqrKasEyFKvHU+1b5s+f3+054XBYhmHIMAyrzUkgELCOGYYhb8Y7bTIlEgn5/X5VVFTIMAyVlZXJ6/V2Wfgf6PPEYjH5/X653W6VlZXJMAy53e6s9gifN2+eJNneAx25l9e7sK1bt06GYViv1vSVy+VSLBbLiz5FAGAr05SeuFpqb+0YG0XSp+6WivL6tVEAAADblEydqtmh5Xrzs59T+65dkqRty+7SqFmzNOm882xOB3Svpa1Fjbsa+3SuKVP79u2TJI07NE6GjB7Pf/jl3veB60n4b2F99ZSvDuoxujN70myVFpfm5LEzC6O5bEESCoXk9XqVSCQUDoetth7ZFovFrJXwPdXaysvLrYJ9ItHxwobD4VB5ebl1Tlc91YPB4BEF/mQyafVU9/l8WrFihdVKZiDPEwgEOhXBUxuipuqAoVBIDQ0Ng25/4/V6FQ6HlUgklEwms9pOB/klr4vjyWRShmF0mhR94XQ6FYvFrIkFAAXrlV9KG55Njz9ymXTUKfblAQAAGAZKKyo06/779dZXvyq1diwy2FIdUMn0GRrnOs3mdEDXGnc16v/9+v/ZHaNLT2x4Qk9seCInj/3Yvz2muWVzc/LYqUJyrgujHo/HWugZCARyVhxvamqybve0YajP57ParlRUVCiRSKimpkbV1dXdXpNZGK+urlZNTY0cDodVHF+0aJEikYhisZji8fiAnyeVvba2Vh6Px/reRKNR6wWGRYsW9Wnvwt6eIyWRSPR74S6GjxG5dLC/xXQAGJEO7JSeuj49nni09PEa+/IAAAAMI+M/crqO+e53rLF58KA2LVmigxs32pgKgB1SRfJcWrFihfVc2WwPkilzEWk2a2epor7U0SK5trbWKlo7HA75fD5t2LBBDodDiURiUO1jampqFI/H5fP5Or1o4fF4rP7tmb3iByrzsVl8O7LldXH88LdW9NW6dR19sHjLA4CC9rvbpT3vpMfn3i6NmWRfHgAAgGFm8r/9m6Ze/nVr3JZMqrHKr0PNzTamAjBUMutKuS6Qu1wuq3VLIBDIyfNlPmY2a2apYrfL5ep2o0+Hw2EVr4PB4IA/v55yZ7a+SdUGByrzxYPMFfcYefK6rYrL5VIikVA0GtXu3bs1ceLEXq/ZuXOnYrGYDMOwmucDQMHZ+pL0Qjg9dn5COik/32IJAACQz6YuWaLWxk3a+atfSZIObtyoTV+/XMf+8AcqKs1Nn2NgIGZPmq3H/u2xPp3bqef4uL71HB9MW5QL5lyQ057juTJv3jyr7/i6dety2ndc6ug9XlFRIUlaunSpVUzOlh07dmT18VJSX6Pevj6ZdbpUD/JsyuaLGUP5wgjsldfF8YsuusjazXbhwoV64YUXer2msrLSup2rnYQBIK+1t0uPXyWZ7R3j4tHS+csko+dfeAEAAHAkwzB09K23qPXtt7XvL3+RJO1vaNDWmut1zLI7ZbDROfJEaXFpn3tvm6apPSV7JEkTJkyQ0cvfCos+tGhQxfGqD1XJ6ei+x3W+qqystAq/oVAo58Vxp9OpqqoqhcNhBYNBq293PsssHKcK+905vI/3YCQSCcViMdXX1yuRSKipqSmr7U9ytcoe+Sev/y/u8/l02mkdm500NDTovPPO00svvdTluW+++abOPfdcRaNRGYYhp9Op//iP/xjKuACQH2I/kTZnvIXsX78pTc3NBjUAAACFwBg9WrPuu1ej56YLP7uefFLb773PxlTA0KlwVGjejIG9O3/+UfOHZWFckqqqqqzCaGozyVzLXC0+mN7cXcksXmdrNXRm4bihoaHHczOL1wMtOEciEVVUVKiiokKVlZUKh8NWC5WeNhkdDPY2HNnyujgudTTyN01TklRfXy+Xy6X58+dr8eLFWrZsmRYvXqxzzz1XFRUV1qt5qesAoODsfVeKfjs9Lju+ozgOAACAQSmeNEmzl4dUPHWqdWxHKKRm/vZEgbh63tUaWzK2X9eMLRmrq9xX5SjR0MgsVmd2K8gVh8Oh6upqSVI4HM7qauhc9dF2uVyS1Kku15WVK1datweyCj8SiaiyslKJREJVVVWqr6+XaZpqbm5WQ0NDVmuBmV8fVo6PbHlfHHc6nVq3bp0mTZok0zRlmqZisZjC4bACgYDC4bCi0ah1nyQ9/fTTOvXUU+0NDgB2qL9JOpBMj89fJo3q3y+wAAAA6NroWTM1+6GHZIxN/3719rdv0Z4//snGVMDQOHnqybrrrLv6XCAfWzJWd511l06eenKOk+VWVVWVqqqqJHWsfHa73b2uuk4mkwoGgwN+zsx2KtlsGTyYPtrxeLzb+2pqaiR1fH26+7wz7/P5fN2u8u7peVLFdY/H02Wbm2y+kJD5WLlakY78kPfFcanjFag333xTixYtkiSrEJ75IXVMrng8roULF9oZFwDssfE56a8/S49P/DfpBK99eQAAAEagsaecrJnL7kzv59LWps1XXqkDr//T3mDAEFgwa4F+eO4PNf+o+T2eN/+o+frhuT/UglkLhihZboVCIatAHovFVFZWpkAgoFgsZhWZk8mkYrGYAoGAdf9AORwOq+AcjUZ7XZHdV5kbYqZakfQmVRhetWqVEomEksmkotFop1X0Pp/P+voEAgFVVlZaLWhSRfFUSxeHw6EVK1YM6HlS50SjUYXD4U4F/mg0mtUXEjJb6FAcH9nyekPOTJMnT1YoFFIwGNSqVavU0NCgpqYmlZeXy+12y+PxaM6cOXbHBAB7tLVKT1ydHo8aL5231L48AAAAI9jEhQs1o6ZG79x+uySpfe9eNfr9On7lSo2aMd3mdEBunTz1ZP3w3B8qnoxr1eur9FrTa9rbulfjR43XB8o/oIvff/Gw7THek1AoJK/Xq0AgYBV8e1od7vP5BvV81dXVWrp0qZLJZNZWRDscDrlcLmsjy1RBuyepTUmTyeQRG24mEgmrcBwKheRwOBQMBhWJRBSJRI54LJfLpbq6ui7blPTleWpqaqyiuN/vP6IYns0i9tq1ayUNrP0LhpdhUxxPmTx5shYtWmStIgcASHp+ubTtH+nxx6+TJs+yLw8AAMAIV37pF3WwsVHN//3fkqRDb7+txsWX6fj//m8VjR9vczog9yocFar5SI3dMYaUz+eTz+dTJBLRypUrFYvFrMK1w+GQ0+mUx+OR3+/PSqG2trY2q6uhpY5ibywW6/PmolVVVWpoaNCqVaskdRSg582bp8rKyiM+x1TeQCBgFbodDofmzZsnv9/f4wsGfXkeh8OhhoYG+f1+rVu3TslkUk6nUy6XSxdffLE8Ho+10nywX//Uav2h6DMPexlmqicJYJO///3vOvnkdA+yV155RSeddJKNiXKjra1Na9askSQtWLBAxcXFNifCiLFzk/T906XWvR3j6R+U/H+QikfZm2sYYp4C+Y95CuS/QpqnZlubNl1+hfY884x1bMJZZ2nWA9+XUTLs1qIhT7366qs68cQTs/qYpmlqz549kqQJEybISLUJwogXi8XkdrsldfT3pmXIkfLlazQc5+lAfl7ZXRfM+57jDz/8sBYvXqyHH36413M3bNig+fPn64QTTtAPfvCDIUgHAHngtzXpwrgkXXA3hXEAAIAhYBQXa+ayOzUm44/6Pc8+q3duv12sQwOQj1wul9UqpLa21uY0+SkUCknqWGXPiwcjX94Xx6urqxUOh1VXV9fruXPmzFF7e7vi8bjuuOOOIUgHADZ7o1569dfp8amfl477F/vyAAAAFJiiceM0+6EHNeqYY6xjzT//hZp+/BMbUwFA91JF8VQLE6Qlk0mFw2FJ6SI5Rra8Lo6vWLHC2nm2r69mpc5LJBJ67LHHchUNAOzXul968pr0eIxD8t5qWxwAAIBCVTJtmmaHlqto4kTr2LZgULueetrGVADQtdTq8WQy2eOmooVo6dKlkjr6y7NqvDDkdXE8tVrc6XTq1FNP7dM1mbvIPvroo7mIBQD54Y/fk5rfTI8935bGT7UrDQAAQEErPeEEzbr/PinVa9w0taW6WvtfesneYADQhbq6OjmdTgUCAWtT0UKXSCQUDAbldDq1YsUKu+NgiOR1cXzdunUyDEMul6tf17lcLpmm2eedd7MpkUjI7/fL7XarrKxMZWVlcrvd1lsyssUwDAWDQX6AAYVqR7yjOJ4y0y25vmRfHgAAAGj8Rz+qo2+7zRqbLS1qXLxEBxsbbUwFAEdyOByqr6+Xw+FQZWWl3XHygtfr7fR1QWHI6+J4qqVKeXl5v65Lve1hqAvHwWBQFRUVSiQSWrFihZqbm9Xc3KyamhoFAgHrvsFKPUbqMQ3D6PMHgBHANDvaqbQd7BgbRR2bcBbl9Y90AACAguD4f5/R1CVLrHFbU5Maq/xqe+/vWwDIF06nU6tXr1YikZDX67U7jq28Xq+ampq0evVq2qkUmBK7A+RCf4vp2RAOhxUIBOTz+Y7YPNTn88nlcsntdsvtdmvDhg2DegVqoCviq6urB/ycAPLI3x+T4s+kx/MXScecalscAAAAdDb18q+rdfMm7fzfjo3TD27YoE2XX6HZP3hYRaNH25wOANJcLpeam5vtjmG7+vp6uyPAJnldHHc6ndqwYUO/V1uvW7dOkobsLRCpViqSjiiMpzidTlVVVSkYDGrRokXdntcXa9eutR7T4XD0+mJANBqVx+Pp86amAPLYgV3Sb2vS4wkzpLO/ZV8eAAAAHMEwDB11221q3bJV+977+23f2rXa+q0bdEywlnf1AgCQJ/K6OO5yuZRIJBSNRrV7925NzNj5uzs7d+5ULBaTYRiaN2/eEKSUVXTO3Ay0K36/X8FgUJFIRMlkcsDF+0Qioerq6j4VuyORiBKJxKCK8QDyyO/vkPa8nR6fe7s0ZrJ9eQAAANClotGjNev79+vNz35OB99b8LXr//5Po2fP0rQrrrA5HQAAkPK85/hFF11k3V64cGGfrsncRCC1mjvXUptt9taTKPP+wWzQGYvFdPHFF/d6XiKRsFaps5EAMAK8/bL0/PL0eM5Z0skX2pcHAAAAPSqePFmzwyEVT5liHXv3wYeU/J/HbEwFAABS8ro47vP5dNppp0mSGhoadN555+mll17q8tw333xT5557rqLRqAzDkNPp1H/8x3/kPGNm/++Kiopez08VqVeuXDng53Q6nXK5XD2ek0wm5fV6tWLFil7PBTAMtLdLj18lmW0d46JR0gV3SbwlFwAAIK+NnjVLsx98QEZpqXVs6003ae+f/2xjKgAAIOV5WxWpo4f33LlzZRiG6uvrVV9fL5fLpXnz5qmiokLxeNxqvXL4dUMh1d9c6n3leOqcWCw24E01pb5tElBZWSmPxyOfzzfg5wGQR/76U2nTC+nxx66Upp5gXx4AAAD02dgPf1jH3BnU5iu/IZmmdOiQNl1xpY7/+c9UegK/0wEAYJe8L447nU6tW7dOCxcu1M6dOyWpy+KyaZrW7aefflqnnnrqkOSLx+MDvnYwfcd7EgwG1dTUZMtOu9u2bdP27dv7dc369es7jdva2tTW1pbNWHkh83MaiZ8fcmjfDhXV36TUGnHTcazaP/YNiX9HWcc8BfIf8xTIf8zTro1fuFDTrrlG2++8U5LUvnu33qry69hf/EIl06banA75zDTNTjWPbD1mV7cB5I/hOE9N0+z3//vt/l0h74vjUsfGnG+++aaqq6u1YsWKbv9B+Hw+1dbWas6cOUOWLZlM9uv88vJy63ZTU1PWi+OxWEyBQEANDQ1Zfdy+evDBB3XLLbcM6jEaGhrU1NSUpUT56bnnnrM7AoaR9792v47e32yNX5l1qXb8xZ45XkiYp0D+Y54C+Y95ehjnHE086yyNe/ZZSdKhrVv1+qWXqunqq6SMtitApsmTJ2vPnj05e/y9e/fm7LEBZMdwmactLS1as2ZNv67ZsGFDjtL0zbAojksd/zMIhUIKBoNatWqVVUAtLy+X2+2Wx+MZ0qJ4ymCKuP0trPdFZWWlfD4ffcaBEWLSzld19NvptlHbp35EO6bOtzERAAAABswwtPuiShU3Nan05ZclSaPeekuTf/BD7bzMLxXl9bZgAACMOMOmOJ4yefJkLVq0SIsWLbI7yhFy0SKlP8LhsBKJhGpra23NMVhut1snnXSS3TGyrq2tzVo5c8YZZ6i4uNjmRMh77YdUtOJ6a2iOGqfyz4a1wDHbxlAjG/MUyH/MUyD/MU971z7/dDV+6UtqefVVSdKYv/1NRz33nKbX1NicDPno9ddf14QJE7L6mKZpWitRx48fL8MwerkCwFAbjvO0tLRUCxYs6Nc1mV027DDsiuP5JvMb2N+V4NkupqeK4nZuwrlkyRJVVlb265r169frM5/5jDUuLi4e8b9AF8LniCx4Ybm07e/W0DirWsVTjrcvT4FhngL5j3kK5D/madeKJ03U7OXL9eYll+jQ1q2SpORPf6bSY49V+aWX2pwO+cYwjJwWxXL9+AAGb7jMU8Mw+v3/fbt/TxjRxfFnnnlGZ599dk6fo78F7sw2LNl8ZSQajSqRSMjpdGbtMQdi+vTpmj59uq0ZgBFh1xbpd7enx9M+IH30a/blAQAAQFaNmjFds5cv18bPf17t7/WTfmfpHRo1c6YmLlxoczoAAArDiGto9uabb6qmpkZTpkzROeeck/Pnq6iosG73t/94NleOh0IhSbK9OA4gS566XjqYsenOBXdJJaPtywMAAICsG/P+92nmvfdIJe+tWzNNbb76Gu1/rx85AADIrRFTHH/44Yc1f/58VVRUKBgMqrm5WaZp5vx5582bZ93uS1uVRCIhKftF7EgkIklsxAmMBOtXS39/LD3+0CXS8f9qXx4AAADkzISPfUxHf/tma2weOKDGyxbr4KbNNqYCAKAwDOvi+F//+lctXrxYxcXF8vv9isViQ1IQz5RZjI7H472enyqgezyerGWIxWLW7SlTpmTtcQHYoPWA9OQ16fGYydI5t9mXBwAAADnn8Pk05TK/NW7bsUONfr/adu2yMRUAACPfsCuO79q1S8uWLdMJJ5wgt9utcDh8REHcNE15PB6tWrVqSDKlNsBct25dj+dlFrH9fn8PZ/ZPNBrN2mMBsNmf7pWaEunxwpukCfTxBwAAGOmmXXmlJl1wgTU+GI9r0+VXyDx40MZUQOFKJpPWJoipj1Q3gJ74/X4ZhiG32531TBUVFVaWvnQvsFtXX8PDPyoqKuT1ehUMBvv0OeXj9wXD27Apjj/zzDM699xzVVZWpkAgoHg83qkobpqm5syZo9raWjU3N+vpp5/WhRdeOCTZampqJHUUv3uakCtXrpTUsdq8u/YnyWRSfr9fgUCgz8+fuWI9m33MAQyxpoS05q70+JjTJPd/2pcHAAAAQ8YwDB299HaNnZcu3Ox7/nltvenmIX+HNICu9adWg84cDoc8Hk+nD6fTqUQioWg0qkAgYNX8+ovvCwajxO4APXnzzTcVCoUUDoetV49M07ReGZKkyZMn66KLLpLf79dpp51mS06Xy6Xq6moFg0H5/X7V19cfcU4ikVAwGJTD4dDq1au7fayFCxd2WmFeW1vb6/P3dyNQAHnINKUnr5XaWt47YEgX3C0VFdsaCwAAAEOnaPRozbr/fm285LM6uHGjJGnnr36lUbNnadrXvmZzOqCzlvXr1fzoSh34xz/UvnevisaP15gPflBll1ys0rlz7Y6XVakibiQSUSwWs22/N4fDMWwXRV500UUKhUJd3heNRlVbW6toNGqtIO/u3Ez58n3B8JaXK8d721zTNE1r/Mwzz2j58uW2FcZTamtrVV1drWg0Kq/X22kFeSQSkdvtltPp1OrVq3v8QZb5FpK+vC3k8GvKy8v7Gx1APnj119L6jBZJ878qzeR/7AAAAIWmpKxMs8MhFZeVWcfevf/72vm//2tjKiBt/8sva+MXL1XiU59W809/qv2xmFpef137YzE1//SnSnzq09r4xUu1/+WX7Y6aNYFAwKrl2LlKuaGhQc3NzWpubh62RfKueDwe1dfXq7q6WpIUDocVDAZ7vS5fvi8Y3vKmON6XzTVTbVPWr19vU8qe1dbWqqGhQU6nU16vV2VlZSorK9PSpUtVU1OjeDze66tYoVBITqdTTqezT6vGpfQPA5fLldWNPgEMkZbd0m+uS4/HT5POvsG+PAAAALDV6OOO06wHH5AxerR1bMsNN2rv8y/YmAqQ9vzhD9p46Ze0b+3aHs/bt3atNl76Je35wx+GKFlulZeXWy11o9Eoe7/lSG1trZxOp6R0a+Ke8H1BNthaHO9uc83MleGTJ09WdXW14vG41q9fr2uvvdaaKPnI5XIpFAopHo9br+Y1NDRYr371xuPxKB6PKx6P9/nz9Hg81vOMpFcOgYLx+zuk3VvS43O+K4112BYHAAAA9ht32mk6JpixYKq1VZsuv1wtGXtOAUNp/8sva9MVV8rcv79P55v792vTld8YMSvIq6urWaU8BFK1sL5uOMr3BYNlW3E8c3PNRCLRqSDucDhUVVWlhoYGNTU16Y477tCcOXPsigoAufPO36W/PJQeH79A+tBF9uUBAABA3ph03nmafu011rh91y41Vvl16N13bUyFQrUteKfMAwf6dY25f7+23bksR4mGXuod/rFYTJFIxOY0I08ymbRWf/enMwLfFwyGbcXx+vr6I1aJ+3w+1dfXq6mpKS/6iANATrW3S49fJZltHeOiEumCu6T3NhwGAAAAyr/yFTkuudgat27erMYlX1N7H1fvAtnQsn59r61UurPvhRdGzDseqqqqrJXNg12lnEgk5Pf7VVFRIcMwVFZWJq/X22Nx1+/3yzAMVVRUHHFfMplUIBCwHi/zMcPhsLUSOxKJWPf3ttdd6rGGYkV2MpmU2+2W1LFotq+thqXsfl9QeErsfHLjvQJQbW2trrnmml7OBoAR5qVfSI1/SY/PuFya9n778gAAACDvGIaho264Qa1btmjvH9ZIkg787W/aUl2tmffcI6O42OaEyBftLS1qfeutPp1rmqZa9+2TJLWMG2fVZ7rzbig8qGzvPrRcU/1Vg3qM7ow69lgVlZbm5LG7EgqF5PV6lUgkFA6HVVXV/88rGAweUcRNrZqORqPy+XxasWJFn1vnxmIxLVy40CqAp67LfMxkMqnq6mr5fD45nU4lEgnV1tYqFAp1+5ip4nmqr3e2JRIJJRIJ1dXVKRzu+DfmcrlUV1fX77bB2fi+oDDZWhw3TdN6BWrVqlXy+/2qrKzUpEmT7IwFALm3r0mqvzE9nnysdOa19uUBAABA3jJKSjTz7u9p4xe+oJbXXpMk7a6PalvwTs2oua6Xq1EoWt96S4lP/5vdMbq06/HHtevxx3Py2M7/+7VKTzghJ4/dFY/HI5fLpVgspkAg0O8ibGZhvLq6WjU1NXI4HFYhe9GiRYpEIorFYor3ccX9okWLlEwm5fF4jigsR6NRhUIhuVwu61ggEJDf71c4HFZtbW2XheilS5dK6ujykI397cLhsFUA74rH45Hf75fP5xvQ4w/2+4LCZVtblebmZt1xxx2aPHmyTNPUunXrVFVVpbKyMp133nl67LHH7IoGALm3+hZp3470+JO10ujx9uUBAABAXiueMF6zQ8tVMmOGdazpJz9R009/ZmMqoDCtWLFCUsfK7GAw2OfrUoVbSaqrq+tUmHY4HPL5fNqwYYMcDocSiUSfW4TEYjFJHW1XDi9kpwrmmT28q6qqrPNSRfBMyWTSau+SzVXjDoej00emdevWaeXKlVbP8YEY6PcFhc224vjkyZNVXV2tpqYm1dfX68ILL7T6j9fX18vn86m4uFgXX3yx/ud//seumACQfY1rpYafpMfv+6T0gfPtywMAAIBhYdSMGZodWq6iceOsY+/cfrt2/+53NqYCCo/L5bKKzYFAwGpn0ptUsdvlcnW7Qjqz33YwGOzTY6dWhQcCgT4Xl1Mrq7tazZ3Z4iRzxflgVFVVqbm5udOHaZqKx+Oqq6vTvHnzFIlE5PV65fV6+/w1zTTQ7wsKm61tVVIWLlyohQsXaufOnVq5cqXC4bD1qlckErFerUq1XfnEJz5hZ1wAGLi2Q9IT35TUsRGxSsZ2rBoHAAAA+mDMBz6gmffeo8bLFkttbVJ7uzZfdbWO++//1tiTT7I7Hmw06thj5fy/X/fpXNM0te+9nuPj+thzfDBtUSZ96lM57Tluh1AoZG2MuXTp0j5tIJkqXGeu4u7KvHnzOl3TW6uRmpoaVVZWKpFIyOv1SuooFM+bN09+v7/LAndNTY1VfI9EIp2eI9WHPFe9xjM5nU45nU75fD7FYjG53W5Fo1EtXLhQDQ0N/X68gXxfUNhsWznelcmTJ6uqqkrr1q1TQ0ODFi1aZLVdMU1ToVBIHo9HU6ZMsTsqAAzM2oelt19Oj8+6Vio7zr48AAAAGHYmLFigo266yRqb+/ercfFlat2yxcZUsFtRaalKTzihzx+jKio0qqKiT+dOvcw/qGxTF1/Wr2z9+RjKzTgzOZ1Oa/V1X1Z4Z96fKt729NgpqU0xe+Lz+VRfX9/pulgspnA4LLfbrcrKyiOucTgcVv7M1irRaFSJRMJq8zKUXC6X6uvrJaXz91d/vy9AXhXHM5122mkKhUJqamrSqlWr5PF4rCJ5c3Oz9armokWL9IMf/MDmtADQB7vflp75Tno89X3Sv1xuXx4AAAAMW2UXX6Qpi75qjdu2v6tGv19tu3fbmAojVencuRo3f/6Arh13+ukq7aUYPFxlrkrurT94Zo/t3lZEZxbE+7oZpsfjUTweVzweVygU6rSRZiQS6bJAnsoci8WsDg5DuWq8Kx6Px8pdV1c3oMfoz/cFyNvieCafz6enn37a2sTT4XBYhfJYLKaqqioVFxfrkksu0TPPPGN3XADo2lPfkg5m/LFy/jKpZLR9eQAAADCsTfvmNzXxk+dZ45Y31mvzlVfKbG21MRVGqunV18oYO7Zf1xhjx2r6tdfkKJH9HA6HqqurJXX06e5tlXeqvUlvfcFXrlxp3e6tBcvhUiun6+rq1NzcbK2iTrUsPvzc1Orw1HOmzktdN9SSyaS12ruvLwwcrr/fFxS2YVEcT+lpE0/TNFVXVyev16spU6bo+uuv11//+le7IwNAh/jvpFcyfhk5pVJynmVfHgAAAAx7RlGRjrnjDo097TTr2N7n/qyt3/62TNO0MRlGorGnnKJZ997T5wK5MXasZt17j8aeckqOk9mrpqbGKuL6/T23n0mtxk4kEgoGg12ek3mfz+fr1CqlOz0Vf7taMd5VpnA4bBXGM1edD7XMvL19PXvSn+8LCtuwKo5nWrhwofUq2PLly+VyuTq1Xamtre20gQEA2OZQi/RkxmqJ0knSOd+1Lw8AAABGjKLSUs168AGNOi69KeHOX/6PdrzXGgHIpglnnqnjHvmJxp1+eo/njTv9dB33yE804cwzhyiZfRwOh1VgjkajPa4K9/l81orsQCCgyspKq51Jqiie6kfucDi0YsWKPmVwu91yu90Kh8PW40kd7VJSbUW62pQzddzj8SiZTNraUiUSiVibcUodrVH6u2o+U3++LyhsJXYHGKzUJp5VVVV68cUXtXz5ctXV1dFwH0D+eO4+acf69PjsG6WJM+zLAwAAgBGlpKxMx4ZCevOSz6rtvb+Ft99zr0bNnKXJn/6UveEw4ow95RQd98hP1LJ+vZofXakDr76q9r17VTR+vMaceKLKPnvJiO0x3p3q6motXbpUyWSy1xYeoVBIDodDwWBQkUiky3YnLpdLdXV1fV69nUwmFYvFelwh3VP/7kAgYBWQXS5Xt4X0wVi1alWXX5tEInFEj/Xa2tqstHXpz/cFhWvYF8czpTbxDIVCikQiA9rVFgCyqmmD9Idl6fHRH5bm/5d9eQAAADAijT7+eM164Pt668v/afUc33r99Rp11IwBb6QI9KR07lwddcO37I6RN2pra/vcviN1bqoonUwm5XA4NG/ePPn9fqsPeF81Nzdr1apVqq+vVywWswrBqZ7imS1GuuLxeOR0OpVIJHK2ajyZTHa7etvlcsnpdMrr9Wa913l/vi8oTIZJIzLY7O9//7tOPvlka/zKK6/opJNOsjFRbrS1tWnNmjWSpAULFqi4uNjmRMg505R+fpH0xtPvHTCkr66WZrltjYXuMU+B/Mc8BfIf89ReO594QluuTrf0K5o8Wcf/4hcqdc6xMRUG49VXX9WJJ56Y1cc0TVN79uyRJE2YMEGGYWT18TH8uN1uJRIJNTc32x0F7xmO83QgP6/srgsO257jAJD3XnsiozAuad5/UhgHAABATk2+4AJNu+oqa9y+c6ca/X4damqyMRWAfJZIJBSLxWzpNQ7YjeI4AOTCwb3SbwLp8bip0sKb7MsDAACAgjFl0VflqEy3ZWhtbNSmxUvUfuCAjakA5KvUpp3ZbmkCDAcUxwEgF56tlXZtSo/PuU0aW2ZfHgAAABQMwzB01E03afzHPmYd2//SS9oSuE5me7uNyQDkg+R7G/dKUiwWUyQSUXV1dZ83AAVGEorjAJBt216V/vxAenzsGdKHP2tfHgAAABQcY9Qozbz3HpW+733Wsd1PPaVtd91lYyoA+cDtdquiokJut1tut1sOh4OWKihYFMcBIJtMU3riaqn9UMe4qES64C5pGGycAQAAgJGleMIEzQ4tV8n06daxph/8UM2PPmpjKgB2c7lcampqUiwWk8vlUkNDA6vGUbBK7A4AACPKS49KG/+UHn90iTTjg/blAQAAQEEbdfTRmr38Ib35hS/K3LdPkvT2rbdp1NFHa8JZZ9mcDoAd6urq7I4A5A1WjgNAtuxvlp6+IT2eNEs6K9D9+QAAAMAQGPPBD2rW9+6Wit4rAbS3a9M3r9KBf/zD3mAAANiM4jgAZMvq26R976bHn7xDKp1gXx4AAADgPRPOOktH3ZheyGHu26fGyxar9e23bUwFAIC9KI4DQDZsbpDW/TA9PuEc6QOfsi8PAAAAcJiyz35W5V/5ijU+tG2bGv2XqW3PHhtTAQBgH4rjADBY7W3S41dJMjvGJWOkTwbZhBMAAAB5Z/o1V2viOedY45bXX9fmb3xTZmurjakAALAHxXEAGKx1P5S2/jU9XnCNVD7HtjgAAABAd4yiIh0TrNXYD3/YOrb3j3/U27feJtM0bUwGAMDQozgOAIOx+52OXuMp5RXSx66wLw8AAADQi6IxYzTrwQc0avZs61iyrk47Hn7YxlToCS9cAMh3w/XnFMVxABiMp2+QWnamxxfcJZWU2pcHAAAA6IOSKVM0OxRS0eTJ1rHtd92tXU8+aWMqdKW4uFiHDh2yOwYA9KitrU3FxcV2x+g3iuMAMFAb/iC9vCo9PvlCqeIT9uUBAAAA+qHUOUezv3+/jFGjrGNbrqvRvljMxlQ43IQJE7Rr1y67YwBAj/bs2aNx48bZHaPfKI4DwEAcOig9cXV6PHqidM537csDAAAADMC4+fN19O23W2Pz4EFtWvI1HXzzTftCoZOysjI1NTXpwIEDdkcBgC61tbWpqalJkyZNsjtKv1EcB4CB+PP3pXf/mR6f/S1p0tH25QEAAAAGaPKnP6VpV6b3zWlLJvWW369Dzc02pkLK2LFjNWPGDL311lt65513tHfvXrW1tQ3b/r4ARgbTNHXo0CElk0lt3LhR48eP18SJE+2O1W8ldgcAgGGneaP0bDA9PuoUaf4i+/IAAAAAgzTlsst0sHGTdv7P/0iSWje+pU1f+7qO/dEPVVTKnjp2mzRpkkpLS7Vr1y5t375dLS0tam9vH/DjmaaplpYWSVJpaakMw8hWVABZMhzmaXFxscaNG6epU6dq4sSJeZmxNxTHAaC/fnuddGh/enzB96RifpwCAABg+DIMQ0ff8m0denur9j73Z0nS/lhMW2tqdMyyZTKKeOO53UpLSzVt2jRNmzZt0I/V1tamNWvWSJIWLFgwLDfRA0Y65unQ4P9uANAfrz0pvf5keuz6kjR7vn15AAAAgCwxRo3SzHvvVekJc61ju578jbZ/7x77QgEAkEMUxwGgrw7ulX4TSI/HTZE837YtDgAAAJBtxRMnavby5SqeNtU6tmPFCjWvWmVjKgAAcoPiOAD01R+WSTvfSo+9t0rjyu3LAwAAAOTAqJkzNfuh5TLGjrWOvX3Lrdqz5o82pgIAIPsojgNAX2x/XXru/vR49kelD3/OvjwAAABADo09+STNvOsuKdVrvK1Nm7/xDR14/XV7gwEAkEUUxwGgN6YpPXG11N7aMTaKpQsy/lAAAAAARqCJZ39CM66/3hq3792rRv9lan3nHRtTAQCQPVR2AKA3L9dJb65Jjz+6WDrqZPvyAAAAAEOk/AufV/mXLrXGh95+W42XLVbbnr02pgIAIDsojgNAT/Ynpae+lR5PPEb6+HW2xQEAAACG2vTqak3wLLTGLa++qs1XXyXz0CEbUwEAMHgUxwGgJ7/7rrR3W3p83lKpdKJ9eQAAAIAhZhQXa+add2rMKadYx/Y++we9/Z3vyDRNG5MBADA4FMcBoDtbXpTWPpweVyyUPvjv9uUBAAAAbFI0dqxmP/SgRs2caR1LPrpSTT/8kY2pAAAYHIrjANCV9jbp8asks71jXFwqnX+nZBj25gIAAABsUjJ1qmaHQyqaNMk6tu3OO7Xrt0/ZmAoAgIGjOA4AXWn4sbQllh4vuEqaUmFbHAAAACAflFZUaNZ990mjRlnHtgQC2vfiizamAgBgYCiOA8Dh9myTVt+SHpc7pY99w7Y4AAAAQD4Z/9GP6OjbbrXGZkuLNi35mg6+9ZaNqQAA6D+K4wBwuPqbpAM70+Pzl0mjxtiXBwAAAMgzjs98RlO//nVr3NbcrMYqv9qSSftCAQDQTxTHASDTm3+UXvpFevzBz0hzF9oWBwAAAMhXU7+2RJP/Pb1h/cE331Tj17+u9oMHbUwFAEDfURwHgJS2VumJq9Pj0ROk85balwcAAADIY4Zh6OjbbtW4j3zEOrZ/XYO2Xv8tmaZpYzIAAPqG4jgApPz5AWn7a+nxx2ukScfYlwcAAADIc8bo0Zp1370aXZHevH7X449r+3332ZgKAIC+oTgOAJKUbJSerU2PZ5wsfeQy+/IAAAAAw0Tx5MmaHQqpeMoU69iOh5Yr+ctf2pgKAIDeURwHAEn67XVS6770+IK7pOIS+/IAAAAAw8joWTM1+6EHZYxJb2S/9eZva+9zz9mYCgCAnlEcB4B/PiW99nh6fNoXpGM/al8eAAAAYBga+6EPaeayOyXD6Dhw6JA2XXGlDvzzn/YGAwCgGxTHARS2g/ukJ69Jj8eWSZ5b7csDAAAADGMTPR7NuC5gjdv37FGj/zK1bttmYyoAALpGcRxAYfvj3VLyrfTYc4s0fkr35wMAAADoUdmll6rsC1+wxoe2btWmyxarfe9eG1MBAHAkiuMACte7b0h/ujc9njVfOu2L9uUBAAAARgDDMDSj5jpN+MQnrGMH/vEPbb76GpltbTYmAwCgM4rjAAqTaUpPXC21HewYG0XSBXdLRfxYBAAAAAbLKC7WzLuWacxJJ1nH9vz+93rn9qUyTdPGZAAApFEFAlCYXvmltOHZ9Pgjl0lHf8i+PAAAAMAIUzRunGY99KBKjjnaOtb8s5+p+ZFHbEwFAEAaxXEAhefATump69PjCUdJH6+xLw8AAAAwQo2aPl3HhkIqmjDBOvbOHbXaVV9vYyoAADpQHAdQeH53u7TnnfT4vNulMZPsywMAAACMYKUnnKBZ998nlZR0HDBNbbm2Wvv/9jd7gwEACh7FcQCFZetL0gvh9Nj5Cemk/7AvDwAAAFAAxv/Lv+joW26xxuaBA2pcvEQHN22yMRUAoNBRHAdQONrbpcevksz2jnHxaOn8ZZJh2JsLAAAAKACOC/9DUxZfZo3bduxQY5VfbTt32pgKAFDIKI4DKBwvPiJtXpcef+wb0tS5tsUBAAAACs20K67QpE99yhofTCS06fIrZB48aGMqAEChojgOoDDsfVeqvzk9LjteWnCVbXEAAACAQmQYho6+/bsaN2+edWzfCy9o6403yjRNG5MBAAoRxXEAhaH+ZulAMj0+f5k0aqxtcQAAAIBCVTR6tGZ9/36NnjPHOrbzf3+td7//gI2pAACFiOI4gJFv45+lv/40PT7x09IJXvvyAAAAAAWu2OHQ7HBIxeXl1rF3H3hAycd+ZV8oAEDBoTgOYGRra5WeyGifMmq8dN4d9uUBAAAAIEkaPXu2Zj/4gIzSUuvY1htv1N6//MXGVACAQkJxHMDI9vxyads/0uOPXydNnmVfHgAAAACWsaeeqmOCQckwOg4cOqRNl1+hlvXr7Q0GACgIFMcBjFw7N0u/W5oeTztR+uhi+/IAAAAAOMKkc8/R9Guvtcbtu3erscqvQ9u325gKAFAIKI4DGLmeqpFa96bHn7pbKh5lXx4AAAAAXSr/zy+r7HOftcatW7aocfESte/bZ2MqAMBIR3EcwMj0RlT6x/+mxx/+nHTcGfblAQAAANAtwzA04/rrNf6sM61jB155RZuvrZbZ1mZjMgDASEZxHMDI07pfevLq9HiMQzrnNtviAAAAAOidUVKiWXffrdIPnmgd27N6tbYFgzamAgCMZBTHAYw8f7xHan4zPfbcLI2falcaAAAAAH1UNH68Zj+0XCVHHWUda/rJI2r675/amAoAMFJRHAcwsuyIS3+8Oz2e6ZZcX7YtDgAAAID+GTVjumaHQioaP9469s7Spdr9zDM2pgIAjEQUxwGMHKYpPXmN1HawY2wUSRfcLRXxow4AAAAYTsa8/32aee+9UnFxx4H2dm2++hrtf/kVe4MBAEYUKkYARo5//EqKZ6wmmb9IOuZUu9IAAAAAGIQJ//oxHfXtm62xuX+/GhcvVuvmzTamAgCMJBTHAYwMB3ZJv61JjyfMkM7+ln15AAAAAAxaWWWlplRVWeO2d99V42WXqW3XLhtTAQBGCorjAEaG398h7d6aHp97uzRmsn15AAAAAGTFtG9cqUnnn2+NW95Yr01XXCnz4EEbUwEARgKK4wCGv7dflp5fnh7POVM6+UL78gAAAADIGqOoSEcvvV1jXS7r2L6//EVbb/62TNO0MRkAYLijOA5geGtvl564WjLbOsZFo6Tz75IMw95cAAAAALKmqLRUsx74vkYfd5x1bOdjj2nH8uU9XAUAQM8ojgMY3v76U6nx+fT4Y1dI095nXx4AAAAAOVFSVqbZ4ZCKHQ7r2PZ779POX//avlAAgGGN4jiA4WvvDqn+pvTYcay04Br78gAAAADIqdHHHadZDz4oY/Ro69iWb92gvS+8YGMqAMBwRXEcwPC1+tvS/ub0+JN3SqPH2RYHAAAAQO6Nc52mY2rvSB9obdWmy69QSyJhXygAwLBEcRzA8PTW81LskfT4/RdI7z/PvjwAAAAAhsykT35S06+52hq379ypxiq/Du3YYWMqAMBwQ3EcwPDTdkh64qr0eNQ46ZN3dH8+AAAAgBGn/L/+S46LLrLGrZs2qXHJErUfOGBjKgDAcEJxHMDw80JYeueV9Pis6o5+4wAAAAAKhmEYOuqmGzV+wQLr2IGX/qYt11bLbG+3MRkAYLigOJ5liURCfr9fbrdbZWVlKisrk9vtVjgczunzRqNR+f1+VVRUqKysTBUVFfJ6vYpGozl9XmDI7doi/e676fHU90sf/Zp9eQAAAADYxigp0czvfU+lH/iAdWx3fb223bnMxlQAgOGC4ngWBYNBVVRUKJFIaMWKFWpublZzc7NqamoUCASs+7IpmUzK6/WqsrJSbrdbDQ0Nam5uVjweVygUUm1trYLBYFafE7DVU9dLB/ekxxfcJZWM7v58AAAAACNa8YTxmr38IZVMn24da/rRj9T085/bmAoAMBxQHM+ScDisQCAgn8+n+vp6uVwu6z6fz6eGhgY1NTXJ7XYrmUxm5TkTiYTmzJmjRCKhhoYGVVVVyeFwWPdHIhFFo1EFAoGsPB9gu/Wrpb8/lh5/6BJpzoLuzwcAAABQEEYddZRmh5araNw469g73/mudv/+9/aFAgDkPYrjWZBqpSJJdXV1XZ7jdDpVVVWlZDKpRYsWDfo5k8mkVWivq6uT0+k84pyVK1d2yggMa60HpCevSY9LJ0vn3GZfHgAAAAB5ZcyJJ2rmPd+Tios7DrS3a/NVV2v/3/9ubzAAQN6iOJ4FtbW1kiSPx9PjeakCeiQSGfTq8crKSiWTSfl8vk6r1Lt6Po/H02XxHBhW/nSv1JTxIs/CG6UJ07s/HwAAAEDBmXDmmTrqxhutsblvnzZdtlitW7famAoAkK8ojmdBarPN3grQmfcPZoPOWCxmbbSZKoB3paqqSqZpqr6+fsDPBeSFpoS05q70+JjTpHlfsS8PAAAAgLxVdsnFmvLV/7LGh7ZvV2OVX227d9uYCgCQjyiOD1IsFrNuV1RU9Hp+qid4ZsuT/lq6dKl1u7fV6sCwZ5rSk9dKbS3vHTCkC+6WioptjQUAAAAgf0276ipNPO88a9zyxhvafOU3ZLa22pgKAJBvKI4P0rp166zbfWldkjons6jeX5FIpM/PBwx7r/6ftD6aHs//L2lm162EAAAAAECSjKIiHXPHUo099VTr2N7nntPbt94q0zTtCwYAyCsldgcY7uLx+ICvTSaT1kryvsosqmcW2pcuXapoNKpkMimn0ymfz6eampp+P/5gbdu2Tdu3b+/XNevXr+80bmtrU1tbWzZj5YXMz2kkfn45cXCPin4bkPHe0Bw/Te0f/5bE1w85wjwF8h/zFMh/zFPkjVGjdPT996nxc59Xa2OjJClZF1HJzJkqX7TI5nD2Yp4C+a9Q5qndnxvF8UHq78aa5eXl1u2mpqZ+F68zV6o7HA5FIhEFAgHV1tZqxYoVcjgcisViqqysVDgc1ooVK+Tz+fr1HIPx4IMP6pZbbhnUYzQ0NKipqSlLifLTc889Z3eEYcEZ/5GO3bXFGr82+wt6Z+3fbEyEQsI8BfIf8xTIf8xT5IPir/6XyoN3qmjvXknSu/fcq/W7d6tl/nybk+UH5imQ/0byPN2wYYOtz09blUEaTBG3v4X1w69JJpMKBAJqaGiQz+ezCu0ul0sNDQ2SZBXJgeFm/J6Nmt34a2ucnHyy3plxlo2JAAAAAAxHbTNmKHmZX2ZJen3g5J88olGHvYsZAFB4WDmeRUPRwmTHjh3W7Wg0qrq6ui6f1+FwqKamRoFAQH6/Xx6PZ9j0KHe73TrppJPsjpF1bW1t1it9Z5xxhoqL2VCyW2a7in6yVIbaO4ZFJZr42bAWTPuAzcEw0jFPgfzHPAXyH/MUeWnBAu2acZTerq6WJBmHDmnqiod17M9/ptHHH29vNhswT4H8VyjzNLPLhh0ojg9S5jewvyvBB1JMnzJlSqdxTy1TfD6fAoGAJCkQCKiurq7fz9dfS5YsUWVlZb+uWb9+vT7zmc9Y4+Li4hE74VMK4XMclBcflRr/Yg2NMy5X8VEj7wUT5DfmKZD/mKdA/mOeIp+U/dun1bZli7bfc48kqX3nTm1evETHP/oLldhcnLET8xTIfyN5ntr9eVEcH6T+Frgz27AM5JWRzOdzuVw9npu5UjwSifT7uQZi+vTpmj59+pA8F0aofU1S/Y3p8eTZ0pnX2pcHAAAAwIgxxV+lg5satTPyS0lS61tvadOSr+nYH/9IRWPG2JwOADDU6Dk+SBUVFdbt/vYfH8jK8cyCen+L6wPpcQ4MudW3SvvS7YP0yVpp9Hj78gAAAAAYMQzD0NE336zxZ5xhHdv/179qy3U1MtvbbUwGALADxfFBmjdvnnW7L8XnRCIhSQPu/+3xeAZ0HTAsbFonNfw4PX7fedL7z7ctDgAAAICRxxg1SjPvvUelJ5xgHdv9299q+/e+Z2MqAIAdKI4PUmZrk3g83uv5qQL6QIvcDofDKqynCu39uRbIW22HpMe/KcnsGJeM7Vg1bhi2xgIAAAAw8hRPnKjZoeUqmTbNOrZjxcNqfnSljakAAEON4ngWpDbFXLduXY/nxWIx67bf7x/w86WuTSQSPa5Wzyye97RxJ5AX1v1Aevtv6fGZ10hlx9sWBwAAAMDINuqYYzRr+UMyxo2zjr19223as2aNjakAAEOJ4ngW1NTUSOoofve0mnvlyo5XoF0uV7ebaSaTSfn9fgUCgW4fp6qqyloFHo1Guz0vcxPO2trabs8DbLf7bemZ76THU06QzrjcvjwAAAAACsLYk07SzLuWSUXvlUfa2rT5ym/owGuv2RsMADAkKI5ngcvlUnV1taTuV4QnEgkFg0E5HA6tXr2628dauHChwuGwgsFgtwVyh8OhFStWSJICgUCXq8eTyaSWLl0qSaqvrx9wj3NgSDz1LallV3p8wV1SSal9eQAAAAAUjImf+IRm3PAta9y+b58a/Zep9e23bUwFABgKFMezpLa2VtXV1YpGo/J6vZ1WkEciEbndbjmdTq1evbrH3t+Zhe6eVqH7fD7V1dUpkUjI7XZbK8gTiYQikYjmzJmjZDKpuro6NvFEfkv8Xnol/S4HnVIpOc+yLQ4AAACAwlP+uc+p/MtftsaH3nlHjZctVtuevfaFAgDkHMXxLKqtrVVDQ4OcTqe8Xq/KyspUVlampUuXqqamRvF4vNt2KimhUEhOp1NOp7PXVig+n0/Nzc3yeDzy+/0yDENut1uhUEhVVVVqbm6m1zjy26EW6Ylr0uPSSdI53+n+fAAAAADIkenV12qi12uNW157TZu/+U2Zhw7ZmAoAkEsldgcYaVwul0Kh0ICv93g8isfjfT7f4XAM6vkAWz13n7TjjfT47BukiUfZlwcAAABAwTKKinRMsFYbv/yODrz0N0nS3jVr9PZt39FR375ZhmHYnBAAkG2sHAdgj+Y3pT8sS4+P+pA0/6u2xQEAAACAorFjNfvBBzVq1izrWHLlSjX98Ic2pgIA5ArFcQBDzzSlJ6ulQwfeO2BIn/qeVFRsaywAAAAAKJkyRbPDIRVNmmQd23bnMu367W9tTAUAyAWK4wCG3mtPSG88lR67vyzNmmdbHAAAAADIVOp0atb375dGjbKObakOaF/sRRtTAQCyjeI4gKF1cK/0m0B6PG6q5LnZvjwAAAAA0IXxp5+uY777HWtsHjyoTV/7mg5u3GhjKgBANlEcBzC0ng1Kuzalx+fcJo0tsy8PAAAAAHRj8r/9m6Zecbk1bmtuVmOVX4eam21MBQDIForjAIbOtlelP38/PT72DOnDn7UvDwAAAAD0YurixZr8//6fNT64caM2ff1ytbe02JgKAJANFMcBDA3TlJ64Wmo/1DEuKpEuuEsyDHtzAQAAAEAPDMPQ0bd8W+M++lHr2P6GBm2tuV5me7uNyQAAg0VxHMDQ+NtKaeOf0uOPLpFmfNC+PAAAAADQR8bo0Zp1370aPbfCOrbrySe1/d77bEwFABgsiuMAcm9/s/T0DenxpJnSWYHuzwcAAACAPFM8aZKODYVUPHWqdWxHKKRkJGJjKgDAYFAcB5B7q2+T9m5Pj8+7QyqdYF8eAAAAABiAUTNnavZDD8kYO9Y6tvXmb2vPH//Uw1UAgHxFcRxAbm1ukNb9MD0+4RzpxE/blwcAAAAABmHsKSdr5l3L0vsntbVp85VX6sDr/7Q3GACg3yiOA8id9jbp8askmR3jkjHSJ4NswgkAAABgWJt49tmaUVNjjdv37lXjZZep9Z1tNqYCAPQXxXEAubPuh9LWv6bHC66WyufYFgcAAAAAsqX80i+q7NIvWuNDW7eqcfFlat+718ZUAID+oDgOIDd2v9PRazylvEL62JX25QEAAACALJsRCGjCwoXWuOUfr2rzVVfLPHTIxlQAgL6iOA4gN+pvlFp2pscXLJNKSu3LAwAAAABZZhQXa+adQY05+WTr2J5nn9U7t98u0zRtTAYA6AuK4wCyb8Ma6W8r0+OT/kOqONu+PAAAAACQI0Xjxmn2Qw9q1DHHWMeaf/4LNf34JzamAgD0BcVxANl16KD0xNXp8eiJ0rm325cHAAAAAHKsZNo0zQ6HVDRxonVsWzCoXU89bWMqAEBvKI4DyK4/f1969/X0+OxvSZOOti8PAAAAAAyB0rlzNev++6SSko4Dpqkt1dXa/9JL9gYDAHSL4jiA7Em+JT0bTI9nnCLNX2RfHgAAAAAYQuM/+lEdfdtt1thsaVHj4iU62NhoYyoAQHcojgPInt8EpEP70+NP3S0Vl9iXBwAAAACGmOP/fUZTlyyxxm1NTWqs8qstmbQvFACgSxTHAWTHa09Krz+ZHru+JM0+3b48AAAAAGCTqZd/XZP//d+s8cENG7Tp8ivUfvCgjakAAIejOA5g8A7u61g1njK2XPJ827Y4AAAAAGAnwzB09G23adzp6QVD+9au1dYbbpBpmjYmAwBkojgOYPDWLJN2vpUee2+VxpXblwcAAAAAbGaMHq1Z99+n0U6ndWzXr/9P797/fRtTAQAyURwHMDjbX5f+dF96PPsj0qmfty8PAAAAAOSJ4smTNTscUvGUKdaxdx98UMn/eczGVACAFIrjAAbONKUnrpbaWzvGRrF0wd1SET9aAAAAAECSRs+apdkPPShjzBjr2NabbtLeP//ZxlQAAIniOIDBeDkivbkmPf7oYumok+3LAwAAAAB5aOyHPqRj7gxKhtFx4NAhbbriSrW88Ya9wQCgwFEcBzAw+5PSU9enxxOPkT5+nW1xAAAAACCfTfJ6NT1QbY3bd+/WW36/Dm3fbmMqAChsFMcBDMzvvivt3ZYen7dUKp1oXx4AAAAAyHPlX/qSyj6f3qPp0Jatarxssdr37bMxFQAULorjAPpvy4vS2ofT44qF0gf/3b48AAAAADAMGIahGdfXaMLHP24dO/D3v2vzNdfKbGuzLxgAFCiK4wD6p71NevwqyWzvGBeXSuffme6dBwAAAADollFcrJl3LdOYD37QOrbnmWf0zh21NqYCgMJEcRxA/zT8WNoSS4//9ZvSlArb4gAAAADAcFM0frxmLX9IJUcfbR1r/u//VtMjj9iYCgAKD8VxAH23Z7u0+pb0uGxOR3EcAAAAANAvo6ZP1+zly1U0YYJ17J2ld2j36tU2pgKAwkJxHEDf1d8kHdiZHp+/TBo1xr48AAAAADCMjXn/+zTrvnulkpKOA6apzVdfo/0vv2xvMAAoEBTHAfTNm3+SXvp5evzBf5dO8NiXBwAAAABGgPFnnKGjb/m2NTYPHFDj4iU6uGmzfaEAoEBQHAfQu7ZW6Ymr0uPRE6Tz7rAvDwAAAACMII4LL9SUy/zWuO3dd9Xo96tt1y4bUwHAyEdxHEDv/vKgtP219PjjNdKkY+zLAwAAAAAjzLQrr9SkT33KGh+Mx7Xp8itkHjxoYyoAGNkojgPo2c5N0u8zVolPP0n6iL/78wEAAAAA/WYYho6+/bsaO89tHdv3/PPaetPNMk3TxmQAMHJRHAfQs98EpNZ96fGn7paKR9mXBwAAAABGqKLRozXr/vs1+vjjrWM7f/Urvfvgg/aFAoARjOI4gO798ynptcfT49O+IB37UfvyAAAAAMAIV1JWptnhkIrLyqxj797/fe383/+1MRUAjEwUxwF0rXW/9OS16fHYMslzq315AAAAAKBAjD72WM168AEZo0dbx7bccKP2Pv+CjakAYOShOA6ga2vukpIb02PPt6XxU2yLAwAAAACFZNxpp+mYYDB9oLVVmy6/XC3xuH2hAGCEoTgO4EjvviH96d70eNZ86bRL7csDAAAAAAVo0nnnavq16Xf0tu/apcYqvw69+66NqQBg5KA4DqAz05SevEZqO9gxNoqkC+6WivhxAQAAAABDrfwr/ynHJRdb49bNm9W45Gtq37/fxlQAMDJQ7QLQ2Su/lBK/T49P90tHf8i2OAAAAABQyAzD0FE33KDxZy6wjh3429+0pbpaZlubjckAYPijOA4g7cBO6anr0+MJR0mfuL778wEAAAAAOWeUlGjm3d9T6YknWsd210e17c5lNqYCgOGP4jiAtN8tlfa8kx6fd7s0ZpJ9eQAAAAAAkqTiCeM1e/lDKpkxwzrW9OMfq+mnP7MxFQAMbxTHAXTY+jfphVB67Py4dNJ/2BYHAAAAANDZqBkzNDscUtH48daxd26/Xbt/9zsbUwHA8EVxHIDU3i49cZVktneMi0dL598lGYa9uQAAAAAAnYx5//s18557pOLijgPt7dp81dXa/8rfbc0FAMMRxXEA0ouPSJvWpscf+4Y0da5tcQAAAAAA3Zuw4F911E03WWNz/341Lr5MrVu22JgKAIafErsDALDZ3nel+pvTY8dx0oKr7MsDAAAAAOhV2cUXqXXTJu1YsUKS1Lb9XTX6L9NxP/+ZiidOtDkdgMFoWb9eTb/4hcr+8ryMAwf01vTpGnvSSSq75GKVzmUxYzZRHAcKXf3N0oFkenz+MmnUWNviAAAAAAD6Zto3v6HWzZu068nfSJJa3nhDm6+8UrNDIRmjRtmcDkB/7X/5ZW0L3ql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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Accuracy\n", "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", @@ -306,20 +324,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "5c336efd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['roc_cn_cert', 'roc_cn_uncert', 'roc_cn_tot', 'roc_noisy_bn'])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "roc.keys()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "9186101e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# ROC curves\n", "fig, axes = plt.subplots(2, 3, figsize=(16, 8))\n", diff --git a/src/mia.py b/src/mia.py index 7d083b9..c94fafe 100644 --- a/src/mia.py +++ b/src/mia.py @@ -10,7 +10,7 @@ import src.attacks from src.config import get_out_path, set_global_seed import src.defenses -from src.utils import check_consistency, get_llr, get_min_max_bns, noisy_bn, safe_assert, save_bn +from src.utils import check_consistency, get_llr, get_min_max_bns, noisy_bn, safe_assert, safe_open_dir, save_bn # Get the attack power related to a fixed error @@ -202,14 +202,16 @@ def get_eps(exp, ess, config): return exp, ess, eps_best -# Learn BN parameters from a given DAG and data -def learn_bn_params(dag, data): +# Learn BN parameters from a given BN and data +def learn_bn_params(bn, data): - learner = gum.BNLearner(data) + bn_copy = gum.BayesNet(bn) + + learner = gum.BNLearner(data, bn_copy) learner.useSmoothingPrior(1e-5) - bn = learner.learnParameters(dag) + bn_learnt = learner.learnParameters(bn_copy) - return bn + return bn_learnt # Estimate BNs from rpop and pool def phase_estimation(exp, config) -> None: @@ -227,8 +229,8 @@ def phase_estimation(exp, config) -> None: # Debug safe_assert(gpop_ss == gpop.shape[0]) - safe_assert(n_nodes == gpop.shape[1]) - + safe_assert(n_nodes == gpop.loc[:, ~gpop.columns.str.contains("in-")].shape[1]) + # For each data sample ... for sample in range(config["n_samples"]): @@ -237,11 +239,11 @@ def phase_estimation(exp, config) -> None: rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes] # ... estimate BN from rpop, ... - bn_learnt = learn_bn_params(bn.dag(), rpop) + bn_learnt = learn_bn_params(bn, rpop) save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config["rpop_path"]) # ... estimate BN from pool, ... - bn_learnt = learn_bn_params(bn.dag(), pool) + bn_learnt = learn_bn_params(bn, pool) save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config["pool_path"]) # Debug @@ -277,6 +279,7 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: # save_bn(bn_min, f"bn_min_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") # save_bn(bn_max, f"bn_max_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") base_path = out_path / config["cns_path"] / f"ESS: {ess}" + safe_open_dir(base_path) cn.saveBNsMinMax(f"{base_path}/bn_min_{exp}_sample{sample}.bif", f"{base_path}/bn_max_{exp}_sample{sample}.bif") diff --git a/src/utils.py b/src/utils.py index 7ae857e..e2af401 100644 --- a/src/utils.py +++ b/src/utils.py @@ -218,7 +218,7 @@ def sample_from_cpts(cpt_min, cpt_max, n_samples) -> list: # Debug safe_assert(cpt_min.shape == cpt_max.shape) - safe_assert(len(credal_dict) == prod(cpt_min.shape)) + safe_assert(len(credal_dict) == cpt_min.shape[0]) safe_assert(len(cpt_samples) == n_samples) return cpt_samples From afebc0e1451662a323dce53e9425e39829da569f Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Wed, 12 Nov 2025 16:11:33 +0100 Subject: [PATCH 07/31] Update `cn_vs_noisybn` accordingly to the `cn_privacy` exp structure --- configs/cn_privacy.yaml | 8 +- configs/cn_vs_noisybn.yaml | 10 +- configs/tests/cn_privacy.yaml | 4 +- configs/tests/cn_vs_noisybn.yaml | 10 +- experiments/cn_privacy/exp.py | 77 +++++---- experiments/cn_vs_noisybn/exp.py | 75 +++++++-- src/data.py | 44 ++--- src/inference.py | 12 +- src/mia.py | 276 +++++++++++++++---------------- src/utils.py | 5 +- 10 files changed, 289 insertions(+), 232 deletions(-) diff --git a/configs/cn_privacy.yaml b/configs/cn_privacy.yaml index 21f8d6c..9fa47d1 100644 --- a/configs/cn_privacy.yaml +++ b/configs/cn_privacy.yaml @@ -2,10 +2,14 @@ # Paths out_path: experiments/cn_privacy/output # Output path -bns_path: bns # Where to save ground-truth BNs +bns_path: bns/gt # Where to save ground-truth BNs +rpop_path: bns/rpop # Where to save BNs learnt from rpop +pool_path: bns/pool # Where to save BNs learnt from pool +cns_path: cns # Where to save CNs learnt from pool +atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results -meta_file: exp_meta.txt # File of metadata +exp_meta: exp_meta.txt # File of metadata for experiments # Models n_nodes_vec: '[10, 20, 50, 100]' # List of models' number of nodes diff --git a/configs/cn_vs_noisybn.yaml b/configs/cn_vs_noisybn.yaml index 91073a8..1098927 100644 --- a/configs/cn_vs_noisybn.yaml +++ b/configs/cn_vs_noisybn.yaml @@ -2,10 +2,16 @@ # Paths out_path: experiments/cn_vs_noisybn/output # Output path -bns_path: bns # Where to save ground-truth BNs +bns_path: bns/gt # Where to save ground-truth BNs +rpop_path: bns/rpop # Where to save BNs learnt from rpop +pool_path: bns/pool # Where to save BNs learnt from pool +cns_path: cns # Where to save CNs learnt from pool +atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN +noisy_path: bns/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results -meta_file: exp_meta.txt # File of metadata +exp_meta: exp_meta.txt # File of metadata for experiments +auc_meta: auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable diff --git a/configs/tests/cn_privacy.yaml b/configs/tests/cn_privacy.yaml index 9e1c9df..5b6b66d 100644 --- a/configs/tests/cn_privacy.yaml +++ b/configs/tests/cn_privacy.yaml @@ -6,10 +6,10 @@ bns_path: bns/gt # Where to save ground-truth rpop_path: bns/rpop # Where to save BNs learnt from rpop pool_path: bns/pool # Where to save BNs learnt from pool cns_path: cns # Where to save CNs learnt from pool -atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CNdata_path: data # Where to save data as generated from ground-truth BNs +atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results -meta_file: exp_meta.txt # File of metadata +exp_meta: exp_meta.txt # File of metadata for experiments # Models n_nodes_vec: '[10, 15]' # List of models' number of nodes diff --git a/configs/tests/cn_vs_noisybn.yaml b/configs/tests/cn_vs_noisybn.yaml index ea8b4e6..1b754c6 100644 --- a/configs/tests/cn_vs_noisybn.yaml +++ b/configs/tests/cn_vs_noisybn.yaml @@ -2,10 +2,16 @@ # Paths out_path: test/cn_vs_noisybn/output # Output path -bns_path: bns # Where to save ground-truth BNs +bns_path: bns/gt # Where to save ground-truth BNs +rpop_path: bns/rpop # Where to save BNs learnt from rpop +pool_path: bns/pool # Where to save BNs learnt from pool +cns_path: cns # Where to save CNs learnt from pool +atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN +noisy_path: bns/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results -meta_file: exp_meta.txt # File of metadata +exp_meta: exp_meta.txt # File of metadata for experiments +auc_meta: auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 948d63a..4c125b4 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -1,13 +1,15 @@ import gc import multiprocessing # noqa: F401 # pylint: disable=unused-import from itertools import product -import numpy as np # noqa: F401 # pylint: disable=unused-import +import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed from src.config import get_out_path, load_config, set_global_seed from src.data import generate_randombn -from src.mia import phase_attack_mechanism, phase_defense_mechanism, phase_estimation, phase_mia_vs_bn, phase_mia_vs_cn, phase_theoretical_power +from src.mia import (phase_attack_mechanism, phase_defense_mechanism, + phase_estimation, phase_mia_vs_bn, phase_mia_vs_cn, + phase_theoretical_power) def main(): @@ -16,65 +18,68 @@ def main(): config = load_config("cn_privacy") out_path = get_out_path(config) set_global_seed(config["seed"]) - results_path = out_path / config["results_path"] - n_samples = config["n_samples"] - error = eval(config["error"]) def_mec = config["def_mec"] atk_mec = config["atk_mec"] + # Generate BNs and data + print("#" * 5, "Generate BNs and data", "#" * 5) + + generate_randombn(config) + # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] ess_vec = eval(config["ess_vec"]) - # Generate BNs and data - print("#"*5, "Generate BNs and data", "#"*5) - generate_randombn(config) - # Estimate BNs from rpop and pool - print("#"*5, "Estimate BNs from rpop and pool", "#"*5) - for exp in exp_vec: - phase_estimation(exp, config) + print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_estimation)(exp, config) for exp in exp_vec + ) # Defense mechanism - print("#"*5, "Defense mechanism", "#"*5) - for exp, ess in product(exp_vec, ess_vec): - phase_defense_mechanism(def_mec, exp, ess, config) + print("#" * 5, "Defense mechanism", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_defense_mechanism)(def_mec, exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) # Attack mechanism - print("#"*5, "Attack mechanism", "#"*5) - for exp, ess in product(exp_vec, ess_vec): - phase_attack_mechanism(atk_mec, exp, ess, config) + print("#" * 5, "Attack mechanism", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) # MIA vs CN - print("#"*5, "MIA vs CN", "#"*5) - for exp, ess in product(exp_vec, ess_vec): - phase_mia_vs_cn(exp, ess, config) + print("#" * 5, "MIA vs CN", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_mia_vs_cn)(exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) # MIA vs BN (for comparison) - print("#"*5, "MIA vs BN", "#"*5) - for exp in exp_vec: - phase_mia_vs_bn(exp, config) + print("#" * 5, "MIA vs BN", "#" * 5) - # Compute theoretical power - print("#"*5, "Compute theoretical power", "#"*5) - for exp in exp_vec: - phase_theoretical_power(exp, config) + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_mia_vs_bn)(exp, config) for exp in exp_vec + ) - # ------------------------- + # Compute theoretical power + print("#" * 5, "Compute theoretical power", "#" * 5) - # OLD! TODO: remove - # # ... run MIA attack on BN and CN - # Parallel(n_jobs=eval(config["num_cores"]))( - # delayed(attack_cn_bn)(exp, ess, config) - # for exp, ess in product(exp_vec, ess_vec) - # ) + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_theoretical_power)(exp, config) for exp in exp_vec + ) # Clean gc.collect() if __name__ == "__main__": - main() diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index ae26aa5..8e7d296 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -2,42 +2,85 @@ import multiprocessing # noqa: F401 # pylint: disable=unused-import from itertools import product +import numpy as np # noqa: F401 # pylint: disable=unused-import +import pandas as pd from joblib import Parallel, delayed -from src.config import get_out_path, load_config +from src.config import get_out_path, load_config, set_global_seed from src.data import generate_naivebayes from src.inference import run_inferences -from src.mia import get_eps +from src.mia import (phase_attack_mechanism, phase_defense_mechanism, + phase_estimation, phase_find_eps, phase_mia_vs_cn) def main(): - # Load config + # Init configs config = load_config("cn_vs_noisybn") + out_path = get_out_path(config) + set_global_seed(config["seed"]) + def_mec = config["def_mec"] + atk_mec = config["atk_mec"] # Generate BNs and data + print("#" * 5, "Generate BNs and data", "#" * 5) generate_naivebayes(config) - # Get output path - out_path = get_out_path(config) - - # Set number of threads for parallelization - num_cores = eval(config["num_cores"]) - - # For each ESS and each model ... + # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] ess_vec = config["ess_dict"].keys() - # ... find eps s.t. |AUC(eps) - AUC(CN)| < tol, ... - res = Parallel(n_jobs=num_cores)( - delayed(get_eps)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) + # Estimate BNs from rpop and pool + print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_estimation)(exp, config) for exp in exp_vec + ) + + # Defense mechanism + print("#" * 5, "Defense mechanism", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_defense_mechanism)(def_mec, exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) + + # Attack mechanism + print("#" * 5, "Attack mechanism", "#" * 5) + + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) + + # MIA vs CN + print("#" * 5, "MIA vs CN", "#" * 5) + + res = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_mia_vs_cn)(exp, ess, config, save_res=False) + for exp, ess in product(exp_vec, ess_vec) ) + res = pd.DataFrame(res) + res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) + + # Find eps s.t. |AUC(eps) - AUC(CN)| < tol + print("#" * 5, "Get epsilon", "#" * 5) + + res = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(phase_find_eps)(exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) + ) + res = pd.DataFrame(res) + res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) + + # Run inferences + print("#" * 5, "Run inferences", "#" * 5) - # ... and run inferences - _ = Parallel(n_jobs=num_cores)( - delayed(run_inferences)(exp, ess, eps, config) for exp, ess, eps in res + _ = Parallel(n_jobs=eval(config["num_cores"]))( + delayed(run_inferences)(exp, ess, config) + for exp, ess in product(exp_vec, ess_vec) ) # Clean diff --git a/src/data.py b/src/data.py index 7e731eb..c82b2fc 100644 --- a/src/data.py +++ b/src/data.py @@ -1,12 +1,11 @@ from itertools import product -from pprint import pformat import numpy as np import pyagrum as gum from numpy.random import randint from src.config import create_clean_dir, get_out_path, set_global_seed -from src.utils import compact_dict, safe_assert, save_bn +from src.utils import safe_assert, save_bn def generate_naivebayes(config): @@ -29,6 +28,7 @@ def generate_naivebayes(config): n_modmax = config["n_modmax"] gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) + rpop_ss = int(gpop_ss * config["rpop_prop"]) n_samples = config["n_samples"] # Set BN (naive Bayes) structure @@ -45,6 +45,11 @@ def generate_naivebayes(config): bn = gum.fastBN(bn_str) save_bn(bn, f"exp{i}", bns_path) + with open(f'{out_path}/{config["exp_meta"]}', "a") as m: + m.write( + f'- exp{i}. Naive Bayes: {config["n_nodes"]} nodes. Complexity: {bn.dim()} Max categories: {n_modmax}\n' + ) + # ... and generate gpop from BN data_gen = gum.BNDatabaseGenerator(bn) data_gen.drawSamples(config["gpop_ss"]) @@ -55,26 +60,22 @@ def generate_naivebayes(config): for sample in range(n_samples): # ... sample pool and rpop - pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False) - gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + shuffled_idx = np.random.permutation(gpop.index) - # Save gpop - gpop.to_csv(f"{data_path}/exp{i}.csv", index=False) + pool_idx = shuffled_idx[:pool_ss] + rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss] - # For each ESS ... - for ess in config["ess_dict"].keys(): + gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) + gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) - # ... create results subdirectories and metadata files - meta_file_path = ( - out_path - / config["results_path"] - / f'results_nodes{config["n_nodes"]}_ess{ess}' - / config["meta_file"] - ) - meta_file_path.parent.mkdir(parents=True, exist_ok=True) + # Debug + safe_assert(pool_ss == len(pool_idx)) + safe_assert(rpop_ss == len(rpop_idx)) + safe_assert(sum(gpop[f"in-pool-{sample}"]) == pool_ss) + safe_assert(sum(gpop[f"in-rpop-{sample}"]) == rpop_ss) - with open(meta_file_path, "w") as f: - f.write(pformat(compact_dict(config)) + "\n\n" + "#" * 50 + "\n\n") + # Save gpop + gpop.to_csv(f"{data_path}/exp{i}.csv", index=False) def generate_randombn(config): @@ -110,12 +111,12 @@ def generate_randombn(config): bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=n_modmax) save_bn(bn, f"exp{i}", bns_path) - with open(f'{out_path}/{config["meta_file"]}', "a") as m: + with open(f'{out_path}/{config["exp_meta"]}', "a") as m: m.write( f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()} Max categories: {n_modmax}\n" ) - # ... and generate gpop from BN #TODO: check if number of levels is coherent, otherwise: rejection sampling of data + # ... and generate gpop from BN data_gen = gum.BNDatabaseGenerator(bn) data_gen.drawSamples(config["gpop_ss"]) data_gen.setDiscretizedLabelModeRandom() @@ -128,7 +129,7 @@ def generate_randombn(config): shuffled_idx = np.random.permutation(gpop.index) pool_idx = shuffled_idx[:pool_ss] - rpop_idx = shuffled_idx[pool_ss:pool_ss + rpop_ss] + rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss] gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) @@ -139,6 +140,5 @@ def generate_randombn(config): safe_assert(sum(gpop[f"in-pool-{sample}"]) == pool_ss) safe_assert(sum(gpop[f"in-rpop-{sample}"]) == rpop_ss) - # Save gpop gpop.to_csv(f"{data_path}/exp{i}.csv", index=False) diff --git a/src/inference.py b/src/inference.py index 02ef6b3..30a7bfd 100644 --- a/src/inference.py +++ b/src/inference.py @@ -9,11 +9,16 @@ from src.utils import add_counts_to_bn, get_min_max_bns, noisy_bn, safe_assert -def run_inferences(exp, ess, eps, config): +def run_inferences(exp, ess, config): out_path = get_out_path(config) target = config["target_var"] + auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') + eps = auc_meta.loc[ + (auc_meta["exp"] == exp) & (auc_meta["ess"] == ess), "eps" + ].values[0] + # Set seed set_global_seed(config["seed"]) @@ -62,10 +67,9 @@ def run_inferences(exp, ess, eps, config): } ) - res_path = ( - out_path / config["results_path"] / f'results_nodes{config["n_nodes"]}_ess{ess}' + results.to_csv( + f'{out_path / config["results_path"]}/{exp}_ess{ess}.csv', index=False ) - results.to_csv(f"{res_path}/{exp}.csv", index=False) # MPE function for BN diff --git a/src/mia.py b/src/mia.py index c94fafe..efe126a 100644 --- a/src/mia.py +++ b/src/mia.py @@ -1,5 +1,4 @@ import math -import traceback import numpy as np import pandas as pd @@ -8,9 +7,9 @@ from sklearn import metrics import src.attacks -from src.config import get_out_path, set_global_seed import src.defenses -from src.utils import check_consistency, get_llr, get_min_max_bns, noisy_bn, safe_assert, safe_open_dir, save_bn +from src.config import get_out_path +from src.utils import get_llr, noisy_bn, safe_assert, safe_open_dir, save_bn # Get the attack power related to a fixed error @@ -55,152 +54,100 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): # Find eps s.t. |AUC(eps) - AUC(CN)| < tol -def get_eps(exp, ess, config): +def phase_find_eps(exp, ess, config) -> dict: + + # TODO: save noisy bn for a given exp. # Get output path out_path = get_out_path(config) - # Set seed - set_global_seed(config["seed"]) - - # Init hyperp. - eps_vec = eval(config["ess_dict"][ess]) - results_path = out_path / config["results_path"] - n_samples = config["n_samples"] - error = eval(eval(config["error"])) - tol = config["tol"] - def_mec = eval(config["def_mec"]) - atk_mec = eval(config["atk_mec"]) - # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - bn = gum.loadBN(f'{out_path / config["bns_path"]}/{exp}.bif') - n_nodes = config["n_nodes"] gpop_ss = config["gpop_ss"] - rpop_ss = int(gpop_ss * config["rpop_prop"]) pool_ss = int(gpop_ss * config["pool_prop"]) + auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') + auc_cn = auc_meta.loc[ + (auc_meta["exp"] == exp) & (auc_meta["ess"] == ess), "auc_cn" + ].values[0] + eps_vec = eval(config["ess_dict"][ess]) - # Debug - safe_assert(gpop_ss == gpop.shape[0]) - safe_assert(n_nodes == gpop.shape[1]) - - bn_theta_vec = [] - bn_theta_hat_vec = [] - cn_vec = [] - - # For any data sample ... - for sample in range(n_samples): - - # ... retrieve pool and rpop, ... - pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes] - - # ... estimate BN from rpop, ... - learner = gum.BNLearner(rpop) - learner.useSmoothingPrior(1e-5) - bn_theta_vec.append(learner.learnParameters(bn.dag())) - - # ... estimate BN from pool, ... - learner = gum.BNLearner(pool) - learner.useSmoothingPrior(1e-5) - bn_theta_hat_vec.append(learner.learnParameters(bn.dag())) - - # ... and run Defense mechanism: estimate the CN - cn = def_mec(bn, ess, pool) - cn_vec.append(cn) - - # Debug - safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) - safe_assert(len(pool) == pool_ss) - safe_assert(len(rpop) == rpop_ss) - - # Debug - safe_assert(len(bn_theta_vec) == n_samples) - safe_assert(len(bn_theta_hat_vec) == n_samples) - safe_assert(len(cn_vec) == n_samples) - - # Run MIA against CN - auc_cn_vec = [] - for sample in range(n_samples): - - # Retrieve sample-related info - y_true = gpop[f"in-pool-{sample}"] - cn = cn_vec[sample] - bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample]) - - # Attack mechanism: extract a BN from the CN - ext_bn = atk_mec(cn, rpop, exp, config) - bn_ie = gum.LazyPropagation(ext_bn) - - # MIA - try: - _, auc = run_mia(bn_ie, bn_theta_ie, rpop, gpop, y_true, error) - auc_cn_vec.append(auc) - - except Exception: - - # Debug - with open(f"{results_path}/log.txt", "a") as log: - log.write(f"{exp}: error with sample {sample} (CN).\n") - log.write(traceback.format_exc()) - - # Compute Avg(AUC(CN)) across data samples - auc_cn = sum(auc_cn_vec) / len(auc_cn_vec) - - # Find eps eps_best = eps_vec[-1] # For each eps ... for eps in eps_vec: - auc_bn_noisy_vec = [] + # Init results + results = pd.DataFrame({"error": eval(config["error"])}) + + auc_noisy_bns = [] - # ... run MIA against noisy BN ... - for sample in range(n_samples): + # ... and for each data sample ... + for sample in range(config["n_samples"]): - # Retrieve sample-related info - y_true = gpop[f"in-pool-{sample}"] - bn_theta_hat = bn_theta_hat_vec[sample] - bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample]) + # ... read the BNs as estimated from rpop and pool, ... + bn_theta = gum.loadBN( + f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif" + ) + bn_theta_hat = gum.loadBN( + f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif" + ) # Get noisy BN - scale = (2 * bn_theta_hat.size()) / (len(pool) * eps) + scale = (2 * bn_theta_hat.size()) / (pool_ss * eps) bn_noisy = noisy_bn(bn_theta_hat, scale) + bn_noisy_ie = gum.LazyPropagation(bn_noisy) + bn_theta_ie = gum.LazyPropagation(bn_theta) + + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] - try: + # try: - # MIA - _, auc = run_mia(bn_noisy_ie, bn_theta_ie, rpop, gpop, y_true, error) - auc_bn_noisy_vec.append(auc) + # ... and perform membership inference on gpop + power_vec, auc = run_mia( + bn_noisy_ie, + bn_theta_ie, + rpop, + gpop, + gpop[f"in-pool-{sample}"], + eval(config["error"]), + ) + results[f"power_noisyBN_sample{sample}"] = power_vec + auc_noisy_bns.append(auc) - except Exception: + # except Exception: - # Debug - with open(f"{results_path}/log.txt", "a") as log: - log.write( - f"{exp}: error with sample {sample} (BN noisy, eps: {eps}).\n" - ) - log.write(traceback.format_exc()) + # # Debug + # with open(f"{results_path}/log.txt", "a") as log: + # log.write(f"{exp}: error with sample {sample} (BN).\n") + # log.write(traceback.format_exc()) - # ... and compute Avg(AUC(eps)) across data samples - auc_bn = sum(auc_bn_noisy_vec) / n_samples + # Compute Avg(AUC(eps)) across data samples + auc_noisy_bn = sum(auc_noisy_bns) / config["n_samples"] # Condition on |AUC(eps) - AUC(CN)| - if abs(auc_cn - auc_bn) <= tol: + if abs(auc_cn - auc_noisy_bn) <= config["tol"]: eps_best = eps break - # Store found eps - meta_file_path = ( - results_path - / f'results_nodes{config["n_nodes"]}_ess{ess}' - / config["meta_file"] - ) - with open(meta_file_path, "a") as m: - m.write(f"- {exp}. Nodes: {n_nodes} Eps: {eps_best}\n") + # Save noisy BNs + for sample in range(config["n_samples"]): + bn_theta_hat = gum.loadBN( + f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif" + ) + scale = (2 * bn_theta_hat.size()) / (pool_ss * eps_best) + bn_noisy = noisy_bn(bn_theta_hat, scale) + save_bn(bn_noisy, f"bn_{exp}_sample{sample}", out_path / config["noisy_path"]) + + return { + "exp": exp, + "ess": ess, + "auc_cn": auc_cn, + "auc_noisy_bn": auc_noisy_bn, + "eps": eps_best, + } - return exp, ess, eps_best # Learn BN parameters from a given BN and data def learn_bn_params(bn, data): @@ -213,6 +160,7 @@ def learn_bn_params(bn, data): return bn_learnt + # Estimate BNs from rpop and pool def phase_estimation(exp, config) -> None: @@ -230,7 +178,7 @@ def phase_estimation(exp, config) -> None: # Debug safe_assert(gpop_ss == gpop.shape[0]) safe_assert(n_nodes == gpop.loc[:, ~gpop.columns.str.contains("in-")].shape[1]) - + # For each data sample ... for sample in range(config["n_samples"]): @@ -250,7 +198,7 @@ def phase_estimation(exp, config) -> None: safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) safe_assert(len(pool) == pool_ss) safe_assert(len(rpop) == rpop_ss) - + return @@ -259,7 +207,7 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: # Get output path out_path = get_out_path(config) - + # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') @@ -270,7 +218,7 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: bn = gum.loadBN(f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif") # ... retrieve pool, ... - pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :len(bn.nodes())] + pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, : len(bn.nodes())] # ... and derive the CN def_mec_fn = getattr(src.defenses, def_mec) @@ -280,17 +228,20 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: # save_bn(bn_max, f"bn_max_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") base_path = out_path / config["cns_path"] / f"ESS: {ess}" safe_open_dir(base_path) - cn.saveBNsMinMax(f"{base_path}/bn_min_{exp}_sample{sample}.bif", f"{base_path}/bn_max_{exp}_sample{sample}.bif") + cn.saveBNsMinMax( + f"{base_path}/bn_min_{exp}_sample{sample}.bif", + f"{base_path}/bn_max_{exp}_sample{sample}.bif", + ) - return + # Apply attack mechanism to a BN, namely, derive a BN from a CN def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: # Get output path out_path = get_out_path(config) - + # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') @@ -298,19 +249,28 @@ def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: for sample in range(config["n_samples"]): # ... read the related CN - bn_min = gum.loadBN(f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_min_{exp}_sample{sample}.bif") - bn_max = gum.loadBN(f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_max_{exp}_sample{sample}.bif") + bn_min = gum.loadBN( + f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_min_{exp}_sample{sample}.bif" + ) + bn_max = gum.loadBN( + f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_max_{exp}_sample{sample}.bif" + ) # ... retrieve rpop, ... - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :len(bn_min.nodes())] + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_min.nodes())] # ... and derive the BN atk_mec_fn = getattr(src.attacks, atk_mec) bn = atk_mec_fn(bn_min, bn_max, rpop, exp, config) - save_bn(bn, f"bn_{exp}_sample{sample}", out_path / config['atk_path'] / f"ESS: {ess}") + save_bn( + bn, + f"bn_{exp}_sample{sample}", + out_path / config["atk_path"] / f"ESS: {ess}", + ) return + # MIA attack vs a BN def phase_mia_vs_bn(exp, config) -> None: @@ -319,7 +279,7 @@ def phase_mia_vs_bn(exp, config) -> None: # Init results results = pd.DataFrame({"error": eval(config["error"])}) - + # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') @@ -327,20 +287,29 @@ def phase_mia_vs_bn(exp, config) -> None: for sample in range(config["n_samples"]): # ... read the BNs as estimated from rpop and pool, ... - bn_theta = gum.loadBN(f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif") - bn_theta_hat = gum.loadBN(f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif") + bn_theta = gum.loadBN( + f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif" + ) + bn_theta_hat = gum.loadBN( + f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif" + ) bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) bn_theta_ie = gum.LazyPropagation(bn_theta) # ... retrieve rpop, ... - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :len(bn_theta.nodes())] + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] # try: # ... and perform membership inference on gpop power_vec, _ = run_mia( - bn_theta_hat_ie, bn_theta_ie, rpop, gpop, gpop[f"in-pool-{sample}"], eval(config["error"]) + bn_theta_hat_ie, + bn_theta_ie, + rpop, + gpop, + gpop[f"in-pool-{sample}"], + eval(config["error"]), ) results[f"power_BN_sample{sample}"] = power_vec @@ -354,40 +323,52 @@ def phase_mia_vs_bn(exp, config) -> None: # Save results results.to_csv(f'{out_path}/{config["results_path"]}/bn_{exp}.csv', index=False) + # MIA attack vs a CN -def phase_mia_vs_cn(exp, ess, config) -> None: +def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # Get output path out_path = get_out_path(config) # Init results results = pd.DataFrame({"error": eval(config["error"])}) - + # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') # For each data sample ... + auc_cns = [] for sample in range(config["n_samples"]): # ... read the BN as inferred from the CN - bn_theta_hat = gum.loadBN(f'{out_path}/{config["atk_path"]}/ESS: {ess}/bn_{exp}_sample{sample}.bif') + bn_theta_hat = gum.loadBN( + f'{out_path}/{config["atk_path"]}/ESS: {ess}/bn_{exp}_sample{sample}.bif' + ) # ... read the BN as estimated from rpop, ... - bn_theta = gum.loadBN(f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif") + bn_theta = gum.loadBN( + f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif" + ) bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) bn_theta_ie = gum.LazyPropagation(bn_theta) # ... retrieve rpop, ... - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :len(bn_theta.nodes())] + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] # try: # ... and perform membership inference on gpop - power_vec, _ = run_mia( - bn_theta_hat_ie, bn_theta_ie, rpop, gpop, gpop[f"in-pool-{sample}"], eval(config["error"]) + power_vec, auc = run_mia( + bn_theta_hat_ie, + bn_theta_ie, + rpop, + gpop, + gpop[f"in-pool-{sample}"], + eval(config["error"]), ) results[f"power_CN_sample{sample}"] = power_vec + auc_cns.append(auc) # except Exception: @@ -396,8 +377,17 @@ def phase_mia_vs_cn(exp, ess, config) -> None: # log.write(f"{exp}: error with sample {sample} (BN).\n") # log.write(traceback.format_exc()) + # Compute Avg(AUC(CN)) across data samples + auc_cn = sum(auc_cns) / len(auc_cns) + # Save results - results.to_csv(f'{out_path}/{config["results_path"]}/cn_{exp}-ess{ess}.csv', index=False) + if save_res: + results.to_csv( + f'{out_path}/{config["results_path"]}/cn_{exp}-ess{ess}.csv', index=False + ) + + return {"exp": exp, "ess": ess, "auc_cn": auc_cn} + # Get theoretical power def phase_theoretical_power(exp, config): @@ -414,5 +404,3 @@ def phase_theoretical_power(exp, config): beta = [norm.cdf(i).item() for i in z_one_minus_beta] return beta - - diff --git a/src/utils.py b/src/utils.py index e2af401..63d17c2 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,7 +1,5 @@ import sys -from math import prod from tempfile import TemporaryDirectory -import os import hopsy import numpy as np @@ -123,6 +121,7 @@ def safe_assert(condition): if IN_PYTEST: assert condition + # Open `path` for writing, creating any parent directories as needed. def safe_open_dir(path): @@ -131,12 +130,14 @@ def safe_open_dir(path): return path + # Save a BN, with its name, into `path` def save_bn(bn, bn_name, path): with safe_open_dir(path) as dir: gum.saveBN(bn, f"{dir}/{bn_name}.bif") + # Extract BN min and BN max from a CN def get_min_max_bns(cn, exp: str): From 664bacd2a6a4ca818322535ec26e121bcd63bdf2 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Wed, 12 Nov 2025 16:32:13 +0100 Subject: [PATCH 08/31] Minor fixes --- .../cn_privacy/config.yaml | 0 experiments/cn_privacy/exp.py | 13 +++++++------ .../cn_vs_noisybn/config.yaml | 0 experiments/cn_vs_noisybn/exp.py | 13 +++++++------ src/config.py | 8 +++----- .../cn_privacy.yaml => test/cn_privacy/config.yaml | 0 .../cn_vs_noisybn/config.yaml | 0 7 files changed, 17 insertions(+), 17 deletions(-) rename configs/cn_privacy.yaml => experiments/cn_privacy/config.yaml (100%) rename configs/cn_vs_noisybn.yaml => experiments/cn_vs_noisybn/config.yaml (100%) rename configs/tests/cn_privacy.yaml => test/cn_privacy/config.yaml (100%) rename configs/tests/cn_vs_noisybn.yaml => test/cn_vs_noisybn/config.yaml (100%) diff --git a/configs/cn_privacy.yaml b/experiments/cn_privacy/config.yaml similarity index 100% rename from configs/cn_privacy.yaml rename to experiments/cn_privacy/config.yaml diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 4c125b4..39b0d28 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -20,6 +20,7 @@ def main(): set_global_seed(config["seed"]) def_mec = config["def_mec"] atk_mec = config["atk_mec"] + num_cores = eval(config["num_cores"]) # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) @@ -35,14 +36,14 @@ def main(): # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_estimation)(exp, config) for exp in exp_vec ) # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_defense_mechanism)(def_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) @@ -50,7 +51,7 @@ def main(): # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) @@ -58,7 +59,7 @@ def main(): # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_mia_vs_cn)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) @@ -66,14 +67,14 @@ def main(): # MIA vs BN (for comparison) print("#" * 5, "MIA vs BN", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_mia_vs_bn)(exp, config) for exp in exp_vec ) # Compute theoretical power print("#" * 5, "Compute theoretical power", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_theoretical_power)(exp, config) for exp in exp_vec ) diff --git a/configs/cn_vs_noisybn.yaml b/experiments/cn_vs_noisybn/config.yaml similarity index 100% rename from configs/cn_vs_noisybn.yaml rename to experiments/cn_vs_noisybn/config.yaml diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 8e7d296..aec339e 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -21,6 +21,7 @@ def main(): set_global_seed(config["seed"]) def_mec = config["def_mec"] atk_mec = config["atk_mec"] + num_cores = eval(config["num_cores"]) # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) @@ -35,14 +36,14 @@ def main(): # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_estimation)(exp, config) for exp in exp_vec ) # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_defense_mechanism)(def_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) @@ -50,7 +51,7 @@ def main(): # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) @@ -58,7 +59,7 @@ def main(): # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) - res = Parallel(n_jobs=eval(config["num_cores"]))( + res = Parallel(n_jobs=num_cores)( delayed(phase_mia_vs_cn)(exp, ess, config, save_res=False) for exp, ess in product(exp_vec, ess_vec) ) @@ -68,7 +69,7 @@ def main(): # Find eps s.t. |AUC(eps) - AUC(CN)| < tol print("#" * 5, "Get epsilon", "#" * 5) - res = Parallel(n_jobs=eval(config["num_cores"]))( + res = Parallel(n_jobs=num_cores)( delayed(phase_find_eps)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) @@ -78,7 +79,7 @@ def main(): # Run inferences print("#" * 5, "Run inferences", "#" * 5) - _ = Parallel(n_jobs=eval(config["num_cores"]))( + _ = Parallel(n_jobs=num_cores)( delayed(run_inferences)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) diff --git a/src/config.py b/src/config.py index 90ccc48..e30086b 100644 --- a/src/config.py +++ b/src/config.py @@ -10,11 +10,9 @@ # Read configuration for experiment def load_config(name: str): - root = get_root_path() - - test_dir = "/tests" if os.getenv("USE_TEST_CONFIG") == "1" else "" - - config_path = root / f"configs{test_dir}" / f"{name}.yaml" + subdir = "test" if os.getenv("USE_TEST_CONFIG") == "1" else "experiments" + + config_path = get_root_path() / subdir / name / "config.yaml" with open(config_path, "r") as f: config = yaml.safe_load(f) diff --git a/configs/tests/cn_privacy.yaml b/test/cn_privacy/config.yaml similarity index 100% rename from configs/tests/cn_privacy.yaml rename to test/cn_privacy/config.yaml diff --git a/configs/tests/cn_vs_noisybn.yaml b/test/cn_vs_noisybn/config.yaml similarity index 100% rename from configs/tests/cn_vs_noisybn.yaml rename to test/cn_vs_noisybn/config.yaml From df79a513a916ac90b937c1c328d72648069f3e1a Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Thu, 13 Nov 2025 14:55:26 +0100 Subject: [PATCH 09/31] Update: simplify how results are saved --- experiments/cn_privacy/config.yaml | 22 +++++----- experiments/cn_privacy/config_BAK.yaml | 36 +++++++++++++++++ experiments/cn_privacy/exp.py | 19 ++++----- experiments/cn_vs_noisybn/config.yaml | 32 ++++++--------- experiments/cn_vs_noisybn/config_BAK.yaml | 49 +++++++++++++++++++++++ experiments/cn_vs_noisybn/exp.py | 16 ++++---- src/data.py | 16 +------- src/inference.py | 26 ++++++------ src/mia.py | 49 +++++++++++++---------- src/utils.py | 5 +-- test/cn_privacy/config.yaml | 8 ++-- test/cn_vs_noisybn/config.yaml | 10 ++--- 12 files changed, 178 insertions(+), 110 deletions(-) create mode 100644 experiments/cn_privacy/config_BAK.yaml create mode 100644 experiments/cn_vs_noisybn/config_BAK.yaml diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 9fa47d1..9c18cbe 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -2,36 +2,34 @@ # Paths out_path: experiments/cn_privacy/output # Output path -bns_path: bns/gt # Where to save ground-truth BNs -rpop_path: bns/rpop # Where to save BNs learnt from rpop -pool_path: bns/pool # Where to save BNs learnt from pool -cns_path: cns # Where to save CNs learnt from pool -atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN +bns_path: bns # Where to save ground-truth BNs +cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments # Models -n_nodes_vec: '[10, 20, 50, 100]' # List of models' number of nodes -edge_ratio_vec: '[1, 2, 4]' # List of models' edge ratio +n_nodes_vec: '[10, 15]' # List of models' number of nodes +edge_ratio_vec: '[1, 1.5]' # List of models' edge ratio n_modmax: 2 # Maximum number of variables categories # Data -gpop_ss: 10000 # Sample size of general population +gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 20 # Number of data samples -error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector +n_samples: 5 # Number of data samples +error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM -ess_vec: '[1, 10, 50, 100, 1000]' # List of ESS +ess_vec: '[1, 100]' # List of ESS # ATK-MLE -n_bns: 500 # Number of BNs to sample within the CN +n_bns: 5 # Number of BNs to sample within the CN # Other seed: 42 # Global seed diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml new file mode 100644 index 0000000..9952d1c --- /dev/null +++ b/experiments/cn_privacy/config_BAK.yaml @@ -0,0 +1,36 @@ +## Configuration file + +# Paths +out_path: experiments/cn_privacy/output # Output path +bns_path: bns # Where to save ground-truth BNs +cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +data_path: data # Where to save data as generated from ground-truth BNs +results_path: results # Where to save the experiment results +exp_meta: exp_meta.txt # File of metadata for experiments + +# Models +n_nodes_vec: '[10, 20, 50, 100]' # List of models' number of nodes +edge_ratio_vec: '[1, 2, 4]' # List of models' edge ratio +n_modmax: 2 # Maximum number of variables categories + +# Data +gpop_ss: 10000 # Sample size of general population +rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop +pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop + +# MIA +def_mec: 'def_idm' # Defense mechanisms to consider +atk_mec: 'atk_mle' # Attack mechanisms to consider +n_samples: 20 # Number of data samples +error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector + +# DEF-IDM +ess_vec: '[1, 10, 50, 100, 1000]' # List of ESS + +# ATK-MLE +n_bns: 500 # Number of BNs to sample within the CN + +# Other +seed: 42 # Global seed +num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 39b0d28..5887cc6 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -5,7 +5,7 @@ import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import get_out_path, load_config, set_global_seed +from src.config import create_clean_dir, get_out_path, load_config, set_global_seed from src.data import generate_randombn from src.mia import (phase_attack_mechanism, phase_defense_mechanism, phase_estimation, phase_mia_vs_bn, phase_mia_vs_cn, @@ -24,7 +24,8 @@ def main(): # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) - + create_clean_dir(out_path / config["bns_path"] / "gt") + create_clean_dir(out_path / config["data_path"]) generate_randombn(config) # Init the vectors of experiments @@ -35,14 +36,15 @@ def main(): # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - + create_clean_dir(out_path / config["bns_path"] / "rpop") + create_clean_dir(out_path / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( delayed(phase_estimation)(exp, config) for exp in exp_vec ) # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) - + create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( delayed(phase_defense_mechanism)(def_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) @@ -50,7 +52,7 @@ def main(): # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) - + create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) @@ -58,22 +60,21 @@ def main(): # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) - + create_clean_dir(out_path / config["results_path"] / "cns") _ = Parallel(n_jobs=num_cores)( delayed(phase_mia_vs_cn)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) ) - # MIA vs BN (for comparison) + # MIA vs BN print("#" * 5, "MIA vs BN", "#" * 5) - + create_clean_dir(out_path / config["results_path"] / "bns") _ = Parallel(n_jobs=num_cores)( delayed(phase_mia_vs_bn)(exp, config) for exp in exp_vec ) # Compute theoretical power print("#" * 5, "Compute theoretical power", "#" * 5) - _ = Parallel(n_jobs=num_cores)( delayed(phase_theoretical_power)(exp, config) for exp in exp_vec ) diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 1098927..49cdd7b 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -2,12 +2,10 @@ # Paths out_path: experiments/cn_vs_noisybn/output # Output path -bns_path: bns/gt # Where to save ground-truth BNs -rpop_path: bns/rpop # Where to save BNs learnt from rpop -pool_path: bns/pool # Where to save BNs learnt from pool -cns_path: cns # Where to save CNs learnt from pool -atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN -noisy_path: bns/noisy # Where to save noisy BNs +bns_path: bns # Where to save ground-truth BNs +cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +noisy_path: noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments @@ -17,34 +15,30 @@ auc_meta: auc_meta.csv # File of metadata for AUCs target_var: 'T' # Target variable n_nodes: 10 # Number of nodes for each BN model n_modmax: 2 # Maximum number of categories for covariates -n_models: 10 # Number of models to evaluate +n_models: 5 # Number of models to evaluate # Data -gpop_ss: 1000 # Sample size of general population +gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 30 # Number of data samples -tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol -error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector +n_samples: 5 # Number of data samples +tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM ess_dict: # Eps list to evaluate for each ess - 1: 'np.arange(0.1, 10, 0.1)' - 10: 'np.arange(0.1, 10, 0.1)' - 20: 'np.arange(0.05, 5, 0.05)' - 30: 'np.arange(1e-3, 1, 1e-3)' - 40: 'np.arange(5e-6, 1e-2, 5e-6)' - 50: 'np.arange(5e-7, 5e-4, 5e-7)' + 1: 'np.arange(0.1, 10, 0.5)' + 50: 'np.arange(5e-7, 5e-4, 1e-6)' # ATK-MLE -n_bns: 50 # Number of BNs to sample within the CN +n_bns: 5 # Number of BNs to sample within the CN # Inferences -n_infer: 1000 # Number of inferences to perform +n_infer: 5 # Number of inferences to perform # Other seed: 42 # Global seed diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml new file mode 100644 index 0000000..f8d72b4 --- /dev/null +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -0,0 +1,49 @@ +## Configuration file + +# Paths +out_path: experiments/cn_vs_noisybn/output # Output path +bns_path: bns # Where to save ground-truth BNs +cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +noisy_path: noisy # Where to save noisy BNs +data_path: data # Where to save data as generated from ground-truth BNs +results_path: results # Where to save the experiment results +exp_meta: exp_meta.txt # File of metadata for experiments +auc_meta: auc_meta.csv # File of metadata for AUCs + +# Models (Naive Bayes) +target_var: 'T' # Target variable +n_nodes: 10 # Number of nodes for each BN model +n_modmax: 2 # Maximum number of categories for covariates +n_models: 10 # Number of models to evaluate + +# Data +gpop_ss: 1000 # Sample size of general population +rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop +pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop + +# MIA +def_mec: 'def_idm' # Defense mechanisms to consider +atk_mec: 'atk_mle' # Attack mechanisms to consider +n_samples: 30 # Number of data samples +tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector + +# DEF-IDM +ess_dict: # Eps list to evaluate for each ess + 1: 'np.arange(0.1, 10, 0.1)' + 10: 'np.arange(0.1, 10, 0.1)' + 20: 'np.arange(0.05, 5, 0.05)' + 30: 'np.arange(1e-3, 1, 1e-3)' + 40: 'np.arange(5e-6, 1e-2, 5e-6)' + 50: 'np.arange(5e-7, 5e-4, 5e-7)' + +# ATK-MLE +n_bns: 50 # Number of BNs to sample within the CN + +# Inferences +n_infer: 1000 # Number of inferences to perform + +# Other +seed: 42 # Global seed +num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization \ No newline at end of file diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index aec339e..2077750 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -6,7 +6,7 @@ import pandas as pd from joblib import Parallel, delayed -from src.config import get_out_path, load_config, set_global_seed +from src.config import create_clean_dir, get_out_path, load_config, set_global_seed from src.data import generate_naivebayes from src.inference import run_inferences from src.mia import (phase_attack_mechanism, phase_defense_mechanism, @@ -25,6 +25,8 @@ def main(): # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) + create_clean_dir(out_path / config["bns_path"] / "gt") + create_clean_dir(out_path / config["data_path"]) generate_naivebayes(config) # Init the vectors of experiments @@ -35,14 +37,15 @@ def main(): # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - + create_clean_dir(out_path / config["bns_path"] / "rpop") + create_clean_dir(out_path / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( delayed(phase_estimation)(exp, config) for exp in exp_vec ) # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) - + create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( delayed(phase_defense_mechanism)(def_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) @@ -50,7 +53,7 @@ def main(): # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) - + create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) for exp, ess in product(exp_vec, ess_vec) @@ -58,7 +61,6 @@ def main(): # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) - res = Parallel(n_jobs=num_cores)( delayed(phase_mia_vs_cn)(exp, ess, config, save_res=False) for exp, ess in product(exp_vec, ess_vec) @@ -68,7 +70,7 @@ def main(): # Find eps s.t. |AUC(eps) - AUC(CN)| < tol print("#" * 5, "Get epsilon", "#" * 5) - + create_clean_dir(out_path / config["noisy_path"]) res = Parallel(n_jobs=num_cores)( delayed(phase_find_eps)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) @@ -78,7 +80,7 @@ def main(): # Run inferences print("#" * 5, "Run inferences", "#" * 5) - + create_clean_dir(out_path / config["results_path"] / "inferences") _ = Parallel(n_jobs=num_cores)( delayed(run_inferences)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec) diff --git a/src/data.py b/src/data.py index c82b2fc..6585451 100644 --- a/src/data.py +++ b/src/data.py @@ -17,12 +17,6 @@ def generate_naivebayes(config): out_path = get_out_path(config) bns_path = out_path / config["bns_path"] data_path = out_path / config["data_path"] - results_path = out_path / config["results_path"] - - # Create empty directories - create_clean_dir(bns_path) - create_clean_dir(data_path) - create_clean_dir(results_path) # Retrieve hyperparameters n_modmax = config["n_modmax"] @@ -43,7 +37,7 @@ def generate_naivebayes(config): # ... generate BN, ... bn = gum.fastBN(bn_str) - save_bn(bn, f"exp{i}", bns_path) + save_bn(bn, f"exp{i}", bns_path / "gt") with open(f'{out_path}/{config["exp_meta"]}', "a") as m: m.write( @@ -87,12 +81,6 @@ def generate_randombn(config): out_path = get_out_path(config) bns_path = out_path / config["bns_path"] data_path = out_path / config["data_path"] - results_path = out_path / config["results_path"] - - # Create empty directories - create_clean_dir(bns_path) - create_clean_dir(data_path) - create_clean_dir(results_path) # Retrieve hyperparameters n_nodes_vec = eval(config["n_nodes_vec"]) @@ -109,7 +97,7 @@ def generate_randombn(config): # ... generate BN, ... bn_gen = gum.BNGenerator() bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=n_modmax) - save_bn(bn, f"exp{i}", bns_path) + save_bn(bn, f"exp{i}", bns_path / "gt") with open(f'{out_path}/{config["exp_meta"]}', "a") as m: m.write( diff --git a/src/inference.py b/src/inference.py index 30a7bfd..610e46c 100644 --- a/src/inference.py +++ b/src/inference.py @@ -6,13 +6,17 @@ from more_itertools import random_product from src.config import get_out_path, set_global_seed -from src.utils import add_counts_to_bn, get_min_max_bns, noisy_bn, safe_assert +from src.mia import learn_bn_params +from src.utils import get_min_max_bns, noisy_bn, safe_assert +import src.defenses + def run_inferences(exp, ess, config): out_path = get_out_path(config) target = config["target_var"] + def_mec = config["def_mec"] auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') eps = auc_meta.loc[ @@ -29,21 +33,17 @@ def run_inferences(exp, ess, config): ] # Store ground-truth BN - gt = gum.loadBN(f'{out_path / config["bns_path"]}/{exp}.bif') + gt = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - # Learn BN from gpop - bn_learner = gum.BNLearner(gpop) - bn_learner.useSmoothingPrior(1e-5) - bn = bn_learner.learnParameters(gt.dag()) + # Learn BN from gpop #TODO: save results + bn = learn_bn_params(gt,gpop) - # Learn CN from gpop - bn_copy = gum.BayesNet(bn) - add_counts_to_bn(bn_copy, gpop) - cn = gum.CredalNet(bn_copy) - cn.idmLearning(ess) + # Learn CN from gpop (defense mechanism) #TODO: save results + def_mec_fn = getattr(src.defenses, def_mec) + cn = def_mec_fn(bn, ess, gpop) - # Learn noisy BN from gpop + # Learn noisy BN from gpop #TODO: save results scale = (2 * bn.size()) / (len(gpop) * eps) bn_noisy = noisy_bn(bn, scale) @@ -68,7 +68,7 @@ def run_inferences(exp, ess, config): ) results.to_csv( - f'{out_path / config["results_path"]}/{exp}_ess{ess}.csv', index=False + f'{out_path / config["results_path"]}/inferences/{exp}_ess{ess}.csv', index=False ) diff --git a/src/mia.py b/src/mia.py index efe126a..d48a17b 100644 --- a/src/mia.py +++ b/src/mia.py @@ -56,8 +56,6 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): # Find eps s.t. |AUC(eps) - AUC(CN)| < tol def phase_find_eps(exp, ess, config) -> dict: - # TODO: save noisy bn for a given exp. - # Get output path out_path = get_out_path(config) @@ -86,10 +84,10 @@ def phase_find_eps(exp, ess, config) -> dict: # ... read the BNs as estimated from rpop and pool, ... bn_theta = gum.loadBN( - f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif" + f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" ) bn_theta_hat = gum.loadBN( - f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif" + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) # Get noisy BN @@ -134,7 +132,7 @@ def phase_find_eps(exp, ess, config) -> dict: # Save noisy BNs for sample in range(config["n_samples"]): bn_theta_hat = gum.loadBN( - f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif" + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) scale = (2 * bn_theta_hat.size()) / (pool_ss * eps_best) bn_noisy = noisy_bn(bn_theta_hat, scale) @@ -169,7 +167,7 @@ def phase_estimation(exp, config) -> None: # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - bn = gum.loadBN(f'{out_path / config["bns_path"]}/{exp}.bif') + bn = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') n_nodes = len(bn.nodes()) gpop_ss = config["gpop_ss"] rpop_ss = int(gpop_ss * config["rpop_prop"]) @@ -188,11 +186,11 @@ def phase_estimation(exp, config) -> None: # ... estimate BN from rpop, ... bn_learnt = learn_bn_params(bn, rpop) - save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config["rpop_path"]) + save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config['bns_path'] / "rpop") # ... estimate BN from pool, ... bn_learnt = learn_bn_params(bn, pool) - save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config["pool_path"]) + save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config['bns_path'] / "pool") # Debug safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) @@ -215,7 +213,7 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: for sample in range(config["n_samples"]): # ... read the related BN - bn = gum.loadBN(f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif") + bn = gum.loadBN(f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif") # ... retrieve pool, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, : len(bn.nodes())] @@ -223,9 +221,6 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: # ... and derive the CN def_mec_fn = getattr(src.defenses, def_mec) cn = def_mec_fn(bn, ess, pool) - # bn_min, bn_max = get_min_max_bns(cn, exp) - # save_bn(bn_min, f"bn_min_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") - # save_bn(bn_max, f"bn_max_{exp}_sample{sample}", out_path / config["cns_path"] / f"ESS: {ess}") base_path = out_path / config["cns_path"] / f"ESS: {ess}" safe_open_dir(base_path) cn.saveBNsMinMax( @@ -262,10 +257,12 @@ def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: # ... and derive the BN atk_mec_fn = getattr(src.attacks, atk_mec) bn = atk_mec_fn(bn_min, bn_max, rpop, exp, config) + base_path = out_path / config["atk_path"] / f"ESS: {ess}" + safe_open_dir(base_path) save_bn( bn, f"bn_{exp}_sample{sample}", - out_path / config["atk_path"] / f"ESS: {ess}", + base_path ) return @@ -288,10 +285,10 @@ def phase_mia_vs_bn(exp, config) -> None: # ... read the BNs as estimated from rpop and pool, ... bn_theta = gum.loadBN( - f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif" + f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" ) bn_theta_hat = gum.loadBN( - f"{out_path}/{config['pool_path']}/bn_{exp}_sample{sample}.bif" + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) @@ -321,7 +318,7 @@ def phase_mia_vs_bn(exp, config) -> None: # log.write(traceback.format_exc()) # Save results - results.to_csv(f'{out_path}/{config["results_path"]}/bn_{exp}.csv', index=False) + results.to_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) # MIA attack vs a CN @@ -347,7 +344,7 @@ def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # ... read the BN as estimated from rpop, ... bn_theta = gum.loadBN( - f"{out_path}/{config['rpop_path']}/bn_{exp}_sample{sample}.bif" + f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" ) bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) @@ -383,17 +380,21 @@ def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # Save results if save_res: results.to_csv( - f'{out_path}/{config["results_path"]}/cn_{exp}-ess{ess}.csv', index=False + f'{out_path}/{config["results_path"]}/cns/cn_{exp}-ess{ess}.csv', index=False ) return {"exp": exp, "ess": ess, "auc_cn": auc_cn} # Get theoretical power -def phase_theoretical_power(exp, config): +def phase_theoretical_power(exp, config) -> None: - # Read BN - bn = gum.loadBN(f'{get_out_path(config) / config["bns_path"]}/{exp}.bif') + # Get output path + out_path = get_out_path(config) + + # Read data + bn = gum.loadBN(f'{get_out_path(config) / config["bns_path"]}/gt/{exp}.bif') + results = pd.read_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv') # Compute bound bound = math.sqrt(bn.dim() / int(config["gpop_ss"] * config["pool_prop"])) @@ -403,4 +404,8 @@ def phase_theoretical_power(exp, config): z_one_minus_beta = [bound - i for i in z_alpha] beta = [norm.cdf(i).item() for i in z_one_minus_beta] - return beta + # Save results + results["power_bound"] = beta + results.to_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) + + return diff --git a/src/utils.py b/src/utils.py index 63d17c2..2b98ba5 100644 --- a/src/utils.py +++ b/src/utils.py @@ -126,7 +126,7 @@ def safe_assert(condition): def safe_open_dir(path): if not path.exists(): - path.mkdir(parents=True, exist_ok=True) + path.mkdir(parents=False, exist_ok=True) return path @@ -134,8 +134,7 @@ def safe_open_dir(path): # Save a BN, with its name, into `path` def save_bn(bn, bn_name, path): - with safe_open_dir(path) as dir: - gum.saveBN(bn, f"{dir}/{bn_name}.bif") + gum.saveBN(bn, f"{path}/{bn_name}.bif") # Extract BN min and BN max from a CN diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index 5b6b66d..06ac0ac 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -2,11 +2,9 @@ # Paths out_path: test/cn_privacy/output # Output path -bns_path: bns/gt # Where to save ground-truth BNs -rpop_path: bns/rpop # Where to save BNs learnt from rpop -pool_path: bns/pool # Where to save BNs learnt from pool -cns_path: cns # Where to save CNs learnt from pool -atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN +bns_path: bns # Where to save ground-truth BNs +cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index 1b754c6..b2ef3bd 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -2,12 +2,10 @@ # Paths out_path: test/cn_vs_noisybn/output # Output path -bns_path: bns/gt # Where to save ground-truth BNs -rpop_path: bns/rpop # Where to save BNs learnt from rpop -pool_path: bns/pool # Where to save BNs learnt from pool -cns_path: cns # Where to save CNs learnt from pool -atk_path: bns/atk # Where to save BN as obtained by atk-mec from a CN -noisy_path: bns/noisy # Where to save noisy BNs +bns_path: bns # Where to save ground-truth BNs +cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +noisy_path: noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments From a5014f1308307bf1a5089ac067033eaf1fbcc3f0 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Fri, 14 Nov 2025 12:10:17 +0100 Subject: [PATCH 10/31] Change how (intermediate) results and metadata files are saved --- experiments/cn_privacy/config.yaml | 2 +- experiments/cn_privacy/config_BAK.yaml | 2 +- experiments/cn_privacy/exp.py | 11 ++-- experiments/cn_vs_noisybn/config.yaml | 2 +- experiments/cn_vs_noisybn/config_BAK.yaml | 2 +- experiments/cn_vs_noisybn/exp.py | 9 +++- src/attacks.py | 5 +- src/config.py | 2 +- src/data.py | 6 +-- src/inference.py | 6 +-- src/mia.py | 62 +++++++++++++++-------- src/utils.py | 26 +++++----- test/cn_privacy/config.yaml | 2 +- test/cn_vs_noisybn/config.yaml | 2 +- 14 files changed, 83 insertions(+), 56 deletions(-) diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 9c18cbe..1287d0a 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -18,11 +18,11 @@ n_modmax: 2 # Maximum number of variable gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop +samples: 5 # Number of data samples # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 5 # Number of data samples error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index 9952d1c..14ed7c2 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -18,11 +18,11 @@ n_modmax: 2 # Maximum number of variable gpop_ss: 10000 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop +samples: 20 # Number of data samples # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 20 # Number of data samples error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector # DEF-IDM diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 5887cc6..84438de 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -7,9 +7,14 @@ from src.config import create_clean_dir, get_out_path, load_config, set_global_seed from src.data import generate_randombn -from src.mia import (phase_attack_mechanism, phase_defense_mechanism, - phase_estimation, phase_mia_vs_bn, phase_mia_vs_cn, - phase_theoretical_power) +from src.mia import ( + phase_attack_mechanism, + phase_defense_mechanism, + phase_estimation, + phase_mia_vs_bn, + phase_mia_vs_cn, + phase_theoretical_power, +) def main(): diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 49cdd7b..62ecfc9 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -21,11 +21,11 @@ n_models: 5 # Number of models to evalua gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop +samples: 5 # Number of data samples # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 5 # Number of data samples tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index f8d72b4..4f61b85 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -21,11 +21,11 @@ n_models: 10 # Number of models to evalua gpop_ss: 1000 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop +samples: 30 # Number of data samples # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 30 # Number of data samples tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 2077750..328a8a8 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -9,8 +9,13 @@ from src.config import create_clean_dir, get_out_path, load_config, set_global_seed from src.data import generate_naivebayes from src.inference import run_inferences -from src.mia import (phase_attack_mechanism, phase_defense_mechanism, - phase_estimation, phase_find_eps, phase_mia_vs_cn) +from src.mia import ( + phase_attack_mechanism, + phase_defense_mechanism, + phase_estimation, + phase_find_eps, + phase_mia_vs_cn, +) def main(): diff --git a/src/attacks.py b/src/attacks.py index 79da39b..628c968 100644 --- a/src/attacks.py +++ b/src/attacks.py @@ -5,11 +5,10 @@ # Get the maximum likelihood BN inside a CN -def atk_mle(bn_min, bn_max, data, exp, config): +def atk_mle(bn_min, bn_max, data, n_bns: int): # Sample from the CN ... - n_bns = config["n_bns"] - bns_sample = sample_from_cn(bn_min, bn_max, exp, n_bns) + bns_sample = sample_from_cn(bn_min, bn_max, n_bns) # ... and take the MLE one bn = mle_bn(bns_sample, data) diff --git a/src/config.py b/src/config.py index e30086b..aef7bc5 100644 --- a/src/config.py +++ b/src/config.py @@ -11,7 +11,7 @@ def load_config(name: str): subdir = "test" if os.getenv("USE_TEST_CONFIG") == "1" else "experiments" - + config_path = get_root_path() / subdir / name / "config.yaml" with open(config_path, "r") as f: diff --git a/src/data.py b/src/data.py index 6585451..c91cac1 100644 --- a/src/data.py +++ b/src/data.py @@ -23,7 +23,6 @@ def generate_naivebayes(config): gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) rpop_ss = int(gpop_ss * config["rpop_prop"]) - n_samples = config["n_samples"] # Set BN (naive Bayes) structure bn_str_gen = ( @@ -51,7 +50,7 @@ def generate_naivebayes(config): gpop = data_gen.to_pandas() # For any data sample ... - for sample in range(n_samples): + for sample in range(config["samples"]): # ... sample pool and rpop shuffled_idx = np.random.permutation(gpop.index) @@ -89,7 +88,6 @@ def generate_randombn(config): gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) rpop_ss = int(gpop_ss * config["rpop_prop"]) - n_samples = config["n_samples"] # For each configuration ... for i, (n, r) in enumerate(product(n_nodes_vec, edge_ratio_vec)): @@ -111,7 +109,7 @@ def generate_randombn(config): gpop = data_gen.to_pandas() # For any data sample ... - for sample in range(n_samples): + for sample in range(config["samples"]): # ... sample pool and rpop shuffled_idx = np.random.permutation(gpop.index) diff --git a/src/inference.py b/src/inference.py index 610e46c..347c4f3 100644 --- a/src/inference.py +++ b/src/inference.py @@ -11,7 +11,6 @@ import src.defenses - def run_inferences(exp, ess, config): out_path = get_out_path(config) @@ -37,7 +36,7 @@ def run_inferences(exp, ess, config): gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') # Learn BN from gpop #TODO: save results - bn = learn_bn_params(gt,gpop) + bn = learn_bn_params(gt, gpop) # Learn CN from gpop (defense mechanism) #TODO: save results def_mec_fn = getattr(src.defenses, def_mec) @@ -68,7 +67,8 @@ def run_inferences(exp, ess, config): ) results.to_csv( - f'{out_path / config["results_path"]}/inferences/{exp}_ess{ess}.csv', index=False + f'{out_path / config["results_path"]}/inferences/{exp}_ess{ess}.csv', + index=False, ) diff --git a/src/mia.py b/src/mia.py index d48a17b..f471c76 100644 --- a/src/mia.py +++ b/src/mia.py @@ -1,3 +1,4 @@ +import inspect import math import numpy as np @@ -80,7 +81,7 @@ def phase_find_eps(exp, ess, config) -> dict: auc_noisy_bns = [] # ... and for each data sample ... - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): # ... read the BNs as estimated from rpop and pool, ... bn_theta = gum.loadBN( @@ -122,7 +123,7 @@ def phase_find_eps(exp, ess, config) -> dict: # log.write(traceback.format_exc()) # Compute Avg(AUC(eps)) across data samples - auc_noisy_bn = sum(auc_noisy_bns) / config["n_samples"] + auc_noisy_bn = sum(auc_noisy_bns) / config["samples"] # Condition on |AUC(eps) - AUC(CN)| if abs(auc_cn - auc_noisy_bn) <= config["tol"]: @@ -130,7 +131,7 @@ def phase_find_eps(exp, ess, config) -> dict: break # Save noisy BNs - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): bn_theta_hat = gum.loadBN( f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) @@ -178,7 +179,7 @@ def phase_estimation(exp, config) -> None: safe_assert(n_nodes == gpop.loc[:, ~gpop.columns.str.contains("in-")].shape[1]) # For each data sample ... - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): # ... retrieve pool and rpop, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] @@ -186,11 +187,19 @@ def phase_estimation(exp, config) -> None: # ... estimate BN from rpop, ... bn_learnt = learn_bn_params(bn, rpop) - save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config['bns_path'] / "rpop") + save_bn( + bn_learnt, + f"bn_{exp}_sample{sample}", + out_path / config["bns_path"] / "rpop", + ) # ... estimate BN from pool, ... bn_learnt = learn_bn_params(bn, pool) - save_bn(bn_learnt, f"bn_{exp}_sample{sample}", out_path / config['bns_path'] / "pool") + save_bn( + bn_learnt, + f"bn_{exp}_sample{sample}", + out_path / config["bns_path"] / "pool", + ) # Debug safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) @@ -210,17 +219,25 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') # For each data sample ... - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): # ... read the related BN - bn = gum.loadBN(f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif") + bn = gum.loadBN( + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + ) # ... retrieve pool, ... pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, : len(bn.nodes())] # ... and derive the CN - def_mec_fn = getattr(src.defenses, def_mec) - cn = def_mec_fn(bn, ess, pool) + def_mec_fn = getattr(src.defenses, def_mec) # Get the related function + sig = inspect.signature(def_mec_fn) # Get its signature + args = { + k: v + for k, v in {"bn": bn, "ess": ess, "data": pool}.items() + if k in sig.parameters + } + cn = def_mec_fn(**args) # Keep only `def_mec`` args base_path = out_path / config["cns_path"] / f"ESS: {ess}" safe_open_dir(base_path) cn.saveBNsMinMax( @@ -241,7 +258,7 @@ def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') # For each data sample ... - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): # ... read the related CN bn_min = gum.loadBN( @@ -255,15 +272,17 @@ def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_min.nodes())] # ... and derive the BN - atk_mec_fn = getattr(src.attacks, atk_mec) - bn = atk_mec_fn(bn_min, bn_max, rpop, exp, config) + atk_mec_fn = getattr(src.attacks, atk_mec) # Get the related function + sig = inspect.signature(atk_mec_fn) # Get its signature + args = { + k: v + for k, v in {"bn_min": bn_min,"bn_max": bn_max, "data": rpop, "n_bns":config["n_bns"]}.items() + if k in sig.parameters + } + bn = atk_mec_fn(**args) base_path = out_path / config["atk_path"] / f"ESS: {ess}" safe_open_dir(base_path) - save_bn( - bn, - f"bn_{exp}_sample{sample}", - base_path - ) + save_bn(bn, f"bn_{exp}_sample{sample}", base_path) return @@ -281,7 +300,7 @@ def phase_mia_vs_bn(exp, config) -> None: gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') # For each data sample ... - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): # ... read the BNs as estimated from rpop and pool, ... bn_theta = gum.loadBN( @@ -335,7 +354,7 @@ def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # For each data sample ... auc_cns = [] - for sample in range(config["n_samples"]): + for sample in range(config["samples"]): # ... read the BN as inferred from the CN bn_theta_hat = gum.loadBN( @@ -380,7 +399,8 @@ def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # Save results if save_res: results.to_csv( - f'{out_path}/{config["results_path"]}/cns/cn_{exp}-ess{ess}.csv', index=False + f'{out_path}/{config["results_path"]}/cns/cn_{exp}-ess{ess}.csv', + index=False, ) return {"exp": exp, "ess": ess, "auc_cn": auc_cn} diff --git a/src/utils.py b/src/utils.py index 2b98ba5..a5e4dbe 100644 --- a/src/utils.py +++ b/src/utils.py @@ -149,7 +149,7 @@ def get_min_max_bns(cn, exp: str): # Sample from a credal set K(x | pi_x), i.e., a constrained polytope. -def sample_from_cset(vec_min, vec_max, n_samples) -> list: +def sample_from_cset(vec_min, vec_max, n_bns) -> list: """ We assume a credal set is a polytope in a space of #X parameters, defined by a: - Multi-dimensional rectangle, i.e., inequality constraint Ax <= b, and @@ -175,7 +175,7 @@ def sample_from_cset(vec_min, vec_max, n_samples) -> list: # Sample from the polytope mc = hopsy.MarkovChain(constrained_rectangle) rng = hopsy.RandomNumberGenerator(42) - _, constrained_samples = hopsy.sample(mc, rng, n_samples, thinning=10) + _, constrained_samples = hopsy.sample(mc, rng, n_bns, thinning=10) constrained_samples = constrained_samples[0] # Debug @@ -183,7 +183,7 @@ def sample_from_cset(vec_min, vec_max, n_samples) -> list: safe_assert(n_par == len(vec_max)) safe_assert(n_par == A.shape[1]) safe_assert(n_par == A_eq.shape[1]) - safe_assert(len(constrained_samples) == n_samples) + safe_assert(len(constrained_samples) == n_bns) for i in constrained_samples: safe_assert(len(i) == n_par) @@ -191,7 +191,7 @@ def sample_from_cset(vec_min, vec_max, n_samples) -> list: # Sample from two extreme CPTs -def sample_from_cpts(cpt_min, cpt_max, n_samples) -> list: +def sample_from_cpts(cpt_min, cpt_max, n_bns) -> list: # Transform CPTs into pandas dataframes cpt_min = np.atleast_2d(cpt_min.topandas()) @@ -201,12 +201,12 @@ def sample_from_cpts(cpt_min, cpt_max, n_samples) -> list: credal_dict = {} for row in range(cpt_min.shape[0]): - # ... sample `n_samples` points from the credal set - credal_dict[row] = sample_from_cset(cpt_min[row, :], cpt_max[row, :], n_samples) + # ... sample `n_bns` points from the credal set + credal_dict[row] = sample_from_cset(cpt_min[row, :], cpt_max[row, :], n_bns) # For each sample ... cpt_samples = [] - for i in range(n_samples): + for i in range(n_bns): # ... build the CPT cpt = [] @@ -219,13 +219,13 @@ def sample_from_cpts(cpt_min, cpt_max, n_samples) -> list: # Debug safe_assert(cpt_min.shape == cpt_max.shape) safe_assert(len(credal_dict) == cpt_min.shape[0]) - safe_assert(len(cpt_samples) == n_samples) + safe_assert(len(cpt_samples) == n_bns) return cpt_samples # BNs sampler from a CN -def sample_from_cn(bn_min, bn_max, exp: str, n_samples: int) -> list: +def sample_from_cn(bn_min, bn_max, n_bns: int) -> list: # Get the DAG and extreme BNs dag = gum.BayesNet(bn_min) @@ -234,12 +234,12 @@ def sample_from_cn(bn_min, bn_max, exp: str, n_samples: int) -> list: cpts_dict = {} for var in dag.names(): - # ... sample `n_samples` CPTs from the CN - cpts_dict[var] = sample_from_cpts(bn_min.cpt(var), bn_max.cpt(var), n_samples) + # ... sample `n_bns` CPTs from the CN + cpts_dict[var] = sample_from_cpts(bn_min.cpt(var), bn_max.cpt(var), n_bns) # For each sample ... bns = [] - for i in range(n_samples): + for i in range(n_bns): # ... init an empty BN ... bn = gum.BayesNet(dag) @@ -255,7 +255,7 @@ def sample_from_cn(bn_min, bn_max, exp: str, n_samples: int) -> list: # Debug safe_assert(len(cpts_dict) == len(dag.names())) - safe_assert(len(bns) == n_samples) + safe_assert(len(bns) == n_bns) return bns diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index 06ac0ac..ef6ddce 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -18,11 +18,11 @@ n_modmax: 2 # Maximum number of variable gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop +samples: 5 # Number of data samples # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 5 # Number of data samples error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index b2ef3bd..17b07c0 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -21,11 +21,11 @@ n_models: 5 # Number of models to evalua gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop +samples: 5 # Number of data samples # MIA def_mec: 'def_idm' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider -n_samples: 5 # Number of data samples tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector From 1cce2b63eb36ea971334b56d1ae58048a905ae00 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Fri, 14 Nov 2025 13:01:46 +0100 Subject: [PATCH 11/31] Update: consider only one `ESS` hyperparameter for each exp, and update `README.md` --- README.md | 22 +++++---------- experiments/cn_privacy/config.yaml | 2 +- experiments/cn_privacy/config_BAK.yaml | 2 +- experiments/cn_privacy/exp.py | 13 +++++---- experiments/cn_vs_noisybn/config.yaml | 18 ++++++++++--- experiments/cn_vs_noisybn/config_BAK.yaml | 22 +++++++++------ experiments/cn_vs_noisybn/exp.py | 21 +++++++-------- src/data.py | 5 ++-- src/inference.py | 8 +++--- src/mia.py | 33 +++++++++++------------ test/cn_privacy/config.yaml | 2 +- test/cn_vs_noisybn/config.yaml | 16 ++++++++--- 12 files changed, 87 insertions(+), 77 deletions(-) diff --git a/README.md b/README.md index c5b4d4c..6d75e81 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,9 @@ pip freeze > requirements.txt ## Experiments -`` is the name of the experiment to run. It can be one of the following. +`` is the name of the experiment to run. Each `` has its own directory, which is named the same way. Each of these contains the experiment logic, configuration file (`config.yaml`), output (specified in configurations), and a `Plot_results.ipynb` notebook to plot results. + +`` can be one of the following: 1. `cn_privacy`: run membership inference attack against a Bayesian network (BN), its related credal network (CN), and compute the theoretical privacy estimate of BN. The pipeline and results are described in the paper. @@ -34,17 +36,13 @@ pip freeze > requirements.txt ### Running code -1. Run the experiment: +Run an experiment with: ```bash python -m experiments..exp ``` -*Notice:* this will delete any existing ground-truth model, data, and result. - -2. Results available at: - -`experiments//output/`. +*Notice:* this will delete any already existing output. For storing intermediate output, comment out code in the `experiments..exp.py` file. ### Using Docker (recommended) @@ -62,7 +60,7 @@ docker run [-d] [--rm] -v bnp:/workspace bnp:2025 python -m experiments..e 3. Results available at: -`/var/lib/docker/volumes/bnp/_data/experiments//output/`. +`/var/lib/docker/volumes/bnp/_data/`. ## Testing code @@ -72,10 +70,6 @@ Run integration tests with: pytest [--cov=src] [--cov-report=term-missing] [--capture=no] ``` -Results are available at: - -`test//output/`. - ## Formatting and linting Format code by running: @@ -97,7 +91,3 @@ Analyze code by running: ```bash pylint $(git ls-files '*.py') ``` - -## Plotting results - -Use the `Plot_results.ipynb` notebook available for each experiment. Plots will be saved at: `experiments//output/plots`. diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 1287d0a..14cfc94 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -26,7 +26,7 @@ atk_mec: 'atk_mle' # Attack mechanisms to consi error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM -ess_vec: '[1, 100]' # List of ESS +ess: 1 # ESS: the amount of injected uncertainty # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index 14ed7c2..e55e681 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -26,7 +26,7 @@ atk_mec: 'atk_mle' # Attack mechanisms to consi error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector # DEF-IDM -ess_vec: '[1, 10, 50, 100, 1000]' # List of ESS +ess: 1 # ESS: the amount of injected uncertainty # ATK-MLE n_bns: 500 # Number of BNs to sample within the CN diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 84438de..638573a 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -37,7 +37,6 @@ def main(): exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] - ess_vec = eval(config["ess_vec"]) # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) @@ -51,24 +50,24 @@ def main(): print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_defense_mechanism)(def_mec, exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_defense_mechanism)(def_mec, exp, config) + for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_attack_mechanism)(atk_mec, exp, config) + for exp in exp_vec ) # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) create_clean_dir(out_path / config["results_path"] / "cns") _ = Parallel(n_jobs=num_cores)( - delayed(phase_mia_vs_cn)(exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_mia_vs_cn)(exp, config) + for exp in exp_vec ) # MIA vs BN diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 62ecfc9..8280a4d 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -29,10 +29,11 @@ atk_mec: 'atk_mle' # Attack mechanisms to consi tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector +# Noisy BN +eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for noisy BN + # DEF-IDM -ess_dict: # Eps list to evaluate for each ess - 1: 'np.arange(0.1, 10, 0.5)' - 50: 'np.arange(5e-7, 5e-4, 1e-6)' +ess: 1 # ESS: the amount of injected uncertainty # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN @@ -42,4 +43,13 @@ n_infer: 5 # Number of inferences to pe # Other seed: 42 # Global seed -num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization \ No newline at end of file +num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization + +## Notes +# 1) Suggested pairs (ess: eps_vec) for n_nodes=10: +# - 1 : 'np.arange(0.1, 10, 0.1)' +# - 10: 'np.arange(0.1, 10, 0.1)' +# - 20: 'np.arange(0.05, 5, 0.05)' +# - 30: 'np.arange(1e-3, 1, 1e-3)' +# - 40: 'np.arange(5e-6, 1e-2, 5e-6)' +# - 50: 'np.arange(5e-7, 5e-4, 5e-7)' \ No newline at end of file diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index 4f61b85..80836f2 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -29,14 +29,11 @@ atk_mec: 'atk_mle' # Attack mechanisms to consi tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector +# Noisy BN +eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for noisy BN + # DEF-IDM -ess_dict: # Eps list to evaluate for each ess - 1: 'np.arange(0.1, 10, 0.1)' - 10: 'np.arange(0.1, 10, 0.1)' - 20: 'np.arange(0.05, 5, 0.05)' - 30: 'np.arange(1e-3, 1, 1e-3)' - 40: 'np.arange(5e-6, 1e-2, 5e-6)' - 50: 'np.arange(5e-7, 5e-4, 5e-7)' +ess: 1 # ESS: the amount of injected uncertainty # ATK-MLE n_bns: 50 # Number of BNs to sample within the CN @@ -46,4 +43,13 @@ n_infer: 1000 # Number of inferences to pe # Other seed: 42 # Global seed -num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization \ No newline at end of file +num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization + +## Notes +# 1) Suggested pairs (ess: eps_vec) for n_nodes=10: +# - 1 : 'np.arange(0.1, 10, 0.1)' +# - 10: 'np.arange(0.1, 10, 0.1)' +# - 20: 'np.arange(0.05, 5, 0.05)' +# - 30: 'np.arange(1e-3, 1, 1e-3)' +# - 40: 'np.arange(5e-6, 1e-2, 5e-6)' +# - 50: 'np.arange(5e-7, 5e-4, 5e-7)' \ No newline at end of file diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 328a8a8..109903e 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -38,7 +38,6 @@ def main(): exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] - ess_vec = config["ess_dict"].keys() # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) @@ -52,23 +51,23 @@ def main(): print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_defense_mechanism)(def_mec, exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_defense_mechanism)(def_mec, exp, config) + for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_attack_mechanism)(atk_mec, exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_attack_mechanism)(atk_mec, exp, config) + for exp in exp_vec ) # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) res = Parallel(n_jobs=num_cores)( - delayed(phase_mia_vs_cn)(exp, ess, config, save_res=False) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_mia_vs_cn)(exp, config, save_res=False) + for exp in exp_vec ) res = pd.DataFrame(res) res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) @@ -77,8 +76,8 @@ def main(): print("#" * 5, "Get epsilon", "#" * 5) create_clean_dir(out_path / config["noisy_path"]) res = Parallel(n_jobs=num_cores)( - delayed(phase_find_eps)(exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(phase_find_eps)(exp, config) + for exp in exp_vec ) res = pd.DataFrame(res) res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) @@ -87,8 +86,8 @@ def main(): print("#" * 5, "Run inferences", "#" * 5) create_clean_dir(out_path / config["results_path"] / "inferences") _ = Parallel(n_jobs=num_cores)( - delayed(run_inferences)(exp, ess, config) - for exp, ess in product(exp_vec, ess_vec) + delayed(run_inferences)(exp, config) + for exp in exp_vec ) # Clean diff --git a/src/data.py b/src/data.py index c91cac1..c480e88 100644 --- a/src/data.py +++ b/src/data.py @@ -84,7 +84,6 @@ def generate_randombn(config): # Retrieve hyperparameters n_nodes_vec = eval(config["n_nodes_vec"]) edge_ratio_vec = eval(config["edge_ratio_vec"]) - n_modmax = config["n_modmax"] gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) rpop_ss = int(gpop_ss * config["rpop_prop"]) @@ -94,12 +93,12 @@ def generate_randombn(config): # ... generate BN, ... bn_gen = gum.BNGenerator() - bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=n_modmax) + bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=config["n_modmax"]) save_bn(bn, f"exp{i}", bns_path / "gt") with open(f'{out_path}/{config["exp_meta"]}', "a") as m: m.write( - f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()} Max categories: {n_modmax}\n" + f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()}\n" ) # ... and generate gpop from BN diff --git a/src/inference.py b/src/inference.py index 347c4f3..133d17e 100644 --- a/src/inference.py +++ b/src/inference.py @@ -11,7 +11,7 @@ import src.defenses -def run_inferences(exp, ess, config): +def run_inferences(exp, config): out_path = get_out_path(config) target = config["target_var"] @@ -19,7 +19,7 @@ def run_inferences(exp, ess, config): auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') eps = auc_meta.loc[ - (auc_meta["exp"] == exp) & (auc_meta["ess"] == ess), "eps" + auc_meta["exp"] == exp, "eps" ].values[0] # Set seed @@ -40,7 +40,7 @@ def run_inferences(exp, ess, config): # Learn CN from gpop (defense mechanism) #TODO: save results def_mec_fn = getattr(src.defenses, def_mec) - cn = def_mec_fn(bn, ess, gpop) + cn = def_mec_fn(bn, config["ess"], gpop) # Learn noisy BN from gpop #TODO: save results scale = (2 * bn.size()) / (len(gpop) * eps) @@ -67,7 +67,7 @@ def run_inferences(exp, ess, config): ) results.to_csv( - f'{out_path / config["results_path"]}/inferences/{exp}_ess{ess}.csv', + f'{out_path / config["results_path"]}/inferences/{exp}.csv', index=False, ) diff --git a/src/mia.py b/src/mia.py index f471c76..da0ad9c 100644 --- a/src/mia.py +++ b/src/mia.py @@ -55,7 +55,7 @@ def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): # Find eps s.t. |AUC(eps) - AUC(CN)| < tol -def phase_find_eps(exp, ess, config) -> dict: +def phase_find_eps(exp, config) -> dict: # Get output path out_path = get_out_path(config) @@ -66,9 +66,9 @@ def phase_find_eps(exp, ess, config) -> dict: pool_ss = int(gpop_ss * config["pool_prop"]) auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') auc_cn = auc_meta.loc[ - (auc_meta["exp"] == exp) & (auc_meta["ess"] == ess), "auc_cn" + auc_meta["exp"] == exp, "auc_cn" ].values[0] - eps_vec = eval(config["ess_dict"][ess]) + eps_vec = eval(config["eps_vec"]) eps_best = eps_vec[-1] @@ -141,7 +141,6 @@ def phase_find_eps(exp, ess, config) -> dict: return { "exp": exp, - "ess": ess, "auc_cn": auc_cn, "auc_noisy_bn": auc_noisy_bn, "eps": eps_best, @@ -210,7 +209,7 @@ def phase_estimation(exp, config) -> None: # Apply defense mechanism to a BN, namely, derive a CN from a BN -def phase_defense_mechanism(def_mec, exp, ess, config) -> None: +def phase_defense_mechanism(def_mec, exp, config) -> None: # Get output path out_path = get_out_path(config) @@ -234,12 +233,11 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: sig = inspect.signature(def_mec_fn) # Get its signature args = { k: v - for k, v in {"bn": bn, "ess": ess, "data": pool}.items() + for k, v in {"bn": bn, "ess": config["ess"], "data": pool}.items() if k in sig.parameters } cn = def_mec_fn(**args) # Keep only `def_mec`` args - base_path = out_path / config["cns_path"] / f"ESS: {ess}" - safe_open_dir(base_path) + base_path = out_path / config["cns_path"] cn.saveBNsMinMax( f"{base_path}/bn_min_{exp}_sample{sample}.bif", f"{base_path}/bn_max_{exp}_sample{sample}.bif", @@ -249,23 +247,24 @@ def phase_defense_mechanism(def_mec, exp, ess, config) -> None: # Apply attack mechanism to a BN, namely, derive a BN from a CN -def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: +def phase_attack_mechanism(atk_mec, exp, config) -> None: # Get output path out_path = get_out_path(config) # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + base_path = out_path / config["cns_path"] # For each data sample ... for sample in range(config["samples"]): # ... read the related CN bn_min = gum.loadBN( - f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_min_{exp}_sample{sample}.bif" + f"{base_path}/bn_min_{exp}_sample{sample}.bif" ) bn_max = gum.loadBN( - f"{out_path}/{config['cns_path']}/ESS: {ess}/bn_max_{exp}_sample{sample}.bif" + f"{base_path}/bn_max_{exp}_sample{sample}.bif" ) # ... retrieve rpop, ... @@ -280,9 +279,7 @@ def phase_attack_mechanism(atk_mec, exp, ess, config) -> None: if k in sig.parameters } bn = atk_mec_fn(**args) - base_path = out_path / config["atk_path"] / f"ESS: {ess}" - safe_open_dir(base_path) - save_bn(bn, f"bn_{exp}_sample{sample}", base_path) + save_bn(bn, f"bn_{exp}_sample{sample}", out_path / config["atk_path"]) return @@ -341,7 +338,7 @@ def phase_mia_vs_bn(exp, config) -> None: # MIA attack vs a CN -def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: +def phase_mia_vs_cn(exp, config, save_res=True) -> dict: # Get output path out_path = get_out_path(config) @@ -358,7 +355,7 @@ def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # ... read the BN as inferred from the CN bn_theta_hat = gum.loadBN( - f'{out_path}/{config["atk_path"]}/ESS: {ess}/bn_{exp}_sample{sample}.bif' + f'{out_path}/{config["atk_path"]}/bn_{exp}_sample{sample}.bif' ) # ... read the BN as estimated from rpop, ... @@ -399,11 +396,11 @@ def phase_mia_vs_cn(exp, ess, config, save_res=True) -> dict: # Save results if save_res: results.to_csv( - f'{out_path}/{config["results_path"]}/cns/cn_{exp}-ess{ess}.csv', + f'{out_path}/{config["results_path"]}/cns/cn_{exp}.csv', index=False, ) - return {"exp": exp, "ess": ess, "auc_cn": auc_cn} + return {"exp": exp, "auc_cn": auc_cn} # Get theoretical power diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index ef6ddce..6e584d6 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -26,7 +26,7 @@ atk_mec: 'atk_mle' # Attack mechanisms to consi error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM -ess_vec: '[1, 100]' # List of ESS +ess: 1 # ESS: the amount of injected uncertainty # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index 17b07c0..3f32819 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -29,10 +29,11 @@ atk_mec: 'atk_mle' # Attack mechanisms to consi tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector +# Noisy BN +eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for noisy BN + # DEF-IDM -ess_dict: # Eps list to evaluate for each ess - 1: 'np.arange(0.1, 10, 0.5)' - 50: 'np.arange(5e-7, 5e-4, 1e-6)' +ess: 1 # ESS: the amount of injected uncertainty # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN @@ -43,3 +44,12 @@ n_infer: 5 # Number of inferences to pe # Other seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization + +## Notes +# 1) Suggested pairs (ess: eps_vec) for n_nodes=10: +# - 1 : 'np.arange(0.1, 10, 0.1)' +# - 10: 'np.arange(0.1, 10, 0.1)' +# - 20: 'np.arange(0.05, 5, 0.05)' +# - 30: 'np.arange(1e-3, 1, 1e-3)' +# - 40: 'np.arange(5e-6, 1e-2, 5e-6)' +# - 50: 'np.arange(5e-7, 5e-4, 5e-7)' From a221817b3b49ad980a303faf6016807df4545cf0 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Fri, 14 Nov 2025 15:41:57 +0100 Subject: [PATCH 12/31] Fix plots for `cn_privacy` --- experiments/cn_privacy/Plot_results.ipynb | 126 +++++++------ experiments/cn_vs_noisybn/Plot_results.ipynb | 187 +++++++++++-------- src/data.py | 4 +- 3 files changed, 177 insertions(+), 140 deletions(-) diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index c3ffb32..b956d2a 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -15,6 +15,7 @@ "import numpy as np\n", "import sys\n", "from pathlib import Path\n", + "import re\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", "from src.config import * # noqa" @@ -22,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -31,10 +32,12 @@ "config = load_config(\"cn_privacy\")\n", "\n", "# Get results path\n", - "res_path = get_out_path(config) / config[\"results_path\"]\n", + "out_path = get_out_path(config)\n", + "res_path = out_path / config[\"results_path\"]\n", + "error = eval(config[\"error\"])\n", "\n", "# Choose where to save plots\n", - "plots_path = get_out_path(config) / \"plots\"\n", + "plots_path = out_path / \"plots\"\n", "create_clean_dir(plots_path)" ] }, @@ -74,6 +77,28 @@ { "cell_type": "code", "execution_count": 4, + "id": "c55e8958", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['bn_exp1.csv', 'bn_exp3.csv', 'bn_exp2.csv', 'bn_exp0.csv']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = os.listdir(res_path/'bns')\n", + "files" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "3bc7788b", "metadata": {}, "outputs": [], @@ -82,14 +107,15 @@ "def plot_bn_bound(exp: str, ax):\n", "\n", " # Import results\n", - " results = os.listdir(res_path)\n", - " r_path = [r for r in results if f\"{exp}-ess1.csv\" in r][0]\n", - " result = pd.read_csv(f\"{res_path}/{r_path}\")\n", - " error = result[\"error\"]\n", - " bound = result[\"power_bound\"]\n", - " bn_cols = [c for c in result.columns if \"BN\" in c]\n", - " bn_mean = result.loc[:, bn_cols].mean(axis=1)\n", - " bn_max = result.loc[:, bn_cols].max(axis=1)\n", + " files = os.listdir(res_path/'bns')\n", + " r_path = [r for r in files if f\"{exp}\" in r][0]\n", + " res = pd.read_csv(f\"{res_path}/bns/{r_path}\")\n", + " bound = res[\"power_bound\"]\n", + "\n", + " # Select what to plot\n", + " bn_cols = [c for c in res.columns if \"BN\" in c]\n", + " bn_mean = res.loc[:, bn_cols].mean(axis=1)\n", + " bn_max = res.loc[:, bn_cols].max(axis=1)\n", "\n", " # Plot bound\n", " ax.semilogx(\n", @@ -122,21 +148,22 @@ " )\n", "\n", "\n", - "# Plot CN (for a given ess)\n", - "def plot_cn(exp, ax, ess: int, color: str, type: str):\n", + "# Plot CN\n", + "def plot_cn(exp, ax, color: str, type: str):\n", "\n", " # Import results\n", - " results = os.listdir(res_path)\n", - " r_path = [r for r in results if f\"{exp}-ess{ess}.csv\" in r][0]\n", - " result = pd.read_csv(f\"{res_path}/{r_path}\")\n", - " error = result[\"error\"]\n", - " cn_cols = [c for c in result.columns if \"CN\" in c]\n", - " cn_mean = result.loc[:, cn_cols].mean(axis=1)\n", - " cn_max = result.loc[:, cn_cols].max(axis=1)\n", + " files = os.listdir(res_path/'cns')\n", + " r_path = [r for r in files if f\"{exp}\" in r][0]\n", + " res = pd.read_csv(f\"{res_path}/cns/{r_path}\")\n", + "\n", + " # Select what to plot \n", + " cn_cols = [c for c in res.columns if \"CN\" in c]\n", + " cn_mean = res.loc[:, cn_cols].mean(axis=1)\n", + " cn_max = res.loc[:, cn_cols].max(axis=1)\n", "\n", " # Plot CN (avg-max)\n", " ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n", - " ax.semilogx(error, cn_mean, type, color=color, label=f\"CN, $S={ess}$\", zorder=3)\n", + " ax.semilogx(error, cn_mean, type, color=color, label=f'CN, $S={config[\"ess\"]}$', zorder=3)\n", "\n", " # Legend\n", " if exp == \"exp0\":\n", @@ -152,7 +179,7 @@ "\n", "# Plot title function\n", "def get_title(exp: str):\n", - " with open(f\"{res_path}/exp_meta.txt\", \"r\") as meta:\n", + " with open(f\"{out_path}/exp_meta.txt\", \"r\") as meta:\n", " for row in meta:\n", " if exp in row:\n", " pieces = row.split()\n", @@ -164,27 +191,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "ade37b54", "metadata": {}, "outputs": [ - { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mIndexError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 23\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m# Loop over subplots\u001b[39;00m\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(axes.flat):\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m \u001b[43mplot_bn_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mexps\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 24\u001b[39m plot_cn(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexps[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, ax, ess=ess[\u001b[32m0\u001b[39m], color=CN_color, \u001b[38;5;28mtype\u001b[39m=\u001b[33m\"\u001b[39m\u001b[33m-\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 25\u001b[39m \u001b[38;5;66;03m# plot_cn(f\"{exps[i]}\", ax, ess=ess[1], color=CN_color, type=\"--\")\u001b[39;00m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36mplot_bn_bound\u001b[39m\u001b[34m(exp, ax)\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mplot_bn_bound\u001b[39m(exp: \u001b[38;5;28mstr\u001b[39m, ax):\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Import results\u001b[39;00m\n\u001b[32m 5\u001b[39m results = os.listdir(res_path)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m r_path = \u001b[43m[\u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mexp\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m-ess1.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 7\u001b[39m result = pd.read_csv(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mres_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mr_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 8\u001b[39m error = result[\u001b[33m\"\u001b[39m\u001b[33merror\u001b[39m\u001b[33m\"\u001b[39m]\n", - "\u001b[31mIndexError\u001b[39m: list index out of range" - ] - }, { "data": { - "image/png": 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ikeiz1RLN/Xhz1el0Gu3t7Ybb7Z4xp5KdbQggfeSZKRzzVf4xn2GQZ5jb2XWlKPXjE/IMeYY8M3vT51A68z0b6WQjAJkh00zhuK/yj/sMg0zD3K7MuU2mIdOQaWYvF5kmWedBOu2VFor2gF9LFQgNI/agM94bYjqBMJM/6ESPKdGB8PR9pmqNm86HMdFv1KkOeuP9xxf9PJTrB/GF/pArkwPzXMlXIMz1fIx+HSSaj9EH3snmLHO78ud29H3nKxDm+n08erzJ3uunt0ZP9tymE+Ciw0uyFvV+vz9mf4nmQaqANdvXX7REYSP6/+lUISofgTDd10t0qC2lgrxU0j0umT6npz/GkZGRmOcg1f8VANJDnpnCMV/lH/MZBnmGuZ39B1ylfHxCnpm5T/JMfOSZxBJ9wOVwOHL23pfuMQ6AzJBppnDcV/nHfYZBpmFuV+bcJtPM3CeZJj4yTWKzzTSpLn1O0V5pmSMAaYtuubpr166Mt/d6vRoYGJA02W42VdtSp9Mph8MhabKFqcfjmbFOpN1xRKqWuKlaQUffj91uT9oqNdIyV5L27t0bd514bXExU/TlqIplZGRExmQxd1pf8doK53o+SpNtpyPWrl0bd53o7ydqd8zcLo5Cz+1IC/GIXF+qLR/v49Eibb7jibT5T2fd6ZcMSCVZi/rpLcejW3CnK9evv4MHD5rvHy0tLeZlESKPtbu7O29t95OJng89PT1x538oFDIvO2G324syzmxEWu9Lk+/d6ba0j/e7sFqtMe+hAwMDKV8bAHKDPBOLY77cIc8kRp4pPaV8fEKekTm2CPJM4VRKnmlpaVF7e7va29vldrvlcDhktVrl9XrV0NAQczmwbGSbjQDkBpkmFsd9yZXycV88ZJopzO3kSnluk2lkji2CTFM4ZJqpyzJH7NmzpxBDxixQtAdkwOl0ymazSZp8w4tcBzxd0QfHkf/sUol+0413cB35TyWyz8j4sjV9f4FAIOlX5D+y6BCwefNm83ZHR4caGxvV1tYmr9dbEh/mlKLpB7HlKtfzUYr9ADCdAJloHeZ2cRR6bk+fc9G/v1zIx/t4tGT7XL58uXnbarUmfX1Fr5sL04NvOiEzWi5ef9GsVqt8Pp+sVqtCoZAaGhrMoNre3i6n05nR+HIp+rmK9weA6D8ob9++vSBjmq2BgQFt2LBB0uRr7NixY0nnn8/nk8/nk9/vTxh4bTZbzO8p+o8OAPKHPMMxX76QZxIjz5SGcjk+Ic/MXDcXyDPpq4Q843A45Ha75Xa71d7err6+Ph07dsycnz09PWpoaMjqfS/TbAQg98g0HPelUi7HfflCpqlc5TK3yTQz180FMk36qj3TbN261fx+e3t7Wu/bKC6K9oAMzeZMriNHjpi3H3jggbS2iT47JXr7CL/fb97ORSV49BkwHR0damhoSPoVCcXRBwdOpzPmP+OBgQF1dHSoublZdXV1amhoKJkzl3Il+j+8dINBpkGnHOR6PkpSY2OjeTvRwWE6ry3mdnbKbW5PD1S5PuM9H+/j0dJ9vorxoUD0fWYaCHPx+os3noMHD8Z8z+VypTyzLt+iQ16844TIe4TVai1qcE2X1+tVY2OjQqGQHA6HGcSTiZydlmqeRr+GAoFAxf9hDSgV5BmO+eIpt2O+fCHPVN7cjiiX4xPyTP6QZ9JTaXkmItIRLzIPAoGAtm7dmtE+sslGAPKDTMNxXzLlctyXL2Qa5nax5zaZJn/INOmp5kzj9XrN4kmbzVb03wXSQ9EekKHZnMkVfWCU7n/mqT48iW4rvGLFirTHkki2bYqnb9fd3Z3wDI5AIKDW1lY1NDRkfFBRqrI5UIp+znJ9xkWx5Ho+StKmTZvM29HtfKNF5prVak14FgxzOzvlOLej58C+fftyuu98vI9HS/f5KsbzOptAmKvX33TT/0gx28sc5UJ00Itusy4p5ozPZO3nS4XH4zFb/rvdbvX19eX0Q6np+6qU902g1JFn0tuOY77Uin3Mlw/kmcqb25kqheMT8kx+kGfSU0l5Jp7plxVLt/NLvrMRgMyQadLbjuO+5ErhuC8fyDTM7VKY22Sa/CDTpKeaM030JZsdDoc8Hk/cr+ji1e7u7pifofAo2gOykO2ZXNEHBakq+SOi/9ON98eg6IOC6DNocsHv98swjLS+RkZGZmzvdrtlGIa6u7vldrtnnNUTCATMM1TLXTYHStH/iVbKH/ryMR+tVqt5JkAgEDDPlPJ6vfJ4PDHha8+ePWk9l8zt9JXj3I4+KA2FQjk9yMzH+3i5iH4cswmks339RWtpaYkZ1/TlYok+Toi+3dnZad4u1bbrEW1tbeZrKdkfwnKpnF8fQLkhz3DMN105HvPlA3lG5jgrZW7PFnmGPDMdeWZSqeeZRKa/36XzOihGNgKQGpmG475cK+fjnGhkGpnjZG5PItOQaaYj00yqxEwTXWTp8XjU2toa9yv6b3rT10PhUbQHZCHbM7miPwhJ92A5+k0z3hkDDQ0N5u10D06SiW4lHF15PhtOp1Pt7e3y+XzmQXTkoCjXB2vFYrPZYg70Up2pGwqFYg5aEp15VG5yPR8jIq18pakzpZqbm9Xa2mo+j+3t7UnbGDO3s1OOczv6PVqa/AN7rsJ5Pt7Hy0X07zXTx5GP119HR4f53tDX1yeHw6FQKKTm5uai/zHGZrOZcz/6zK3IeJ1OZ0n/caClpUUdHR2yWq3y+Xx5axE//TVUzq8PoNyQZzLDMd9MpXDMlw/kmcqb25kqheMT8kx+kGfSV+55JhOpnutCZSMAmSPTZIbjvplK4bgvH8g0zO1SmNtkmvwg06SPTINyQtEekKXoqux0DzY2b95s3k73QHHv3r1xt4+I/nBkYGBg1m/M0fuLrjbPJafTGXON+76+vrzcT6FFtwiP/r3FE90OulI+4JJyPx8j+wkEAnI4HPL7/TEH+3a7XS6XS36/3zzTK52xMbczU45zu7u727wdCoW0YcOGnOw3H+/j5SD6D59Wq3XGmTyp5Pr119PTY16Kob29XQ6Hw/yDTCAQyNnvO1qm7eOjLxWxb9++mCBcymdwNTc3q6enRzabTceOHcv4d53Je3/0c5Lp/QCYPfJM9jjmK51jvlwjz1Tm3C7H4xPyTG6RZyo7z2TyoeP0ovRkc2G22QhA/pFpssdxX+kc9+UamYa5XSpzm0yTW2QaMk1EskwzMjKSVgfF6G36+vpifoYiMAAYhmEY3d3dhiRDkuFwONLaxm63m9u43W7D6XSay52dnXG3sVqt5joulyvp/tvb2811JRkjIyMpx+F0OpPu0+FwpFw3en+JHkcyicY5fZ1Mn+9Civ5dut3utLbx+/3mNlarNenzED0P+vr6cjTqzKQ7vzKV6/nY2dmZs3nC3K6OuR0xfY47HI6M5nlfX5/h8/lmfD/X7+Ppvg4z+X8q+rXV3d0dd53ox5FoHcOI/f1LMtrb2+OuF71OPLN9/UX4fL6E7xvRP0v1HKUar2HEvp5tNlvGY408ZofDYbjd7qz3UwgjIyMx482W1WpNa/vIe3s6cxBAesgz8ffHMV/lHvORZ5jb6c7tcj0+Ic+QZ5Ihz0yKPGfpvl6jf49WqzXuOrnKRgAyR6aJvz+O+yr3uI9Mw9yu1LkdQaYh0yRDppmUj0yTyT6K/VkuJqslARjZBcK+vr4ZBxup/rObvk2ig4nIfxzp/Oc5fZ+JDvJcLlfMeokOwKP/Q0110Ojz+Qyn0xnzOFwul2Gz2ZL+5xI9lnj7HxkZMXw+n+Hz+Qy/359wP/mSzUHz9O0SHXxGz5Nkcy3fz0G+AmGu52P0a3O2zwNzuzrmdrTp76VWqzVlGOnr6zMfb7x1c/0+XgqBUFLc8Nvd3R2zXrIAkCpgzfb1ZxiT4TQynkTBKvoPE8kCezqBcPp605+jzs7OpHM4+ncWCUHphuFCvk78fr9hs9lSPmepRD9em80Wd05NXy+T4y4AyZFnpnDMVx3HfOQZ5nY6czsfxyfkmVjkmfjrkmcK8zqZ/qFgote4Ycx834734VSushGA7JBppnDcVx3HfWQa5nalzu1oZBoyTbbjjbcemSZ1pkkHRXulpcYw6HEISJNtXFtaWiRNtohNt2VyY2PjjDak0mRrWZfLFXcbj8ej1tbWmO85HA7ZbDYFAgEdOXIkps2x2+2OafUeT3Nzc0zrVKvVqk2bNqmhoUGvvfaaeb12q9Vq7tvpdMa0J44W/XxE9udwOMxr3vv9fnm9XgUCgRn7mv6c2O122Ww22Ww2hUKhmO0i+4q00o7wer1qbm4273tkZCTp45+NQCAQMx5psmVu5DE4nc4Zv6+1a9cmvNZ99OOP/j1cvHhRHo/HfP6tVquOHTuWcD/5fg46OjpiWgOn00o5Xvvtzs7OGZfEyuV8DIVCqqurM5enz5XI/m02m5qbm7V27dqkj4W5Xflze7p477nS1PuuNNlWOxAIzHg/T/T+m8v38ZqaGvP2yMhIwuctk/+nol+D3d3dcjqdM9apq6ub8Zp2Op164IEH5Pf7deTIkRnPR6J9TX8ciQ4vZ/P6k6SGhgbzZ/FeXxGtra1ma/z29va4l2ZIZ7xS/Hk/PDxsvo8l2n9E9POcyXwv1OskFApp1apV5hjtdruWL18uKb12893d3TG/h7a2tphW/TabTU6nUytWrNDFixfl9XpnvI/6fL4cPRqgupFnYnHMV/nHfOSZqW2Z21Pize1cH5+QZ2KRZ8gz8RTydRJ9X5H7i8znFStWxLxvR8R73LnORgAyR6aJxXFf5R/3kWmmtmVuT6mEuT0dmYZMQ6ZJ/74i95dNpklX9HPa19c34/8QFFjx6gWB0pLNWVyGMbOaP/KVzlkC1mkV9NO/rGmcbRAtuio63ld7e3tMdXuqttg+n888uzTZl81mi6nCnn7mQrLHl6haPPp5tWbZ2jVd09tFp/OVrOp8ZGQk5gyKeF92uz1lVX6+n4PpZ3Fl+5XoDJFczsdUz2emzy9zu7LndqKxR5/Bls5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"text/plain": [ - "
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" ] }, "metadata": {}, @@ -192,33 +207,20 @@ } ], "source": [ + "# Names of experiments\n", + "exp_names = [re.findall('bn_(\\w+\\d+)\\.csv', r)[0] for r in os.listdir(res_path/'bns')]\n", + "\n", "# Layout 4x3\n", - "fig, axes = plt.subplots(4, 3, figsize=(10, 9.5))\n", + "fig, axes = plt.subplots(len(exp_names)//3+1, 3)\n", "# fig.suptitle(\"Power vs Error\", fontsize=18)\n", "\n", - "exps = [\n", - " \"exp0\",\n", - " \"exp1\",\n", - " \"exp2\",\n", - " \"exp3\",\n", - " \"exp4\",\n", - " \"exp5\",\n", - " \"exp6\",\n", - " \"exp7\",\n", - " \"exp8\",\n", - " \"exp9\",\n", - " \"exp10\",\n", - " \"exp11\",\n", - "]\n", - "ess = [1, 1000]\n", - "\n", - "# Loop over subplots\n", - "for i, ax in enumerate(axes.flat):\n", - " plot_bn_bound(f\"{exps[i]}\", ax)\n", - " plot_cn(f\"{exps[i]}\", ax, ess=ess[0], color=CN_color, type=\"-\")\n", - " plot_cn(f\"{exps[i]}\", ax, ess=ess[1], color=CN_color, type=\"--\")\n", - " (n, e, c) = get_title(exps[i])\n", - " ax.set_title(f\"Nodes: {n}, Edges: {e}, Complexity: {c}\")\n", + "# Loop over results\n", + "for i, exp in enumerate(exp_names):\n", + " ax = axes.flat[i]\n", + " plot_bn_bound(exp, ax)\n", + " plot_cn(f\"{exp_names[i]}\", ax, color=CN_color, type=\"-\")\n", + " (n, e, c) = get_title(exp_names[i])\n", + " ax.set_title(f\"N: {n}, E: {e}, Compl: {c}\")\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", "plt.show()\n", diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 831eab9..ececb50 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -1,5 +1,13 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "80ba8653", + "metadata": {}, + "source": [ + "### TO BE FIXED" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -24,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "id": "7b61e02b", "metadata": {}, "outputs": [], @@ -33,10 +41,11 @@ "config = load_config(\"cn_vs_noisybn\")\n", "\n", "# Get results path\n", - "res_path = get_out_path(config) / config[\"results_path\"]\n", + "out_path = get_out_path(config)\n", + "res_path = out_path / config[\"results_path\"] / 'inferences'\n", "\n", "# Choose where to save plots\n", - "plots_path = get_out_path(config) / \"plots\"\n", + "plots_path = out_path / \"plots\"\n", "create_clean_dir(plots_path)" ] }, @@ -58,7 +67,7 @@ " return data_cert, data_uncert\n", "\n", "\n", - "# Accuracy function for BN\n", + "# Accuracy function for a BN\n", "def get_acc_bn(data: pd.DataFrame, col: str, vs_col: str) -> float:\n", "\n", " return sum(data[col] == data[vs_col]) / len(data)" @@ -66,18 +75,38 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, + "id": "da082d37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['exp0.csv', 'exp1.csv', 'exp4.csv', 'exp2.csv', 'exp3.csv']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dirs = os.listdir(res_path)\n", + "dirs" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "38f1e202", "metadata": {}, "outputs": [], "source": [ - "res_path = get_out_path(config) / config[\"results_path\"]\n", - "dirs = natsorted(\n", - " [f\"{res_path}/{dir}\" for dir in os.listdir(f\"{res_path}/\") if \"results_\" in dir]\n", - ")\n", + "# Names of experiments\n", + "exp_names = [re.findall('bn_(\\w+\\d+)\\.csv', r)[0] for r in os.listdir(res_path/'bns')]\n", "\n", + "# Initialize results\n", "res = {\n", - " \"ess\": [],\n", " \"eps\": [],\n", " \"acc_cn_cert\": [],\n", " \"acc_cn_uncert\": [],\n", @@ -93,77 +122,75 @@ " \"roc_noisy_bn\": dict(),\n", "}\n", "\n", - "# For each ess...\n", - "for dir in dirs:\n", - "\n", - " # Get results\n", - " files = [f for f in os.listdir(dir) if \".csv\" in f]\n", - " data = pd.concat([pd.read_csv(dir + \"/\" + f) for f in files])\n", - " data.reset_index(inplace=True)\n", - " data[\"bn_noisy_probs_1\"] = data.apply(\n", - " lambda row: (\n", - " row[\"bn_noisy_probs\"]\n", - " if row[\"bn_noisy_mpes\"] == 1\n", - " else (1 - row[\"bn_noisy_probs\"])\n", - " ),\n", - " axis=1,\n", - " )\n", - " data[\"cn_probs_1\"] = data.apply(\n", - " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", - " axis=1,\n", - " )\n", "\n", - " # Store ess\n", - " reg = re.search(\"nodes(\\d+)_ess(\\d+)\", dir)\n", - " n_nodes = reg.group(1)\n", - " ess = reg.group(2)\n", + "# Get results\n", + "files = [f for f in os.listdir(dir) if \".csv\" in f]\n", + "data = pd.concat([pd.read_csv(dir + \"/\" + f) for f in files])\n", + "data.reset_index(inplace=True)\n", + "data[\"bn_noisy_probs_1\"] = data.apply(\n", + " lambda row: (\n", + " row[\"bn_noisy_probs\"]\n", + " if row[\"bn_noisy_mpes\"] == 1\n", + " else (1 - row[\"bn_noisy_probs\"])\n", + " ),\n", + " axis=1,\n", + ")\n", + "data[\"cn_probs_1\"] = data.apply(\n", + " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", + " axis=1,\n", + ")\n", "\n", - " # Store avg of eps and std\n", - " with open(dir + \"/exp_meta.txt\", \"r\") as f:\n", - " eps_vec = [\n", - " float(re.search(\"Eps: (.+)\\n\", line).group(1))\n", - " for line in f\n", - " if \"Eps: \" in line\n", - " ]\n", - " eps = (float(np.mean(eps_vec)), float(np.std(eps_vec)))\n", + "# Store ess\n", + "reg = re.search(\"nodes(\\d+)_ess(\\d+)\", dir)\n", + "n_nodes = reg.group(1)\n", + "ess = reg.group(2)\n", "\n", - " # Split CN results based on probabilities\n", - " data_cert, data_uncert = split_data(data, \"cn_probs\", 0.5)\n", + "# Store avg of eps and std\n", + "with open(dir + \"/exp_meta.txt\", \"r\") as f:\n", + " eps_vec = [\n", + " float(re.search(\"Eps: (.+)\\n\", line).group(1))\n", + " for line in f\n", + " if \"Eps: \" in line\n", + " ]\n", + "eps = (float(np.mean(eps_vec)), float(np.std(eps_vec)))\n", "\n", - " # Compute accuracies\n", - " vs = \"gt\"\n", + "# Split CN results based on probabilities\n", + "data_cert, data_uncert = split_data(data, \"cn_probs\", 0.5)\n", "\n", - " acc_cn_cert = (\n", - " get_acc_bn(data_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_cert) > 0 else None\n", - " )\n", - " acc_cn_uncert = (\n", - " get_acc_bn(data_uncert, f\"{vs}_mpes\", \"cn_mpes\")\n", - " if len(data_uncert) > 0\n", - " else None\n", - " )\n", - " acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n", - " acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n", + "# Compute accuracies\n", + "vs = \"gt\"\n", "\n", - " # Compute CN certainty\n", - " cert_cn = sum(data[\"cn_probs\"] > 0.5) / len(data)\n", + "acc_cn_cert = (\n", + " get_acc_bn(data_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_cert) > 0 else None\n", + ")\n", + "acc_cn_uncert = (\n", + " get_acc_bn(data_uncert, f\"{vs}_mpes\", \"cn_mpes\")\n", + " if len(data_uncert) > 0\n", + " else None\n", + ")\n", + "acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n", + "acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n", "\n", - " # Compute ROC\n", - " roc_cn_cert = roc_curve(data_cert[f\"{vs}_mpes\"], data_cert[\"cn_probs_1\"])\n", - " roc_cn_uncert = roc_curve(data_uncert[f\"{vs}_mpes\"], data_uncert[\"cn_probs_1\"])\n", - " roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n", - " roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n", + "# Compute CN certainty\n", + "cert_cn = sum(data[\"cn_probs\"] > 0.5) / len(data)\n", "\n", - " # Store results\n", - " for key in res.keys():\n", - " res[key].append(eval(key))\n", - " for key in roc.keys():\n", - " roc[key][ess] = eval(key)\n", + "# Compute ROC\n", + "roc_cn_cert = roc_curve(data_cert[f\"{vs}_mpes\"], data_cert[\"cn_probs_1\"])\n", + "roc_cn_uncert = roc_curve(data_uncert[f\"{vs}_mpes\"], data_uncert[\"cn_probs_1\"])\n", + "roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n", + "roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n", "\n", - " # Debug\n", - " assert (data[\"cn_probs\"] >= data[\"cn_probs_alt\"]).all()\n", - " assert (data[\"bn_noisy_probs\"] >= 0.5).all()\n", - " assert (data[\"bn_probs\"] >= 0.5).all()\n", - " assert len(data) == len(pd.read_csv(dir + \"/\" + files[0])) * len(files)\n", + "# Store results\n", + "for key in res.keys():\n", + " res[key].append(eval(key))\n", + "for key in roc.keys():\n", + " roc[key][ess] = eval(key)\n", + "\n", + "# Debug\n", + "assert (data[\"cn_probs\"] >= data[\"cn_probs_alt\"]).all()\n", + "assert (data[\"bn_noisy_probs\"] >= 0.5).all()\n", + "assert (data[\"bn_probs\"] >= 0.5).all()\n", + "assert len(data) == len(pd.read_csv(dir + \"/\" + files[0])) * len(files)\n", "\n", "# Debug\n", "length = len(res[\"ess\"])\n", @@ -209,7 +236,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -252,7 +279,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -286,7 +313,7 @@ "outputs": [ { "data": { - "image/png": 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oWq/Xo9lsRqlUikqlMpIaiFhcXIxCoRD1ej1ardaoy8kk4TgAAAAAR/Jbv/eV+C//0W9E5Rc+E+/9+V+Lyi98Jv7Lf/Qb8Vu/95VRl3YqqtVqzM3Npd3DCwsLsb6+HltbW7G1tRXr6+uxsrISpVIpIiIKhcK+x+vf32q1otPpnGrtd0qSJA3lG43Gma49afL5fPp1WvojVQ77gQrDZeY4AAAAAIfyud9J4r/6p1+If3Fr86771r64Ff/tZ16IP/7IhfiZ/+Bt8YNvzp99gaegWq1Gs9mMiIhisRgrKyt3hd/FYjGKxWJUKpVDjUrpXwSzH1Svrq6eSu27uXz5ckRsj/U4KMQfZ/V6PTY2NmJubi79UGLYzmIWe/+90+l0ol6vx+Li4qmvybfpHAcAAADgQJ/6zRfjx5r/fNdgfNC/uLUZP9b85/Gp33zxjCo7Pa1WKw3GC4VCXL9+/VBd4Qdtc+HChVhaWoqIiHa7He12ezgFHyBJkqjX6xExupEuw3L16tVoNptn3nl/GqrVakR8+4MLzo5wHAAAAIB9fe53knj/r6zHq9+8fajtX/3m7fjAr3Tic7+TnG5hp2xw1EWj0RjqeI3FxcX0eGcVVF+7di29fVrd1hzdwsJCRGx/eGH2+NkSjgMAAACwr//qn34hvv7N1460z6vfvB2X/99fOKWKTl+r1UpnjFcqlVMJk5eXlyMiotPpnEko2h/f4iKc46dYLEZEnOmIHYTjAAAAAOzjt37vKweOUtnLP+9uxs0XJ/MinVevXk1v98deDNvCwkI6guUsusf741suXbp06H263W5Uq9WYnp6OXC4XuVwupqamYm5ubt+RJu12O8rlckxNTUUul4vp6el9H2O9Xo+pqakdz3Wz2YyZmZmYmppKv+/X0F+7VqulP8vlclEul+86dqfTiWq1mh4rl8vFzMxMOmJmL9VqNa39TrVaLX0e9nq8Bx1/0MWLF9PjcHZckBMAAABgwn39m7fjtze/dirH/m8+dfNE+/8/Pnkz/uN3PTqkanb63gtvjNe/7typHHswpDzNESSNRiPK5XJ0u91oNpvpiI1h63Q6aSd8v0v5IK1WKw1/B/XHf7RarVhcXEw74PtqtdpdwXC32416vR6tVivW19fvGlHz5S9/OZIkic3NzUiSJGZnZ+8K3y9cuJB+mNC/8Gk+n48LFy6k29w57/3OWvL5fOTz+eh0OtHpdKLRaOxaz0GSJEm/9nq8tVotbty4ESsrKwcer1wuR7PZjG63G0mSDHWED3sTjgMAAABMuN/e/Fq8+//+a6MuY1f/8LP/Ov7hZ//1qRz7uf/8T8Zb//B3ncqx+0HyaYeUpVIpisVidDqdqNVqpxaOb25+u/v/oAuGRmx3afe7uEulUtRqtbh48WLk8/nodrvRarXi8uXLd3VVDwbFjUYjfTz9sLgfuO81PqTT6cQjjzwSSZJEoVBI143YHgfTHwkzPT0d3W43lpaWYnFxcd/HUigUYnl5OUqlUvp69ju9u91uzM/PHyrA3k3/gqqDj7XT6cTc3Fz6PHW73UNdyLWv2+0e+gMMTsZYFQAAAADYQz8kP01XrlxJ1zrKKI6j6HdaR8SOTuvdJEmSBuMLCwuxurq6I1guFAqxuLgYW1tbO8L8fnd4xPbs7MH7CoVCrKysRKlUina7vedIln7n9PLycmxsbMTCwsKJguKlpaXY2NiISqWy44OOUqmUdrwPzpc/jsXFxR2PtVgsRqPRSL8/zKiUwdoGXytOl3AcAAAAAO4wGFaedkBeLBbT0S21Wu1U1hs85kHd8JcvX05v3zkyZT/9bfe7gGl/m8GZ7neqVCoHdoMf1n6PdbDGtbW1Y6+x20z6frd7xOHeP4MfWAx2+XO6jFUBAAAAmHDfe+GN8dx//idP5dj/zadunmgsyp9//N861Znjp+XixYtpx+/a2tqpzh2P2B5B0h9Rcvny5SOF0ofx5S9/+dDb9h/3YLf4YfQD5larFblcbt9t9+uOXlpaOvSaJzGsD0B2G5ly1HE8Z/lhDN8mHAcAAACYcK9/3blTm739n7zr0ROF43/tRx6NRx8+ndpO09zcXBoSNxqNUw/HC4VCLCwsRLPZjHq9HktLSyO7KGM/uD7MbPJB/VEphULhSDO273Qa87a73W50Op1YXV2Nbrcbm5ubQxlfMqzX6Cid/QyPcBwAAACAPb3lD39X/PFHLsS/uHX0UQ8/XLgwkcF4xPas7f6Ik1arFZ1O59Qvkri8vBzNZjMitserDM6tPqnBC2cmSbJvAFsoFKLT6Rw5PM7n85EkyYkuLDrsYLjVakWtVtvxWPL5fBrg7zX7fJQOmgnP8Jg5DgAAAMC+fuY/eFu84XXnjrTPG153Lpb+D287pYrOxuBok7m5uVNfL5/Pp7O2m83mUC/MeJSZ1v152Uedw93vrl9ZWTlidaej1WrF3NxcdLvd9MKivV4vtra2Yn19fWzqjNj5mugcPzvCcQAAAAD29YNvzsdH/mLx0AH5G153Lj7yF4vxg2/On25hp2xhYSHtgO52uzEzM3PgPOgkSaJerx97zcFxKrtd6PG4jjLTularpdsdVMNggN//MKHdbker1dp3v2HN1d7Y2Njzvv5FP0ul0q6jcYb54cNJDdZy1HE2HJ9wHAAAAIADveuPPRwfW/jh+OHC/iMffrhwIT628MPxrj/28BlVdroajUYakHc6nZiamoparRadTicNeJMkiU6nE7VaLb3/uPL5fHpByna7nc49P6l+N3jEwR3hhUIhDbqbzWaUy+Vot9uRJEn6WOv1ekxPT+94rIP7zc3NRbVa3RH6drvddL/Lly+f6PH0A+Rr165Ft9uNJEmi3W7v6PDvb9Nut6PZbO4I5Nvt9lA/fDipwfEuwvGzY+Y4AAAAAIfyg2/Ox8cW/r34rd/7Svy//sVvx2/865filW/cjjc9cC4e+7fOx1/84e+d2Bnj+2k0GlEul9PZ1fV6fd/u8EqlcqL1FhcX4/Lly5EkydC6m/P5fBSLxfSilAfNBF9cXIx8Ph/VanXfkP7OOez9sTC1Wi2azWY6Q/1ODz300DEexbf1L5iaJMmOeeoR2yF8oVCIpaWlNBSvVqt3heHjFELfuHEjIuLUL/zKTsJxAAAAAI7kLX/4u+K//HOPjbqMM1WpVKJSqUSr1YqrV6/uuGBl/wKPpVIpqtXqUELX5eXloXc2l0ql6HQ6h74I5cLCQpRKpVheXo52ux3dbjd9rBcvXoxqtbrrRUoXFxejUqnsul+pVNoxOua4FhYWYn19Pa5duxYRkdY0NzeXPv/5fD7W19ejWq3G2tpaJEkShUIhisViPP3001EqldJO81EH5f0PH85itj3fluv1er1RFwGT5jd+4zfi7W9/e/r95z//+XjssWz9jwLG2+3bt+PTn/50RES8853vjHPnjnbhHODknIcwWs5BGD3n4XB84QtfiLe9bbIvasno9Hq9eOWVVyIi4k1velM8//zzMTMzExHbs7pHHQizrdPp3BOvy3F+X406YzNzHAAAAAAyoFgspmM7+rPBGb1GoxER2539kxqMTyrhOAAAAABkRD8U748jYbSSJEnnsvdDcs6OcBwAAAAAMqLfPZ4kyb4XFeVsXL58OSK2Z9rrGj97wnEAAAAAyJCVlZUoFApRq9XSi4py9rrdbtTr9SgUCnHlypVRl5NJwnEAAAAAyJB8Ph+rq6uRz+djbm5u1OVkVrlc3vFacPaE4wAAAACQMYVCIa5fvx7dbjfK5fKoy8mccrkcm5ubcf36deNURuj+URcAAAAAAJy9YrEYW1tboy4jk1ZXV0ddAqFzHAAAAACADBKOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAACAjKrValGr1UZdBmesVqtFvV4fdRkjd/+oCwAAAAAAzl65XI52ux3r6+ujLoUzVi6Xo1wux40bN2JlZWXU5YyMznEAAAAAOKRWqxVzc3MxPT0duVwucrlcTE1NxfT0dFSr1Wi1WpEkyV37JUmSbt//6na7B65XrVYjl8vFzMzMUB9HtVqNdrsdKysrUSwWh3pshmPwPbbbe+okSqVSLC8vR6vVyvRfDgjHJ1i3241qtRozMzMxNTUVU1NTMTMzE81mcyLXazabMTU1NZRjAQAAAKfoxX8V8U8XIz76nohf+Pe3//tPF7d/fo9qtVoxNTUVc3Nz0Wq1dgTbSZJEt9uNZrMZc3NzMT8/f6hjjiqUrNfr0Ww2o1QqRaVSGUkNjN7i4mIUCoWo1+vRarVGXc5ICMcnVL1ej+np6eh2u3HlypXY2tqKra2tWFpailqtlt43Kev1g/ckSYb+SRgAAAAwJP/besTf/dMRH/njEf+yEfE7/zzi9z6//d9/2dj++d/909vb3UOq1WrMzc2lmcXCwkKsr6+n+cj6+nqsrKxEqVSKiIhCobDv8fr3t1qt6HQ6p1r7nZIkSUP5RqNxpmtzNPl8Pv06Lf2RKof9QOdeIxyfQM1mM2q1WlQqlVhdXd3xpy+VSiXW19djc3MzZmZmhhI0n8V6c3NzJ64TAAAAOEW/tRrx3/6ZiC/+z/tv98X/eXu731o9m7pOWbVaTf9qvlgsxsbGRjQajSgWi2lwWSwW09xkY2MjqtXqvses1Wpp4HnW3eOXL1+OiO2xGgeF+OytXq+no2lOy+AHMKcVkBeLxSgWi5EkSSYv0CkcnzD9DuuI2HNYfqFQiIWFhUiS5MSf+pzFevV6/cw/JQUAAACO4H9bj7j6H0Z882uH2/6bX4u49hMT30HearXSYLxQKMT169cP1RV+0DYXLlyIpaWliIhot9unGrAOGgxAszxnehiuXr0azWbznsi0+tlf/4OTLBGOT5jl5eWIiPTPdPbSf1PvdRGIcVmv0+nE5cuX03UAAACAMfTcz0Z869Wj7fPNr0Ws/l9Pp54zMtgE2Gg0htq9u7i4eObd49euXUtvH5T1kB0LCwsRsf3hSdZmjwvHJ8zgp5X7Gbz/JBfMPO315ubmYmVl5VRnJwEAAAAn8OK/OniUyl5e+HTEl35zuPWckcEGwEqlciphcr9ZsNPpnEkoubq6PerGRTi5U3+Mcv89khXC8Qky+Gca09PTB27fD5yvXr06luvVarUolUo+qQQAAICT+ubXI178wul8ffpvn6y2X/tbp1fbN78+nOdvF4P5xkEzxI9rYWEhbTg8i+7x/viWS5cuHbhttVqNXC4XU1NTe26Ty+Uil8vtOqu6VqvF1NRUep25drsd5XI5pqamIpfLxfT09KFmXPdH/k5PT6fr9Y+730iT3dbb7zmu1+sxNTW147VuNpsxMzOTPgfNZjOtob92rVZLf5bL5aJcLt917E6nE9VqNT1WLpeLmZmZAx9//zXYLZcb1vPbd/HixfQ4WXL/qAvg8NbW1tLbh7lgQqFQiE6nc+zZR6e5Xv8T0Y2NjWPVBgAAAAzYuhXxkR8edRW7+/WV7a/T8IF/HvHw207l0IMh4Wk29jUajSiXy9HtdqPZbKYjLoat0+mknfD9LuHTlCRJ+lWr1e4KarvdbtRqtbhx48ae17lrtVpp+HvnsVutVrRarVhcXLxrXO9e69Xr9Wi1WrG+vn7XFIMvf/nLkSRJbG5uRpIkMTs7e1fGdeHChTQj63a7EbHdLHrhwoV0mzsztDtr6V/EtZ+hNRqNXes5yDCe30HlcjmazWZ0u91IkiQzUx50jk+QkwTJx5k7fprr9cepAAAAAIyjfrZx2iFhqVRKw+rT7B7f3NxMbx+mCXJY2u121Ov1aDQa0ev1otfrxfr6elpDq9VKg+ZBzWYzDcZLpVKsrq7G1tZW9Hq92NjYiOXl5cjn83d1VQ8GxYNrbmxsRKVSiW63u2vg3tfpdOKRRx6JTqcThUIhDa8jtsfRbGxsxMbGRlr/0tJS+rONjY1oNBp3HbNQKMTKykpsbW2lX/3xJd1ud8ds+6M67vO7W419h9n+XqFzfIIcNeAe/NRqc3PzWJ9AncZ61Wo1KpXKmXxKeRgvvvhifOlLXzrSPjdv3tzx/e3bt+P27dvDLAtOZPD96L0Jo+E8hNFyDsLoOQ+Hox92HWLDyJ1+OWOn1+tFHOb5OYEkSQ73Ghxg8Bh3vq7NZjMuXrwYSZLE8vJyLC4u7rnfcdccbIKcmpo68FhHWXe39+ng9x/84Adjfn4+/dkTTzwRv/iLvxjvfve7I2J7zvVgx3ySJOl4k/n5+R2Bc6/Xi0ceeSQ++MEPxgc/+MEda/W7wyMinnvuuSiVSul9jzzySFy7di3e/e53R7vdjvX19R3Z1OAxIiKeeeaZPV+Hgx77oA996EPxzDPP3HWc2dnZeOaZZ+JDH/pQtFqt2NrauitL2+81OMnzu5vz58+ntzc2NuKJJ57Yd/vd9Hq9I/++H/W/D8LxCTL4Cd9RHadz/DTWa7fb0W63x2qcykc+8pH48Ic/fKJjrK+vn+j5gtP0mc98ZtQlQOY5D2G0nIMwes7D4zt//ny88sorB2/4HX8o7vuPrp9KDd/xL/6f8bp/9fFj7//N7/8L8Qd//D8ZYkXf9tp3/KGIwzw/x5DP59N843d/93dP3EE++Dp+/etf3/H9W9/61njXu94Vn/rUp+JDH/pQ/PiP/3i63re+9a2IiHjttdcO917Yw2Bj4P3333/gsfrr3ln7bv7gD/7grm0G9/+Lf/Ev3nX/93//96e3X3zxxR33/42/8TfS2//Ff/FfHPpx/82/+TcjIuLJJ5+MH/7hH951v5/92Z+Ndrsdv/IrvxJvfetb059/85vfTG8/+eST8YEPfGDfdV977bWI2P2xD9rvuX7HO96R3v70pz8d73rXu3bcv99rf5Lndzff8R3fkd7+3//3//1Y77VvfOMb8elPf/pI+9y6devI6wyTcHxCnfXcn2GslyRJzM3NxfXrp/OPNQAAAGTW/a+P1777j53Kof/gh//aicLxP/jh/zRee+gtQ6zobDzxxBPxqU99KiIinn/++buCy2H7+Z//+fjBH/zBiIj4O3/n7+wIiIdhlE19jzzyyF0/2y9r+mf/7J9FRMS73vWuI2VSzz//fEREfOITn4gHH3xw321feOGFPe/7yZ/8yUOveRKD3dovvfTSsY9z1Od3N4Pbn6SWSSMcnyCDY0uO2gl+nHB72OvNz8/H0tLS2IxTGaaZmZl47LHHRl0GpG7fvp1257zjHe+Ic+fOjbgiyB7nIYyWcxBGz3k4HL/5m78Zb3rTm0ZbxJuK0fuj/37kvvj/OfKuve/7E/HGP3r08Qzj4Omnn07D8b//9/9+/Nk/+2dPdLzBTt/Xv/71d72uP/ADPxDz8/Nx5cqV+Pmf//n42Z/92cjn83H//dvx3X333Xfk90Kv14uvfvWrERHxute9Lv35YY7TX/cw23/Hd3zHXdv098/n80fevx9cP/roo0d6zJ/97GcjYnt+9kFz1d/ylrfsOPbg8/Mn/sSfOHCt++7bvpTjbo99N91uNzqdTqyursatW7dic3Nzx2zv3d4T+732J3l+dzOY/T388MPH+r3zwAMPxDvf+c4j7TOYP46CcHyCHDXgHvxE8DhvtGGu1x/+P44X4fzABz6w74UYdnPz5s148skn0+/PnTvnf+gxtrw/YfSchzBazkEYPefh8eVyucjlxmCa+Lv/bxH/7Z+J+ObXDr/P694YufLfiBiH+o+hWq3Ghz70oUiSJFqtVjz//PMnavgbfB33el3r9XpcuXIlIrZnVTcajbv2O67BC1e+9NJLB+Y+R1l3t8dzkv0LhUJ0Op24devWkR5zfxROrVY7cMb2bjX0j3GUNQ86R1utVtRqtR1BeD6fTwP8Tqez53H2ew5P+vrstk3fQw89dKz3Wi6XO/Lv+lH/23DfSFfnSAZ/iR31T2GO0zk+rPWSJIn5+fmxDMYjtj8Ne+yxx4709eijj466bAAAADg7//ZMxFO/HPG6Nx5u+9e9cXv7f3vmdOs6ZcvLy+ntozbWHUc+n08vAtlsNncEqic12Mg4jBErx7m+3WFdvHgxIiLW1taOtF+pVIqIGJsMqtVqxdzcXHS73VhYWIjV1dXo9XqxtbUV6+vrY1NnxM73xFmPcx4l4fgE6f9iiDjcL6D+L9CD/ozktNebm5uLhYWFuHDhQiRJsudX3+bm5l0/AwAAAEbsLeWI/9M/jvi+A8YmfN87t7d7S/ls6jpFCwsLaQdyt9uNmZmZA/OKJEmiXq8fe82lpaU0nKxWq8c+zp3ubGQ8qXa7feJj7KVWq0XEdp0HPQeDHyD0P8xot9vRarX23W9YudPGxsae9129ejUitkP7RqORhvd9w/zw46QGazluljiJhOMTZPBPd/Y78fr6J/mdJ95Zr9dut6Ner8fU1NSeX/1fehHbHev9nw/zHwEAAADghP7tme3g+wP/IuKHqhHf++9F/OEf2P7vD1Uj/uN/uX3/hHeMD2o0GmlA3ul00hyj0+mkWUiSJNHpdKJWq92VcxxVPp+PpaWliNjOVIYVQg82QR6mI7s/USBJkrtC3Ha7HfPz80OpazeFQiENupvNZpTL5Wi322kzZafTiXq9HtPT0zue68H95ubmolqt7qi92+2m+12+fPnENUZEXLt2LbrdbiRJEu12e8dfGPS3abfb0Ww2dwTy7XZ7rHKv/niXiGyF42aOT5hKpRKtVuvAX2KDb+iTnGjDWK/X6x24ztzcXPqJ3tbWVqb+fAMAAAAmzsPfH/EfHL87etI0Go0ol8vp7Oh6vb5vd3ilUjnReouLi3H58uVdg+njyufzUSwW04tCHjSTe7D5cXp6OorFYly4cCG63W50u90olUqn2j2+uLgY+Xw+qtXqvh8S3DkHvj+WplarRbPZjGazuet+Dz300Inqm5ubSwP7wdHEEdshfKFQiKWlpTQUr1ard2Vm4xRC37hxIyKO32Q7qXSOT5j+J4edTmffX479P9soFot7Xiyif2Lu92nmMNcDAAAAmFSVSiU2NjZiZWUlKpXKjmCzHzwvLi6m25zU4LzzYekHn4NNjnspFovRaDTSx9nPhkqlUqyvr8fq6mpUKpUolUqnFvIuLCzExsZGLCwspGv0n+uFhYU953b3X4fd9ltcXIytra00RD9JbQsLC5HP53fUtLq6umPN9fX1KJVKaSNooVCISqUSKysr6X2n+RweVv/Dh7OYrT9Ocr3DtPUyVmq1WtTr9SiVSrG6unrX/d1uN6anpyOfz8etW7f27MKemZlJfxkuLi7u+Ut3WOvtZ9I6x3/jN34j3v72t6fff/7zn4/HHntshBXBTrdv345Pf/rTERHxzne+c+RXf4Ysch7CaDkHYfSch8PxhS98Id72treNugwmVK/Xi1deeSUiIt70pjfF888/HzMz2yNvNjY2Rh7IMh46nc5Q3hfH+X016oxN5/gEWl5ejsXFxWi321Eul3d0dLdarZiZmYlCoRDXr1/fN2QenHO0X1f4sNbbbf1ut3vXn8b0r8bsgpwAAAAAw1MsFtPu8dPoTGcyNRqNiIix6GA/a8LxCbW8vBzr6+tRKBSiXC6nF7C8fPlyLC0txcbGxoHjTfp/GjN4sYLTXO9OU1NTMT09nf65Rv/PUC5fvpxelPMkV3cGAAAAYKd+BnTt2rURV8I4SJIkncveD8mzxAU5J1h/9tNxlUql2NjYOLP17mSiDwAAAMDZ6nePt9vtqNfrJ569zWS7fPlyRMRdc/SzQuc4AAAAAGTIyspKFAqFqNVq+47a5d7W7XajXq9HoVCIK1eujLqckRCOAwAAAECG5PP5WF1djXw+n467JXvK5fKO90IWCccBAAAAIGMKhUJcv349ut1ulMvlUZfDGSuXy7G5uRnXr1/P5DiVPjPHAQAAACCDisVibG1tjboMRmB1dXXUJYwFneMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAGRUrVaLWq026jJGqlarRb1eH3UZjMD9oy4AAAAAADh75XI52u12rK+vj7qUkSqXy1Eul+PGjRuxsrIy6nI4QzrHAQAAAOCQWq1WzM3NxfT0dORyucjlcjE1NRXT09NRrVaj1WpFkiR37ZckSbp9/6vb7R64XrVajVwuFzMzM0N9HNVqNdrtdqysrESxWBzqscdR/3mcnp6+675SqRTLy8vRarUy30WfNTrHAQAAADiSjWQjrv3mtfjC5hfiq9/8anzn674z3nbhbfHUH3sqpvN3h4/3glarFfPz83sG30mSRLPZjGazGZVK5VAdyLVabSSdyvV6PZrNZpRKpahUKme+/jhaXFyMRqMR9Xo9Ll265HnJCJ3jAAAAABzK53//8/GX/se/FE/+wyfjH/yrfxDPv/h8/K9b/2s8/+Lz8Q/+1T+IJ//hk/GX/se/FJ///c+PutShqlarMTc3lwbjCwsLsb6+HltbW7G1tRXr6+uxsrISpVIpIiIKhcK+x+vf32q1otPpnGrtd0qSJO2ObjQaZ7r2uOt/UDE/Pz/iSjgrwnEAAAAADvTp3/10/OX/6S/H2u+t7bvd2u+txV/+n/5yfPp3P31GlZ2uarUazWYzIiKKxWJsbGxEo9GIYrEY+Xw+8vl8FIvFqFQqsbq6GhsbG1GtVvc9Zq1Wi3w+n94+S5cvX46I7VEiB4X49Xo9Hb9yms5qnYMUi8UoFouRJIkLdGaEcBwAAACAfX3+9z8fP/nPfjJe/darh9r+1W+9Gj/1qz818R3krVYrDcYLhUJcv379UF3hB21z4cKFWFpaioiIdrt9ZqHwYOh7mFD+6tWr0Ww2T727/azWOYz+Bxv9DxG4twnHAQAAANjXz639XHz99tePtM+r33o1/s763zmlis7G4HiNRqORdnsPw+Li4pl3j1+7di293R8Bw04LCwsRsf1BQqvVGnE1nDbhOAAAAAB72kg2Dhylspcb/+ZGdJPukCs6G61WK50xXqlUTiVMXl5ejoiITqdzJkHs6upqRISLTR6gWCxGxLefL+5d94+6AAAAAABO5hu3vxG/8/LvnMqxn/31Z0+0f/P/24y/8gN/ZUjV7PTmB98cD5x74FSOffXq1fT2QTPEj2thYSGWl5ej2+1GrVY79dC6P77l0qVLe27TbDbvery1Wm1Hd3upVNo1OO52u7G8vBztdju63W7k8/m4ePFiVKvVux7bcdfpdDrRaDRibW0tut1uJEkSxWIxnn766VhcXDzgGTicixcvRqfTGfkMdE6fcBwAAABgwv3Oy78Tf+Ef/YVRl7Grf3Lrn8Q/ufVPTuXYH/9zH49Hpx49lWMPBqOnOYKk0WhEuVyObrcbzWYzHesxbJ1OJ+2E73dG7+bChQvpzPRud7vrP5/Px4ULF9JtdpupXq/X7xoPkyRJOlO9UqnElStX0lEyx1mnVqvtuFBm/4KonU4nDc3X19dPPP6mXC5Hs9lMw/dhjtNhvBirAgAAAAB36AfJpx2MlkqlNKw+zdnjm5ub6e39LhhaqVRiY2MjNjY20u2WlpbSn21sbESj0dixz2Awvri4GFtbW9Hr9WJraytWVlYin89Hq9WKmZmZE63Tr31lZSW2trbSr353ebfb3TEn/rgGn59+cM+9STgOAAAAAHvoh+Sn6cqVK+lag53RwzQY8g52Z59Up9NJg/GVlZVYXl5OP1DI5/NRqVTi1q1bkc/n0/Exx9UPzyuVyo4PLUqlUjq/fXBW/HENHls4fm8zVgUAAABgwr35wTfHx//cx0/l2M/++rMnGovypx/506c6c/y05PP5NGQ97dEaxWIxSqVStNvtqNVqsbCwMPT1BgPjYR67H3YXi8U9Z6bn8/lYXl6OarUa9Xo9lpaWjlXDfvsMjr5ZW1s70SicwQ8PBjvuufcIxwEAAAAm3APnHji12dvz/+78icLxhX93IQr5vcd4jKuLFy+mc8dPGrYeRqPRiOnp6YiIuHz5ctoJPSxf/vKXh3q8vv5zdNDzc/HixR37DPvio4PB+TA7x8/iLwcYHWNVAAAAANjTdH46Lv7hiwdvuItLf+TSRAbjERFzc3Pp7d1mXw9boVBIL8ZZr9cnIpQdrLEf7O9lmHO8u91utFqtqFarUS6XY2ZmZsc885M6rS57xo9wHAAAAIB9/dTFn4o33P+GI+3zhvvfED8585OnVNHpGxxt0mq1otPpnPqag93iw74452B4PazgfTA4Xl9f33fbwUD8uIFzq9WK6enpmJ6ejrm5uWg2m7G2thYR+19k9CSGOZ+d8SMcBwAAAGBfb//ut8fP/amfO3RA/ob73xA/96d+Lt7+3W8/5cpO12BYPdhJflry+XwsLi5GRESz2RzqxSBPa452sViMiG+PV9nL1atX09vHGVHTarVibm4uut1uLCwsxOrqavR6vdja2or19fVYWVk58jH3Mvj86By/twnHAQAAADjQO7/nnfFL7/mluPRHLu273aU/cil+6T2/FO/8nneeUWWnZ2FhIR110u12Y2Zm5sCu6yRJol6vH3vNwYtVVqvVYx/nTieZo72xsbHnfUtLSxGx/fzs9bgH76tUKnt2ee+3Tj9cL5VK0Wg07grYh/lBwuCxTqsjnfEgHAcAAADgUN7+3W+PX3rPL8Un/vwn4se//8ej+HAx/tjUH4viw8X48e//8fiHf/4fxi+955cmvmN8UKPRSAPyTqcTU1NTUavVotPppCFzkiTR6XSiVqul9x9XPp9PA+d2u31gR/ZhDV4Qsz+K5CD9YPjatWvR7XYjSZJot9s7uugrlUr6/NRqtZibm0tH0PRD8f5Il3w+H1euXDnWOv1t2u12NJvNHQF/u90e6gcJgyN0hOP3tvtHXQAAAAAAk2U6Px1Lf3xp1GWcmUajEeVyOWq1Whr47tcdXqlUTrTe4uJiXL58OZIkGVpHdD6fj2KxGJ1OJ1ZXV9NAez9zc3PRbrcjSZK7LrjZ7XbT4LjRaEQ+n496vR6tVitardZdxyoWi7GysrLrmJLDrLO0tJSG4tVq9a4wfJgh9o0bNyLieONfmCw6xwEAAADgAJVKJTY2NmJlZeWu0SD94HlxcTHd5qQG550PSz/sPezFRftjZfL5fPoY+/O+7wyjl5eXY2NjIyqVShqA5/P5KJVKsbKyEuvr63sG2IdZJ5/Px/r6epRKpfT4hUIhKpVKevxSqRSlUunEQXm/W/8s5swzWrler9cbdREwaX7jN34j3v72b/+J2Oc///l47LHHRlgR7HT79u349Kc/HRER73znO+PcuXMjrgiyx3kIo+UchNFzHg7HF77whXjb29426jKYUL1eL1555ZWIiHjTm94Uzz//fMzMzETE9nxvI0Pu1ul0PEfHdJzfV6PO2HSOAwAAAEAGFIvFtHv8NDrT7wWNRiMiYigd6Iw/4TgAAAAAZEQ/FL927dqIKxk/SZJEs9mMiG+H5NzbhOMAAAAAkBH97vEkSfa9qGgWXb58OSLirpny3LuE4wAAAACQISsrK1EoFKJWq0W32x11OWOh2+1GvV6PQqEQV65cGXU5nBHhOAAAAABkSD6fj9XV1cjn8zE3NzfqcsZCuVze8byQDcJxAAAAAMiYQqEQ169fj263G+VyedTljFS5XI7Nzc24fv26cSoZc/+oCwAAAAAAzl6xWIytra1RlzFyq6uroy6BEdE5DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAABiRJErlcbsdXt9s9cL9qtRq5XC5mZmaGXtP09HRaS5IkQz/+sO32HN75NT09HeVyOer1+qEe0zi+Lkw24TgAAAAAHKBWq426hImVz+ejVCrt+CoUCtHtdqPdbketVoupqaljPcdeF07i/lEXAAAAAMBk+cbNm7H1savx9f/lf4nXvvrVuO87vzNe/+/8OzH1Y0/HA48+Ouryhqof4rZareh0OlEsFkdSRz6fj3w+P5K1T+qpp56KRqOx633tdjuWl5ej3W6nHeR7bTtoXF4XJpvOcQAAAAAO5dVf//X44n/4E9H9M382tn7lV+LVTie+8Zu/Ga92OrH1K78S3T/zZ+OL/+FPxKu//uujLnVoarVaGkqPskt5fX09tra2Ymtra2JD8t2USqVYXV2NxcXFiIhoNptRr9cP3G9cXhcmm3AcAAAAgAO98mu/Fl/8if8ovnbjxr7bfe3GjfjiT/xH8cqv/doZVXa6Lly4EEtLSxGx3eXcbrdHXNG9aXl5OQqFQkREXL169cDtvS4Mg3AcAAAAgH29+uu/Hr/7n/5n0Xv11UNt33v11fjd/+z/fM90kC8uLupSPgP9cPywFxz1unBSwnEAAAAA9vVi/W9F7+tfP9I+vVdfjRf/1t8+pYrO3vLyckREdDqdaLVaI67m3pMkSdr9XSqVDr2f14WTEI4DAAAAsKdv3Lx54CiVvXztX/7L+MbGxpArGo2FhYW0s/mkXcrdbjeq1WpMT09HLpeLqampKJfL+4a71Wo1crlcTE9P33VfkiRRq9XS4/WP+e53vzv+7t/9u2kndqvVSu/vdrv71tg/1ll0ZCdJEjMzMxGxfeHRfuB9GMN8Xcie+0ddAAAAAAAn89o3vhHf/O3fPpVj/36jebL9f+EX47urC0OqZqfXfe/3xn0PPHAqx95No9GIcrkc3W43ms1mLCwc/XHV6/W7Qtx+13S73Y5KpRJXrlw59EU3O51OzM7OpgF4f7/BY7700kvx1//6X49KpRKFQiG63W4sLy9Ho9HY85j98Lw/13vYut1udLvdWFlZiWZz+z1WLBZjZWXlyBccHcbrQjYJxwEAAAAm3Dd/+7ej+2f/3KjL2NXL//gfx8v/+B+fyrEL/8M/igfe8pZTOfZuSqVSFIvF6HQ6UavVjhzCDgbji4uLsbS0FPl8Pg2y5+fno9VqRafTiY1DdtzPz89HkiRRKpXuCpZXV1fjIx/5SPzgD/5g+rNarRbVajWazWYsLy/vGkRfvnw5IiIqlcqRg+rdNJvNNADfTalUimq1GpVK5VjHP+nrQnYZqwIAAAAAh3TlypWI2O7Mrtfrh96vH9xGRKysrOwIpvP5fFQqlbh161bk8/nodruHHhHS6XQiYnvsyp1BdqlUil/+5V+Od73rXenPFhYW0u36IfigJEnS8S7D7BrP5/M7vgatra3F1atX05njx3Hc14VsE44DAAAAwCEVi8X0gpG1Wi0dZ3KQfthdLBb37JAenLddr9cPdexisZge/7Dhcr+zerdu7sERJ/1jn9TCwkJsbW3t+Or1erGxsRErKytx8eLFaLVaUS6Xo1wuH/o5HXTc14VsM1YFAAAAYMK97nu/Nwr/wz86lWP/fqN5orEoD/6ZP3OqM8dHodFopBfGvHz58qEuINkPrvsB7l4uXry4Y5+DRo0sLS3F3NxcdLvdKJfLEbEdFF+8eDEWFhbirW9966779MP3Vqu1Y43+HPLTmjU+qFAoRKFQiEqlEp1OJ2ZmZqLdbsfs7Gysr68f+XjHeV3INuE4AAAAwIS774EHTm329nf/1eqJwvHvfv9fjQf+/4HlvaJQKMTCwkI0m82o1+vp7PC9DHYxTx/wXBQKhfR2/6KY+6lUKrG6uhrVajXdvtPpRKfTiWazGU8++WT88i//8o598vl8Wv/ly5fTcLzdbke3203HvJylYrEYq6urUS6X09qPOjv8qK8LGKsCAAAAwJ4eePTReOOlS8fa940/9EP3XDDeN9iVfNB88MGA9qCO6MFA/LDBbqlUio2NjdjY2IhGo7HjQpqf+MQn4id+4ifu2qdfcz9IjzjbrvHdlEqltO6VlZVjHeMorwsIxwEAAADY18OLH4zcG95wpH1yb3hDPPzBnz6likYvn8/H4uJiRGzP6T6oy7s/v/ugueBXr15Nbx80guVO/c7plZWV2Nraivn5+YjYDsh327bfHd5fs38hzqN2bA9LkiRpl/1xO76P+rqQbcJxAAAAAPb1hh/4gfie//rnDx2Q597whvie//rn4w0/8AOnXNloDY7tqFarB24bsd0ZXq/Xd91m8L5KpbJjxMpe9gt/5+bmDlVTs9lMg/HBrvOzNljvQc/nfo7yupBtwnEAAAAADvSmP/kn44/+8t+LN/7QD+273Rt/6Ifij/7y34s3/ck/eUaVjU4+n08D5na7vW9XeKVSSTuya7VazM3NpeNM+qF4fx55Pp+PK1euHKqGmZmZmJmZiWazmR4vYntcyoc+9KGIiHj88cd33bdYLEapVIokSUY6UqXVaqUX44zYHo1y1K75QUd5Xcg2F+QEAAAA4FDe8AM/EH/0l/9efOPmzdj62NX4+he+EK999atx33d+Z7z+bW+Lqf/jj92zM8b3sri4GJcvX44kSQ4c4dFoNCKfz0e9Xo9Wq5V2aw8qFouxsrJy6O7tJEmi0+ns2yH99/7e39vzvlqtlgbIxWIxHf8yTNeuXdv1uel2u3fNWF9eXh7KWJejvC5kl3AcAAAAgCN54NFH44/89f/LqMsYG8vLy4ce39Hfth9KJ0kS+Xw+Ll68GNVqNZ0DflhbW1tx7dq1WF1djU6nkwbBhUIh3ve+98Vf+2t/bd+gvVQqRaFQiG63e2pd40mS7Nm9XSwWo1AoRLlcHvqs86O8LmSTcBwAAAAABuTz+ej1eofefmFh4UjBbqFQiJWVlSPV1Gg00tEng/L5/J7r93q9eOWVVw48dj6fj3w+f+Rg/qBjHuU5PI1jHvV1IXuE4wAAAACQUd1uNzqdTiwvL4+6FDhzLsgJAAAAABlVq9UiInRYk0nCcQAAAADIiCRJ0tudTidarVYsLi4e+gKgcC8xVgUAAAAAMmJmZiYitud3dzqdyOfzp3YhThh3OscBAAAAICOKxWJsbm5Gp9OJYrEY6+vrusbJLJ3jAAAAAJARKysroy4BxobOcQAAAAAAMkc4PsG63W5Uq9WYmZmJqampmJqaipmZmWg2m2O9XqvVinK5HFNTU5HL5WJmZibm5uZOrW4AAAAAgDsJxydUvV6P6enp6Ha7ceXKldja2oqtra1YWlqKWq2W3jdO6yVJEuVyOS5fvhxzc3Oxvr4e6+vr8fTTT0e73Y5qtRrT09PR6XSGVjcAAAAAwG7MHJ9AzWYzarVaVCqVu+ZEVSqVKBaLMTMzEzMzM3Hr1q0TX1RhWOvNzs7GxYsXY3V1dcfPi8ViLCwsxOzsbHQ6nZiZmYn19fUoFosnqhsAAAAAYC86xydMf7RJxN4XUCgUCrGwsBBJksT8/PxYrFer1SIiotFo7Hp/Pp+PK1eupN/Pzc2dpGwAAAAAgH0JxyfM8vJyRESUSqV9t+sH2q1WK5IkGfl6zWYzCoXCvqNXisViuk632zWDHAAAACKi1+uNugSAfU3q7ynh+ITpB8aFQmHf7QbvP0nIPIz1kiSJJEmi1WrF9PT0vscZHKWyV6c6AAAAZMV9990Xr7322qjLANjXa6+9FvfdN3lR8+RVnGGDF6o8KGSOiHT299WrV0e63ubm5o7v9+sev3Tp0p77AQAAQNa84Q1viK9+9aujLgNgX1//+tfjgQceGHUZRyYcnyBra2vp7YM6uQe3GQy5R7FefyZ5Pp+PhYWFfY81GJxfuHDhqCUDAADAPeW7vuu74itf+cqoywDY1yuvvBLf+Z3fOeoyjkw4PkE2NjaOve9x5o4Pc71GoxFbW1t7XpCz78aNG+ntwRErAAAAkEUPPvhgfO1rX4utra1RlwKwq69//evx0ksvxYMPPjjqUo7s/lEXwOEdNeAe7Lze3NxMx56M83rtdjv9fmlp6Uj7H9eLL74YX/rSl460z82bN3d8f/v27bh9+/Ywy4ITGXw/em/CaDgPYbScgzB6zsPh+Z7v+Z743d/93fjqV78aDz74YLzxjW+M++67L3K53KhLY8wNXiRxUi+YyHjq9XrxrW99K15++eXY3NyMhx9+OO6///4j/74f9b8PwvEJcpIZ3MfpHD/r9S5fvpzut7y8fORw/bg+8pGPxIc//OETHWN9fd2MdMbWZz7zmVGXAJnnPITRcg7C6DkPh+P++++PBx54IO6/X5wDjF6v14s/+IM/iD/4gz+If/2v//WxjnHr1q0hV3U0fptOqLMKjs9qvW63G/V6PSIiKpVKLC4unup6AAAAMGm+9a1vxbe+9a1RlwFwzxCOT5DBsSVH7cw+Trh9VuslSRLlcjkitoPxlZWVI601DmZmZuKxxx4bdRmQun37dtqd8453vCPOnTs34ooge5yHMFrOQRg95yGMnvOQcTeYP46CcHyCHDXgHhzzcZw32lmtNzs7G91uNxYWFg68YOdp+MAHPhBzc3NH2ufmzZvx5JNPpt+fO3fOPzCMLe9PGD3nIYyWcxBGz3kIo+c8ZByN+j0pHJ8g09PT6e2jzrc+Tuf4WaxXLpej0+nEyspKVCqVI60xLA8//HA8/PDDI1kbAAAAABgN4fgEuXjxYnr7MGNOut1uREQUCoWxXK9cLsfa2lqsr69HsVg8Vo0AAAAAAMdx36gL4PAGA+SNjY0Dt+8H2qVSaezWm5ubi263u2cw3ul0jjzqBAAAAADgsITjE6Y/emRtbW3f7TqdTnq7Wq2O1XqDwfheXebtdnvkA/kBAAAAgHuXcHzCLC0tRcR2GN0fY7Kbq1evRsR29/deI0uSJIlqtRq1Wu1M1ovYDsaTJIn19fV955Kvrq7umHkOAAAAADBMZo5PmGKxGIuLi1Gv16Narcbq6upd23S73ajX65HP5+P69et7Hmt2dnZHx/fy8vKprletVqPVakWpVIpyubzrNpubm5EkSXS73X1DewAAAACAkxCOT6B+iF2v16NcLkej0UjHk7RarZifn49CoRArKyv7dmcPXmRzv67wYaxXrVaj2WxGxPbIlMMYvCAoAAAAAMAwGasyoZaXl9OZ3eVyOaampmJqaiouX74cS0tLsbGxse94k4hIQ+5CobBr1/iw1ut0OmkwfhT7BfsAAAAAACehc3yCFYvFaDQax96/VCrFxsbGqa9XLBaj1+sdeT8AAAAAgNOicxwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5ox1OP7QQw/Fz/3cz8XLL7886lIAAAAAALiHjHU4vrW1FYuLizE1NRU/9mM/Fp/97GdHXRIAAAAAAPeAsQ7HIyJ6vV70er1YWVmJmZmZuHTpUnz84x8fdVkAAAAAAEywsQ7Ht7a24plnnolCoZCG5J1OJyqVSjz00EPxMz/zM0auAAAAAABwZGMdjp8/fz4WFxfj5s2b8dxzz8Xs7Gwakm9tbcXy8rKRKwAAAAAAHNlYh+ODSqVSrK6uxtbWVnzwgx+MfD5/18iVt771rfHRj3501KUCAAAAADDmJiYc7zt//nwsLy/H5uZmrK6uxvve9740JL9582YsLCykI1deeOGFUZcLAAAAAMAYmrhwfNDs7GysrKzEa6+9Fqurq7G4uBhPPPFEOnJleno63vve98anPvWpUZcKAAAAAMAYmehwvO/ll1+OW7duxfPPPx/PP/985HK5tJv8ueeei1KpZOQKAAAAAACpiQ7HP/nJT8Z73vOemJqaimq1Gu12O3q9Xjp6ZWNjI5555pl48MEH05Erb33rW+Nzn/vcqEsHAAAAAGCEJi4cf/nll+Nv/+2/HQ899FCUy+U0EO/1evHEE0/EyspKbG5uxgc/+MF45JFHYnFxMba2tuLatWvxfd/3fXHz5s0oFotGrQAAAAAAZNjEhOOf/exn4+mnn46pqamo1WqRJEkaipdKpVhdXY21tbV43/vet+v+lUolNjY20gt41mq1M34EAAAAAACMi7EPxz/5yU/GpUuXYmZmJlqtVhqI93q9NPB+7rnnYnZ29lDH+6t/9a9GRMT6+vpplg0AAAAAwBi7f9QF7Octb3lLdLvdiIjo9XrpzxcXF2NpaSnOnz9/5GMmSTKs8gAAAAAAmFBjHY5vbGykt/P5fCwtLcUHP/jBEx3zxo0bERFRKBROdBwAAAAAACbXWIfjERHFYjGWlpb2nCV+VA899FAsLi7GpUuXhnI8AAAAAAAmz1iH46urq4eeJX5YJ+08BwAAAABg8o31BTmHHYwDAAAAAEDEmIfjERHPPvtsvP/9749nn332wG1v3boVly5dire85S3x0Y9+9AyqAwAAAABgEo19OL64uBjNZjNWVlYO3PaRRx6J1157LTY2NuKZZ545g+oAAAAAAJhEYx2OX7lyJZIkiYiI5eXlQ+3T367b7cbHP/7x0yoNAAAAAIAJNtbheL9bvFAoxOOPP36ofUqlUnr7Yx/72GmUBQAAAADAhBvrcHxtbS1yuVwUi8Uj7VcsFqPX60Wn0zmlygAAAAAAmGRjHY73R6pcuHDhSPsVCoWI2B6tAgAAAAAAdxrrcPy4jhqmAwAAAACQLWMdjh+3A3xtbS0iIvL5/LBLAgAAAADgHjDW4Xh/dni73Y6vfOUrh9rnpZdeik6nE7lcLi5evHjKFQIAAAAAMInGOhx/6qmn0tuzs7OH2mdubi69Xa1Wh14TAAAAAACTb6zD8UqlEk888URERKyvr8d73/ve+NznPrfrti+88EK85z3viXa7HblcLgqFQvzoj/7oWZYLAAAAAMCEuH/UBRxkZWUlHn300cjlcrG6uhqrq6tRLBbj4sWLMT09HRsbG9HtdqPdbt+1HwAAAAAA7Gbsw/FCoRBra2sxOzsbL730UkREdDqd6HQ6O7br9Xrp7eeeey4ef/zxsywTAAAAAIAJMtZjVfqKxWK88MILMT8/HxHbQfidXxHbY1g2NjYOPZ8cAAAAAIBsGvvO8b7z589Ho9GIer0e165di/X19djc3IwLFy7EzMxMlEqleOSRR0ZdJgAAAAAAE2BiwvG+8+fPx/z8fNpFDgAAAAAARzURY1UAAAAAAGCYhOMAAAAAAGTOxI1VeeGFF6LT6Rxq20KhEI8//vjpFgQAAAAAwMSZiHD85Zdfjvn5+Wi1Wkfab2FhIX7hF37hlKoCAAAAAGBSjX04/vzzz0epVIokSaLX6426HAAAAAAA7gFjH47Pzc3F1tZW+n2hUIhCoRAREe12O3K5XMzOzkZERLfbjW63G7lcLkqlUpTL5ZHUDAAAAADAeBvrcPxv/a2/lYbds7Oz0Wg04pFHHknvv3DhQrz00kvx3HPPpT+r1+vxoQ99KPL5fPzoj/7oKMoGAAAAAGDM3TfqAvazuroaERHFYjGee+65HcF4xHY4HrE9k7xvcXEx8vl8tFqteOGFF86sVgAAAAAAJsdYh+Nra2uRy+Xi6aef3vX+fD4fEdvjVAY99dRTERHx3/13/92p1gcAAAAAwGQa63C8rx+C36k/e/zOcHx6ejp6vV587GMfO+3SAAAAAACYQGMdjvfD7/X19T3v7/V6cePGjV3vvzM0BwAAAACAiAkIx3u93p4hd7lcjoiIVqu14+f9sDxJklOtDwAAAACAyTTW4filS5ciIqLdbu96/+zsbERsd4h/7nOfi4iIl156KQ3L9xrHAgAAAABAto11OL6wsJDe/rmf+7ldt3nf+94XvV4vfuRHfiTe//73p6NYcrlclEqlM6kTAAAAAIDJMtbh+Pnz52N+fj56vV784i/+Yrz88st3bbO8vBwR2yNUms3mjlEq/fsAAAAAAGDQWIfjERGNRiNee+21+K3f+q148MEH77q/UCjEtWvXotfrpV/FYjHW1tbi+77v+86+YAAAAAAAxt79oy5gGCqVSmxtbUW73Y5CoRBPPPHEqEsCAAAAAGCM3RPheMT2CJb3ve99oy4DAAAAAIAJMNbh+NLSUkRETE9Px1/5K39lxNUAAAAAAHCvGOtwfGVlJW7duhX5fF44DgAAAADA0Iz1BTmLxWL0er1IkiRefvnlUZcDAAAAAMA9YqzD8Wq1mt5+5plnRlgJAAAAAAD3krEOx2dnZ+OJJ56IXq8Xy8vL8cUvfnHUJQEAAAAAcA8Y63A8YnvueEREr9eLYrEYn/rUp0ZcEQAAAAAAk26sL8gZEVEoFGJ9fT1mZ2dja2srSqVSVKvVqFQqUSgU4sKFC/vu/+CDD55RpQAAAAAATIqxDsefeuqpeP7553f8rNfrRaPRiEajceD+uVwuvvWtb51WeQAAAAAATKixDse73W5sbGxELpeLiEj/G7EdkgMAAAAAwHGM9czxqampyOfzcf78+bu+8vn8ob7uZd1uN6rVaszMzMTU1FRMTU3FzMxMNJvNsV7vrOsGAAAAALjTWIfjq6ursbm5eeyvL3/5y6N+CKemXq/H9PR0dLvduHLlSmxtbcXW1lYsLS1FrVZL7xu39c66bgAAAACA3Yx1OM7ums1m1Gq1qFQqsbq6GsViMb2vUqnE+vp6bG5uxszMTCRJMjbrnXXdAAAAAAB7EY5PmP5IkoiIlZWVXbcpFAqxsLAQSZLE/Pz8WKx31nUDAAAAAOxHOD5hlpeXIyKiVCrtu10/iG61Wifqwh7WemddNwAAAADAfoTjE6Z/0cpCobDvdoP3n+RCl8Na76zrBgAAAADYj3B8gnQ6nfT29PT0gdvn8/mIiLh69epI1zvrugEAAAAADnL/qAvYz3ve854T7V8ul+Onf/qnh1TN6K2traW3D+rA7m/T6XR2hNOjWO+s6wYAAAAAOMhYh+Orq6uRy+WOvF+v14tcLneoIHaSbGxsHHvfJEnSjuyzXu+s6z6qF198Mb70pS8daZ+bN2/u+P727dtx+/btYZYFJzL4fvTehNFwHsJoOQdh9JyHMHrOQ8bdqN+XYx2OR2wH3UfRD9OPut8kOOoFKi9cuJDe3tzcPHLIPKz1zrruo/rIRz4SH/7wh090jPX19djc3BxSRTBcn/nMZ0ZdAmSe8xBGyzkIo+c8hNFzHjKObt26NdL1xzocX1lZOfS2N27ciE6nE+12O3K5XKyursYjjzxyitWdvZOEr0cNqIe53lnXDQAAAABwkLEOx9/3vvcdedt6vR4f+tCHotlsxsc+9rHTKm3kTrub+rTWO+u6z8rMzEw89thjoy4DUrdv3067At7xjnfEuXPnRlwRZI/zEEbLOQij5zyE0XMeMu4GJ0iMwliH48exuLgYq6ursbKyEs1mMx588MFRlzQ0g2+Wo3ZUHyeUHtZ6Z133UX3gAx+Iubm5I+1z8+bNePLJJ9Pvz5075x8Yxpb3J4ye8xBGyzkIo+c8hNFzHjKORv2evOfC8YiIp556Kq5fvx5XrlyJn/qpnxp1OUNz1KB4cJzJcT6FGdZ6Z133UT388MPx8MMPn/o6AAAAAMD4uG/UBZyGixcvRkTEc889N+JKhmt6ejq9fdQ53sfpwB7WemddNwAAAADAQe7JcLxvbW1t1CUMVT/0jzjceJJutxsREYVCYaTrnXXdAAAAAAAHuSfD8X4oftT51uOuWCymtzc2Ng7cvv/4S6XSSNc767oBAAAAAA4y1uH4yy+/fOSvT37yk7G8vDzq0k9NpVKJiIO74judTnq7Wq2OfL2zrhsAAAAAYD9jHY7n8/mYmpo60le5XI5bt25FLpfb0bF8r1haWoqI7RC5P35kN1evXo2I7a7tvZ6HJEmiWq1GrVY79fWGWTcAAAAAwEmNdTje1+v1jvwVEfdkB3mxWIzFxcWI2LuzutvtRr1ej3w+H9evX9/zWLOzs9FsNqNer+8ZkA9rvWHWDQAAAABwUmMfjveD7sMqFApRKpXiueeeix/5kR85papGa3l5ORYXF6Pdbke5XN7Rid1qtWJmZiYKhUJcv3498vn8nscZnMm+Xzf3sNYb1nEAAAAAAE7q/lEXsJ/XXntt1CWMreXl5Xj66aej0WhEuVyOzc3NiNj+cGBpaSnt0t5Po9FIu7gP6rIfxnrDPA4AAAAAwEmMdTjO/orFYjQajWPvXyqVYmNj48zWG/ZxAAAAAACOa+zHqgAAAAAAwLAJxwEAAAAAyJyxD8efffbZeP/73x/PPvvsgdveunUrLl26FG95y1viox/96BlUBwAAAADAJBr7cHxxcTGazWasrKwcuO0jjzwSr732WmxsbMQzzzxzBtUBAAAAADCJxjocv3LlSiRJEhERy8vLh9qnv123242Pf/zjp1UaAAAAAAATbKzD8X63eKFQiMcff/xQ+5RKpfT2xz72sdMoCwAAAACACTfW4fja2lrkcrkoFotH2q9YLEav14tOp3NKlQEAAAAAMMnGOhzvj1S5cOHCkfYrFAoRsT1aBQAAAAAA7jTW4fhxHTVMBwAAAAAgW8Y6HD9uB/ja2lpEROTz+WGXBAAAAADAPWCsw/H+7PB2ux1f+cpXDrXPSy+9FJ1OJ3K5XFy8ePGUKwQAAAAAYBKNdTj+1FNPpbdnZ2cPtc/c3Fx6u1qtDr0mAAAAAAAm31iH45VKJZ544omIiFhfX4/3vve98bnPfW7XbV944YV4z3veE+12O3K5XBQKhfjRH/3RsywXAAAAAIAJcf+oCzjIyspKPProo5HL5WJ1dTVWV1ejWCzGxYsXY3p6OjY2NqLb7Ua73b5rPwAAAAAA2M3Yh+OFQiHW1tZidnY2XnrppYiI6HQ60el0dmzX6/XS288991w8/vjjZ1kmAAAAAAATZKzHqvQVi8V44YUXYn5+PiK2g/A7vyK2x7BsbGwcej45AAAAAADZNPad433nz5+PRqMR9Xo9rl27Fuvr67G5uRkXLlyImZmZKJVK8cgjj4y6TAAAAAAAJsDEhON958+fj/n5+bSLHAAAAAAAjmoixqoAAAAAAMAwjX04/uyzz8b73//+ePbZZw/c9tatW3Hp0qV4y1veEh/96EfPoDoAAAAAACbR2Ifji4uL0Ww2Y2Vl5cBtH3nkkXjttddiY2MjnnnmmTOoDgAAAACASTTW4fiVK1ciSZKIiFheXj7UPv3tut1ufPzjHz+t0gAAAAAAmGBjHY73u8ULhUI8/vjjh9qnVCqltz/2sY+dRlkAAAAAAEy4sQ7H19bWIpfLRbFYPNJ+xWIxer1edDqdU6oMAAAAAIBJNtbheH+kyoULF460X6FQiIjt0SoAAAAAAHCnsQ7Hj+uoYToAAAAAANky1uH4cTvA19bWIiIin88PuyQAAAAAAO4BYx2O92eHt9vt+MpXvnKofV566aXodDqRy+Xi4sWLp1whAAAAAACTaKzD8aeeeiq9PTs7e6h95ubm0tvVanXoNQEAAAAAMPnGOhyvVCrxxBNPRETE+vp6vPe9743Pfe5zu277wgsvxHve855ot9uRy+WiUCjEj/7oj55luQAAAAAATIj7R13AQVZWVuLRRx+NXC4Xq6ursbq6GsViMS5evBjT09OxsbER3W432u32XfsBAAAAAMBuxj4cLxQKsba2FrOzs/HSSy9FRESn04lOp7Nju16vl95+7rnn4vHHHz/LMgEAAAAAmCBjPValr1gsxgsvvBDz8/MRsR2E3/kVsT2GZWNj49DzyQEAAAAAyKax7xzvO3/+fDQajajX63Ht2rVYX1+Pzc3NuHDhQszMzESpVIpHHnlk1GUCAAAAADABJiYc7zt//nzMz8+nXeQAAAAAAHBUEzFWBQAAAAAAhumeDsc/+clPjroEAAAAAADG0D0Xjr/wwguxtLQUDz30ULz73e8edTkAAAAAAIyhiZs5vpdnn302Go1GdDqdiIjo9XqRy+VGXBUAAAAAAONoosPxz372s9FoNKLZbKY/E4oDAAAAAHCQiQvHX3755Wg2m9FoNKLb7UbE3YF4r9eLUqkU1Wp1VGUCAAAAADDGJiYc/+QnPxnLy8vRbrcjYjsAj4g0FO/1elEoFKJarcbCwkKcP39+ZLUCAAAAADDexjocf+GFF9KxKUmSRMS3u8T7ofj58+fjqaeeimq1Gk888cQIqwUAAAAAYFKMZTi+28U1I3Z2ife//+QnPxmPP/74SOoEAAAAAGAy3TfqAvo++9nPxvvf//44d+5cVKvV6HQ6aQje98gjj8Ty8nLcvHlzRFUCAAAAAHAvGGnn+F4X1xx0/vz5WFhYiGq1Go888sgoygQAAAAA4B4zsnD8Pe95T3pxzYidoXg+nzdHHAAAAACAUzOycHx1dfWun1UqlahWqzE7OzuCigAAAAAAyIqRjlXpX2BzeXk5fvqnf3qUpQAAAAAAkCEjvSBnf5RKrVaLH/qhH4qPfvSj8fLLL4+yJAAAAAAAMmBk4fjW1lY888wzcf78+ej1erG2thYLCwsxNTUV733ve+PjH//4qEoDAAAAAOAeN7Jw/Pz587G4uBibm5uxuroa73vf+6LX60Wv14vV1dWoVCpx7ty5ePrpp+O//+//+1GVCQAAAADAPWikY1X6ZmdnY2VlJba2tuIXf/EX44knnkiD8larFXNzc3Hu3Ln4wAc+EJ/61KdGXS4AAAAAABNuLMLxvvPnz8fCwkKsra3F+vp6zM/Pp2NXer1eNBqNKJVK8dBDD426VAAAAAAAJthYheODnnjiiWg0GrG5uRnXrl2LUqmUhuRbW1uRy+UiImJ+fj4++tGPjrhaAAAAAAAmydiG44MqlUo899xz6UU88/l8GpR3Op1YWFiIc+fOxY/92I/FJz/5yVGXCwAAAADAmJuIcLxvv4t49nq9WFlZiXK5HA899FD8zM/8THz2s58ddckAAAAAAIyhiQrHB915Ec9isbhj7Mry8nJcvHhx1GUCAAAAADCGJjYc79vtIp6DY1cAAAAAAOBOEx+OD7rzIp6zs7OjLgkAAAAAgDF0T4Xjg/oX8QQAAAAAgDvds+E4AAAAAADsRTgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHJ9Q3W43qtVqzMzMxNTUVExNTcXMzEw0m82xXq/VakW5XI6pqanI5XIxMzMTc3Nzp1Y3AAAAAMBuhOMTqF6vx/T0dHS73bhy5UpsbW3F1tZWLC0tRa1WS+8bp/WSJIlyuRyXL1+Oubm5WF9fj/X19Xj66aej3W5HtVqN6enp6HQ6Q6sbAAAAAGAv94+6AI6m2WxGrVaLSqUSKysrO+6rVCpRLBZjZmYmZmZm4tatW5HP58divdnZ2bh48WKsrq7u+HmxWIyFhYWYnZ2NTqcTMzMzsb6+HsVi8UR1AwAAAADsR+f4BOmPNomIu4LqvkKhEAsLC5EkSczPz4/FerVaLSIiGo3Grvfn8/m4cuVK+v3c3NxJygYAAAAAOJBwfIIsLy9HRESpVNp3u36g3Wq1IkmSka/XbDajUCjsO3qlWCym63S7XTPIAQAAAIBTJRyfIP3AuFAo7Lvd4P0nCZmHsV6SJJEkSbRarZient73OIOjVPbqVAcAAAAAGAbh+IQYvFDlQSFzRKSzv69evTrS9TY3N3d8v1/3+KVLl/bcDwAAAABgmITjE2JtbS29fVAn9+A2gyH3KNbrzyTP5/OxsLCw77EGg/MLFy4ctWQAAAAAgEO7f9QFcDgbGxvH3jdJkrSzexTrNRqNPS/GOejGjRvp7cERK6ftxRdfjC996UtH2ufmzZs7vr99+3bcvn17mGXBiQy+H703YTSchzBazkEYPechjJ7zkHE36velcHxCHPXCmoOd15ubm0cOx0exXrvdTr9fWlo60v4n8ZGPfCQ+/OEPn+gY6+vrRsEwtj7zmc+MugTIPOchjJZzEEbPeQij5zxkHN26dWuk6xurMiFOErweNegexXqXL19O91teXj5yuA4AAAAAcBQ6xyfQWQfHp71et9uNer0eERGVSiUWFxdPdb3TMDMzE4899tioy4DU7du3066Ad7zjHXHu3LkRVwTZ4zyE0XIOwug5D2H0nIeMu1Ffd1A4PiEG3yhH7cw+Trh9VuslSRLlcjkitoPxlZWVI601DB/4wAdibm7uSPvcvHkznnzyyfT7c+fO+QeGseX9CaPnPITRcg7C6DkPYfSch4yjUb8nheNDMD09Hd1ud6jHXF1djVKplH5/1IB7cCzKcT6BOav1Zmdno9vtxsLCwqEu2nkaHn744Xj44YdHsjYAAAAAMBrC8SFYXl6OGzduDO14Dz30UFy8eHHHz6anp9PbR50HfpzO8bNYr1wuR6fTiZWVlahUKkdaAwAAAADgJITjQ1CpVE493B0Myw8z5qTfyV4oFMZyvXK5HGtra7G+vh7FYvFYNQIAAAAAHNd9oy6AwxkMkDc2Ng7cvh9oD45mGZf15ubmotvt7hmMdzqdI88ABwAAAAA4CuH4BOl3p6+tre27XafTSW9Xq9WxWm8wGN+ry7zdbo/8SrUAAAAAwL1NOD5BlpaWImI7jN7vAqBXr16NiO3u771GliRJEtVqNWq12pmsF7EdjCdJEuvr6/vOJV9dXd0x8xwAAAAAYNjMHJ8gxWIxFhcXo16vR7VajdXV1bu26Xa7Ua/XI5/Px/Xr1/c81uzs7I6O7+Xl5VNdr1qtRqvVilKpFOVyeddtNjc3I0mS6Ha7+4b2AAAAAAAnJRyfMP0Qu16vR7lcjkajkY4nabVaMT8/H4VCIVZWVvbtzh68yOZ+XeHDWK9arUaz2YyI7ZEphzF4QVAAAAAAgGEzVmUCLS8vpzO7y+VyTE1NxdTUVFy+fDmWlpZiY2Nj3/EmEZGG3IVCYdeu8WGt1+l00mD8KPYL9gEAAAAATkrn+IQqFovRaDSOvX+pVIqNjY1TX69YLEav1zvyfgAAAAAAp0nnOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzh+ITqdrtRrVZjZmYmpqamYmpqKmZmZqLZbE7kes1mM6ampoZyLAAAAACAgwjHJ1C9Xo/p6enodrtx5cqV2Nraiq2trVhaWoparZbeNynr9YP3JEkiSZKh1Q0AAAAAsBfh+IRpNptRq9WiUqnE6upqFIvF9L5KpRLr6+uxubkZMzMzQwmaz2K9ubm5E9cJAAAAAHAUwvEJ0u+wjohYWVnZdZtCoRALCwuRJEnMz8+P/Xr1ej06nc6J6gQAAAAAOCrh+ARZXl6OiIhSqbTvdv1Au9Vqnah7/LTX63Q6cfny5XQdAAAAAICzIhyfIP2LXxYKhX23G7z/JBfMPO315ubmYmVlJfL5/LHqAwAAAAA4LuH4hBgcPTI9PX3g9v3A+erVq2O5Xq1Wi1KpdGBXOgAAAADAaRCOT4i1tbX09kGd3IPbHHee92mu1+l0otVqRaPROFZtAAAAAAAnJRyfEBsbG8fe9zhzx09zvf44FQAAAACAUbl/1AVwOEcNuC9cuJDe3tzcPPJc79Nar1qtRqVSiWKxeKTjn6YXX3wxvvSlLx1pn5s3b+74/vbt23H79u1hlgUnMvh+9N6E0XAewmg5B2H0nIcwes5Dxt2o35fC8Qmxubl57H2P0zl+Guu12+1ot9sn6ko/DR/5yEfiwx/+8ImOsb6+fqLnDE7TZz7zmVGXAJnnPITRcg7C6DkPYfSch4yjW7dujXR94fgEOmoX+DislyRJzM3NxfXr109eEAAAAADACQnHJ8Tg2JKjdoIfJ9we9nrz8/OxtLQ0VuNUhmlmZiYee+yxUZcBqdu3b6ddAe94xzvi3LlzI64Issd5CKPlHITRcx7C6DkPGXeDGeQoCMeHYHp6Orrd7lCPubq6GqVSKf3+qAH34IiP47zJhrleq9WKbrc7thfh/MAHPhBzc3NH2ufmzZvx5JNPpt+fO3fOPzCMLe9PGD3nIYyWcxBGz3kIo+c8ZByN+j0pHB+C5eXluHHjxtCO99BDD8XFixd3/Gx6ejq9fdTZ1sfpHB/WekmSxPz8fKyvrx+5hrPy8MMPx8MPPzzqMgAAAACAMyQcH4JKpRKVSuVU1xgMyw8z5qTfyV4oFEa63tzcXCwsLMSFCxf2PM7gzweD+LOerQ4AAAAAZIdwfEIMzure2Ng4cPt+4Dw4mmUU67Xb7Wi321Gv1w+17mDH+sLCQjQajUPtBwAAAABwFPeNugAOr9+dvra2tu92nU4nvV2tVke6Xq/XO/BrsOt+a2sr/blgHAAAAAA4LcLxCbK0tBQR22H0fhcAvXr1akRsd38PdoAPSpIkqtVq1Gq1M1kPAAAAAGCcCMcnSLFYjMXFxYjYuyO82+1GvV6PfD4f169f3/NYs7Oz0Ww2o16v7xmQD3M9AAAAAIBxIhyfMMvLy7G4uBjtdjvK5fKOju5WqxUzMzNRKBTi+vXr+17QcvAimPt1hQ9rvd3W73a76UzyvmazGd1u91AXAQUAAAAAOC7h+ARaXl6O9fX1KBQKUS6XY2pqKqampuLy5cuxtLQUGxsbB443aTQaUSgUolAoxPLy8qmvd6epqamYnp6Oubm5iIjI5/ORz+fj8uXLMT09HVNTU4e+iCcAAAAAwFHdP+oCOJ5isXiiC1aWSqXY2Ng4s/Xu1Ov1hnYsAAAAAICj0jkOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGO///au3vlNM63D8C33mQm7aJMyjTIXTpI6hSGOilgfARGZ4BGVUqPdAboDBxUpAcXqS04AkMK92ZbF5l9i4wYyX99W3w8PNc1owkgtM+d4je7+/OyAAAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZ+XbTA0CKPn/+fO35hw8fNjQJ3Ozff/+Nf/75JyIi9vf345tvvtnwRJAfOYTNkkHYPDmEzZNDtt2XndqXnduqKcfhCT5+/Hjt+e+//76ZQQAAAABgR3z8+DEajcba1nNbFQAAAAAAsqMcBwAAAAAgO3tVVVWbHgJSU5Zl/P3338vnP/74Y3z33XcbnAiu+/Dhw7Xb/fz111/x4sWLzQ0EGZJD2CwZhM2TQ9g8OWTbff78+drti3/99dcoimJt67vnODxBURTx22+/bXoMeLAXL17ETz/9tOkxIGtyCJslg7B5cgibJ4dso3XeY/xLbqsCAAAAAEB2lOMAAAAAAGRHOQ4AAAAAQHaU4wAAAAAAZEc5DgAAAABAdpTjAAAAAABkRzkOAAAAAEB2lOMAAAAAAGRHOQ4AAAAAQHaU4wAAAAAAZEc5DgAAAABAdr7d9AAAPL8ffvgh/vjjj2vPgfWSQ9gsGYTNk0PYPDmEu+1VVVVteggAAAAAAFgnt1UBAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcB9hy8/k8Dg8Po9lsRq1Wi1qtFs1mM87OzpJc7+zsLGq12rNsC9Yl1Ryen59Hu92OWq0We3t70Ww2o9vtrmxueKhUM7XuuWGVUs2hfRu7ItUM3sZ5HsmqANhaJycnVURUrVarmkwmy9eHw2FVFEVVr9er2WyWzHqz2ayKiCoiqsVi8QwTw+qlmMPFYlG1Wq2q0WhUg8Ggms1m1WQyqU5OTqqiKKqIqOr1+rXtw7qkmKlNzA2rlGIO7dvYJSlm8C7O80iZchxgSw0Ggyoiqk6nc+PvZ7NZVRRFVRTFsxyArGO9RqPhoImkpJrDRqNR9Xq9G3+3WCyuZVGJwDqlmql1zw2rlGoO7dvYFalm8C7O80iZchxgC139l/e79Pv9Ow90tmm9y6sVHDSRilRz2O/3q0ajcec2JpPJcq16vf7kmeExUs3UuueGVUo1h/Zt7IpUM3gX53mkTjkOsIV6vd7yY293ea6Pr616vclkUhVFce3AyUET2y7VHBZFUXU6nXs/GttqtZbbGQwGT54bHirVTK17blilVHNo38auSDWDt3Gexy7whZwAW+jyS1Hq9fqd77v6+6/5IpVVr9ftdmM4HEZRFE+aDzYhxRyWZRllWcb5+XkcHBzcuZ1Go7F8PBwOHzsuPFqKmXrO7cA2SDGH9m3skhQzeBfneewC5TjAlplOp8vH950ARMTyQOTt27dbud7R0VG0Wq1otVpPmg82IdUcfvr06drz+Xx+6zZ++eWXW/8OnluqmVr33LBKqebQvo1dkWoGb+M8j12hHAfYMhcXF8vH9/0L/9X3XD342Zb1ptNpnJ+fx2AweNJssCmp5rBer0ev14uiKKLX6925ravlwv7+/mNHhkdJNVPrnhtWKdUc2rexK1LN4E2c57FLlOMAW2Y2mz35b8uy3Kr1Lj9mB6lJOYeDwSAWi8W9Jyvv379fPr76MXRYhVQzte65YZVSzWGEfRu7IeUMfsl5HrtEOQ6wZR574HP1qpinfHx0VesdHh5Gp9NxYkKSdiWHd603Ho+Xz4+Pjx+9DXiMVDO17rlhlVLN4WPWs29jm+1KBp3nsWu+3fQAAFz3NSfTT7miYBXrjcfjGI/HX3W1AmzSLuTwLm/evFn+3cnJiS9RYuVSzdS654ZVSjWHD2XfxrbbhQw6z2MXKccBtti6D+qfY72yLKPb7ca7d+++fiDYAinm8C7z+TxOT08jIqLT6US/31/pevClVDOlaGOXpJrD29i3kZoUM+g8j13ltioAW+bqx9kee4XAUw56nnu9169fx/HxsY/ZkbTUc3ibsiyj3W5HxH/lgXtFsi6pZmrdc8MqpZrD+9i3kYrUM+g8j12lHAd4hIODg9jb23vWn6v3Rox4/IHP1Y/LXT0AeqjnXO/8/Dzm87mrdVgpOXz6ei9fvoz5fB69Xk95wFqlmql1zw2rlGoO72PfRipSzqDzPHaZ26oAPMLJyUm8f//+2bb3/fffx88//3zttYODg+Xjx94n7ilXFDzXemVZxuvXr2MymTx6BngMOXzaeu12O6bTaQyHw+h0Oo9aA75Wqpla99ywSqnm8C72baQk1Qw6z2PXKccBHqHT6az8wPtqSfeQj7/N5/OIiKjX6xtdr9vtRq/Xi/39/Vu3c9uXnCkQeAw5fPx67XY7Li4uYjKZ+CgsG5FqptY9N6xSqjm8jX0bqUk1g87z2HVuqwKwZa4e3D/kW8AvD0RardZG1xuPx3F6ehq1Wu3Wn6Ojo+X7Dw4Olq8fHh4+aXZYlVRzeJNutxvz+fzW8mA6nUa32334sPAEqWZq3XPDKqWaw5vYt5GiVDPoPI9dpxwH2EKXV8VeXFzc+b7pdLp8/DUHHs+xXlVV9/5cvdp3sVgsXx8MBk+eHVYlxRx+6Wp5cNtVR+Px2L2RWYtUM7XuuWGVUs3hVfZtpCzFDDrPY9cpxwG20PHxcUT8d5By+fG2m7x9+zYi/rsq4LaPk5ZlGYeHh9f+NX+V68GuSD2H3W43yrKMyWRy50daR6PRtXtSwqqkmin7SHZJqjm8ZN9G6lLPIOykCoCt1O/3q4ioWq3Wjb+fzWZVRFRFUVSLxeLW7TQajSoiqoio+v3+yte7S6fTWc7y1G3AOqWaw16vt9zObT+NRqOq1+tVRFSj0ejWbcFzSjVT69hHwrqkmkP7NnZFqhm8i/M8UqYcB9hiVw9kZrPZ8vXhcFgVRVHV6/VqMpncuY3LE4SIqDqdzsrX+9Jisahms1k1Go2qoiiWs5ycnFSz2czBE1svtRxelgeP+ZFD1im1TD33dmAbpJZD+zZ2TWoZvInzPHbFXlVV1YMvMwdg7abTaQwGgxiPx8tv/q7X6/Hq1avo9/v3/v14PF7eN240Gt37bedfu96X9vb2IuLmbyq//NKXk5OTJ20b1iWVHE6n02g2mw/931pyOMi6pZKpVW0HtkEqObRvY1elksHbOM9jVyjHAQAAAADIji/kBAAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAHbG6elptNvtqNVqsbe3F3t7e1Gr1aLdbsfR0VHM5/NNjwgAwJbYq6qq2vQQAAAAX+P8/Dxev34dZVne+95WqxVHR0fRarVWPxgAAFvr200PAAAA8DXa7XaMx+Nrr9Xr9ajX6xER8enTp5hOp8vfXb5XOQ4AkDe3VQEAAJLV7XavFeP9fj+qqorZbBaj0ShGo1FMJpOoqiqGw2E0Go2IiOV/AQDIl9uqAAAASZpOp9FsNpfPR6PRg64GPzs7i3q97spxAIDMua0KAACQpMFgsHzc6/UeXHb3er1VjQQAQELcVgUAAEjSxcXF8nG73d7gJAAApEg5DgAAJKksy+Xj+Xy+uUEAAEiSchwAAEjS1S/VfPPmzbWyHAAA7qMcBwAAknR4eLh8XJZlNJtNV5ADAPBge1VVVZseAgAA4Cna7XaMx+Nrr7VarWi329Fqta5dXQ4AAFcpxwEAgGSVZRkvX76M6XR663sajUa8evUqer1eFEWxvuEAANhqynEAACB5R0dHcXp6eu/7+v1+nJycrGEiAAC2nXIcAADYCWVZxp9//hmj0SjOz89vfV+j0Yh37965ihwAIHPKcQAAYCdNp9MYj8cxGo3+577knU4nhsPhhiYDAGAbKMcBAICdN5/Po91ux3w+X742HA6j0+lscCoAADZJOQ4AAGTj4OBgWZA3Go2YTCYbnggAgE35v00PAAAAsC5Xv4xzOp1ucBIAADbNleMAAEA2yrKMWq22fL5YLHwxJwBAplw5DgAAZOPqPccjQjEOAJAx5TgAAJCk8Xj86L95+/bt8nGr1XrOcQAASIxyHAAASFK3241ms/ngknw6ncbp6eny+eHh4apGAwAgAcpxAAAgOWVZRlmWMZ1Oo91uR7PZjLOzs/+5bcrle4+OjqLZbC5fa7Va0el01jkyAABbxhdyAgAAyZnP59Fut28sw4uiiP39/SiKIubzeZRlee33jUYjJpPJmiYFAGBbKccBAIBknZ+fx9HR0Y0l+U0Gg0H0er0VTwUAQAqU4wAAQPLm83mcn5/H+/fvYzqdLsvyoiiiXq9Hq9WK4+PjKIpis4MCALA1lOMAAAAAAGTHF3ICAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZ+X9ar0YoRGEogwAAAABJRU5ErkJggg==", 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" ] @@ -349,9 +376,17 @@ "id": "9186101e", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_14042/3610192424.py:36: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " plt.legend(loc=\"best\")\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/src/data.py b/src/data.py index c480e88..be4bcf9 100644 --- a/src/data.py +++ b/src/data.py @@ -38,7 +38,7 @@ def generate_naivebayes(config): bn = gum.fastBN(bn_str) save_bn(bn, f"exp{i}", bns_path / "gt") - with open(f'{out_path}/{config["exp_meta"]}', "a") as m: + with open(f'{out_path}/{config["exp_meta"]}', "w") as m: m.write( f'- exp{i}. Naive Bayes: {config["n_nodes"]} nodes. Complexity: {bn.dim()} Max categories: {n_modmax}\n' ) @@ -96,7 +96,7 @@ def generate_randombn(config): bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=config["n_modmax"]) save_bn(bn, f"exp{i}", bns_path / "gt") - with open(f'{out_path}/{config["exp_meta"]}', "a") as m: + with open(f'{out_path}/{config["exp_meta"]}', "w") as m: m.write( f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()}\n" ) From 39f91bc3543b9df8c81d4c6f93ad66913f7e637b Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Fri, 14 Nov 2025 16:43:07 +0100 Subject: [PATCH 13/31] Add `def-ran` as defense mechanism --- experiments/cn_privacy/config.yaml | 3 ++ experiments/cn_privacy/config_BAK.yaml | 3 ++ experiments/cn_vs_noisybn/config.yaml | 3 ++ experiments/cn_vs_noisybn/config_BAK.yaml | 3 ++ src/defenses.py | 41 ++++++++++++++++++++++- src/inference.py | 11 ++++-- src/mia.py | 4 +-- test/cn_privacy/config.yaml | 5 ++- test/cn_vs_noisybn/config.yaml | 5 ++- 9 files changed, 71 insertions(+), 7 deletions(-) diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 14cfc94..9f1c2e9 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -28,6 +28,9 @@ error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM ess: 1 # ESS: the amount of injected uncertainty +# DEF-RAN +delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) + # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index e55e681..01e3205 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -28,6 +28,9 @@ error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector # DEF-IDM ess: 1 # ESS: the amount of injected uncertainty +# DEF-RAN +delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) + # ATK-MLE n_bns: 500 # Number of BNs to sample within the CN diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 8280a4d..6de3bd7 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -35,6 +35,9 @@ eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for no # DEF-IDM ess: 1 # ESS: the amount of injected uncertainty +# DEF-RAN +delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) + # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index 80836f2..b28fe9c 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -35,6 +35,9 @@ eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for no # DEF-IDM ess: 1 # ESS: the amount of injected uncertainty +# DEF-RAN +delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) + # ATK-MLE n_bns: 50 # Number of BNs to sample within the CN diff --git a/src/defenses.py b/src/defenses.py index 442483e..91796f1 100644 --- a/src/defenses.py +++ b/src/defenses.py @@ -1,6 +1,7 @@ +import numpy as np import pyagrum as gum -from src.utils import add_counts_to_bn +from src.utils import add_counts_to_bn, check_consistency, safe_assert # Estimate a CN from data by local IDM @@ -11,3 +12,41 @@ def def_idm(bn, ess, data): cn.idmLearning(ess) return cn + +# Build a CN by bloating each BN parameter with a fixed-size random interval +def def_ran(bn, delta): + + # Initialize the extreme BNs + bn_min = gum.BayesNet(bn) + bn_max = gum.BayesNet(bn) + + # For each node ... + for n in bn.nodes(): + + # ... get the CPT, ... + cpt = bn.cpt(n).toarray() + + # ... get a matrix of eta's, ... + eta = np.random.uniform(0, delta, cpt.size).reshape(cpt.shape) + + # ... perturb the CPT, ... + cpt_min = np.minimum(1-delta, np.maximum(0, cpt-eta)) + cpt_max = np.minimum(1, np.maximum(delta, cpt - eta + delta)) + + # ... and store it into the extreme BNs + bn_min.cpt(n).fillWith(cpt_min.flatten()) + bn_max.cpt(n).fillWith(cpt_max.flatten()) + + # Debug + safe_assert(np.all(cpt_min <= cpt)) + safe_assert(np.all(cpt_max >= cpt)) + safe_assert(np.all(np.abs(cpt_max-cpt_min-delta)<1e-6)) + + # Build the CN from the extreme BNs + cn = gum.CredalNet(bn_min, bn_max) + cn.intervalToCredal() + + # Debug + safe_assert(check_consistency(bn, bn_min, bn_max)) + + return cn diff --git a/src/inference.py b/src/inference.py index 133d17e..7c608d1 100644 --- a/src/inference.py +++ b/src/inference.py @@ -1,3 +1,4 @@ +import inspect import math import numpy as np @@ -39,8 +40,14 @@ def run_inferences(exp, config): bn = learn_bn_params(gt, gpop) # Learn CN from gpop (defense mechanism) #TODO: save results - def_mec_fn = getattr(src.defenses, def_mec) - cn = def_mec_fn(bn, config["ess"], gpop) + def_mec_fn = getattr(src.defenses, def_mec) # Get the related function + sig = inspect.signature(def_mec_fn) # Get its signature + args = { + k: v + for k, v in {"bn": bn, "ess": config["ess"], "delta": config["delta"], "data": gpop}.items() + if k in sig.parameters + } + cn = def_mec_fn(**args) # Keep only `def_mec`` args # Learn noisy BN from gpop #TODO: save results scale = (2 * bn.size()) / (len(gpop) * eps) diff --git a/src/mia.py b/src/mia.py index da0ad9c..ab04c40 100644 --- a/src/mia.py +++ b/src/mia.py @@ -233,9 +233,9 @@ def phase_defense_mechanism(def_mec, exp, config) -> None: sig = inspect.signature(def_mec_fn) # Get its signature args = { k: v - for k, v in {"bn": bn, "ess": config["ess"], "data": pool}.items() + for k, v in {"bn": bn, "ess": config["ess"], "delta": config["delta"],"data": pool}.items() if k in sig.parameters - } + } cn = def_mec_fn(**args) # Keep only `def_mec`` args base_path = out_path / config["cns_path"] cn.saveBNsMinMax( diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index 6e584d6..0e493ec 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -21,13 +21,16 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_idm' # Defense mechanisms to consider +def_mec: 'def_ran' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # DEF-IDM ess: 1 # ESS: the amount of injected uncertainty +# DEF-RAN +delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) + # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index 3f32819..1110645 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -24,7 +24,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_idm' # Defense mechanisms to consider +def_mec: 'def_ran' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector @@ -35,6 +35,9 @@ eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for no # DEF-IDM ess: 1 # ESS: the amount of injected uncertainty +# DEF-RAN +delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) + # ATK-MLE n_bns: 5 # Number of BNs to sample within the CN From c308982934e8b055963ec983340a7af0cf41ab2b Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Fri, 14 Nov 2025 17:41:35 +0100 Subject: [PATCH 14/31] Code refactoring --- experiments/cn_privacy/Plot_results.ipynb | 35 +- experiments/cn_privacy/config.yaml | 2 +- experiments/cn_privacy/config_BAK.yaml | 2 +- experiments/cn_privacy/exp.py | 32 +- experiments/cn_vs_noisybn/Plot_results.ipynb | 12 +- experiments/cn_vs_noisybn/config.yaml | 2 +- experiments/cn_vs_noisybn/config_BAK.yaml | 2 +- experiments/cn_vs_noisybn/exp.py | 35 +- src/attack.py | 87 ++++ src/attacks.py | 41 -- src/config.py | 29 +- src/data.py | 15 +- src/defense.py | 134 ++++++ src/defenses.py | 52 --- src/inference.py | 28 +- src/learning.py | 63 +++ src/mia.py | 439 +++++++------------ src/utils.py | 247 ++++------- 18 files changed, 620 insertions(+), 637 deletions(-) create mode 100644 src/attack.py delete mode 100644 src/attacks.py create mode 100644 src/defense.py delete mode 100644 src/defenses.py create mode 100644 src/learning.py diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index b956d2a..5ec788b 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "52eff632", "metadata": {}, "outputs": [], @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "c55e8958", "metadata": {}, "outputs": [ @@ -86,19 +86,19 @@ "['bn_exp1.csv', 'bn_exp3.csv', 'bn_exp2.csv', 'bn_exp0.csv']" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "files = os.listdir(res_path/'bns')\n", + "files = os.listdir(res_path / \"bns\")\n", "files" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "3bc7788b", "metadata": {}, "outputs": [], @@ -107,7 +107,7 @@ "def plot_bn_bound(exp: str, ax):\n", "\n", " # Import results\n", - " files = os.listdir(res_path/'bns')\n", + " files = os.listdir(res_path / \"bns\")\n", " r_path = [r for r in files if f\"{exp}\" in r][0]\n", " res = pd.read_csv(f\"{res_path}/bns/{r_path}\")\n", " bound = res[\"power_bound\"]\n", @@ -152,18 +152,23 @@ "def plot_cn(exp, ax, color: str, type: str):\n", "\n", " # Import results\n", - " files = os.listdir(res_path/'cns')\n", + " files = os.listdir(res_path / \"cns\")\n", " r_path = [r for r in files if f\"{exp}\" in r][0]\n", " res = pd.read_csv(f\"{res_path}/cns/{r_path}\")\n", "\n", - " # Select what to plot \n", + " # Select what to plot\n", " cn_cols = [c for c in res.columns if \"CN\" in c]\n", " cn_mean = res.loc[:, cn_cols].mean(axis=1)\n", " cn_max = res.loc[:, cn_cols].max(axis=1)\n", "\n", " # Plot CN (avg-max)\n", " ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n", - " ax.semilogx(error, cn_mean, type, color=color, label=f'CN, $S={config[\"ess\"]}$', zorder=3)\n", + " label = (\n", + " f'CN, $S={config[\"ess\"]}$'\n", + " if config[\"def_mec\"] == \"def_idm\"\n", + " else f'CN, $delta={config[\"delta\"]}$'\n", + " )\n", + " ax.semilogx(error, cn_mean, type, color=color, label=label, zorder=3)\n", "\n", " # Legend\n", " if exp == \"exp0\":\n", @@ -191,13 +196,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "ade37b54", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -208,10 +213,10 @@ ], "source": [ "# Names of experiments\n", - "exp_names = [re.findall('bn_(\\w+\\d+)\\.csv', r)[0] for r in os.listdir(res_path/'bns')]\n", + "exp_names = [re.findall(\"bn_(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(res_path / \"bns\")]\n", "\n", "# Layout 4x3\n", - "fig, axes = plt.subplots(len(exp_names)//3+1, 3)\n", + "fig, axes = plt.subplots(len(exp_names) // 3 + 1, 3)\n", "# fig.suptitle(\"Power vs Error\", fontsize=18)\n", "\n", "# Loop over results\n", diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 9f1c2e9..290dac4 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -21,7 +21,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_idm' # Defense mechanisms to consider +def_mec: 'def_ran' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index 01e3205..197880e 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -21,7 +21,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 20 # Number of data samples # MIA -def_mec: 'def_idm' # Defense mechanisms to consider +def_mec: 'def_ran' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 638573a..2d1906d 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -1,20 +1,16 @@ import gc import multiprocessing # noqa: F401 # pylint: disable=unused-import -from itertools import product import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import create_clean_dir, get_out_path, load_config, set_global_seed +from src.attack import attack_mechanism +from src.config import (create_clean_dir, get_out_path, load_config, + set_global_seed) from src.data import generate_randombn -from src.mia import ( - phase_attack_mechanism, - phase_defense_mechanism, - phase_estimation, - phase_mia_vs_bn, - phase_mia_vs_cn, - phase_theoretical_power, -) +from src.defense import defense_mechanism +from src.learning import estimate_bns +from src.mia import mia_vs_bn, mia_vs_cn, theoretical_power def main(): @@ -31,6 +27,7 @@ def main(): print("#" * 5, "Generate BNs and data", "#" * 5) create_clean_dir(out_path / config["bns_path"] / "gt") create_clean_dir(out_path / config["data_path"]) + open(f'{out_path}/{config["exp_meta"]}', "a").close() generate_randombn(config) # Init the vectors of experiments @@ -43,44 +40,41 @@ def main(): create_clean_dir(out_path / config["bns_path"] / "rpop") create_clean_dir(out_path / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( - delayed(phase_estimation)(exp, config) for exp in exp_vec + delayed(estimate_bns)(exp, config) for exp in exp_vec ) # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_defense_mechanism)(def_mec, exp, config) - for exp in exp_vec + delayed(defense_mechanism)(def_mec, exp, config) for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_attack_mechanism)(atk_mec, exp, config) - for exp in exp_vec + delayed(attack_mechanism)(atk_mec, exp, config) for exp in exp_vec ) # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) create_clean_dir(out_path / config["results_path"] / "cns") _ = Parallel(n_jobs=num_cores)( - delayed(phase_mia_vs_cn)(exp, config) - for exp in exp_vec + delayed(mia_vs_cn)(exp, config) for exp in exp_vec ) # MIA vs BN print("#" * 5, "MIA vs BN", "#" * 5) create_clean_dir(out_path / config["results_path"] / "bns") _ = Parallel(n_jobs=num_cores)( - delayed(phase_mia_vs_bn)(exp, config) for exp in exp_vec + delayed(mia_vs_bn)(exp, config) for exp in exp_vec ) # Compute theoretical power print("#" * 5, "Compute theoretical power", "#" * 5) _ = Parallel(n_jobs=num_cores)( - delayed(phase_theoretical_power)(exp, config) for exp in exp_vec + delayed(theoretical_power)(exp, config) for exp in exp_vec ) # Clean diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index ececb50..49dd048 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -42,7 +42,7 @@ "\n", "# Get results path\n", "out_path = get_out_path(config)\n", - "res_path = out_path / config[\"results_path\"] / 'inferences'\n", + "res_path = out_path / config[\"results_path\"] / \"inferences\"\n", "\n", "# Choose where to save plots\n", "plots_path = out_path / \"plots\"\n", @@ -103,7 +103,7 @@ "outputs": [], "source": [ "# Names of experiments\n", - "exp_names = [re.findall('bn_(\\w+\\d+)\\.csv', r)[0] for r in os.listdir(res_path/'bns')]\n", + "exp_names = [re.findall(\"bn_(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(res_path / \"bns\")]\n", "\n", "# Initialize results\n", "res = {\n", @@ -148,9 +148,7 @@ "# Store avg of eps and std\n", "with open(dir + \"/exp_meta.txt\", \"r\") as f:\n", " eps_vec = [\n", - " float(re.search(\"Eps: (.+)\\n\", line).group(1))\n", - " for line in f\n", - " if \"Eps: \" in line\n", + " float(re.search(\"Eps: (.+)\\n\", line).group(1)) for line in f if \"Eps: \" in line\n", " ]\n", "eps = (float(np.mean(eps_vec)), float(np.std(eps_vec)))\n", "\n", @@ -164,9 +162,7 @@ " get_acc_bn(data_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_cert) > 0 else None\n", ")\n", "acc_cn_uncert = (\n", - " get_acc_bn(data_uncert, f\"{vs}_mpes\", \"cn_mpes\")\n", - " if len(data_uncert) > 0\n", - " else None\n", + " get_acc_bn(data_uncert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_uncert) > 0 else None\n", ")\n", "acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n", "acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n", diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 6de3bd7..e12d866 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -24,7 +24,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_idm' # Defense mechanisms to consider +def_mec: 'def_ran' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index b28fe9c..b8f0ca6 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -24,7 +24,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 30 # Number of data samples # MIA -def_mec: 'def_idm' # Defense mechanisms to consider +def_mec: 'def_ran' # Defense mechanisms to consider atk_mec: 'atk_mle' # Attack mechanisms to consider tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 109903e..5794061 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -1,21 +1,18 @@ import gc import multiprocessing # noqa: F401 # pylint: disable=unused-import -from itertools import product import numpy as np # noqa: F401 # pylint: disable=unused-import import pandas as pd from joblib import Parallel, delayed -from src.config import create_clean_dir, get_out_path, load_config, set_global_seed +from src.attack import attack_mechanism +from src.config import (create_clean_dir, get_out_path, load_config, + set_global_seed) from src.data import generate_naivebayes -from src.inference import run_inferences -from src.mia import ( - phase_attack_mechanism, - phase_defense_mechanism, - phase_estimation, - phase_find_eps, - phase_mia_vs_cn, -) +from src.defense import defense_mechanism +from src.inference import inferences +from src.learning import estimate_bns +from src.mia import find_epsilon, mia_vs_cn def main(): @@ -32,6 +29,7 @@ def main(): print("#" * 5, "Generate BNs and data", "#" * 5) create_clean_dir(out_path / config["bns_path"] / "gt") create_clean_dir(out_path / config["data_path"]) + open(f'{out_path}/{config["exp_meta"]}', "a").close() generate_naivebayes(config) # Init the vectors of experiments @@ -44,30 +42,27 @@ def main(): create_clean_dir(out_path / config["bns_path"] / "rpop") create_clean_dir(out_path / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( - delayed(phase_estimation)(exp, config) for exp in exp_vec + delayed(estimate_bns)(exp, config) for exp in exp_vec ) # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_defense_mechanism)(def_mec, exp, config) - for exp in exp_vec + delayed(defense_mechanism)(def_mec, exp, config) for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(phase_attack_mechanism)(atk_mec, exp, config) - for exp in exp_vec + delayed(attack_mechanism)(atk_mec, exp, config) for exp in exp_vec ) # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) res = Parallel(n_jobs=num_cores)( - delayed(phase_mia_vs_cn)(exp, config, save_res=False) - for exp in exp_vec + delayed(mia_vs_cn)(exp, config, save_res=False) for exp in exp_vec ) res = pd.DataFrame(res) res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) @@ -76,8 +71,7 @@ def main(): print("#" * 5, "Get epsilon", "#" * 5) create_clean_dir(out_path / config["noisy_path"]) res = Parallel(n_jobs=num_cores)( - delayed(phase_find_eps)(exp, config) - for exp in exp_vec + delayed(find_epsilon)(exp, config) for exp in exp_vec ) res = pd.DataFrame(res) res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) @@ -86,8 +80,7 @@ def main(): print("#" * 5, "Run inferences", "#" * 5) create_clean_dir(out_path / config["results_path"] / "inferences") _ = Parallel(n_jobs=num_cores)( - delayed(run_inferences)(exp, config) - for exp in exp_vec + delayed(inferences)(exp, config) for exp in exp_vec ) # Clean diff --git a/src/attack.py b/src/attack.py new file mode 100644 index 0000000..45a10e8 --- /dev/null +++ b/src/attack.py @@ -0,0 +1,87 @@ +import inspect + +import numpy as np +import pandas as pd +import pyagrum as gum + +from src.config import get_out_path +from src.mia import get_ll +from src.utils import sample_from_cn + + +# Apply attack mechanism to a BN, namely, derive a BN from a CN +def attack_mechanism(atk_mec, exp, config) -> None: + + # Get output path + out_path = get_out_path(config) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + base_path = out_path / config["cns_path"] + + # For each data sample ... + for sample in range(config["samples"]): + + # ... read the related CN + bn_min = gum.loadBN(f"{base_path}/bn_min_{exp}_sample{sample}.bif") + bn_max = gum.loadBN(f"{base_path}/bn_max_{exp}_sample{sample}.bif") + + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_min.nodes())] + + # ... and derive the BN + atk_mec_fn = globals()[atk_mec] # Get the related function + sig = inspect.signature(atk_mec_fn) # Get its signature + args = { + k: v + for k, v in { + "bn_min": bn_min, + "bn_max": bn_max, + "data": rpop, + "n_bns": config["n_bns"], + }.items() + if k in sig.parameters + } + bn = atk_mec_fn(**args) + gum.saveBN( + bn, f'{out_path / config["atk_path"]}/{f"bn_{exp}_sample{sample}"}.bif' + ) + + return + + +# Get the maximum likelihood BN inside a CN +def atk_mle(bn_min, bn_max, data, n_bns: int): + + # Sample from the CN ... + bns_sample = sample_from_cn(bn_min, bn_max, n_bns) + + # ... and take the MLE one + bn = mle_bn(bns_sample, data) + + return bn + + +# Get the maximum likelihood BN within a set +def mle_bn(bns_sample, data): + """ + Given a list `bns_sample` of BNs, + find argmax_{BN in bns_sample} ll(BN | data), + where ll is the log-likelihood function. + """ + + mle_bn = None + mle = -np.inf + + for bn in bns_sample: + + # Estimate the likelihood of data + bn_ie = gum.LazyPropagation(bn) + llr_im = data.apply(lambda x: get_ll(x.to_dict(), bn_ie), axis=1).dropna() + llr = np.sum(llr_im) + + if llr > mle: + mle_bn = bn + mle = llr + + return mle_bn diff --git a/src/attacks.py b/src/attacks.py deleted file mode 100644 index 628c968..0000000 --- a/src/attacks.py +++ /dev/null @@ -1,41 +0,0 @@ -import numpy as np -import pyagrum as gum - -from src.utils import get_ll, sample_from_cn - - -# Get the maximum likelihood BN inside a CN -def atk_mle(bn_min, bn_max, data, n_bns: int): - - # Sample from the CN ... - bns_sample = sample_from_cn(bn_min, bn_max, n_bns) - - # ... and take the MLE one - bn = mle_bn(bns_sample, data) - - return bn - - -# Get the maximum likelihood BN within a set -def mle_bn(bns_sample, data): - """ - Given a list `bns_sample` of BNs, - find argmax_{BN in bns_sample} ll(BN | data), - where ll is the log-likelihood function. - """ - - mle_bn = None - mle = -np.inf - - for bn in bns_sample: - - # Estimate the likelihood of data - bn_ie = gum.LazyPropagation(bn) - llr_im = data.apply(lambda x: get_ll(x.to_dict(), bn_ie), axis=1).dropna() - llr = np.sum(llr_im) - - if llr > mle: - mle_bn = bn - mle = llr - - return mle_bn diff --git a/src/config.py b/src/config.py index aef7bc5..bdd519e 100644 --- a/src/config.py +++ b/src/config.py @@ -1,11 +1,14 @@ import os import random import shutil +import sys from pathlib import Path import pyagrum as gum import yaml +IN_PYTEST = "pytest" in sys.modules + # Read configuration for experiment def load_config(name: str): @@ -27,9 +30,15 @@ def set_global_seed(seed: int): gum.initRandom(seed) -# Get root directory -def get_root_path(): - return Path(__file__).resolve().parents[1] +# Create an empty directory +def create_clean_dir(path: Path): + + # Remove the folder if already exists + if path.exists() and path.is_dir(): + shutil.rmtree(path) + + # Create a new folder + path.mkdir(parents=True, exist_ok=True) # Get output path @@ -41,12 +50,12 @@ def get_out_path(config): return root_path / out_path -# Create an empty directory -def create_clean_dir(path: Path): +# Get root directory +def get_root_path(): + return Path(__file__).resolve().parents[1] - # Remove the folder if already exists - if path.exists() and path.is_dir(): - shutil.rmtree(path) - # Create a new folder - path.mkdir(parents=True, exist_ok=True) +# Only perform an `assert` if code is running in `pytest` +def safe_assert(condition): + if IN_PYTEST: + assert condition diff --git a/src/data.py b/src/data.py index be4bcf9..0b92887 100644 --- a/src/data.py +++ b/src/data.py @@ -4,8 +4,7 @@ import pyagrum as gum from numpy.random import randint -from src.config import create_clean_dir, get_out_path, set_global_seed -from src.utils import safe_assert, save_bn +from src.config import get_out_path, safe_assert, set_global_seed def generate_naivebayes(config): @@ -36,9 +35,9 @@ def generate_naivebayes(config): # ... generate BN, ... bn = gum.fastBN(bn_str) - save_bn(bn, f"exp{i}", bns_path / "gt") + gum.saveBN(bn, f'{bns_path / "gt"}/{f"exp{i}"}.bif') - with open(f'{out_path}/{config["exp_meta"]}', "w") as m: + with open(f'{out_path}/{config["exp_meta"]}', "a") as m: m.write( f'- exp{i}. Naive Bayes: {config["n_nodes"]} nodes. Complexity: {bn.dim()} Max categories: {n_modmax}\n' ) @@ -56,7 +55,7 @@ def generate_naivebayes(config): shuffled_idx = np.random.permutation(gpop.index) pool_idx = shuffled_idx[:pool_ss] - rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss] + rpop_idx = shuffled_idx[pool_ss: pool_ss + rpop_ss] gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) @@ -94,9 +93,9 @@ def generate_randombn(config): # ... generate BN, ... bn_gen = gum.BNGenerator() bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=config["n_modmax"]) - save_bn(bn, f"exp{i}", bns_path / "gt") + gum.saveBN(bn, f'{bns_path / "gt"}/{f"exp{i}"}.bif') - with open(f'{out_path}/{config["exp_meta"]}', "w") as m: + with open(f'{out_path}/{config["exp_meta"]}', "a") as m: m.write( f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()}\n" ) @@ -114,7 +113,7 @@ def generate_randombn(config): shuffled_idx = np.random.permutation(gpop.index) pool_idx = shuffled_idx[:pool_ss] - rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss] + rpop_idx = shuffled_idx[pool_ss: pool_ss + rpop_ss] gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) diff --git a/src/defense.py b/src/defense.py new file mode 100644 index 0000000..b5f8106 --- /dev/null +++ b/src/defense.py @@ -0,0 +1,134 @@ +import inspect + +import numpy as np +import pandas as pd +import pyagrum as gum + +from src.config import get_out_path, safe_assert +from src.utils import add_counts_to_bn, check_consistency + + +# Apply defense mechanism to a BN, namely, derive a CN from a BN +def defense_mechanism(def_mec, exp, config) -> None: + + # Get output path + out_path = get_out_path(config) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + + # For each data sample ... + for sample in range(config["samples"]): + + # ... read the related BN + bn = gum.loadBN( + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + ) + + # ... retrieve pool, ... + pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, : len(bn.nodes())] + + # ... and derive the CN + def_mec_fn = globals()[def_mec] # Get the related function + sig = inspect.signature(def_mec_fn) # Get its signature + args = { + k: v + for k, v in { + "bn": bn, + "ess": config["ess"], + "delta": config["delta"], + "data": pool, + }.items() + if k in sig.parameters + } + cn = def_mec_fn(**args) # Keep only `def_mec`` args + base_path = out_path / config["cns_path"] + cn.saveBNsMinMax( + f"{base_path}/bn_min_{exp}_sample{sample}.bif", + f"{base_path}/bn_max_{exp}_sample{sample}.bif", + ) + + return + + +# Estimate a CN from data by local IDM +def def_idm(bn, ess, data): + bn_counts = gum.BayesNet(bn) + add_counts_to_bn(bn_counts, data) + cn = gum.CredalNet(bn_counts) + cn.idmLearning(ess) + + return cn + + +# Build a CN by bloating each BN parameter with a fixed-size random interval +def def_ran(bn, delta): + + # Initialize the extreme BNs + bn_min = gum.BayesNet(bn) + bn_max = gum.BayesNet(bn) + + # For each node ... + for n in bn.nodes(): + + # ... get the CPT, ... + cpt = bn.cpt(n).toarray() + + # ... get a matrix of eta's, ... + eta = np.random.uniform(0, delta, cpt.size).reshape(cpt.shape) + + # ... perturb the CPT, ... + cpt_min = np.minimum(1 - delta, np.maximum(0, cpt - eta)) + cpt_max = np.minimum(1, np.maximum(delta, cpt - eta + delta)) + + # ... and store it into the extreme BNs + bn_min.cpt(n).fillWith(cpt_min.flatten()) + bn_max.cpt(n).fillWith(cpt_max.flatten()) + + # Debug + safe_assert(np.all(cpt_min <= cpt)) + safe_assert(np.all(cpt_max >= cpt)) + safe_assert(np.all(np.abs(cpt_max - cpt_min - delta) < 1e-6)) + + # Build the CN from the extreme BNs + cn = gum.CredalNet(bn_min, bn_max) + cn.intervalToCredal() + + # Debug + safe_assert(check_consistency(bn, bn_min, bn_max)) + + return cn + + +# Create noisy BN by adding Laplacian noise (Zhang et al., 2017) +def noisy_bn(bn, scale: float): + + bn_ie = gum.LazyPropagation(bn) + bn_ie.makeInference() + + bn_noisy = gum.BayesNet(bn) + + # For each node X ... + for node in bn.names(): + + # Get the joint P(X, Pa(X)) + joint = bn_ie.jointPosterior(bn.family(node)) + + # Add noise to P(X, Pa(X)) and normalize + noise = np.random.laplace(scale=scale, size=np.prod(joint.shape)) + noisy_joint = np.clip( + joint.toarray().flatten() + noise, a_min=10e-10, a_max=None + ) + noisy_joint = noisy_joint / np.sum(noisy_joint) + joint.fillWith(noisy_joint) + + # Compute the conditional P(X | Pa(X)) + cond = joint / joint.sumOut(node) + + # Fill noisy BN + bn_noisy.cpt(node).fillWith(cond) + + # Check noisy bn + bn_noisy.check() # OK if = (). + + return bn_noisy diff --git a/src/defenses.py b/src/defenses.py deleted file mode 100644 index 91796f1..0000000 --- a/src/defenses.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np -import pyagrum as gum - -from src.utils import add_counts_to_bn, check_consistency, safe_assert - - -# Estimate a CN from data by local IDM -def def_idm(bn, ess, data): - bn_counts = gum.BayesNet(bn) - add_counts_to_bn(bn_counts, data) - cn = gum.CredalNet(bn_counts) - cn.idmLearning(ess) - - return cn - -# Build a CN by bloating each BN parameter with a fixed-size random interval -def def_ran(bn, delta): - - # Initialize the extreme BNs - bn_min = gum.BayesNet(bn) - bn_max = gum.BayesNet(bn) - - # For each node ... - for n in bn.nodes(): - - # ... get the CPT, ... - cpt = bn.cpt(n).toarray() - - # ... get a matrix of eta's, ... - eta = np.random.uniform(0, delta, cpt.size).reshape(cpt.shape) - - # ... perturb the CPT, ... - cpt_min = np.minimum(1-delta, np.maximum(0, cpt-eta)) - cpt_max = np.minimum(1, np.maximum(delta, cpt - eta + delta)) - - # ... and store it into the extreme BNs - bn_min.cpt(n).fillWith(cpt_min.flatten()) - bn_max.cpt(n).fillWith(cpt_max.flatten()) - - # Debug - safe_assert(np.all(cpt_min <= cpt)) - safe_assert(np.all(cpt_max >= cpt)) - safe_assert(np.all(np.abs(cpt_max-cpt_min-delta)<1e-6)) - - # Build the CN from the extreme BNs - cn = gum.CredalNet(bn_min, bn_max) - cn.intervalToCredal() - - # Debug - safe_assert(check_consistency(bn, bn_min, bn_max)) - - return cn diff --git a/src/inference.py b/src/inference.py index 7c608d1..63ae8fb 100644 --- a/src/inference.py +++ b/src/inference.py @@ -6,22 +6,21 @@ import pyagrum as gum from more_itertools import random_product -from src.config import get_out_path, set_global_seed -from src.mia import learn_bn_params -from src.utils import get_min_max_bns, noisy_bn, safe_assert -import src.defenses +import src.defense +from src.config import get_out_path, safe_assert, set_global_seed +from src.defense import noisy_bn +from src.learning import learn_bn_params +from src.utils import get_min_max_bns -def run_inferences(exp, config): +def inferences(exp, config): out_path = get_out_path(config) target = config["target_var"] def_mec = config["def_mec"] auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') - eps = auc_meta.loc[ - auc_meta["exp"] == exp, "eps" - ].values[0] + eps = auc_meta.loc[auc_meta["exp"] == exp, "eps"].values[0] # Set seed set_global_seed(config["seed"]) @@ -40,14 +39,19 @@ def run_inferences(exp, config): bn = learn_bn_params(gt, gpop) # Learn CN from gpop (defense mechanism) #TODO: save results - def_mec_fn = getattr(src.defenses, def_mec) # Get the related function - sig = inspect.signature(def_mec_fn) # Get its signature + def_mec_fn = getattr(src.defense, def_mec) # Get the related function + sig = inspect.signature(def_mec_fn) # Get its signature args = { k: v - for k, v in {"bn": bn, "ess": config["ess"], "delta": config["delta"], "data": gpop}.items() + for k, v in { + "bn": bn, + "ess": config["ess"], + "delta": config["delta"], + "data": gpop, + }.items() if k in sig.parameters } - cn = def_mec_fn(**args) # Keep only `def_mec`` args + cn = def_mec_fn(**args) # Keep only `def_mec`` args # Learn noisy BN from gpop #TODO: save results scale = (2 * bn.size()) / (len(gpop) * eps) diff --git a/src/learning.py b/src/learning.py new file mode 100644 index 0000000..ba303a0 --- /dev/null +++ b/src/learning.py @@ -0,0 +1,63 @@ +import pandas as pd +import pyagrum as gum + +from src.config import get_out_path, safe_assert + + +# Learn BN parameters from a given BN and data +def learn_bn_params(bn, data): + + bn_copy = gum.BayesNet(bn) + + learner = gum.BNLearner(data, bn_copy) + learner.useSmoothingPrior(1e-5) + bn_learnt = learner.learnParameters(bn_copy) + + return bn_learnt + + +# Estimate BNs from rpop and pool +def estimate_bns(exp, config) -> None: + + # Get output path + out_path = get_out_path(config) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + bn = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') + n_nodes = len(bn.nodes()) + gpop_ss = config["gpop_ss"] + rpop_ss = int(gpop_ss * config["rpop_prop"]) + pool_ss = int(gpop_ss * config["pool_prop"]) + + # Debug + safe_assert(gpop_ss == gpop.shape[0]) + safe_assert(n_nodes == gpop.loc[:, ~gpop.columns.str.contains("in-")].shape[1]) + + # For each data sample ... + for sample in range(config["samples"]): + + # ... retrieve pool and rpop, ... + pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes] + + # ... estimate BN from rpop, ... + bn_learnt = learn_bn_params(bn, rpop) + gum.saveBN( + bn_learnt, + f'{out_path / config["bns_path"] / "rpop"}/{f"bn_{exp}_sample{sample}"}.bif', + ) + + # ... estimate BN from pool, ... + bn_learnt = learn_bn_params(bn, pool) + gum.saveBN( + bn_learnt, + f'{out_path / config["bns_path"] / "pool"}/{f"bn_{exp}_sample{sample}"}.bif', + ) + + # Debug + safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) + safe_assert(len(pool) == pool_ss) + safe_assert(len(rpop) == rpop_ss) + + return diff --git a/src/mia.py b/src/mia.py index ab04c40..5a893b1 100644 --- a/src/mia.py +++ b/src/mia.py @@ -1,4 +1,3 @@ -import inspect import math import numpy as np @@ -7,285 +6,12 @@ from scipy.stats import norm from sklearn import metrics -import src.attacks -import src.defenses from src.config import get_out_path -from src.utils import get_llr, noisy_bn, safe_assert, safe_open_dir, save_bn - - -# Get the attack power related to a fixed error -def get_power(llr_ref, llr_gen, ground_truth, error) -> float: - - # Compute the threshold - t = np.quantile(llr_ref, 1 - error).item() - - # Test: L(x) > t => reject H_0 => assign `x` to target_pop - y_pred = llr_gen > t - - # Compute power (i.e., true positive rate) - power = sum(ground_truth & y_pred) / sum(ground_truth) - - return power - - -# MIA: membership inference attack -def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): - - # Compute llr(x) on reference and general populations - llr_ref = ( - rpop.apply(lambda x: get_llr(x.to_dict(), baseline, model), axis=1) - .dropna() - .sort_values() - ) - llr_gen = gpop[[*rpop.columns]].apply( - lambda x: get_llr(x.to_dict(), baseline, model), axis=1 - ) - - power_vec = [] - - # Get the power for each error - for error in error_vec: - power = get_power(llr_ref, llr_gen, ground_truth, error) - power_vec.append(power) - - # Compute and store AUC - auc = metrics.auc(error_vec, power_vec) - - return power_vec, auc - - -# Find eps s.t. |AUC(eps) - AUC(CN)| < tol -def phase_find_eps(exp, config) -> dict: - - # Get output path - out_path = get_out_path(config) - - # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - gpop_ss = config["gpop_ss"] - pool_ss = int(gpop_ss * config["pool_prop"]) - auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') - auc_cn = auc_meta.loc[ - auc_meta["exp"] == exp, "auc_cn" - ].values[0] - eps_vec = eval(config["eps_vec"]) - - eps_best = eps_vec[-1] - - # For each eps ... - for eps in eps_vec: - - # Init results - results = pd.DataFrame({"error": eval(config["error"])}) - - auc_noisy_bns = [] - - # ... and for each data sample ... - for sample in range(config["samples"]): - - # ... read the BNs as estimated from rpop and pool, ... - bn_theta = gum.loadBN( - f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" - ) - bn_theta_hat = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" - ) - - # Get noisy BN - scale = (2 * bn_theta_hat.size()) / (pool_ss * eps) - bn_noisy = noisy_bn(bn_theta_hat, scale) - - bn_noisy_ie = gum.LazyPropagation(bn_noisy) - bn_theta_ie = gum.LazyPropagation(bn_theta) - - # ... retrieve rpop, ... - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] - - # try: - - # ... and perform membership inference on gpop - power_vec, auc = run_mia( - bn_noisy_ie, - bn_theta_ie, - rpop, - gpop, - gpop[f"in-pool-{sample}"], - eval(config["error"]), - ) - results[f"power_noisyBN_sample{sample}"] = power_vec - auc_noisy_bns.append(auc) - - # except Exception: - - # # Debug - # with open(f"{results_path}/log.txt", "a") as log: - # log.write(f"{exp}: error with sample {sample} (BN).\n") - # log.write(traceback.format_exc()) - - # Compute Avg(AUC(eps)) across data samples - auc_noisy_bn = sum(auc_noisy_bns) / config["samples"] - - # Condition on |AUC(eps) - AUC(CN)| - if abs(auc_cn - auc_noisy_bn) <= config["tol"]: - eps_best = eps - break - - # Save noisy BNs - for sample in range(config["samples"]): - bn_theta_hat = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" - ) - scale = (2 * bn_theta_hat.size()) / (pool_ss * eps_best) - bn_noisy = noisy_bn(bn_theta_hat, scale) - save_bn(bn_noisy, f"bn_{exp}_sample{sample}", out_path / config["noisy_path"]) - - return { - "exp": exp, - "auc_cn": auc_cn, - "auc_noisy_bn": auc_noisy_bn, - "eps": eps_best, - } - - -# Learn BN parameters from a given BN and data -def learn_bn_params(bn, data): - - bn_copy = gum.BayesNet(bn) - - learner = gum.BNLearner(data, bn_copy) - learner.useSmoothingPrior(1e-5) - bn_learnt = learner.learnParameters(bn_copy) - - return bn_learnt - - -# Estimate BNs from rpop and pool -def phase_estimation(exp, config) -> None: - - # Get output path - out_path = get_out_path(config) - - # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - bn = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') - n_nodes = len(bn.nodes()) - gpop_ss = config["gpop_ss"] - rpop_ss = int(gpop_ss * config["rpop_prop"]) - pool_ss = int(gpop_ss * config["pool_prop"]) - - # Debug - safe_assert(gpop_ss == gpop.shape[0]) - safe_assert(n_nodes == gpop.loc[:, ~gpop.columns.str.contains("in-")].shape[1]) - - # For each data sample ... - for sample in range(config["samples"]): - - # ... retrieve pool and rpop, ... - pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes] - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes] - - # ... estimate BN from rpop, ... - bn_learnt = learn_bn_params(bn, rpop) - save_bn( - bn_learnt, - f"bn_{exp}_sample{sample}", - out_path / config["bns_path"] / "rpop", - ) - - # ... estimate BN from pool, ... - bn_learnt = learn_bn_params(bn, pool) - save_bn( - bn_learnt, - f"bn_{exp}_sample{sample}", - out_path / config["bns_path"] / "pool", - ) - - # Debug - safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"])) - safe_assert(len(pool) == pool_ss) - safe_assert(len(rpop) == rpop_ss) - - return - - -# Apply defense mechanism to a BN, namely, derive a CN from a BN -def phase_defense_mechanism(def_mec, exp, config) -> None: - - # Get output path - out_path = get_out_path(config) - - # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - - # For each data sample ... - for sample in range(config["samples"]): - - # ... read the related BN - bn = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" - ) - - # ... retrieve pool, ... - pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, : len(bn.nodes())] - - # ... and derive the CN - def_mec_fn = getattr(src.defenses, def_mec) # Get the related function - sig = inspect.signature(def_mec_fn) # Get its signature - args = { - k: v - for k, v in {"bn": bn, "ess": config["ess"], "delta": config["delta"],"data": pool}.items() - if k in sig.parameters - } - cn = def_mec_fn(**args) # Keep only `def_mec`` args - base_path = out_path / config["cns_path"] - cn.saveBNsMinMax( - f"{base_path}/bn_min_{exp}_sample{sample}.bif", - f"{base_path}/bn_max_{exp}_sample{sample}.bif", - ) - - return - - -# Apply attack mechanism to a BN, namely, derive a BN from a CN -def phase_attack_mechanism(atk_mec, exp, config) -> None: - - # Get output path - out_path = get_out_path(config) - - # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - base_path = out_path / config["cns_path"] - - # For each data sample ... - for sample in range(config["samples"]): - - # ... read the related CN - bn_min = gum.loadBN( - f"{base_path}/bn_min_{exp}_sample{sample}.bif" - ) - bn_max = gum.loadBN( - f"{base_path}/bn_max_{exp}_sample{sample}.bif" - ) - - # ... retrieve rpop, ... - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_min.nodes())] - - # ... and derive the BN - atk_mec_fn = getattr(src.attacks, atk_mec) # Get the related function - sig = inspect.signature(atk_mec_fn) # Get its signature - args = { - k: v - for k, v in {"bn_min": bn_min,"bn_max": bn_max, "data": rpop, "n_bns":config["n_bns"]}.items() - if k in sig.parameters - } - bn = atk_mec_fn(**args) - save_bn(bn, f"bn_{exp}_sample{sample}", out_path / config["atk_path"]) - - return +from src.defense import noisy_bn # MIA attack vs a BN -def phase_mia_vs_bn(exp, config) -> None: +def mia_vs_bn(exp, config) -> None: # Get output path out_path = get_out_path(config) @@ -338,7 +64,7 @@ def phase_mia_vs_bn(exp, config) -> None: # MIA attack vs a CN -def phase_mia_vs_cn(exp, config, save_res=True) -> dict: +def mia_vs_cn(exp, config, save_res=True) -> dict: # Get output path out_path = get_out_path(config) @@ -404,7 +130,7 @@ def phase_mia_vs_cn(exp, config, save_res=True) -> dict: # Get theoretical power -def phase_theoretical_power(exp, config) -> None: +def theoretical_power(exp, config) -> None: # Get output path out_path = get_out_path(config) @@ -426,3 +152,160 @@ def phase_theoretical_power(exp, config) -> None: results.to_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) return + + +# Find eps s.t. |AUC(eps) - AUC(CN)| < tol +def find_epsilon(exp, config) -> dict: + + # Get output path + out_path = get_out_path(config) + + # Read data + gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + gpop_ss = config["gpop_ss"] + pool_ss = int(gpop_ss * config["pool_prop"]) + auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') + auc_cn = auc_meta.loc[auc_meta["exp"] == exp, "auc_cn"].values[0] + eps_vec = eval(config["eps_vec"]) + + eps_best = eps_vec[-1] + + # For each eps ... + for eps in eps_vec: + + # Init results + results = pd.DataFrame({"error": eval(config["error"])}) + + auc_noisy_bns = [] + + # ... and for each data sample ... + for sample in range(config["samples"]): + + # ... read the BNs as estimated from rpop and pool, ... + bn_theta = gum.loadBN( + f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" + ) + bn_theta_hat = gum.loadBN( + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + ) + + # Get noisy BN + scale = (2 * bn_theta_hat.size()) / (pool_ss * eps) + bn_noisy = noisy_bn(bn_theta_hat, scale) + + bn_noisy_ie = gum.LazyPropagation(bn_noisy) + bn_theta_ie = gum.LazyPropagation(bn_theta) + + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] + + # try: + + # ... and perform membership inference on gpop + power_vec, auc = run_mia( + bn_noisy_ie, + bn_theta_ie, + rpop, + gpop, + gpop[f"in-pool-{sample}"], + eval(config["error"]), + ) + results[f"power_noisyBN_sample{sample}"] = power_vec + auc_noisy_bns.append(auc) + + # except Exception: + + # # Debug + # with open(f"{results_path}/log.txt", "a") as log: + # log.write(f"{exp}: error with sample {sample} (BN).\n") + # log.write(traceback.format_exc()) + + # Compute Avg(AUC(eps)) across data samples + auc_noisy_bn = sum(auc_noisy_bns) / config["samples"] + + # Condition on |AUC(eps) - AUC(CN)| + if abs(auc_cn - auc_noisy_bn) <= config["tol"]: + eps_best = eps + break + + # Save noisy BNs + for sample in range(config["samples"]): + bn_theta_hat = gum.loadBN( + f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + ) + scale = (2 * bn_theta_hat.size()) / (pool_ss * eps_best) + bn_noisy = noisy_bn(bn_theta_hat, scale) + gum.saveBN( + bn_noisy, + f'{out_path / config["noisy_path"]}/{f"bn_{exp}_sample{sample}"}.bif', + ) + + return { + "exp": exp, + "auc_cn": auc_cn, + "auc_noisy_bn": auc_noisy_bn, + "eps": eps_best, + } + + +# MIA: membership inference attack +def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec): + + # Compute llr(x) on reference and general populations + llr_ref = ( + rpop.apply(lambda x: get_llr(x.to_dict(), baseline, model), axis=1) + .dropna() + .sort_values() + ) + llr_gen = gpop[[*rpop.columns]].apply( + lambda x: get_llr(x.to_dict(), baseline, model), axis=1 + ) + + power_vec = [] + + # Get the power for each error + for error in error_vec: + power = get_power(llr_ref, llr_gen, ground_truth, error) + power_vec.append(power) + + # Compute and store AUC + auc = metrics.auc(error_vec, power_vec) + + return power_vec, auc + + +# Get the attack power related to a fixed error +def get_power(llr_ref, llr_gen, ground_truth, error) -> float: + + # Compute the threshold + t = np.quantile(llr_ref, 1 - error).item() + + # Test: L(x) > t => reject H_0 => assign `x` to target_pop + y_pred = llr_gen > t + + # Compute power (i.e., true positive rate) + power = sum(ground_truth & y_pred) / sum(ground_truth) + + return power + + +# Log-likelihood function +def get_ll(x: dict, theta): + + # Erase all evidences and apply addEvidence(key,value) for every pairs in x + theta.setEvidence(x) + + # Compute P(x | theta) + ll = theta.evidenceProbability() + + return np.log(ll) + + +# Log-likelihood ratio (llr) function +def get_llr(x: dict, theta, theta_hat): + + # Compute log-likelihoods + ll_theta = get_ll(x, theta) + ll_theta_hat = get_ll(x, theta_hat) + + return ll_theta_hat - ll_theta diff --git a/src/utils.py b/src/utils.py index a5e4dbe..95c87ec 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,51 +1,10 @@ -import sys from tempfile import TemporaryDirectory import hopsy import numpy as np import pyagrum as gum -IN_PYTEST = "pytest" in sys.modules - - -# Log-likelihood function -def get_ll(x: dict, theta): - - # Erase all evidences and apply addEvidence(key,value) for every pairs in x - theta.setEvidence(x) - - # Compute P(x | theta) - ll = theta.evidenceProbability() - - return np.log(ll) - - -# Log-likelihood ratio (llr) function -def get_llr(x: dict, theta, theta_hat): - - # Compute log-likelihoods - ll_theta = get_ll(x, theta) - ll_theta_hat = get_ll(x, theta_hat) - - return ll_theta_hat - ll_theta - - -# Check BNs sampled from a CN -def are_all_bns_different(bn_vec) -> bool: - - signatures = set() - for bn in bn_vec: - cpt_data = [] - for var in bn.names(): - cpt = bn.cpt(var) - flat = [f"{v:.8f}" for v in cpt.toarray().flatten()] - cpt_data.append(f"{var}:" + ",".join(flat)) - sig = "|".join(cpt_data) - signatures.add(sig) - - print(f"({len(signatures)}/{len(bn_vec)} different BNs.)") - - return len(signatures) == len(bn_vec) +from src.config import safe_assert # Add counts of events to a BN @@ -69,83 +28,74 @@ def add_counts_to_bn(bn, data): bn.cpt(node).fillWith(counts_array.flatten().tolist()) -# Compact a dictionary to be printable -def compact_dict(d): - new_dict = {} - for k, v in d.items(): - if isinstance(v, np.ndarray): - new_dict[k] = ( - f"np.ndarray: [{v[0]:.2g}, {v[1]:.2g}, ..., {v[-1]:.2g}], length={len(v)}" - ) - else: - new_dict[k] = v - return new_dict - - -# Create noisy BN by adding Laplacian noise (Zhang et al., 2017) -def noisy_bn(bn, scale: float): - - bn_ie = gum.LazyPropagation(bn) - bn_ie.makeInference() - - bn_noisy = gum.BayesNet(bn) +# BNs sampler from a CN +def sample_from_cn(bn_min, bn_max, n_bns: int) -> list: - # For each node X ... - for node in bn.names(): + # Get the DAG and extreme BNs + dag = gum.BayesNet(bn_min) - # Get the joint P(X, Pa(X)) - joint = bn_ie.jointPosterior(bn.family(node)) + # For each variable ... + cpts_dict = {} + for var in dag.names(): - # Add noise to P(X, Pa(X)) and normalize - noise = np.random.laplace(scale=scale, size=np.prod(joint.shape)) - noisy_joint = np.clip( - joint.toarray().flatten() + noise, a_min=10e-10, a_max=None - ) - noisy_joint = noisy_joint / np.sum(noisy_joint) - joint.fillWith(noisy_joint) + # ... sample `n_bns` CPTs from the CN + cpts_dict[var] = sample_from_cpts(bn_min.cpt(var), bn_max.cpt(var), n_bns) - # Compute the conditional P(X | Pa(X)) - cond = joint / joint.sumOut(node) + # For each sample ... + bns = [] + for i in range(n_bns): - # Fill noisy BN - bn_noisy.cpt(node).fillWith(cond) + # ... init an empty BN ... + bn = gum.BayesNet(dag) - # Check noisy bn - bn_noisy.check() # OK if = (). + # ... and fill its CPTs + for var in dag.names(): + bn.cpt(var).fillWith(cpts_dict[var][i]) - return bn_noisy + bns.append(bn) + # Debug + safe_assert(check_consistency(bn, bn_min, bn_max)) -# Only perform an `assert` if code is running in `pytest` -def safe_assert(condition): - if IN_PYTEST: - assert condition + # Debug + safe_assert(len(cpts_dict) == len(dag.names())) + safe_assert(len(bns) == n_bns) + return bns -# Open `path` for writing, creating any parent directories as needed. -def safe_open_dir(path): - if not path.exists(): - path.mkdir(parents=False, exist_ok=True) +# Sample from two extreme CPTs +def sample_from_cpts(cpt_min, cpt_max, n_bns) -> list: - return path + # Transform CPTs into pandas dataframes + cpt_min = np.atleast_2d(cpt_min.topandas()) + cpt_max = np.atleast_2d(cpt_max.topandas()) + # For each row in the CPT ... + credal_dict = {} + for row in range(cpt_min.shape[0]): -# Save a BN, with its name, into `path` -def save_bn(bn, bn_name, path): + # ... sample `n_bns` points from the credal set + credal_dict[row] = sample_from_cset(cpt_min[row, :], cpt_max[row, :], n_bns) - gum.saveBN(bn, f"{path}/{bn_name}.bif") + # For each sample ... + cpt_samples = [] + for i in range(n_bns): + # ... build the CPT + cpt = [] + for row in range(cpt_min.shape[0]): + cpt.append(credal_dict[row][i]) -# Extract BN min and BN max from a CN -def get_min_max_bns(cn, exp: str): + cpt = np.array(cpt).flatten() + cpt_samples.append(cpt) - with TemporaryDirectory() as tmp_path: - cn.saveBNsMinMax(f"{tmp_path}/bn_min_{exp}.bif", f"{tmp_path}/bn_max_{exp}.bif") - bn_min = gum.loadBN(f"{tmp_path}/bn_min_{exp}.bif") - bn_max = gum.loadBN(f"{tmp_path}/bn_max_{exp}.bif") + # Debug + safe_assert(cpt_min.shape == cpt_max.shape) + safe_assert(len(credal_dict) == cpt_min.shape[0]) + safe_assert(len(cpt_samples) == n_bns) - return bn_min, bn_max + return cpt_samples # Sample from a credal set K(x | pi_x), i.e., a constrained polytope. @@ -190,76 +140,6 @@ def sample_from_cset(vec_min, vec_max, n_bns) -> list: return constrained_samples -# Sample from two extreme CPTs -def sample_from_cpts(cpt_min, cpt_max, n_bns) -> list: - - # Transform CPTs into pandas dataframes - cpt_min = np.atleast_2d(cpt_min.topandas()) - cpt_max = np.atleast_2d(cpt_max.topandas()) - - # For each row in the CPT ... - credal_dict = {} - for row in range(cpt_min.shape[0]): - - # ... sample `n_bns` points from the credal set - credal_dict[row] = sample_from_cset(cpt_min[row, :], cpt_max[row, :], n_bns) - - # For each sample ... - cpt_samples = [] - for i in range(n_bns): - - # ... build the CPT - cpt = [] - for row in range(cpt_min.shape[0]): - cpt.append(credal_dict[row][i]) - - cpt = np.array(cpt).flatten() - cpt_samples.append(cpt) - - # Debug - safe_assert(cpt_min.shape == cpt_max.shape) - safe_assert(len(credal_dict) == cpt_min.shape[0]) - safe_assert(len(cpt_samples) == n_bns) - - return cpt_samples - - -# BNs sampler from a CN -def sample_from_cn(bn_min, bn_max, n_bns: int) -> list: - - # Get the DAG and extreme BNs - dag = gum.BayesNet(bn_min) - - # For each variable ... - cpts_dict = {} - for var in dag.names(): - - # ... sample `n_bns` CPTs from the CN - cpts_dict[var] = sample_from_cpts(bn_min.cpt(var), bn_max.cpt(var), n_bns) - - # For each sample ... - bns = [] - for i in range(n_bns): - - # ... init an empty BN ... - bn = gum.BayesNet(dag) - - # ... and fill its CPTs - for var in dag.names(): - bn.cpt(var).fillWith(cpts_dict[var][i]) - - bns.append(bn) - - # Debug - safe_assert(check_consistency(bn, bn_min, bn_max)) - - # Debug - safe_assert(len(cpts_dict) == len(dag.names())) - safe_assert(len(bns) == n_bns) - - return bns - - # Check the consistency of a BN as sampled from a CN def check_consistency(bn, bn_min, bn_max) -> bool: @@ -289,3 +169,32 @@ def check_consistency(bn, bn_min, bn_max) -> bool: return False return True + + +# Check BNs sampled from a CN +def are_all_bns_different(bn_vec) -> bool: + + signatures = set() + for bn in bn_vec: + cpt_data = [] + for var in bn.names(): + cpt = bn.cpt(var) + flat = [f"{v:.8f}" for v in cpt.toarray().flatten()] + cpt_data.append(f"{var}:" + ",".join(flat)) + sig = "|".join(cpt_data) + signatures.add(sig) + + print(f"({len(signatures)}/{len(bn_vec)} different BNs.)") + + return len(signatures) == len(bn_vec) + + +# Extract BN min and BN max from a CN +def get_min_max_bns(cn, exp: str): + + with TemporaryDirectory() as tmp_path: + cn.saveBNsMinMax(f"{tmp_path}/bn_min_{exp}.bif", f"{tmp_path}/bn_max_{exp}.bif") + bn_min = gum.loadBN(f"{tmp_path}/bn_min_{exp}.bif") + bn_max = gum.loadBN(f"{tmp_path}/bn_max_{exp}.bif") + + return bn_min, bn_max From b336f0e784e8bcc49a37e8597433424d853d76e8 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Sat, 15 Nov 2025 10:09:32 +0100 Subject: [PATCH 15/31] Update: separate models and data generation from main exps --- .dockerignore | 7 ++-- Dockerfile | 7 ++++ README.md | 10 ++++-- experiments/cn_privacy/exp.py | 17 ---------- experiments/cn_privacy/generate.py | 46 +++++++++++++++++++++++++ experiments/cn_vs_noisybn/exp.py | 17 ---------- experiments/cn_vs_noisybn/generate.py | 47 ++++++++++++++++++++++++++ test/cn_privacy/test_integration.py | 5 ++- test/cn_vs_noisybn/test_integration.py | 5 ++- 9 files changed, 119 insertions(+), 42 deletions(-) create mode 100644 experiments/cn_privacy/generate.py create mode 100644 experiments/cn_vs_noisybn/generate.py diff --git a/.dockerignore b/.dockerignore index 2e6e678..4791eba 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,5 +1,4 @@ venv -output -bin -__pycache__ -.pytest_cache +**/output +**/__pycache__ +**/.pytest_cache diff --git a/Dockerfile b/Dockerfile index 3b514b1..ff2a25d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,5 +1,6 @@ FROM python:3.12-slim +# Install system dependencies RUN apt-get update && apt-get install -y \ build-essential \ swig \ @@ -7,10 +8,16 @@ RUN apt-get update && apt-get install -y \ python3-dev \ && rm -rf /var/lib/apt/lists/* +# Set working directory WORKDIR /workspace COPY . . +# Install python packages RUN pip install --upgrade pip RUN if [ -f requirements.txt ]; then pip install -r requirements.txt; fi +# Generate models and data +RUN python -m experiments.cn_privacy.generate +RUN python -m experiments.cn_vs_noisybn.generate + CMD /bin/sh diff --git a/README.md b/README.md index 6d75e81..8184fcc 100644 --- a/README.md +++ b/README.md @@ -36,13 +36,19 @@ pip freeze > requirements.txt ### Running code -Run an experiment with: +1) Generate models and data: + +```bash +python -m experiments..generate +``` + +2) Run an experiment: ```bash python -m experiments..exp ``` -*Notice:* this will delete any already existing output. For storing intermediate output, comment out code in the `experiments..exp.py` file. +*Notice:* each command will overwrite any related output. ### Using Docker (recommended) diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 2d1906d..d681a18 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -7,9 +7,7 @@ from src.attack import attack_mechanism from src.config import (create_clean_dir, get_out_path, load_config, set_global_seed) -from src.data import generate_randombn from src.defense import defense_mechanism -from src.learning import estimate_bns from src.mia import mia_vs_bn, mia_vs_cn, theoretical_power @@ -23,26 +21,11 @@ def main(): atk_mec = config["atk_mec"] num_cores = eval(config["num_cores"]) - # Generate BNs and data - print("#" * 5, "Generate BNs and data", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "gt") - create_clean_dir(out_path / config["data_path"]) - open(f'{out_path}/{config["exp_meta"]}', "a").close() - generate_randombn(config) - # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] - # Estimate BNs from rpop and pool - print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "rpop") - create_clean_dir(out_path / config["bns_path"] / "pool") - _ = Parallel(n_jobs=num_cores)( - delayed(estimate_bns)(exp, config) for exp in exp_vec - ) - # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py new file mode 100644 index 0000000..53dd7a2 --- /dev/null +++ b/experiments/cn_privacy/generate.py @@ -0,0 +1,46 @@ +import gc +import multiprocessing # noqa: F401 # pylint: disable=unused-import + +import numpy as np # noqa: F401 # pylint: disable=unused-import +from joblib import Parallel, delayed + +from src.config import (create_clean_dir, get_out_path, load_config, + set_global_seed) +from src.data import generate_randombn +from src.learning import estimate_bns + + +def main(): + + # Init configs + config = load_config("cn_privacy") + out_path = get_out_path(config) + set_global_seed(config["seed"]) + num_cores = eval(config["num_cores"]) + + # Generate BNs and data + print("#" * 5, "Generate BNs and data", "#" * 5) + create_clean_dir(out_path / config["bns_path"] / "gt") + create_clean_dir(out_path / config["data_path"]) + open(f'{out_path}/{config["exp_meta"]}', "a").close() + generate_randombn(config) + + # Init the vectors of experiments + exp_vec = [ + f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() + ] + + # Estimate BNs from rpop and pool + print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) + create_clean_dir(out_path / config["bns_path"] / "rpop") + create_clean_dir(out_path / config["bns_path"] / "pool") + _ = Parallel(n_jobs=num_cores)( + delayed(estimate_bns)(exp, config) for exp in exp_vec + ) + + # Clean + gc.collect() + + +if __name__ == "__main__": + main() diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 5794061..7421ecf 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -8,10 +8,8 @@ from src.attack import attack_mechanism from src.config import (create_clean_dir, get_out_path, load_config, set_global_seed) -from src.data import generate_naivebayes from src.defense import defense_mechanism from src.inference import inferences -from src.learning import estimate_bns from src.mia import find_epsilon, mia_vs_cn @@ -25,26 +23,11 @@ def main(): atk_mec = config["atk_mec"] num_cores = eval(config["num_cores"]) - # Generate BNs and data - print("#" * 5, "Generate BNs and data", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "gt") - create_clean_dir(out_path / config["data_path"]) - open(f'{out_path}/{config["exp_meta"]}', "a").close() - generate_naivebayes(config) - # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() ] - # Estimate BNs from rpop and pool - print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "rpop") - create_clean_dir(out_path / config["bns_path"] / "pool") - _ = Parallel(n_jobs=num_cores)( - delayed(estimate_bns)(exp, config) for exp in exp_vec - ) - # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py new file mode 100644 index 0000000..e30bde6 --- /dev/null +++ b/experiments/cn_vs_noisybn/generate.py @@ -0,0 +1,47 @@ +import gc +import multiprocessing # noqa: F401 # pylint: disable=unused-import + +import numpy as np # noqa: F401 # pylint: disable=unused-import +from joblib import Parallel, delayed + +from src.config import (create_clean_dir, get_out_path, load_config, + set_global_seed) +from src.data import generate_naivebayes +from src.learning import estimate_bns + + +def main(): + + # Init configs + config = load_config("cn_vs_noisybn") + out_path = get_out_path(config) + set_global_seed(config["seed"]) + num_cores = eval(config["num_cores"]) + + # Generate BNs and data + print("#" * 5, "Generate BNs and data", "#" * 5) + create_clean_dir(out_path / config["bns_path"] / "gt") + create_clean_dir(out_path / config["data_path"]) + open(f'{out_path}/{config["exp_meta"]}', "a").close() + generate_naivebayes(config) + + # Init the vectors of experiments + exp_vec = [ + f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() + ] + + # Estimate BNs from rpop and pool + print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) + create_clean_dir(out_path / config["bns_path"] / "rpop") + create_clean_dir(out_path / config["bns_path"] / "pool") + _ = Parallel(n_jobs=num_cores)( + delayed(estimate_bns)(exp, config) for exp in exp_vec + ) + + # Clean + gc.collect() + + +if __name__ == "__main__": + + main() diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 39542de..89ddc37 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,7 +1,10 @@ -from experiments.cn_privacy import exp +from experiments.cn_privacy import generate, exp def test_integration(): + # Generate models and data + generate.main() + # Run experiment exp.main() diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index a67e4bb..1b23814 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,7 +1,10 @@ -from experiments.cn_vs_noisybn import exp +from experiments.cn_vs_noisybn import generate, exp def test_integration(): + # Generate models and data + generate.main() + # Run experiment exp.main() From 289c1af65bf0a4dc6f4361f4b091fb3e15a10686 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Sat, 15 Nov 2025 12:34:30 +0100 Subject: [PATCH 16/31] Update: def and atk parameters are now passed to command-line instead of defined inside `config.yaml` files --- README.md | 38 ++++++++++----- experiments/cn_privacy/Plot_results.ipynb | 51 ++++++++------------ experiments/cn_privacy/config.yaml | 12 ----- experiments/cn_privacy/config_BAK.yaml | 12 ----- experiments/cn_privacy/exp.py | 21 ++++---- experiments/cn_privacy/generate.py | 4 +- experiments/cn_vs_noisybn/Plot_results.ipynb | 29 ++--------- experiments/cn_vs_noisybn/config.yaml | 1 - experiments/cn_vs_noisybn/config_BAK.yaml | 1 - experiments/cn_vs_noisybn/exp.py | 15 +++--- experiments/cn_vs_noisybn/generate.py | 4 +- src/attack.py | 9 ++-- src/config.py | 45 +++++++++++++++-- src/data.py | 23 ++++----- src/defense.py | 13 +++-- src/inference.py | 12 ++++- src/learning.py | 5 +- src/mia.py | 14 +++++- test/cn_privacy/config.yaml | 1 - test/cn_privacy/test_integration.py | 17 ++++++- test/cn_vs_noisybn/config.yaml | 1 - test/cn_vs_noisybn/test_integration.py | 17 ++++++- 22 files changed, 194 insertions(+), 151 deletions(-) diff --git a/README.md b/README.md index 8184fcc..a74428e 100644 --- a/README.md +++ b/README.md @@ -4,29 +4,30 @@ Code for paper ["Towards Privacy-Aware Bayesian Networks: A Credal Approach"](ht ## Setting up Python environment -Create and activate a Python virtual environment with: +Create and activate a Python virtual environment: ```bash python3 -m venv venv source venv/bin/activate[.fish] # use `.fish` suffix if using fish shell ``` -Install dependencies with: +Install dependencies: ```bash pip install -r requirements.txt ``` -Upgrade dependencies with: +Upgrade dependencies: ```bash pip install --upgrade $(pip freeze | cut -d '=' -f 1) pip freeze > requirements.txt ``` +## Preliminaries -## Experiments +### Experiments -`` is the name of the experiment to run. Each `` has its own directory, which is named the same way. Each of these contains the experiment logic, configuration file (`config.yaml`), output (specified in configurations), and a `Plot_results.ipynb` notebook to plot results. +`` is the name of the experiment to run. Each `` has its own directory, which is named the same way. Each of these contains the experiment logic, configuration file (`config.yaml`), output (which path specified in configurations), and a `Plot_results.ipynb` notebook to plot results. `` can be one of the following: @@ -34,7 +35,22 @@ pip freeze > requirements.txt 2. `cn_vs_noisybn`: additional experiment, not reported in the paper. It compares two privacy techniques, namely the CN and a noisy version of BN. All models are naive Bayes with target variable T. First, the CN and noisy BN hyperparameters are fine-tuned so that they achieve the same privacy level; then, their accuracy is computed in terms of most probable explanation (MPE) on variable T. -### Running code +### Attacks and defenses + +Each experiment requires the user to specify one defense and one attack mechanisms, plus additional related hyperparameters. Below, the mechanisms and hyperparameters names are reported. Further details are provided in the paper. + +Implemented defenses: +- `def_idm`. Requires: `ess` +- `def_ran`. Requires: `delta` + +Implemented attacks: +- `atk_mle`. Requires: `n_bns` + +## Running code + +### Local computation + +*Notice:* each of the following command will overwrite any related output. 1) Generate models and data: @@ -45,11 +61,9 @@ python -m experiments..generate 2) Run an experiment: ```bash -python -m experiments..exp +python -m experiments..exp def_mec= [def_params] atk_mec= [atk_params] ``` -*Notice:* each command will overwrite any related output. - ### Using Docker (recommended) 1. Build the Docker image: @@ -61,16 +75,18 @@ docker build . -t bnp:2025 2. Run the Docker container: ```bash -docker run [-d] [--rm] -v bnp:/workspace bnp:2025 python -m experiments..exp +docker run [-d] [--rm] -v bnp:/workspace bnp:2025 ``` +where `` follows the same syntax as in the local computation. + 3. Results available at: `/var/lib/docker/volumes/bnp/_data/`. ## Testing code -Run integration tests with: +Run integration tests: ```bash pytest [--cov=src] [--cov-report=term-missing] [--capture=no] diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index 5ec788b..c2b4161 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -16,6 +16,7 @@ "import sys\n", "from pathlib import Path\n", "import re\n", + "import ast\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", "from src.config import * # noqa" @@ -23,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -34,7 +35,13 @@ "# Get results path\n", "out_path = get_out_path(config)\n", "res_path = out_path / config[\"results_path\"]\n", + "\n", + "# Get some hyperparameters\n", "error = eval(config[\"error\"])\n", + "with open(f\"{out_path}/exp_meta.txt\", \"r\") as meta:\n", + " for row in meta:\n", + " if re.search(\"\\{.*\\}\", row):\n", + " params = ast.literal_eval(row)\n", "\n", "# Choose where to save plots\n", "plots_path = out_path / \"plots\"\n", @@ -43,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "52eff632", "metadata": {}, "outputs": [], @@ -76,29 +83,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "c55e8958", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bn_exp1.csv', 'bn_exp3.csv', 'bn_exp2.csv', 'bn_exp0.csv']" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files = os.listdir(res_path / \"bns\")\n", - "files" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "3bc7788b", "metadata": {}, "outputs": [], @@ -164,9 +149,9 @@ " # Plot CN (avg-max)\n", " ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n", " label = (\n", - " f'CN, $S={config[\"ess\"]}$'\n", - " if config[\"def_mec\"] == \"def_idm\"\n", - " else f'CN, $delta={config[\"delta\"]}$'\n", + " f'CN, $S={params[\"ess\"]}$'\n", + " if params[\"def_mec\"] == \"def_idm\"\n", + " else f'CN, $\\delta={params[\"delta\"]}$'\n", " )\n", " ax.semilogx(error, cn_mean, type, color=color, label=label, zorder=3)\n", "\n", @@ -196,13 +181,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "ade37b54", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -217,7 +202,9 @@ "\n", "# Layout 4x3\n", "fig, axes = plt.subplots(len(exp_names) // 3 + 1, 3)\n", - "# fig.suptitle(\"Power vs Error\", fontsize=18)\n", + "fig.suptitle(\n", + " f\"Power vs Error - {params['def_mec']} vs {params['atk_mec']}\", fontsize=15\n", + ")\n", "\n", "# Loop over results\n", "for i, exp in enumerate(exp_names):\n", diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 290dac4..8ba91a1 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -21,19 +21,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_ran' # Defense mechanisms to consider -atk_mec: 'atk_mle' # Attack mechanisms to consider error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector -# DEF-IDM -ess: 1 # ESS: the amount of injected uncertainty - -# DEF-RAN -delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) - -# ATK-MLE -n_bns: 5 # Number of BNs to sample within the CN - # Other -seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index 197880e..806fbf9 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -21,19 +21,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 20 # Number of data samples # MIA -def_mec: 'def_ran' # Defense mechanisms to consider -atk_mec: 'atk_mle' # Attack mechanisms to consider error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector -# DEF-IDM -ess: 1 # ESS: the amount of injected uncertainty - -# DEF-RAN -delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) - -# ATK-MLE -n_bns: 500 # Number of BNs to sample within the CN - # Other -seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index d681a18..51141a2 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -1,12 +1,13 @@ import gc import multiprocessing # noqa: F401 # pylint: disable=unused-import +import sys import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed from src.attack import attack_mechanism from src.config import (create_clean_dir, get_out_path, load_config, - set_global_seed) + map_sys_args) from src.defense import defense_mechanism from src.mia import mia_vs_bn, mia_vs_cn, theoretical_power @@ -16,11 +17,11 @@ def main(): # Init configs config = load_config("cn_privacy") out_path = get_out_path(config) - set_global_seed(config["seed"]) - def_mec = config["def_mec"] - atk_mec = config["atk_mec"] num_cores = eval(config["num_cores"]) + # Get command-line hyperparameters + def_mec, def_args, atk_mec, atk_args = map_sys_args(sys.argv, config) + # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() @@ -30,29 +31,25 @@ def main(): print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(defense_mechanism)(def_mec, exp, config) for exp in exp_vec + delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(attack_mechanism)(atk_mec, exp, config) for exp in exp_vec + delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec ) # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) create_clean_dir(out_path / config["results_path"] / "cns") - _ = Parallel(n_jobs=num_cores)( - delayed(mia_vs_cn)(exp, config) for exp in exp_vec - ) + _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_cn)(exp, config) for exp in exp_vec) # MIA vs BN print("#" * 5, "MIA vs BN", "#" * 5) create_clean_dir(out_path / config["results_path"] / "bns") - _ = Parallel(n_jobs=num_cores)( - delayed(mia_vs_bn)(exp, config) for exp in exp_vec - ) + _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_bn)(exp, config) for exp in exp_vec) # Compute theoretical power print("#" * 5, "Compute theoretical power", "#" * 5) diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py index 53dd7a2..6028001 100644 --- a/experiments/cn_privacy/generate.py +++ b/experiments/cn_privacy/generate.py @@ -4,8 +4,7 @@ import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import (create_clean_dir, get_out_path, load_config, - set_global_seed) +from src.config import create_clean_dir, get_out_path, load_config from src.data import generate_randombn from src.learning import estimate_bns @@ -15,7 +14,6 @@ def main(): # Init configs config = load_config("cn_privacy") out_path = get_out_path(config) - set_global_seed(config["seed"]) num_cores = eval(config["num_cores"]) # Generate BNs and data diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 49dd048..67d9b6b 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -21,6 +21,7 @@ "import numpy as np\n", "import re\n", "import sys\n", + "import ast\n", "from pathlib import Path\n", "from natsort import natsorted\n", "from sklearn.metrics import roc_curve\n", @@ -73,28 +74,6 @@ " return sum(data[col] == data[vs_col]) / len(data)" ] }, - { - "cell_type": "code", - "execution_count": 15, - "id": "da082d37", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['exp0.csv', 'exp1.csv', 'exp4.csv', 'exp2.csv', 'exp3.csv']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dirs = os.listdir(res_path)\n", - "dirs" - ] - }, { "cell_type": "code", "execution_count": null, @@ -178,9 +157,9 @@ "\n", "# Store results\n", "for key in res.keys():\n", - " res[key].append(eval(key))\n", + " res[key].append(ast.literal_eval(key))\n", "for key in roc.keys():\n", - " roc[key][ess] = eval(key)\n", + " roc[key][ess] = ast.literal_eval(key)\n", "\n", "# Debug\n", "assert (data[\"cn_probs\"] >= data[\"cn_probs_alt\"]).all()\n", diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index e12d866..4c7106e 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -45,7 +45,6 @@ n_bns: 5 # Number of BNs to sample wi n_infer: 5 # Number of inferences to perform # Other -seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization ## Notes diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index b8f0ca6..bebc3af 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -45,7 +45,6 @@ n_bns: 50 # Number of BNs to sample wi n_infer: 1000 # Number of inferences to perform # Other -seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization ## Notes diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 7421ecf..ec7e54d 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -1,5 +1,6 @@ import gc import multiprocessing # noqa: F401 # pylint: disable=unused-import +import sys import numpy as np # noqa: F401 # pylint: disable=unused-import import pandas as pd @@ -7,7 +8,7 @@ from src.attack import attack_mechanism from src.config import (create_clean_dir, get_out_path, load_config, - set_global_seed) + map_sys_args) from src.defense import defense_mechanism from src.inference import inferences from src.mia import find_epsilon, mia_vs_cn @@ -18,11 +19,13 @@ def main(): # Init configs config = load_config("cn_vs_noisybn") out_path = get_out_path(config) - set_global_seed(config["seed"]) def_mec = config["def_mec"] atk_mec = config["atk_mec"] num_cores = eval(config["num_cores"]) + # Get command-line hyperparameters + def_mec, def_args, atk_mec, atk_args = map_sys_args(sys.argv, config) + # Init the vectors of experiments exp_vec = [ f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() @@ -32,14 +35,14 @@ def main(): print("#" * 5, "Defense mechanism", "#" * 5) create_clean_dir(out_path / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(defense_mechanism)(def_mec, exp, config) for exp in exp_vec + delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) create_clean_dir(out_path / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( - delayed(attack_mechanism)(atk_mec, exp, config) for exp in exp_vec + delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec ) # MIA vs CN @@ -62,9 +65,7 @@ def main(): # Run inferences print("#" * 5, "Run inferences", "#" * 5) create_clean_dir(out_path / config["results_path"] / "inferences") - _ = Parallel(n_jobs=num_cores)( - delayed(inferences)(exp, config) for exp in exp_vec - ) + _ = Parallel(n_jobs=num_cores)(delayed(inferences)(exp, config) for exp in exp_vec) # Clean gc.collect() diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py index e30bde6..84b052f 100644 --- a/experiments/cn_vs_noisybn/generate.py +++ b/experiments/cn_vs_noisybn/generate.py @@ -4,8 +4,7 @@ import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import (create_clean_dir, get_out_path, load_config, - set_global_seed) +from src.config import create_clean_dir, get_out_path, load_config from src.data import generate_naivebayes from src.learning import estimate_bns @@ -15,7 +14,6 @@ def main(): # Init configs config = load_config("cn_vs_noisybn") out_path = get_out_path(config) - set_global_seed(config["seed"]) num_cores = eval(config["num_cores"]) # Generate BNs and data diff --git a/src/attack.py b/src/attack.py index 45a10e8..6ecf059 100644 --- a/src/attack.py +++ b/src/attack.py @@ -4,13 +4,13 @@ import pandas as pd import pyagrum as gum -from src.config import get_out_path +from src.config import get_out_path, set_seed from src.mia import get_ll from src.utils import sample_from_cn # Apply attack mechanism to a BN, namely, derive a BN from a CN -def attack_mechanism(atk_mec, exp, config) -> None: +def attack_mechanism(exp, config, atk_mec, atk_args) -> None: # Get output path out_path = get_out_path(config) @@ -19,6 +19,9 @@ def attack_mechanism(atk_mec, exp, config) -> None: gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') base_path = out_path / config["cns_path"] + # Set seed + set_seed() + # For each data sample ... for sample in range(config["samples"]): @@ -38,7 +41,7 @@ def attack_mechanism(atk_mec, exp, config) -> None: "bn_min": bn_min, "bn_max": bn_max, "data": rpop, - "n_bns": config["n_bns"], + "n_bns": atk_args.get("n_bns", None), }.items() if k in sig.parameters } diff --git a/src/config.py b/src/config.py index bdd519e..7ef0d5f 100644 --- a/src/config.py +++ b/src/config.py @@ -10,6 +10,45 @@ IN_PYTEST = "pytest" in sys.modules +# Get arguments as passed from command-line for experiment +def map_sys_args(sys_args, config) -> tuple: + + # Store parameters + params = dict([arg.split("=") for arg in sys_args]) + with open(f'{config["out_path"]}/{config["exp_meta"]}', "a") as m: + m.write(f"\n Defense & attack parameters: \n {params}") + + # Get defense and attack mechanisms + def_mec = params.pop("def_mec") + atk_mec = params.pop("atk_mec") + + # Get defense parameters + def_args = dict() + if def_mec == "def_idm": + def_args["ess"] = int(params.pop("ess")) + assert def_args["ess"] >= 0 + elif def_mec == "def_ran": + def_args["delta"] = float(params.pop("delta")) + assert def_args["delta"] >= 0 + assert def_args["delta"] <= 1 + else: + raise Exception("Defense not implemented") + + # Save attack parameters + atk_args = dict() + if atk_mec == "atk_mle": + atk_args["n_bns"] = int(params.pop("n_bns")) + assert atk_args["n_bns"] >= 1 + else: + raise Exception("Attack not implemented") + + # Exceptions + if len(params) != 0: + raise Exception(f"Unused parameters: {params}") + + return (def_mec, def_args, atk_mec, atk_args) + + # Read configuration for experiment def load_config(name: str): @@ -24,10 +63,10 @@ def load_config(name: str): # Set global seed -def set_global_seed(seed: int): +def set_seed(): - random.seed(seed) - gum.initRandom(seed) + random.seed(42) + gum.initRandom(42) # Create an empty directory diff --git a/src/data.py b/src/data.py index 0b92887..04bb839 100644 --- a/src/data.py +++ b/src/data.py @@ -1,22 +1,23 @@ +import ast from itertools import product import numpy as np import pyagrum as gum from numpy.random import randint -from src.config import get_out_path, safe_assert, set_global_seed +from src.config import get_out_path, safe_assert, set_seed def generate_naivebayes(config): - # Set seed - set_global_seed(config["seed"]) - # Set paths out_path = get_out_path(config) bns_path = out_path / config["bns_path"] data_path = out_path / config["data_path"] + # Set seed + set_seed() + # Retrieve hyperparameters n_modmax = config["n_modmax"] gpop_ss = config["gpop_ss"] @@ -55,7 +56,7 @@ def generate_naivebayes(config): shuffled_idx = np.random.permutation(gpop.index) pool_idx = shuffled_idx[:pool_ss] - rpop_idx = shuffled_idx[pool_ss: pool_ss + rpop_ss] + rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss] gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) @@ -72,21 +73,21 @@ def generate_naivebayes(config): def generate_randombn(config): - # Set seed - set_global_seed(config["seed"]) - # Set paths out_path = get_out_path(config) bns_path = out_path / config["bns_path"] data_path = out_path / config["data_path"] # Retrieve hyperparameters - n_nodes_vec = eval(config["n_nodes_vec"]) - edge_ratio_vec = eval(config["edge_ratio_vec"]) + n_nodes_vec = ast.literal_eval(config["n_nodes_vec"]) + edge_ratio_vec = ast.literal_eval(config["edge_ratio_vec"]) gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) rpop_ss = int(gpop_ss * config["rpop_prop"]) + # Set seed + set_seed() + # For each configuration ... for i, (n, r) in enumerate(product(n_nodes_vec, edge_ratio_vec)): @@ -113,7 +114,7 @@ def generate_randombn(config): shuffled_idx = np.random.permutation(gpop.index) pool_idx = shuffled_idx[:pool_ss] - rpop_idx = shuffled_idx[pool_ss: pool_ss + rpop_ss] + rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss] gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx) gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx) diff --git a/src/defense.py b/src/defense.py index b5f8106..ad614b9 100644 --- a/src/defense.py +++ b/src/defense.py @@ -4,12 +4,12 @@ import pandas as pd import pyagrum as gum -from src.config import get_out_path, safe_assert +from src.config import get_out_path, safe_assert, set_seed from src.utils import add_counts_to_bn, check_consistency # Apply defense mechanism to a BN, namely, derive a CN from a BN -def defense_mechanism(def_mec, exp, config) -> None: +def defense_mechanism(exp, config, def_mec, def_args) -> None: # Get output path out_path = get_out_path(config) @@ -17,6 +17,9 @@ def defense_mechanism(def_mec, exp, config) -> None: # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + # Set seed + set_seed() + # For each data sample ... for sample in range(config["samples"]): @@ -35,13 +38,13 @@ def defense_mechanism(def_mec, exp, config) -> None: k: v for k, v in { "bn": bn, - "ess": config["ess"], - "delta": config["delta"], + "ess": def_args.get("ess", None), + "delta": def_args.get("delta", None), "data": pool, }.items() if k in sig.parameters } - cn = def_mec_fn(**args) # Keep only `def_mec`` args + cn = def_mec_fn(**args) # Keep only `def_mec` args base_path = out_path / config["cns_path"] cn.saveBNsMinMax( f"{base_path}/bn_min_{exp}_sample{sample}.bif", diff --git a/src/inference.py b/src/inference.py index 63ae8fb..cfbd7a9 100644 --- a/src/inference.py +++ b/src/inference.py @@ -7,7 +7,7 @@ from more_itertools import random_product import src.defense -from src.config import get_out_path, safe_assert, set_global_seed +from src.config import get_out_path, safe_assert, set_seed from src.defense import noisy_bn from src.learning import learn_bn_params from src.utils import get_min_max_bns @@ -15,15 +15,17 @@ def inferences(exp, config): + # Read config out_path = get_out_path(config) target = config["target_var"] def_mec = config["def_mec"] + # Read data auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') eps = auc_meta.loc[auc_meta["exp"] == exp, "eps"].values[0] # Set seed - set_global_seed(config["seed"]) + set_seed() # Set list of evidence evid_vec = [ @@ -180,6 +182,9 @@ def run_inference_bn(bn, target: str, evid_vec): cov = sorted(list(bn.names())) cov.remove(target) + # Set seed + set_seed() + # Debug safe_assert(len(cov) == bn.size() - 1) @@ -213,6 +218,9 @@ def run_inference_cn(cn, target: str, evid_vec, exp: str): cov = sorted(list(bn_min.names())) cov.remove(target) + # Set seed + set_seed() + # Debug safe_assert(len(cov) == bn_min.size() - 1) diff --git a/src/learning.py b/src/learning.py index ba303a0..1b2910f 100644 --- a/src/learning.py +++ b/src/learning.py @@ -1,7 +1,7 @@ import pandas as pd import pyagrum as gum -from src.config import get_out_path, safe_assert +from src.config import get_out_path, safe_assert, set_seed # Learn BN parameters from a given BN and data @@ -22,6 +22,9 @@ def estimate_bns(exp, config) -> None: # Get output path out_path = get_out_path(config) + # Set seed + set_seed() + # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') bn = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') diff --git a/src/mia.py b/src/mia.py index 5a893b1..33fd960 100644 --- a/src/mia.py +++ b/src/mia.py @@ -6,7 +6,7 @@ from scipy.stats import norm from sklearn import metrics -from src.config import get_out_path +from src.config import get_out_path, set_seed from src.defense import noisy_bn @@ -22,6 +22,9 @@ def mia_vs_bn(exp, config) -> None: # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + # Set seed + set_seed() + # For each data sample ... for sample in range(config["samples"]): @@ -75,6 +78,9 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: # Read data gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + # Set seed + set_seed() + # For each data sample ... auc_cns = [] for sample in range(config["samples"]): @@ -139,6 +145,9 @@ def theoretical_power(exp, config) -> None: bn = gum.loadBN(f'{get_out_path(config) / config["bns_path"]}/gt/{exp}.bif') results = pd.read_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv') + # Set seed + set_seed() + # Compute bound bound = math.sqrt(bn.dim() / int(config["gpop_ss"] * config["pool_prop"])) @@ -168,6 +177,9 @@ def find_epsilon(exp, config) -> dict: auc_cn = auc_meta.loc[auc_meta["exp"] == exp, "auc_cn"].values[0] eps_vec = eval(config["eps_vec"]) + # Set seed + set_seed() + eps_best = eps_vec[-1] # For each eps ... diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index 0e493ec..b7ba234 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -35,5 +35,4 @@ delta: 0.3 # Size of random interval fo n_bns: 5 # Number of BNs to sample within the CN # Other -seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 89ddc37..27f77ba 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,7 +1,20 @@ -from experiments.cn_privacy import generate, exp +from experiments.cn_privacy import exp, generate +import sys -def test_integration(): +def test_def_ran_atk_mle(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"]) + + # Generate models and data + generate.main() + + # Run experiment + exp.main() + +def test_def_idm_atk_mle(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"]) # Generate models and data generate.main() diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index 1110645..f2d1b32 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -45,7 +45,6 @@ n_bns: 5 # Number of BNs to sample wi n_infer: 5 # Number of inferences to perform # Other -seed: 42 # Global seed num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization ## Notes diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 1b23814..d648d80 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,7 +1,20 @@ -from experiments.cn_vs_noisybn import generate, exp +import sys +from experiments.cn_vs_noisybn import exp, generate -def test_integration(): +def test_def_ran_atk_mle(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"]) + + # Generate models and data + generate.main() + + # Run experiment + exp.main() + +def test_def_idm_atk_mle(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"]) # Generate models and data generate.main() From f016f0c1bde89f2b39f4bc57cd3e75b891c4735e Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Sat, 15 Nov 2025 18:03:04 +0100 Subject: [PATCH 17/31] Add `docker compose` support --- .gitignore | 3 + Dockerfile | 4 - README.md | 82 ++++++++++---------- compose.yaml | 43 ++++++++++ experiments/cn_privacy/Plot_results.ipynb | 12 +-- experiments/cn_privacy/config.yaml | 8 +- experiments/cn_privacy/config_BAK.yaml | 9 ++- experiments/cn_privacy/exp.py | 14 ++-- experiments/cn_privacy/generate.py | 16 ++-- experiments/cn_vs_noisybn/Plot_results.ipynb | 6 +- experiments/cn_vs_noisybn/config.yaml | 12 +-- experiments/cn_vs_noisybn/config_BAK.yaml | 12 +-- experiments/cn_vs_noisybn/exp.py | 18 ++--- experiments/cn_vs_noisybn/generate.py | 16 ++-- src/attack.py | 10 +-- src/config.py | 12 +-- src/data.py | 18 ++--- src/defense.py | 10 +-- src/inference.py | 12 +-- src/learning.py | 12 +-- src/mia.py | 44 +++++------ test/cn_privacy/config.yaml | 8 +- test/cn_privacy/test_integration.py | 10 +-- test/cn_vs_noisybn/config.yaml | 12 +-- test/cn_vs_noisybn/test_integration.py | 10 +-- 25 files changed, 228 insertions(+), 185 deletions(-) create mode 100644 compose.yaml diff --git a/.gitignore b/.gitignore index 7d18de2..c07aa87 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,8 @@ venv output* +bns +data +*meta.txt bin __pycache__ .pytest_cache diff --git a/Dockerfile b/Dockerfile index ff2a25d..6d65d46 100644 --- a/Dockerfile +++ b/Dockerfile @@ -16,8 +16,4 @@ COPY . . RUN pip install --upgrade pip RUN if [ -f requirements.txt ]; then pip install -r requirements.txt; fi -# Generate models and data -RUN python -m experiments.cn_privacy.generate -RUN python -m experiments.cn_vs_noisybn.generate - CMD /bin/sh diff --git a/README.md b/README.md index a74428e..7b0d962 100644 --- a/README.md +++ b/README.md @@ -2,42 +2,23 @@ Code for paper ["Towards Privacy-Aware Bayesian Networks: A Credal Approach"](https://doi.org/10.3233/FAIA251419) presented at [ECAI 2025](https://ecai2025.org/). -## Setting up Python environment - -Create and activate a Python virtual environment: - -```bash -python3 -m venv venv -source venv/bin/activate[.fish] # use `.fish` suffix if using fish shell -``` - -Install dependencies: - -```bash -pip install -r requirements.txt -``` - -Upgrade dependencies: - -```bash -pip install --upgrade $(pip freeze | cut -d '=' -f 1) -pip freeze > requirements.txt -``` ## Preliminaries ### Experiments -`` is the name of the experiment to run. Each `` has its own directory, which is named the same way. Each of these contains the experiment logic, configuration file (`config.yaml`), output (which path specified in configurations), and a `Plot_results.ipynb` notebook to plot results. +`` is the name of the experiment to run. Each `` has its own directory, which is named the same way. Each of these contains the experiment logic, configuration file (`config.yaml`), eventually generated models and data, output directory, and a `Plot_results.ipynb` notebook to plot results. `` can be one of the following: -1. `cn_privacy`: run membership inference attack against a Bayesian network (BN), its related credal network (CN), and compute the theoretical privacy estimate of BN. The pipeline and results are described in the paper. +1. `cn_privacy`: run membership inference attack against a Bayesian network (BN), its related credal network (CN), and compute the theoretical privacy estimate of BN. -2. `cn_vs_noisybn`: additional experiment, not reported in the paper. It compares two privacy techniques, namely the CN and a noisy version of BN. All models are naive Bayes with target variable T. First, the CN and noisy BN hyperparameters are fine-tuned so that they achieve the same privacy level; then, their accuracy is computed in terms of most probable explanation (MPE) on variable T. +2. `cn_vs_noisybn`: compare two privacy techniques, namely the CN and a noisy version of BN. All models are naive Bayes with target variable T. First, the CN and noisy BN hyperparameters are fine-tuned so that they achieve the same privacy level; then, their accuracy is computed in terms of most probable explanation (MPE) on variable T. + +For additional details, we refer to the paper. ### Attacks and defenses -Each experiment requires the user to specify one defense and one attack mechanisms, plus additional related hyperparameters. Below, the mechanisms and hyperparameters names are reported. Further details are provided in the paper. +Each experiment requires the user to specify one defense and one attack mechanisms, plus additional related hyperparameters. Below, the mechanisms and hyperparameters names are reported. Implemented defenses: - `def_idm`. Requires: `ess` @@ -48,41 +29,64 @@ Implemented attacks: ## Running code -### Local computation +### Using Docker (recommended) -*Notice:* each of the following command will overwrite any related output. +The `compose.yaml` file contains a set of pre-set experiments. Additional ones can also be specified. -1) Generate models and data: +Generate models and data for all experiments: ```bash -python -m experiments..generate +python -m experiments.cn_privacy.generate +python -m experiments.cn_vs_noisybn.generate ``` -2) Run an experiment: +Run one or more experiments with: ```bash -python -m experiments..exp def_mec= [def_params] atk_mec= [atk_params] +docker compose up [service name] ``` -### Using Docker (recommended) +Results will be available under `experiments//output_*`. -1. Build the Docker image: + +### Local computation + +Create and activate a Python virtual environment: ```bash -docker build . -t bnp:2025 +python3 -m venv venv +source venv/bin/activate[.fish] # use `.fish` suffix if using fish shell ``` -2. Run the Docker container: +Install dependencies: ```bash -docker run [-d] [--rm] -v bnp:/workspace bnp:2025 +pip install -r requirements.txt ``` -where `` follows the same syntax as in the local computation. +Upgrade dependencies: + +```bash +pip install --upgrade $(pip freeze | cut -d '=' -f 1) +pip freeze > requirements.txt +``` + +*Notice:* each of the following command will overwrite any related output. + +Generate models and data: + +```bash +python -m experiments..generate +``` + +Run an experiment: + +```bash +python -m experiments..exp def_mec= [param=value] atk_mec= [param=value] +``` -3. Results available at: +Results will be available under `experiments//output`. -`/var/lib/docker/volumes/bnp/_data/`. ## Testing code diff --git a/compose.yaml b/compose.yaml new file mode 100644 index 0000000..6ac16e5 --- /dev/null +++ b/compose.yaml @@ -0,0 +1,43 @@ +version: "3.9" + +# cn_privacy paths +x-cn_privacy_bns: &cn_privacy_bns ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns +x-cn_privacy_data: &cn_privacy_data ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data + +# cn_vs_noisybn paths +x-cn_vs_noisybn_bns: &cn_vs_noisybn_bns ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns +x-cn_vs_noisybn_data: &cn_vs_noisybn_data ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + +services: + cn_privacy_def_ran_atk_mle: + image: bnp:2025 + build: . + volumes: + - *cn_privacy_bns + - *cn_privacy_data + - ./experiments/cn_privacy/output_def_ran_atk_mle:/workspace/experiments/cn_privacy/output + command: [python, -m, experiments.cn_privacy.exp, + def_mec=def_ran, delta=0.6, + atk_mec=atk_mle, n_bns=5] + + cn_privacy_def_idm_atk_mle: + image: bnp:2025 + build: . + volumes: + - *cn_privacy_bns + - *cn_privacy_data + - ./experiments/cn_privacy/output_def_idm_atk_mle:/workspace/experiments/cn_privacy/output + command: [python, -m, experiments.cn_privacy.exp, + def_mec=def_idm, ess=1, + atk_mec=atk_mle, n_bns=5] + + cn_vs_noisybn_def_ran_atk_mle: + image: bnp:2025 + build: . + volumes: + - *cn_vs_noisybn_bns + - *cn_vs_noisybn_data + - ./experiments/cn_vs_noisybn/output_def_ran_atk_mle-prova:/workspace/experiments/cn_vs_noisybn/output + command: [python, -m, experiments.cn_vs_noisybn.exp, + def_mec=def_ran, delta=0.6, + atk_mec=atk_mle, n_bns=5] \ No newline at end of file diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index c2b4161..1930fef 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -33,18 +33,18 @@ "config = load_config(\"cn_privacy\")\n", "\n", "# Get results path\n", - "out_path = get_out_path(config)\n", - "res_path = out_path / config[\"results_path\"]\n", + "cur_dir = get_cur_dir(config)\n", + "res_path = cur_dir / config[\"results_path\"]\n", "\n", "# Get some hyperparameters\n", "error = eval(config[\"error\"])\n", - "with open(f\"{out_path}/exp_meta.txt\", \"r\") as meta:\n", + "with open(f\"{cur_dir}/exp_meta.txt\", \"r\") as meta:\n", " for row in meta:\n", " if re.search(\"\\{.*\\}\", row):\n", " params = ast.literal_eval(row)\n", "\n", "# Choose where to save plots\n", - "plots_path = out_path / \"plots\"\n", + "plots_path = cur_dir / \"plots\"\n", "create_clean_dir(plots_path)" ] }, @@ -169,7 +169,7 @@ "\n", "# Plot title function\n", "def get_title(exp: str):\n", - " with open(f\"{out_path}/exp_meta.txt\", \"r\") as meta:\n", + " with open(f\"{cur_dir}/exp_meta.txt\", \"r\") as meta:\n", " for row in meta:\n", " if exp in row:\n", " pieces = row.split()\n", diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml index 8ba91a1..ff72094 100644 --- a/experiments/cn_privacy/config.yaml +++ b/experiments/cn_privacy/config.yaml @@ -1,12 +1,12 @@ ## Configuration file # Paths -out_path: experiments/cn_privacy/output # Output path +cur_dir: experiments/cn_privacy # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs -cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool -atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs data_path: data # Where to save data as generated from ground-truth BNs -results_path: results # Where to save the experiment results +results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments # Models diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index 806fbf9..245f5df 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -1,12 +1,13 @@ ## Configuration file # Paths -out_path: experiments/cn_privacy/output # Output path +# Paths +cur_dir: experiments/cn_privacy # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs -cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool -atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs data_path: data # Where to save data as generated from ground-truth BNs -results_path: results # Where to save the experiment results +results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments # Models diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 51141a2..77125c7 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -6,7 +6,7 @@ from joblib import Parallel, delayed from src.attack import attack_mechanism -from src.config import (create_clean_dir, get_out_path, load_config, +from src.config import (create_clean_dir, get_cur_dir, load_config, map_sys_args) from src.defense import defense_mechanism from src.mia import mia_vs_bn, mia_vs_cn, theoretical_power @@ -16,7 +16,7 @@ def main(): # Init configs config = load_config("cn_privacy") - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) num_cores = eval(config["num_cores"]) # Get command-line hyperparameters @@ -24,31 +24,31 @@ def main(): # Init the vectors of experiments exp_vec = [ - f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() + f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() ] # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) - create_clean_dir(out_path / config["cns_path"]) + create_clean_dir(cur_dir / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) - create_clean_dir(out_path / config["atk_path"]) + create_clean_dir(cur_dir / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec ) # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) - create_clean_dir(out_path / config["results_path"] / "cns") + create_clean_dir(cur_dir / config["results_path"] / "cns") _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_cn)(exp, config) for exp in exp_vec) # MIA vs BN print("#" * 5, "MIA vs BN", "#" * 5) - create_clean_dir(out_path / config["results_path"] / "bns") + create_clean_dir(cur_dir / config["results_path"] / "bns") _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_bn)(exp, config) for exp in exp_vec) # Compute theoretical power diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py index 6028001..7e5552c 100644 --- a/experiments/cn_privacy/generate.py +++ b/experiments/cn_privacy/generate.py @@ -4,7 +4,7 @@ import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import create_clean_dir, get_out_path, load_config +from src.config import create_clean_dir, get_cur_dir, load_config from src.data import generate_randombn from src.learning import estimate_bns @@ -13,25 +13,25 @@ def main(): # Init configs config = load_config("cn_privacy") - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) num_cores = eval(config["num_cores"]) # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "gt") - create_clean_dir(out_path / config["data_path"]) - open(f'{out_path}/{config["exp_meta"]}', "a").close() + create_clean_dir(cur_dir / config["bns_path"] / "gt") + create_clean_dir(cur_dir / config["data_path"]) + open(f'{cur_dir}/{config["exp_meta"]}', "a").close() generate_randombn(config) # Init the vectors of experiments exp_vec = [ - f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() + f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() ] # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "rpop") - create_clean_dir(out_path / config["bns_path"] / "pool") + create_clean_dir(cur_dir / config["bns_path"] / "rpop") + create_clean_dir(cur_dir / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( delayed(estimate_bns)(exp, config) for exp in exp_vec ) diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 67d9b6b..514cfed 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -42,11 +42,11 @@ "config = load_config(\"cn_vs_noisybn\")\n", "\n", "# Get results path\n", - "out_path = get_out_path(config)\n", - "res_path = out_path / config[\"results_path\"] / \"inferences\"\n", + "cur_dir = get_cur_dir(config)\n", + "res_path = cur_dir / config[\"results_path\"] / \"inferences\"\n", "\n", "# Choose where to save plots\n", - "plots_path = out_path / \"plots\"\n", + "plots_path = cur_dir / \"plots\"\n", "create_clean_dir(plots_path)" ] }, diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 4c7106e..f3fe153 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -1,15 +1,15 @@ ## Configuration file # Paths -out_path: experiments/cn_vs_noisybn/output # Output path +cur_dir: experiments/cn_vs_noisybn # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs -cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool -atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs -noisy_path: noisy # Where to save noisy BNs +cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs +noisy_path: output/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs -results_path: results # Where to save the experiment results +results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments -auc_meta: auc_meta.csv # File of metadata for AUCs +auc_meta: output/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index bebc3af..7ab71ca 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -1,15 +1,15 @@ ## Configuration file # Paths -out_path: experiments/cn_vs_noisybn/output # Output path +cur_dir: experiments/cn_vs_noisybn # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs -cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool -atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs -noisy_path: noisy # Where to save noisy BNs +cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs +noisy_path: output/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs -results_path: results # Where to save the experiment results +results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments -auc_meta: auc_meta.csv # File of metadata for AUCs +auc_meta: output/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index ec7e54d..f2a07f0 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -7,7 +7,7 @@ from joblib import Parallel, delayed from src.attack import attack_mechanism -from src.config import (create_clean_dir, get_out_path, load_config, +from src.config import (create_clean_dir, get_cur_dir, load_config, map_sys_args) from src.defense import defense_mechanism from src.inference import inferences @@ -18,7 +18,7 @@ def main(): # Init configs config = load_config("cn_vs_noisybn") - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) def_mec = config["def_mec"] atk_mec = config["atk_mec"] num_cores = eval(config["num_cores"]) @@ -28,19 +28,19 @@ def main(): # Init the vectors of experiments exp_vec = [ - f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() + f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() ] # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) - create_clean_dir(out_path / config["cns_path"]) + create_clean_dir(cur_dir / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec ) # Attack mechanism print("#" * 5, "Attack mechanism", "#" * 5) - create_clean_dir(out_path / config["atk_path"]) + create_clean_dir(cur_dir / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec ) @@ -51,20 +51,20 @@ def main(): delayed(mia_vs_cn)(exp, config, save_res=False) for exp in exp_vec ) res = pd.DataFrame(res) - res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) + res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Find eps s.t. |AUC(eps) - AUC(CN)| < tol print("#" * 5, "Get epsilon", "#" * 5) - create_clean_dir(out_path / config["noisy_path"]) + create_clean_dir(cur_dir / config["noisy_path"]) res = Parallel(n_jobs=num_cores)( delayed(find_epsilon)(exp, config) for exp in exp_vec ) res = pd.DataFrame(res) - res.to_csv(f'{out_path}/{config["auc_meta"]}', index=False) + res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Run inferences print("#" * 5, "Run inferences", "#" * 5) - create_clean_dir(out_path / config["results_path"] / "inferences") + create_clean_dir(cur_dir / config["results_path"] / "inferences") _ = Parallel(n_jobs=num_cores)(delayed(inferences)(exp, config) for exp in exp_vec) # Clean diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py index 84b052f..0f31b1b 100644 --- a/experiments/cn_vs_noisybn/generate.py +++ b/experiments/cn_vs_noisybn/generate.py @@ -4,7 +4,7 @@ import numpy as np # noqa: F401 # pylint: disable=unused-import from joblib import Parallel, delayed -from src.config import create_clean_dir, get_out_path, load_config +from src.config import create_clean_dir, get_cur_dir, load_config from src.data import generate_naivebayes from src.learning import estimate_bns @@ -13,25 +13,25 @@ def main(): # Init configs config = load_config("cn_vs_noisybn") - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) num_cores = eval(config["num_cores"]) # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "gt") - create_clean_dir(out_path / config["data_path"]) - open(f'{out_path}/{config["exp_meta"]}', "a").close() + create_clean_dir(cur_dir / config["bns_path"] / "gt") + create_clean_dir(cur_dir / config["data_path"]) + open(f'{cur_dir}/{config["exp_meta"]}', "a").close() generate_naivebayes(config) # Init the vectors of experiments exp_vec = [ - f.stem for f in (out_path / config["data_path"]).iterdir() if f.is_file() + f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() ] # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) - create_clean_dir(out_path / config["bns_path"] / "rpop") - create_clean_dir(out_path / config["bns_path"] / "pool") + create_clean_dir(cur_dir / config["bns_path"] / "rpop") + create_clean_dir(cur_dir / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( delayed(estimate_bns)(exp, config) for exp in exp_vec ) diff --git a/src/attack.py b/src/attack.py index 6ecf059..996cd5f 100644 --- a/src/attack.py +++ b/src/attack.py @@ -4,7 +4,7 @@ import pandas as pd import pyagrum as gum -from src.config import get_out_path, set_seed +from src.config import get_cur_dir, set_seed from src.mia import get_ll from src.utils import sample_from_cn @@ -13,11 +13,11 @@ def attack_mechanism(exp, config, atk_mec, atk_args) -> None: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - base_path = out_path / config["cns_path"] + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') + base_path = cur_dir / config["cns_path"] # Set seed set_seed() @@ -47,7 +47,7 @@ def attack_mechanism(exp, config, atk_mec, atk_args) -> None: } bn = atk_mec_fn(**args) gum.saveBN( - bn, f'{out_path / config["atk_path"]}/{f"bn_{exp}_sample{sample}"}.bif' + bn, f'{cur_dir / config["atk_path"]}/{f"bn_{exp}_sample{sample}"}.bif' ) return diff --git a/src/config.py b/src/config.py index 7ef0d5f..c485995 100644 --- a/src/config.py +++ b/src/config.py @@ -14,8 +14,8 @@ def map_sys_args(sys_args, config) -> tuple: # Store parameters - params = dict([arg.split("=") for arg in sys_args]) - with open(f'{config["out_path"]}/{config["exp_meta"]}', "a") as m: + params = dict([arg.split("=") for arg in sys_args if "=" in arg]) + with open(f'{config["cur_dir"]}/{config["exp_meta"]}', "a") as m: m.write(f"\n Defense & attack parameters: \n {params}") # Get defense and attack mechanisms @@ -81,15 +81,15 @@ def create_clean_dir(path: Path): # Get output path -def get_out_path(config): +def get_cur_dir(config): root_path = get_root_path() - out_path = config["out_path"] + cur_dir = config["cur_dir"] - return root_path / out_path + return root_path / cur_dir -# Get root directory +# Get root (project) directory def get_root_path(): return Path(__file__).resolve().parents[1] diff --git a/src/data.py b/src/data.py index 04bb839..3e1cb3d 100644 --- a/src/data.py +++ b/src/data.py @@ -5,15 +5,15 @@ import pyagrum as gum from numpy.random import randint -from src.config import get_out_path, safe_assert, set_seed +from src.config import get_cur_dir, safe_assert, set_seed def generate_naivebayes(config): # Set paths - out_path = get_out_path(config) - bns_path = out_path / config["bns_path"] - data_path = out_path / config["data_path"] + cur_dir = get_cur_dir(config) + bns_path = cur_dir / config["bns_path"] + data_path = cur_dir / config["data_path"] # Set seed set_seed() @@ -38,7 +38,7 @@ def generate_naivebayes(config): bn = gum.fastBN(bn_str) gum.saveBN(bn, f'{bns_path / "gt"}/{f"exp{i}"}.bif') - with open(f'{out_path}/{config["exp_meta"]}', "a") as m: + with open(f'{cur_dir}/{config["exp_meta"]}', "a") as m: m.write( f'- exp{i}. Naive Bayes: {config["n_nodes"]} nodes. Complexity: {bn.dim()} Max categories: {n_modmax}\n' ) @@ -74,9 +74,9 @@ def generate_naivebayes(config): def generate_randombn(config): # Set paths - out_path = get_out_path(config) - bns_path = out_path / config["bns_path"] - data_path = out_path / config["data_path"] + cur_dir = get_cur_dir(config) + bns_path = cur_dir / config["bns_path"] + data_path = cur_dir / config["data_path"] # Retrieve hyperparameters n_nodes_vec = ast.literal_eval(config["n_nodes_vec"]) @@ -96,7 +96,7 @@ def generate_randombn(config): bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=config["n_modmax"]) gum.saveBN(bn, f'{bns_path / "gt"}/{f"exp{i}"}.bif') - with open(f'{out_path}/{config["exp_meta"]}', "a") as m: + with open(f'{cur_dir}/{config["exp_meta"]}', "a") as m: m.write( f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()}\n" ) diff --git a/src/defense.py b/src/defense.py index ad614b9..a72aa65 100644 --- a/src/defense.py +++ b/src/defense.py @@ -4,7 +4,7 @@ import pandas as pd import pyagrum as gum -from src.config import get_out_path, safe_assert, set_seed +from src.config import get_cur_dir, safe_assert, set_seed from src.utils import add_counts_to_bn, check_consistency @@ -12,10 +12,10 @@ def defense_mechanism(exp, config, def_mec, def_args) -> None: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') # Set seed set_seed() @@ -25,7 +25,7 @@ def defense_mechanism(exp, config, def_mec, def_args) -> None: # ... read the related BN bn = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) # ... retrieve pool, ... @@ -45,7 +45,7 @@ def defense_mechanism(exp, config, def_mec, def_args) -> None: if k in sig.parameters } cn = def_mec_fn(**args) # Keep only `def_mec` args - base_path = out_path / config["cns_path"] + base_path = cur_dir / config["cns_path"] cn.saveBNsMinMax( f"{base_path}/bn_min_{exp}_sample{sample}.bif", f"{base_path}/bn_max_{exp}_sample{sample}.bif", diff --git a/src/inference.py b/src/inference.py index cfbd7a9..cddef45 100644 --- a/src/inference.py +++ b/src/inference.py @@ -7,7 +7,7 @@ from more_itertools import random_product import src.defense -from src.config import get_out_path, safe_assert, set_seed +from src.config import get_cur_dir, safe_assert, set_seed from src.defense import noisy_bn from src.learning import learn_bn_params from src.utils import get_min_max_bns @@ -16,12 +16,12 @@ def inferences(exp, config): # Read config - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) target = config["target_var"] def_mec = config["def_mec"] # Read data - auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') + auc_meta = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') eps = auc_meta.loc[auc_meta["exp"] == exp, "eps"].values[0] # Set seed @@ -34,8 +34,8 @@ def inferences(exp, config): ] # Store ground-truth BN - gt = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + gt = gum.loadBN(f'{cur_dir / config["bns_path"]}/gt/{exp}.bif') + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') # Learn BN from gpop #TODO: save results bn = learn_bn_params(gt, gpop) @@ -80,7 +80,7 @@ def inferences(exp, config): ) results.to_csv( - f'{out_path / config["results_path"]}/inferences/{exp}.csv', + f'{cur_dir / config["results_path"]}/inferences/{exp}.csv', index=False, ) diff --git a/src/learning.py b/src/learning.py index 1b2910f..955375b 100644 --- a/src/learning.py +++ b/src/learning.py @@ -1,7 +1,7 @@ import pandas as pd import pyagrum as gum -from src.config import get_out_path, safe_assert, set_seed +from src.config import get_cur_dir, safe_assert, set_seed # Learn BN parameters from a given BN and data @@ -20,14 +20,14 @@ def learn_bn_params(bn, data): def estimate_bns(exp, config) -> None: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Set seed set_seed() # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') - bn = gum.loadBN(f'{out_path / config["bns_path"]}/gt/{exp}.bif') + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') + bn = gum.loadBN(f'{cur_dir / config["bns_path"]}/gt/{exp}.bif') n_nodes = len(bn.nodes()) gpop_ss = config["gpop_ss"] rpop_ss = int(gpop_ss * config["rpop_prop"]) @@ -48,14 +48,14 @@ def estimate_bns(exp, config) -> None: bn_learnt = learn_bn_params(bn, rpop) gum.saveBN( bn_learnt, - f'{out_path / config["bns_path"] / "rpop"}/{f"bn_{exp}_sample{sample}"}.bif', + f'{cur_dir / config["bns_path"] / "rpop"}/{f"bn_{exp}_sample{sample}"}.bif', ) # ... estimate BN from pool, ... bn_learnt = learn_bn_params(bn, pool) gum.saveBN( bn_learnt, - f'{out_path / config["bns_path"] / "pool"}/{f"bn_{exp}_sample{sample}"}.bif', + f'{cur_dir / config["bns_path"] / "pool"}/{f"bn_{exp}_sample{sample}"}.bif', ) # Debug diff --git a/src/mia.py b/src/mia.py index 33fd960..bc1049c 100644 --- a/src/mia.py +++ b/src/mia.py @@ -6,7 +6,7 @@ from scipy.stats import norm from sklearn import metrics -from src.config import get_out_path, set_seed +from src.config import get_cur_dir, set_seed from src.defense import noisy_bn @@ -14,13 +14,13 @@ def mia_vs_bn(exp, config) -> None: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Init results results = pd.DataFrame({"error": eval(config["error"])}) # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') # Set seed set_seed() @@ -30,10 +30,10 @@ def mia_vs_bn(exp, config) -> None: # ... read the BNs as estimated from rpop and pool, ... bn_theta = gum.loadBN( - f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" ) bn_theta_hat = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) @@ -63,20 +63,20 @@ def mia_vs_bn(exp, config) -> None: # log.write(traceback.format_exc()) # Save results - results.to_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) + results.to_csv(f'{cur_dir}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) # MIA attack vs a CN def mia_vs_cn(exp, config, save_res=True) -> dict: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Init results results = pd.DataFrame({"error": eval(config["error"])}) # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') # Set seed set_seed() @@ -87,12 +87,12 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: # ... read the BN as inferred from the CN bn_theta_hat = gum.loadBN( - f'{out_path}/{config["atk_path"]}/bn_{exp}_sample{sample}.bif' + f'{cur_dir}/{config["atk_path"]}/bn_{exp}_sample{sample}.bif' ) # ... read the BN as estimated from rpop, ... bn_theta = gum.loadBN( - f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" ) bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat) @@ -128,7 +128,7 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: # Save results if save_res: results.to_csv( - f'{out_path}/{config["results_path"]}/cns/cn_{exp}.csv', + f'{cur_dir}/{config["results_path"]}/cns/cn_{exp}.csv', index=False, ) @@ -139,11 +139,11 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: def theoretical_power(exp, config) -> None: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Read data - bn = gum.loadBN(f'{get_out_path(config) / config["bns_path"]}/gt/{exp}.bif') - results = pd.read_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv') + bn = gum.loadBN(f'{get_cur_dir(config) / config["bns_path"]}/gt/{exp}.bif') + results = pd.read_csv(f'{cur_dir}/{config["results_path"]}/bns/bn_{exp}.csv') # Set seed set_seed() @@ -158,7 +158,7 @@ def theoretical_power(exp, config) -> None: # Save results results["power_bound"] = beta - results.to_csv(f'{out_path}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) + results.to_csv(f'{cur_dir}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) return @@ -167,13 +167,13 @@ def theoretical_power(exp, config) -> None: def find_epsilon(exp, config) -> dict: # Get output path - out_path = get_out_path(config) + cur_dir = get_cur_dir(config) # Read data - gpop = pd.read_csv(f'{out_path / config["data_path"]}/{exp}.csv') + gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) - auc_meta = pd.read_csv(f'{out_path}/{config["auc_meta"]}') + auc_meta = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') auc_cn = auc_meta.loc[auc_meta["exp"] == exp, "auc_cn"].values[0] eps_vec = eval(config["eps_vec"]) @@ -195,10 +195,10 @@ def find_epsilon(exp, config) -> dict: # ... read the BNs as estimated from rpop and pool, ... bn_theta = gum.loadBN( - f"{out_path}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" ) bn_theta_hat = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) # Get noisy BN @@ -243,13 +243,13 @@ def find_epsilon(exp, config) -> dict: # Save noisy BNs for sample in range(config["samples"]): bn_theta_hat = gum.loadBN( - f"{out_path}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" ) scale = (2 * bn_theta_hat.size()) / (pool_ss * eps_best) bn_noisy = noisy_bn(bn_theta_hat, scale) gum.saveBN( bn_noisy, - f'{out_path / config["noisy_path"]}/{f"bn_{exp}_sample{sample}"}.bif', + f'{cur_dir / config["noisy_path"]}/{f"bn_{exp}_sample{sample}"}.bif', ) return { diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index b7ba234..a689a88 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -1,12 +1,12 @@ ## Configuration file # Paths -out_path: test/cn_privacy/output # Output path +cur_dir: test/cn_privacy # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs -cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool -atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs +cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs data_path: data # Where to save data as generated from ground-truth BNs -results_path: results # Where to save the experiment results +results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments # Models diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 27f77ba..d6e8397 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,14 +1,15 @@ from experiments.cn_privacy import exp, generate import sys +def test_generation(): + + # Generate models and data + generate.main() def test_def_ran_atk_mle(monkeypatch): monkeypatch.setattr(sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"]) - # Generate models and data - generate.main() - # Run experiment exp.main() @@ -16,8 +17,5 @@ def test_def_idm_atk_mle(monkeypatch): monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"]) - # Generate models and data - generate.main() - # Run experiment exp.main() diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index f2d1b32..ea694b1 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -1,15 +1,15 @@ ## Configuration file # Paths -out_path: test/cn_vs_noisybn/output # Output path +cur_dir: experiments/cn_vs_noisybn # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs -cns_path: cns # Where to save CNs as obtained by def-mec from BNs learnt from pool -atk_path: bns_atk # Where to save BNs as obtained by atk-mec from CNs -noisy_path: noisy # Where to save noisy BNs +cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool +atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs +noisy_path: output/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs -results_path: results # Where to save the experiment results +results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments -auc_meta: auc_meta.csv # File of metadata for AUCs +auc_meta: output/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index d648d80..37feed5 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,14 +1,15 @@ import sys from experiments.cn_vs_noisybn import exp, generate +def test_generation(): + + # Generate models and data + generate.main() def test_def_ran_atk_mle(monkeypatch): monkeypatch.setattr(sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"]) - # Generate models and data - generate.main() - # Run experiment exp.main() @@ -16,8 +17,5 @@ def test_def_idm_atk_mle(monkeypatch): monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"]) - # Generate models and data - generate.main() - # Run experiment exp.main() From b3a682631b6a4a3be60fa13a9d70221a74f33e83 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Mon, 17 Nov 2025 12:50:50 +0100 Subject: [PATCH 18/31] Add `generate_compose.py` to automate the creation of Docker compose file (`compose.yaml`) --- README.md | 6 +- compose.yaml | 89 ++++++++++++++++---------- experiments/cn_privacy/exp.py | 7 +- experiments/cn_privacy/generate.py | 4 +- experiments/cn_vs_noisybn/exp.py | 7 +- experiments/cn_vs_noisybn/generate.py | 4 +- generate_compose.py | 49 ++++++++++++++ src/mia.py | 6 ++ test/cn_privacy/test_integration.py | 11 +++- test/cn_vs_noisybn/test_integration.py | 11 +++- 10 files changed, 136 insertions(+), 58 deletions(-) create mode 100644 generate_compose.py diff --git a/README.md b/README.md index 7b0d962..9731ca1 100644 --- a/README.md +++ b/README.md @@ -31,9 +31,9 @@ Implemented attacks: ### Using Docker (recommended) -The `compose.yaml` file contains a set of pre-set experiments. Additional ones can also be specified. +The `compose.yaml` file contains a set of pre-set experiments. Additional ones can also be specified. The `generate_compose.py` file helps in generating them automatically. -Generate models and data for all experiments: +Generate models and data for all experiments (controlled by `config.yaml`): ```bash python -m experiments.cn_privacy.generate @@ -73,7 +73,7 @@ pip freeze > requirements.txt *Notice:* each of the following command will overwrite any related output. -Generate models and data: +Generate models and data (controlled by `config.yaml`): ```bash python -m experiments..generate diff --git a/compose.yaml b/compose.yaml index 6ac16e5..450b399 100644 --- a/compose.yaml +++ b/compose.yaml @@ -1,43 +1,62 @@ -version: "3.9" - -# cn_privacy paths -x-cn_privacy_bns: &cn_privacy_bns ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns -x-cn_privacy_data: &cn_privacy_data ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - -# cn_vs_noisybn paths -x-cn_vs_noisybn_bns: &cn_vs_noisybn_bns ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns -x-cn_vs_noisybn_data: &cn_vs_noisybn_data ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - +version: '3.9' services: - cn_privacy_def_ran_atk_mle: - image: bnp:2025 + cn_privacy_def_idm_atk_mle_ess1: build: . - volumes: - - *cn_privacy_bns - - *cn_privacy_data - - ./experiments/cn_privacy/output_def_ran_atk_mle:/workspace/experiments/cn_privacy/output - command: [python, -m, experiments.cn_privacy.exp, - def_mec=def_ran, delta=0.6, - atk_mec=atk_mle, n_bns=5] - - cn_privacy_def_idm_atk_mle: + command: + - python + - -m + - experiments.cn_privacy.exp + - def_mec=def_idm + - ess=1 + - atk_mec=atk_mle + - n_bns=5 image: bnp:2025 + volumes: + - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns + - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data + - ./experiments/cn_privacy/output_def_idm_atk_mle_ess1:/workspace/experiments/cn_privacy/output + cn_privacy_def_idm_atk_mle_ess2: build: . + command: + - python + - -m + - experiments.cn_privacy.exp + - def_mec=def_idm + - ess=2 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 volumes: - - *cn_privacy_bns - - *cn_privacy_data - - ./experiments/cn_privacy/output_def_idm_atk_mle:/workspace/experiments/cn_privacy/output - command: [python, -m, experiments.cn_privacy.exp, - def_mec=def_idm, ess=1, - atk_mec=atk_mle, n_bns=5] - - cn_vs_noisybn_def_ran_atk_mle: + - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns + - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data + - ./experiments/cn_privacy/output_def_idm_atk_mle_ess2:/workspace/experiments/cn_privacy/output + cn_privacy_def_ran_atk_mle_delta0.2: + build: . + command: + - python + - -m + - experiments.cn_privacy.exp + - def_mec=def_ran + - delta=0.2 + - atk_mec=atk_mle + - n_bns=5 image: bnp:2025 + volumes: + - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns + - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data + - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.2:/workspace/experiments/cn_privacy/output + cn_privacy_def_ran_atk_mle_delta0.4: build: . + command: + - python + - -m + - experiments.cn_privacy.exp + - def_mec=def_ran + - delta=0.4 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 volumes: - - *cn_vs_noisybn_bns - - *cn_vs_noisybn_data - - ./experiments/cn_vs_noisybn/output_def_ran_atk_mle-prova:/workspace/experiments/cn_vs_noisybn/output - command: [python, -m, experiments.cn_vs_noisybn.exp, - def_mec=def_ran, delta=0.6, - atk_mec=atk_mle, n_bns=5] \ No newline at end of file + - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns + - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data + - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.4:/workspace/experiments/cn_privacy/output diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 77125c7..5d83581 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -6,8 +6,7 @@ from joblib import Parallel, delayed from src.attack import attack_mechanism -from src.config import (create_clean_dir, get_cur_dir, load_config, - map_sys_args) +from src.config import create_clean_dir, get_cur_dir, load_config, map_sys_args from src.defense import defense_mechanism from src.mia import mia_vs_bn, mia_vs_cn, theoretical_power @@ -23,9 +22,7 @@ def main(): def_mec, def_args, atk_mec, atk_args = map_sys_args(sys.argv, config) # Init the vectors of experiments - exp_vec = [ - f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() - ] + exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py index 7e5552c..1ab6c46 100644 --- a/experiments/cn_privacy/generate.py +++ b/experiments/cn_privacy/generate.py @@ -24,9 +24,7 @@ def main(): generate_randombn(config) # Init the vectors of experiments - exp_vec = [ - f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() - ] + exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index f2a07f0..c294ad0 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -7,8 +7,7 @@ from joblib import Parallel, delayed from src.attack import attack_mechanism -from src.config import (create_clean_dir, get_cur_dir, load_config, - map_sys_args) +from src.config import create_clean_dir, get_cur_dir, load_config, map_sys_args from src.defense import defense_mechanism from src.inference import inferences from src.mia import find_epsilon, mia_vs_cn @@ -27,9 +26,7 @@ def main(): def_mec, def_args, atk_mec, atk_args = map_sys_args(sys.argv, config) # Init the vectors of experiments - exp_vec = [ - f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() - ] + exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Defense mechanism print("#" * 5, "Defense mechanism", "#" * 5) diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py index 0f31b1b..2087d74 100644 --- a/experiments/cn_vs_noisybn/generate.py +++ b/experiments/cn_vs_noisybn/generate.py @@ -24,9 +24,7 @@ def main(): generate_naivebayes(config) # Init the vectors of experiments - exp_vec = [ - f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file() - ] + exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Estimate BNs from rpop and pool print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) diff --git a/generate_compose.py b/generate_compose.py new file mode 100644 index 0000000..45d39bb --- /dev/null +++ b/generate_compose.py @@ -0,0 +1,49 @@ +import yaml +from itertools import product + +# Set hyperparameters +names = ["cn_privacy"] +def_mecs = {"def_idm":{"ess":[1, 2]}, "def_ran":{"delta":[0.2, 0.4]}} +atk_mecs = {"atk_mle":{"n_bns":[5]}} + +# Initialize the `compose.yaml` file +init = {"version": "3.9"} +with open("compose.yaml", "w") as f: + yaml.dump(init, f, default_flow_style=False) + +# For any configuration ... +data = {"services": dict()} + +for name, def_mec, atk_mec in product(names, def_mecs.keys(), atk_mecs.keys()): + + def_params = def_mecs[def_mec] + atk_params = atk_mecs[atk_mec] + + # Assumption: each defense and attack has only 1 hyperparameter + for def_par, atk_par in product(list(def_params.values())[0], list(atk_params.values())[0]): + + # ... set the related volume, ... + volumes = [ + f"./experiments/{name}/bns:/workspace/experiments/{name}/bns", + f"./experiments/{name}/data:/workspace/experiments/{name}/data", + f"./experiments/{name}/output_{def_mec}_{atk_mec}_{list(def_params.keys())[0]}{def_par}:/workspace/experiments/{name}/output", + ] + + # ... and create the experiment + data["services"][f"{name}_{def_mec}_{atk_mec}_{list(def_params.keys())[0]}{def_par}"] = { + "image": "bnp:2025", + "build": ".", + "volumes": volumes, + "command": [ + "python", + "-m", + f"experiments.{name}.exp", + f"def_mec={def_mec}", + f"{list(def_params.keys())[0]}={def_par}", + f"atk_mec={atk_mec}", + f"{list(atk_params.keys())[0]}={atk_par}", ], + } + +# Write file +with open("compose.yaml", "a") as f: + yaml.dump(data, f, default_flow_style=False) diff --git a/src/mia.py b/src/mia.py index bc1049c..6122a79 100644 --- a/src/mia.py +++ b/src/mia.py @@ -163,6 +163,12 @@ def theoretical_power(exp, config) -> None: return +def find_epsilon_new(): + # TODO + + return + + # Find eps s.t. |AUC(eps) - AUC(CN)| < tol def find_epsilon(exp, config) -> dict: diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index d6e8397..9b63b13 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,21 +1,28 @@ from experiments.cn_privacy import exp, generate import sys + def test_generation(): # Generate models and data generate.main() + def test_def_ran_atk_mle(monkeypatch): - monkeypatch.setattr(sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"]) + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"] + ) # Run experiment exp.main() + def test_def_idm_atk_mle(monkeypatch): - monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"]) + monkeypatch.setattr( + sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"] + ) # Run experiment exp.main() diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 37feed5..7a1f85e 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,21 +1,28 @@ import sys from experiments.cn_vs_noisybn import exp, generate + def test_generation(): # Generate models and data generate.main() + def test_def_ran_atk_mle(monkeypatch): - monkeypatch.setattr(sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"]) + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle", "n_bns=5"] + ) # Run experiment exp.main() + def test_def_idm_atk_mle(monkeypatch): - monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"]) + monkeypatch.setattr( + sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle", "n_bns=5"] + ) # Run experiment exp.main() From b548562939f06f54ed78501a332335b1d37b0f6b Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Mon, 17 Nov 2025 17:49:26 +0100 Subject: [PATCH 19/31] Get `eps` for each data sample instead of mean --- compose.yaml | 90 ++++++++++++++++++ experiments/cn_vs_noisybn/exp.py | 12 +-- generate_compose.py | 7 +- src/attack.py | 2 +- src/config.py | 2 +- src/defense.py | 2 +- src/inference.py | 4 +- src/learning.py | 2 +- src/mia.py | 158 ++++++++++++++----------------- 9 files changed, 174 insertions(+), 105 deletions(-) diff --git a/compose.yaml b/compose.yaml index 450b399..200d4f8 100644 --- a/compose.yaml +++ b/compose.yaml @@ -15,6 +15,21 @@ services: - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - ./experiments/cn_privacy/output_def_idm_atk_mle_ess1:/workspace/experiments/cn_privacy/output + cn_privacy_def_idm_atk_mle_ess10: + build: . + command: + - python + - -m + - experiments.cn_privacy.exp + - def_mec=def_idm + - ess=10 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 + volumes: + - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns + - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data + - ./experiments/cn_privacy/output_def_idm_atk_mle_ess10:/workspace/experiments/cn_privacy/output cn_privacy_def_idm_atk_mle_ess2: build: . command: @@ -60,3 +75,78 @@ services: - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.4:/workspace/experiments/cn_privacy/output + cn_vs_noisybn_def_idm_atk_mle_ess1: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_idm + - ess=1 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess1:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_idm_atk_mle_ess10: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_idm + - ess=10 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess10:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_idm_atk_mle_ess2: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_idm + - ess=2 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess2:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_ran_atk_mle_delta0.2: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_ran + - delta=0.2 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_ran_atk_mle_delta0.2:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_ran_atk_mle_delta0.4: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_ran + - delta=0.4 + - atk_mec=atk_mle + - n_bns=5 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_ran_atk_mle_delta0.4:/workspace/experiments/cn_vs_noisybn/output diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index c294ad0..86aec22 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -18,8 +18,6 @@ def main(): # Init configs config = load_config("cn_vs_noisybn") cur_dir = get_cur_dir(config) - def_mec = config["def_mec"] - atk_mec = config["atk_mec"] num_cores = eval(config["num_cores"]) # Get command-line hyperparameters @@ -45,10 +43,10 @@ def main(): # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) res = Parallel(n_jobs=num_cores)( - delayed(mia_vs_cn)(exp, config, save_res=False) for exp in exp_vec + delayed(mia_vs_cn)(exp, config, save_power_res=False) for exp in exp_vec ) - res = pd.DataFrame(res) - res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) + auc_res = pd.concat((i for i in res), axis=0) + auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Find eps s.t. |AUC(eps) - AUC(CN)| < tol print("#" * 5, "Get epsilon", "#" * 5) @@ -56,8 +54,8 @@ def main(): res = Parallel(n_jobs=num_cores)( delayed(find_epsilon)(exp, config) for exp in exp_vec ) - res = pd.DataFrame(res) - res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) + auc_res = pd.concat((i for i in res), axis=0) + auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Run inferences print("#" * 5, "Run inferences", "#" * 5) diff --git a/generate_compose.py b/generate_compose.py index 45d39bb..cb3dc60 100644 --- a/generate_compose.py +++ b/generate_compose.py @@ -2,8 +2,8 @@ from itertools import product # Set hyperparameters -names = ["cn_privacy"] -def_mecs = {"def_idm":{"ess":[1, 2]}, "def_ran":{"delta":[0.2, 0.4]}} +names = ["cn_privacy", "cn_vs_noisybn"] +def_mecs = {"def_idm":{"ess":[1, 2, 10]}, "def_ran":{"delta":[0.2, 0.4]}} atk_mecs = {"atk_mle":{"n_bns":[5]}} # Initialize the `compose.yaml` file @@ -13,13 +13,12 @@ # For any configuration ... data = {"services": dict()} - for name, def_mec, atk_mec in product(names, def_mecs.keys(), atk_mecs.keys()): def_params = def_mecs[def_mec] atk_params = atk_mecs[atk_mec] - # Assumption: each defense and attack has only 1 hyperparameter + # (assumption: each defense and attack mechanism has only 1 hyperparameter to be set) for def_par, atk_par in product(list(def_params.values())[0], list(atk_params.values())[0]): # ... set the related volume, ... diff --git a/src/attack.py b/src/attack.py index 996cd5f..27eab77 100644 --- a/src/attack.py +++ b/src/attack.py @@ -12,7 +12,7 @@ # Apply attack mechanism to a BN, namely, derive a BN from a CN def attack_mechanism(exp, config, atk_mec, atk_args) -> None: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Read data diff --git a/src/config.py b/src/config.py index c485995..5178e91 100644 --- a/src/config.py +++ b/src/config.py @@ -80,7 +80,7 @@ def create_clean_dir(path: Path): path.mkdir(parents=True, exist_ok=True) -# Get output path +# Get current directory def get_cur_dir(config): root_path = get_root_path() diff --git a/src/defense.py b/src/defense.py index a72aa65..35c1e41 100644 --- a/src/defense.py +++ b/src/defense.py @@ -11,7 +11,7 @@ # Apply defense mechanism to a BN, namely, derive a CN from a BN def defense_mechanism(exp, config, def_mec, def_args) -> None: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Read data diff --git a/src/inference.py b/src/inference.py index cddef45..a84ab16 100644 --- a/src/inference.py +++ b/src/inference.py @@ -21,8 +21,8 @@ def inferences(exp, config): def_mec = config["def_mec"] # Read data - auc_meta = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') - eps = auc_meta.loc[auc_meta["exp"] == exp, "eps"].values[0] + auc_res = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') + eps = np.mean(auc_res.loc[auc_res["exp"] == exp, "epsilon"].values[0]) # Set seed set_seed() diff --git a/src/learning.py b/src/learning.py index 955375b..5d7c603 100644 --- a/src/learning.py +++ b/src/learning.py @@ -19,7 +19,7 @@ def learn_bn_params(bn, data): # Estimate BNs from rpop and pool def estimate_bns(exp, config) -> None: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Set seed diff --git a/src/mia.py b/src/mia.py index 6122a79..696999b 100644 --- a/src/mia.py +++ b/src/mia.py @@ -1,23 +1,27 @@ import math +import sys import numpy as np import pandas as pd import pyagrum as gum from scipy.stats import norm from sklearn import metrics +from scipy.optimize import minimize from src.config import get_cur_dir, set_seed from src.defense import noisy_bn # MIA attack vs a BN -def mia_vs_bn(exp, config) -> None: +def mia_vs_bn(exp, config) -> dict: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Init results - results = pd.DataFrame({"error": eval(config["error"])}) + power_res = pd.DataFrame({"error": eval(config["error"])}) + auc_res = pd.DataFrame({"sample": range(config["samples"])}) + auc_res["exp"] = exp # Read data gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') @@ -26,6 +30,7 @@ def mia_vs_bn(exp, config) -> None: set_seed() # For each data sample ... + auc_bns_dict = dict() for sample in range(config["samples"]): # ... read the BNs as estimated from rpop and pool, ... @@ -45,7 +50,7 @@ def mia_vs_bn(exp, config) -> None: # try: # ... and perform membership inference on gpop - power_vec, _ = run_mia( + power_vec, auc = run_mia( bn_theta_hat_ie, bn_theta_ie, rpop, @@ -53,7 +58,8 @@ def mia_vs_bn(exp, config) -> None: gpop[f"in-pool-{sample}"], eval(config["error"]), ) - results[f"power_BN_sample{sample}"] = power_vec + power_res[f"power_BN_sample{sample}"] = power_vec + auc_bns_dict[sample] = auc # except Exception: @@ -63,17 +69,24 @@ def mia_vs_bn(exp, config) -> None: # log.write(traceback.format_exc()) # Save results - results.to_csv(f'{cur_dir}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) + power_res.to_csv(f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False) + + # Return + auc_res["auc_bn"] = auc_res.apply(lambda row: auc_bns_dict[row["sample"]], axis=1) + + return auc_res # MIA attack vs a CN -def mia_vs_cn(exp, config, save_res=True) -> dict: +def mia_vs_cn(exp, config, save_power_res=True) -> pd.DataFrame: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Init results - results = pd.DataFrame({"error": eval(config["error"])}) + power_res = pd.DataFrame({"error": eval(config["error"])}) + auc_res = pd.DataFrame({"sample": range(config["samples"])}) + auc_res["exp"] = exp # Read data gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') @@ -82,7 +95,7 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: set_seed() # For each data sample ... - auc_cns = [] + auc_cns_dict = dict() for sample in range(config["samples"]): # ... read the BN as inferred from the CN @@ -112,8 +125,8 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: gpop[f"in-pool-{sample}"], eval(config["error"]), ) - results[f"power_CN_sample{sample}"] = power_vec - auc_cns.append(auc) + power_res[f"power_CN_sample{sample}"] = power_vec + auc_cns_dict[sample] = auc # except Exception: @@ -122,23 +135,23 @@ def mia_vs_cn(exp, config, save_res=True) -> dict: # log.write(f"{exp}: error with sample {sample} (BN).\n") # log.write(traceback.format_exc()) - # Compute Avg(AUC(CN)) across data samples - auc_cn = sum(auc_cns) / len(auc_cns) - # Save results - if save_res: - results.to_csv( - f'{cur_dir}/{config["results_path"]}/cns/cn_{exp}.csv', + if save_power_res: + power_res.to_csv( + f'{cur_dir}/{config["results_path"]}/cns/power_cn_{exp}.csv', index=False, ) - return {"exp": exp, "auc_cn": auc_cn} + # Return + auc_res["auc_cn"] = auc_res.apply(lambda row: auc_cns_dict[row["sample"]], axis=1) + + return auc_res # Get theoretical power def theoretical_power(exp, config) -> None: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Read data @@ -162,51 +175,49 @@ def theoretical_power(exp, config) -> None: return - -def find_epsilon_new(): - # TODO - - return - - # Find eps s.t. |AUC(eps) - AUC(CN)| < tol def find_epsilon(exp, config) -> dict: - # Get output path + # Get current directory cur_dir = get_cur_dir(config) # Read data gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') gpop_ss = config["gpop_ss"] pool_ss = int(gpop_ss * config["pool_prop"]) - auc_meta = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') - auc_cn = auc_meta.loc[auc_meta["exp"] == exp, "auc_cn"].values[0] + auc_res = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') + auc_res = auc_res[auc_res["exp"] == exp] eps_vec = eval(config["eps_vec"]) # Set seed set_seed() - eps_best = eps_vec[-1] - - # For each eps ... - for eps in eps_vec: + # For each data sample ... + eps_dict = dict() + auc_noisy_dict = dict() + for sample in range(config["samples"]): - # Init results - results = pd.DataFrame({"error": eval(config["error"])}) + # ... read the BNs as estimated from rpop and pool, ... + bn_theta = gum.loadBN( + f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" + ) + bn_theta_hat = gum.loadBN( + f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" + ) - auc_noisy_bns = [] + # ... retrieve rpop, ... + rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] - # ... and for each data sample ... - for sample in range(config["samples"]): + # ... get CN AUC, ... + auc_cn = auc_res.loc[auc_res["sample"] == sample, "auc_cn"].values[0] - # ... read the BNs as estimated from rpop and pool, ... - bn_theta = gum.loadBN( - f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif" - ) - bn_theta_hat = gum.loadBN( - f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" - ) + # ... init results, ... + eps_dict[sample] = eps_vec[-1] + auc_noisy_dict[sample] = None + # ... and find epsilon + for eps in eps_vec: + # Get noisy BN scale = (2 * bn_theta_hat.size()) / (pool_ss * eps) bn_noisy = noisy_bn(bn_theta_hat, scale) @@ -214,13 +225,8 @@ def find_epsilon(exp, config) -> dict: bn_noisy_ie = gum.LazyPropagation(bn_noisy) bn_theta_ie = gum.LazyPropagation(bn_theta) - # ... retrieve rpop, ... - rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_theta.nodes())] - - # try: - - # ... and perform membership inference on gpop - power_vec, auc = run_mia( + # Perform membership inference on gpop + _, auc = run_mia( bn_noisy_ie, bn_theta_ie, rpop, @@ -228,42 +234,18 @@ def find_epsilon(exp, config) -> dict: gpop[f"in-pool-{sample}"], eval(config["error"]), ) - results[f"power_noisyBN_sample{sample}"] = power_vec - auc_noisy_bns.append(auc) - - # except Exception: - - # # Debug - # with open(f"{results_path}/log.txt", "a") as log: - # log.write(f"{exp}: error with sample {sample} (BN).\n") - # log.write(traceback.format_exc()) - - # Compute Avg(AUC(eps)) across data samples - auc_noisy_bn = sum(auc_noisy_bns) / config["samples"] - - # Condition on |AUC(eps) - AUC(CN)| - if abs(auc_cn - auc_noisy_bn) <= config["tol"]: - eps_best = eps - break - - # Save noisy BNs - for sample in range(config["samples"]): - bn_theta_hat = gum.loadBN( - f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif" - ) - scale = (2 * bn_theta_hat.size()) / (pool_ss * eps_best) - bn_noisy = noisy_bn(bn_theta_hat, scale) - gum.saveBN( - bn_noisy, - f'{cur_dir / config["noisy_path"]}/{f"bn_{exp}_sample{sample}"}.bif', - ) - return { - "exp": exp, - "auc_cn": auc_cn, - "auc_noisy_bn": auc_noisy_bn, - "eps": eps_best, - } + # Condition on |AUC(eps) - AUC(CN)| + if abs(auc_cn - auc) < config["tol"]: + eps_dict[sample] = eps + auc_noisy_dict[sample] = auc + break + + # Return + auc_res["epsilon"] = auc_res.apply(lambda row: eps_dict[row["sample"]], axis=1) + auc_res["auc_noisy_bn"] = auc_res.apply(lambda row: auc_noisy_dict[row["sample"]], axis=1) + + return auc_res # MIA: membership inference attack From 100d6577455f16ddf0ef5be74570fca765ace875 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 18 Nov 2025 11:32:14 +0100 Subject: [PATCH 20/31] Fix plot notebooks, and other fixes --- .gitignore | 1 + experiments/cn_privacy/Plot_results.ipynb | 16 +- experiments/cn_privacy/config_BAK.yaml | 2 +- experiments/cn_privacy/exp.py | 1 + experiments/cn_privacy/generate.py | 3 +- experiments/cn_vs_noisybn/Plot_results.ipynb | 394 +++++++------------ experiments/cn_vs_noisybn/config.yaml | 18 +- experiments/cn_vs_noisybn/config_BAK.yaml | 18 +- experiments/cn_vs_noisybn/exp.py | 8 +- experiments/cn_vs_noisybn/generate.py | 3 +- src/config.py | 2 +- src/inference.py | 14 +- src/mia.py | 27 +- test/cn_privacy/config.yaml | 11 - test/cn_vs_noisybn/config.yaml | 20 +- 15 files changed, 198 insertions(+), 340 deletions(-) diff --git a/.gitignore b/.gitignore index c07aa87..322a84d 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ venv output* bns data +plots *meta.txt bin __pycache__ diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index 1930fef..f39732c 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -181,13 +181,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "ade37b54", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -198,7 +198,7 @@ ], "source": [ "# Names of experiments\n", - "exp_names = [re.findall(\"bn_(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(res_path / \"bns\")]\n", + "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", "# Layout 4x3\n", "fig, axes = plt.subplots(len(exp_names) // 3 + 1, 3)\n", @@ -220,6 +220,14 @@ " f\"{plots_path}/results_logx.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n", ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65698b92", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/experiments/cn_privacy/config_BAK.yaml b/experiments/cn_privacy/config_BAK.yaml index 245f5df..9251cec 100644 --- a/experiments/cn_privacy/config_BAK.yaml +++ b/experiments/cn_privacy/config_BAK.yaml @@ -1,7 +1,7 @@ ## Configuration file # Paths -# Paths + cur_dir: experiments/cn_privacy # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 5d83581..6c12744 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -16,6 +16,7 @@ def main(): # Init configs config = load_config("cn_privacy") cur_dir = get_cur_dir(config) + create_clean_dir(cur_dir / "output") num_cores = eval(config["num_cores"]) # Get command-line hyperparameters diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py index 1ab6c46..aa4d368 100644 --- a/experiments/cn_privacy/generate.py +++ b/experiments/cn_privacy/generate.py @@ -18,9 +18,10 @@ def main(): # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) + create_clean_dir(cur_dir / config["bns_path"]) create_clean_dir(cur_dir / config["bns_path"] / "gt") create_clean_dir(cur_dir / config["data_path"]) - open(f'{cur_dir}/{config["exp_meta"]}', "a").close() + open(f'{cur_dir}/{config["exp_meta"]}', "w").close() generate_randombn(config) # Init the vectors of experiments diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 514cfed..e2765f3 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "7b61e02b", "metadata": {}, "outputs": [], @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "49a48f2f", "metadata": {}, "outputs": [], @@ -77,36 +77,23 @@ { "cell_type": "code", "execution_count": null, - "id": "38f1e202", + "id": "84251635", "metadata": {}, "outputs": [], "source": [ "# Names of experiments\n", - "exp_names = [re.findall(\"bn_(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(res_path / \"bns\")]\n", - "\n", - "# Initialize results\n", - "res = {\n", - " \"eps\": [],\n", - " \"acc_cn_cert\": [],\n", - " \"acc_cn_uncert\": [],\n", - " \"acc_cn_tot\": [],\n", - " \"acc_noisy_bn\": [],\n", - " \"cert_cn\": [],\n", - "}\n", - "\n", - "roc = {\n", - " \"roc_cn_cert\": dict(),\n", - " \"roc_cn_uncert\": dict(),\n", - " \"roc_cn_tot\": dict(),\n", - " \"roc_noisy_bn\": dict(),\n", - "}\n", - "\n", - "\n", - "# Get results\n", - "files = [f for f in os.listdir(dir) if \".csv\" in f]\n", - "data = pd.concat([pd.read_csv(dir + \"/\" + f) for f in files])\n", - "data.reset_index(inplace=True)\n", - "data[\"bn_noisy_probs_1\"] = data.apply(\n", + "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", + "\n", + "# Build a data set for each result folder\n", + "res = dict()\n", + "for dir_name in os.listdir(res_path):\n", + " files = [os.path.join(res_path, dir_name, f) for f in os.listdir(f\"{res_path}/{dir_name}\")]\n", + " data = pd.concat((pd.read_csv(f) for f in files if \"auc_meta\" not in f), axis=0)\n", + " data[\"cn_probs_1\"] = data.apply(\n", + " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", + " axis=1,\n", + ")\n", + " data[\"bn_noisy_probs_1\"] = data.apply(\n", " lambda row: (\n", " row[\"bn_noisy_probs\"]\n", " if row[\"bn_noisy_mpes\"] == 1\n", @@ -114,265 +101,161 @@ " ),\n", " axis=1,\n", ")\n", - "data[\"cn_probs_1\"] = data.apply(\n", - " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", - " axis=1,\n", - ")\n", - "\n", - "# Store ess\n", - "reg = re.search(\"nodes(\\d+)_ess(\\d+)\", dir)\n", - "n_nodes = reg.group(1)\n", - "ess = reg.group(2)\n", - "\n", - "# Store avg of eps and std\n", - "with open(dir + \"/exp_meta.txt\", \"r\") as f:\n", - " eps_vec = [\n", - " float(re.search(\"Eps: (.+)\\n\", line).group(1)) for line in f if \"Eps: \" in line\n", - " ]\n", - "eps = (float(np.mean(eps_vec)), float(np.std(eps_vec)))\n", - "\n", - "# Split CN results based on probabilities\n", - "data_cert, data_uncert = split_data(data, \"cn_probs\", 0.5)\n", - "\n", - "# Compute accuracies\n", - "vs = \"gt\"\n", - "\n", - "acc_cn_cert = (\n", - " get_acc_bn(data_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_cert) > 0 else None\n", - ")\n", - "acc_cn_uncert = (\n", - " get_acc_bn(data_uncert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_uncert) > 0 else None\n", - ")\n", - "acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n", - "acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n", + " res[dir_name] = data\n", "\n", - "# Compute CN certainty\n", - "cert_cn = sum(data[\"cn_probs\"] > 0.5) / len(data)\n", - "\n", - "# Compute ROC\n", - "roc_cn_cert = roc_curve(data_cert[f\"{vs}_mpes\"], data_cert[\"cn_probs_1\"])\n", - "roc_cn_uncert = roc_curve(data_uncert[f\"{vs}_mpes\"], data_uncert[\"cn_probs_1\"])\n", - "roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n", - "roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n", - "\n", - "# Store results\n", - "for key in res.keys():\n", - " res[key].append(ast.literal_eval(key))\n", - "for key in roc.keys():\n", - " roc[key][ess] = ast.literal_eval(key)\n", - "\n", - "# Debug\n", - "assert (data[\"cn_probs\"] >= data[\"cn_probs_alt\"]).all()\n", - "assert (data[\"bn_noisy_probs\"] >= 0.5).all()\n", - "assert (data[\"bn_probs\"] >= 0.5).all()\n", - "assert len(data) == len(pd.read_csv(dir + \"/\" + files[0])) * len(files)\n", - "\n", - "# Debug\n", - "length = len(res[\"ess\"])\n", - "for key in res.keys():\n", - " assert len(res[key]) == length\n", - "for key in roc.keys():\n", - " assert len(roc[key]) == length\n", - "assert res[\"ess\"] == sorted(res[\"ess\"])" + "# Retrieve AUCs for each result folder\n", + "aucs = dict()\n", + "for dir_name in os.listdir(res_path):\n", + " data = pd.read_csv(os.path.join(res_path, dir_name, \"auc_meta.csv\"))\n", + " aucs[dir_name] = data" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "b6274696", + "execution_count": null, + "id": "2ce6f5be", "metadata": {}, "outputs": [], "source": [ - "# Style\n", - "plt.style.use(\"seaborn-v0_8-paper\")\n", - "plt.rcParams.update(\n", - " {\n", - " \"font.size\": 9,\n", - " \"axes.labelsize\": 9,\n", - " \"axes.titlesize\": 9,\n", - " \"legend.fontsize\": 7,\n", - " \"xtick.labelsize\": 8,\n", - " \"ytick.labelsize\": 8,\n", - " \"lines.linewidth\": 0.8,\n", - " \"figure.dpi\": 300,\n", - " \"savefig.dpi\": 300,\n", - " \"axes.edgecolor\": \"black\",\n", - " \"axes.linewidth\": 0.8,\n", - " \"text.usetex\": True,\n", - " }\n", - ")" + "# Get ESS/delta list\n", + "type = re.findall(\"(\\w+)_\", os.listdir(res_path)[0])[0]\n", + "x_values = sorted([re.findall(\"_(\\d+)\", i)[0] for i in os.listdir(res_path)])\n", + "\n", + "# Retrieve related epsilon\n", + "eps_median = []\n", + "eps_up, eps_lp = [], []\n", + "for x in x_values:\n", + " data = aucs[f\"{type}_{x}\"]\n", + " data_eps = [i for i in data[\"epsilon\"].values if i is not None]\n", + " eps_median.append(np.median(data_eps))\n", + " eps_up.append(np.percentile(data_eps, 75))\n", + " eps_lp.append(np.percentile(data_eps, 25))\n", + "\n", + "# Plot: ess vs eps\n", + "fig, ax = plt.subplots(1, 1)\n", + "\n", + "ax.semilogy(x_values, eps_median, \"-o\", label=\"Mean\", markersize=4)\n", + "ax.semilogy(x_values, eps_up, \"-o\", label=\"Up 75\", markersize=4)\n", + "ax.semilogy(x_values, eps_lp, \"-o\", label=\"Lp 75\", markersize=4)\n", + "ax.set_xlabel(type)\n", + "ax.set_ylabel(\"$\\epsilon$\")\n", + "ax.set_title(f\"{type} vs $\\epsilon$\")\n", + "\n", + "ax.set_ylim([1e-9, 2])\n", + "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", + "ax.legend(loc=\"best\")" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "0e9e8e36", + "execution_count": null, + "id": "a29cb66d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Ess vs eps\n", - "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", + "# Get CN certainty\n", + "cn_certainty = []\n", + "for x in x_values:\n", + " data = res[f\"{type}_{x}\"]\n", + " cn_certainty.append(sum(data[\"cn_probs\"] > 0.5) / len(data))\n", "\n", - "eps_mean = np.array([x[0] for x in res[\"eps\"]])\n", - "ax.semilogy(res[\"ess\"], eps_mean, \"-o\", label=\"Mean\", markersize=4)\n", - "ax.set_xlabel(\"S\")\n", - "ax.set_ylabel(\"$\\epsilon$\")\n", - "ax.set_title(\"S vs $\\epsilon$\")\n", - "ax.set_ylim([1e-5, 10])\n", - "ax.set_yticks([4, 1, 1e-1, 1e-2, 1e-3, 1e-4])\n", - "ax.set_yticklabels([\"4\", \"1\", \"1e-1\", \"1e-2\", \"1e-3\", \"1e-4\"])\n", - "ax.yaxis.set_minor_locator(LogLocator(base=10.0, subs=\"auto\"))\n", - "ax.tick_params(axis=\"y\", which=\"minor\", length=2, width=0.5)\n", - "ax.tick_params(axis=\"y\", which=\"major\", length=3, width=0.9)\n", - "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", - "# ax.grid(True, which='minor', linestyle='-', linewidth=0.5, color='#bfbfbf', zorder=1)\n", - "ax.legend(loc=\"best\")\n", + "# Plot: ess vs CN certainty\n", + "fig, ax = plt.subplots(1, 1)\n", "\n", - "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", - "plt.show()\n", - "fig.savefig(\n", - " f\"{plots_path}/s_vs_eps.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n", - ")" + "ax.plot(x_values, cn_certainty, \"-o\", markersize=4)\n", + "ax.set_xlabel(type)\n", + "ax.set_ylabel(\"Ratio of (maxmin CN prob. $> 0.5$)\")\n", + "ax.set_title(\"CN certainty\")\n", + "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "90bf5cc9", + "execution_count": null, + "id": "1f7ebeae", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Ess vs CN certainty\n", - "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", + "# Store accuracy results\n", + "acc = {\"acc_noisy_bn\": [],\n", + " \"acc_cn_tot\": [],\n", + " \"acc_cn_cert\": [],\n", + " \"acc_cn_uncert\": []}\n", "\n", - "ax.plot(res[\"ess\"], res[\"cert_cn\"], \"-o\", markersize=4)\n", - "ax.set_xlabel(\"S\")\n", - "ax.set_ylabel(\"Ratio of (maxmin CN prob. $> 0.5$)\")\n", - "ax.set_title(\"CN certainty\")\n", - "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", + "roc = {\n", + " \"roc_cn_cert\": dict(),\n", + " \"roc_cn_uncert\": dict(),\n", + " \"roc_cn_tot\": dict(),\n", + " \"roc_noisy_bn\": dict(),\n", + "}\n", "\n", - "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", - "plt.show()\n", - "fig.savefig(\n", - " f\"{plots_path}/cn_certainty.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n", - ")" + "for x in x_values:\n", + " data = res[f\"{type}_{x}\"]\n", + " \n", + " # Split CN results based on probabilities\n", + " cn_cert, cn_uncert = split_data(data, \"cn_probs\", 0.5)\n", + "\n", + " # Comput accuracies\n", + " vs = \"gt\"\n", + " acc_cn_cert = (\n", + " get_acc_bn(cn_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(cn_cert) > 0 else None\n", + " )\n", + " acc_cn_uncert = (\n", + " get_acc_bn(cn_uncert, f\"{vs}_mpes\", \"cn_mpes\") if len(cn_uncert) > 0 else None\n", + " )\n", + " acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n", + " acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n", + "\n", + " # Compute ROC\n", + " roc_cn_cert = roc_curve(cn_cert[f\"{vs}_mpes\"], cn_cert[\"cn_probs_1\"]) if len(cn_cert) > 0 else None\n", + " roc_cn_uncert = roc_curve(cn_uncert[f\"{vs}_mpes\"], cn_uncert[\"cn_probs_1\"]) if len(cn_uncert) > 0 else None\n", + " roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n", + " roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n", + "\n", + " # Store results\n", + " for key in acc.keys():\n", + " value = eval(key)\n", + " acc[key].append(value)\n", + " for key in roc.keys():\n", + " roc[key][x] = eval(key)" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "b5c19b7e", + "execution_count": null, + "id": "c158f122", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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oWq/Xo9lsRqlUikqlMpIaiFhcXIxCoRD1ej1ardaoy8kk4TgAAAAAR/Jbv/eV+C//0W9E5Rc+E+/9+V+Lyi98Jv7Lf/Qb8Vu/95VRl3YqqtVqzM3Npd3DCwsLsb6+HltbW7G1tRXr6+uxsrISpVIpIiIKhcK+x+vf32q1otPpnGrtd0qSJA3lG43Gma49afL5fPp1WvojVQ77gQrDZeY4AAAAAIfyud9J4r/6p1+If3Fr86771r64Ff/tZ16IP/7IhfiZ/+Bt8YNvzp99gaegWq1Gs9mMiIhisRgrKyt3hd/FYjGKxWJUKpVDjUrpXwSzH1Svrq6eSu27uXz5ckRsj/U4KMQfZ/V6PTY2NmJubi79UGLYzmIWe/+90+l0ol6vx+Li4qmvybfpHAcAAADgQJ/6zRfjx5r/fNdgfNC/uLUZP9b85/Gp33zxjCo7Pa1WKw3GC4VCXL9+/VBd4Qdtc+HChVhaWoqIiHa7He12ezgFHyBJkqjX6xExupEuw3L16tVoNptn3nl/GqrVakR8+4MLzo5wHAAAAIB9fe53knj/r6zHq9+8fajtX/3m7fjAr3Tic7+TnG5hp2xw1EWj0RjqeI3FxcX0eGcVVF+7di29fVrd1hzdwsJCRGx/eGH2+NkSjgMAAACwr//qn34hvv7N1460z6vfvB2X/99fOKWKTl+r1UpnjFcqlVMJk5eXlyMiotPpnEko2h/f4iKc46dYLEZEnOmIHYTjAAAAAOzjt37vKweOUtnLP+9uxs0XJ/MinVevXk1v98deDNvCwkI6guUsusf741suXbp06H263W5Uq9WYnp6OXC4XuVwupqamYm5ubt+RJu12O8rlckxNTUUul4vp6el9H2O9Xo+pqakdz3Wz2YyZmZmYmppKv+/X0F+7VqulP8vlclEul+86dqfTiWq1mh4rl8vFzMxMOmJmL9VqNa39TrVaLX0e9nq8Bx1/0MWLF9PjcHZckBMAAABgwn39m7fjtze/dirH/m8+dfNE+/8/Pnkz/uN3PTqkanb63gtvjNe/7typHHswpDzNESSNRiPK5XJ0u91oNpvpiI1h63Q6aSd8v0v5IK1WKw1/B/XHf7RarVhcXEw74PtqtdpdwXC32416vR6tVivW19fvGlHz5S9/OZIkic3NzUiSJGZnZ+8K3y9cuJB+mNC/8Gk+n48LFy6k29w57/3OWvL5fOTz+eh0OtHpdKLRaOxaz0GSJEm/9nq8tVotbty4ESsrKwcer1wuR7PZjG63G0mSDHWED3sTjgMAAABMuN/e/Fq8+//+a6MuY1f/8LP/Ov7hZ//1qRz7uf/8T8Zb//B3ncqx+0HyaYeUpVIpisVidDqdqNVqpxaOb25+u/v/oAuGRmx3afe7uEulUtRqtbh48WLk8/nodrvRarXi8uXLd3VVDwbFjUYjfTz9sLgfuO81PqTT6cQjjzwSSZJEoVBI143YHgfTHwkzPT0d3W43lpaWYnFxcd/HUigUYnl5OUqlUvp69ju9u91uzM/PHyrA3k3/gqqDj7XT6cTc3Fz6PHW73UNdyLWv2+0e+gMMTsZYFQAAAADYQz8kP01XrlxJ1zrKKI6j6HdaR8SOTuvdJEmSBuMLCwuxurq6I1guFAqxuLgYW1tbO8L8fnd4xPbs7MH7CoVCrKysRKlUina7vedIln7n9PLycmxsbMTCwsKJguKlpaXY2NiISqWy44OOUqmUdrwPzpc/jsXFxR2PtVgsRqPRSL8/zKiUwdoGXytOl3AcAAAAAO4wGFaedkBeLBbT0S21Wu1U1hs85kHd8JcvX05v3zkyZT/9bfe7gGl/m8GZ7neqVCoHdoMf1n6PdbDGtbW1Y6+x20z6frd7xOHeP4MfWAx2+XO6jFUBAAAAmHDfe+GN8dx//idP5dj/zadunmgsyp9//N861Znjp+XixYtpx+/a2tqpzh2P2B5B0h9Rcvny5SOF0ofx5S9/+dDb9h/3YLf4YfQD5larFblcbt9t9+uOXlpaOvSaJzGsD0B2G5ly1HE8Z/lhDN8mHAcAAACYcK9/3blTm739n7zr0ROF43/tRx6NRx8+ndpO09zcXBoSNxqNUw/HC4VCLCwsRLPZjHq9HktLSyO7KGM/uD7MbPJB/VEphULhSDO273Qa87a73W50Op1YXV2Nbrcbm5ubQxlfMqzX6Cid/QyPcBwAAACAPb3lD39X/PFHLsS/uHX0UQ8/XLgwkcF4xPas7f6Ik1arFZ1O59Qvkri8vBzNZjMitserDM6tPqnBC2cmSbJvAFsoFKLT6Rw5PM7n85EkyYkuLDrsYLjVakWtVtvxWPL5fBrg7zX7fJQOmgnP8Jg5DgAAAMC+fuY/eFu84XXnjrTPG153Lpb+D287pYrOxuBok7m5uVNfL5/Pp7O2m83mUC/MeJSZ1v152Uedw93vrl9ZWTlidaej1WrF3NxcdLvd9MKivV4vtra2Yn19fWzqjNj5mugcPzvCcQAAAAD29YNvzsdH/mLx0AH5G153Lj7yF4vxg2/On25hp2xhYSHtgO52uzEzM3PgPOgkSaJerx97zcFxKrtd6PG4jjLTularpdsdVMNggN//MKHdbker1dp3v2HN1d7Y2Njzvv5FP0ul0q6jcYb54cNJDdZy1HE2HJ9wHAAAAIADveuPPRwfW/jh+OHC/iMffrhwIT628MPxrj/28BlVdroajUYakHc6nZiamoparRadTicNeJMkiU6nE7VaLb3/uPL5fHpByna7nc49P6l+N3jEwR3hhUIhDbqbzWaUy+Vot9uRJEn6WOv1ekxPT+94rIP7zc3NRbVa3RH6drvddL/Lly+f6PH0A+Rr165Ft9uNJEmi3W7v6PDvb9Nut6PZbO4I5Nvt9lA/fDipwfEuwvGzY+Y4AAAAAIfyg2/Ox8cW/r34rd/7Svy//sVvx2/865filW/cjjc9cC4e+7fOx1/84e+d2Bnj+2k0GlEul9PZ1fV6fd/u8EqlcqL1FhcX4/Lly5EkydC6m/P5fBSLxfSilAfNBF9cXIx8Ph/VanXfkP7OOez9sTC1Wi2azWY6Q/1ODz300DEexbf1L5iaJMmOeeoR2yF8oVCIpaWlNBSvVqt3heHjFELfuHEjIuLUL/zKTsJxAAAAAI7kLX/4u+K//HOPjbqMM1WpVKJSqUSr1YqrV6/uuGBl/wKPpVIpqtXqUELX5eXloXc2l0ql6HQ6h74I5cLCQpRKpVheXo52ux3dbjd9rBcvXoxqtbrrRUoXFxejUqnsul+pVNoxOua4FhYWYn19Pa5duxYRkdY0NzeXPv/5fD7W19ejWq3G2tpaJEkShUIhisViPP3001EqldJO81EH5f0PH85itj3fluv1er1RFwGT5jd+4zfi7W9/e/r95z//+XjssWz9jwLG2+3bt+PTn/50RES8853vjHPnjnbhHODknIcwWs5BGD3n4XB84QtfiLe9bbIvasno9Hq9eOWVVyIi4k1velM8//zzMTMzExHbs7pHHQizrdPp3BOvy3F+X406YzNzHAAAAAAyoFgspmM7+rPBGb1GoxER2539kxqMTyrhOAAAAABkRD8U748jYbSSJEnnsvdDcs6OcBwAAAAAMqLfPZ4kyb4XFeVsXL58OSK2Z9rrGj97wnEAAAAAyJCVlZUoFApRq9XSi4py9rrdbtTr9SgUCnHlypVRl5NJwnEAAAAAyJB8Ph+rq6uRz+djbm5u1OVkVrlc3vFacPaE4wAAAACQMYVCIa5fvx7dbjfK5fKoy8mccrkcm5ubcf36deNURuj+URcAAAAAAJy9YrEYW1tboy4jk1ZXV0ddAqFzHAAAAACADBKOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAACAjKrValGr1UZdBmesVqtFvV4fdRkjd/+oCwAAAAAAzl65XI52ux3r6+ujLoUzVi6Xo1wux40bN2JlZWXU5YyMznEAAAAAOKRWqxVzc3MxPT0duVwucrlcTE1NxfT0dFSr1Wi1WpEkyV37JUmSbt//6na7B65XrVYjl8vFzMzMUB9HtVqNdrsdKysrUSwWh3pshmPwPbbbe+okSqVSLC8vR6vVyvRfDgjHJ1i3241qtRozMzMxNTUVU1NTMTMzE81mcyLXazabMTU1NZRjAQAAAKfoxX8V8U8XIz76nohf+Pe3//tPF7d/fo9qtVoxNTUVc3Nz0Wq1dgTbSZJEt9uNZrMZc3NzMT8/f6hjjiqUrNfr0Ww2o1QqRaVSGUkNjN7i4mIUCoWo1+vRarVGXc5ICMcnVL1ej+np6eh2u3HlypXY2tqKra2tWFpailqtlt43Kev1g/ckSYb+SRgAAAAwJP/besTf/dMRH/njEf+yEfE7/zzi9z6//d9/2dj++d/909vb3UOq1WrMzc2lmcXCwkKsr6+n+cj6+nqsrKxEqVSKiIhCobDv8fr3t1qt6HQ6p1r7nZIkSUP5RqNxpmtzNPl8Pv06Lf2RKof9QOdeIxyfQM1mM2q1WlQqlVhdXd3xpy+VSiXW19djc3MzZmZmhhI0n8V6c3NzJ64TAAAAOEW/tRrx3/6ZiC/+z/tv98X/eXu731o9m7pOWbVaTf9qvlgsxsbGRjQajSgWi2lwWSwW09xkY2MjqtXqvses1Wpp4HnW3eOXL1+OiO2xGgeF+OytXq+no2lOy+AHMKcVkBeLxSgWi5EkSSYv0CkcnzD9DuuI2HNYfqFQiIWFhUiS5MSf+pzFevV6/cw/JQUAAACO4H9bj7j6H0Z882uH2/6bX4u49hMT30HearXSYLxQKMT169cP1RV+0DYXLlyIpaWliIhot9unGrAOGgxAszxnehiuXr0azWbznsi0+tlf/4OTLBGOT5jl5eWIiPTPdPbSf1PvdRGIcVmv0+nE5cuX03UAAACAMfTcz0Z869Wj7fPNr0Ws/l9Pp54zMtgE2Gg0htq9u7i4eObd49euXUtvH5T1kB0LCwsRsf3hSdZmjwvHJ8zgp5X7Gbz/JBfMPO315ubmYmVl5VRnJwEAAAAn8OK/OniUyl5e+HTEl35zuPWckcEGwEqlciphcr9ZsNPpnEkoubq6PerGRTi5U3+Mcv89khXC8Qky+Gca09PTB27fD5yvXr06luvVarUolUo+qQQAAICT+ubXI178wul8ffpvn6y2X/tbp1fbN78+nOdvF4P5xkEzxI9rYWEhbTg8i+7x/viWS5cuHbhttVqNXC4XU1NTe26Ty+Uil8vtOqu6VqvF1NRUep25drsd5XI5pqamIpfLxfT09KFmXPdH/k5PT6fr9Y+730iT3dbb7zmu1+sxNTW147VuNpsxMzOTPgfNZjOtob92rVZLf5bL5aJcLt917E6nE9VqNT1WLpeLmZmZAx9//zXYLZcb1vPbd/HixfQ4WXL/qAvg8NbW1tLbh7lgQqFQiE6nc+zZR6e5Xv8T0Y2NjWPVBgAAAAzYuhXxkR8edRW7+/WV7a/T8IF/HvHw207l0IMh4Wk29jUajSiXy9HtdqPZbKYjLoat0+mknfD9LuHTlCRJ+lWr1e4KarvdbtRqtbhx48ae17lrtVpp+HvnsVutVrRarVhcXLxrXO9e69Xr9Wi1WrG+vn7XFIMvf/nLkSRJbG5uRpIkMTs7e1fGdeHChTQj63a7EbHdLHrhwoV0mzsztDtr6V/EtZ+hNRqNXes5yDCe30HlcjmazWZ0u91IkiQzUx50jk+QkwTJx5k7fprr9cepAAAAAIyjfrZx2iFhqVRKw+rT7B7f3NxMbx+mCXJY2u121Ov1aDQa0ev1otfrxfr6elpDq9VKg+ZBzWYzDcZLpVKsrq7G1tZW9Hq92NjYiOXl5cjn83d1VQ8GxYNrbmxsRKVSiW63u2vg3tfpdOKRRx6JTqcThUIhDa8jtsfRbGxsxMbGRlr/0tJS+rONjY1oNBp3HbNQKMTKykpsbW2lX/3xJd1ud8ds+6M67vO7W419h9n+XqFzfIIcNeAe/NRqc3PzWJ9AncZ61Wo1KpXKmXxKeRgvvvhifOlLXzrSPjdv3tzx/e3bt+P27dvDLAtOZPD96L0Jo+E8hNFyDsLoOQ+Hox92HWLDyJ1+OWOn1+tFHOb5OYEkSQ73Ghxg8Bh3vq7NZjMuXrwYSZLE8vJyLC4u7rnfcdccbIKcmpo68FhHWXe39+ng9x/84Adjfn4+/dkTTzwRv/iLvxjvfve7I2J7zvVgx3ySJOl4k/n5+R2Bc6/Xi0ceeSQ++MEPxgc/+MEda/W7wyMinnvuuSiVSul9jzzySFy7di3e/e53R7vdjvX19R3Z1OAxIiKeeeaZPV+Hgx77oA996EPxzDPP3HWc2dnZeOaZZ+JDH/pQtFqt2NrauitL2+81OMnzu5vz58+ntzc2NuKJJ57Yd/vd9Hq9I/++H/W/D8LxCTL4Cd9RHadz/DTWa7fb0W63x2qcykc+8pH48Ic/fKJjrK+vn+j5gtP0mc98ZtQlQOY5D2G0nIMwes7D4zt//ny88sorB2/4HX8o7vuPrp9KDd/xL/6f8bp/9fFj7//N7/8L8Qd//D8ZYkXf9tp3/KGIwzw/x5DP59N843d/93dP3EE++Dp+/etf3/H9W9/61njXu94Vn/rUp+JDH/pQ/PiP/3i63re+9a2IiHjttdcO917Yw2Bj4P3333/gsfrr3ln7bv7gD/7grm0G9/+Lf/Ev3nX/93//96e3X3zxxR33/42/8TfS2//Ff/FfHPpx/82/+TcjIuLJJ5+MH/7hH951v5/92Z+Ndrsdv/IrvxJvfetb059/85vfTG8/+eST8YEPfGDfdV977bWI2P2xD9rvuX7HO96R3v70pz8d73rXu3bcv99rf5Lndzff8R3fkd7+3//3//1Y77VvfOMb8elPf/pI+9y6devI6wyTcHxCnfXcn2GslyRJzM3NxfXrp/OPNQAAAGTW/a+P1777j53Kof/gh//aicLxP/jh/zRee+gtQ6zobDzxxBPxqU99KiIinn/++buCy2H7+Z//+fjBH/zBiIj4O3/n7+wIiIdhlE19jzzyyF0/2y9r+mf/7J9FRMS73vWuI2VSzz//fEREfOITn4gHH3xw321feOGFPe/7yZ/8yUOveRKD3dovvfTSsY9z1Od3N4Pbn6SWSSMcnyCDY0uO2gl+nHB72OvNz8/H0tLS2IxTGaaZmZl47LHHRl0GpG7fvp1257zjHe+Ic+fOjbgiyB7nIYyWcxBGz3k4HL/5m78Zb3rTm0ZbxJuK0fuj/37kvvj/OfKuve/7E/HGP3r08Qzj4Omnn07D8b//9/9+/Nk/+2dPdLzBTt/Xv/71d72uP/ADPxDz8/Nx5cqV+Pmf//n42Z/92cjn83H//dvx3X333Xfk90Kv14uvfvWrERHxute9Lv35YY7TX/cw23/Hd3zHXdv098/n80fevx9cP/roo0d6zJ/97GcjYnt+9kFz1d/ylrfsOPbg8/Mn/sSfOHCt++7bvpTjbo99N91uNzqdTqyursatW7dic3Nzx2zv3d4T+732J3l+dzOY/T388MPH+r3zwAMPxDvf+c4j7TOYP46CcHyCHDXgHvxE8DhvtGGu1x/+P44X4fzABz6w74UYdnPz5s148skn0+/PnTvnf+gxtrw/YfSchzBazkEYPefh8eVyucjlxmCa+Lv/bxH/7Z+J+ObXDr/P694YufLfiBiH+o+hWq3Ghz70oUiSJFqtVjz//PMnavgbfB33el3r9XpcuXIlIrZnVTcajbv2O67BC1e+9NJLB+Y+R1l3t8dzkv0LhUJ0Op24devWkR5zfxROrVY7cMb2bjX0j3GUNQ86R1utVtRqtR1BeD6fTwP8Tqez53H2ew5P+vrstk3fQw89dKz3Wi6XO/Lv+lH/23DfSFfnSAZ/iR31T2GO0zk+rPWSJIn5+fmxDMYjtj8Ne+yxx4709eijj466bAAAADg7//ZMxFO/HPG6Nx5u+9e9cXv7f3vmdOs6ZcvLy+ntozbWHUc+n08vAtlsNncEqic12Mg4jBErx7m+3WFdvHgxIiLW1taOtF+pVIqIGJsMqtVqxdzcXHS73VhYWIjV1dXo9XqxtbUV6+vrY1NnxM73xFmPcx4l4fgE6f9iiDjcL6D+L9CD/ozktNebm5uLhYWFuHDhQiRJsudX3+bm5l0/AwAAAEbsLeWI/9M/jvi+A8YmfN87t7d7S/ls6jpFCwsLaQdyt9uNmZmZA/OKJEmiXq8fe82lpaU0nKxWq8c+zp3ubGQ8qXa7feJj7KVWq0XEdp0HPQeDHyD0P8xot9vRarX23W9YudPGxsae9129ejUitkP7RqORhvd9w/zw46QGazluljiJhOMTZPBPd/Y78fr6J/mdJ95Zr9dut6Ner8fU1NSeX/1fehHbHev9nw/zHwEAAADghP7tme3g+wP/IuKHqhHf++9F/OEf2P7vD1Uj/uN/uX3/hHeMD2o0GmlA3ul00hyj0+mkWUiSJNHpdKJWq92VcxxVPp+PpaWliNjOVIYVQg82QR6mI7s/USBJkrtC3Ha7HfPz80OpazeFQiENupvNZpTL5Wi322kzZafTiXq9HtPT0zue68H95ubmolqt7qi92+2m+12+fPnENUZEXLt2LbrdbiRJEu12e8dfGPS3abfb0Ww2dwTy7XZ7rHKv/niXiGyF42aOT5hKpRKtVuvAX2KDb+iTnGjDWK/X6x24ztzcXPqJ3tbWVqb+fAMAAAAmzsPfH/EfHL87etI0Go0ol8vp7Oh6vb5vd3ilUjnReouLi3H58uVdg+njyufzUSwW04tCHjSTe7D5cXp6OorFYly4cCG63W50u90olUqn2j2+uLgY+Xw+qtXqvh8S3DkHvj+WplarRbPZjGazuet+Dz300Inqm5ubSwP7wdHEEdshfKFQiKWlpTQUr1ard2Vm4xRC37hxIyKO32Q7qXSOT5j+J4edTmffX479P9soFot7Xiyif2Lu92nmMNcDAAAAmFSVSiU2NjZiZWUlKpXKjmCzHzwvLi6m25zU4LzzYekHn4NNjnspFovRaDTSx9nPhkqlUqyvr8fq6mpUKpUolUqnFvIuLCzExsZGLCwspGv0n+uFhYU953b3X4fd9ltcXIytra00RD9JbQsLC5HP53fUtLq6umPN9fX1KJVKaSNooVCISqUSKysr6X2n+RweVv/Dh7OYrT9Ocr3DtPUyVmq1WtTr9SiVSrG6unrX/d1uN6anpyOfz8etW7f27MKemZlJfxkuLi7u+Ut3WOvtZ9I6x3/jN34j3v72t6fff/7zn4/HHntshBXBTrdv345Pf/rTERHxzne+c+RXf4Ysch7CaDkHYfSch8PxhS98Id72treNugwmVK/Xi1deeSUiIt70pjfF888/HzMz2yNvNjY2Rh7IMh46nc5Q3hfH+X016oxN5/gEWl5ejsXFxWi321Eul3d0dLdarZiZmYlCoRDXr1/fN2QenHO0X1f4sNbbbf1ut3vXn8b0r8bsgpwAAAAAw1MsFtPu8dPoTGcyNRqNiIix6GA/a8LxCbW8vBzr6+tRKBSiXC6nF7C8fPlyLC0txcbGxoHjTfp/GjN4sYLTXO9OU1NTMT09nf65Rv/PUC5fvpxelPMkV3cGAAAAYKd+BnTt2rURV8I4SJIkncveD8mzxAU5J1h/9tNxlUql2NjYOLP17mSiDwAAAMDZ6nePt9vtqNfrJ569zWS7fPlyRMRdc/SzQuc4AAAAAGTIyspKFAqFqNVq+47a5d7W7XajXq9HoVCIK1eujLqckRCOAwAAAECG5PP5WF1djXw+n467JXvK5fKO90IWCccBAAAAIGMKhUJcv349ut1ulMvlUZfDGSuXy7G5uRnXr1/P5DiVPjPHAQAAACCDisVibG1tjboMRmB1dXXUJYwFneMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAGRUrVaLWq026jJGqlarRb1eH3UZjMD9oy4AAAAAADh75XI52u12rK+vj7qUkSqXy1Eul+PGjRuxsrIy6nI4QzrHAQAAAOCQWq1WzM3NxfT0dORyucjlcjE1NRXT09NRrVaj1WpFkiR37ZckSbp9/6vb7R64XrVajVwuFzMzM0N9HNVqNdrtdqysrESxWBzqscdR/3mcnp6+675SqRTLy8vRarUy30WfNTrHAQAAADiSjWQjrv3mtfjC5hfiq9/8anzn674z3nbhbfHUH3sqpvN3h4/3glarFfPz83sG30mSRLPZjGazGZVK5VAdyLVabSSdyvV6PZrNZpRKpahUKme+/jhaXFyMRqMR9Xo9Ll265HnJCJ3jAAAAABzK53//8/GX/se/FE/+wyfjH/yrfxDPv/h8/K9b/2s8/+Lz8Q/+1T+IJ//hk/GX/se/FJ///c+PutShqlarMTc3lwbjCwsLsb6+HltbW7G1tRXr6+uxsrISpVIpIiIKhcK+x+vf32q1otPpnGrtd0qSJO2ObjQaZ7r2uOt/UDE/Pz/iSjgrwnEAAAAADvTp3/10/OX/6S/H2u+t7bvd2u+txV/+n/5yfPp3P31GlZ2uarUazWYzIiKKxWJsbGxEo9GIYrEY+Xw+8vl8FIvFqFQqsbq6GhsbG1GtVvc9Zq1Wi3w+n94+S5cvX46I7VEiB4X49Xo9Hb9yms5qnYMUi8UoFouRJIkLdGaEcBwAAACAfX3+9z8fP/nPfjJe/darh9r+1W+9Gj/1qz818R3krVYrDcYLhUJcv379UF3hB21z4cKFWFpaioiIdrt9ZqHwYOh7mFD+6tWr0Ww2T727/azWOYz+Bxv9DxG4twnHAQAAANjXz639XHz99tePtM+r33o1/s763zmlis7G4HiNRqORdnsPw+Li4pl3j1+7di293R8Bw04LCwsRsf1BQqvVGnE1nDbhOAAAAAB72kg2Dhylspcb/+ZGdJPukCs6G61WK50xXqlUTiVMXl5ejoiITqdzJkHs6upqRISLTR6gWCxGxLefL+5d94+6AAAAAABO5hu3vxG/8/LvnMqxn/31Z0+0f/P/24y/8gN/ZUjV7PTmB98cD5x74FSOffXq1fT2QTPEj2thYSGWl5ej2+1GrVY79dC6P77l0qVLe27TbDbvery1Wm1Hd3upVNo1OO52u7G8vBztdju63W7k8/m4ePFiVKvVux7bcdfpdDrRaDRibW0tut1uJEkSxWIxnn766VhcXDzgGTicixcvRqfTGfkMdE6fcBwAAABgwv3Oy78Tf+Ef/YVRl7Grf3Lrn8Q/ufVPTuXYH/9zH49Hpx49lWMPBqOnOYKk0WhEuVyObrcbzWYzHesxbJ1OJ+2E73dG7+bChQvpzPRud7vrP5/Px4ULF9JtdpupXq/X7xoPkyRJOlO9UqnElStX0lEyx1mnVqvtuFBm/4KonU4nDc3X19dPPP6mXC5Hs9lMw/dhjtNhvBirAgAAAAB36AfJpx2MlkqlNKw+zdnjm5ub6e39LhhaqVRiY2MjNjY20u2WlpbSn21sbESj0dixz2Awvri4GFtbW9Hr9WJraytWVlYin89Hq9WKmZmZE63Tr31lZSW2trbSr353ebfb3TEn/rgGn59+cM+9STgOAAAAAHvoh+Sn6cqVK+lag53RwzQY8g52Z59Up9NJg/GVlZVYXl5OP1DI5/NRqVTi1q1bkc/n0/Exx9UPzyuVyo4PLUqlUjq/fXBW/HENHls4fm8zVgUAAABgwr35wTfHx//cx0/l2M/++rMnGovypx/506c6c/y05PP5NGQ97dEaxWIxSqVStNvtqNVqsbCwMPT1BgPjYR67H3YXi8U9Z6bn8/lYXl6OarUa9Xo9lpaWjlXDfvsMjr5ZW1s70SicwQ8PBjvuufcIxwEAAAAm3APnHji12dvz/+78icLxhX93IQr5vcd4jKuLFy+mc8dPGrYeRqPRiOnp6YiIuHz5ctoJPSxf/vKXh3q8vv5zdNDzc/HixR37DPvio4PB+TA7x8/iLwcYHWNVAAAAANjTdH46Lv7hiwdvuItLf+TSRAbjERFzc3Pp7d1mXw9boVBIL8ZZr9cnIpQdrLEf7O9lmHO8u91utFqtqFarUS6XY2ZmZsc885M6rS57xo9wHAAAAIB9/dTFn4o33P+GI+3zhvvfED8585OnVNHpGxxt0mq1otPpnPqag93iw74452B4PazgfTA4Xl9f33fbwUD8uIFzq9WK6enpmJ6ejrm5uWg2m7G2thYR+19k9CSGOZ+d8SMcBwAAAGBfb//ut8fP/amfO3RA/ob73xA/96d+Lt7+3W8/5cpO12BYPdhJflry+XwsLi5GRESz2RzqxSBPa452sViMiG+PV9nL1atX09vHGVHTarVibm4uut1uLCwsxOrqavR6vdja2or19fVYWVk58jH3Mvj86By/twnHAQAAADjQO7/nnfFL7/mluPRHLu273aU/cil+6T2/FO/8nneeUWWnZ2FhIR110u12Y2Zm5sCu6yRJol6vH3vNwYtVVqvVYx/nTieZo72xsbHnfUtLSxGx/fzs9bgH76tUKnt2ee+3Tj9cL5VK0Wg07grYh/lBwuCxTqsjnfEgHAcAAADgUN7+3W+PX3rPL8Un/vwn4se//8ej+HAx/tjUH4viw8X48e//8fiHf/4fxi+955cmvmN8UKPRSAPyTqcTU1NTUavVotPppCFzkiTR6XSiVqul9x9XPp9PA+d2u31gR/ZhDV4Qsz+K5CD9YPjatWvR7XYjSZJot9s7uugrlUr6/NRqtZibm0tH0PRD8f5Il3w+H1euXDnWOv1t2u12NJvNHQF/u90e6gcJgyN0hOP3tvtHXQAAAAAAk2U6Px1Lf3xp1GWcmUajEeVyOWq1Whr47tcdXqlUTrTe4uJiXL58OZIkGVpHdD6fj2KxGJ1OJ1ZXV9NAez9zc3PRbrcjSZK7LrjZ7XbT4LjRaEQ+n496vR6tVitardZdxyoWi7GysrLrmJLDrLO0tJSG4tVq9a4wfJgh9o0bNyLieONfmCw6xwEAAADgAJVKJTY2NmJlZeWu0SD94HlxcTHd5qQG550PSz/sPezFRftjZfL5fPoY+/O+7wyjl5eXY2NjIyqVShqA5/P5KJVKsbKyEuvr63sG2IdZJ5/Px/r6epRKpfT4hUIhKpVKevxSqRSlUunEQXm/W/8s5swzWrler9cbdREwaX7jN34j3v72b/+J2Oc///l47LHHRlgR7HT79u349Kc/HRER73znO+PcuXMjrgiyx3kIo+UchNFzHg7HF77whXjb29426jKYUL1eL1555ZWIiHjTm94Uzz//fMzMzETE9nxvI0Pu1ul0PEfHdJzfV6PO2HSOAwAAAEAGFIvFtHv8NDrT7wWNRiMiYigd6Iw/4TgAAAAAZEQ/FL927dqIKxk/SZJEs9mMiG+H5NzbhOMAAAAAkBH97vEkSfa9qGgWXb58OSLirpny3LuE4wAAAACQISsrK1EoFKJWq0W32x11OWOh2+1GvV6PQqEQV65cGXU5nBHhOAAAAABkSD6fj9XV1cjn8zE3NzfqcsZCuVze8byQDcJxAAAAAMiYQqEQ169fj263G+VyedTljFS5XI7Nzc24fv26cSoZc/+oCwAAAAAAzl6xWIytra1RlzFyq6uroy6BEdE5DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAABiRJErlcbsdXt9s9cL9qtRq5XC5mZmaGXtP09HRaS5IkQz/+sO32HN75NT09HeVyOer1+qEe0zi+Lkw24TgAAAAAHKBWq426hImVz+ejVCrt+CoUCtHtdqPdbketVoupqaljPcdeF07i/lEXAAAAAMBk+cbNm7H1savx9f/lf4nXvvrVuO87vzNe/+/8OzH1Y0/HA48+Ouryhqof4rZareh0OlEsFkdSRz6fj3w+P5K1T+qpp56KRqOx633tdjuWl5ej3W6nHeR7bTtoXF4XJpvOcQAAAAAO5dVf//X44n/4E9H9M382tn7lV+LVTie+8Zu/Ga92OrH1K78S3T/zZ+OL/+FPxKu//uujLnVoarVaGkqPskt5fX09tra2Ymtra2JD8t2USqVYXV2NxcXFiIhoNptRr9cP3G9cXhcmm3AcAAAAgAO98mu/Fl/8if8ovnbjxr7bfe3GjfjiT/xH8cqv/doZVXa6Lly4EEtLSxGx3eXcbrdHXNG9aXl5OQqFQkREXL169cDtvS4Mg3AcAAAAgH29+uu/Hr/7n/5n0Xv11UNt33v11fjd/+z/fM90kC8uLupSPgP9cPywFxz1unBSwnEAAAAA9vVi/W9F7+tfP9I+vVdfjRf/1t8+pYrO3vLyckREdDqdaLVaI67m3pMkSdr9XSqVDr2f14WTEI4DAAAAsKdv3Lx54CiVvXztX/7L+MbGxpArGo2FhYW0s/mkXcrdbjeq1WpMT09HLpeLqampKJfL+4a71Wo1crlcTE9P33VfkiRRq9XS4/WP+e53vzv+7t/9u2kndqvVSu/vdrv71tg/1ll0ZCdJEjMzMxGxfeHRfuB9GMN8Xcie+0ddAAAAAAAn89o3vhHf/O3fPpVj/36jebL9f+EX47urC0OqZqfXfe/3xn0PPHAqx95No9GIcrkc3W43ms1mLCwc/XHV6/W7Qtx+13S73Y5KpRJXrlw59EU3O51OzM7OpgF4f7/BY7700kvx1//6X49KpRKFQiG63W4sLy9Ho9HY85j98Lw/13vYut1udLvdWFlZiWZz+z1WLBZjZWXlyBccHcbrQjYJxwEAAAAm3Dd/+7ej+2f/3KjL2NXL//gfx8v/+B+fyrEL/8M/igfe8pZTOfZuSqVSFIvF6HQ6UavVjhzCDgbji4uLsbS0FPl8Pg2y5+fno9VqRafTiY1DdtzPz89HkiRRKpXuCpZXV1fjIx/5SPzgD/5g+rNarRbVajWazWYsLy/vGkRfvnw5IiIqlcqRg+rdNJvNNADfTalUimq1GpVK5VjHP+nrQnYZqwIAAAAAh3TlypWI2O7Mrtfrh96vH9xGRKysrOwIpvP5fFQqlbh161bk8/nodruHHhHS6XQiYnvsyp1BdqlUil/+5V+Od73rXenPFhYW0u36IfigJEnS8S7D7BrP5/M7vgatra3F1atX05njx3Hc14VsE44DAAAAwCEVi8X0gpG1Wi0dZ3KQfthdLBb37JAenLddr9cPdexisZge/7Dhcr+zerdu7sERJ/1jn9TCwkJsbW3t+Or1erGxsRErKytx8eLFaLVaUS6Xo1wuH/o5HXTc14VsM1YFAAAAYMK97nu/Nwr/wz86lWP/fqN5orEoD/6ZP3OqM8dHodFopBfGvHz58qEuINkPrvsB7l4uXry4Y5+DRo0sLS3F3NxcdLvdKJfLEbEdFF+8eDEWFhbirW9966779MP3Vqu1Y43+HPLTmjU+qFAoRKFQiEqlEp1OJ2ZmZqLdbsfs7Gysr68f+XjHeV3INuE4AAAAwIS774EHTm329nf/1eqJwvHvfv9fjQf+/4HlvaJQKMTCwkI0m82o1+vp7PC9DHYxTx/wXBQKhfR2/6KY+6lUKrG6uhrVajXdvtPpRKfTiWazGU8++WT88i//8o598vl8Wv/ly5fTcLzdbke3203HvJylYrEYq6urUS6X09qPOjv8qK8LGKsCAAAAwJ4eePTReOOlS8fa940/9EP3XDDeN9iVfNB88MGA9qCO6MFA/LDBbqlUio2NjdjY2IhGo7HjQpqf+MQn4id+4ifu2qdfcz9IjzjbrvHdlEqltO6VlZVjHeMorwsIxwEAAADY18OLH4zcG95wpH1yb3hDPPzBnz6likYvn8/H4uJiRGzP6T6oy7s/v/ugueBXr15Nbx80guVO/c7plZWV2Nraivn5+YjYDsh327bfHd5fs38hzqN2bA9LkiRpl/1xO76P+rqQbcJxAAAAAPb1hh/4gfie//rnDx2Q597whvie//rn4w0/8AOnXNloDY7tqFarB24bsd0ZXq/Xd91m8L5KpbJjxMpe9gt/5+bmDlVTs9lMg/HBrvOzNljvQc/nfo7yupBtwnEAAAAADvSmP/kn44/+8t+LN/7QD+273Rt/6Ifij/7y34s3/ck/eUaVjU4+n08D5na7vW9XeKVSSTuya7VazM3NpeNM+qF4fx55Pp+PK1euHKqGmZmZmJmZiWazmR4vYntcyoc+9KGIiHj88cd33bdYLEapVIokSUY6UqXVaqUX44zYHo1y1K75QUd5Xcg2F+QEAAAA4FDe8AM/EH/0l/9efOPmzdj62NX4+he+EK999atx33d+Z7z+bW+Lqf/jj92zM8b3sri4GJcvX44kSQ4c4dFoNCKfz0e9Xo9Wq5V2aw8qFouxsrJy6O7tJEmi0+ns2yH99/7e39vzvlqtlgbIxWIxHf8yTNeuXdv1uel2u3fNWF9eXh7KWJejvC5kl3AcAAAAgCN54NFH44/89f/LqMsYG8vLy4ce39Hfth9KJ0kS+Xw+Ll68GNVqNZ0DflhbW1tx7dq1WF1djU6nkwbBhUIh3ve+98Vf+2t/bd+gvVQqRaFQiG63e2pd40mS7Nm9XSwWo1AoRLlcHvqs86O8LmSTcBwAAAAABuTz+ej1eofefmFh4UjBbqFQiJWVlSPV1Gg00tEng/L5/J7r93q9eOWVVw48dj6fj3w+f+Rg/qBjHuU5PI1jHvV1IXuE4wAAAACQUd1uNzqdTiwvL4+6FDhzLsgJAAAAABlVq9UiInRYk0nCcQAAAADIiCRJ0tudTidarVYsLi4e+gKgcC8xVgUAAAAAMmJmZiYitud3dzqdyOfzp3YhThh3OscBAAAAICOKxWJsbm5Gp9OJYrEY6+vrusbJLJ3jAAAAAJARKysroy4BxobOcQAAAAAAMkc4PsG63W5Uq9WYmZmJqampmJqaipmZmWg2m2O9XqvVinK5HFNTU5HL5WJmZibm5uZOrW4AAAAAgDsJxydUvV6P6enp6Ha7ceXKldja2oqtra1YWlqKWq2W3jdO6yVJEuVyOS5fvhxzc3Oxvr4e6+vr8fTTT0e73Y5qtRrT09PR6XSGVjcAAAAAwG7MHJ9AzWYzarVaVCqVu+ZEVSqVKBaLMTMzEzMzM3Hr1q0TX1RhWOvNzs7GxYsXY3V1dcfPi8ViLCwsxOzsbHQ6nZiZmYn19fUoFosnqhsAAAAAYC86xydMf7RJxN4XUCgUCrGwsBBJksT8/PxYrFer1SIiotFo7Hp/Pp+PK1eupN/Pzc2dpGwAAAAAgH0JxyfM8vJyRESUSqV9t+sH2q1WK5IkGfl6zWYzCoXCvqNXisViuk632zWDHAAAACKi1+uNugSAfU3q7ynh+ITpB8aFQmHf7QbvP0nIPIz1kiSJJEmi1WrF9PT0vscZHKWyV6c6AAAAZMV9990Xr7322qjLANjXa6+9FvfdN3lR8+RVnGGDF6o8KGSOiHT299WrV0e63ubm5o7v9+sev3Tp0p77AQAAQNa84Q1viK9+9aujLgNgX1//+tfjgQceGHUZRyYcnyBra2vp7YM6uQe3GQy5R7FefyZ5Pp+PhYWFfY81GJxfuHDhqCUDAADAPeW7vuu74itf+cqoywDY1yuvvBLf+Z3fOeoyjkw4PkE2NjaOve9x5o4Pc71GoxFbW1t7XpCz78aNG+ntwRErAAAAkEUPPvhgfO1rX4utra1RlwKwq69//evx0ksvxYMPPjjqUo7s/lEXwOEdNeAe7Lze3NxMx56M83rtdjv9fmlp6Uj7H9eLL74YX/rSl460z82bN3d8f/v27bh9+/Ywy4ITGXw/em/CaDgPYbScgzB6zsPh+Z7v+Z743d/93fjqV78aDz74YLzxjW+M++67L3K53KhLY8wNXiRxUi+YyHjq9XrxrW99K15++eXY3NyMhx9+OO6///4j/74f9b8PwvEJcpIZ3MfpHD/r9S5fvpzut7y8fORw/bg+8pGPxIc//OETHWN9fd2MdMbWZz7zmVGXAJnnPITRcg7C6DkPh+P++++PBx54IO6/X5wDjF6v14s/+IM/iD/4gz+If/2v//WxjnHr1q0hV3U0fptOqLMKjs9qvW63G/V6PSIiKpVKLC4unup6AAAAMGm+9a1vxbe+9a1RlwFwzxCOT5DBsSVH7cw+Trh9VuslSRLlcjkitoPxlZWVI601DmZmZuKxxx4bdRmQun37dtqd8453vCPOnTs34ooge5yHMFrOQRg95yGMnvOQcTeYP46CcHyCHDXgHhzzcZw32lmtNzs7G91uNxYWFg68YOdp+MAHPhBzc3NH2ufmzZvx5JNPpt+fO3fOPzCMLe9PGD3nIYyWcxBGz3kIo+c8ZByN+j0pHJ8g09PT6e2jzrc+Tuf4WaxXLpej0+nEyspKVCqVI60xLA8//HA8/PDDI1kbAAAAABgN4fgEuXjxYnr7MGNOut1uREQUCoWxXK9cLsfa2lqsr69HsVg8Vo0AAAAAAMdx36gL4PAGA+SNjY0Dt+8H2qVSaezWm5ubi263u2cw3ul0jjzqBAAAAADgsITjE6Y/emRtbW3f7TqdTnq7Wq2O1XqDwfheXebtdnvkA/kBAAAAgHuXcHzCLC0tRcR2GN0fY7Kbq1evRsR29/deI0uSJIlqtRq1Wu1M1ovYDsaTJIn19fV955Kvrq7umHkOAAAAADBMZo5PmGKxGIuLi1Gv16Narcbq6upd23S73ajX65HP5+P69et7Hmt2dnZHx/fy8vKprletVqPVakWpVIpyubzrNpubm5EkSXS73X1DewAAAACAkxCOT6B+iF2v16NcLkej0UjHk7RarZifn49CoRArKyv7dmcPXmRzv67wYaxXrVaj2WxGxPbIlMMYvCAoAAAAAMAwGasyoZaXl9OZ3eVyOaampmJqaiouX74cS0tLsbGxse94k4hIQ+5CobBr1/iw1ut0OmkwfhT7BfsAAAAAACehc3yCFYvFaDQax96/VCrFxsbGqa9XLBaj1+sdeT8AAAAAgNOicxwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5gjHAQAAAADIHOE4AAAAAACZIxwHAAAAACBzhOMAAAAAAGSOcBwAAAAAgMwRjgMAAAAAkDnCcQAAAAAAMkc4DgAAAABA5ox1OP7QQw/Fz/3cz8XLL7886lIAAAAAALiHjHU4vrW1FYuLizE1NRU/9mM/Fp/97GdHXRIAAAAAAPeAsQ7HIyJ6vV70er1YWVmJmZmZuHTpUnz84x8fdVkAAAAAAEywsQ7Ht7a24plnnolCoZCG5J1OJyqVSjz00EPxMz/zM0auAAAAAABwZGMdjp8/fz4WFxfj5s2b8dxzz8Xs7Gwakm9tbcXy8rKRKwAAAAAAHNlYh+ODSqVSrK6uxtbWVnzwgx+MfD5/18iVt771rfHRj3501KUCAAAAADDmJiYc7zt//nwsLy/H5uZmrK6uxvve9740JL9582YsLCykI1deeOGFUZcLAAAAAMAYmrhwfNDs7GysrKzEa6+9Fqurq7G4uBhPPPFEOnJleno63vve98anPvWpUZcKAAAAAMAYmehwvO/ll1+OW7duxfPPPx/PP/985HK5tJv8ueeei1KpZOQKAAAAAACpiQ7HP/nJT8Z73vOemJqaimq1Gu12O3q9Xjp6ZWNjI5555pl48MEH05Erb33rW+Nzn/vcqEsHAAAAAGCEJi4cf/nll+Nv/+2/HQ899FCUy+U0EO/1evHEE0/EyspKbG5uxgc/+MF45JFHYnFxMba2tuLatWvxfd/3fXHz5s0oFotGrQAAAAAAZNjEhOOf/exn4+mnn46pqamo1WqRJEkaipdKpVhdXY21tbV43/vet+v+lUolNjY20gt41mq1M34EAAAAAACMi7EPxz/5yU/GpUuXYmZmJlqtVhqI93q9NPB+7rnnYnZ29lDH+6t/9a9GRMT6+vpplg0AAAAAwBi7f9QF7Octb3lLdLvdiIjo9XrpzxcXF2NpaSnOnz9/5GMmSTKs8gAAAAAAmFBjHY5vbGykt/P5fCwtLcUHP/jBEx3zxo0bERFRKBROdBwAAAAAACbXWIfjERHFYjGWlpb2nCV+VA899FAsLi7GpUuXhnI8AAAAAAAmz1iH46urq4eeJX5YJ+08BwAAAABg8o31BTmHHYwDAAAAAEDEmIfjERHPPvtsvP/9749nn332wG1v3boVly5dire85S3x0Y9+9AyqAwAAAABgEo19OL64uBjNZjNWVlYO3PaRRx6J1157LTY2NuKZZ545g+oAAAAAAJhEYx2OX7lyJZIkiYiI5eXlQ+3T367b7cbHP/7x0yoNAAAAAIAJNtbheL9bvFAoxOOPP36ofUqlUnr7Yx/72GmUBQAAAADAhBvrcHxtbS1yuVwUi8Uj7VcsFqPX60Wn0zmlygAAAAAAmGRjHY73R6pcuHDhSPsVCoWI2B6tAgAAAAAAdxrrcPy4jhqmAwAAAACQLWMdjh+3A3xtbS0iIvL5/LBLAgAAAADgHjDW4Xh/dni73Y6vfOUrh9rnpZdeik6nE7lcLi5evHjKFQIAAAAAMInGOhx/6qmn0tuzs7OH2mdubi69Xa1Wh14TAAAAAACTb6zD8UqlEk888URERKyvr8d73/ve+NznPrfrti+88EK85z3viXa7HblcLgqFQvzoj/7oWZYLAAAAAMCEuH/UBRxkZWUlHn300cjlcrG6uhqrq6tRLBbj4sWLMT09HRsbG9HtdqPdbt+1HwAAAAAA7Gbsw/FCoRBra2sxOzsbL730UkREdDqd6HQ6O7br9Xrp7eeeey4ef/zxsywTAAAAAIAJMtZjVfqKxWK88MILMT8/HxHbQfidXxHbY1g2NjYOPZ8cAAAAAIBsGvvO8b7z589Ho9GIer0e165di/X19djc3IwLFy7EzMxMlEqleOSRR0ZdJgAAAAAAE2BiwvG+8+fPx/z8fNpFDgAAAAAARzURY1UAAAAAAGCYhOMAAAAAAGTOxI1VeeGFF6LT6Rxq20KhEI8//vjpFgQAAAAAwMSZiHD85Zdfjvn5+Wi1Wkfab2FhIX7hF37hlKoCAAAAAGBSjX04/vzzz0epVIokSaLX6426HAAAAAAA7gFjH47Pzc3F1tZW+n2hUIhCoRAREe12O3K5XMzOzkZERLfbjW63G7lcLkqlUpTL5ZHUDAAAAADAeBvrcPxv/a2/lYbds7Oz0Wg04pFHHknvv3DhQrz00kvx3HPPpT+r1+vxoQ99KPL5fPzoj/7oKMoGAAAAAGDM3TfqAvazuroaERHFYjGee+65HcF4xHY4HrE9k7xvcXEx8vl8tFqteOGFF86sVgAAAAAAJsdYh+Nra2uRy+Xi6aef3vX+fD4fEdvjVAY99dRTERHx3/13/92p1gcAAAAAwGQa63C8rx+C36k/e/zOcHx6ejp6vV587GMfO+3SAAAAAACYQGMdjvfD7/X19T3v7/V6cePGjV3vvzM0BwAAAACAiAkIx3u93p4hd7lcjoiIVqu14+f9sDxJklOtDwAAAACAyTTW4filS5ciIqLdbu96/+zsbERsd4h/7nOfi4iIl156KQ3L9xrHAgAAAABAto11OL6wsJDe/rmf+7ldt3nf+94XvV4vfuRHfiTe//73p6NYcrlclEqlM6kTAAAAAIDJMtbh+Pnz52N+fj56vV784i/+Yrz88st3bbO8vBwR2yNUms3mjlEq/fsAAAAAAGDQWIfjERGNRiNee+21+K3f+q148MEH77q/UCjEtWvXotfrpV/FYjHW1tbi+77v+86+YAAAAAAAxt79oy5gGCqVSmxtbUW73Y5CoRBPPPHEqEsCAAAAAGCM3RPheMT2CJb3ve99oy4DAAAAAIAJMNbh+NLSUkRETE9Px1/5K39lxNUAAAAAAHCvGOtwfGVlJW7duhX5fF44DgAAAADA0Iz1BTmLxWL0er1IkiRefvnlUZcDAAAAAMA9YqzD8Wq1mt5+5plnRlgJAAAAAAD3krEOx2dnZ+OJJ56IXq8Xy8vL8cUvfnHUJQEAAAAAcA8Y63A8YnvueEREr9eLYrEYn/rUp0ZcEQAAAAAAk26sL8gZEVEoFGJ9fT1mZ2dja2srSqVSVKvVqFQqUSgU4sKFC/vu/+CDD55RpQAAAAAATIqxDsefeuqpeP7553f8rNfrRaPRiEajceD+uVwuvvWtb51WeQAAAAAATKixDse73W5sbGxELpeLiEj/G7EdkgMAAAAAwHGM9czxqampyOfzcf78+bu+8vn8ob7uZd1uN6rVaszMzMTU1FRMTU3FzMxMNJvNsV7vrOsGAAAAALjTWIfjq6ursbm5eeyvL3/5y6N+CKemXq/H9PR0dLvduHLlSmxtbcXW1lYsLS1FrVZL7xu39c66bgAAAACA3Yx1OM7ums1m1Gq1qFQqsbq6GsViMb2vUqnE+vp6bG5uxszMTCRJMjbrnXXdAAAAAAB7EY5PmP5IkoiIlZWVXbcpFAqxsLAQSZLE/Pz8WKx31nUDAAAAAOxHOD5hlpeXIyKiVCrtu10/iG61Wifqwh7WemddNwAAAADAfoTjE6Z/0cpCobDvdoP3n+RCl8Na76zrBgAAAADYj3B8gnQ6nfT29PT0gdvn8/mIiLh69epI1zvrugEAAAAADnL/qAvYz3ve854T7V8ul+Onf/qnh1TN6K2traW3D+rA7m/T6XR2hNOjWO+s6wYAAAAAOMhYh+Orq6uRy+WOvF+v14tcLneoIHaSbGxsHHvfJEnSjuyzXu+s6z6qF198Mb70pS8daZ+bN2/u+P727dtx+/btYZYFJzL4fvTehNFwHsJoOQdh9JyHMHrOQ8bdqN+XYx2OR2wH3UfRD9OPut8kOOoFKi9cuJDe3tzcPHLIPKz1zrruo/rIRz4SH/7wh090jPX19djc3BxSRTBcn/nMZ0ZdAmSe8xBGyzkIo+c8hNFzHjKObt26NdL1xzocX1lZOfS2N27ciE6nE+12O3K5XKyursYjjzxyitWdvZOEr0cNqIe53lnXDQAAAABwkLEOx9/3vvcdedt6vR4f+tCHotlsxsc+9rHTKm3kTrub+rTWO+u6z8rMzEw89thjoy4DUrdv3067At7xjnfEuXPnRlwRZI/zEEbLOQij5zyE0XMeMu4GJ0iMwliH48exuLgYq6ursbKyEs1mMx588MFRlzQ0g2+Wo3ZUHyeUHtZ6Z133UX3gAx+Iubm5I+1z8+bNePLJJ9Pvz5075x8Yxpb3J4ye8xBGyzkIo+c8hNFzHjKORv2evOfC8YiIp556Kq5fvx5XrlyJn/qpnxp1OUNz1KB4cJzJcT6FGdZ6Z133UT388MPx8MMPn/o6AAAAAMD4uG/UBZyGixcvRkTEc889N+JKhmt6ejq9fdQ53sfpwB7WemddNwAAAADAQe7JcLxvbW1t1CUMVT/0jzjceJJutxsREYVCYaTrnXXdAAAAAAAHuSfD8X4oftT51uOuWCymtzc2Ng7cvv/4S6XSSNc767oBAAAAAA4y1uH4yy+/fOSvT37yk7G8vDzq0k9NpVKJiIO74judTnq7Wq2OfL2zrhsAAAAAYD9jHY7n8/mYmpo60le5XI5bt25FLpfb0bF8r1haWoqI7RC5P35kN1evXo2I7a7tvZ6HJEmiWq1GrVY79fWGWTcAAAAAwEmNdTje1+v1jvwVEfdkB3mxWIzFxcWI2LuzutvtRr1ej3w+H9evX9/zWLOzs9FsNqNer+8ZkA9rvWHWDQAAAABwUmMfjveD7sMqFApRKpXiueeeix/5kR85papGa3l5ORYXF6Pdbke5XN7Rid1qtWJmZiYKhUJcv3498vn8nscZnMm+Xzf3sNYb1nEAAAAAAE7q/lEXsJ/XXntt1CWMreXl5Xj66aej0WhEuVyOzc3NiNj+cGBpaSnt0t5Po9FIu7gP6rIfxnrDPA4AAAAAwEmMdTjO/orFYjQajWPvXyqVYmNj48zWG/ZxAAAAAACOa+zHqgAAAAAAwLAJxwEAAAAAyJyxD8efffbZeP/73x/PPvvsgdveunUrLl26FG95y1viox/96BlUBwAAAADAJBr7cHxxcTGazWasrKwcuO0jjzwSr732WmxsbMQzzzxzBtUBAAAAADCJxjocv3LlSiRJEhERy8vLh9qnv123242Pf/zjp1UaAAAAAAATbKzD8X63eKFQiMcff/xQ+5RKpfT2xz72sdMoCwAAAACACTfW4fja2lrkcrkoFotH2q9YLEav14tOp3NKlQEAAAAAMMnGOhzvj1S5cOHCkfYrFAoRsT1aBQAAAAAA7jTW4fhxHTVMBwAAAAAgW8Y6HD9uB/ja2lpEROTz+WGXBAAAAADAPWCsw/H+7PB2ux1f+cpXDrXPSy+9FJ1OJ3K5XFy8ePGUKwQAAAAAYBKNdTj+1FNPpbdnZ2cPtc/c3Fx6u1qtDr0mAAAAAAAm31iH45VKJZ544omIiFhfX4/3vve98bnPfW7XbV944YV4z3veE+12O3K5XBQKhfjRH/3RsywXAAAAAIAJcf+oCzjIyspKPProo5HL5WJ1dTVWV1ejWCzGxYsXY3p6OjY2NqLb7Ua73b5rPwAAAAAA2M3Yh+OFQiHW1tZidnY2XnrppYiI6HQ60el0dmzX6/XS288991w8/vjjZ1kmAAAAAAATZKzHqvQVi8V44YUXYn5+PiK2g/A7vyK2x7BsbGwcej45AAAAAADZNPad433nz5+PRqMR9Xo9rl27Fuvr67G5uRkXLlyImZmZKJVK8cgjj4y6TAAAAAAAJsDEhON958+fj/n5+bSLHAAAAAAAjmoixqoAAAAAAMAwjX04/uyzz8b73//+ePbZZw/c9tatW3Hp0qV4y1veEh/96EfPoDoAAAAAACbR2Ifji4uL0Ww2Y2Vl5cBtH3nkkXjttddiY2MjnnnmmTOoDgAAAACASTTW4fiVK1ciSZKIiFheXj7UPv3tut1ufPzjHz+t0gAAAAAAmGBjHY73u8ULhUI8/vjjh9qnVCqltz/2sY+dRlkAAAAAAEy4sQ7H19bWIpfLRbFYPNJ+xWIxer1edDqdU6oMAAAAAIBJNtbheH+kyoULF460X6FQiIjt0SoAAAAAAHCnsQ7Hj+uoYToAAAAAANky1uH4cTvA19bWIiIin88PuyQAAAAAAO4BYx2O92eHt9vt+MpXvnKofV566aXodDqRy+Xi4sWLp1whAAAAAACTaKzD8aeeeiq9PTs7e6h95ubm0tvVanXoNQEAAAAAMPnGOhyvVCrxxBNPRETE+vp6vPe9743Pfe5zu277wgsvxHve855ot9uRy+WiUCjEj/7oj55luQAAAAAATIj7R13AQVZWVuLRRx+NXC4Xq6ursbq6GsViMS5evBjT09OxsbER3W432u32XfsBAAAAAMBuxj4cLxQKsba2FrOzs/HSSy9FRESn04lOp7Nju16vl95+7rnn4vHHHz/LMgEAAAAAmCBjPValr1gsxgsvvBDz8/MRsR2E3/kVsT2GZWNj49DzyQEAAAAAyKax7xzvO3/+fDQajajX63Ht2rVYX1+Pzc3NuHDhQszMzESpVIpHHnlk1GUCAAAAADABJiYc7zt//nzMz8+nXeQAAAAAAHBUEzFWBQAAAAAAhumeDsc/+clPjroEAAAAAADG0D0Xjr/wwguxtLQUDz30ULz73e8edTkAAAAAAIyhiZs5vpdnn302Go1GdDqdiIjo9XqRy+VGXBUAAAAAAONoosPxz372s9FoNKLZbKY/E4oDAAAAAHCQiQvHX3755Wg2m9FoNKLb7UbE3YF4r9eLUqkU1Wp1VGUCAAAAADDGJiYc/+QnPxnLy8vRbrcjYjsAj4g0FO/1elEoFKJarcbCwkKcP39+ZLUCAAAAADDexjocf+GFF9KxKUmSRMS3u8T7ofj58+fjqaeeimq1Gk888cQIqwUAAAAAYFKMZTi+28U1I3Z2ife//+QnPxmPP/74SOoEAAAAAGAy3TfqAvo++9nPxvvf//44d+5cVKvV6HQ6aQje98gjj8Ty8nLcvHlzRFUCAAAAAHAvGGnn+F4X1xx0/vz5WFhYiGq1Go888sgoygQAAAAA4B4zsnD8Pe95T3pxzYidoXg+nzdHHAAAAACAUzOycHx1dfWun1UqlahWqzE7OzuCigAAAAAAyIqRjlXpX2BzeXk5fvqnf3qUpQAAAAAAkCEjvSBnf5RKrVaLH/qhH4qPfvSj8fLLL4+yJAAAAAAAMmBk4fjW1lY888wzcf78+ej1erG2thYLCwsxNTUV733ve+PjH//4qEoDAAAAAOAeN7Jw/Pz587G4uBibm5uxuroa73vf+6LX60Wv14vV1dWoVCpx7ty5ePrpp+O//+//+1GVCQAAAADAPWikY1X6ZmdnY2VlJba2tuIXf/EX44knnkiD8larFXNzc3Hu3Ln4wAc+EJ/61KdGXS4AAAAAABNuLMLxvvPnz8fCwkKsra3F+vp6zM/Pp2NXer1eNBqNKJVK8dBDD426VAAAAAAAJthYheODnnjiiWg0GrG5uRnXrl2LUqmUhuRbW1uRy+UiImJ+fj4++tGPjrhaAAAAAAAmydiG44MqlUo899xz6UU88/l8GpR3Op1YWFiIc+fOxY/92I/FJz/5yVGXCwAAAADAmJuIcLxvv4t49nq9WFlZiXK5HA899FD8zM/8THz2s58ddckAAAAAAIyhiQrHB915Ec9isbhj7Mry8nJcvHhx1GUCAAAAADCGJjYc79vtIp6DY1cAAAAAAOBOEx+OD7rzIp6zs7OjLgkAAAAAgDF0T4Xjg/oX8QQAAAAAgDvds+E4AAAAAADsRTgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHJ9Q3W43qtVqzMzMxNTUVExNTcXMzEw0m82xXq/VakW5XI6pqanI5XIxMzMTc3Nzp1Y3AAAAAMBuhOMTqF6vx/T0dHS73bhy5UpsbW3F1tZWLC0tRa1WS+8bp/WSJIlyuRyXL1+Oubm5WF9fj/X19Xj66aej3W5HtVqN6enp6HQ6Q6sbAAAAAGAv94+6AI6m2WxGrVaLSqUSKysrO+6rVCpRLBZjZmYmZmZm4tatW5HP58divdnZ2bh48WKsrq7u+HmxWIyFhYWYnZ2NTqcTMzMzsb6+HsVi8UR1AwAAAADsR+f4BOmPNomIu4LqvkKhEAsLC5EkSczPz4/FerVaLSIiGo3Grvfn8/m4cuVK+v3c3NxJygYAAAAAOJBwfIIsLy9HRESpVNp3u36g3Wq1IkmSka/XbDajUCjsO3qlWCym63S7XTPIAQAAAIBTJRyfIP3AuFAo7Lvd4P0nCZmHsV6SJJEkSbRarZient73OIOjVPbqVAcAAAAAGAbh+IQYvFDlQSFzRKSzv69evTrS9TY3N3d8v1/3+KVLl/bcDwAAAABgmITjE2JtbS29fVAn9+A2gyH3KNbrzyTP5/OxsLCw77EGg/MLFy4ctWQAAAAAgEO7f9QFcDgbGxvH3jdJkrSzexTrNRqNPS/GOejGjRvp7cERK6ftxRdfjC996UtH2ufmzZs7vr99+3bcvn17mGXBiQy+H703YTSchzBazkEYPechjJ7zkHE36velcHxCHPXCmoOd15ubm0cOx0exXrvdTr9fWlo60v4n8ZGPfCQ+/OEPn+gY6+vrRsEwtj7zmc+MugTIPOchjJZzEEbPeQij5zxkHN26dWuk6xurMiFOErweNegexXqXL19O91teXj5yuA4AAAAAcBQ6xyfQWQfHp71et9uNer0eERGVSiUWFxdPdb3TMDMzE4899tioy4DU7du3066Ad7zjHXHu3LkRVwTZ4zyE0XIOwug5D2H0nIeMu1Ffd1A4PiEG3yhH7cw+Trh9VuslSRLlcjkitoPxlZWVI601DB/4wAdibm7uSPvcvHkznnzyyfT7c+fO+QeGseX9CaPnPITRcg7C6DkPYfSch4yjUb8nheNDMD09Hd1ud6jHXF1djVKplH5/1IB7cCzKcT6BOav1Zmdno9vtxsLCwqEu2nkaHn744Xj44YdHsjYAAAAAMBrC8SFYXl6OGzduDO14Dz30UFy8eHHHz6anp9PbR50HfpzO8bNYr1wuR6fTiZWVlahUKkdaAwAAAADgJITjQ1CpVE493B0Myw8z5qTfyV4oFMZyvXK5HGtra7G+vh7FYvFYNQIAAAAAHNd9oy6AwxkMkDc2Ng7cvh9oD45mGZf15ubmotvt7hmMdzqdI88ABwAAAAA4CuH4BOl3p6+tre27XafTSW9Xq9WxWm8wGN+ry7zdbo/8SrUAAAAAwL1NOD5BlpaWImI7jN7vAqBXr16NiO3u771GliRJEtVqNWq12pmsF7EdjCdJEuvr6/vOJV9dXd0x8xwAAAAAYNjMHJ8gxWIxFhcXo16vR7VajdXV1bu26Xa7Ua/XI5/Px/Xr1/c81uzs7I6O7+Xl5VNdr1qtRqvVilKpFOVyeddtNjc3I0mS6Ha7+4b2AAAAAAAnJRyfMP0Qu16vR7lcjkajkY4nabVaMT8/H4VCIVZWVvbtzh68yOZ+XeHDWK9arUaz2YyI7ZEphzF4QVAAAAAAgGEzVmUCLS8vpzO7y+VyTE1NxdTUVFy+fDmWlpZiY2Nj3/EmEZGG3IVCYdeu8WGt1+l00mD8KPYL9gEAAAAATkrn+IQqFovRaDSOvX+pVIqNjY1TX69YLEav1zvyfgAAAAAAp0nnOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzhOAAAAAAAmSMcBwAAAAAgc4TjAAAAAABkjnAcAAAAAIDMEY4DAAAAAJA5wnEAAAAAADJHOA4AAAAAQOYIxwEAAAAAyBzh+ITqdrtRrVZjZmYmpqamYmpqKmZmZqLZbE7kes1mM6ampoZyLAAAAACAgwjHJ1C9Xo/p6enodrtx5cqV2Nraiq2trVhaWoparZbeNynr9YP3JEkiSZKh1Q0AAAAAsBfh+IRpNptRq9WiUqnE6upqFIvF9L5KpRLr6+uxubkZMzMzQwmaz2K9ubm5E9cJAAAAAHAUwvEJ0u+wjohYWVnZdZtCoRALCwuRJEnMz8+P/Xr1ej06nc6J6gQAAAAAOCrh+ARZXl6OiIhSqbTvdv1Au9Vqnah7/LTX63Q6cfny5XQdAAAAAICzIhyfIP2LXxYKhX23G7z/JBfMPO315ubmYmVlJfL5/LHqAwAAAAA4LuH4hBgcPTI9PX3g9v3A+erVq2O5Xq1Wi1KpdGBXOgAAAADAaRCOT4i1tbX09kGd3IPbHHee92mu1+l0otVqRaPROFZtAAAAAAAnJRyfEBsbG8fe9zhzx09zvf44FQAAAACAUbl/1AVwOEcNuC9cuJDe3tzcPPJc79Nar1qtRqVSiWKxeKTjn6YXX3wxvvSlLx1pn5s3b+74/vbt23H79u1hlgUnMvh+9N6E0XAewmg5B2H0nIcwes5Dxt2o35fC8Qmxubl57H2P0zl+Guu12+1ot9sn6ko/DR/5yEfiwx/+8ImOsb6+fqLnDE7TZz7zmVGXAJnnPITRcg7C6DkPYfSch4yjW7dujXR94fgEOmoX+DislyRJzM3NxfXr109eEAAAAADACQnHJ8Tg2JKjdoIfJ9we9nrz8/OxtLQ0VuNUhmlmZiYee+yxUZcBqdu3b6ddAe94xzvi3LlzI64Issd5CKPlHITRcx7C6DkPGXeDGeQoCMeHYHp6Orrd7lCPubq6GqVSKf3+qAH34IiP47zJhrleq9WKbrc7thfh/MAHPhBzc3NH2ufmzZvx5JNPpt+fO3fOPzCMLe9PGD3nIYyWcxBGz3kIo+c8ZByN+j0pHB+C5eXluHHjxtCO99BDD8XFixd3/Gx6ejq9fdTZ1sfpHB/WekmSxPz8fKyvrx+5hrPy8MMPx8MPPzzqMgAAAACAMyQcH4JKpRKVSuVU1xgMyw8z5qTfyV4oFEa63tzcXCwsLMSFCxf2PM7gzweD+LOerQ4AAAAAZIdwfEIMzure2Ng4cPt+4Dw4mmUU67Xb7Wi321Gv1w+17mDH+sLCQjQajUPtBwAAAABwFPeNugAOr9+dvra2tu92nU4nvV2tVke6Xq/XO/BrsOt+a2sr/blgHAAAAAA4LcLxCbK0tBQR22H0fhcAvXr1akRsd38PdoAPSpIkqtVq1Gq1M1kPAAAAAGCcCMcnSLFYjMXFxYjYuyO82+1GvV6PfD4f169f3/NYs7Oz0Ww2o16v7xmQD3M9AAAAAIBxIhyfMMvLy7G4uBjtdjvK5fKOju5WqxUzMzNRKBTi+vXr+17QcvAimPt1hQ9rvd3W73a76UzyvmazGd1u91AXAQUAAAAAOC7h+ARaXl6O9fX1KBQKUS6XY2pqKqampuLy5cuxtLQUGxsbB443aTQaUSgUolAoxPLy8qmvd6epqamYnp6Oubm5iIjI5/ORz+fj8uXLMT09HVNTU4e+iCcAAAAAwFHdP+oCOJ5isXiiC1aWSqXY2Ng4s/Xu1Ov1hnYsAAAAAICj0jkOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGOAwAAAACQOcJxAAAAAAAyRzgOAAAAAEDmCMcBAAAAAMgc4TgAAAAAAJkjHAcAAAAAIHOE4wAAAAAAZI5wHAAAAACAzBGO///au3vlNM63D8C33mQm7aJMyjTIXTpI6hSGOilgfARGZ4BGVUqPdAboDBxUpAcXqS04AkMK92ZbF5l9i4wYyX99W3w8PNc1owkgtM+d4je7+/OyAAAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZ+XbTA0CKPn/+fO35hw8fNjQJ3Ozff/+Nf/75JyIi9vf345tvvtnwRJAfOYTNkkHYPDmEzZNDtt2XndqXnduqKcfhCT5+/Hjt+e+//76ZQQAAAABgR3z8+DEajcba1nNbFQAAAAAAsqMcBwAAAAAgO3tVVVWbHgJSU5Zl/P3338vnP/74Y3z33XcbnAiu+/Dhw7Xb/fz111/x4sWLzQ0EGZJD2CwZhM2TQ9g8OWTbff78+drti3/99dcoimJt67vnODxBURTx22+/bXoMeLAXL17ETz/9tOkxIGtyCJslg7B5cgibJ4dso3XeY/xLbqsCAAAAAEB2lOMAAAAAAGRHOQ4AAAAAQHaU4wAAAAAAZEc5DgAAAABAdpTjAAAAAABkRzkOAAAAAEB2lOMAAAAAAGRHOQ4AAAAAQHaU4wAAAAAAZEc5DgAAAABAdr7d9AAAPL8ffvgh/vjjj2vPgfWSQ9gsGYTNk0PYPDmEu+1VVVVteggAAAAAAFgnt1UBAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcB9hy8/k8Dg8Po9lsRq1Wi1qtFs1mM87OzpJc7+zsLGq12rNsC9Yl1Ryen59Hu92OWq0We3t70Ww2o9vtrmxueKhUM7XuuWGVUs2hfRu7ItUM3sZ5HsmqANhaJycnVURUrVarmkwmy9eHw2FVFEVVr9er2WyWzHqz2ayKiCoiqsVi8QwTw+qlmMPFYlG1Wq2q0WhUg8Ggms1m1WQyqU5OTqqiKKqIqOr1+rXtw7qkmKlNzA2rlGIO7dvYJSlm8C7O80iZchxgSw0Ggyoiqk6nc+PvZ7NZVRRFVRTFsxyArGO9RqPhoImkpJrDRqNR9Xq9G3+3WCyuZVGJwDqlmql1zw2rlGoO7dvYFalm8C7O80iZchxgC139l/e79Pv9Ow90tmm9y6sVHDSRilRz2O/3q0ajcec2JpPJcq16vf7kmeExUs3UuueGVUo1h/Zt7IpUM3gX53mkTjkOsIV6vd7yY293ea6Pr616vclkUhVFce3AyUET2y7VHBZFUXU6nXs/GttqtZbbGQwGT54bHirVTK17blilVHNo38auSDWDt3Gexy7whZwAW+jyS1Hq9fqd77v6+6/5IpVVr9ftdmM4HEZRFE+aDzYhxRyWZRllWcb5+XkcHBzcuZ1Go7F8PBwOHzsuPFqKmXrO7cA2SDGH9m3skhQzeBfneewC5TjAlplOp8vH950ARMTyQOTt27dbud7R0VG0Wq1otVpPmg82IdUcfvr06drz+Xx+6zZ++eWXW/8OnluqmVr33LBKqebQvo1dkWoGb+M8j12hHAfYMhcXF8vH9/0L/9X3XD342Zb1ptNpnJ+fx2AweNJssCmp5rBer0ev14uiKKLX6925ravlwv7+/mNHhkdJNVPrnhtWKdUc2rexK1LN4E2c57FLlOMAW2Y2mz35b8uy3Kr1Lj9mB6lJOYeDwSAWi8W9Jyvv379fPr76MXRYhVQzte65YZVSzWGEfRu7IeUMfsl5HrtEOQ6wZR574HP1qpinfHx0VesdHh5Gp9NxYkKSdiWHd603Ho+Xz4+Pjx+9DXiMVDO17rlhlVLN4WPWs29jm+1KBp3nsWu+3fQAAFz3NSfTT7miYBXrjcfjGI/HX3W1AmzSLuTwLm/evFn+3cnJiS9RYuVSzdS654ZVSjWHD2XfxrbbhQw6z2MXKccBtti6D+qfY72yLKPb7ca7d+++fiDYAinm8C7z+TxOT08jIqLT6US/31/pevClVDOlaGOXpJrD29i3kZoUM+g8j13ltioAW+bqx9kee4XAUw56nnu9169fx/HxsY/ZkbTUc3ibsiyj3W5HxH/lgXtFsi6pZmrdc8MqpZrD+9i3kYrUM+g8j12lHAd4hIODg9jb23vWn6v3Rox4/IHP1Y/LXT0AeqjnXO/8/Dzm87mrdVgpOXz6ei9fvoz5fB69Xk95wFqlmql1zw2rlGoO72PfRipSzqDzPHaZ26oAPMLJyUm8f//+2bb3/fffx88//3zttYODg+Xjx94n7ilXFDzXemVZxuvXr2MymTx6BngMOXzaeu12O6bTaQyHw+h0Oo9aA75Wqpla99ywSqnm8C72baQk1Qw6z2PXKccBHqHT6az8wPtqSfeQj7/N5/OIiKjX6xtdr9vtRq/Xi/39/Vu3c9uXnCkQeAw5fPx67XY7Li4uYjKZ+CgsG5FqptY9N6xSqjm8jX0bqUk1g87z2HVuqwKwZa4e3D/kW8AvD0RardZG1xuPx3F6ehq1Wu3Wn6Ojo+X7Dw4Olq8fHh4+aXZYlVRzeJNutxvz+fzW8mA6nUa32334sPAEqWZq3XPDKqWaw5vYt5GiVDPoPI9dpxwH2EKXV8VeXFzc+b7pdLp8/DUHHs+xXlVV9/5cvdp3sVgsXx8MBk+eHVYlxRx+6Wp5cNtVR+Px2L2RWYtUM7XuuWGVUs3hVfZtpCzFDDrPY9cpxwG20PHxcUT8d5By+fG2m7x9+zYi/rsq4LaPk5ZlGYeHh9f+NX+V68GuSD2H3W43yrKMyWRy50daR6PRtXtSwqqkmin7SHZJqjm8ZN9G6lLPIOykCoCt1O/3q4ioWq3Wjb+fzWZVRFRFUVSLxeLW7TQajSoiqoio+v3+yte7S6fTWc7y1G3AOqWaw16vt9zObT+NRqOq1+tVRFSj0ejWbcFzSjVT69hHwrqkmkP7NnZFqhm8i/M8UqYcB9hiVw9kZrPZ8vXhcFgVRVHV6/VqMpncuY3LE4SIqDqdzsrX+9Jisahms1k1Go2qoiiWs5ycnFSz2czBE1svtRxelgeP+ZFD1im1TD33dmAbpJZD+zZ2TWoZvInzPHbFXlVV1YMvMwdg7abTaQwGgxiPx8tv/q7X6/Hq1avo9/v3/v14PF7eN240Gt37bedfu96X9vb2IuLmbyq//NKXk5OTJ20b1iWVHE6n02g2mw/931pyOMi6pZKpVW0HtkEqObRvY1elksHbOM9jVyjHAQAAAADIji/kBAAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAAAAIDvKcQAAAAAAsqMcBwAAAAAgO8pxAAAAAACyoxwHAAAAACA7ynEAAAAAALKjHAcAAHbG6elptNvtqNVqsbe3F3t7e1Gr1aLdbsfR0VHM5/NNjwgAwJbYq6qq2vQQAAAAX+P8/Dxev34dZVne+95WqxVHR0fRarVWPxgAAFvr200PAAAA8DXa7XaMx+Nrr9Xr9ajX6xER8enTp5hOp8vfXb5XOQ4AkDe3VQEAAJLV7XavFeP9fj+qqorZbBaj0ShGo1FMJpOoqiqGw2E0Go2IiOV/AQDIl9uqAAAASZpOp9FsNpfPR6PRg64GPzs7i3q97spxAIDMua0KAACQpMFgsHzc6/UeXHb3er1VjQQAQELcVgUAAEjSxcXF8nG73d7gJAAApEg5DgAAJKksy+Xj+Xy+uUEAAEiSchwAAEjS1S/VfPPmzbWyHAAA7qMcBwAAknR4eLh8XJZlNJtNV5ADAPBge1VVVZseAgAA4Cna7XaMx+Nrr7VarWi329Fqta5dXQ4AAFcpxwEAgGSVZRkvX76M6XR663sajUa8evUqer1eFEWxvuEAANhqynEAACB5R0dHcXp6eu/7+v1+nJycrGEiAAC2nXIcAADYCWVZxp9//hmj0SjOz89vfV+j0Yh37965ihwAIHPKcQAAYCdNp9MYj8cxGo3+577knU4nhsPhhiYDAGAbKMcBAICdN5/Po91ux3w+X742HA6j0+lscCoAADZJOQ4AAGTj4OBgWZA3Go2YTCYbnggAgE35v00PAAAAsC5Xv4xzOp1ucBIAADbNleMAAEA2yrKMWq22fL5YLHwxJwBAplw5DgAAZOPqPccjQjEOAJAx5TgAAJCk8Xj86L95+/bt8nGr1XrOcQAASIxyHAAASFK3241ms/ngknw6ncbp6eny+eHh4apGAwAgAcpxAAAgOWVZRlmWMZ1Oo91uR7PZjLOzs/+5bcrle4+OjqLZbC5fa7Va0el01jkyAABbxhdyAgAAyZnP59Fut28sw4uiiP39/SiKIubzeZRlee33jUYjJpPJmiYFAGBbKccBAIBknZ+fx9HR0Y0l+U0Gg0H0er0VTwUAQAqU4wAAQPLm83mcn5/H+/fvYzqdLsvyoiiiXq9Hq9WK4+PjKIpis4MCALA1lOMAAAAAAGTHF3ICAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZUY4DAAAAAJAd5TgAAAAAANlRjgMAAAAAkB3lOAAAAAAA2VGOAwAAAACQHeU4AAAAAADZ+X9ar0YoRGEogwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Accuracy\n", - "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", + "# Plot accuracies\n", + "fig, ax = plt.subplots(1, 1)\n", "\n", "labels = {\n", - " \"acc_noisy_bn\": \"Noisy BN\",\n", - " \"acc_cn_tot\": \"CN (total)\",\n", - " \"acc_cn_cert\": \"CN (certain)\",\n", - " \"acc_cn_uncert\": \"CN (uncertain)\",\n", + "\"acc_noisy_bn\": \"Noisy BN\",\n", + "\"acc_cn_tot\": \"CN (total)\",\n", + "\"acc_cn_cert\": \"CN (certain)\",\n", + "\"acc_cn_uncert\": \"CN (uncertain)\",\n", "}\n", - "for key in res.keys():\n", - " if \"acc\" in key:\n", - " ax.plot(res[\"ess\"], res[key], \"-o\", label=labels[key], markersize=4)\n", "\n", - "ax.set_xlabel(\"S\")\n", + "for key in acc.keys():\n", + " ax.plot(x_values, acc[key], \"-o\", label=labels[key], markersize=4)\n", + "\n", + "ax.set_xlabel(type)\n", "ax.set_ylabel(\"Accuracy\")\n", - "ax.set_title(\"Accuracy\")\n", + "ax.set_title(\"Accuracies (MAP estimation)\")\n", "ax.legend(loc=\"best\")\n", - "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", - "\n", - "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", - "plt.show()\n", - "fig.savefig(\n", - " f\"{plots_path}/accuracy.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n", - ")" + "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "5c336efd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['roc_cn_cert', 'roc_cn_uncert', 'roc_cn_tot', 'roc_noisy_bn'])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "roc.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9186101e", + "execution_count": null, + "id": "089a1d9a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_14042/3610192424.py:36: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", - " plt.legend(loc=\"best\")\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# ROC curves\n", - "fig, axes = plt.subplots(2, 3, figsize=(16, 8))\n", + "# Plot ROCs\n", + "fig, axes = plt.subplots(len(x_values) // 3 + 1, 3, figsize=(14,7))\n", "\n", "labels = {\n", " \"roc_cn_cert\": \"CN (certain)\",\n", @@ -382,12 +265,15 @@ "}\n", "\n", "i = 0\n", - "for ess in res[\"ess\"]:\n", + "for x in x_values:\n", " ax = axes.flatten()[i]\n", "\n", " for key in roc.keys():\n", - " fpr, tpr, _ = roc[key][ess]\n", - " ax.plot(fpr, tpr, label=labels[key], linewidth=1.3)\n", + " try:\n", + " fpr, tpr, _ = roc[key][x]\n", + " ax.plot(fpr, tpr, label=labels[key], linewidth=1.3)\n", + " except:\n", + " continue\n", "\n", " ax.plot(\n", " [0, 1],\n", @@ -397,21 +283,19 @@ " linewidth=1.3,\n", " label=\"baseline\",\n", " )\n", + " \n", " ax.set_xlabel(\"FPR\")\n", " ax.set_ylabel(\"TPR\")\n", - " ax.set_title(f\"ROC (S = {ess})\")\n", " ax.grid(\n", " True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1\n", " )\n", + " ax.set_title(f\"ROC ({type} = {x})\")\n", + " if i == 0:\n", + " ax.legend(loc=\"best\")\n", "\n", " i += 1\n", "\n", - "plt.legend(loc=\"best\")\n", - "plt.subplots_adjust(hspace=0.5)\n", - "\n", - "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", - "plt.show()\n", - "fig.savefig(f\"{plots_path}/roc.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False)" + "plt.subplots_adjust(hspace=.3)" ] } ], diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index f3fe153..f19e026 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -5,11 +5,10 @@ cur_dir: experiments/cn_vs_noisybn # Current directory (contain bns_path: bns # Where to save ground-truth BNs cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs -noisy_path: output/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments -auc_meta: output/auc_meta.csv # File of metadata for AUCs +auc_meta: output/results/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable @@ -24,22 +23,11 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_ran' # Defense mechanisms to consider -atk_mec: 'atk_mle' # Attack mechanisms to consider -tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # Noisy BN -eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for noisy BN - -# DEF-IDM -ess: 1 # ESS: the amount of injected uncertainty - -# DEF-RAN -delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) - -# ATK-MLE -n_bns: 5 # Number of BNs to sample within the CN +tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=100)' # Epsilon to consider for noisy BN # Inferences n_infer: 5 # Number of inferences to perform diff --git a/experiments/cn_vs_noisybn/config_BAK.yaml b/experiments/cn_vs_noisybn/config_BAK.yaml index 7ab71ca..6cb9d4f 100644 --- a/experiments/cn_vs_noisybn/config_BAK.yaml +++ b/experiments/cn_vs_noisybn/config_BAK.yaml @@ -5,11 +5,10 @@ cur_dir: experiments/cn_vs_noisybn # Current directory (contain bns_path: bns # Where to save ground-truth BNs cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs -noisy_path: output/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments -auc_meta: output/auc_meta.csv # File of metadata for AUCs +auc_meta: output/results/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable @@ -24,22 +23,11 @@ pool_prop: 0.25 # Sample size of pool popula samples: 30 # Number of data samples # MIA -def_mec: 'def_ran' # Defense mechanisms to consider -atk_mec: 'atk_mle' # Attack mechanisms to consider -tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 25, endpoint=False)' # Type-I errors vector # Noisy BN -eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for noisy BN - -# DEF-IDM -ess: 1 # ESS: the amount of injected uncertainty - -# DEF-RAN -delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) - -# ATK-MLE -n_bns: 50 # Number of BNs to sample within the CN +tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=1000)' # Epsilon to consider for noisy BN # Inferences n_infer: 1000 # Number of inferences to perform diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 86aec22..3fec559 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -18,6 +18,7 @@ def main(): # Init configs config = load_config("cn_vs_noisybn") cur_dir = get_cur_dir(config) + create_clean_dir(cur_dir / "output") num_cores = eval(config["num_cores"]) # Get command-line hyperparameters @@ -42,15 +43,16 @@ def main(): # MIA vs CN print("#" * 5, "MIA vs CN", "#" * 5) + create_clean_dir(cur_dir / config["results_path"] / "cns") res = Parallel(n_jobs=num_cores)( - delayed(mia_vs_cn)(exp, config, save_power_res=False) for exp in exp_vec + delayed(mia_vs_cn)(exp, config) for exp in exp_vec ) auc_res = pd.concat((i for i in res), axis=0) auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Find eps s.t. |AUC(eps) - AUC(CN)| < tol print("#" * 5, "Get epsilon", "#" * 5) - create_clean_dir(cur_dir / config["noisy_path"]) + create_clean_dir(cur_dir / config["results_path"] / "bn_noisy") res = Parallel(n_jobs=num_cores)( delayed(find_epsilon)(exp, config) for exp in exp_vec ) @@ -60,7 +62,7 @@ def main(): # Run inferences print("#" * 5, "Run inferences", "#" * 5) create_clean_dir(cur_dir / config["results_path"] / "inferences") - _ = Parallel(n_jobs=num_cores)(delayed(inferences)(exp, config) for exp in exp_vec) + _ = Parallel(n_jobs=num_cores)(delayed(inferences)(exp, config, def_mec, def_args) for exp in exp_vec) # Clean gc.collect() diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py index 2087d74..1103116 100644 --- a/experiments/cn_vs_noisybn/generate.py +++ b/experiments/cn_vs_noisybn/generate.py @@ -18,9 +18,10 @@ def main(): # Generate BNs and data print("#" * 5, "Generate BNs and data", "#" * 5) + create_clean_dir(cur_dir / config["bns_path"]) create_clean_dir(cur_dir / config["bns_path"] / "gt") create_clean_dir(cur_dir / config["data_path"]) - open(f'{cur_dir}/{config["exp_meta"]}', "a").close() + open(f'{cur_dir}/{config["exp_meta"]}', "w").close() generate_naivebayes(config) # Init the vectors of experiments diff --git a/src/config.py b/src/config.py index 5178e91..c485995 100644 --- a/src/config.py +++ b/src/config.py @@ -80,7 +80,7 @@ def create_clean_dir(path: Path): path.mkdir(parents=True, exist_ok=True) -# Get current directory +# Get output path def get_cur_dir(config): root_path = get_root_path() diff --git a/src/inference.py b/src/inference.py index a84ab16..e23a11d 100644 --- a/src/inference.py +++ b/src/inference.py @@ -13,16 +13,16 @@ from src.utils import get_min_max_bns -def inferences(exp, config): +def inferences(exp, config, def_mec, def_args): # Read config cur_dir = get_cur_dir(config) target = config["target_var"] - def_mec = config["def_mec"] # Read data auc_res = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') - eps = np.mean(auc_res.loc[auc_res["exp"] == exp, "epsilon"].values[0]) + eps_vec = [i for i in auc_res.loc[auc_res["exp"] == exp, "epsilon"].values if i is not None] + eps = np.mean(eps_vec) # Set seed set_seed() @@ -41,14 +41,14 @@ def inferences(exp, config): bn = learn_bn_params(gt, gpop) # Learn CN from gpop (defense mechanism) #TODO: save results - def_mec_fn = getattr(src.defense, def_mec) # Get the related function - sig = inspect.signature(def_mec_fn) # Get its signature + def_mec_fn = getattr(src.defense, def_mec) # Get the related function + sig = inspect.signature(def_mec_fn) # Get its signature args = { k: v for k, v in { "bn": bn, - "ess": config["ess"], - "delta": config["delta"], + "ess": def_args.get("ess", None), + "delta": def_args.get("delta", None), "data": gpop, }.items() if k in sig.parameters diff --git a/src/mia.py b/src/mia.py index 696999b..3b12431 100644 --- a/src/mia.py +++ b/src/mia.py @@ -78,7 +78,7 @@ def mia_vs_bn(exp, config) -> dict: # MIA attack vs a CN -def mia_vs_cn(exp, config, save_power_res=True) -> pd.DataFrame: +def mia_vs_cn(exp, config) -> pd.DataFrame: # Get current directory cur_dir = get_cur_dir(config) @@ -136,11 +136,9 @@ def mia_vs_cn(exp, config, save_power_res=True) -> pd.DataFrame: # log.write(traceback.format_exc()) # Save results - if save_power_res: - power_res.to_csv( - f'{cur_dir}/{config["results_path"]}/cns/power_cn_{exp}.csv', - index=False, - ) + power_res.to_csv( + f'{cur_dir}/{config["results_path"]}/cns/power_cn_{exp}.csv', + index=False) # Return auc_res["auc_cn"] = auc_res.apply(lambda row: auc_cns_dict[row["sample"]], axis=1) @@ -156,7 +154,7 @@ def theoretical_power(exp, config) -> None: # Read data bn = gum.loadBN(f'{get_cur_dir(config) / config["bns_path"]}/gt/{exp}.bif') - results = pd.read_csv(f'{cur_dir}/{config["results_path"]}/bns/bn_{exp}.csv') + results = pd.read_csv(f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv') # Set seed set_seed() @@ -171,7 +169,7 @@ def theoretical_power(exp, config) -> None: # Save results results["power_bound"] = beta - results.to_csv(f'{cur_dir}/{config["results_path"]}/bns/bn_{exp}.csv', index=False) + results.to_csv(f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False) return @@ -181,6 +179,9 @@ def find_epsilon(exp, config) -> dict: # Get current directory cur_dir = get_cur_dir(config) + # Init results + power_res = pd.DataFrame({"error": eval(config["error"])}) + # Read data gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv') gpop_ss = config["gpop_ss"] @@ -212,7 +213,7 @@ def find_epsilon(exp, config) -> dict: auc_cn = auc_res.loc[auc_res["sample"] == sample, "auc_cn"].values[0] # ... init results, ... - eps_dict[sample] = eps_vec[-1] + eps_dict[sample] = None auc_noisy_dict[sample] = None # ... and find epsilon @@ -226,7 +227,7 @@ def find_epsilon(exp, config) -> dict: bn_theta_ie = gum.LazyPropagation(bn_theta) # Perform membership inference on gpop - _, auc = run_mia( + power_vec, auc = run_mia( bn_noisy_ie, bn_theta_ie, rpop, @@ -239,8 +240,14 @@ def find_epsilon(exp, config) -> dict: if abs(auc_cn - auc) < config["tol"]: eps_dict[sample] = eps auc_noisy_dict[sample] = auc + power_res[f"power_BN_noisy_sample{sample}"] = power_vec break + # Save results + power_res.to_csv( + f'{cur_dir}/{config["results_path"]}/bn_noisy/power_bn_{exp}.csv', + index=False) + # Return auc_res["epsilon"] = auc_res.apply(lambda row: eps_dict[row["sample"]], axis=1) auc_res["auc_noisy_bn"] = auc_res.apply(lambda row: auc_noisy_dict[row["sample"]], axis=1) diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index a689a88..462f4a0 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -21,18 +21,7 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_ran' # Defense mechanisms to consider -atk_mec: 'atk_mle' # Attack mechanisms to consider error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector -# DEF-IDM -ess: 1 # ESS: the amount of injected uncertainty - -# DEF-RAN -delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) - -# ATK-MLE -n_bns: 5 # Number of BNs to sample within the CN - # Other num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index ea694b1..0a3e644 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -1,15 +1,14 @@ ## Configuration file # Paths -cur_dir: experiments/cn_vs_noisybn # Current directory (contains all the following) +cur_dir: test/cn_vs_noisybn # Current directory (contains all the following) bns_path: bns # Where to save ground-truth BNs cns_path: output/cns # Where to save CNs as obtained by def-mec from BNs learnt from pool atk_path: output/bns_atk # Where to save BNs as obtained by atk-mec from CNs -noisy_path: output/noisy # Where to save noisy BNs data_path: data # Where to save data as generated from ground-truth BNs results_path: output/results # Where to save the experiment results exp_meta: exp_meta.txt # File of metadata for experiments -auc_meta: output/auc_meta.csv # File of metadata for AUCs +auc_meta: output/results/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable @@ -24,22 +23,11 @@ pool_prop: 0.25 # Sample size of pool popula samples: 5 # Number of data samples # MIA -def_mec: 'def_ran' # Defense mechanisms to consider -atk_mec: 'atk_mle' # Attack mechanisms to consider -tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # Noisy BN -eps_vec: 'np.arange(0.1, 10, 0.1)' # Epsilon to consider for noisy BN - -# DEF-IDM -ess: 1 # ESS: the amount of injected uncertainty - -# DEF-RAN -delta: 0.3 # Size of random interval for each BN parameter (should be 0 < delta <= 1) - -# ATK-MLE -n_bns: 5 # Number of BNs to sample within the CN +tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=50)' # Epsilon to consider for noisy BN # Inferences n_infer: 5 # Number of inferences to perform From 57b04e6c0aab8b78c85d8e0701d04008324045f8 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 18 Nov 2025 13:34:18 +0100 Subject: [PATCH 21/31] Fix the way CN uncertainty is computed in plots --- experiments/cn_vs_noisybn/Plot_results.ipynb | 86 ++++++++++++++++---- 1 file changed, 70 insertions(+), 16 deletions(-) diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index e2765f3..5d972a9 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7b61e02b", "metadata": {}, "outputs": [], @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "49a48f2f", "metadata": {}, "outputs": [], @@ -61,9 +61,9 @@ "def split_data(data: pd.DataFrame, col: str, threshold: float = 0.5) -> tuple:\n", "\n", " # Split results based on probabilities\n", - " cert_idx = data[(data[col] > threshold)].index\n", - " data_cert = data.iloc[cert_idx]\n", - " data_uncert = data[~data.index.isin(cert_idx)]\n", + " cond = data[col] > threshold\n", + " data_cert = data[cond]\n", + " data_uncert = data[~cond]\n", "\n", " return data_cert, data_uncert\n", "\n", @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "84251635", "metadata": {}, "outputs": [], @@ -112,10 +112,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "2ce6f5be", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get ESS/delta list\n", "type = re.findall(\"(\\w+)_\", os.listdir(res_path)[0])[0]\n", @@ -148,10 +169,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a29cb66d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get CN certainty\n", "cn_certainty = []\n", @@ -171,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "1f7ebeae", "metadata": {}, "outputs": [], @@ -222,10 +254,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "c158f122", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot accuracies\n", "fig, ax = plt.subplots(1, 1)\n", @@ -249,10 +292,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "089a1d9a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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CCCGEEEIIIYQQokRSPBJCCCGEEEIIIYQQJZLikRBCCCGEEEIIIYQokYPaAVQ3s9nM1atX8fT0RKPRqB2OEEJUC0VRyMzMJCQkBK225t03kNwvhKiJJPdL7hdC1DxVlftrXPHo6tWrhIaGqh2GEEKo4tKlS9StW1ftMKqd5H4hRE0muV8IIWqeys79Na545OnpCRR8I728vMq9/9GjR2nZsmVlh2V15Drti1ynfanIdWZkZBAaGmrJgTWN5P6ykeu0L3Kd9kVyf/lJ7i8buU77ItdpX6wp99e44tGtLqteXl4VehHx8PCo0H62Rq7Tvsh12pd7uc6a2m1fcn/ZyHXaF7lO+yK5v/wk95eNXKd9keu0L9aU+2ve4GchhBBCCCGEEEIIUWZSPBJCCCGEEEIIIYQQJZLikRBCCCGEEEIIIYQokarFo99++43hw4cTEhKCRqNh5cqVpe6zdetW2rdvj7OzM40bN2bRokVVHqcQQojKI7lfCCFqHsn9Qghh21QtHmVnZ9OmTRvmzp1bpu3Pnz/PsGHD6NevH4cOHeLll1/mqaeeYt26dVUcqRBCiMoiuV8IIWoeyf1CCGHbVF1tbciQIQwZMqTM28+bN48GDRrw4YcfAhAeHs6OHTv4v//7P6KioqoqTCGEEJVIcr8QQtQ8kvuFEMK2qVo8Kq/du3cTGRlZqC0qKoqXX365xH3y8/PJz8+3PM7IyLjnOGasOsKvRxLv+TiVxVArDpPr4co9qAL8UbmHtEpynTbBQ8nFCX3ZNt5btbGUh9ak4GQEJwPF/u9YbLuCRin+eAqgOLijd84l4rOj1XotalI7989YdYSfDl7GYXVShY9hK4wGg1xnGfzD9CX9zXsqMaKqEQykxBVtn+Pnwm9uNvUWsHRWlPtL8+BGM+1OlX17s0ZDvosrZk0ObD9edYFZGcn91Udyv3qq4vWkpNxfUVb9mmFDub+sgm8otD4DrvkaFJ0z6d75RPyfdbzvt9LfguIlJiYSFBRUqC0oKIiMjAxyc3NxdXUtss+7777LzJkzi7QfPXoUDw+Pcsfw8c5ENlwo+BDr41J41J/JbyWKW3y5j3nPHDIL/jd6Vu5xS/gAa3fkOq2eE3q0KJjRlHtfZ71C34Pgkn9v3wAN4Fhcweevrx1vtd3WrjPf0ykLyXNx5veuXUmvVYvW+9YSH1++XJOVlVV5wVQztXP/Twcvk5ZnphaG8gVugxRFwWiQ6yxNP/bgSzo38K7EqCpujp8L28vxxj7VoeD9i6+xEpNUDVPeAtDtvLML/k93L33bXBc34jt0Jc/FjTZ710nul9xfJST3q6dKXk8UUMr/lrlYc/xcWOXlBMhrRlWqnaLQ/iS0P6VQJwWyPDzY3b0bJp2OFuc2Wk3ut6niUUVMmzaNyZMnWx5nZGQQGhpKy5Yt8fLyKvfx9sVdBuCxbvV5a2SrQs/1++Hf3MjNws/V796CLjdnIutF8lrX1yrtiPHx8URERFTa8ayVXKeN+E/Tgv+n3P2d+p3XacrK5uJDD5F/+nRVRlfAwQGtiwsadxe0Lq4FX7u4FPzv+lebqwsaZ5eC/289dil4TuPibPlfoyucmi+mprLx2FFy9HpCvL0JePLVcv88K6PXpS2pzNzvsDqJWhg4+GbZh1vYKpvPFWV0z9f5H2cgkIBSclJle3vP22xM2FikPSU3BQB/V/9C7UaDEQfHom/1/KHS3zeo6V5+nolvzSJjw/py72dKLvie6wL8S9myGG7gNXAQ4TOm33WzkydP8tuqVeTm5tKwYUN8739Acn8pJPdXjOR+FayZAsdXQ1Y6eFTu60llXufOH/pBbgoPNnvQ6l4zrOrnWQGKwcCNrxeS/tNq9GfOFjRqtST278vu2sEYzGYa+fvTpFdvq8n9NlU8Cg4OJimpcFfDpKQkvLy8ir37AODs7Iyzs3OlxhHg6VykcHSLn6sfWx7YUqnnE8JW6S9dIn3VahST8d4OtO+v2ydz5tx1M+3161zfvNnyOH3VKoxXrwHgPXo0XkPv7Q2gxtn5f0Uh178KRK6uaJ2d0Tg63tOxi2Mymdi6dSs7Dh0EoFevXvTt25ejR62j62p1sZbcLypPRT+wVxadwcipYooqZZb1V8/jVb3KtHm2Pot8UxmH3t5FP8VMP0CrKbreibPOCXenwrm2oHhU0tHWcoq19xyTNbiXn2dFi0C6AH+8Bg4iuJQCUEUYjUY2bNjA3r170Wg0DBgwgB49enDkyJFKP5c1k9wv7Nrx1ZB1HTwCIXyE2tHclb+rv9UVjmydYjBw5R//j8y1a0Grxa1rV1wHDWSXgwOHjx9Hp9EwePBgOnfubFW536aKR926deOXX34p1LZhwwa6deumUkQFbt0JvJF7Q4VeR0JYJ/2lS5wdOKiSjvbXB6VD80rd6kYx7T6PPELQP6eh0ekqKZ7qkZGRwd69e/Hw8GDMmDE0aNBA7ZBUYa2539YVV8C556JKGd1Trw0bk63PIteYBxRf9CkPrUb7V5Go/MPuRfGqsghUUampqezfvx9vb2+io6MJDQ1VOyRVSO4Xds8jsNRe9cL+KAYDV6b8g8x163Bp0YK6n3+OY1AgV65c4c+vvsLPz4/o6Ghq166tdqhFqFo8ysrK4syZM5bH58+f59ChQ/j6+lKvXj2mTZvGlStX+PbbbwGYNGkSn376Kf/v//0/nnjiCTZv3swPP/zAmjVrqjVuQ604+v3wtuXx7d3FI+tFlrTbXWVt30HuwYOVEl9l0F6/TvKWrWqHUeXkOqtOymefWb4OfvNNHIKD7rJ1KZZPLPh/zBd33ezihYvUD6tfqE3n7o5r+/Y2VTgyGAw4Ojri4+PD2LFjCQoKwt29DJNj2Ahbzf2VoaRhRxXxwM8ZtDuWV+H9vbMK5i5I97i9oKGAsZImSrgbDy0HW7jww33qvA0xGih2OFeJ8jLA+L9JeFG0oNGCR+nHSMk1Ag6qdPmPj4+nhQ136S8re7nOW7k/MDCQBx98kDp16pTYw8YW1eTcX1kq8zXkdkaDEYfjNtWnoEKs6jp9C+YR4od+lX7oyrxO6RxRuRSDgSuvTCFz/XpcWrQg9KsFKH/Nx1anTh0efPBBwsLCcHJyUjnS4qn617Nv3z769fvfH8ytMcqPP/44ixYt4tq1ayQkJFieb9CgAWvWrOHvf/87c+bMoW7duixYsKBal+s0+a1E8dhFSu7/5ha4VTSq6JvCrO07uDRhQmWGec+0QIraQVQDuc6qV3fe53j27XtvB9n31/+lHEeJj8fThj9AGAwG1q1bR2JiIuPHj0en09GwYUO1w6p0tpj7K8Pbe95m6cmlQNG5ae5UlsJQ8cWfsku3FHD+Nw9ISXPk1HjGfFDMBQUjKPjfoWxDY+71PYKwf3q9njVr1pCZmcmjjz6KRqOhcePGaodV6Wpq7q9MGxM2kpKbUupriBCVxc/Vr8KdI0RhdxaOAj7/jOUbNqAoCg888AAajYamTZuqHeZdqfoOsW/fvihKySsgLVq0qNh9DqrYQ+fWamqVeQfx6pQplq/r3tZbQ00XL16gfv0wtcOocnKdVcsxOAiXFi2q/by2KDk5mdjYWK5fv46Pjw8ZGRn4+PioHVaVsMXcXxal3RG+1Uu1tNePxLdmcXPv90Apw7pcyzbp7t10BZ657bGtTz5ZVuW+zjJO2i9EeV27do3Y2FhSU1Px9/cnKysLT89KXj3XSthr7q9u/q7+lT6/quR+Fdx6XXmi8ufKtarrFMAdhaOWLdH+axYL/vtf0tPTCQ4OJi8vzyZ6msrtxYoweVZK4Shrx05y/zyMKbtgzdbGv23DMTDwno9bGWy9B0dZyXUKtSmKwqFDh/jll18wGo20bNmS++67DxcXF7VDE+VU2tx3d+uBcvv8Q7fmA/J5+GGrmodFCFF5FEVh7969bNiwAZPJRLt27Rg8eLDVDlUQQghRMYrBwJXJr5C5YQPOLVtyadLTbP3xRxRFoXPnzgwcOBAHB9soy9hGlFZixqojmLFM3XvPrvz975gzMwFwbdPGagpHQojqoSgKK1asID4+HgcHB4YPH067du3QaKphzhlRae5cNKEsd4TvnKz69gmkrXECXyFE5TGZTPz444+cPHkSJycnRo4cKb0EhBDCDhUUjiaTuWEjuogIdgwdwvldu3B1dWXkyJE0a9ZM7RDLRYpH5fDrkUQIBmeHe5t4N2vHTvKOxGPOzsYhOJig1/6Ja+s2lRSlEMJWaDQa3N3dCQwMJCYmhoCAALVDEhVwe+GorPMCZGxYjynlBjr/gl5KUjASoubQ6XS4uroSEhJCdHQ0vr6+aockhBCikil6PVdeeYXMDRtxadWK0C+/4ND69dSrV48xY8bg7e2tdojlJsWjctICni4V/7Zl7djJpaeesjx2DAnBa+DASohMCGELFEXh3LlzNGrUCIDIyEjMZjOOjo4qRybuRVl7HN1O5+9H0+3bqygiIYQ1MZvNXLhwwbIIwtChQ9FqtehsaCVQoY7b59Or9Mmy10yB46tpbjTCOvv/WGhV15l1HTxk1Ik9yj93nuwd28n6bTuZO3eS3qULXT75GJ2XF6NGjcLBwQGttrLGMlUvK/nrqRnuLBzVmT0b13Zt1QtICFGtsrOzWblyJWfOnCEmJoaWLVui0+nkw4MNuv3NvCxjK4S4m4yMDJYvX87Fixd55JFHaNy4sdwwEGV2e+/WW3PnVZrjqwuKGC7S+63aeQRC+Ai1oxCVQFEU8k+dJnPdOjI3rCf/9BkAstzd2TtiODfc3amblkY9Ly+bn9dOikflYKgVBw6ZQNmW572doihcfu45y+PG27bhGCTVZiFqivPnz7N8+XKysrKoW7cuderUUTskUUFv73mbpSeXAgWTYJdnuNqtuY5uH7ImhLBfp06dYtWqVeTk5BAWFkZQUJDaIQkbVJHerWXmEciJqLgaMe/WCVmFTFQSRVHIO3KUzPXryVy/Hv3FiwVPaLW4depEYvdubL5xA73JRJPGjfHzs4/3fFI8KgeT62GAClf8lfx8QApHQtQkZrOZbdu28dtvvwHQo0cP+vXrJ72NbNitHkcPNnuwTCtvFreS2q05joQQ9slkMrFx40b27NmDRqOhX79+9OzZ02aHKoiqc3tP1uJI71YhrINiNpN76LClYGS4erXgCZ0O9+7d8YyKwqVvHzb98Qf79+9Hq9UyaNAgunbtajeL4UjxqLyMnmX6sFAS5xbhUjgSogbZuHEju3fvxt3dndGjR1vmOhK2zd/Vv8yFo5vffw/ISmpC1CRr1qzh4MGDeHl5MWbMGOrXr692SMIKFFcoSsktuKlQ0lxG5endKoSoXIrJRM6+/QUFow0bMF6/DoDG0RGPvn3xHDQIz/790NWqBcCPP/7IsWPH8PHxISYmhpCQEBWjr3xSPBJCiCrUrVs3MjIyGDx4MB4eHmqHI+7BrTf95bkLfKvHkc/DD0vBSIgapGfPnhiNRoYMGYKrq6va4QgrsTFhY5FJr2/NY3QvN6eFEJVHMRjI/n1vQcFo40ZMqakAaFxc8Bw4EM9Bg/Do1xddMe/re/fujZOTE4MHD8bZufxT3Vg7KR5Vk5w9e9QOQQhRDYxGIxs3bqR9+/YEBgbi6elJTEyM2mGJSnB74agsd4ET35qFKTkFXYC/FI6EsHN6vZ4NGzbQvXt3fHx88PX1ZcyYMWqHJayQv6t/1c1fJISoELNeT/bOnWSu30Dm5s2Y09MB0Lq54TV0KJ5RUXj06onWza3Qfnl5eWzYsIG+ffvi6elJUFAQI0eOVOMSqoUUj6qBYjZzadIzAOg8PFWORghRVVJSUoiLiyMxMZHU1FQefvhhtUMSlaw8k5be6nUkcxsJYd+SkpKIjY0lJSWFvLw8oqOj1Q5JWJmK9FwVQlQtc24uWdu3k7l+A1lbtmDOzgZA6+WF98iReEZF4d6jO9oSehBduXKF2NhY0tLS0Gg03HfffdUZviqkeFQdFMUyWXbIe++qHIwQoiocPnyYNWvWYDAYCA8PZ8QIWX5VIL2OhLBjiqKwb98+1q1bh8lkok2bNgwdOlTtsIQVuHNuo9vnNZL5i4RQjykrm6xtW8lct56s7dtRcnMB0Pn4UGvoUDwHDcK9S2c0Tk4lHkNRFHbt2sXmzZsxm8107NiRQYNqxo1CKR5Vsezf95J/4jgALi1b4mhnk2YJUdPl5+fzyy+/8Oeff+Lg4MCwYcPo0KGD3ayqIAqY/FYWmadCCFFz5ebm8tNPP3H8+HEcHR0ZPnw4bdq0UTssYSXu7GUk8xoJoR5TejqZm7eQuX492Tt3ouj1ADgEBOA5ejSegwbh1rEDGofSSyPZ2dmsXLmSM2fO4OLiwogRIwgPD6/qS7AaUjyqQorRyKUJEyy/oFpPGbImhL25fv068fHx+Pv7ExMTQ1BQkNohiSqguMUDlPmO8e3zHQkh7M/Vq1c5fvw4wcHBxMTE4OcnQ5FEYeUZ5iyEqFzG1FQyN24kc/0GsvfsAaMRAIeQ2ngNHIRn1CBc27ZFo9WW67gXL17kzJkz1K1bl+joaGr9tcpaTSHFo6pkNqPo9TiGhuL/zDO4d+msdkRCiEqgKApGoxFHR0dCQ0N58MEHCQsLw+kuXVyF7fN39S/xrnHiW7MscxwBmJILhijIfEdC2A+z2YzZbMbBwYFGjRoxduxYGjdujEMZ7lYL+3bnMDWZ20iI6mdIuk7mxg1krt9Azh9/gNkMgGP9engNGoTnoEG4tGpV7tEBZrMZRVHQ6XS0aNGCBx54gKZNm6LT6ariMqyavNpVA52vD7XGjFY7DCFEJcjJyWHVqlU4ODgQExODRqOhadOmaoclVJT41ixufv89gKWnkS7AH6+Bg2S+IyHsRGZmJitWrKBWrVqWOe2aN2+uclTCGry9522WnlwKYBnaXNZVOavVmilwfHXhtqzr4BGoTjxCVALDlStkbCgoGOUePAiKAoBT40YFBaOoKJybNq3wdBJpaWksX76cunXrWuY1qknD1O4kxSMhhCijixcvEhcXR2ZmJiEhIeTn5+Pi4qJ2WEJlt3oc+Tz8sBSLhLBDZ86cYcWKFeTk5FCvXj0MBgOOjo5qhyWsxK0eRw82e9C65zQ6vrposcgjEMJlgQ9hW/QXL5Kxfj26las4c/aspd05PByvqIIeRs4NG97zeY4fP87q1avJy8vD2dkZs9mMtpzD3OyNFI+EEKIUZrOZ7du3s23bNhRFoVu3bgwYMKBGdletiQy14sAhEyh+qVaQVdWEsEcmk4nNmzeza9cuNBoNffr0oXfv3jX+w0NNV9wQtbsNa7YqHoEw5VTR9vj46o9FiHLIP3OGjPXryVy3nvyTJwHQAC6tWxcUjAYOxKlevUo5l9FoZN26dezbtw+tVktkZCTdu3eXxXCQ4pEQQtxVfn4+//3vf7l48SJubm6MGjWKJk2aqB2WqEYm18NA2SfLtkvFDXewYc2NRlhXjrdAMrSjxsnJyeH777/nypUreHp6MmbMGMLCwtQOS1iBO1dSs8ohakLYAWNqKqmLF5O5bj36c+cKGjUaXDt2wGvQIK6EhtKgX79KPWdmZiZLliwhKSmJWrVqER0dTd26dSv1HLZMikdCCHEXTk5OuLi40KBBA0aPHo2nrJpYMxk9beOuclUpbrhDTSJDO2ocFxcXnJycaNKkCaNGjcLNzU3tkIQVkZXUhKhaisHAlRdfImffPgDcunUtmMMoMhKHgAAArlRBjzk3NzccHBxo0aIFw4cPl+kp7iDFIyGEuIPRaCQhIYGGDRui0WgYM2YMDg4OMlRBFJH41ixMySmWibLtWknDHWzQifh4IiIi1A5DWBmDwcCVK1cICwtDq9UyduxYnJycZKiCEEJUA2NyMmlxy8nZu5ecgwdRcnMBqL/4W9w6daqy8+bn55OUlES9evXQ6XQ8+uijkvtLIMUjIYS4zY0bN4iLiyMxMZEnnniCunXr4uTkpHZYwkrdmizba+AglSMRQtyL69evExsby82bN5kwYQKBgYE4O5c8z5moeW7NdXT7kDUhROXQX75CwmOPYbh6FQCH4GDcBkbi0acPrh07Vtl5r169SmxsLNnZ2UyaNAkfHx/J/XchxaMqlCuTzwlhU/7880/WrFmDXq+nefPm+PnJm0NROpksWwjbpSgKBw4cYO3atRiNRiIiIvD29lY7LGGFbi8cWcUcR+Wdi64mDz0WVs1w5X+Fo1oPjsXviSdwDA2t0p4/iqKwZ88eNm7ciNlspn379nh4eFTZ+eyFFI+qiGIwkDBuPABaGScvhFXT6/X8+uuvHDp0CJ1Ox5AhQ+jUqZN0VxV3ZddD1u78UCIfOoQdysvL46effuLYsWM4OjoycuRI2rRpI7lflMiq5joq71x0MnebqCaGpOtk79oFilKm7RNnzULJzcXn0UcJ+ue0Ks/BOTk5rFy5ktOnT+Ps7Mzw4cNp2bJllZ7TXkjxqIooZjOKwQBA7TfeUDkaIcTdLF++nJMnT+Ln50dMTAzBwcFqhyRsgF0PWbvzQ4l86BB2aNmyZVy4cIGgoCBiYmLw97fDQrCoFG/veZuU3BT8Xavxd6S0nkW3crSdzEUn1KW/cIGcg4cq5VjXpk2r0H5+4x6v8sKRoigsXryYxMRE6tSpQ3R0ND4+PlV6TnsixaMq5tq2LU6ytKsQVq1v3764ubkxePBgmd9IlItdD1mTDyXCzvXv35/4+HgGDRqEg4O8JRYl25iwEaB6h6uV1rNIivqiDPJPnyb36NFSt7v2asUKPncT8PLLZR6B41gnBMc6dSo9hjtpNBoGDBjA+fPn6d+/PzqdrsrPaU/klVIIUePk5uayfv16BgwYgIeHB8HBwYwYIW/AhBDCnmVlZbFp0yYGDRqEq6sroaGhhIaGqh2WsHK39zp6retr1XtyKeKLe5QwYSLGxMQybx/0z8opIjk1aIhHr56Vcqx7lZ6eztatWxkyZAhOTk40btyYxo0bqx2WTZLikRCiRklISCAuLo6MjAycnJwYMmSI2iEJIYSoYufOnWP58uVkZ2fj6elJ//791Q5J2AhVeh0JUQnyz5zBmJiIztsbv2cmlbq9W9u2uLZtW/WBVaOTJ0+yatUqcnNzCQgIoHv37mqHZNOkeCSEqBHMZjM7duxg69atKIpCly5diIyUN4LChpR3ZZ0KaG40wjoHmSBb2A2TycTWrVvZsWMHAL169aJv377qBiVsjiq9joTdyTlwAMOVq1V+Hs2lS6RfuMjVf/wDAIegIPzGjavy81oTo9HIhg0b2Lt3LxqNhv79+9OtWze1w7J5UjwSQti9zMxMVqxYwfnz53F1dWXkyJE0a9ZM7bCEKJ/yrqxzL2QuDWEH0tLSiIuL4/Lly3h4eDB69GgaNmyodlhCCDuU/ftejMnJJT5vvH6d6//+d7XEogNuL1GFvPdutZzXWty4cYPY2FgSExPx9vYmOjpahihXEikeCSHs3sWLFzl//jz169dnzJgxeHl5qR2SEBVTxfNfnIiPJyIiosqOL0R1OnPmDJcvX6Zx48aMGjUKd3d3tUMS4u5u72EqPUBtxs2ly0h8880ybx/4V4+gqnIt8Rq1g2sD4Na1Cy4tWlTp+azN8ePHSUxMJDw8nOHDh+Pq6qp2SHZDikdCCLtkMpkA0Ol0tGrVCgcHB5o2bYpWq1U5MiGEEFXFaDSi0+nQaDR06NABd3d3mjdvXuXLPwv7ZPJbaZksu1rc3sNUeoBWO8VsJnv3bswZGWXeJ//MWVLmzrU8DpzySskba3V4DY7CMSTkXsIs1dX4ePxq2I0gg8GAg4MDGo2GHj164OfnJ7m/CqhePJo7dy4ffPABiYmJtGnThk8++YTOnTuXuP3s2bP5/PPPSUhIwN/fn5iYGN59911cXFyqMWohhDW7efMmcXFxhIWFWeY1at68ucpRidvZeu5PfGsWpuQUdAHV9IFCCFGq5ORk4uLiaNWqFT179kSj0RAeHq52WOI2tpb7Fbd4oAomyy5pDrtbhSNZYU0VOb//zqUnn6rQvhpHRxpv3oRDQEAlRyVKk5iYSGxsLF26dKFTp06S+6uQqsWjZcuWMXnyZObNm0eXLl2YPXs2UVFRnDx5ksDAot00v//+e1599VW+/vprunfvzqlTpxg3bhwajYaPPvpIhSsQQlibI0eO8PPPP5Ofn4+Hhwdms1l6G1kZe8j9GRvWA+A1cJAq5xdC/I+iKBw8eJBff/0Vg8FAYGAgiqLIHWcrY6u5v0omyy5pDjvpbaQqU0YmAO7du+PWtWuZ99M4OOA9/D4pHFUzRVHYu3cv69evx2Qycf36dbVDsnuqFo8++ugjJkyYwPjx4wGYN28ea9as4euvv+bVV18tsv2uXbvo0aMHDz/8MABhYWE89NBD/P7779UatxDC+hgMBg4cOMCFCxfQarVERUXRpUsX+fBghewl9+sC/AmeMV3VGISo6fLz8/njjz+4fPkyDg4ODB8+nHbt2knut0L2kvsrjfQwUoViMpHz+++YsrKKPJd7+DAAbp064j9xQnWHJsohJyeHPXv2cO3aNZycnBg5cqTM2VgNVCse6fV69u/fz7Rp0yxtWq2WyMhIdu/eXew+3bt357vvvmPv3r107tyZc+fO8csvv/Doo4+WeJ78/Hzy8/MtjzPKMYZV2JESugdblqW2czZ/naVMGpmens6SJUtITk7G19eX6OhoQqp4PLmoGMn9QojKcuPGDZYsWcLNmzcJCAggJiam2B4sQn22mPsNteLAIRNwrvAxhDoUo5HsPb9jzs0p1K65mMC1Jd+TvnLlXffXOMt0KNYsKSmJ77//noyMDEJCQoiOjsbX11ftsGoE1T5NpqSkYDKZCAoKKtQeFBTEiRMnit3n4YcfJiUlhZ49e6IoCkajkUmTJvHPf/6zxPO8++67zJw5s0j70aNH8fDwKF/QSsF/8fHxpW+r1+NAQVW0TNtbmYyMDJuMuyTN45fjkJeK0aVwYlHMZgxGo0pRVR+bv04XXzICe3C1hN9Jk8mEwWAgODiYTp06cePGDW7cuFHNQVafivx9ZhVzh00N9pL7dQZjkbaq1vyvv+ETVXhOe8v9JZHrtA9GoxGj0UidOnXo2LEjSUlJJCUlqR1WlZHcX7253+RS0AulnUe7Sv87qo58Xh72lis027ej+/iTIu06IP2vrxV/f8yRA4ru7OTE1ebNS3zPaQvs7ed5J71ej9FopF69erRv354rV65w5coVtcOqMtaU+22qK8LWrVt55513+Oyzz+jSpQtnzpzhpZdeYtasWUyfXvzQgWnTpjF58mTL44yMDEJDQ2nZsmX5l+v+A1AoU5c4c34+JwE3NzfCbLALXby9Lde8zgE8AnG8o3uw3V1nCezhOv3++ndLXl4e169fp169egCEh4dz8uRJWrdurUp81akiP09b7nljTbk/8a1ZZGxYjyk9HZ2/Hy2q8+/qr96DVfm3bA+5oizkOm1XdnY2aWlp1KlTB4AWLVpw6tQpu7vO4kjuVyH3Gz35aGgVzK9UDfm8POwlVyhGIzl795Jx7RppgEfkgELL1CclJREUFITWzY1aMTHoynszyUbYy8/zdpmZmWRlZVG7dm0AWrVqJbn/Lqoq96tWPPL390en0xW5Q5SUlERwcHCx+0yfPp1HH32Up54qmAU/IiKC7OxsJk6cyGuvvVbspLjOzs44O0t3UyHsyeXLl4mLiyM3N5dJkyZRq1YtnJ2dZY4LG2DruT9jw3pMKTfQ+ftVzWTZJa3AA6UO3xTC3l24cIHly5djMpl45pln8PDwkPd4NsLWc3+FlbaqmqhU6T/9zLXbhkZ6DR6C933DLI8T4+MJqAHFBntz+vRpVq5ciYODA8888wwuLi7W9Xdeg6i2BJGTkxMdOnRg06ZNljaz2cymTZvo1q1bsfvk5OQUeaHQ6XRAwWzrQgj7pigKO3bsYOHChaSlpdG6devyD0ESqrKH3K/z96Pp9u1VM1n2rRV4iiOr8Igaymw2s2XLFr755hsyMzNp164drq6uaoclysEecn+FlJTTJZ9XCVN6GlDQ4yhw6lQ8+/dTNyBxT0wmE+vWreP7778nNzeXDh064OTkpHZYNZqqw9YmT57M448/TseOHencuTOzZ88mOzvbsgrDY489Rp06dXj33XcBGD58OB999BHt2rWzdF+dPn06w4cPt7yYCCHsU1ZWFitWrODcuXO4uLgwcuRImjdvrnZYogIk95dCVuARwiI9PZ3ly5eTkJCAu7s7o0ePplGjRmqHJSqgRuX+Wz2ObvUwkpxerTwjI6k1apTaYYh7kJqaSlxcHFevXsXLy4sxY8ZQv359tcOq8VQtHo0dO5bk5GRmzJhBYmIibdu2Ze3atZbJ9BISEgrdcXj99dfRaDS8/vrrXLlyhYCAAIYPH87bb7+t1iUIIaqBoih8++23JCcnU69ePcaMGYO3t7faYYkKktwvhCgLk8nEokWLSEtLo2HDhowePVp6m9qwGpX7by8cSQ+jSqcYjeTs24+Sn1eoXX/2rEoRicpkMBj4+uuvyc7OplmzZowYMQI3Nze1wxJYwYTZzz//PM8//3yxz23durXQYwcHB9544w3eeOONaohMCGEtNBoNkZGRXL58mb59+xY7z4GwLZL7hRCl0el0DBgwgLS0NHr06CHz2tmBGpX7pcdRlUmLjSXxzaKr6t2ilaFNNs3R0ZH+/ftjMBjo3Lmz5H4ronrxSAghipOWlsa2bdsYOnQojo6ONG3alKZNm6odlhBCiCp048YNdu7cybBhw9DpdLRq1UrtkIQQVkIxGsk5cIDcg4cA8Bw4EKeGDQtto/P0wKNv3+oPTtyTpKQk/vjjD4YOHYpWq6V9+/ZqhySKIcUjIYTVOXbsGKtXryY/P5/g4GC6dOmidkhCCCGq2OHDh1mzZg0Gg4H69evTpk0btUMSQlgJxWjkypR/kLl2raXNO3oMnlIosmmKorBv3z7WrVuHyWSicePGMqepFZPikRDCahgMBtatW8f+/fvRarUMHDiQzp07qx2WEEKIKqTX61mzZg1//vknOp2OYcOG0bp1a7XDEqJ0tybGvtOt+Y7EPVGMRnIPHULR60n9bglZmzcD4BwejvewobiXsFKfsA25ubn89NNPHD9+HEdHR4YPHy6FIysnxSMhhFVITk4mNjaW69ev4+PjQ3R0NHXq1FE7LCGEEFXo2rVrxMbGkpqair+/PzExMZYJlIWwerdPjH07mSj7nigGA7mHD5Py5Zdkb/ut0HNu3boSOn++zGtk4y5dukRcXBzp6ekEBwcTExODn5+f2mGJUkjxSAhhFY4fP87169dp2bIl9913Hy4uLmqHJEQhiW/NwpScgi7AX+1QhLAbR44cITU1lXbt2jF48GCc5AOhsDUyMfY9uVUoUoxGS9uNBV+RvWOH5bFLRARunTuhq1UL38cfl8KRHTh06BDp6el06dKFyMhIHBykLGEL5KckhFCNXq/H0dERjUZDz549CQoKomnTprKqgrBKGRvWA+A1cJDKkQhh2/R6vaVI1L9/f+rXry8LIghRQ6XM/4KUTz8t9jmXNq3xjIzEb9w4NI6O1RyZqGy35/7BgwcTHh5O48aNVY5KlIcUj4QQqrh69SqxsbH06NGDDh06oNVqadasmdphCXFXugB/gmdMVzsMIWzWxYsXWb58OQMHDqRVq1bodDopHAlRQykGA5mbNwHgNXQoDrcNWdX5+kjRyI6cPXuWFStWMHLkSJo0aYKjo6MUjmyQFI+EENVKURT27NnDxo0bMZvNpKSkqB2SEEKIKmY2m9m+fTvbtm1DURTJ/UIIUubNJ//YcQB8x4/HNaKVyhGJymYymdiyZQs7d+4ECuY4bdKkicpRiYqS4pEQotpkZ2ezcuVKzpw5g7OzMyNGjKBFixZqhyWEEKIKZWRksGLFCi5cuICbmxujRo2SDw/Cdt2+wpqsqnZPjKk3APCIHIBLi3CVoxGV7ebNm8TFxXHlyhU8PT0ZM2YMYWFhaocl7oEUj4QQ1eLatWt8//33ZGVlUbduXaKjo6lVq5baYQkhhKhCCQkJLF26lNzcXMLCwhgzZgyenp5qhyVExd2+wpqsqlYp/J96Co1Op3YYohKdPXuWH3/8kfz8fJo0acKoUaNwc3NTOyxxj6R4JISoFt7e3uh0Onr06EG/fv3QyZsEUZPcfqf6buQutrAzPj4+6HQ6+vXrR8+ePdFqtWqHJMS9kxXWhLgrX19fdDodgwYNomvXrrIYjp2Q4pEQosqkp6eTm5tLcHAwbm5uPPvss7IMs6iZbr9TfTdyF1vYgdTUVEwmEwEBAXh6evLCCy9I7hdCoBiN5B0/jmI0AmCSuc/syvXr13F0dMTHxwcfHx9eeuklyf12RopHQogqceLECVatWoWLiwtPP/00Li4u8gIiaja5Uy1qgPj4eH7++We8vLyYOHEijo6OkvuFEAAkf/IpN+bPL/qEg6yoZssUReHAgQOsXbuWwMBAnnjiCXQ6neR+OyTFIyFEpTIajaxfv54//vgDrVZLz549cXZ2VjssIYQQVUiv1/Prr79y6NAhdDodnTp1wsFB3mYKUdMV9DY6AWYT+SdOAOA5eDAOvj4AOAQE4BLeXM0QxT3Iy8vj559/5ujRozg6OtKpUycZnmzH5FVdCFFpUlJSiIuLIzExkVq1ahEdHU3dunXVDksIIUQVSkpKIjY2lpSUFPz8/IiJiSE4OFjtsISoXLfmrpO56cpEMRrJO3mS6+//m5y9ews95//sM7g0bapSZKKyXLlyhdjYWNLS0ggKCiImJgZ/f3+1wxJVSIpHQohKkZ+fz9dff01ubi4tWrRg+PDhuLi4qB2WEOq4c4Js+bAh7FROTg5fffUVBoOBNm3aMHToUBmqIOzT7YUjmZuuVNf/8yGpixZZHrt16YJTWBiOwUE4N26sXmCiUqSnp7Nw4UJMJhMdO3Zk0KBBODrK8EN7J8UjIUSlcHZ2pn///mg0Gtq3by+rKgi78sTGHEzJ+egCynhH7c670/JhQ9gpNzc3+vXrh7u7O61bt1Y7HCGqlsxdV2bG69cB8BwyGJdmzfF7eqK8N7Qj3t7e9OrVi8DAQMLDw9UOR1QTKR4JISrs2rVr7N+/n2HDhqHRaOjYsaPaIQlRJbqc0gPgNXBQ2XeSDxnCTl26dIkjR44wePBgNBoN3bp1UzskIYQVUIxG8k+fRjGbMaWnAxDwwos4N2ygcmSiMpw7d45z584RGRkJQJ8+fVSOSFQ3KR4JIcpNURR+//13Nm7ciMlkolmzZjRp0kTtsISoUroAf4JnTFc7DCFUYzab2blzJ1u2bEFRFFq2bEm9evXUDkuIqrdmCmQlgUeQ2pFYLcVkIuGJJ4vMb6Rx0KkUkagsZrOZLVu2sGPHDgAiIiIICpK/hZpIikdCiHLJyclh1apVnDp1CmdnZ0aNGiWFIyGEsHOZmZmsWLGC8+fP4+rqysiRI6VwJGqOW3PYyfDjEiW9+56lcOTevRsOtWvjVLcujqGhKkcm7kVaWhrLly/n0qVLeHh4MHr0aCkc1WBSPBJClNmFCxdYvnw5mZmZhISEEBMTg4+Pj9phCSGEqEJnzpxhxYoV5OTkUK9ePaKjo/Hy8lI7LCGql0cQDPuP2lFYJcVkInv7dgBq3X8/wW/NlPmN7MDx48dZvXo1eXl5NGrUiFGjRuHh4aF2WEJFUjwSQpTZ4cOHyczMpFu3bgwYMACdTroiCyGEvTtw4AA5OTn06dOH3r17o9Vq1Q5JCGElbg1X01+8CID/c89K4cgOKIrCH3/8gV6vJzIyku7du8vPVUjxSAhxd3q93rLs8pAhQ4iIiKBhw4YqRyWEEKIq3Z77hw8fTpcuXahfv77KUQkhrIViMqE/f57Et2ZZhqvVeuhBHGRIk027lfs1Gg2jR48mPT2dunXrqh2WsBJy60gIUaJTp04xZ84czp49C4CTk5MUjoQQws4dPXqU2bNnc+nSJQBcXV2lcCSEKCTxrVmcu2+4pXDk8/DDBM+YIb1TbJSiKBw8eJDZs2eTlJQEgKenpxSORCHS80gIUYTRaGTjxo38/vvvaDQaUlJSaNSokdphCSGEqEIGg4G1a9dy4MABtFotKSkphMpkt0KIYhiuXQXAc9AgXMKb4//MMypHJCoqPz+fNWvWEB8fj4ODAykpKTIptiiWFI+EEIWkpqYSGxvLtWvX8PLyIjo6WlbUEUIIO3f9+nViY2NJTk7Gx8eHmJgYQkJC1A5LCGHlgqe/jkNAgNphiAq6evUqcXFxpKamEhAQQExMDIGBgWqHJayUFI+EEBanT58mNjYWvV5P8+bNGTFiBK6urmqHJYQQogodPXqUlStXYjQaiYiIYNiwYTg7O6sdlhDCSilms2WCbGG7Dh48yM8//4zZbKZ9+/YMHjwYR0dHtcMSVkyKR0IIC19fXxwcHBgwYACdOnWScetCCFED+Pn54ejoyLBhw2jTpo3kfiHEXSW+9RaGiwkFD2TlXZvl7++Ps7Mzw4YNo2XLlmqHI2yAFI+EqOESExNxcXGhVq1a+Pn58dJLL1lW2BFCCGGfLl++jLe3N56engQHB/Pyyy9L7hdClInh8hUAfB5+CAdfX5WjEeVx8eJFAgMDcXV1JTQ0VHK/KBdZbU2IGkpRFPbu3cuCBQuIjY3FbDYDyAuIEELYMUVR2LlzJwsXLmTlypUoigJI7heiOO7kwH+aQtZ1tUNRnWI2o798Gf2lS5jzcgHwf+45laMSZWU2m9myZQuLFi3ip59+srRL7hflIT2PhKiBcnNzWb16NSdOnMDJyYnOnTuj1UotWQgh7FlWVhYrV67k7NmzuLi40LFjRxmiJsRdOGMoKBx5BEL4CLXDUVXiG2+S9uOPhRvlvaNNyMjIYPny5Vy8eBF3d3fat2+vdkjCRknxSIgaJiEhgbi4ODIyMqhduzbR0dH4+fmpHZYQtmnNFDi+umj7rQ8bQliJc+fOsXz5crKzswkNDSU6Ohpvb2+1wxLC+nkEwpRTakehOv2lSwB49OuHxtER58aNcfDxUTkqUZqTJ0+yatUqcnNzadiwIaNHj8bDw0PtsISNUr1cPHfuXMLCwnBxcaFLly7s3bv3rtunpaXx3HPPUbt2bZydnWnatCm//PJLNUUrhG27efMm33zzDRkZGXTp0oUnnnhCCkdCFXaT+4+vLn44g9ylFlYkKSmJxYsXk52dTa9evRg3bpwUjoQq7Cb312Ah771L3Y/nEPDiC2qHIkqRkJDA0qVLycvLo3///vztb3+TwpG4J6r2PFq2bBmTJ09m3rx5dOnShdmzZxMVFcXJkycJDCx6x1av1zNw4EACAwOJjY2lTp06XLx4kVq1alV/8ELYIB8fH3r16kXt2rVp1qyZ2uGIGsrucr/clRZWLigoiO7du9OoUSMaNmyodjiihrK73C+ElQsNDaVz5860atWK0NBQtcMRdkDV4tFHH33EhAkTGD9+PADz5s1jzZo1fP3117z66qtFtv/6669JTU1l165dODo6AhAWFladIQthc86cOcORI0eIiIgAoG/fvuoGJGo8yf1CVL3jx49z4sQJS+4fOHCgyhGJmk5yv21SFAXj9eso+flqhyLK4PDhw5w9e5aIiAg0Gg1DhgxROyRhR1QbtqbX69m/fz+RkZH/C0arJTIykt27dxe7z+rVq+nWrRvPPfccQUFBtGrVinfeeQeTyVTiefLz88nIyCj0T4iawGQysX79epYsWcKpU6dITk5WOyQhJPcLUcUMBgNr1qzhhx9+4MSJE6Snp6sdkhCS+22UoihcnvQMZ/r0JffgwYJGmSTbKun1elauXMnKlSs5duwYOTk5aock7JBqPY9SUlIwmUwEBQUVag8KCuLEiRPF7nPu3Dk2b97MI488wi+//MKZM2d49tlnMRgMvPHGG8Xu8+677zJz5swi7UePHi3/mM+C1WyJj48vfVu9HgcgJyenbNtbmYyMDJuMuyTNjUYATtxxTfZ2nbdkZ2ezd+9ebt68iYuLCy1btiQxMZHExES1Q6tS9vrzvFNFrjMrK6uKoikfW879RoOxyPe9pNxiq+RvyLZlZGSwd+9eMjIycHNzo2XLliQkJKgdVpWz15/nnST3q5P7DUaj3eT4kpT0u6WdNw/ttm0AmFu2hObNOHbhQjVHV3nsNVekpaWxd+9esrKy8PT0pEWLFpw9e1btsKqcvf4872RNud+mVlszm80EBgbyxRdfoNPp6NChA1euXOGDDz4o8UVk2rRpTJ482fI4IyOD0NBQWrZsiZeXV/kC+ANQsHQBv2us+fmcBNzc3Agrw/bWJj4+vkzXaTPWFfyq33lNdnedwJEjR9i6dSv5+fk0bdqUkSNHWrqv2jt7/HkWpyLXact3X60i9wMOjg60uPP7XkJusVXyN2SbFEXh0KFDbNu2DYPBQMuWLbnvvvs4ffq0XV1nSezt51kSyf3Vm/s99uaiRcHRwcHuf79K+t26kJZOLuA7bhxBr06t/sAqmb3lCkVR2Lt3L9u2bcNkMtG2bVuGDBnCyZMn7eo6S2JvP8+SWFPuV6145O/vj06nIykpqVB7UlISwcHBxe5Tu3ZtHB0d0el0lrbw8HASExPR6/U4OTkV2cfZ2RlnZ+fKDV4IK6UoCvv378dgMBAVFUWXLl3QaDRqhyWEheR+ISqfoijs27cPRVEYPnw47dq1k9wvrIot5n4n9H+dtGatnKkoCqYbNwq+NhgAZGU1K2U0Gtm3bx86nY6RI0fWiEKKUJdqg1adnJzo0KEDmzZtsrSZzWY2bdpEt27dit2nR48enDlzBrPZbGk7deoUtWvXLvYFRIiaQq8veIOj0WgYPXo0Tz75JF27dpUPD8LqSO4XovLcyv1arZbo6GgmTJhA+/btJfcLq2Orud+MBob9p1rOZS2uvfY6p3v24nTPXuT9+WdBo+QUq3Ir9zs6OvLAAw/w9NNPS+FIVAtVZzybPHkyX375Jd988w3Hjx/nmWeeITs727IKw2OPPca0adMs2z/zzDOkpqby0ksvcerUKdasWcM777zDc889p9YlCKGqW3ebZ8+ezfXr1wHw8vIiJCRE5ciEKJnkfiHujaIo7N69mzlz5nDz5k0AfH19i13uXAhrIbnfNuSfOQOAe48euPfqhd+kp9G6uqoclYCCguu2bdv45JNPyMzMBCAgIABfX1+VIxM1hapzHo0dO5bk5GRmzJhBYmIibdu2Ze3atZbJ9BISEtDeNqN/aGgo69at4+9//zutW7emTp06vPTSS0ydavtjcIUor7y8PH766SeOHTuGo6Mjqamp8sFB2ATJ/UJUXHZ2NqtWreL06dM4OzuTmpqKj4+P2mEJUSrJ/bYl9PPP0EjvXquRmZnJ8uXLuXDhAm5ubty8eRNPT0+1wxI1jOoTZj///PM8//zzxT63devWIm3dunVjz549VRyVENbt8uXLxMbGkp6eTlBQEDExMfj7+6sdlhBlJrlfiPI7f/48y5cvJysrizp16hAdHS2FI2FTJPcLUX6nTp1i1apV5OTkEBYWxujRo8u/+IcQlUD14pEQonz27dvHL7/8gqIodOrUiUGDBuHgIH/KQghhz3bt2sWGDRuAgrlg+vXrV2giYSGEEPZny5Yt/Pbbb2g0Gvr27UuvXr0K9dATojrJJ04hbExAQACurq4MHz6c5s2bqx2OEEKIahAQEIC7uzujR4+mUaNGaocjhLBDiqJgrqIlvkXFBAQE4OXlxZgxY6hfv77a4YgaTopHQtiA8+fPU7t2bVxcXKhfvz4vvfSSrDIlhBB27syZM9SvXx9HR0eaNGnCiy++KLlfCFFlEmfMQH/hgtph1HinTp2iUaNG6HQ6WrVqRdOmTSX3C6tQaX3eli9fTuvWrSvrcEIIwGQysXHjRr799lt+/vlnS7u8gAhrIblfiMpnNBr59ddfWbJkCWvXrrW0S+4X1kJyv51RFExZ2eQePQqA7/jxMlm2CvR6PatWreK///0vmzdvtrRL7hfWolzFo/nz5xMTE8PDDz/M77//DsDmzZtp164djz76KD169KiSIIWoidLS0li0aBE7d+7Ew8ODDh06qB2SqKEk9wtRfW7cuMFXX33F3r178fb2pm3btmqHJGooyf01g6IoaGfP4VTHjuQfOw5A4CuTVY6q5klKSuLLL7/k0KFD+Pn5ERERoXZIQhRR5mFr7733HjNmzKB169acOHGCVatW8dprr/HJJ5/w0ksv8fTTT8uKH0JUkmPHjrF69Wry8/Np3Lgxo0aNwt3dXe2wRA0kuV+I6nP48GHWrFmDwWAgPDycESNG4OLionZYogaS3F9zXHv9dbS7dgHg1rEjru3aopGFWKqNoijs27ePdevWYTKZaNOmDUOHDpXeRsIqlTkzLFy4kC+//JLHH3+c7du306dPH3bt2sWZM2fkQ60QlejChQv8+OOPaLVaBg4cSLdu3dBoNGqHJWooyf1CVI8TJ06wcuVKdDodw4YNo0OHDpL7hWok99cceUePAeA3YYL0OFLB4cOH+eWXX3B0dGT48OG0adNG7ZCEKFGZi0cJCQn0798fgF69euHo6MjMmTPlBUSISla/fn06d+5M69atqVOnjtrhiBpOcr8Q1aNp06Z06NCBTp06ERQUpHY4ooaT3F+zKBqNFI5UEhERwcWLF+nZsyd+fn5qhyPEXZW5eJSfn1+o67STkxO+vr5VEpQQNYmiKBw8eBC9Xk/Xrl3RaDQMGTJE7bCEACT3F7FmChxf/b/HWdfBI1C9eITNUhSFvXv34ujoSPv27dFqtdx3331qhyUEILlfiKqiKAo7duzAx8eHVq1aodPpGDlypNphCVEm5RrQOn36dNzc3ICC2eD/9a9/4e3tXWibjz76qPKiE8LO5eXlsWbNGo4cOYKzszNt2rTB1dVV7bCEKERy/22Ory5cMPIIhPAR6sYkbE5OTg6rVq3i1KlTuLu7ExERgaOjo9phCVGI5H4hKldWVhYrVqzg3Llz1KpVi/DwcHQ6ndphCVFmZS4e9e7dm5MnT1oed+/enXPnzhXaRsbmC1F2V65cIS4ujps3bxIYGEhMTIwUjoTVkdxfDI9AmHJK7SiEjbp48SJxcXFkZmYSEhJCdHS0FI6E1ZHcL0TlOnPmDCtXriQ7O5t69eoRHR0thSNhc8pcPNq6dWsVhiFEzaEoCrt372bTpk2YzWY6dOhAVFSUfHgQVklyPzyxIQefbAXc1I5E2DKz2cz27dvZtm0biqLQrVs3BgwYIB8ehFWS3C9E5TCZTGzevJldf61o16dPH3r37o1Wq1U5MiHKr1zD1jIyMvj999/R6/V07tyZgICAqopLCLtlMBg4cOAAjo6OjBgxghYtWqgdkhB3VdNzf5dTegC8Bg4CFqsbjLBZer2eAwcO4OrqyqhRo2jSpInaIQlxVzU99wtRGXJzczl06BCenp6MGTOGsLAwtUMSosLKXDw6dOgQQ4cOJTExEQBPT09++OEHoqKiqiw4IexJfn4+zs7OODk58cADD+Dk5EStWrXUDkuIu5LcX+Cmu4bwGdPhP1I8EuVzK/e7uLjw4IMP4uHhgaenp9phCXFXkvuFuDe3cr+HhwcPPfQQPj4+slqhsHllLh5NnTqVBg0aEBcXh4uLC7NmzeL555/n9OnTVRmfEDbPbDazdetWDh48yNNPP42HhweBgQWT7ZpMJgwGQ5XHoCgKeXl5VX4etdX063Rycqr0btCS+4WoGKPRyKZNmzhx4gRPP/00Li4u1K5dW+2whCgTyf1CVIzBYGDt2rUkJCQwYcIEnJycqFu3rtphCVEpylw82r9/P+vXr6d9+/YAfP311/j6+pKRkYGXl1eVBSiELUtPTycuLo5Lly7h7u5OWloaHh4eKIpCYmIiaWlp1RKHoiicP3++Ws6lppp+nVqtlgYNGuDk5FRp55LcL0T5paamEhsby7Vr1/Dy8iI9Pb3QsudCWDvJ/UKU3/Xr14mNjSU5ORkfHx8yMzPx8/NTOywhKk2Zi0epqamFqqa1atXC3d2dGzduyIuIEMU4ceIEq1atIi8vj4YNGzJ69Gg8PDwALIWjwMBA3NzcqnzFktzc3BqxkltNvk6z2czVq1e5du0a9erVq7TfKcn9QpRPfHw8P//8M3q9nubNmzNixIgakZeEfZHcb/+uzZxJ2tJloCggK+fdE0VROHDgAGvXrsVoNBIREcGwYcNwdnZWOzQhKlW5Jsw+duyYZewzFPyhHD9+nMzMTEtb69atKy86IWzUxo0b2blzJxqNhgEDBtCjRw/Lh3mTyWQpHFXX3Qiz2Vwj7nrX9OsMCAjg6tWrGI3GSl29T3K/EGWzZs0a9u3bh06nY8iQIXTq1EmWMxc2S3K/fcvdtw8UBefmzcltEKZ2ODZLURRWrFhBfHw8jo6OjBw5kjZt2kjuF3apXMWjAQMGoChKobb77rsPjUaDoihoNBpMJlOlBiiELQoMDMTb25vo6GhCQ0MLPXdrjiM3N1n3W1SuW8PVTCZTpRaPJPcLUTYBAQH4+fkRExNDcHCw2uEIcU8k99cAjo40XFlQ+BAVo9FoCAgIICgoiJiYGPz9/dUOSYgqU+biUU2YR0SIilIUhZMnT9KkSRN0Oh2tW7cmPDz8rh/g5Y6EqGxV8TsluV+IkimKwokTJ2jevDkajYZOnTrRrl27Si3eCqEGyf1ClOzO3N+zZ0+6deuGg0O5+mUIYXPK/Bv+zTffMGXKFOktIcQd8vPz+eWXX/jzzz/p2bMnAwYMAJAPD8IuSO4Xoni5ubmsXr2aEydOEBkZaRmeLLlf2APJ/UIULzs7mxUrVnD27FmGDRtGx44d0Wg0UjgSNUKZ13SeOXMmWVlZVRmLEDbn2rVrfPHFF/z5558EBAQQERGhdkhCVCrJ/UIUlZCQwPz58zlx4gS1a9emefPmaockRKWS3C9EUefOnWPevHmcPXuW0NBQGjdurHZIQlSrMpdI7xzzLERNpigKv//+Oxs2bMBsNtOuXTuGDBkid5wrwfTp00lKSuKLL75QO5Ri9e3bl7Zt2zJ79uwybZ+SkkKLFi04cOBAoZVrbIXkfiH+x2w2s3PnTrZs2YKiKHTp0oXIyEi54yzsjuR++5b4r7fJP30GJHeViclkYuvWrezYsQOAXr160bdvX7TaMvfDEMIulOs3XuZoEaLAwYMHWbduHQ4ODkRHRzNixIgaUThKTEzkhRdeoGHDhjg7OxMaGsrw4cPZtGmTZZuwsDA0Gg179uwptO/LL79M3759Sz3+nDlzeO2116oi/ELCwsLKXAC63fLly5k1a1aZt/f39+exxx7jjTfeKPe5rIXkfiEK7Nmzh82bN+Pi4sKDDz7I4MGDpXAk7JbkfvuVvXs3ALXuj1E5Etvw22+/sWPHDjw8PHj00Ufp37+/FI5EjVSudzxNmzYt9YUkNTX1ngISwha0bt2ay5cv06tXL3x8fNQOp1pcuHCBHj16UKtWLT744AMiIiIwGAysW7eO5557jhMnTli2dXFxYerUqWzbtq1c51iwYAHdu3enfv36lR2+hV6vt6xKVhG+vr7l3mf8+PF06NCBDz74oEL7q01yvxAFOnbsSHJyMv369cPLy0vtcISoUpL77ZvGyYnaNnxjqzp17dqVjIwMBgwYgIeHh9rhCKGachWPZs6cibe3d1XFIoTVMpvN/PbbbwQGBtKiRQscHBwYMWKE2mFVq2effRaNRsPevXtxd3e3tLds2ZInnnii0LYTJ05k3rx5/PLLLwwdOrTM51i6dCnPPPNMoTaz2cx//vMfvvjiCy5dukRQUBBPP/20pXfSpUuXeOWVV1i/fj1arZZevXoxZ84cwsLCABg3bhxpaWl06tSJuXPn4uzsTP369bl48SJ///vf+fvf/w4UdNG/ceMGzz//PL/99hs3b96kUaNG/POf/+Shhx6yxHPnsLWwsDAmTpzImTNn+PHHH/Hx8eH1119n4sSJhb5HISEhrFixgieffLLM3w9rIblf1FQmk4nNmzfToEEDGjdujJOTEyNHjlQ7LCGqheR+UVMZjUbWr19Pq1atqFevHq6urpL7haCcxaMHH3yQwMDAqopFCKuUkZHB8uXLuXjxIr6+vjRv3rxKuqoOnv0bN7L1lX5cKCiM3Hn30M/dibUv9y7T/qmpqaxdu5a33367UOHollq1ahV63KBBAyZNmsS0adMYPHhwmb5fqampHDt2jI4dOxZqnzZtGl9++SX/93//R8+ePbl27Zqll5PBYCAqKopu3bqxfft2DAYDH374IYMHD+bPP/+09DDatGkTXl5ebNiwAYDatWvTpk0bJk6cyIQJEyznysvLo0OHDkydOhUvLy/WrFnDo48+SqNGjejcuXOJsX/44YfMmjWLf/7zn8TGxvLMM8/Qp08fmjVrZtmmc+fObN++3SaLR5L7RU108+ZN4uLiuHLlCufPn6dRo0YyjEfUKJL77UvirH+RvmoVAObsbDQ1YLqFikhOTiYuLo6kpCSSk5N5/PHH1Q5JCKtR5uKRvGESNdGpU6dYuXIlubm5NGjQgNGjR9fIMc5nzpxBUZRyrSj0+uuvs3DhQpYsWcKjjz5a6vYJCQkoikJISIilLTMzkzlz5vDpp59aXrwbNWpEz549AVi2bBlms5kFCxag0WjIyclh4cKF1KpVi61btzJo0CAA3N3dWbBgQaHhajqdDk9PT4KDgy1tderUYcqUKZbHL7zwAuvWreOHH364a/Fo6NChPPvsswBMnTqV//u//2PLli2FikchISEcPHiw1O+DtZHcL2qiI0eO8PPPP5Ofn0/Tpk0ZOXKk/C2IGkV+3+1P1o7tmLOycAwNRefjg3u3bmqHZFUUReHQoUP8+uuvGAwGWrZsyX333ad2WEJYFVltTYhiGI1GNm7cyO+//45Go6Ffv3707NmzSgtHZe0FVBE5OTm4ublVeP+K/P0HBAQwZcoUZsyYwdixY0vdPjc3FyiYL+mW48ePk5+fz4ABA4rd5/Dhw5w5cwZPT89C7Xl5eZw9e9byOCIiokzzHJlMJt555x1++OEHrly5gl6vJz8/v9TvXevWrS1fazQagoODuX79eqFtXF1dycnJKTUGayO5X9QkBoOBX3/9lYMHD6LVaomKiqJLly7yQVrUOJL77Uviv97GcDEBjZsbjTesVzscq5Ofn8/PP//MkSNHcHBwYPjw4bRr105yvxB3KHPxyGw2V2UcQliV3Nxc/vzzT7y8vIiOjqZevXpqh6SqJk2aoNFoCk2KXRaTJ0/ms88+47PPPit1W39/f6BgqEhAQABQUHC5m6ysLDp06MCSJUuAgp/brX1uHQModqhdcT744APmzJnD7NmziYiIwN3dnZdffhm9/u7DCe9caU+j0RTJmampqYVishWS+0VNkpWVxdGjR/H19SU6OrpQT0ghahLJ/fYl668FTLxHDFc5EuuUnp7OiRMnCAgIICYmRoZrClECWV9WiNvk5+fj7OyMp6cnDz/8MH5+fqUWMGoCX19foqKimDt3Li+++GKRYkxaWlqReY8APDw8mD59Om+++WapE4w3atQILy8vjh07RtOmTYGCopWrqyubNm3iqaeeKrJP+/btWbZsGYGBgXh5eZWrh5WTkxMmk6lQ286dOxk5ciR/+9vfgII3z6dOnaJFixZlOubdHDlyhL59+97zcYQQlUtRFPR6Pc7Ozvj4+PDII48QFBSEs7Oz2qEJIcQ9S3z7HQyXLqF1d6f2m2+qHY7VuD33BwYG8sgjj1CnTp0iNwSFEP9jFZO3zJ07l7CwMFxcXOjSpQt79+4t035Lly5Fo9EwatSoqg1Q2D29Xs+qVav46quvMBgMANStW1cKR7eZO3cuJpOJzp07ExcXx+nTpzl+/Dgff/wx3e4ybn7ixIl4e3vz/fff3/X4Wq2WyMhIduzYYWlzcXFh6tSp/L//9//49ttvOXv2LHv27OGrr74C4JFHHsHf35+RI0eyfft2Lly4wNatW3nxxRe5fPnyXc8XFhbGb7/9xpUrV0hJSQEKilUbNmxg165dHD9+nKeffpqkpKSyfotKlJOTw/79+y1zMAnJ+8I65OXlERsby+LFiy3F5Hr16knhSIgqIrm/+mVt3gyAl8zfY5Gdnc1///tfy9yZUPC+UApHQtyd6sWjZcuWMXnyZN544w0OHDhAmzZtiIqKKjJfyJ0uXLjAlClT6NWrVzVFKuxVYmIiX3zxBYcOHcJsNpOVlaV2SFapYcOGHDhwgH79+vHKK6/QqlUrBg4cyKZNm/j8889L3M/R0ZFZs2aRl5dX6jmeeuopli5dWqi7/PTp03nllVeYMWMG4eHhjB071pIf3Nzc+O2336hXrx5jxoyhffv2PPnkk+Tl5eHl5XXXc7311ltcuHCBRo0aWYaTvf7667Rv356oqCj69u1LcHBwpbxRXbVqFfXq1ZN89RfJ+8IaXL58mfnz53Ps2DGMRqNNzkkmhC2R3K8erZcXtWe+qXYYVuHChQvMnz+f06dPo9fry/T+VAhRQPVhax999BETJkxg/PjxAMybN481a9bw9ddf8+qrrxa7j8lk4pFHHmHmzJls376dtLS0aoxY2AtFUTh79iyrVq3CZDLRtm1bhgwZUqaJlWuq2rVr8+mnn/Lpp5+WuM2FCxeKtD300EM89NBDpR5/8ODBhISEsGzZMsv2Wq2W1157jddee63YfYKDg/nmm2+A4icGX7RoUbH7de3alcOHDxdq8/X1ZeXKlXeNcevWrYUeF3e9hw4dKvR4zpw5zJgx467HrUlsLe9rUNAA/KcpZF0HD5kLwZYpisKpU6c4duwYZrOZTp06MWjQIBwcVH9LJIRds7XcL+yL2Wzm2LFjlvk7u3fvTv/+/dHpdCpHJoTtULXnkV6vZ//+/URGRlrabg1d2b17d4n7vfXWWwQGBvLkk0+Weo78/HwyMjIK/RNCURRiY2M5fPgwOp2O0aNHM3LkSCkcqUyj0fDFF19gNBrVDqXSpKSkMGbMmDIVz2qC6sj7ULm5v9BaKx6BEH73+buE9TKbzXz//fccOXIEJycnHnjgAYYOHSqFIyGqmC3mfmE/jEYj33zzDSdOnMDd3Z1HHnmEgQMHSuFIiHJS9d1SSkoKJpOJoKCgQu1BQUElruq0Y8cOvvrqqyJ39kvy7rvvMnPmzCLtR48excPDo3wB/7VqaXx8fOnb6vU4UNATokzbW5mMjAybjLskzf8qRpy47ZoURcHT05Nu3bqh0Wiq7XoVRUFRFHJzc6ttNROz2WwzQzKaNm1K06ZNKxSvNV6nm5sbzz//PLm5uZV2zJKuMz8/H71ez6lTp4osL2stwzGrI+9DJef+v8RHxd32wH7y4+3sLfeXxNvbm27dumE0Gu36emvKz1Ous2SS+wvcU+638d8tnV4PJtNdr6Mm/A1pNBp8fX3p2rUrubm5dn29NeHnCXKdd1NVud+mbrVlZmby6KOP8uWXX1qW9S7NtGnTmDx5suVxRkYGoaGhtGzZstQ5UYr4A1AgIiKi1E3N+fmcpOCDY1gZtrc28fHxZbpOm7HOAbOiQafTER4ejkajoWXLlvz555+0bdu2WkPJy8vj/PnzuLq64uLiUi3nLM8qZLaspl+nVqvFycmJBg0aFPndstW7rxXJ+1C5uX/PX//bVU4sgd3lfgqGvZw6dYrw8HAAWrRowZEjR2jTpo3KkVU9e/x5Fkeus2SS++/hff9fc3nb8u9W0nvvk5qcjNbTkxZ3uQ57/BsyGo2cOXOG5s2bAxAeHs6xY8do3bq1ypFVPXv8eRZHrrNkVZX7VS0e+fv7o9PpiqxmlJSURHBwcJHtz549y4ULFxg+fLil7VbPDQcHB06ePEmjRo0K7ePs7Cyrpggyza6syO/O+R9/ZMSIEbRr1w6tVivdVYWoZtWR90FyvyiQlpZGXFwcly9f5v7776dFixbodDq0WtXXCxGiRpHcr46M9esA8Bw0UOVIqteNGzeIjY0lMTGRv/3tbzRq1AgHB4civbKFEOWj6rsnJycnOnTowKZNmyxtZrOZTZs2Fbv0d/PmzYmPj+fQoUOWfyNGjKBfv34cOnSI0NDQ6gxf2IjTp08zL3co583B1K9fv9g3G0KI6iF5X1SXY8eOMX/+fC5fvkzjxo2pX7++2iEJUWNJ7q9+Se//G+PVa2i9vQl5+221w6k2hw8fZv78+SQmJhIeHk5ISIjaIQlhN1QftjZ58mQef/xxOnbsSOfOnZk9ezbZ2dmWlRgee+wx6tSpw7vvvouLiwutWrUqtH+tWrUAirQLYTKZ2LRpE7t370aDE30c/6T3Y9PljrMQKpO8L6qSwWBg/fr17Nu3D61Wy8CBAy1z2wkh1CO5v3pl/PorAJ4DBqgcSfXQ6/X88ssvlsVwhg4dSseOHSX3C1GJVC8ejR07luTkZGbMmEFiYiJt27Zl7dq1lgn1EhISrPLDvmIycemZZ9CfOVv887dm1xaq2bJlC7t378bT05MxhuWE6a6DFf4uCVHT2GreF7Zh3bp17N+/n1q1ahETE0OdOnXUDkkIgeR+Neh8fAh5p2b0Ovrpp584cuQI/v7+xMTEFJmcXQhx71QvHgE8//zzPP/888U+t3Xr1rvuu2jRosoPqAxMN2+S/dt2ALQlTMCn9fLCvXev6gxL3KZ79+7k5OQQGRmJ22fz1A5HCHEbW8z7wjb06dMHRVEYOHBgtS1KIIQoG8n9VS/p/X+TuWEDxuvX0Xl7qx1Otenfvz8uLi4MHDgQJycntcMRwi5ZRfHIlrn36kW9L79QOwxBwVCFtWvX0q5dO+rWrYubmxsjRoxQOywhhBBVKD8/n19//ZXu3bsTGBiIp6dnoUl2hRCipkh6/9+kLlwIgNbTE48+fVSOqOrk5OSwdu1a+vbti6+vLz4+PgwbNkztsISwa1I8KiPFaGTaj5nUvmnmzLIoMBrVDkncJikpidjYWFJSUkhLS+PRRx9VOyRRQdOnTycpKYkvvrDfouzWrVvp168fN2/etMzhUJpXX32V7OxsPvnkk6oNTggbcvXqVWJjY7l58yYGg4H7779f7ZCEEEI16T/9BID3mDF2PVzt4sWLLF++nIyMDBwcHORmsRDVRAYWl5ExKYl2540EpJsx3biBKT0drYcH7t27qx1ajaYoCvv27WPBggWkpKQQERHBAw88oHZYdisxMZEXXniBhg0b4uzsTGhoKMOHDy+0ekpYWBgajYY9e/YU2vfll1+mb9++pR5/zpw5vPbaa1URfqVbtGhRmYs/t+vevTvXrl3DuxzdyadMmcI333zDuXPnyn0+IeyNoijs3r2br776ips3b9KhQwdGjRqldlhCCKE6h4AAuy0cmc1mtm3bxjfffENGRgZdu3aV3kZCVCPpeVROBxo58tjP+9QOQwB5eXn89NNPHDt2DEdHR0aOHEmbNm1kVYUqcuHCBXr06EGtWrX44IMPiIiIwGAwsG7dOp577jlOnDhh2dbFxYWpU6eybdu2cp1jwYIFdO/e3SaW1DYYDBXe18nJieDg4HLt4+/vT1RUFJ9//jkzZ86s8LmFsHXZ2dmsWrWK06dP4+zszPDhw2nZsqXaYQkhhKhCmZmZLF++nAsXLuDm5sbIkSNp2rSp2mEJUaNIzyNhs9LS0jh58iRBQUFMnDiRtm3bSuGoCj377LNoNBr27t1LdHQ0TZs2pWXLlkyePLlIL6OJEyeyZ88efvnll3KdY+nSpUXmKgkLC2P27NmF2tq2bcubb75peazRaFiwYAEPPvggbm5uNGnShNWrVxfa5+jRo9x33314eXnh6elJr169OHv2f6slLliwgPDwcFxcXGjevDmfffaZ5bkLFy6g0WhYtmwZffr0wcXFhSVLljB+/HjS09PRaDRoNBpLTIsXL6Zjx454enoSHBzMww8/zPXr1y3H27p1KxqNhrS0NOB/PZjWrVtHeHg4Hh4eDB48mGvXrhW6huHDh7N06dJyfU+FsDepqamcOXOGOnXq8PTTT0vhSAghaoCkpCQuXLhAWFgYTz/9tBSOhFCB9DwSNkVRFPR6Pc7OzgQHB/O3v/2NunXr4uBgB7/Kn/eA7OQqObSrosCdhTX3AHhmZ5n2T01NZe3atbz99tu4u7sXef7OoVsNGjRg0qRJTJs2jcGDB5dp6d3U1FSOHTtGx44dyxTTnWbOnMmsWbP46KOP+OSTT3jkkUe4ePEivr6+XLlyhd69e9O3b182b96Ml5cXO3fuxPjX3GVLlixhxowZfPrpp7Rr146DBw8yYcIE3N3defzxxy3nePXVV/nwww9p164dWq2W2bNnM2PGDE6ePAmAh4cHUNAradasWTRr1ozr168zefJkxo0bd9diWk5ODv/5z39YvHgxWq2Wv/3tb0yZMoUlS5ZYtuncuTOXL1/m4sWLhIeHV+j7JIQtMpvNGI1GnJycCA0N5dFHH6VevXrodDq1QxNCCFFFTCYTZrMZR0dHGjduzKOPPkpYWFiZ3lcKISqfHXziFjVFVlYWK1asQKPR8Mgjj6DRaAgLC1M7rBrhzJkzKIpC8+bNy7zP66+/zsKFC1myZEmZJjBPSEhAURRCQkIqFOO4ceN44IEHcHNz45133uHjjz9m7969DB48mLlz5+Lt7c3SpUtxdHQEKHTH6o033uDDDz9kzJgxQEHx69ixY8yfP79Q8ejll1+2bAPg7e2NRqMpMgTtiSeesHzdsGFDPv74Yzp16kRWVpalwHQng8HAvHnzaNSoEVCwnPFbb71VaJtb35uEhAQpHokaIz09neXLl+Pu7s7999+PRqOhQYMGaoclhBCiCqWmphIXF0dQUJBlQuyGDRuqHJUQNZsUj4RNOHv2LCtWrCA7O5vQ0FDy8vJwdXVVO6zKVcZeQBWRm5ODm5tbhfdXFKXc+wQEBDBlyhRmzJjB2LFjS90+NzcXKJgvqSJat25t+drd3R0vLy/LULFDhw7Rq1cvS+HodtnZ2Zw9e5Ynn3ySCRMmWNqNRmORCa3L2itq//79vPnmmxw+fJibN29iNpuBgqJPixYtit3Hzc3NUjgCqF27dqGhboDldz4nJ6dMcQhh606cOMGqVavIy8ujYcOGGAwGnJyc1A5LCCFEFYqPj+fnn39Gr9fj6emJyWSSnqZCWAEpHgmrZjKZ2LJlCzt3FhRWevXqRd++faW7ajVr0qQJGo2m0KTYZTF58mQ+++yzQvMHlcTf3x+AmzdvEhAQYGnXarVFilfFTVZ9Z2FIo9FYijZ3KzRmZWUB8OWXX9KlS5dCz935RqW4IXt3ys7OJioqiqioKJYsWUJAQAAJCQlERUWh1+tL3K+4+O+87tTUVIBC3x8h7JHRaGT9+vX88ccfaDQaBgwYQI8ePWReOyGEsGN6vZ5ff/2VQ4cOodPpGDx4MJ07d5bcL4SVkOKRsFp6vZ7Fixdz+fJlPDw8GDNmjAxVUImvry9RUVHMnTuXF198sUgRJS0trdgl6z08PJg+fTpvvvmmpctxSRo1aoSXlxfHjh0rNKQsICCg0MTRGRkZnD9/vlzxt27dmm+++QaDwVCkSBMUFERISAjnzp3jkUceKddxnZycMJlMhdpOnDjBjRs3eO+99wgNDQVg377KWaHxyJEjODo6ypA1Yddyc3P59ttvSUxMxNvbm+joaMvfkhBCCPuUmZnJt99+S0pKCn5+fkRHR1O7dm21wxJC3Ea6bwir5eTkhJ+fH40bN2bSpElSOFLZ3LlzMZlMdO7cmbi4OE6fPs3x48f5+OOP6datW4n7TZw4EW9vb77//vu7Hl+r1RIZGcmOHTsKtffv35/Fixezfft24uPjefzxx8vddfn5558nIyODBx98kH379nH69GkWL15smeh65syZvPvuu3z88cecOnWK+Ph4Fi5cyEcffXTX44aFhZGVlcWmTZtISUkhJyeHevXq4eTkxCeffMK5c+dYvXo1s2bNKle8Jdm+fTu9evWyvyGbQtzGxcWFWrVqER4ezqRJk6RwJIQQNYC7uzuenp60adOGiRMnSuFICCskxSNhVQwGQ6GhUffddx8PP/xwmYYLiarVsGFDDhw4QL9+/XjllVdo1aoVAwcOZNOmTXz++ecl7ufo6MisWbPIy8sr9RxPPfUUS5cutQw3A5g2bRp9+vThvvvuY9iwYYwaNarQ3EBl4efnx+bNm8nKyqJPnz506NCBL7/80tIL6amnnmLBggUsXLiQiIgI+vTpw6JFi0otWHbv3p1JkyYxduxYAgIC+Pe//01AQACLFi3ixx9/pEWLFrz33nv85z//KVe8JVm6dGmheZmEsBf5+fmcOnUKKBiyOWbMGO6///4Kz4EmhBDC+uXm5nLmzBmg4CbiQw89xKhRo2RuOyGslAxbE1YjOTmZ2NhYrl+/zmOPPUaDBg1wcJBfUWtSu3ZtPv30Uz799NMSt7lw4UKRtoceeoiHHnqo1OMPHjyYkJAQli1bZtney8uLpUuXFtru9hXQ4H8Tet8+kXRaWlqhbVq3bs26detKPPfDDz/Mww8/XOxzYWFhJU4a/vnnnxcpnhV3vbfv37dv30KPx40bx7hx4wptP2rUqELb/Prrr2i1WmJiYu46d5IQtubatWvExsZy8+ZNnnzySerUqVPs5PZCCCHsx6VLl4iLiyMrK4uJEycSGBgouV8IKyefzIXqFEXh4MGD/PrrrxiNRlq2bCldVWsojUbDF198QXx8vNqhWJ3s7GwWLlyIg4ODFI+EXVAUhb1797JhwwZMJhPt2rWTyeCFEMLOKYrCjh072LJlC4qi0LlzZ3x9fdUOSwhRBlI8EqrKy8tjzZo1HDlyBAcHB4YPH067du1kVYUarG3btrRt21btMKxOTEyM2iEIUWlycnJYtWoVp06dwsnJiVGjRtGqVSu1wxJCCFGFsrKyWLFiBefOncPV1ZWRI0fSrFkztcMSQpSRFI+EqlavXs3x48cJDAwkJiZG7joLIUQNEBcXx7lz5wgJCSE6OlruOgshhJ1TFIWlS5dy5coV6tWrR3R0NF5eXmqHJYQoBykeCVUNGDAALy8vBgwYIOOchRCihhg4cCDx8fH079+/3KsnCiGEsD0ajYaoqCjOnDlDnz590Gpl3SYhbI381YpqlZ2dTWxsrGUyYz8/PwYPHiyFIyGEsGMZGRn8+OOPZGVlARAcHMzAgQOlcCSEEHbs5s2b/Pjjj+Tm5gIQGhpKv379pHAkhI2Snkei2pw/f57ly5eTlZWFq6srw4YNUzskIYQQVezUqVOsXLmS3NxcfHx8iIyMVDskIYQQVezo0aP89NNP5OfnU7t2bXr27Kl2SEKIeyTFI1HlzGYzW7duZfv27QD06NGDfv36qRyVEEKIqmQ0Gtm0aRN79uxBo9HQr18/+fAghBB2zmAwsHbtWg4cOIBWq2XQoEF07dpV7bCEEJVAikeiSqWnpxMXF8elS5dwd3dn9OjRNGrUSO2whBBCVKHU1FRiY2O5du0aXl5eREdHU69ePbXDEkIIUYWuX79ObGwsycnJ+Pj4EBMTQ0hIiNphCSEqiRSPRJVKSkri0qVLNGzYkNGjR+Ph4aF2SEIIIarYlStXuHbtGs2aNWPkyJG4urqqHZIQQogqlpCQQHJyMhEREQwbNgxnZ2e1QxJCVCIpHolKZzQaURQFR0dHmjZtyqOPPkqDBg3QaDRqhyZswPTp00lKSuKLL75Q5fzjxo0jLS2NlStXAvDggw/SqVMnXnnlFVXiEcJW6PV6dDodOp2OiIgI3N3dJfcLIYSdy8/Px9HREa1WS4cOHfD19ZXcL4SdkqnuRaVKSUlhwYIFrFu3ztLWsGFDeQGxE4mJibzwwgs0bNgQZ2dnQkNDGT58OJs2bbJsExYWhkajYc+ePYX2ffnll+nbt2+px58zZw6vvfaapa1v3768/PLL5Y61ovvd6fXXX+ftt98mPT39no8lhL1KTEzkiy++YPPmzZY2yf1CCGHfLl++zLx589i5cycAGo1Gcr8Qdkx6HolKoSgKhw8f5pdffsFgMODn54fZbJalOO3IhQsX6NGjB7Vq1eKDDz4gIiICg8HAunXreO655zhx4oRlWxcXF6ZOncq2bdvKdY4FCxbQvXt36tevX9nhV1irVq1o1KgR3333Hc8995za4QhhVRRF4Y8//mD9+vWYTCZycnJQFEU+OAghhB1TFIVdu3axefNmzGYzWVlZkvuFqAHkk724Z/n5+axcuZJVq1ahKArDhg0jJiZGCkd25tlnn0Wj0bB3716io6Np2rQpLVu2ZPLkyUV6GU2cOJE9e/bwyy+/lOscS5cuZfjw4ZbH48aNY9u2bcyZMweNRoNGo+HChQsAbNu2jc6dO+Ps7Ezt2rV59dVXMRqNd93PZDLx5JNP0qBBA1xdXWnWrBlz5swpNa7hw4ezdOnScl2LEPYuNzeXH374gV9//RWtVsuoUaMYOXKkfHgQQgg7lpWVxZIlS9i4cSNOTk488MADDBkyRHK/EDWA9DwS9yQjI4NvvvmG1NRU/P39iYmJISgoSO2wbFL06mhS81Kr5NjF3Q3ydfElbkRcmfZPTU1l7dq1vP3227i7uxd5vlatWoUeN2jQgEmTJjFt2jQGDx5cpkJiamoqx44do2PHjpa2OXPmcOrUKVq1asVbb70FQEBAAFeuXGHo0KGMGzeOb7/9lhMnTjBhwgR0Oh1vv/12ifuZzWbq1q3Ljz/+iJ+fH7t27WLixInUrl2bBx54oMTYOnfuzNtvv01+fr5M/igEBX+v3377Lenp6QQHBxMTE4Ofn5/aYQkhhKhCSUlJLF68mOzsbEJDQxkzZkyR94BCCPslxSNxTzw8PPDy8iIsLIzBgwfj6OiodkiiCpw5cwZFUWjevHmZ93n99ddZuHAhS5Ys4dFHHy11+4SEBBRFKbSkq7e3N05OTri5uREcHGxp/+yzzwgNDeXTTz9Fo9HQvHlzrl69ytSpU5k1a1aJ++l0OmbOnGl53KBBA3bv3s0PP/xw1+JRSEgIer2exMREqxpSJ4RavL29cXd3p3nz5kRGRuLgIG8nhBDC3vn4+ODm5kb79u3p27evjDIQooaRd3ui3HJycrh69SqNGzdGq9XyyCOPyAeHSlDWXkAVkZOTg5ubW4X3VxSl3PsEBAQwZcoUZsyYwdixY0vdPjc3FyiYL6k0x48fp1u3boV6U/Xo0YOsrCwuX75MvXr1Stx37ty5fP311yQkJJCbm4ter6dt27Z3Pd+tZcZzcnJKjU0Ie5WZmUlKSgoNGjRAp9Mxfvx4yf1CCGHn0tLSyMjIoF69ejg5OTFx4kTJ/ULUUFIuFuVy4cIF5s2bx7Jly0hJSQGQF5AaoEmTJmg0mkKTYpfF5MmTyc3N5bPPPit1W39/fwBu3rxZoRjLYunSpUyZMoUnn3yS9evXc+jQIcaPH49er7/rfqmpBcMJAwICqiw2IazZ6dOnLbn/1sqDkvuFEMK+HTt2jPnz57Ns2TKys7MByf1C1GRSPBJlYjab2bp1K99++y2ZmZl06tQJHx8ftcMS1cTX15eoqCjmzp1refNwu7S0tGL38/DwYPr06bz99ttkZmbe9RyNGjXCy8uLY8eOFWp3cnLCZDIVagsPD2f37t2FekTt3LkTT09P6tatW+J+O3fupHv37jz77LO0a9eOxo0bc/bs2bvGBXDkyBHq1q1rKXAJUVOYTCbWr1/P999/T25uLl27dsXT01PtsIQQQlQhg8HAmjVr+PHHH9Hr9XTv3v2eerALIeyDFI9EqTIyMvj222/Ztm0brq6uPPzwwwwaNAidTqd2aKIazZ07F5PJROfOnYmLi+P06dMcP36cjz/+mG7dupW438SJE/H29ub777+/6/G1Wi2RkZHs2LGjUHtYWBi///47Fy5cICUlBbPZzLPPPsulS5d44YUXOHHiBKtWreKNN97ghRdesIy/L26/Jk2asG/fPtatW8epU6eYPn06f/zxR6nXvn37dgYNGlSG75IQ9uPmzZt8/fXX7N69G09PTx577DGZ40IIIexccnIyCxYsYN++fdSqVYvx48fTo0cPWU1NCGEdxaO5c+cSFhaGi4sLXbp0Ye/evSVu++WXX9KrVy98fHzw8fEhMjLyrtuLe6MoCv/973+5ePGiZQWtJk2aqB2WUEHDhg05cOAA/fr145VXXqFVq1YMHDiQTZs28fnnn5e4n6OjI7NmzSIvL6/Uczz11FMsXboUs9lsaZsyZQo6nY4WLVoQEBBAQkICderU4ZdffmHv3r20adOGSZMm8eSTTzJ16tS77vf0008zZswYxo4dS5cuXbhx4wbPPvvsXWPKy8tj5cqVTJgwoQzfJVFWkvetm9ls5rvvvuPq1as0adKESZMmERYWpnZYQggbJ7nfuhmNRr799luuX79OixYtePrppy09uoUQQvVBq8uWLWPy5MnMmzePLl26MHv2bKKiojh58iSBgYFFtt+6dSsPPfQQ3bt3x8XFhffff59BgwZx9OhR6tSpo8IV2DeNRsPgwYO5ePEiPXv2lDvONVzt2rX59NNP+fTTT0vc5sKFC0XaHnroIR566KFSjz948GBCQkJYtmyZZfumTZuye/fuItv26dOnyJvI2ye0Lmm/hQsXsnDhwkJt7777ruXrRYsWFdm+c+fOdO3atdT4RdlI3rd+Wq2WwYMHc+PGDbp06SJ3nIUQ90xyv/VzcHBg8ODB5OXl0b59e8n9QohCVK8EfPTRR0yYMIHx48fTokUL5s2bh5ubG19//XWx2y9ZsoRnn32Wtm3b0rx5cxYsWIDZbGbTpk3VHLn9unHjBnv27LH0FKlfvz69e/eWwpGochqNhi+++AKj0ah2KBaOjo588sknaodhVyTvW6fr16+zZ88eDAYDUDBRfteuXeXDgxCiUkjut05Xr15l7969lnkiW7ZsSYcOHST3CyGKULXnkV6vZ//+/UybNs3Sdmvek+J6DBQnJycHg8GAr69vsc/n5+eTn59veZyRkXFvQdu5P//8kzVr1qDX6zl48GDJc9msmQLHV1dvcPci6zp4FL2rJaxP27Ztadu2rdphWDz11FNqh2BXqiPvg+T+8lAUhf3797Nu3TqMRiNHjhyhXbt2aoclhLAjkvutj6Io7Nmzh40bN2I2mzl58iQtWrRQOywhhBVTtXiUkpKCyWQiKCioUHtQUFCZlwSfOnUqISEhREZGFvv8u+++y8yZM4u0Hz16FA8Pj7IHm5xs+WbFx8eXfT8bYTQaOXToEAkJCWi1Wpo1a4a7u3uJ19o8fjkOeakYXUp+AbcqLr5kBPbg6h3Xk5GRUe0/T0VRUBSF3NzcQnP7VCWz2VxoSJe9qunXmZ+fj16v59SpU0XuGGZlZVVXeHdVHXkfKjH338Yec/+tGwVXrlyxzBGm0+ns8lpvp0buV4Ncp32pyHVK7i9QU3K/zmAArbbUmPPz89m3bx9JSUk4ODjQsmVLTCaTTV1rRUiusC9ynSWrqtyv+pxH9+K9995j6dKlbN26FRcXl2K3mTZtGpMnT7Y8zsjIIDQ0lJYtW+Ll5VXmcxmuXOHMX19HRETcS9hWJzExkdjYWG7cuIGfnx8xMTEkJyff/TrXOYBHII5TTlVfoPfI769/t4uPj6/2n2deXh7nz5/H1dW1xN/bypaTk1Mjllit6dep1WpxcnKiQYMGRX637OXua1nyPlRe7gfY89f/9pb7L1++TGxsLOnp6QQFBRETE8O1a9fs7jqLo0buV4Ncp32pyHVK7q947uevaRVt6XfrlKMjGo2GFneJ+fz586xfv56srCzq1KlDdHQ0ly9ftqnrrCjJFfZFrrNkVZX7VS0e+fv7o9PpSEpKKtSelJREcHDwXff9z3/+w3vvvcfGjRtp3bp1ids5Ozvj7OxcKfHaq4sXL3Ljxg3atm3LkCFDcHJyIjk5We2whBB2qDryPkjuL4tz586Rnp5Op06dGDRoEA4ODly7dk3tsIQQdkhyv/U4ffo0WVlZ9OjRg379+qHT6bh8+bLaYQkhbICqMyA7OTnRoUOHQhPf3ZoIr8S5doB///vfzJo1i7Vr19KxY8fqCNXu5OXloSgKAJ07d+axxx5j5MiRODk5qRyZEMKeSd5XV25uriX39+zZk8cff5yhQ4fi4GDTHZGFEFZOcr+6cnNzLV8PGDCAcePGERkZiU6nUzEqIYStUX35rMmTJ/Pll1/yzTffcPz4cZ555hmys7MZP348AI899lihyfXef/99pk+fztdff01YWBiJiYkkJiZazZhuW5CQkMDnn3/Orl27gIIVrho0aKByVEKImkLyvjrOnj3L3Llz2b9/P1AwzDEsLEzdoIQQNYbkfnWcOHGCjz/+2DJnik6no379+ipHJYSwRarfahw7dizJycnMmDGDxMRE2rZty9q1ay0T6t2awPmWzz//HL1eT0xMTKHjvPHGG7z55pvVGbrNMZvN7Nixg61bt6IoSo2YWFgIYX0k71cvk8nEli1b2LlzJwDZ2dkqRySEqIkk91cvo9HIhg0b2Lt3LxqNRnK/EOKeqV48Anj++ed5/vnni31u69athR5fuHCh6gOyQ5mZmaxYscIyUfOoUaNo2rSp2mGJGiosLIyXX36Zl19+We1QhEok71ePtLQ04uLiuHz5Mh4eHowZM0Z6mgohVCO5v3rcuHGD2NhYEhMT8fb2Jjo6mtDQULXDEkLYONWHrYmqd+3aNebNm8f58+epX78+kyZNksKRKLdx48ah0Wh47733CrWvXLmyyLLwpfnjjz+YOHFiZYZnceHCBTQajeWfk5MTjRs35l//+pdlrheAN998E41Gw6RJkwrtf+jQITQajbxpFTYvISGB+fPnc/nyZRo3bsykSZOkcCSEEHbumq8v8+fPJzExkfDwcJ5++mkpHAkhKoVV9DwSVcvPzw93d3c6depE7969C3UJFqI8XFxceP/993n66afx8fGp8HECAgIqMaribdy4kZYtW5Kfn8+OHTt46qmnqF27Nk8++aRlGxcXF7766iteeeUVmjRpUuUxCVGdAgICcHV1pVevXnTr1q3cRV4hhBC2p1ZWFm5ubvTs2ZMOHTpI7hdCVBqpItipmzdvkpCQABSscDFx4kT69u0rhSNxTyIjIwkODubdd9+963ZxcXG0bNkSZ2dnwsLC+PDDDws9HxYWxuzZswFQFIU333yTevXq4ezsTEhICC+++CIAb731Fq1atSpy/LZt2zJ9+vS7xuDn50dwcDD169fnkUceoUePHhw4cKDQNs2aNaNfv3689tprpV26EDYhOTmZq1evAuDq6sqzzz5L9+7d5cODEELYsWvXrnH9+nUAXPV6nn/+eTp27Ci5XwhRqaSSYIeOHDnCvHnz+OGHHyyTYssyzKIy6HQ63nnnHT755BMuX75c7Db79+/ngQce4MEHHyQ+Pp4333yT6dOns2jRomK3j4uL4//+7/+YP38+p0+fZuXKlURERADwxBNPcPz4cf744w/L9gcPHuTPP/+0rM5SFvv27WP//v106dKlyHPvvfcecXFx7Nu3r8zHE8LaKIrCwYMH+eKLL/jhhx/Q6/WA5H4hhLBniqLw+++/89VXX/Hjjz9i+qtYJLlfCFEVJLPYEYPBwK+//srBgwfR6XT0798fV1dXtcMSZXRu5CiMqTeq5uCKAnfcfXLw9aPhqpXlPtTo0aNp27Ytb7zxBl999VWR5z/66CMGDBhg6RnUtGlTjh07xgcffMC4ceOKbJ+QkEBwcDCRkZE4OjpSr149OnfuDEDdunWJiopi4cKFdOrUCYCFCxfSp08fGjZseNc4u3fvjlarRa/XYzAYmDhxIo899liR7dq3b88DDzzA1KlT2bRpU3m/HUKoLj8/n59//pkjR47g4OBA7969cXR0VDssIYQQVSgnJ4fVq1dz8uRJnJyc6N27N7r/Li3yfk8IISqL9DyyE0lJSXzxxRccPHgQX19fnnzySbp06SLdVUWVeP/99/nmm284fvx4keeOHz9Ojx49CrX16NGD06dPYzKZimx///33k5ubS8OGDZkwYQIrVqzAaDRanp8wYQL//e9/ycvLQ6/X8/333/PEE0+UGuOyZcs4dOgQhw8f5ocffmDVqlW8+uqrxW77r3/9i+3bt7N+/fpSjyuENbl69Srz58/nyJEjBAYGMnHiRNq3by+5Xwgh7NjFixeZP38+J0+eJCQkhKefftrSa1sIIaqK9DyyAwaDgW+//ZacnBxat27N0KFDcXZ2VjssUU4V6QVUVjk5Obi5uVXa8Xr37k1UVBTTpk0rtjdReYSGhnLy5Ek2btzIhg0bePbZZ/nggw/Ytm0bjo6ODB8+HGdnZ1asWIGTkxMGg4GYmJgyHbdx48YAhIeHc/bsWaZPn86bb76Ji4tLoW0bNWrEhAkTePXVV4vtTSWENcrNzeWbb75Br9fToUMHoqKipMeREELYuczMTBYvXozJZKJbt24MGDAAnU6ndlhCiBpAikd2wNHRkSFDhmAymWjTpo3a4Yga4r333qNt27Y0a9asUHt4eDg7d+4s1LZz506aNm1a4psbV1dXhg8fzvDhw3nuuedo3rw58fHxtG/fHgcHBx5//HEWLlyIk5MTDz74YIWGY+p0OoxGI3q9vkjxCGDGjBk0atSIpUuXlvvYQqjB1dWVqKgoXFxcaNGihdrhCCGEqAaenp4MHDgQX19fWSlWCFGtpHhkoy5dusTvv//O6NGj0el0xa5IJURVioiI4JFHHuHjjz8u1P7KK6/QqVMnZs2axdixY9m9ezeffvopn332WbHHWbRoESaTiS5duuDm5sZ3332Hq6sr9evXt2zz1FNPER4eDlCkMFWSGzdukJiYiNFoJD4+njlz5tCvXz+8vLyK3T4oKIjJkyfzwQcflOn4Qqjh/PnzHDp0iFGjRqHRaGjfvr3aIQkhhKhip06d4uTJk9x3331oNJpiFwARQoiqJnMe2RhFUdixYwcLFy7k6NGjnDlzRu2QRA321ltvYTabC7W1b9+eH374gaVLl9KqVStmzJjBW2+9VeLwtlq1avHll1/So0cPWrduzcaNG/npp5/w8/OzbNOkSRO6d+9O8+bNy/yGKTIyktq1axMWFsbEiRMZOnQoy5Ytu+s+U6ZMwcPDo0zHF6I6mc1mNm/ezLfffsuff/7JxYsX1Q5JCCFEFTOZTKxbt47//ve/HDx4kGvXrqkdkhCiBpOeRzYkKyuLFStWcO7cOVxcXBg5cmSRIUNCVJVFixYVaQsLCyM/P79Ie3R0NNHR0SUe68KFC5avR40axahRo+56bkVRuHr1Ks8++2ypcYaFhaEoSqnbvfnmm7z55puF2ry8vEhOTi51XyGqU3p6OsuXLychIQF3d3dGjx5NWFiY2mEJIYSoQqmpqcTGxnLt2jW8vLwYM2YMISEhaoclhKjBpHhkI86ePcuKFSvIzs6mXr16jBkzBm9vb7XDEqLKJScns3TpUhITExk/frza4QhRrU6cOMGqVavIy8ujYcOGjB49WnrHCfH/27vv8KjKtI/jv5lJZpKQBoQUIEiTIlU6CCIaCUXKCoLiUlxFUXDVrK4iKKKruBZWV1l51UVw1UUFQaSKQVSkSXMjBBABqQmEkkb6nPcPNrPEJJA2M5nM93Ndc5k55zlz7ock95h7ngLUcAkJCVq+fLlyc3PVsmVLDR06tEo3PgGAiqB45CF+/vlnZWZm6vrrr1ffvn1lNjPjEN4hPDxcYWFhevvtt1W7dm13hwO41L59+5STk6ObbrpJ1113nUwmk7tDAgA4kWEYSkxMVEFBgQYOHKiuXbuS+wFUCxSPqrGsrCzHrlIxMTFq06aNoqOj3RwV4FplmYIG1CSX5v6BAweqS5cuatCggZujAgA4U2HuN5lMGjJkiFJTUxUZGenusADAgeEr1dSePXv0+uuva/fu3ZIkHx8fCkcAUMP9+OOPeu211xybIVitVgpHAFCDGYahH374QX/729909OhRSZK/vz+FIwDVDiOPqpm8vDytWbNG27dvl9ls1oULF9wdEgDAyXJycrRy5Ur95z//kY+PjzIzM90dEgDAybKysvTFF18oMTFRvr6+ysjIcHdIAFAqikfVyOnTp7Vo0SKdOnVKtWvX1ogRI/jEGQBquJMnT2rRokU6e/aswsLCNHLkSEVERLg7LACAEx09elSLFy92TE8bOXKk6tat6+6wAKBUFI+qif379+vTTz9Vfn6+2rZtq1tuuUU2m83dYQEAnGj37t1asmSJCgoKdO2112rAgAGyWq3uDgsA4EQ7duzQ8uXLZRiGunfvrpiYGPn48GcZgOqNLFVNREZGqlatWurbt686duzIrgoA4AWioqIUEBCg/v37q23btu4OBwDgAg0aNFBgYKAGDx6sli1bujscACgTikdudPz4cVmtVtWrV0/BwcGaMmUKnzoAQA3366+/Kjg4WLVr11adOnX0xz/+kdwPADXcL7/8ovDwcAUFBSkiIoLcD8DjsNuaGxiGoY0bN2revHlatGiRCgoKJIk3EFR7N9xwgx5++GG33X/ChAkaPnx4tYkHKA+73a7169drwYIFWrx4sQzDkETuB4CarKCgQGvXrtUHH3ygpUuXkvsBeCyylotlZmZq6dKlOnDggGw2m/r27SuLxeLusACP9Nlnn8nX19fdYQBXlJaWps8++0y//vqrAgIC1LdvX6YnA0ANd+7cOS1evFjHjx9XUFCQ+vTpQ+4H4LEoHrnQoUOH9NlnnykjI0MNGzbUiBEjFBoa6u6wAI9Vp04dd4cAXNH+/fu1dOlSZWVlqUmTJvrd736noKAgd4cFAHCi3bt364svvlBOTo6uvvpqDR8+XAEBAe4OCwAqjGlrLpKamqoPPvhAGRkZ6t27tyZMmEDhCB4pPz9fU6ZMUUhIiMLCwvTUU085hmD/61//UpcuXRQUFKTIyEiNGTNGp06dclx77tw53XnnnapXr578/f119dVX67333nOcP3r0qEaNGqXQ0FDVqVNHw4YN0+HDh0uN5bfT1lq3bq0XXnhBf/jDHxQUFKRGjRrp7bffLnJNee8BVEZKSor+/e9/Kzs7W/369dPvf/97CkcAUMMdP35cixYtUl5enmJjY3XHHXdQOALg8SgeuUhISIhuvvlm/f73v9dNN93EVDV4rAULFsjHx0dbt27V66+/rtmzZ+vdd9+VJOXl5em5557Tjz/+qKVLl+rw4cOaMGGC49qnnnpKe/bs0apVq5SYmKi33npLYWFhjmtjY2MVFBSk7777Tt9//70CAwM1YMAA5ebmljm+V199VV26dNHOnTv1wAMP6P7779e+ffuq9B5AWYWFhemmm27ShAkTdP3118ts5m0XAGq6Bg0aqG/fvrr77rvVo0cPl0xVy/juOxWkpDj9PgC8F9PWnGjv3r36+eefdcstt8hkMqlHjx7uDgnV3Kuvvlri8ebNm2vYsGGSpB07dujrr78usd2oUaMUHR0tSfrnP/+p8+fPS7q4SHvh/7iEhYVp/PjxFY4xOjpaf/vb32QymdSyZUslJCTob3/7myZOnKg//OEPjnZNmzbV3//+d3Xt2lUZGRkKDAzUkSNHdO2116pLly6SpMaNGzvaf/zxx7Lb7Xr33Xcdsb733nsKDQ3V+vXr1b9//zLFN2jQID3wwAOSpMcff1x/+9vf9PXXX6tly5ZVdg/gchISEnTixAnFxsZKknr37u3miAAAzmQYhnbs2KHU1FTdeOONki6Ojq7w6xUUKHvvXl3YslUF586V6Zoz77wjSbIwpR+Ak1A8coL8/Hx9+eWX+uGHH2Q2m9WtWzdFRES4OyygSvz2E7SePXvq1VdfVUFBgXbt2qVnnnlGP/74o86dOye73S5JOnLkiK655hrdf//9GjFihHbs2KH+/ftr+PDh6tWrlyTpxx9/1IEDB4pN6cnOztYvv/xS5vjat2/v+NpkMikyMtIxda6q7gGUJDc3V6tWrdKuXbtksVjUrVs31a5d291hAQCcKDs7W8uXL9fu3bvl6+urbt26KTAwsFyvYRiGcg8dUubmzbqwabMyt26VPTW1QvE0+ue7FboOAK6E4lEVS0lJ0aJFi5ScnKzQ0FCNGDGCwhHK7E9/+tMV23Tq1EmdOnW6Yru7777b8fWFCxecPtc+OztbsbGxio2N1Ycffqh69erpyJEjio2NdUwJGzhwoH799VetXLlSa9eu1U033aTJkyfrlVdeUUZGhjp37qwPP/yw2GvXq1evzHH8dvc1k8nkKGJV1T2A30pKStKiRYt05swZ1a1bVyNHjqRwBAA13LFjx7R48WKdP39eERERGjlyZJkLR3knTypz02Zlbt6kC5u3KP+SNSItISEK6t9fAT26yxodLals0978WreSz3+XAwCAqkbxqIoYhqEff/xRK1euVF5enq655hoNGTJEfn5+7g4NqFJbtmwp8nzz5s26+uqrtXfvXp05c0YvvviiY+rctm3bil1fr149jR8/XuPHj1efPn302GOP6ZVXXlGnTp308ccfKzw8XMHBwU6J3RX3gHcxDEPbtm3TmjVrVFBQoI4dO2rgwIGyWq3uDg0A4CSGYWjjxo1at26d7Ha7unbtqv79+8vHp/Q/rfLPndOFLVscBaO8X484zpn8/VWrTx/V6tFDtXr2kK1VK5lYIw9ANUPxqArt3btXhmHolltuUadOnVyyOB7gakeOHFFcXJzuu+8+7dixQ2+88YZeffVVNWrUSFarVW+88YYmTZqkn376Sc8991yRa59++ml17txZbdq0UU5OjpYvX67WrVtLku688069/PLLGjZsmJ599lk1bNhQv/76qz777DP9+c9/VsOGDSsduyvuAe9iGIb27Nkji8WioUOHFpk2CQComex2u/bs2SOr1aqhQ4c6/l/mUgUZmbqw7Qdd2LxFmZs3K2fv3v+d9PVVQJcuCvhvsci/XTuZ+NABQDVH8aiSCqcDmUwmDR06VBkZGQoPD3d3WIDTjBs3TllZWerWrZssFoseeugh3XvvvTKZTJo/f76efPJJ/f3vf1enTp30yiuvaOjQoY5rrVarpk6dqsOHD8vf3199+vTRwoULJUkBAQH69ttv9fjjj+vWW29Venq6GjRooJtuuqnKRgm54h7wDoW532w269Zbb1Vubq7q1q3r7rAAAE5UmPstFotuu+02SVJoaKgkyZ6bq6yduxzT0LISEqT8/IsXmkzya9NGtXr2UECPngrodK3MTl5OAACqGsWjCjIMQ1u2bNG6des0btw4NWzYUAEBAU5fVwZwp/Xr1zu+fuutt4qdv+OOO3THHXcUOWYYhuPr6dOna/r06aW+fmRkpBYsWFDq+fnz55cajyQlJiYW+x3ctWtXue4BXI7dbteGDRv0/fff6w9/+IMiIiKKLcAOAKhZCgoKtH79ev3www+69957VadOHYWGhsooKNDZf32gjK/X6cL2HTJychzXWJs1U60ePRTQo7tqdesmS0iIG3sAAJVXLSbTzpkzR40bN5afn5+6d++urVu3Xrb9p59+qlatWsnPz0/t2rXTypUrXRTpRRcuXNDChQu1Zs0amc1mZWZmuvT+AODpPC3vS1J6ero++OADff3117JYLOR+ACgnT8z958+f14IFC7Rhwwb5+voqMzNTRl6esnbt0onHHlPy888rc+MmWerWUcitt6r+yy+p+TffqNmK5Yp8arqCb76ZwhGAGsHtxaOPP/5YcXFxmjFjhnbs2KEOHTooNjbWsbX2b23cuFF33HGH7r77bu3cuVPDhw/X8OHD9dNPP7kk3qygMM2dO1f79+9X/fr1dd9996lly5YuuTcA1ASelvcl6XS9SM2dO1eHDh3SVVddpUmTJqlp06Yuuz8AeDpPzP31shro//7v/3T06FE1rlNXt/n7y5j5rPZ176HDt9+htJWrZG3eTE1XrlTz+HjVf+F5hQwZIt8IlrAAUPOYjEvnlLhB9+7d1bVrV7355puSLk4JiI6O1oMPPqgnnniiWPvRo0crMzNTy5cvdxzr0aOHOnbsqLlz5xZrn5OTo5xLhpCmpaUpOjpaqamp5VrjJO/4ca2+7z7t6NJZMpnVy3ePbvT9URaTvTzd9Rh5+fnyvcyOEco4JQWGS4/ud11QTpCQkKB27dq59J7Z2dk6dOiQmjRp4rLd+Arn6Nd03t7Py/1spaWlKSQkpNy5zxmcnfelqsv9kvTvUb/T/jYdZTKZdP311+v666+XuYbuguOOnOgO9LNmoZ+lI/dXLPfnHjuuv/7tEdmD28hkt6vdjz+q5d59KtwKxyc8XAHduimga1cFDxooi4dPX+Z3qGahnzVLdcr9bl3zKDc3V9u3b9fUqVMdx8xms2JiYrRp06YSr9m0aZPi4uKKHIuNjdXSpUtLbD9r1izNnDmz2PHdu3crMDCw7MGePq26Z87INzdLtwZuVDPzCdkLpJpZOpIMu115hYv8lcSvjtLCr9OJhATXBeUEaWlpSnBxHwzDkGEYysrKkt3ump8gu92uCxcuuORe7uTt/czJyVFubq72799fbLfHjIwMV4V3Wa7I+1IV5n5JtgspsmVlqlv/Aapbt652795drus9iTtyojvQz5qFfpaO3H9RuXP/nj26bkeKdnTOUI8fflCdkBAZAwfI3rSpjJatlB8ZoWyTSWcl6fDhsr9uNcXvUM1CP2uW6pT73Vo8SklJUUFBgSIiIoocj4iI0N5Lt7O8RFJSUontk5KSSmw/derUIm88hZ9AtGnTplxVOHtWljJmzlSt86lqNeqlMl/nqcpS4az734cnc+fIIz8/P/n7+7vknt4+IqemKa2fJpNJVqu11JFH1YEr8r5Udblfktot/04//vijOnToUK7rPBGf4tUs9LNmqeinz9WBp+V+e9OmSrPaVNfHovYvzpLpcqPxawB+h2oW+lmzVKfcX7MzoSSbzSabzVbp1zH7+yu4f3+ZvaC6Cefy9fWVdLEA4KriEbxDbm6uJMlisbg5EverqtxfqKZOUwOAmqTK/r+/Vi2F/m64jiYk1PjCEQCUlVuzYVhYmCwWi5KTk4scT05OVmRkZInXREZGlqs9UN1YLBaFhoY6FogMCAgoNsWoquXk5HjFH7/e3E+73a7Tp08rICBAPtX4f3TJ+wDgfcj9AOD53PoXhtVqVefOnRUfH6/hw4dLuvgHUHx8vKZMmVLiNT179lR8fLwefvhhx7G1a9eqZ8+eLogYqBqF/+NT2g4jVS03N1dWq9Ul93Inb++n2WxWo0aNnF6MrAzyPgB4H3I/AHg+t388HRcXp/Hjx6tLly7q1q2bXnvtNWVmZuquu+6SJI0bN04NGjTQrFmzJEkPPfSQ+vbtq1dffVWDBw/WwoULtW3bNr399tvu7AZQLiaTSVFRUQoPD1deXp7T77d//341adLE6fdxN2/vp9Vq9YiRV+R9APA+5H4A8GxuLx6NHj1ap0+f1tNPP62kpCR17NhRq1evdiyQd+TIkSJ/DPXq1UsfffSRpk+frieffFJXX321li5dqrZt27qrC0CFWSwWl6xPYzKZii2gXBPRT89A3gcA70PuBwDP5vbikSRNmTKl1CGr69evL3bstttu02233ebkqAAAzkLeBwDvQ+4HAM9V/ec3AAAAAAAAwG0oHgEAAAAAAKBU1WLamisZhiFJSktLq9D1GRkZFb7Wk9DPmoV+1iwV6Wdh+8Ic6G3I/WVDP2sW+lmzkPvLj9xfNvSzZqGfNUt1yv1eVzxKT0+XJEVHR7s5EgBwvfT0dIWEhLg7DJcj9wPwZuR+cj8A71PVud9keNlHEXa7XSdOnFBQUJBMJlO5rk1LS1N0dLSOHj2q4OBgJ0XofvSzZqGfNUtF+2kYhtLT01W/fv0iu9l4C3L/ldHPmoV+1izk/ooh918Z/axZ6GfNUt1yv9eNPDKbzWrYsGGlXiM4OLhG/5AWop81C/2sWSrST2/81LkQub/s6GfNQj9rFnJ/+ZD7y45+1iz0s2apLrnf+z6CAAAAAAAAQJlRPAIAAAAAAECpKB6Vg81m04wZM2Sz2dwdilPRz5qFftYs3tLP6sRb/s3pZ81CP2sWb+lndeIt/+b0s2ahnzVLdeun1y2YDQAAAAAAgLJj5BEAAAAAAABKRfEIAAAAAAAApaJ4BAAAAAAAgFJRPAIAAAAAAECpKB79xpw5c9S4cWP5+fmpe/fu2rp162Xbf/rpp2rVqpX8/PzUrl07rVy50kWRVk55+vnOO++oT58+ql27tmrXrq2YmJgr/rtUF+X9fhZauHChTCaThg8f7twAq0h5+3n+/HlNnjxZUVFRstlsatGihUf87Ja3n6+99ppatmwpf39/RUdH65FHHlF2draLoi2/b7/9VkOGDFH9+vVlMpm0dOnSK16zfv16derUSTabTc2bN9f8+fOdHmdNRO4vjtxf/ZH7S0buR1mR+4sj91d/5P6SkftdwIDDwoULDavVasybN8/YvXu3MXHiRCM0NNRITk4usf33339vWCwW46WXXjL27NljTJ8+3fD19TUSEhJcHHn5lLefY8aMMebMmWPs3LnTSExMNCZMmGCEhIQYx44dc3Hk5VPefhY6dOiQ0aBBA6NPnz7GsGHDXBNsJZS3nzk5OUaXLl2MQYMGGRs2bDAOHTpkrF+/3ti1a5eLIy+f8vbzww8/NGw2m/Hhhx8ahw4dMtasWWNERUUZjzzyiIsjL7uVK1ca06ZNMz777DNDkrFkyZLLtj948KAREBBgxMXFGXv27DHeeOMNw2KxGKtXr3ZNwDUEuZ/cbxjk/uqK3F8cub9qkPvJ/YZB7q+uyP3FVYfcT/HoEt26dTMmT57seF5QUGDUr1/fmDVrVontR40aZQwePLjIse7duxv33XefU+OsrPL287fy8/ONoKAgY8GCBc4KsUpUpJ/5+flGr169jHfffdcYP368R7yJlLefb731ltG0aVMjNzfXVSFWifL2c/LkycaNN95Y5FhcXJxx3XXXOTXOqlKWN5E///nPRps2bYocGz16tBEbG+vEyGoecj+5n9xffZH7iyP3Vw1yP7mf3F99kfuLqw65n2lr/5Wbm6vt27crJibGccxsNismJkabNm0q8ZpNmzYVaS9JsbGxpbavDirSz9+6cOGC8vLyVKdOHWeFWWkV7eezzz6r8PBw3X333a4Is9Iq0s9ly5apZ8+emjx5siIiItS2bVu98MILKigocFXY5VaRfvbq1Uvbt293DHE9ePCgVq5cqUGDBrkkZlfwxBxU3ZD7yf0Sub+6IveXzBNzUHVD7if3S+T+6orcX7LqkIN8XHanai4lJUUFBQWKiIgocjwiIkJ79+4t8ZqkpKQS2yclJTktzsqqSD9/6/HHH1f9+vWL/fBWJxXp54YNG/TPf/5Tu3btckGEVaMi/Tx48KDWrVunO++8UytXrtSBAwf0wAMPKC8vTzNmzHBF2OVWkX6OGTNGKSkp6t27twzDUH5+viZNmqQnn3zSFSG7RGk5KC0tTVlZWfL393dTZJ6D3E/uJ/eT+z0Nub/yyP3kfnI/ud/TVIfcz8gjlMuLL76ohQsXasmSJfLz83N3OFUmPT1dY8eO1TvvvKOwsDB3h+NUdrtd4eHhevvtt9W5c2eNHj1a06ZN09y5c90dWpVav369XnjhBf3jH//Qjh079Nlnn2nFihV67rnn3B0a4HHI/Z6P3E/uB8qL3O/5yP3k/qrEyKP/CgsLk8ViUXJycpHjycnJioyMLPGayMjIcrWvDirSz0KvvPKKXnzxRX311Vdq3769M8OstPL285dfftHhw4c1ZMgQxzG73S5J8vHx0b59+9SsWTPnBl0BFfl+RkVFydfXVxaLxXGsdevWSkpKUm5urqxWq1NjroiK9POpp57S2LFjdc8990iS2rVrp8zMTN17772aNm2azGbPr52XloOCg4P55LmMyP3kfnI/ud/TkPsrj9xP7if3k/s9TXXI/Z7/r1hFrFarOnfurPj4eMcxu92u+Ph49ezZs8RrevbsWaS9JK1du7bU9tVBRfopSS+99JK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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot ROCs\n", "fig, axes = plt.subplots(len(x_values) // 3 + 1, 3, figsize=(14,7))\n", From 5927431f59a0d15723e251b7a52b1defc22cff02 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 18 Nov 2025 14:03:04 +0100 Subject: [PATCH 22/31] Fix `create_clean_dir` function --- README.md | 7 ++ compose.yaml | 112 +++----------------------- experiments/cn_vs_noisybn/config.yaml | 14 ++-- generate_compose.py | 6 +- src/config.py | 10 ++- 5 files changed, 34 insertions(+), 115 deletions(-) diff --git a/README.md b/README.md index 9731ca1..db158dc 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,13 @@ docker compose up [service name] Results will be available under `experiments//output_*`. +To check the status, run one or more of the following: + +```bash +docker compose ps +docker compose logs [service name] +docker stats +``` ### Local computation diff --git a/compose.yaml b/compose.yaml index 200d4f8..2f2724e 100644 --- a/compose.yaml +++ b/compose.yaml @@ -1,80 +1,5 @@ version: '3.9' services: - cn_privacy_def_idm_atk_mle_ess1: - build: . - command: - - python - - -m - - experiments.cn_privacy.exp - - def_mec=def_idm - - ess=1 - - atk_mec=atk_mle - - n_bns=5 - image: bnp:2025 - volumes: - - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - - ./experiments/cn_privacy/output_def_idm_atk_mle_ess1:/workspace/experiments/cn_privacy/output - cn_privacy_def_idm_atk_mle_ess10: - build: . - command: - - python - - -m - - experiments.cn_privacy.exp - - def_mec=def_idm - - ess=10 - - atk_mec=atk_mle - - n_bns=5 - image: bnp:2025 - volumes: - - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - - ./experiments/cn_privacy/output_def_idm_atk_mle_ess10:/workspace/experiments/cn_privacy/output - cn_privacy_def_idm_atk_mle_ess2: - build: . - command: - - python - - -m - - experiments.cn_privacy.exp - - def_mec=def_idm - - ess=2 - - atk_mec=atk_mle - - n_bns=5 - image: bnp:2025 - volumes: - - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - - ./experiments/cn_privacy/output_def_idm_atk_mle_ess2:/workspace/experiments/cn_privacy/output - cn_privacy_def_ran_atk_mle_delta0.2: - build: . - command: - - python - - -m - - experiments.cn_privacy.exp - - def_mec=def_ran - - delta=0.2 - - atk_mec=atk_mle - - n_bns=5 - image: bnp:2025 - volumes: - - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.2:/workspace/experiments/cn_privacy/output - cn_privacy_def_ran_atk_mle_delta0.4: - build: . - command: - - python - - -m - - experiments.cn_privacy.exp - - def_mec=def_ran - - delta=0.4 - - atk_mec=atk_mle - - n_bns=5 - image: bnp:2025 - volumes: - - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns - - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data - - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.4:/workspace/experiments/cn_privacy/output cn_vs_noisybn_def_idm_atk_mle_ess1: build: . command: @@ -84,7 +9,7 @@ services: - def_mec=def_idm - ess=1 - atk_mec=atk_mle - - n_bns=5 + - n_bns=50 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns @@ -99,54 +24,39 @@ services: - def_mec=def_idm - ess=10 - atk_mec=atk_mle - - n_bns=5 + - n_bns=50 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess10:/workspace/experiments/cn_vs_noisybn/output - cn_vs_noisybn_def_idm_atk_mle_ess2: + cn_vs_noisybn_def_idm_atk_mle_ess30: build: . command: - python - -m - experiments.cn_vs_noisybn.exp - def_mec=def_idm - - ess=2 - - atk_mec=atk_mle - - n_bns=5 - image: bnp:2025 - volumes: - - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess2:/workspace/experiments/cn_vs_noisybn/output - cn_vs_noisybn_def_ran_atk_mle_delta0.2: - build: . - command: - - python - - -m - - experiments.cn_vs_noisybn.exp - - def_mec=def_ran - - delta=0.2 + - ess=30 - atk_mec=atk_mle - - n_bns=5 + - n_bns=50 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_ran_atk_mle_delta0.2:/workspace/experiments/cn_vs_noisybn/output - cn_vs_noisybn_def_ran_atk_mle_delta0.4: + - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess30:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_idm_atk_mle_ess50: build: . command: - python - -m - experiments.cn_vs_noisybn.exp - - def_mec=def_ran - - delta=0.4 + - def_mec=def_idm + - ess=50 - atk_mec=atk_mle - - n_bns=5 + - n_bns=50 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_ran_atk_mle_delta0.4:/workspace/experiments/cn_vs_noisybn/output + - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess50:/workspace/experiments/cn_vs_noisybn/output diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index f19e026..80ac02b 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -17,20 +17,20 @@ n_modmax: 2 # Maximum number of categori n_models: 5 # Number of models to evaluate # Data -gpop_ss: 500 # Sample size of general population +gpop_ss: 1000 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop -samples: 5 # Number of data samples +samples: 10 # Number of data samples # MIA -error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector +error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector # Noisy BN -tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol -eps_vec: 'np.logspace(-8, 2, num=100)' # Epsilon to consider for noisy BN +tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=300)' # Epsilon to consider for noisy BN # Inferences -n_infer: 5 # Number of inferences to perform +n_infer: 100 # Number of inferences to perform # Other num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization @@ -42,4 +42,4 @@ num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use f # - 20: 'np.arange(0.05, 5, 0.05)' # - 30: 'np.arange(1e-3, 1, 1e-3)' # - 40: 'np.arange(5e-6, 1e-2, 5e-6)' -# - 50: 'np.arange(5e-7, 5e-4, 5e-7)' \ No newline at end of file +# - 50: 'np.arange(5e-7, 5e-4, 5e-7)' diff --git a/generate_compose.py b/generate_compose.py index cb3dc60..f1c5dfc 100644 --- a/generate_compose.py +++ b/generate_compose.py @@ -2,9 +2,9 @@ from itertools import product # Set hyperparameters -names = ["cn_privacy", "cn_vs_noisybn"] -def_mecs = {"def_idm":{"ess":[1, 2, 10]}, "def_ran":{"delta":[0.2, 0.4]}} -atk_mecs = {"atk_mle":{"n_bns":[5]}} +names = ["cn_vs_noisybn"] +def_mecs = {"def_idm":{"ess":[1, 10, 30, 50]}} +atk_mecs = {"atk_mle":{"n_bns":[50]}} # Initialize the `compose.yaml` file init = {"version": "3.9"} diff --git a/src/config.py b/src/config.py index c485995..382e5c3 100644 --- a/src/config.py +++ b/src/config.py @@ -72,12 +72,14 @@ def set_seed(): # Create an empty directory def create_clean_dir(path: Path): - # Remove the folder if already exists + # If directory exists, clean it if path.exists() and path.is_dir(): - shutil.rmtree(path) + for item in path.iterdir(): + shutil.rmtree(item) if item.is_dir() else item.unlink() - # Create a new folder - path.mkdir(parents=True, exist_ok=True) + # Else, create a new one + else: + path.mkdir(parents=True, exist_ok=True) # Get output path From 30674c0126a155d5025e0fb3240b6f36ee44a4c8 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 18 Nov 2025 16:09:51 +0100 Subject: [PATCH 23/31] Minor fixes --- experiments/cn_privacy/exp.py | 10 +- experiments/cn_privacy/generate.py | 4 +- experiments/cn_vs_noisybn/Plot_results.ipynb | 137 +++++++------------ experiments/cn_vs_noisybn/config.yaml | 12 +- experiments/cn_vs_noisybn/exp.py | 10 +- experiments/cn_vs_noisybn/generate.py | 4 +- 6 files changed, 69 insertions(+), 108 deletions(-) diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py index 6c12744..0a0349a 100644 --- a/experiments/cn_privacy/exp.py +++ b/experiments/cn_privacy/exp.py @@ -26,31 +26,31 @@ def main(): exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Defense mechanism - print("#" * 5, "Defense mechanism", "#" * 5) + print("## Defense mechanism: [", def_mec, def_args, "] ##", flush=True) create_clean_dir(cur_dir / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec ) # Attack mechanism - print("#" * 5, "Attack mechanism", "#" * 5) + print("## Attack mechanism: [", atk_mec, atk_args, "] ##", flush=True) create_clean_dir(cur_dir / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec ) # MIA vs CN - print("#" * 5, "MIA vs CN", "#" * 5) + print("## MIA vs CN ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "cns") _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_cn)(exp, config) for exp in exp_vec) # MIA vs BN - print("#" * 5, "MIA vs BN", "#" * 5) + print("## MIA vs BN ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "bns") _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_bn)(exp, config) for exp in exp_vec) # Compute theoretical power - print("#" * 5, "Compute theoretical power", "#" * 5) + print("## Compute theoretical power ##", flush=True) _ = Parallel(n_jobs=num_cores)( delayed(theoretical_power)(exp, config) for exp in exp_vec ) diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py index aa4d368..bee8241 100644 --- a/experiments/cn_privacy/generate.py +++ b/experiments/cn_privacy/generate.py @@ -17,7 +17,7 @@ def main(): num_cores = eval(config["num_cores"]) # Generate BNs and data - print("#" * 5, "Generate BNs and data", "#" * 5) + print("## Generate BNs and data ##") create_clean_dir(cur_dir / config["bns_path"]) create_clean_dir(cur_dir / config["bns_path"] / "gt") create_clean_dir(cur_dir / config["data_path"]) @@ -28,7 +28,7 @@ def main(): exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Estimate BNs from rpop and pool - print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) + print("## Estimate BNs from rpop and pool ##") create_clean_dir(cur_dir / config["bns_path"] / "rpop") create_clean_dir(cur_dir / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 5d972a9..b6e856c 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -5,12 +5,12 @@ "id": "80ba8653", "metadata": {}, "source": [ - "### TO BE FIXED" + "Ensure the output directories are names as `output_<...>_`, where `def_arg` can be `ess` or `delta`, and `def_value` its value." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "7b61e02b", "metadata": {}, "outputs": [], @@ -43,7 +43,7 @@ "\n", "# Get results path\n", "cur_dir = get_cur_dir(config)\n", - "res_path = cur_dir / config[\"results_path\"] / \"inferences\"\n", + "# res_path = cur_dir / config[\"results_path\"] / \"inferences\"\n", "\n", "# Choose where to save plots\n", "plots_path = cur_dir / \"plots\"\n", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "49a48f2f", "metadata": {}, "outputs": [], @@ -76,19 +76,35 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "84251635", + "execution_count": null, + "id": "e07e1ceb", "metadata": {}, "outputs": [], "source": [ "# Names of experiments\n", "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", - "# Build a data set for each result folder\n", + "# Names of output directories\n", + "out_dirs = [item for item in os.listdir(f\"{cur_dir}\") if Path(item).is_dir() and \"output\" in item]\n", + "\n", + "# Get ESS/delta list\n", + "type = re.findall(\"output_(\\w+)\\d+\", out_dirs[0])[0]\n", + "x_values = sorted([re.findall(\"output_\\w+(\\d+)\", i)[0] for i in out_dirs])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84251635", + "metadata": {}, + "outputs": [], + "source": [ + "# Build an inferences data set for each output folder\n", "res = dict()\n", - "for dir_name in os.listdir(res_path):\n", - " files = [os.path.join(res_path, dir_name, f) for f in os.listdir(f\"{res_path}/{dir_name}\")]\n", - " data = pd.concat((pd.read_csv(f) for f in files if \"auc_meta\" not in f), axis=0)\n", + "for out_dir in out_dirs:\n", + " inferences_path = os.path.join(cur_dir, out_dir, \"results/inferences\")\n", + " files = [os.path.join(inferences_path, f) for f in os.listdir(inferences_path)]\n", + " data = pd.concat((pd.read_csv(f) for f in files), axis=0)\n", " data[\"cn_probs_1\"] = data.apply(\n", " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", " axis=1,\n", @@ -101,52 +117,30 @@ " ),\n", " axis=1,\n", ")\n", - " res[dir_name] = data\n", + " x = re.findall(\"output_\\w+(\\d+)\", out_dir)[0]\n", + " res[f\"{type}{x}\"] = data\n", "\n", "# Retrieve AUCs for each result folder\n", "aucs = dict()\n", - "for dir_name in os.listdir(res_path):\n", - " data = pd.read_csv(os.path.join(res_path, dir_name, \"auc_meta.csv\"))\n", - " aucs[dir_name] = data" + "for out_dir in out_dirs:\n", + " auc_path = os.path.join(cur_dir, out_dir, \"results/auc_meta.csv\")\n", + " data = pd.read_csv(auc_path)\n", + " x = re.findall(\"output_\\w+(\\d+)\", out_dir)[0]\n", + " aucs[f\"{type}{x}\"] = data" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "2ce6f5be", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Get ESS/delta list\n", - "type = re.findall(\"(\\w+)_\", os.listdir(res_path)[0])[0]\n", - "x_values = sorted([re.findall(\"_(\\d+)\", i)[0] for i in os.listdir(res_path)])\n", - "\n", "# Retrieve related epsilon\n", "eps_median = []\n", "eps_up, eps_lp = [], []\n", "for x in x_values:\n", - " data = aucs[f\"{type}_{x}\"]\n", + " data = aucs[f\"{type}{x}\"]\n", " data_eps = [i for i in data[\"epsilon\"].values if i is not None]\n", " eps_median.append(np.median(data_eps))\n", " eps_up.append(np.percentile(data_eps, 75))\n", @@ -155,9 +149,9 @@ "# Plot: ess vs eps\n", "fig, ax = plt.subplots(1, 1)\n", "\n", - "ax.semilogy(x_values, eps_median, \"-o\", label=\"Mean\", markersize=4)\n", - "ax.semilogy(x_values, eps_up, \"-o\", label=\"Up 75\", markersize=4)\n", - "ax.semilogy(x_values, eps_lp, \"-o\", label=\"Lp 75\", markersize=4)\n", + "ax.semilogy(x_values, eps_median, \"-o\", label=\"Median\", markersize=4)\n", + "ax.semilogy(x_values, eps_up, \"-o\", label=\"Quantile 0.75\", markersize=4)\n", + "ax.semilogy(x_values, eps_lp, \"-o\", label=\"Quantile 0.25\", markersize=4)\n", "ax.set_xlabel(type)\n", "ax.set_ylabel(\"$\\epsilon$\")\n", "ax.set_title(f\"{type} vs $\\epsilon$\")\n", @@ -169,26 +163,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "a29cb66d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get CN certainty\n", "cn_certainty = []\n", "for x in x_values:\n", - " data = res[f\"{type}_{x}\"]\n", + " data = res[f\"{type}{x}\"]\n", " cn_certainty.append(sum(data[\"cn_probs\"] > 0.5) / len(data))\n", "\n", "# Plot: ess vs CN certainty\n", @@ -203,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "1f7ebeae", "metadata": {}, "outputs": [], @@ -222,7 +205,7 @@ "}\n", "\n", "for x in x_values:\n", - " data = res[f\"{type}_{x}\"]\n", + " data = res[f\"{type}{x}\"]\n", " \n", " # Split CN results based on probabilities\n", " cn_cert, cn_uncert = split_data(data, \"cn_probs\", 0.5)\n", @@ -254,21 +237,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "c158f122", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot accuracies\n", "fig, ax = plt.subplots(1, 1)\n", @@ -292,21 +264,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "089a1d9a", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot ROCs\n", "fig, axes = plt.subplots(len(x_values) // 3 + 1, 3, figsize=(14,7))\n", diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 80ac02b..7d362f3 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -14,23 +14,23 @@ auc_meta: output/results/auc_meta.csv # File of metadata for AUCs target_var: 'T' # Target variable n_nodes: 10 # Number of nodes for each BN model n_modmax: 2 # Maximum number of categories for covariates -n_models: 5 # Number of models to evaluate +n_models: 2 # Number of models to evaluate # Data -gpop_ss: 1000 # Sample size of general population +gpop_ss: 500 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop samples: 10 # Number of data samples # MIA -error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector +error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector # Noisy BN -tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol -eps_vec: 'np.logspace(-8, 2, num=300)' # Epsilon to consider for noisy BN +tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=50)' # Epsilon to consider for noisy BN # Inferences -n_infer: 100 # Number of inferences to perform +n_infer: 10 # Number of inferences to perform # Other num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 3fec559..366ab19 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -28,21 +28,21 @@ def main(): exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Defense mechanism - print("#" * 5, "Defense mechanism", "#" * 5) + print("## Defense mechanism: [", def_mec, def_args, "] ##", flush=True) create_clean_dir(cur_dir / config["cns_path"]) _ = Parallel(n_jobs=num_cores)( delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec ) # Attack mechanism - print("#" * 5, "Attack mechanism", "#" * 5) + print("## Attack mechanism: [", atk_mec, atk_args, "] ##", flush=True) create_clean_dir(cur_dir / config["atk_path"]) _ = Parallel(n_jobs=num_cores)( delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec ) # MIA vs CN - print("#" * 5, "MIA vs CN", "#" * 5) + print("## MIA vs CN ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "cns") res = Parallel(n_jobs=num_cores)( delayed(mia_vs_cn)(exp, config) for exp in exp_vec @@ -51,7 +51,7 @@ def main(): auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Find eps s.t. |AUC(eps) - AUC(CN)| < tol - print("#" * 5, "Get epsilon", "#" * 5) + print("## Find epsilon ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "bn_noisy") res = Parallel(n_jobs=num_cores)( delayed(find_epsilon)(exp, config) for exp in exp_vec @@ -60,7 +60,7 @@ def main(): auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) # Run inferences - print("#" * 5, "Run inferences", "#" * 5) + print("## Inferences ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "inferences") _ = Parallel(n_jobs=num_cores)(delayed(inferences)(exp, config, def_mec, def_args) for exp in exp_vec) diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py index 1103116..557f062 100644 --- a/experiments/cn_vs_noisybn/generate.py +++ b/experiments/cn_vs_noisybn/generate.py @@ -17,7 +17,7 @@ def main(): num_cores = eval(config["num_cores"]) # Generate BNs and data - print("#" * 5, "Generate BNs and data", "#" * 5) + print("## Generate BNs and data ##") create_clean_dir(cur_dir / config["bns_path"]) create_clean_dir(cur_dir / config["bns_path"] / "gt") create_clean_dir(cur_dir / config["data_path"]) @@ -28,7 +28,7 @@ def main(): exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()] # Estimate BNs from rpop and pool - print("#" * 5, "Estimate BNs from rpop and pool", "#" * 5) + print("## Estimate BNs from rpop and pool ##") create_clean_dir(cur_dir / config["bns_path"] / "rpop") create_clean_dir(cur_dir / config["bns_path"] / "pool") _ = Parallel(n_jobs=num_cores)( From 356ac9787e519c99c555dc4ec15faee6689c2208 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 18 Nov 2025 19:04:57 +0100 Subject: [PATCH 24/31] Add `atk_cen`, taking the centroid of a CN --- Dockerfile | 2 + README.md | 5 +- experiments/cn_privacy/Plot_results.ipynb | 58 ++++++++++++- experiments/cn_vs_noisybn/Plot_results.ipynb | 84 ++++++++++--------- experiments/cn_vs_noisybn/exp.py | 8 +- generate_compose.py | 18 ++-- requirements.txt | 1 + src/attack.py | 10 ++- src/config.py | 2 + src/inference.py | 8 +- src/mia.py | 31 ++++--- src/utils.py | 86 +++++++++++++++++++- test/cn_privacy/test_integration.py | 21 ++++- test/cn_vs_noisybn/test_integration.py | 19 +++++ 14 files changed, 282 insertions(+), 71 deletions(-) diff --git a/Dockerfile b/Dockerfile index 6d65d46..160dec4 100644 --- a/Dockerfile +++ b/Dockerfile @@ -6,6 +6,8 @@ RUN apt-get update && apt-get install -y \ swig \ libglpk-dev \ python3-dev \ + libcdd-dev \ + libgmp-dev \ && rm -rf /var/lib/apt/lists/* # Set working directory diff --git a/README.md b/README.md index db158dc..3d0bfdb 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,7 @@ Implemented defenses: Implemented attacks: - `atk_mle`. Requires: `n_bns` +- `atk_cen` ## Running code @@ -71,6 +72,8 @@ Install dependencies: pip install -r requirements.txt ``` +*Notice*: if some package is missing locally, see the `Dockerfile` for additional packages to be installed (names refer to Ubuntu/Debian). + Upgrade dependencies: ```bash @@ -116,7 +119,7 @@ Lint code by running: ```bash flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude=venv -flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude=venv +flake8 . --count --exit-zero --max-complexity=10 --ignore=E203 --max-line-length=140 --statistics --exclude=venv ``` Analyze code by running: diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index f39732c..9c8b817 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -22,6 +22,62 @@ "from src.config import * # noqa" ] }, + { + "cell_type": "code", + "execution_count": 27, + "id": "57313fdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5, 6])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate(([1, 2, 3], [4, 5, 6]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f286be9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., -1., -0., -0.],\n", + " [ 2., -0., -1., -0.],\n", + " [ 3., -0., -0., -1.],\n", + " [ 7., 1., 0., 0.],\n", + " [ 8., 0., 1., 0.],\n", + " [ 9., 0., 0., 1.],\n", + " [-1., 1., 1., 1.]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_par = 3\n", + "A = np.concatenate(\n", + " (-np.eye(n_par), np.eye(n_par), np.atleast_2d(np.ones(n_par))), axis=0\n", + ")\n", + "b = np.array([1, 2, 3, 7, 8, 9, -1]).reshape(7, 1)\n", + "\n", + "bA = np.concatenate((b, A), axis=1)\n", + "\n", + "bA" + ] + }, { "cell_type": "code", "execution_count": 7, diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index b6e856c..8a20282 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -82,14 +82,19 @@ "outputs": [], "source": [ "# Names of experiments\n", + "pattern = re.compile(\"output_.*_(ess|delta)(\\d+)\")\n", "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", "# Names of output directories\n", - "out_dirs = [item for item in os.listdir(f\"{cur_dir}\") if Path(item).is_dir() and \"output\" in item]\n", + "out_dirs = [\n", + " item\n", + " for item in os.listdir(f\"{cur_dir}\")\n", + " if Path(item).is_dir() and pattern.match(item)\n", + "]\n", "\n", "# Get ESS/delta list\n", - "type = re.findall(\"output_(\\w+)\\d+\", out_dirs[0])[0]\n", - "x_values = sorted([re.findall(\"output_\\w+(\\d+)\", i)[0] for i in out_dirs])" + "def_arg = pattern.findall(out_dirs[0])[0][0]\n", + "x_values = sorted([pattern.findall(i)[0][1] for i in out_dirs])" ] }, { @@ -106,19 +111,19 @@ " files = [os.path.join(inferences_path, f) for f in os.listdir(inferences_path)]\n", " data = pd.concat((pd.read_csv(f) for f in files), axis=0)\n", " data[\"cn_probs_1\"] = data.apply(\n", - " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", - " axis=1,\n", - ")\n", + " lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n", + " axis=1,\n", + " )\n", " data[\"bn_noisy_probs_1\"] = data.apply(\n", - " lambda row: (\n", - " row[\"bn_noisy_probs\"]\n", - " if row[\"bn_noisy_mpes\"] == 1\n", - " else (1 - row[\"bn_noisy_probs\"])\n", - " ),\n", - " axis=1,\n", - ")\n", + " lambda row: (\n", + " row[\"bn_noisy_probs\"]\n", + " if row[\"bn_noisy_mpes\"] == 1\n", + " else (1 - row[\"bn_noisy_probs\"])\n", + " ),\n", + " axis=1,\n", + " )\n", " x = re.findall(\"output_\\w+(\\d+)\", out_dir)[0]\n", - " res[f\"{type}{x}\"] = data\n", + " res[f\"{def_arg}{x}\"] = data\n", "\n", "# Retrieve AUCs for each result folder\n", "aucs = dict()\n", @@ -126,7 +131,7 @@ " auc_path = os.path.join(cur_dir, out_dir, \"results/auc_meta.csv\")\n", " data = pd.read_csv(auc_path)\n", " x = re.findall(\"output_\\w+(\\d+)\", out_dir)[0]\n", - " aucs[f\"{type}{x}\"] = data" + " aucs[f\"{def_arg}{x}\"] = data" ] }, { @@ -140,7 +145,7 @@ "eps_median = []\n", "eps_up, eps_lp = [], []\n", "for x in x_values:\n", - " data = aucs[f\"{type}{x}\"]\n", + " data = aucs[f\"{def_arg}{x}\"]\n", " data_eps = [i for i in data[\"epsilon\"].values if i is not None]\n", " eps_median.append(np.median(data_eps))\n", " eps_up.append(np.percentile(data_eps, 75))\n", @@ -152,9 +157,9 @@ "ax.semilogy(x_values, eps_median, \"-o\", label=\"Median\", markersize=4)\n", "ax.semilogy(x_values, eps_up, \"-o\", label=\"Quantile 0.75\", markersize=4)\n", "ax.semilogy(x_values, eps_lp, \"-o\", label=\"Quantile 0.25\", markersize=4)\n", - "ax.set_xlabel(type)\n", + "ax.set_xlabel(def_arg)\n", "ax.set_ylabel(\"$\\epsilon$\")\n", - "ax.set_title(f\"{type} vs $\\epsilon$\")\n", + "ax.set_title(f\"{def_arg} vs $\\epsilon$\")\n", "\n", "ax.set_ylim([1e-9, 2])\n", "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", @@ -171,14 +176,14 @@ "# Get CN certainty\n", "cn_certainty = []\n", "for x in x_values:\n", - " data = res[f\"{type}{x}\"]\n", + " data = res[f\"{def_arg}{x}\"]\n", " cn_certainty.append(sum(data[\"cn_probs\"] > 0.5) / len(data))\n", "\n", "# Plot: ess vs CN certainty\n", "fig, ax = plt.subplots(1, 1)\n", "\n", "ax.plot(x_values, cn_certainty, \"-o\", markersize=4)\n", - "ax.set_xlabel(type)\n", + "ax.set_xlabel(def_arg)\n", "ax.set_ylabel(\"Ratio of (maxmin CN prob. $> 0.5$)\")\n", "ax.set_title(\"CN certainty\")\n", "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)" @@ -192,10 +197,7 @@ "outputs": [], "source": [ "# Store accuracy results\n", - "acc = {\"acc_noisy_bn\": [],\n", - " \"acc_cn_tot\": [],\n", - " \"acc_cn_cert\": [],\n", - " \"acc_cn_uncert\": []}\n", + "acc = {\"acc_noisy_bn\": [], \"acc_cn_tot\": [], \"acc_cn_cert\": [], \"acc_cn_uncert\": []}\n", "\n", "roc = {\n", " \"roc_cn_cert\": dict(),\n", @@ -205,8 +207,8 @@ "}\n", "\n", "for x in x_values:\n", - " data = res[f\"{type}{x}\"]\n", - " \n", + " data = res[f\"{def_arg}{x}\"]\n", + "\n", " # Split CN results based on probabilities\n", " cn_cert, cn_uncert = split_data(data, \"cn_probs\", 0.5)\n", "\n", @@ -222,8 +224,16 @@ " acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n", "\n", " # Compute ROC\n", - " roc_cn_cert = roc_curve(cn_cert[f\"{vs}_mpes\"], cn_cert[\"cn_probs_1\"]) if len(cn_cert) > 0 else None\n", - " roc_cn_uncert = roc_curve(cn_uncert[f\"{vs}_mpes\"], cn_uncert[\"cn_probs_1\"]) if len(cn_uncert) > 0 else None\n", + " roc_cn_cert = (\n", + " roc_curve(cn_cert[f\"{vs}_mpes\"], cn_cert[\"cn_probs_1\"])\n", + " if len(cn_cert) > 0\n", + " else None\n", + " )\n", + " roc_cn_uncert = (\n", + " roc_curve(cn_uncert[f\"{vs}_mpes\"], cn_uncert[\"cn_probs_1\"])\n", + " if len(cn_uncert) > 0\n", + " else None\n", + " )\n", " roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n", " roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n", "\n", @@ -246,16 +256,16 @@ "fig, ax = plt.subplots(1, 1)\n", "\n", "labels = {\n", - "\"acc_noisy_bn\": \"Noisy BN\",\n", - "\"acc_cn_tot\": \"CN (total)\",\n", - "\"acc_cn_cert\": \"CN (certain)\",\n", - "\"acc_cn_uncert\": \"CN (uncertain)\",\n", + " \"acc_noisy_bn\": \"Noisy BN\",\n", + " \"acc_cn_tot\": \"CN (total)\",\n", + " \"acc_cn_cert\": \"CN (certain)\",\n", + " \"acc_cn_uncert\": \"CN (uncertain)\",\n", "}\n", "\n", "for key in acc.keys():\n", " ax.plot(x_values, acc[key], \"-o\", label=labels[key], markersize=4)\n", "\n", - "ax.set_xlabel(type)\n", + "ax.set_xlabel(def_arg)\n", "ax.set_ylabel(\"Accuracy\")\n", "ax.set_title(\"Accuracies (MAP estimation)\")\n", "ax.legend(loc=\"best\")\n", @@ -270,7 +280,7 @@ "outputs": [], "source": [ "# Plot ROCs\n", - "fig, axes = plt.subplots(len(x_values) // 3 + 1, 3, figsize=(14,7))\n", + "fig, axes = plt.subplots(len(x_values) // 3 + 1, 3, figsize=(14, 7))\n", "\n", "labels = {\n", " \"roc_cn_cert\": \"CN (certain)\",\n", @@ -298,19 +308,19 @@ " linewidth=1.3,\n", " label=\"baseline\",\n", " )\n", - " \n", + "\n", " ax.set_xlabel(\"FPR\")\n", " ax.set_ylabel(\"TPR\")\n", " ax.grid(\n", " True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1\n", " )\n", - " ax.set_title(f\"ROC ({type} = {x})\")\n", + " ax.set_title(f\"ROC ({def_arg} = {x})\")\n", " if i == 0:\n", " ax.legend(loc=\"best\")\n", "\n", " i += 1\n", "\n", - "plt.subplots_adjust(hspace=.3)" + "plt.subplots_adjust(hspace=0.3)" ] } ], diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py index 366ab19..5110a16 100644 --- a/experiments/cn_vs_noisybn/exp.py +++ b/experiments/cn_vs_noisybn/exp.py @@ -44,9 +44,7 @@ def main(): # MIA vs CN print("## MIA vs CN ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "cns") - res = Parallel(n_jobs=num_cores)( - delayed(mia_vs_cn)(exp, config) for exp in exp_vec - ) + res = Parallel(n_jobs=num_cores)(delayed(mia_vs_cn)(exp, config) for exp in exp_vec) auc_res = pd.concat((i for i in res), axis=0) auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False) @@ -62,7 +60,9 @@ def main(): # Run inferences print("## Inferences ##", flush=True) create_clean_dir(cur_dir / config["results_path"] / "inferences") - _ = Parallel(n_jobs=num_cores)(delayed(inferences)(exp, config, def_mec, def_args) for exp in exp_vec) + _ = Parallel(n_jobs=num_cores)( + delayed(inferences)(exp, config, def_mec, def_args) for exp in exp_vec + ) # Clean gc.collect() diff --git a/generate_compose.py b/generate_compose.py index f1c5dfc..6d397af 100644 --- a/generate_compose.py +++ b/generate_compose.py @@ -1,10 +1,11 @@ -import yaml from itertools import product +import yaml + # Set hyperparameters names = ["cn_vs_noisybn"] -def_mecs = {"def_idm":{"ess":[1, 10, 30, 50]}} -atk_mecs = {"atk_mle":{"n_bns":[50]}} +def_mecs = {"def_idm": {"ess": [1, 10, 30, 50]}} +atk_mecs = {"atk_mle": {"n_bns": [50]}} # Initialize the `compose.yaml` file init = {"version": "3.9"} @@ -19,7 +20,9 @@ atk_params = atk_mecs[atk_mec] # (assumption: each defense and attack mechanism has only 1 hyperparameter to be set) - for def_par, atk_par in product(list(def_params.values())[0], list(atk_params.values())[0]): + for def_par, atk_par in product( + list(def_params.values())[0], list(atk_params.values())[0] + ): # ... set the related volume, ... volumes = [ @@ -29,7 +32,9 @@ ] # ... and create the experiment - data["services"][f"{name}_{def_mec}_{atk_mec}_{list(def_params.keys())[0]}{def_par}"] = { + data["services"][ + f"{name}_{def_mec}_{atk_mec}_{list(def_params.keys())[0]}{def_par}" + ] = { "image": "bnp:2025", "build": ".", "volumes": volumes, @@ -40,7 +45,8 @@ f"def_mec={def_mec}", f"{list(def_params.keys())[0]}={def_par}", f"atk_mec={atk_mec}", - f"{list(atk_params.keys())[0]}={atk_par}", ], + f"{list(atk_params.keys())[0]}={atk_par}", + ], } # Write file diff --git a/requirements.txt b/requirements.txt index 9c22126..0c2c7a0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -59,6 +59,7 @@ ptyprocess==0.7.0 pure_eval==0.2.3 pyAgrum==2.2.0 pyarrow==21.0.0 +pycddlib==3.0.2 pycodestyle==2.14.0 pydot==4.0.1 pyflakes==3.4.0 diff --git a/src/attack.py b/src/attack.py index 27eab77..461981e 100644 --- a/src/attack.py +++ b/src/attack.py @@ -6,7 +6,7 @@ from src.config import get_cur_dir, set_seed from src.mia import get_ll -from src.utils import sample_from_cn +from src.utils import centroid_cn, sample_from_cn # Apply attack mechanism to a BN, namely, derive a BN from a CN @@ -53,6 +53,14 @@ def attack_mechanism(exp, config, atk_mec, atk_args) -> None: return +# Get the centroid of a CN +def atk_cen(bn_min, bn_max): + + bn = centroid_cn(bn_min, bn_max) + + return bn + + # Get the maximum likelihood BN inside a CN def atk_mle(bn_min, bn_max, data, n_bns: int): diff --git a/src/config.py b/src/config.py index 382e5c3..11911d2 100644 --- a/src/config.py +++ b/src/config.py @@ -39,6 +39,8 @@ def map_sys_args(sys_args, config) -> tuple: if atk_mec == "atk_mle": atk_args["n_bns"] = int(params.pop("n_bns")) assert atk_args["n_bns"] >= 1 + elif atk_mec == "atk_cen": + pass else: raise Exception("Attack not implemented") diff --git a/src/inference.py b/src/inference.py index e23a11d..056fffb 100644 --- a/src/inference.py +++ b/src/inference.py @@ -21,7 +21,9 @@ def inferences(exp, config, def_mec, def_args): # Read data auc_res = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}') - eps_vec = [i for i in auc_res.loc[auc_res["exp"] == exp, "epsilon"].values if i is not None] + eps_vec = [ + i for i in auc_res.loc[auc_res["exp"] == exp, "epsilon"].values if i is not None + ] eps = np.mean(eps_vec) # Set seed @@ -41,8 +43,8 @@ def inferences(exp, config, def_mec, def_args): bn = learn_bn_params(gt, gpop) # Learn CN from gpop (defense mechanism) #TODO: save results - def_mec_fn = getattr(src.defense, def_mec) # Get the related function - sig = inspect.signature(def_mec_fn) # Get its signature + def_mec_fn = getattr(src.defense, def_mec) # Get the related function + sig = inspect.signature(def_mec_fn) # Get its signature args = { k: v for k, v in { diff --git a/src/mia.py b/src/mia.py index 3b12431..5c843b5 100644 --- a/src/mia.py +++ b/src/mia.py @@ -1,12 +1,10 @@ import math -import sys import numpy as np import pandas as pd import pyagrum as gum from scipy.stats import norm from sklearn import metrics -from scipy.optimize import minimize from src.config import get_cur_dir, set_seed from src.defense import noisy_bn @@ -69,8 +67,10 @@ def mia_vs_bn(exp, config) -> dict: # log.write(traceback.format_exc()) # Save results - power_res.to_csv(f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False) - + power_res.to_csv( + f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False + ) + # Return auc_res["auc_bn"] = auc_res.apply(lambda row: auc_bns_dict[row["sample"]], axis=1) @@ -137,8 +137,8 @@ def mia_vs_cn(exp, config) -> pd.DataFrame: # Save results power_res.to_csv( - f'{cur_dir}/{config["results_path"]}/cns/power_cn_{exp}.csv', - index=False) + f'{cur_dir}/{config["results_path"]}/cns/power_cn_{exp}.csv', index=False + ) # Return auc_res["auc_cn"] = auc_res.apply(lambda row: auc_cns_dict[row["sample"]], axis=1) @@ -169,10 +169,13 @@ def theoretical_power(exp, config) -> None: # Save results results["power_bound"] = beta - results.to_csv(f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False) + results.to_csv( + f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False + ) return + # Find eps s.t. |AUC(eps) - AUC(CN)| < tol def find_epsilon(exp, config) -> dict: @@ -218,7 +221,7 @@ def find_epsilon(exp, config) -> dict: # ... and find epsilon for eps in eps_vec: - + # Get noisy BN scale = (2 * bn_theta_hat.size()) / (pool_ss * eps) bn_noisy = noisy_bn(bn_theta_hat, scale) @@ -242,15 +245,17 @@ def find_epsilon(exp, config) -> dict: auc_noisy_dict[sample] = auc power_res[f"power_BN_noisy_sample{sample}"] = power_vec break - + # Save results power_res.to_csv( - f'{cur_dir}/{config["results_path"]}/bn_noisy/power_bn_{exp}.csv', - index=False) - + f'{cur_dir}/{config["results_path"]}/bn_noisy/power_bn_{exp}.csv', index=False + ) + # Return auc_res["epsilon"] = auc_res.apply(lambda row: eps_dict[row["sample"]], axis=1) - auc_res["auc_noisy_bn"] = auc_res.apply(lambda row: auc_noisy_dict[row["sample"]], axis=1) + auc_res["auc_noisy_bn"] = auc_res.apply( + lambda row: auc_noisy_dict[row["sample"]], axis=1 + ) return auc_res diff --git a/src/utils.py b/src/utils.py index 95c87ec..ad6b6ca 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,5 +1,6 @@ from tempfile import TemporaryDirectory +import cdd import hopsy import numpy as np import pyagrum as gum @@ -28,6 +29,83 @@ def add_counts_to_bn(bn, data): bn.cpt(node).fillWith(counts_array.flatten().tolist()) +# Get the centroid of a CN +def centroid_cn(bn_min, bn_max) -> gum.BayesNet: + + # Init an empty BN + bn = gum.BayesNet(bn_min) + + # For each variable ... + for var in bn.names(): + + # ... get the centroid CPT, ... + cpt = centroid_cpt(bn_min.cpt(var), bn_max.cpt(var)) + + # ... and fill the BN + bn.cpt(var).fillWith(cpt.flatten()) + + # Debug + safe_assert(check_consistency(bn, bn_min, bn_max)) + + return bn + + +# Get the centroid of a CN CPT +def centroid_cpt(cpt_min, cpt_max) -> np.array: + + # Transform CPTs into pandas dataframes + cpt_min = np.atleast_2d(cpt_min.topandas()) + cpt_max = np.atleast_2d(cpt_max.topandas()) + + # For each row in the CPT ... + cpt = [] + for row in range(cpt_min.shape[0]): + + # ... get the centroid credal set, ... + c = centroid_cset(cpt_min[row, :], cpt_max[row, :]) + cpt.append(c) + + # Reshape the CPT + cpt = np.array(cpt) + + # Debug + safe_assert(cpt_min.shape == cpt_max.shape) + safe_assert(cpt.shape == cpt_min.shape) + + return cpt + + +# Get the centroid of a credal set as the average of its extreme points +def centroid_cset(vec_min, vec_max) -> np.array: + + # Define the (in)equalities (i.e., get the H-representation of the credal set) + n_par = len(vec_min) + A = np.concatenate( + (-np.eye(n_par), np.eye(n_par), np.atleast_2d(np.ones(n_par))), axis=0 + ) + b = np.concatenate((vec_max, -vec_min, np.atleast_1d(-1))).reshape(len(A), 1) + bA = np.concatenate((b, A), axis=1) + mat = cdd.matrix_from_array( + array=bA, rep_type=cdd.RepType.INEQUALITY, lin_set=set([len(A) - 1]) + ) + + # Get the polytope and extreme points. Each point is a row of the matrix `vertices` + poly = cdd.polyhedron_from_matrix(mat) + ext = cdd.copy_generators(poly) + vertices = np.array(ext.array)[:, 1:] + + # Compute the centroid as the average across extreme points + centroid = np.sum(vertices, axis=0) / len(vertices) + + # Debug + safe_assert(len(vec_min) == len(vec_max)) + safe_assert(len(b) == 2 * len(vec_min) + 1) + safe_assert(A.shape == (len(b), len(vec_min))) + safe_assert(bA.shape == (2 * len(vec_min) + 1, len(vec_min) + 1)) + + return centroid + + # BNs sampler from a CN def sample_from_cn(bn_min, bn_max, n_bns: int) -> list: @@ -104,20 +182,20 @@ def sample_from_cset(vec_min, vec_max, n_bns) -> list: We assume a credal set is a polytope in a space of #X parameters, defined by a: - Multi-dimensional rectangle, i.e., inequality constraint Ax <= b, and - Hyperplane (provided all the variables sum up to 1), i.e., equality constraint A_eq x = b_eq. - In the case of local IDM, this is ensured. + This is true if the CN has been learnt by local IDM, for instance. """ # Define the rectangle n_par = len(vec_min) - A = np.concat((np.eye(n_par), -np.eye(n_par)), axis=0) - b = np.array(np.concatenate((vec_max, -vec_min))) + A = np.concatenate((np.eye(n_par), -np.eye(n_par)), axis=0) + b = np.concatenate((vec_max, -vec_min)) rectangle = hopsy.Problem(A=A, b=b) # Define the hyperplane A_eq = np.array([np.ones(n_par)]) b_eq = np.array([1.0]) - # Define the polytope as a constrained rectangle + # Define the polytope as a constrained rectangle (i.e., get the H-representation of the credal set) constrained_rectangle = hopsy.add_equality_constraints( rectangle, A_eq=A_eq, b_eq=b_eq ) diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 9b63b13..b048c7d 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -1,6 +1,7 @@ -from experiments.cn_privacy import exp, generate import sys +from experiments.cn_privacy import exp, generate + def test_generation(): @@ -26,3 +27,21 @@ def test_def_idm_atk_mle(monkeypatch): # Run experiment exp.main() + + +def test_def_ran_atk_cen(monkeypatch): + + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_cen"] + ) + + # Run experiment + exp.main() + + +def test_def_idm_atk_cen(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_cen"]) + + # Run experiment + exp.main() diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 7a1f85e..88313fe 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -1,4 +1,5 @@ import sys + from experiments.cn_vs_noisybn import exp, generate @@ -26,3 +27,21 @@ def test_def_idm_atk_mle(monkeypatch): # Run experiment exp.main() + + +def test_def_ran_atk_cen(monkeypatch): + + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_cen"] + ) + + # Run experiment + exp.main() + + +def test_def_idm_atk_cen(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_cen"]) + + # Run experiment + exp.main() From 93f743486222d9c28bf94f0a821f2c8caba88879 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Wed, 19 Nov 2025 11:09:09 +0100 Subject: [PATCH 25/31] Add `atk_ran` --- README.md | 9 +++++---- src/attack.py | 6 ++++++ src/config.py | 2 +- test/cn_privacy/config.yaml | 8 ++++---- test/cn_privacy/test_integration.py | 17 +++++++++++++++++ test/cn_vs_noisybn/config.yaml | 16 ++++++++-------- test/cn_vs_noisybn/test_integration.py | 17 +++++++++++++++++ 7 files changed, 58 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 3d0bfdb..9197813 100644 --- a/README.md +++ b/README.md @@ -21,12 +21,13 @@ For additional details, we refer to the paper. Each experiment requires the user to specify one defense and one attack mechanisms, plus additional related hyperparameters. Below, the mechanisms and hyperparameters names are reported. Implemented defenses: -- `def_idm`. Requires: `ess` -- `def_ran`. Requires: `delta` +- `def_idm`. Requires: `ess`. +- `def_ran`. Requires: `delta`. Implemented attacks: -- `atk_mle`. Requires: `n_bns` -- `atk_cen` +- `atk_mle`. Requires: `n_bns`. +- `atk_cen`. +- `atk_ran`. ## Running code diff --git a/src/attack.py b/src/attack.py index 461981e..396b403 100644 --- a/src/attack.py +++ b/src/attack.py @@ -52,6 +52,12 @@ def attack_mechanism(exp, config, atk_mec, atk_args) -> None: return +# Get a random BN inside a CN +def atk_ran(bn_min, bn_max): + + bn = sample_from_cn(bn_min, bn_max, 1) + + return bn[0] # Get the centroid of a CN def atk_cen(bn_min, bn_max): diff --git a/src/config.py b/src/config.py index 11911d2..0e8c9ba 100644 --- a/src/config.py +++ b/src/config.py @@ -39,7 +39,7 @@ def map_sys_args(sys_args, config) -> tuple: if atk_mec == "atk_mle": atk_args["n_bns"] = int(params.pop("n_bns")) assert atk_args["n_bns"] >= 1 - elif atk_mec == "atk_cen": + elif atk_mec == "atk_cen" or atk_mec == "atk_ran": pass else: raise Exception("Attack not implemented") diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml index 462f4a0..1144679 100644 --- a/test/cn_privacy/config.yaml +++ b/test/cn_privacy/config.yaml @@ -10,18 +10,18 @@ results_path: output/results # Where to save the experime exp_meta: exp_meta.txt # File of metadata for experiments # Models -n_nodes_vec: '[10, 15]' # List of models' number of nodes +n_nodes_vec: '[4, 5]' # List of models' number of nodes edge_ratio_vec: '[1, 1.5]' # List of models' edge ratio n_modmax: 2 # Maximum number of variables categories # Data -gpop_ss: 500 # Sample size of general population +gpop_ss: 50 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop -samples: 5 # Number of data samples +samples: 2 # Number of data samples # MIA -error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector +error: 'np.logspace(-4, 0, 5, endpoint=False)' # Type-I errors vector # Other num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index b048c7d..7ed42cd 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -45,3 +45,20 @@ def test_def_idm_atk_cen(monkeypatch): # Run experiment exp.main() + +def test_def_ran_atk_ran(monkeypatch): + + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ran"] + ) + + # Run experiment + exp.main() + + +def test_def_idm_atk_ran(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ran"]) + + # Run experiment + exp.main() diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml index 0a3e644..19cf415 100644 --- a/test/cn_vs_noisybn/config.yaml +++ b/test/cn_vs_noisybn/config.yaml @@ -12,25 +12,25 @@ auc_meta: output/results/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable -n_nodes: 10 # Number of nodes for each BN model +n_nodes: 5 # Number of nodes for each BN model n_modmax: 2 # Maximum number of categories for covariates -n_models: 5 # Number of models to evaluate +n_models: 2 # Number of models to evaluate # Data -gpop_ss: 500 # Sample size of general population +gpop_ss: 50 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop -samples: 5 # Number of data samples +samples: 2 # Number of data samples # MIA -error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector +error: 'np.logspace(-4, 0, 5, endpoint=False)' # Type-I errors vector # Noisy BN -tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol -eps_vec: 'np.logspace(-8, 2, num=50)' # Epsilon to consider for noisy BN +tol: 0.05 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=10)' # Epsilon to consider for noisy BN # Inferences -n_infer: 5 # Number of inferences to perform +n_infer: 2 # Number of inferences to perform # Other num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 88313fe..6e20cfb 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -45,3 +45,20 @@ def test_def_idm_atk_cen(monkeypatch): # Run experiment exp.main() + +def test_def_ran_atk_ran(monkeypatch): + + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ran"] + ) + + # Run experiment + exp.main() + + +def test_def_idm_atk_ran(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ran"]) + + # Run experiment + exp.main() \ No newline at end of file From d61444a5f4a4e0d06a6cc650434474691460e1b4 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Wed, 19 Nov 2025 16:26:12 +0100 Subject: [PATCH 26/31] Minor fixes --- experiments/cn_vs_noisybn/Plot_results.ipynb | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 8a20282..c7cbca5 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -82,7 +82,7 @@ "outputs": [], "source": [ "# Names of experiments\n", - "pattern = re.compile(\"output_.*_(ess|delta)(\\d+)\")\n", + "pattern = re.compile(\"output_.*_(ess|delta)(\\d+\\.?\\d*)\")\n", "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", "# Names of output directories\n", @@ -94,7 +94,7 @@ "\n", "# Get ESS/delta list\n", "def_arg = pattern.findall(out_dirs[0])[0][0]\n", - "x_values = sorted([pattern.findall(i)[0][1] for i in out_dirs])" + "x_values = natsorted([pattern.findall(i)[0][1] for i in out_dirs])" ] }, { @@ -122,7 +122,7 @@ " ),\n", " axis=1,\n", " )\n", - " x = re.findall(\"output_\\w+(\\d+)\", out_dir)[0]\n", + " x = pattern.findall(out_dir)[0][1]\n", " res[f\"{def_arg}{x}\"] = data\n", "\n", "# Retrieve AUCs for each result folder\n", @@ -130,7 +130,7 @@ "for out_dir in out_dirs:\n", " auc_path = os.path.join(cur_dir, out_dir, \"results/auc_meta.csv\")\n", " data = pd.read_csv(auc_path)\n", - " x = re.findall(\"output_\\w+(\\d+)\", out_dir)[0]\n", + " x = pattern.findall(out_dir)[0][1]\n", " aucs[f\"{def_arg}{x}\"] = data" ] }, @@ -161,7 +161,7 @@ "ax.set_ylabel(\"$\\epsilon$\")\n", "ax.set_title(f\"{def_arg} vs $\\epsilon$\")\n", "\n", - "ax.set_ylim([1e-9, 2])\n", + "ax.set_ylim([1e-9, 100])\n", "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", "ax.legend(loc=\"best\")" ] @@ -293,6 +293,7 @@ "for x in x_values:\n", " ax = axes.flatten()[i]\n", "\n", + "\n", " for key in roc.keys():\n", " try:\n", " fpr, tpr, _ = roc[key][x]\n", From 77d23baf60af0c0e6238b47bfec176145fc90a43 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Wed, 19 Nov 2025 17:00:57 +0100 Subject: [PATCH 27/31] Fix cdd numerical stability with `Fraction` and `cdd.gmp` module --- experiments/cn_vs_noisybn/Plot_results.ipynb | 3 ++- src/utils.py | 17 ++++++++++++----- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index c7cbca5..38a4aae 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -86,10 +86,11 @@ "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", "# Names of output directories\n", + "atk_mec = \"atk_mle\"\n", "out_dirs = [\n", " item\n", " for item in os.listdir(f\"{cur_dir}\")\n", - " if Path(item).is_dir() and pattern.match(item)\n", + " if Path(item).is_dir() and pattern.match(item) and atk_mec in item\n", "]\n", "\n", "# Get ESS/delta list\n", diff --git a/src/utils.py b/src/utils.py index ad6b6ca..dec0808 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,6 +1,8 @@ +from fractions import Fraction from tempfile import TemporaryDirectory import cdd +import cdd.gmp import hopsy import numpy as np import pyagrum as gum @@ -85,14 +87,18 @@ def centroid_cset(vec_min, vec_max) -> np.array: ) b = np.concatenate((vec_max, -vec_min, np.atleast_1d(-1))).reshape(len(A), 1) bA = np.concatenate((b, A), axis=1) - mat = cdd.matrix_from_array( - array=bA, rep_type=cdd.RepType.INEQUALITY, lin_set=set([len(A) - 1]) + bA_frac = np.array([[Fraction(x).limit_denominator() for x in row] + for row in bA], dtype=object) # Needed for numerical stability + mat_frac = cdd.gmp.matrix_from_array( + array=bA_frac, rep_type=cdd.RepType.INEQUALITY, lin_set=set([len(A) - 1]) ) # Get the polytope and extreme points. Each point is a row of the matrix `vertices` - poly = cdd.polyhedron_from_matrix(mat) - ext = cdd.copy_generators(poly) - vertices = np.array(ext.array)[:, 1:] + poly_frac = cdd.gmp.polyhedron_from_matrix(mat_frac) + ext_frac = cdd.gmp.copy_generators(poly_frac) + vertices_frac = np.array(ext_frac.array)[:, 1:] + vertices = np.array([[float(x) for x in row] + for row in vertices_frac], dtype=object) # Compute the centroid as the average across extreme points centroid = np.sum(vertices, axis=0) / len(vertices) @@ -102,6 +108,7 @@ def centroid_cset(vec_min, vec_max) -> np.array: safe_assert(len(b) == 2 * len(vec_min) + 1) safe_assert(A.shape == (len(b), len(vec_min))) safe_assert(bA.shape == (2 * len(vec_min) + 1, len(vec_min) + 1)) + safe_assert(vertices.shape[1] == n_par) return centroid From 9985c1f0b0d3b4b77ca54de2ddbc38cac894683c Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Thu, 20 Nov 2025 13:42:19 +0100 Subject: [PATCH 28/31] Fix inferences: skip numerically unstable experiments --- compose.yaml | 64 ++++++++++++++------ experiments/cn_vs_noisybn/Plot_results.ipynb | 16 +++-- experiments/cn_vs_noisybn/config.yaml | 16 ++--- generate_compose.py | 56 ++++++++++------- src/attack.py | 2 + src/inference.py | 13 ++-- src/utils.py | 10 +-- test/cn_privacy/test_integration.py | 1 + test/cn_vs_noisybn/test_integration.py | 3 +- 9 files changed, 119 insertions(+), 62 deletions(-) diff --git a/compose.yaml b/compose.yaml index 2f2724e..66d6217 100644 --- a/compose.yaml +++ b/compose.yaml @@ -1,62 +1,86 @@ version: '3.9' services: - cn_vs_noisybn_def_idm_atk_mle_ess1: + cn_vs_noisybn_def_idm_atk_ran_ess1: build: . command: - python - -m - experiments.cn_vs_noisybn.exp - def_mec=def_idm + - atk_mec=atk_ran - ess=1 - - atk_mec=atk_mle - - n_bns=50 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess1:/workspace/experiments/cn_vs_noisybn/output - cn_vs_noisybn_def_idm_atk_mle_ess10: + - ./experiments/cn_vs_noisybn/output_def_idm_atk_ran_ess1:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_idm_atk_ran_ess100: build: . command: - python - -m - experiments.cn_vs_noisybn.exp - def_mec=def_idm - - ess=10 - - atk_mec=atk_mle - - n_bns=50 + - atk_mec=atk_ran + - ess=100 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess10:/workspace/experiments/cn_vs_noisybn/output - cn_vs_noisybn_def_idm_atk_mle_ess30: + - ./experiments/cn_vs_noisybn/output_def_idm_atk_ran_ess100:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_idm_atk_ran_ess50: build: . command: - python - -m - experiments.cn_vs_noisybn.exp - def_mec=def_idm - - ess=30 - - atk_mec=atk_mle - - n_bns=50 + - atk_mec=atk_ran + - ess=50 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess30:/workspace/experiments/cn_vs_noisybn/output - cn_vs_noisybn_def_idm_atk_mle_ess50: + - ./experiments/cn_vs_noisybn/output_def_idm_atk_ran_ess50:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_ran_atk_ran_delta0.001: build: . command: - python - -m - experiments.cn_vs_noisybn.exp - - def_mec=def_idm - - ess=50 - - atk_mec=atk_mle - - n_bns=50 + - def_mec=def_ran + - atk_mec=atk_ran + - delta=0.001 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_ran_atk_ran_delta0.001:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_ran_atk_ran_delta0.05: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_ran + - atk_mec=atk_ran + - delta=0.05 + image: bnp:2025 + volumes: + - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns + - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data + - ./experiments/cn_vs_noisybn/output_def_ran_atk_ran_delta0.05:/workspace/experiments/cn_vs_noisybn/output + cn_vs_noisybn_def_ran_atk_ran_delta0.1: + build: . + command: + - python + - -m + - experiments.cn_vs_noisybn.exp + - def_mec=def_ran + - atk_mec=atk_ran + - delta=0.1 image: bnp:2025 volumes: - ./experiments/cn_vs_noisybn/bns:/workspace/experiments/cn_vs_noisybn/bns - ./experiments/cn_vs_noisybn/data:/workspace/experiments/cn_vs_noisybn/data - - ./experiments/cn_vs_noisybn/output_def_idm_atk_mle_ess50:/workspace/experiments/cn_vs_noisybn/output + - ./experiments/cn_vs_noisybn/output_def_ran_atk_ran_delta0.1:/workspace/experiments/cn_vs_noisybn/output diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 38a4aae..4f47f37 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -86,16 +86,25 @@ "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", "# Names of output directories\n", - "atk_mec = \"atk_mle\"\n", + "def_mec = \"def_idm\"\n", + "atk_mec = \"atk_ran\"\n", "out_dirs = [\n", " item\n", " for item in os.listdir(f\"{cur_dir}\")\n", - " if Path(item).is_dir() and pattern.match(item) and atk_mec in item\n", + " if Path(item).is_dir()\n", + " and pattern.match(item)\n", + " and atk_mec in item\n", + " and def_mec in item\n", "]\n", "\n", "# Get ESS/delta list\n", "def_arg = pattern.findall(out_dirs[0])[0][0]\n", - "x_values = natsorted([pattern.findall(i)[0][1] for i in out_dirs])" + "x_values = [pattern.findall(i)[0][1] for i in out_dirs]\n", + "x_values = (\n", + " sorted([int(x) for x in x_values])\n", + " if def_mec == \"def_idm\"\n", + " else sorted([float(x) for x in x_values])\n", + ")" ] }, { @@ -294,7 +303,6 @@ "for x in x_values:\n", " ax = axes.flatten()[i]\n", "\n", - "\n", " for key in roc.keys():\n", " try:\n", " fpr, tpr, _ = roc[key][x]\n", diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml index 7d362f3..fb9f76e 100644 --- a/experiments/cn_vs_noisybn/config.yaml +++ b/experiments/cn_vs_noisybn/config.yaml @@ -12,25 +12,25 @@ auc_meta: output/results/auc_meta.csv # File of metadata for AUCs # Models (Naive Bayes) target_var: 'T' # Target variable -n_nodes: 10 # Number of nodes for each BN model -n_modmax: 2 # Maximum number of categories for covariates -n_models: 2 # Number of models to evaluate +n_nodes: 20 # Number of nodes for each BN model +n_modmax: 4 # Maximum number of categories for covariates +n_models: 10 # Number of models to evaluate # Data -gpop_ss: 500 # Sample size of general population +gpop_ss: 1000 # Sample size of general population rpop_prop: 0.5 # Sample size of reference population = gpop_ss * rpop_prop pool_prop: 0.25 # Sample size of pool population = gpop_ss * pool_prop samples: 10 # Number of data samples # MIA -error: 'np.logspace(-4, 0, 10, endpoint=False)' # Type-I errors vector +error: 'np.logspace(-4, 0, 20, endpoint=False)' # Type-I errors vector # Noisy BN -tol: 0.03 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol -eps_vec: 'np.logspace(-8, 2, num=50)' # Epsilon to consider for noisy BN +tol: 0.01 # To find eps s.t. |AUC(eps) - AUC(CN)| < tol +eps_vec: 'np.logspace(-8, 2, num=200)' # Epsilon to consider for noisy BN # Inferences -n_infer: 10 # Number of inferences to perform +n_infer: 100 # Number of inferences to perform # Other num_cores: 'multiprocessing.cpu_count() - 1' # Number of threads to use for parallelization diff --git a/generate_compose.py b/generate_compose.py index 6d397af..c395af5 100644 --- a/generate_compose.py +++ b/generate_compose.py @@ -1,11 +1,16 @@ +import sys from itertools import product import yaml # Set hyperparameters names = ["cn_vs_noisybn"] -def_mecs = {"def_idm": {"ess": [1, 10, 30, 50]}} -atk_mecs = {"atk_mle": {"n_bns": [50]}} +def_mecs = {"def_idm": {"ess": [1, 50, 100]}, "def_ran": {"delta": [0.001, 0.05, 0.1]}} +atk_mecs = { + "atk_mle": {"n_bns": [100]}, + "atk_cen": {None: [None]}, + "atk_ran": {None: [None]}, +} # Initialize the `compose.yaml` file init = {"version": "3.9"} @@ -13,41 +18,50 @@ yaml.dump(init, f, default_flow_style=False) # For any configuration ... +# (assumption: each defense and attack mechanism has at most 1 hyperparameter to be set) data = {"services": dict()} for name, def_mec, atk_mec in product(names, def_mecs.keys(), atk_mecs.keys()): - def_params = def_mecs[def_mec] - atk_params = atk_mecs[atk_mec] + def_params = list(def_mecs[def_mec].values())[0] + atk_params = list(atk_mecs[atk_mec].values())[0] - # (assumption: each defense and attack mechanism has only 1 hyperparameter to be set) - for def_par, atk_par in product( - list(def_params.values())[0], list(atk_params.values())[0] - ): + for def_par, atk_par in product(def_params, atk_params): # ... set the related volume, ... + app = ( + f"_{list(def_mecs[def_mec].keys())[0]}{def_par}" + if def_par is not None + else "" + ) volumes = [ f"./experiments/{name}/bns:/workspace/experiments/{name}/bns", f"./experiments/{name}/data:/workspace/experiments/{name}/data", - f"./experiments/{name}/output_{def_mec}_{atk_mec}_{list(def_params.keys())[0]}{def_par}:/workspace/experiments/{name}/output", + f"./experiments/{name}/output_{def_mec}_{atk_mec}{app}:/workspace/experiments/{name}/output", ] + # ... set the command, ... + command = [ + "python", + "-m", + f"experiments.{name}.exp", + f"def_mec={def_mec}", + f"atk_mec={atk_mec}", + ] + + if def_par is not None: + command.append(f"{list(def_mecs[def_mec].keys())[0]}={def_par}") + if atk_par is not None: + command.append(f"{list(atk_mecs[atk_mec].keys())[0]}={atk_par}") + # ... and create the experiment - data["services"][ - f"{name}_{def_mec}_{atk_mec}_{list(def_params.keys())[0]}{def_par}" - ] = { + data["services"][f"{name}_{def_mec}_{atk_mec}{app}"] = { "image": "bnp:2025", "build": ".", "volumes": volumes, - "command": [ - "python", - "-m", - f"experiments.{name}.exp", - f"def_mec={def_mec}", - f"{list(def_params.keys())[0]}={def_par}", - f"atk_mec={atk_mec}", - f"{list(atk_params.keys())[0]}={atk_par}", - ], + "command": command, } +# Print number of services +print("Number of services: ", len(data["services"])) # Write file with open("compose.yaml", "a") as f: diff --git a/src/attack.py b/src/attack.py index 396b403..428118a 100644 --- a/src/attack.py +++ b/src/attack.py @@ -52,6 +52,7 @@ def attack_mechanism(exp, config, atk_mec, atk_args) -> None: return + # Get a random BN inside a CN def atk_ran(bn_min, bn_max): @@ -59,6 +60,7 @@ def atk_ran(bn_min, bn_max): return bn[0] + # Get the centroid of a CN def atk_cen(bn_min, bn_max): diff --git a/src/inference.py b/src/inference.py index 056fffb..273a6b7 100644 --- a/src/inference.py +++ b/src/inference.py @@ -62,10 +62,13 @@ def inferences(exp, config, def_mec, def_args): bn_noisy = noisy_bn(bn, scale) # Run inferences - gt_mpes, _ = run_inference_bn(gt, target, evid_vec) - bn_mpes, bn_probs = run_inference_bn(bn, target, evid_vec) - bn_noisy_mpes, bn_noisy_probs = run_inference_bn(bn_noisy, target, evid_vec) - cn_mpes, cn_probs, cn_probs_alt = run_inference_cn(cn, target, evid_vec, exp) + try: + gt_mpes, _ = run_inference_bn(gt, target, evid_vec) + bn_mpes, bn_probs = run_inference_bn(bn, target, evid_vec) + bn_noisy_mpes, bn_noisy_probs = run_inference_bn(bn_noisy, target, evid_vec) + cn_mpes, cn_probs, cn_probs_alt = run_inference_cn(cn, target, evid_vec, exp) + except: + return # Save results results = pd.DataFrame( @@ -86,6 +89,8 @@ def inferences(exp, config, def_mec, def_args): index=False, ) + return + # MPE function for BN def mpe_bn(bn_ie: gum.LazyPropagation, target: str, evid: dict) -> tuple: diff --git a/src/utils.py b/src/utils.py index dec0808..ef08d3b 100644 --- a/src/utils.py +++ b/src/utils.py @@ -87,8 +87,9 @@ def centroid_cset(vec_min, vec_max) -> np.array: ) b = np.concatenate((vec_max, -vec_min, np.atleast_1d(-1))).reshape(len(A), 1) bA = np.concatenate((b, A), axis=1) - bA_frac = np.array([[Fraction(x).limit_denominator() for x in row] - for row in bA], dtype=object) # Needed for numerical stability + bA_frac = np.array( + [[Fraction(x).limit_denominator() for x in row] for row in bA], dtype=object + ) # Needed for numerical stability mat_frac = cdd.gmp.matrix_from_array( array=bA_frac, rep_type=cdd.RepType.INEQUALITY, lin_set=set([len(A) - 1]) ) @@ -97,8 +98,9 @@ def centroid_cset(vec_min, vec_max) -> np.array: poly_frac = cdd.gmp.polyhedron_from_matrix(mat_frac) ext_frac = cdd.gmp.copy_generators(poly_frac) vertices_frac = np.array(ext_frac.array)[:, 1:] - vertices = np.array([[float(x) for x in row] - for row in vertices_frac], dtype=object) + vertices = np.array( + [[float(x) for x in row] for row in vertices_frac], dtype=object + ) # Compute the centroid as the average across extreme points centroid = np.sum(vertices, axis=0) / len(vertices) diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 7ed42cd..7a33b77 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -46,6 +46,7 @@ def test_def_idm_atk_cen(monkeypatch): # Run experiment exp.main() + def test_def_ran_atk_ran(monkeypatch): monkeypatch.setattr( diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 6e20cfb..72d0500 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -46,6 +46,7 @@ def test_def_idm_atk_cen(monkeypatch): # Run experiment exp.main() + def test_def_ran_atk_ran(monkeypatch): monkeypatch.setattr( @@ -61,4 +62,4 @@ def test_def_idm_atk_ran(monkeypatch): monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ran"]) # Run experiment - exp.main() \ No newline at end of file + exp.main() From 32414533d28c5f9a3727aac942ccdd9b3596d79f Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 2 Dec 2025 16:46:25 +0100 Subject: [PATCH 29/31] Add `atk_ent` --- README.md | 1 + experiments/cn_privacy/Plot_results.ipynb | 266 ++++++++++--------- experiments/cn_vs_noisybn/Plot_results.ipynb | 182 +++++++++---- generate_compose.py | 1 - src/attack.py | 10 +- src/config.py | 2 +- src/utils.py | 96 +++++++ test/cn_privacy/test_integration.py | 18 ++ test/cn_vs_noisybn/test_integration.py | 18 ++ test/unit/__init__.py | 0 test/unit/utils.py | 12 + 11 files changed, 418 insertions(+), 188 deletions(-) create mode 100644 test/unit/__init__.py create mode 100644 test/unit/utils.py diff --git a/README.md b/README.md index 9197813..511e356 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ Implemented attacks: - `atk_mle`. Requires: `n_bns`. - `atk_cen`. - `atk_ran`. +- `atk_ent`. ## Running code diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb index 9c8b817..9c157ed 100644 --- a/experiments/cn_privacy/Plot_results.ipynb +++ b/experiments/cn_privacy/Plot_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b53ad4f3", "metadata": {}, "outputs": [], @@ -17,70 +17,15 @@ "from pathlib import Path\n", "import re\n", "import ast\n", + "from itertools import cycle, product\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", "from src.config import * # noqa" ] }, - { - "cell_type": "code", - "execution_count": 27, - "id": "57313fdd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5, 6])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.concatenate(([1, 2, 3], [4, 5, 6]))" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "2f286be9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., -1., -0., -0.],\n", - " [ 2., -0., -1., -0.],\n", - " [ 3., -0., -0., -1.],\n", - " [ 7., 1., 0., 0.],\n", - " [ 8., 0., 1., 0.],\n", - " [ 9., 0., 0., 1.],\n", - " [-1., 1., 1., 1.]])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_par = 3\n", - "A = np.concatenate(\n", - " (-np.eye(n_par), np.eye(n_par), np.atleast_2d(np.ones(n_par))), axis=0\n", - ")\n", - "b = np.array([1, 2, 3, 7, 8, 9, -1]).reshape(7, 1)\n", - "\n", - "bA = np.concatenate((b, A), axis=1)\n", - "\n", - "bA" - ] - }, - { - "cell_type": "code", - "execution_count": 7, "id": "ce91ef75", "metadata": {}, "outputs": [], @@ -88,16 +33,26 @@ "# Choose config file\n", "config = load_config(\"cn_privacy\")\n", "\n", - "# Get results path\n", + "# Choose what to plot\n", + "folder = \"cn_privacy_20251127_cat_ln\"\n", + "params = dict()\n", + "params[\"def_mec\"] = [\"def_idm\", \"def_ran\"]\n", + "params[\"atk_mec\"] = [\"atk_mle\"]\n", + "params[\"ess\"] = [1]\n", + "params[\"delta\"] = [1.0]\n", "cur_dir = get_cur_dir(config)\n", - "res_path = cur_dir / config[\"results_path\"]\n", "\n", - "# Get some hyperparameters\n", - "error = eval(config[\"error\"])\n", - "with open(f\"{cur_dir}/exp_meta.txt\", \"r\") as meta:\n", - " for row in meta:\n", - " if re.search(\"\\{.*\\}\", row):\n", - " params = ast.literal_eval(row)\n", + "# Get results paths\n", + "res_path = {}\n", + "for def_mec, atk_mec in product(params[\"def_mec\"], params[\"atk_mec\"]):\n", + " arg_str = \"ess\" if def_mec == \"def_idm\" else \"delta\"\n", + " arg_vals = [x for x in params[arg_str]]\n", + " for arg_val in arg_vals:\n", + " res_path[(def_mec, atk_mec, arg_str, arg_val)] = (\n", + " cur_dir\n", + " / folder\n", + " / f\"output_{def_mec}_{atk_mec}_{f'{arg_str}{arg_val}'}/results\"\n", + " )\n", "\n", "# Choose where to save plots\n", "plots_path = cur_dir / \"plots\"\n", @@ -106,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "52eff632", "metadata": {}, "outputs": [], @@ -131,47 +86,40 @@ ")\n", "\n", "# Colors\n", - "bound_color = \"#ff441a\" # red\n", - "BN_color = \"#3934fe\" # blue\n", - "CN_color = \"#28bd6b\" # green\n", + "palette_cn = dict(\n", + " zip(res_path.keys(), sns.color_palette(palette=\"viridis\", n_colors=len(res_path)))\n", + ")\n", + "palette_bn = sns.color_palette(palette=\"afmhot\")\n", + "bound_color = palette_bn[3]\n", + "BN_color = palette_bn[2]\n", "alpha = 0.2" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "3bc7788b", "metadata": {}, "outputs": [], "source": [ - "# Plot BN and bound (semilogx)\n", - "def plot_bn_bound(exp: str, ax):\n", + "# Plot BN (semilogx)\n", + "def plot_bn(path: Path, exp: str, ax, fill: bool = True):\n", "\n", " # Import results\n", - " files = os.listdir(res_path / \"bns\")\n", + " files = os.listdir(path / \"bns\")\n", " r_path = [r for r in files if f\"{exp}\" in r][0]\n", - " res = pd.read_csv(f\"{res_path}/bns/{r_path}\")\n", - " bound = res[\"power_bound\"]\n", + " res = pd.read_csv(f\"{path}/bns/{r_path}\")\n", + " error = res[\"error\"]\n", "\n", " # Select what to plot\n", " bn_cols = [c for c in res.columns if \"BN\" in c]\n", " bn_mean = res.loc[:, bn_cols].mean(axis=1)\n", " bn_max = res.loc[:, bn_cols].max(axis=1)\n", "\n", - " # Plot bound\n", - " ax.semilogx(\n", - " error,\n", - " bound,\n", - " \"^\",\n", - " color=bound_color,\n", - " label=\"Theoretical estimate\",\n", - " markersize=4,\n", - " zorder=4,\n", - " )\n", - "\n", " # Plot BN (avg-max)\n", - " ax.fill_between(error, bn_mean, bn_max, color=BN_color, alpha=alpha, zorder=2)\n", - " ax.semilogx(error, bn_mean, \"-\", color=BN_color, label=\"BN\", zorder=3)\n", + " (line,) = ax.semilogx(error, bn_mean, \"-\", color=BN_color, label=\"BN\", zorder=3)\n", + " if fill:\n", + " ax.fill_between(error, bn_mean, bn_max, color=BN_color, alpha=alpha, zorder=2)\n", "\n", " # Title and axes\n", " ax.set_xlabel(\"Error\")\n", @@ -188,14 +136,42 @@ " True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1\n", " )\n", "\n", + " return line\n", + "\n", + "\n", + "# Plot bound (semilogx)\n", + "def plot_bound(path: Path, exp: str, ax):\n", + "\n", + " # Import results\n", + " files = os.listdir(path / \"bns\")\n", + " r_path = [r for r in files if f\"{exp}\" in r][0]\n", + " res = pd.read_csv(f\"{path}/bns/{r_path}\")\n", + " bound = res[\"power_bound\"]\n", + " error = res[\"error\"]\n", + "\n", + " # Plot bound\n", + " (line,) = ax.semilogx(\n", + " error,\n", + " bound,\n", + " \"^\",\n", + " color=bound_color,\n", + " markersize=3,\n", + " zorder=4,\n", + " label=\"Theoretical estimate\",\n", + " mec=None,\n", + " )\n", + "\n", + " return line\n", + "\n", "\n", "# Plot CN\n", - "def plot_cn(exp, ax, color: str, type: str):\n", + "def plot_cn(path: Path, color, exp, ax, type: str, fill: bool = True):\n", "\n", " # Import results\n", - " files = os.listdir(res_path / \"cns\")\n", + " files = os.listdir(path / \"cns\")\n", " r_path = [r for r in files if f\"{exp}\" in r][0]\n", - " res = pd.read_csv(f\"{res_path}/cns/{r_path}\")\n", + " res = pd.read_csv(f\"{path}/cns/{r_path}\")\n", + " error = res[\"error\"]\n", "\n", " # Select what to plot\n", " cn_cols = [c for c in res.columns if \"CN\" in c]\n", @@ -203,24 +179,11 @@ " cn_max = res.loc[:, cn_cols].max(axis=1)\n", "\n", " # Plot CN (avg-max)\n", - " ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n", - " label = (\n", - " f'CN, $S={params[\"ess\"]}$'\n", - " if params[\"def_mec\"] == \"def_idm\"\n", - " else f'CN, $\\delta={params[\"delta\"]}$'\n", - " )\n", - " ax.semilogx(error, cn_mean, type, color=color, label=label, zorder=3)\n", - "\n", - " # Legend\n", - " if exp == \"exp0\":\n", - " ax.legend(\n", - " loc=\"best\",\n", - " frameon=True,\n", - " fancybox=False,\n", - " framealpha=1,\n", - " facecolor=\"#e6e6e6\",\n", - " edgecolor=\"#8c8c8c\",\n", - " )\n", + " (line,) = ax.semilogx(error, cn_mean, type, color=color, zorder=3)\n", + " if fill:\n", + " ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n", + "\n", + " return line\n", "\n", "\n", "# Plot title function\n", @@ -240,35 +203,54 @@ "execution_count": null, "id": "ade37b54", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Names of experiments\n", - "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", + "from natsort import natsorted\n", "\n", - "# Layout 4x3\n", - "fig, axes = plt.subplots(len(exp_names) // 3 + 1, 3)\n", - "fig.suptitle(\n", - " f\"Power vs Error - {params['def_mec']} vs {params['atk_mec']}\", fontsize=15\n", + "exp_names = natsorted(\n", + " [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", ")\n", "\n", + "# Layout 4x3\n", + "fig, axes = plt.subplots(len(exp_names) // 3 + 1, 3, figsize=(9, 8))\n", + "fig.suptitle(f\"Power vs Error\", fontsize=15)\n", + "\n", "# Loop over results\n", "for i, exp in enumerate(exp_names):\n", + "\n", + " # Plot BN & bound\n", " ax = axes.flat[i]\n", - " plot_bn_bound(exp, ax)\n", - " plot_cn(f\"{exp_names[i]}\", ax, color=CN_color, type=\"-\")\n", + " path_bn = list(res_path.values())[0]\n", + " bn = plot_bn(path_bn, exp, ax)\n", + " plot_bound(path_bn, exp, ax)\n", + "\n", + " # Plot CNs\n", + " for res in res_path:\n", + " (def_mec, atk_mec, arg_str, arg_val) = res\n", + " path = res_path[res]\n", + "\n", + " cn = plot_cn(path, palette_cn[res], exp, ax, type=\"-\", fill=True)\n", + "\n", + " # Legend\n", + " cn.set_label(f\"CN, {def_mec}: {arg_str}={arg_val}, {atk_mec}\")\n", + " if i == 0:\n", + " ax.legend(\n", + " loc=\"best\",\n", + " frameon=True,\n", + " fancybox=False,\n", + " framealpha=1,\n", + " facecolor=\"#e6e6e6\",\n", + " edgecolor=\"#8c8c8c\",\n", + " )\n", + "\n", + " # Title\n", " (n, e, c) = get_title(exp_names[i])\n", - " ax.set_title(f\"N: {n}, E: {e}, Compl: {c}\")\n", + " ax.set_title(f\"$N$: {n}, $E$: {e}, $C(\\mathcal G)$: {c}\")\n", + "\n", + " # # Log Y scale\n", + " # ax.set_yscale('log')\n", + " # ax.set_ylim(0.9*cn.get_ydata().min(), 1.1*bn.get_ydata().max())\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", "plt.show()\n", @@ -277,6 +259,32 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "699bb07e", + "metadata": {}, + "source": [ + "def_idm:\n", + " * ess1 is more private than ess1000 (weird!), but only in huge nets\n", + " * atk_mle and atk_cen behave similarly, no real differences for each ess\n", + " * atk_ran only slighty more private only in huge nets, for each ess. With ess1000 it is more visible\n", + "\n", + "def_ran:\n", + " * Privacy increases with delta (expected), for each atk\n", + " * atk_mle and atk_cen behave similarly, no real differences for each delta\n", + " * atk_ran is more private, but not that much\n", + " * delta0.1 is similar to BN, while delta1.0 still leaks info, for each atk\n", + " * delta1.0 is less private than def_idm with ess1, for each atk, in huge nets (weird!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc764066", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb index 4f47f37..29f30f3 100644 --- a/experiments/cn_vs_noisybn/Plot_results.ipynb +++ b/experiments/cn_vs_noisybn/Plot_results.ipynb @@ -5,7 +5,7 @@ "id": "80ba8653", "metadata": {}, "source": [ - "Ensure the output directories are names as `output_<...>_`, where `def_arg` can be `ess` or `delta`, and `def_value` its value." + "Ensure the output directories are named as `output_<...>_`, where `def_arg` can be `ess` or `delta`, and `def_value` its value." ] }, { @@ -22,13 +22,15 @@ "import re\n", "import sys\n", "import ast\n", + "import seaborn as sns\n", "from pathlib import Path\n", "from natsort import natsorted\n", "from sklearn.metrics import roc_curve\n", "from matplotlib.ticker import LogLocator\n", + "from statsmodels.stats.proportion import proportion_confint\n", "\n", "sys.path.insert(0, str(Path().resolve().parents[1]))\n", - "from src.config import * # noqa" + "from src.config import load_config, get_cur_dir, create_clean_dir" ] }, { @@ -50,30 +52,6 @@ "create_clean_dir(plots_path)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "49a48f2f", - "metadata": {}, - "outputs": [], - "source": [ - "# Split data set accordingly to a probability threshold\n", - "def split_data(data: pd.DataFrame, col: str, threshold: float = 0.5) -> tuple:\n", - "\n", - " # Split results based on probabilities\n", - " cond = data[col] > threshold\n", - " data_cert = data[cond]\n", - " data_uncert = data[~cond]\n", - "\n", - " return data_cert, data_uncert\n", - "\n", - "\n", - "# Accuracy function for a BN\n", - "def get_acc_bn(data: pd.DataFrame, col: str, vs_col: str) -> float:\n", - "\n", - " return sum(data[col] == data[vs_col]) / len(data)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -85,16 +63,14 @@ "pattern = re.compile(\"output_.*_(ess|delta)(\\d+\\.?\\d*)\")\n", "exp_names = [re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / \"data\")]\n", "\n", - "# Names of output directories\n", - "def_mec = \"def_idm\"\n", - "atk_mec = \"atk_ran\"\n", + "# Choose what to plot\n", + "folder = \"cn_vs_noisybn_20251120_ln\"\n", + "def_mec = \"def_ran\"\n", + "atk_mec = \"atk_mle\"\n", "out_dirs = [\n", " item\n", - " for item in os.listdir(f\"{cur_dir}\")\n", - " if Path(item).is_dir()\n", - " and pattern.match(item)\n", - " and atk_mec in item\n", - " and def_mec in item\n", + " for item in os.listdir(f\"{cur_dir}/{folder}\")\n", + " if pattern.match(item) and atk_mec in item and def_mec in item\n", "]\n", "\n", "# Get ESS/delta list\n", @@ -107,6 +83,36 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad31867b", + "metadata": {}, + "outputs": [], + "source": [ + "# Split data set accordingly to a probability threshold\n", + "def split_data(data: pd.DataFrame, col: str, threshold: float = 0.5) -> tuple:\n", + "\n", + " # Split results based on probabilities\n", + " cond = data[col] > threshold\n", + " data_cert = data[cond]\n", + " data_uncert = data[~cond]\n", + "\n", + " return data_cert, data_uncert\n", + "\n", + "\n", + "# Accuracy function for a BN\n", + "def get_acc_bn(data: pd.DataFrame, col: str, vs_col: str, alpha=0.05) -> list:\n", + "\n", + " succ = sum(data[col] == data[vs_col])\n", + " acc = succ / len(data)\n", + " lower, upper = proportion_confint(\n", + " count=succ, nobs=len(data), alpha=alpha, method=\"wilson\"\n", + " )\n", + "\n", + " return [acc, lower, upper]" + ] + }, { "cell_type": "code", "execution_count": null, @@ -117,7 +123,7 @@ "# Build an inferences data set for each output folder\n", "res = dict()\n", "for out_dir in out_dirs:\n", - " inferences_path = os.path.join(cur_dir, out_dir, \"results/inferences\")\n", + " inferences_path = os.path.join(cur_dir, folder, out_dir, \"results/inferences\")\n", " files = [os.path.join(inferences_path, f) for f in os.listdir(inferences_path)]\n", " data = pd.concat((pd.read_csv(f) for f in files), axis=0)\n", " data[\"cn_probs_1\"] = data.apply(\n", @@ -138,12 +144,39 @@ "# Retrieve AUCs for each result folder\n", "aucs = dict()\n", "for out_dir in out_dirs:\n", - " auc_path = os.path.join(cur_dir, out_dir, \"results/auc_meta.csv\")\n", + " auc_path = os.path.join(cur_dir, folder, out_dir, \"results/auc_meta.csv\")\n", " data = pd.read_csv(auc_path)\n", " x = pattern.findall(out_dir)[0][1]\n", " aucs[f\"{def_arg}{x}\"] = data" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "581cee1c", + "metadata": {}, + "outputs": [], + "source": [ + "# Style\n", + "# plt.style.use(\"seaborn-v0_8-paper\")\n", + "# plt.rcParams.update(\n", + "# {\n", + "# \"font.size\": 9,\n", + "# \"axes.labelsize\": 9,\n", + "# \"axes.titlesize\": 9,\n", + "# \"legend.fontsize\": 7,\n", + "# \"xtick.labelsize\": 8,\n", + "# \"ytick.labelsize\": 8,\n", + "# \"lines.linewidth\": 0.8,\n", + "# \"figure.dpi\": 300,\n", + "# \"savefig.dpi\": 300,\n", + "# \"axes.edgecolor\": \"black\",\n", + "# \"axes.linewidth\": 0.8,\n", + "# \"text.usetex\": True,\n", + "# }\n", + "# )" + ] + }, { "cell_type": "code", "execution_count": null, @@ -153,23 +186,45 @@ "source": [ "# Retrieve related epsilon\n", "eps_median = []\n", + "eps_uq, eps_lq = [], []\n", "eps_up, eps_lp = [], []\n", "for x in x_values:\n", " data = aucs[f\"{def_arg}{x}\"]\n", - " data_eps = [i for i in data[\"epsilon\"].values if i is not None]\n", + " data_eps = [i for i in data[\"epsilon\"].values if i is not None and not np.isnan(i)]\n", " eps_median.append(np.median(data_eps))\n", - " eps_up.append(np.percentile(data_eps, 75))\n", - " eps_lp.append(np.percentile(data_eps, 25))\n", + " eps_uq.append(np.percentile(data_eps, 75))\n", + " eps_lq.append(np.percentile(data_eps, 25))\n", + " eps_up.append(np.percentile(data_eps, 95))\n", + " eps_lp.append(np.percentile(data_eps, 5))\n", "\n", "# Plot: ess vs eps\n", "fig, ax = plt.subplots(1, 1)\n", "\n", - "ax.semilogy(x_values, eps_median, \"-o\", label=\"Median\", markersize=4)\n", - "ax.semilogy(x_values, eps_up, \"-o\", label=\"Quantile 0.75\", markersize=4)\n", - "ax.semilogy(x_values, eps_lp, \"-o\", label=\"Quantile 0.25\", markersize=4)\n", - "ax.set_xlabel(def_arg)\n", + "ax.semilogy(x_values, eps_median, \"-\", color=\"black\", label=\"Median\")\n", + "ax.fill_between(\n", + " x_values,\n", + " eps_lq,\n", + " eps_uq,\n", + " color=\"#ff4d4d\",\n", + " alpha=0.25,\n", + " linewidth=0,\n", + " label=\"Quartiles (1st \\& 3rd)\",\n", + " zorder=2,\n", + ")\n", + "ax.fill_between(\n", + " x_values,\n", + " eps_lp,\n", + " eps_up,\n", + " color=\"#ff9999\",\n", + " alpha=0.20,\n", + " linewidth=0,\n", + " label=\"Percentiles (5th \\& 95th)\",\n", + " zorder=2,\n", + ")\n", + "label = \"$S$\" if def_mec == \"def_idm\" else \"$\\delta$\"\n", + "ax.set_xlabel(label)\n", "ax.set_ylabel(\"$\\epsilon$\")\n", - "ax.set_title(f\"{def_arg} vs $\\epsilon$\")\n", + "ax.set_title(\"Balancing privacy\")\n", "\n", "ax.set_ylim([1e-9, 100])\n", "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n", @@ -192,8 +247,9 @@ "# Plot: ess vs CN certainty\n", "fig, ax = plt.subplots(1, 1)\n", "\n", - "ax.plot(x_values, cn_certainty, \"-o\", markersize=4)\n", - "ax.set_xlabel(def_arg)\n", + "ax.plot(x_values, cn_certainty, \"-\", color=\"black\")\n", + "label = \"$S$\" if def_mec == \"def_idm\" else \"$\\delta$\"\n", + "ax.set_xlabel(label)\n", "ax.set_ylabel(\"Ratio of (maxmin CN prob. $> 0.5$)\")\n", "ax.set_title(\"CN certainty\")\n", "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)" @@ -207,7 +263,7 @@ "outputs": [], "source": [ "# Store accuracy results\n", - "acc = {\"acc_noisy_bn\": [], \"acc_cn_tot\": [], \"acc_cn_cert\": [], \"acc_cn_uncert\": []}\n", + "acc = {\"acc_noisy_bn\": {}, \"acc_cn_tot\": {}, \"acc_cn_cert\": {}, \"acc_cn_uncert\": {}}\n", "\n", "roc = {\n", " \"roc_cn_cert\": dict(),\n", @@ -223,7 +279,7 @@ " cn_cert, cn_uncert = split_data(data, \"cn_probs\", 0.5)\n", "\n", " # Comput accuracies\n", - " vs = \"gt\"\n", + " vs = \"bn\"\n", " acc_cn_cert = (\n", " get_acc_bn(cn_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(cn_cert) > 0 else None\n", " )\n", @@ -249,8 +305,7 @@ "\n", " # Store results\n", " for key in acc.keys():\n", - " value = eval(key)\n", - " acc[key].append(value)\n", + " acc[key][x] = eval(key)\n", " for key in roc.keys():\n", " roc[key][x] = eval(key)" ] @@ -266,18 +321,29 @@ "fig, ax = plt.subplots(1, 1)\n", "\n", "labels = {\n", - " \"acc_noisy_bn\": \"Noisy BN\",\n", - " \"acc_cn_tot\": \"CN (total)\",\n", " \"acc_cn_cert\": \"CN (certain)\",\n", " \"acc_cn_uncert\": \"CN (uncertain)\",\n", + " \"acc_cn_tot\": \"CN (total)\",\n", + " \"acc_noisy_bn\": \"Noisy BN\",\n", "}\n", "\n", + "colors = dict(\n", + " zip(labels.keys(), sns.color_palette(palette=\"seismic\", n_colors=len(labels)))\n", + ")\n", + "\n", "for key in acc.keys():\n", - " ax.plot(x_values, acc[key], \"-o\", label=labels[key], markersize=4)\n", + " accuracy = [x[0] if x else np.nan for x in acc[key].values()]\n", + " lower = [x[1] if x else np.nan for x in acc[key].values()]\n", + " upper = [x[2] if x else np.nan for x in acc[key].values()]\n", + " ax.plot(x_values, accuracy, \"-\", label=labels[key], color=colors[key])\n", + " ax.fill_between(\n", + " x_values, lower, upper, color=colors[key], alpha=0.25, zorder=2, linewidth=0\n", + " )\n", "\n", - "ax.set_xlabel(def_arg)\n", + "label = \"$S$\" if def_mec == \"def_idm\" else \"$\\delta$\"\n", + "ax.set_xlabel(label)\n", "ax.set_ylabel(\"Accuracy\")\n", - "ax.set_title(\"Accuracies (MAP estimation)\")\n", + "ax.set_title(\"MAP estimation (Wilson CI 95/%)\")\n", "ax.legend(loc=\"best\")\n", "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)" ] @@ -299,6 +365,10 @@ " \"roc_noisy_bn\": \"Noisy BN\",\n", "}\n", "\n", + "colors = dict(\n", + " zip(labels.keys(), sns.color_palette(palette=\"seismic\", n_colors=len(labels)))\n", + ")\n", + "\n", "i = 0\n", "for x in x_values:\n", " ax = axes.flatten()[i]\n", @@ -306,7 +376,7 @@ " for key in roc.keys():\n", " try:\n", " fpr, tpr, _ = roc[key][x]\n", - " ax.plot(fpr, tpr, label=labels[key], linewidth=1.3)\n", + " ax.plot(fpr, tpr, label=labels[key], linewidth=1.3, color=colors[key])\n", " except:\n", " continue\n", "\n", diff --git a/generate_compose.py b/generate_compose.py index c395af5..d2b74d0 100644 --- a/generate_compose.py +++ b/generate_compose.py @@ -1,4 +1,3 @@ -import sys from itertools import product import yaml diff --git a/src/attack.py b/src/attack.py index 428118a..6406d87 100644 --- a/src/attack.py +++ b/src/attack.py @@ -6,7 +6,7 @@ from src.config import get_cur_dir, set_seed from src.mia import get_ll -from src.utils import centroid_cn, sample_from_cn +from src.utils import centroid_cn, maxent_cn, sample_from_cn # Apply attack mechanism to a BN, namely, derive a BN from a CN @@ -53,6 +53,14 @@ def attack_mechanism(exp, config, atk_mec, atk_args) -> None: return +# Get the BN inside a CN with max entropy distribution +def atk_ent(bn_min, bn_max): + + bn = maxent_cn(bn_min, bn_max) + + return bn + + # Get a random BN inside a CN def atk_ran(bn_min, bn_max): diff --git a/src/config.py b/src/config.py index 0e8c9ba..8d9cc66 100644 --- a/src/config.py +++ b/src/config.py @@ -39,7 +39,7 @@ def map_sys_args(sys_args, config) -> tuple: if atk_mec == "atk_mle": atk_args["n_bns"] = int(params.pop("n_bns")) assert atk_args["n_bns"] >= 1 - elif atk_mec == "atk_cen" or atk_mec == "atk_ran": + elif atk_mec == "atk_cen" or atk_mec == "atk_ran" or atk_mec == "atk_ent": pass else: raise Exception("Attack not implemented") diff --git a/src/utils.py b/src/utils.py index ef08d3b..9b75f3e 100644 --- a/src/utils.py +++ b/src/utils.py @@ -31,6 +31,102 @@ def add_counts_to_bn(bn, data): bn.cpt(node).fillWith(counts_array.flatten().tolist()) +# Get the BN inside a CN with max entropy distribution +def maxent_cn(bn_min, bn_max) -> gum.BayesNet: + + # Init an empty BN + bn = gum.BayesNet(bn_min) + + # For each variable ... + for var in bn.names(): + + # ... get the centroid CPT, ... + cpt = maxent_cpt(bn_min.cpt(var), bn_max.cpt(var)) + + # ... and fill the BN + bn.cpt(var).fillWith(cpt.flatten()) + + # Debug + safe_assert(check_consistency(bn, bn_min, bn_max)) + + return bn + + +# Get the BN CPT inside a CN CPT with max entropy distribution +def maxent_cpt(cpt_min, cpt_max) -> np.array: + + # Transform CPTs into pandas dataframes + cpt_min = np.atleast_2d(cpt_min.topandas()) + cpt_max = np.atleast_2d(cpt_max.topandas()) + + # For each row in the CPT ... + cpt = [] + for row in range(cpt_min.shape[0]): + + # ... get the centroid credal set, ... + c = maxent_cset(cpt_min[row, :], cpt_max[row, :]) + cpt.append(c) + + # Reshape the CPT + cpt = np.array(cpt) + + # Debug + safe_assert(cpt_min.shape == cpt_max.shape) + safe_assert(cpt.shape == cpt_min.shape) + + return cpt + + +# Get the max-entropy distribution inside a credal set +def maxent_cset(vec_min, vec_max) -> np.array: + + rank = {v: k for k, v in enumerate(sorted(set(vec_min)))} + vec_order = np.array([rank[val] for val in vec_min]) + + s = 1 - np.sum(vec_min) + + out = vec_min + while s > 0: + idx0 = np.where(vec_order == 0)[0] + idx1 = np.where(vec_order == 1)[0] + idx_len = len(idx0) + + + + try: + s_cond = s / idx_len < out[idx1[0]] - out[idx0[0]] + mat = np.stack( + [ + ( + (s / idx_len) * np.ones(len(idx0)) + if s_cond + else out[idx1] - out[idx0] + ), + vec_max[idx0] - out[idx0], + ] + ) + except IndexError: + s_cond = True + mat = np.stack( + [(s / idx_len) * np.ones(len(idx0)), vec_max[idx0] - out[idx0]] + ) + + mat_min = np.min(mat) + q = np.argwhere(mat == mat_min) + + if np.any(q[:, 0] == 1): + if len(idx0) > len(q): + vec_order[~np.isin(np.arange(len(out)), idx0[q[:, 1]])] += 1 + elif not s_cond: + vec_order[idx0[q[:, 1]]] += 1 + + out[idx0] += mat_min + s -= mat_min * len(idx0) + vec_order -= 1 + + return out + + # Get the centroid of a CN def centroid_cn(bn_min, bn_max) -> gum.BayesNet: diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py index 7a33b77..fe165d2 100644 --- a/test/cn_privacy/test_integration.py +++ b/test/cn_privacy/test_integration.py @@ -63,3 +63,21 @@ def test_def_idm_atk_ran(monkeypatch): # Run experiment exp.main() + + +def test_def_ran_atk_ent(monkeypatch): + + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ent"] + ) + + # Run experiment + exp.main() + + +def test_def_idm_atk_ent(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ent"]) + + # Run experiment + exp.main() diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py index 72d0500..abfb7c9 100644 --- a/test/cn_vs_noisybn/test_integration.py +++ b/test/cn_vs_noisybn/test_integration.py @@ -63,3 +63,21 @@ def test_def_idm_atk_ran(monkeypatch): # Run experiment exp.main() + + +def test_def_ran_atk_ent(monkeypatch): + + monkeypatch.setattr( + sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ent"] + ) + + # Run experiment + exp.main() + + +def test_def_idm_atk_ent(monkeypatch): + + monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ent"]) + + # Run experiment + exp.main() diff --git a/test/unit/__init__.py b/test/unit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/unit/utils.py b/test/unit/utils.py new file mode 100644 index 0000000..0949ca5 --- /dev/null +++ b/test/unit/utils.py @@ -0,0 +1,12 @@ +import numpy as np + +from src.utils import maxent_cset + + +def test_maxent_cset(): + vec_min = np.array([0.3, 0.4, 0, 0.1]) + vec_max = np.array([0.6, 0.8, 0.12, 0.17]) + + out = maxent_cset(vec_min, vec_max) + + assert np.allclose(out, np.array([0.31, 0.4, 0.12, 0.17])) From 792bf0607297da618c2e41de6496a4b8d61422b8 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 2 Dec 2025 17:07:18 +0100 Subject: [PATCH 30/31] Fix GitHub actions --- .github/workflows/pylint.yml | 3 +-- .github/workflows/python-app.yml | 2 ++ README.md | 38 ++++++++++++++++++++++---------- 3 files changed, 29 insertions(+), 14 deletions(-) diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index c15fac9..bb488f9 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -19,7 +19,6 @@ jobs: run: | python -m pip install --upgrade pip pip install pylint - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - name: Analysing the code with pylint + - name: Analyzing code with pylint run: | pylint $(git ls-files '*.py') \ No newline at end of file diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index 9516be8..ad17c15 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -17,6 +17,8 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | + apt-get update + apt-get install -y build-essential swig libglpk-dev python3-dev libcdd-dev libgmp-dev python -m pip install --upgrade pip pip install flake8 pytest pytest-cov if [ -f requirements.txt ]; then pip install -r requirements.txt; fi diff --git a/README.md b/README.md index 511e356..98c7757 100644 --- a/README.md +++ b/README.md @@ -38,14 +38,14 @@ The `compose.yaml` file contains a set of pre-set experiments. Additional ones c Generate models and data for all experiments (controlled by `config.yaml`): -```bash +```sh python -m experiments.cn_privacy.generate python -m experiments.cn_vs_noisybn.generate ``` Run one or more experiments with: -```bash +```sh docker compose up [service name] ``` @@ -53,7 +53,7 @@ Results will be available under `experiments//output_*`. To check the status, run one or more of the following: -```bash +```sh docker compose ps docker compose logs [service name] docker stats @@ -63,14 +63,14 @@ docker stats Create and activate a Python virtual environment: -```bash +```sh python3 -m venv venv source venv/bin/activate[.fish] # use `.fish` suffix if using fish shell ``` Install dependencies: -```bash +```sh pip install -r requirements.txt ``` @@ -78,7 +78,7 @@ pip install -r requirements.txt Upgrade dependencies: -```bash +```sh pip install --upgrade $(pip freeze | cut -d '=' -f 1) pip freeze > requirements.txt ``` @@ -87,13 +87,13 @@ pip freeze > requirements.txt Generate models and data (controlled by `config.yaml`): -```bash +```sh python -m experiments..generate ``` Run an experiment: -```bash +```sh python -m experiments..exp def_mec= [param=value] atk_mec= [param=value] ``` @@ -104,7 +104,7 @@ Results will be available under `experiments//output`. Run integration tests: -```bash +```sh pytest [--cov=src] [--cov-report=term-missing] [--capture=no] ``` @@ -112,20 +112,34 @@ pytest [--cov=src] [--cov-report=term-missing] [--capture=no] Format code by running: -```bash +```sh black . isort . ``` Lint code by running: -```bash +```sh flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude=venv flake8 . --count --exit-zero --max-complexity=10 --ignore=E203 --max-line-length=140 --statistics --exclude=venv ``` Analyze code by running: -```bash +```sh pylint $(git ls-files '*.py') ``` + +## Running actions locally + +Install `act`: + +```sh +curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/nektos/act/master/install.sh | sudo bash +``` + +Run `act` with: + +```sh +sudo ./bin/act [-W ] +``` From 2770d6fbbc3d6948b7d6f87432e1563e7d514ad9 Mon Sep 17 00:00:00 2001 From: Niccolo-Rocchi Date: Tue, 2 Dec 2025 17:09:39 +0100 Subject: [PATCH 31/31] Fix GitHub actions by using `sudo` --- .github/workflows/python-app.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index ad17c15..8312b7a 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -17,8 +17,8 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | - apt-get update - apt-get install -y build-essential swig libglpk-dev python3-dev libcdd-dev libgmp-dev + sudo apt-get update + sudo apt-get install -y build-essential swig libglpk-dev python3-dev libcdd-dev libgmp-dev python -m pip install --upgrade pip pip install flake8 pytest pytest-cov if [ -f requirements.txt ]; then pip install -r requirements.txt; fi