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bartzbeielsteinbartzbeielstein
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0.23.0_alpha
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Lines changed: 648 additions & 238 deletions

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‎notebooks/00_spotPython_tests.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n",
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"Seed set to 123\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"S.X: [[ 0. 0. ]\n",
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" [ 0. 1. ]\n",
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" [ 1. 0. ]\n",
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" [ 1. 1. ]\n",
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" [-0.90924339 -0.15823458]\n",
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" [-0.20581711 -0.48124909]\n",
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" [ 0.94974117 -0.94631272]\n",
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" [-0.12095571 0.06383589]\n",
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" [-0.66278702 0.17431637]\n",
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" [ 0.28200844 0.93001011]\n",
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" [ 0.47878812 0.65321058]]\n",
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"S.y: [0. 1. 1. 2. 0.85176172 0.27396137\n",
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" 1.79751605 0.01870531 0.46967283 0.94444757 0.65592212]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from spotpython.fun.objectivefunctions import Analytical\n",
@@ -7256,25 +7228,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Moving TENSORBOARD_PATH: runs/ to TENSORBOARD_PATH_OLD: runs_OLD/runs_2025_01_12_10_59_57\n",
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"Created spot_tensorboard_path: runs/spot_logs/00_p040025_2025-01-12_10-59-57 for SummaryWriter()\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from spotpython.fun import Analytical\n",
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Design matrix: [[ 0.1 0.2 ]\n",
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" [ 0.3 0.4 ]\n",
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" [ 0.86352963 0.7892358 ]\n",
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" [-0.24407197 -0.83687436]\n",
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" [ 0.36481882 0.8375811 ]\n",
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" [ 0.415331 0.54468512]\n",
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" [-0.56395091 -0.77797854]\n",
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" [-0.90259409 -0.04899292]\n",
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" [-0.16484832 0.35724741]\n",
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" [ 0.05170659 0.07401196]\n",
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" [-0.78548145 -0.44638164]\n",
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" [ 0.64017497 -0.30363301]]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from spotpython.fun import Analytical\n",
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"S.X: [[ 0. 0. ]\n",
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" [ 0. 1. ]\n",
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" [ 1. 0. ]\n",
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" [ 1. 1. ]\n",
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" [ 0.86352963 0.7892358 ]\n",
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" [-0.24407197 -0.83687436]\n",
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" [ 0.36481882 0.8375811 ]\n",
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" [ 0.415331 0.54468512]\n",
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" [-0.56395091 -0.77797854]\n",
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" [-0.90259409 -0.04899292]\n",
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" [-0.16484832 0.35724741]\n",
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" [ 0.05170659 0.07401196]\n",
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" [-0.78548145 -0.44638164]\n",
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" [ 0.64017497 -0.30363301]]\n",
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"S.y: [0. 1. 1. 2. 1.36857656 0.75992983\n",
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" 0.83463487 0.46918172 0.92329124 0.8170764 0.15480068 0.00815134\n",
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" 0.81623768 0.502017 ]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from spotpython.fun.objectivefunctions import Analytical\n",
@@ -7526,6 +7425,217 @@
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" # Implement the logic to generate a filename\n",
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" return f\"{prefix}_experiment.pkl\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## transform_hyper_parameter_values()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'max_depth': 4, 'leaf_prediction': 'mean'}"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from spotpython.utils.transform import transform_hyper_parameter_values\n",
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"fun_control = {\n",
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" \"core_model_hyper_dict\": {\n",
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" \"leaf_prediction\": {\n",
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" \"type\": \"factor\",\n",
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" \"transform\": \"None\",\n",
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" \"default\": \"mean\",\n",
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" \"levels\": [\"mean\", \"model\", \"adaptive\"],\n",
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" \"core_model_parameter_type\": \"str\"\n",
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" },\n",
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" \"max_depth\": {\n",
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" \"type\": \"int\",\n",
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" \"default\": 20,\n",
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" \"transform\": \"transform_power_2\",\n",
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" \"lower\": 2,\n",
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" \"upper\": 20}\n",
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" }\n",
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" }\n",
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"hyper_parameter_values = {\n",
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" 'max_depth': 2,\n",
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" 'leaf_prediction': 'mean'}\n",
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"transform_hyper_parameter_values(fun_control, hyper_parameter_values)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'l1': 4,\n",
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" 'epochs': 8,\n",
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" 'batch_size': 16,\n",
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" 'act_fn': 'ReLU',\n",
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" 'optimizer': 'SGD',\n",
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" 'dropout_prob': 0.01,\n",
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" 'lr_mult': 1.0,\n",
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" 'patience': 8,\n",
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" 'batch_norm': 0,\n",
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" 'initialization': 'Default'}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from spotpython.utils.transform import transform_hyper_parameter_values\n",
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"fun_control = {\n",
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" \"core_model_hyper_dict\": {\n",
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" \"l1\": {\n",
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" \"type\": \"int\",\n",
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" \"default\": 3,\n",
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" \"transform\": \"transform_power_2_int\",\n",
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" \"lower\": 3,\n",
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" \"upper\": 8\n",
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" },\n",
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" \"epochs\": {\n",
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" \"type\": \"int\",\n",
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" \"default\": 4,\n",
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" \"transform\": \"transform_power_2_int\",\n",
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" \"lower\": 4,\n",
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" \"upper\": 9\n",
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" },\n",
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" \"batch_size\": {\n",
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" \"type\": \"int\",\n",
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" \"default\": 4,\n",
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" \"transform\": \"transform_power_2_int\",\n",
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" \"lower\": 1,\n",
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" \"upper\": 4\n",
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" },\n",
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" \"act_fn\": {\n",
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" \"levels\": [\n",
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" \"Sigmoid\",\n",
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" \"Tanh\",\n",
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" \"ReLU\",\n",
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" \"LeakyReLU\",\n",
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" \"ELU\",\n",
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" \"Swish\"\n",
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" ],\n",
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" \"type\": \"factor\",\n",
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" \"default\": \"ReLU\",\n",
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" \"transform\": \"None\",\n",
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" \"class_name\": \"spotpython.torch.activation\",\n",
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" \"core_model_parameter_type\": \"instance()\",\n",
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" \"lower\": 0,\n",
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" \"upper\": 5\n",
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" },\n",
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" \"optimizer\": {\n",
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" \"levels\": [\n",
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" \"Adadelta\",\n",
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" \"Adagrad\",\n",
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" \"Adam\",\n",
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" \"AdamW\",\n",
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" \"SparseAdam\",\n",
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" \"Adamax\",\n",
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" \"ASGD\",\n",
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" \"NAdam\",\n",
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" \"RAdam\",\n",
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" \"RMSprop\",\n",
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" \"Rprop\",\n",
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" \"SGD\"\n",
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" ],\n",
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" \"type\": \"factor\",\n",
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" \"default\": \"SGD\",\n",
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" \"transform\": \"None\",\n",
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" \"class_name\": \"torch.optim\",\n",
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" \"core_model_parameter_type\": \"str\",\n",
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" \"lower\": 0,\n",
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" \"upper\": 11\n",
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" },\n",
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" \"dropout_prob\": {\n",
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" \"type\": \"float\",\n",
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" \"default\": 0.01,\n",
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" \"transform\": \"None\",\n",
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" \"lower\": 0.0,\n",
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" \"upper\": 0.25\n",
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" },\n",
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" \"lr_mult\": {\n",
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" \"type\": \"float\",\n",
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" \"default\": 1.0,\n",
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" \"transform\": \"None\",\n",
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" \"lower\": 0.1,\n",
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" \"upper\": 10.0\n",
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" },\n",
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" \"patience\": {\n",
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" \"type\": \"int\",\n",
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" \"default\": 2,\n",
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" \"transform\": \"transform_power_2_int\",\n",
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" \"lower\": 2,\n",
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" \"upper\": 6\n",
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" },\n",
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" \"batch_norm\": {\n",
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" \"levels\": [\n",
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" 0,\n",
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" 1\n",
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" ],\n",
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" \"type\": \"factor\",\n",
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" \"default\": 0,\n",
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" \"transform\": \"None\",\n",
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" \"core_model_parameter_type\": \"bool\",\n",
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" \"lower\": 0,\n",
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" \"upper\": 1\n",
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" },\n",
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" \"initialization\": {\n",
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" \"levels\": [\n",
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" \"Default\",\n",
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" \"kaiming_uniform\",\n",
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" \"kaiming_normal\",\n",
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" \"xavier_uniform\",\n",
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" \"xavier_normal\"\n",
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" ],\n",
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" \"type\": \"factor\",\n",
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" \"default\": \"Default\",\n",
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" \"transform\": \"None\",\n",
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" \"core_model_parameter_type\": \"str\",\n",
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" \"lower\": 0,\n",
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" \"upper\": 4\n",
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" }\n",
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" }\n",
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"}\n",
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"\n",
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"hyper_parameter_values = {\n",
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" 'l1': 2,\n",
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" 'epochs': 3,\n",
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" 'batch_size': 4,\n",
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" 'act_fn': 'ReLU',\n",
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" 'optimizer': 'SGD',\n",
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" 'dropout_prob': 0.01,\n",
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" 'lr_mult': 1.0,\n",
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" 'patience': 3,\n",
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" 'batch_norm': 0,\n",
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" 'initialization': 'Default', \n",
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" }\n",
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"transform_hyper_parameter_values(fun_control, hyper_parameter_values)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎pyproject.toml‎

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@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "spotpython"
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version = "0.22.7"
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version = "0.23.0_alpha"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

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