v0.5.0

Tensorboard in core spot

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.4.0"
+version = "0.5.0"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/build/kriging.py

@@ -58,6 +58,8 @@ def __init__(
             n_p=1,
             optim_p=False,
             log_level=50,
+            spot_writer=None,
+            counter=None,
             **kwargs
     ):
         """
@@ -172,6 +174,8 @@ def __init__(
         self.name = name
         self.seed = seed
         self.log_level = log_level
+        self.spot_writer = spot_writer
+        self.counter = counter
 
         self.sigma = 0
         self.eps = sqrt(spacing(1))
@@ -299,6 +303,21 @@ def update_log(self):
         self.log["theta"] = append(self.log["theta"], self.theta)
         self.log["p"] = append(self.log["p"], self.p)
         self.log["Lambda"] = append(self.log["Lambda"], self.Lambda)
+        # get the length of the log
+        self.log_length = len(self.log["negLnLike"])
+        if self.spot_writer is not None:
+            writer = self.spot_writer
+            negLnLike = self.negLnLike.copy()
+            theta = self.theta.copy()
+            p = self.p.copy()
+            Lambda = self.Lambda.copy()
+            writer.add_scalar("negLnLike", negLnLike, self.counter+self.log_length)
+            writer.add_scalar("Lambda", Lambda, self.counter+self.log_length)
+            # add the self.n_theta theta values to the writer with one key "theta", i.e, the same key for all theta values
+            writer.add_scalars("theta", {f"theta_{i}": theta[i] for i in range(self.n_theta)}, self.counter+self.log_length)
+            # add the self.n_p p values to the writer with one key "p", i.e, the same key for all p values
+            writer.add_scalars("p", {f"p_{i}": p[i] for i in range(self.n_p)}, self.counter+self.log_length)
+            writer.flush()
 
     def fit_old(self, nat_X, nat_y):
         """

src/spotPython/fun/hypertorch.py

@@ -79,7 +79,7 @@ def fun_torch(self, X, fun_control=None):
                         device=self.fun_control["device"],
                         show_batch_interval=self.fun_control["show_batch_interval"],
                         task=self.fun_control["task"],
-                        writer=self.fun_control["writer"],
+                        writer=self.fun_control["spot_writer"],
                         writerId=config_id,
                     )
                 elif self.fun_control["eval"] == "test_cv":
@@ -90,7 +90,7 @@ def fun_torch(self, X, fun_control=None):
                         device=self.fun_control["device"],
                         show_batch_interval=self.fun_control["show_batch_interval"],
                         task=self.fun_control["task"],
-                        writer=self.fun_control["writer"],
+                        writer=self.fun_control["spot_writer"],
                         writerId=config_id,
                     )
                 elif self.fun_control["eval"] == "test_hold_out":
@@ -105,7 +105,7 @@ def fun_torch(self, X, fun_control=None):
                         show_batch_interval=self.fun_control["show_batch_interval"],
                         path=self.fun_control["path"],
                         task=self.fun_control["task"],
-                        writer=self.fun_control["writer"],
+                        writer=self.fun_control["spot_writer"],
                         writerId=config_id,
                     )
                 else:  # eval == "train_hold_out"
@@ -119,16 +119,16 @@ def fun_torch(self, X, fun_control=None):
                         show_batch_interval=self.fun_control["show_batch_interval"],
                         path=self.fun_control["path"],
                         task=self.fun_control["task"],
-                        writer=self.fun_control["writer"],
+                        writer=self.fun_control["spot_writer"],
                         writerId=config_id,
                     )
             except Exception as err:
                 print(f"Error in fun_torch(). Call to evaluate_model failed. {err=}, {type(err)=}")
                 print("Setting df_eval to np.nan")
                 df_eval = np.nan
             z_val = fun_control["weights"] * df_eval
-            if self.fun_control["writer"] is not None:
-                writer = self.fun_control["writer"]
+            if self.fun_control["spot_writer"] is not None:
+                writer = self.fun_control["spot_writer"]
                 writer.add_hparams(config, {"fun_torch: loss": z_val})
                 writer.flush()
             z_res = np.append(z_res, z_val)

src/spotPython/spot/spot.py

@@ -213,6 +213,18 @@ def __init__(
             "seed": 124,
             "use_cod_y": False,
         }
+        # Logging information:
+        self.counter = 0
+        self.min_y = None
+        self.min_X = None
+        self.min_mean_X = None
+        self.min_mean_y = None
+        self.mean_X = None
+        self.mean_y = None
+        self.var_y = None
+        logger.setLevel(self.log_level)
+        logger.info(f"Starting the logger at level {self.log_level} for module {__name__}:")
+
         self.surrogate_control.update(surrogate_control)
         # If no surrogate model is specified, use the internal
         # spotPython kriging surrogate:
@@ -233,24 +245,15 @@ def __init__(
                 cod_type=self.surrogate_control["cod_type"],
                 var_type=self.surrogate_control["var_type"],
                 use_cod_y=self.surrogate_control["use_cod_y"],
+                spot_writer=self.fun_control["spot_writer"],
+                counter=self.design_control["init_size"] * self.design_control["repeats"] - 1,
             )
         # Optimizer related information:
         self.optimizer = optimizer
         self.optimizer_control = {"max_iter": 1000, "seed": 125}
         self.optimizer_control.update(optimizer_control)
         if self.optimizer is None:
             self.optimizer = optimize.differential_evolution
-        # Logging information:
-        self.counter = 0
-        self.min_y = None
-        self.min_X = None
-        self.min_mean_X = None
-        self.min_mean_y = None
-        self.mean_X = None
-        self.mean_y = None
-        self.var_y = None
-        logger.setLevel(self.log_level)
-        logger.info(f"Starting the logger at level {self.log_level} for module {__name__}:")
 
     def to_red_dim(self):
         self.all_lower = self.lower
@@ -334,6 +337,9 @@ def run(self, X_start=None):
             self.fit_surrogate()
             # progress bar:
             self.show_progress_if_needed(timeout_start)
+        if self.fun_control["spot_writer"] is not None:
+            writer = self.fun_control["spot_writer"]
+            writer.close()
         return self
 
     def initialize_design(self, X_start=None):
@@ -404,6 +410,8 @@ def update_stats(self):
 
         """
         self.min_y = min(self.y)
+        # get the last y value:
+        self.last_y = self.y[-1]
         self.min_X = self.X[argmin(self.y)]
         self.counter = self.y.size
         # Update aggregated x and y values (if noise):
@@ -414,6 +422,14 @@ def update_stats(self):
             self.var_y = Z[2]
             self.min_mean_y = min(self.mean_y)
             self.min_mean_X = self.mean_X[argmin(self.mean_y)]
+        if self.fun_control["spot_writer"] is not None:
+            writer = self.fun_control["spot_writer"]
+            y_min = self.min_y.copy()
+            y_last = self.last_y.copy()
+            X_min = self.min_X.copy()
+            writer.add_scalars("spot_y", {"min": y_min, "last": y_last}, self.counter)
+            writer.add_scalars("spot_X", {f"X_{i}": X_min[i] for i in range(self.k)}, self.counter)
+            writer.flush()
 
     def suggest_new_X_old(self):
         """

src/spotPython/utils/file.py

@@ -3,6 +3,7 @@
 import socket
 from datetime import datetime
 from dateutil.tz import tzlocal
+import pickle
 
 
 def load_data(data_dir="./data"):
@@ -41,3 +42,35 @@ def get_experiment_name(prefix: str = "00") -> str:
     experiment_name = prefix + "_" + HOSTNAME + "_" + str(start_time).split(".", 1)[0].replace(" ", "_")
     experiment_name = experiment_name.replace(":", "-")
     return experiment_name
+
+
+def save_pickle(obj, filename: str):
+    """Saves an object as a pickle file.
+        Add .pkl to the filename.
+    Args:
+        obj (object): Object to be saved.
+        filename (str): Name of the pickle file.
+    Examples:
+        >>> from spotPython.utils.file import save_pickle
+        >>> save_pickle(obj, filename="obj.pkl")
+    """
+    filename = filename + ".pkl"
+    with open(filename, "wb") as f:
+        pickle.dump(obj, f)
+
+
+def load_pickle(filename: str):
+    """Loads a pickle file.
+        Add .pkl to the filename.
+    Args:
+        filename (str): Name of the pickle file.
+    Returns:
+        object: Loaded object.
+    Examples:
+        >>> from spotPython.utils.file import load_pickle
+        >>> obj = load_pickle(filename="obj.pkl")
+    """
+    filename = filename + ".pkl"
+    with open(filename, "rb") as f:
+        obj = pickle.load(f)
+    return obj

src/spotPython/utils/init.py

@@ -71,9 +71,9 @@ def fun_control_init(
         # Starting with v0.2.41, Summary Writer should be not initialized here but by Lightning
         # it is only available for compatibility reasons.
         # So, set this to None and let Lightning manage the logging.
-        writer = SummaryWriter(tensorboard_path)
+        spot_writer = SummaryWriter(tensorboard_path)
     else:
-        writer = None
+        spot_writer = None
 
     # Path to the folder where the pretrained models are saved
     CHECKPOINT_PATH = os.environ.get("PATH_CHECKPOINT", "saved_models/")
@@ -121,7 +121,7 @@ def fun_control_init(
         "task": task,
         "tensorboard_path": tensorboard_path,
         "weights": 1.0,
-        "writer": writer,
+        "spot_writer": spot_writer,
     }
     return fun_control