[Metrics] Cleaned code

src/skit/ModelMetrics.py

@@ -39,10 +39,16 @@ class Metric(Enum):
 
     @property
     def train_metric_key(self):
+        """
+        Get the training metric key corresponding to the Metric enum value.
+        """
         return self.value
 
     @property
     def val_metric_key(self):
+        """
+        Get the validation metric key corresponding to the Metric enum value.
+        """
         if self == Metric.ACCURACY:
             return "val_accuracy"
         elif self == Metric.AUC:
@@ -56,12 +62,6 @@ def val_metric_key(self):
     def plot_labels(self):
         """
         Get the curve labels corresponding to the given Metric enum.
-
-        Parameters:
-        metric_enum (Metric): The Metric enum value.
-
-        Returns:
-        dict: A dictionary mapping curve labels to metric names.
         """
         if self == Metric.ACCURACY or self == Metric.VAL_ACCURACY:
             return {
@@ -88,6 +88,16 @@ def plot_labels(self):
 
 class ModelMetrics:
     def __init__(self, versions, metric_to_monitor=Metric.ACCURACY):
+        """
+        Initialize ModelMetrics class.
+
+        Parameters
+        ----------
+        versions : list
+            List of model versions to track.
+        metric_to_monitor : Metric
+            The metric to monitor (default is Accuracy).
+        """
         self.output = {}
         self.metric_to_monitor = metric_to_monitor
         for version in versions:
@@ -99,6 +109,14 @@ def __init__(self, versions, metric_to_monitor=Metric.ACCURACY):
             }
 
     def reset(self, version=None):
+        """
+        Reset the tracking for a specific version or all versions.
+
+        Parameters
+        ----------
+        version : str, optional
+            The specific version to reset. If None, reset all versions.
+        """
         default_dict = {
             "history":         None,
             "duration":        None,
@@ -114,6 +132,17 @@ def reset(self, version=None):
                 self.output[version] = default_dict.copy()
 
     def get_best_metric(self, version):
+        """
+        Get the best training and validation metrics for a specific model version.
+
+        Parameters
+        ----------
+        Args:
+            version (str): The model version to retrieve metrics for.
+
+        Returns:
+            dict: Dictionary containing best training and validation metrics.
+        """
         history = self.output[version]['history'].history
 
         train_metric_key = self.metric_to_monitor.train_metric_key
@@ -137,11 +166,9 @@ def get_best_report(self, version):
         version : str
             The model version for which to get the best model report.
 
-        Returns
-        -------
-        dict or None
-            The best model report containing training and validation metrics, duration, and paths.
-            Returns None if the specified version is not found in the output.
+        Returns:
+            dict or None: The best model report containing training and validation metrics,
+            duration, and paths. Returns None if the specified version is not found in the output.
         """
         if version not in self.output:
             return None
@@ -287,6 +314,47 @@ def show_history(
         version,
         figsize=(8,6)
     ):
+        """
+        Visualizes the training and validation metrics from the model's history using matplotlib.
+
+        The function generates separate plots for each main category (like 'Accuracy' and 'Loss')
+        defined in the `plot` parameter. For each main category, multiple curves (like 'Training Accuracy'
+        and 'Validation Accuracy') can be plotted based on the nested dictionary values.
+
+        Parameters:
+        -----------
+        history : dict
+            The history object typically returned from the .fit() method of a Keras model. It should
+            have a 'history' attribute containing the training and validation metrics.
+
+        figsize : tuple, optional
+            The width and height in inches for the figure. Defaults to (8,6).
+
+        plot : dict, optional
+            A nested dictionary defining the metrics to be plotted.
+            - The top-level key corresponds to the main category (e.g., 'Accuracy' or 'Loss').
+            - The associated nested dictionary's keys are the curve labels (e.g., 'Training Accuracy')
+              and the values are the corresponding metric names in the 'history' object (e.g., 'accuracy').
+            Defaults to plotting both training and validation accuracy and loss.
+
+        Example:
+        --------
+        show_history(
+            model_history,
+            figsize=(10,8),
+            plot={
+                "Title A": {
+                    "Legend Title 1": "metric_name_1",
+                    "Legend Title 2": "metric_name_2"
+                    }
+                }
+        )
+
+        Note:
+        -----
+        The `plot` parameter allows you to customize which metrics to plot and how they are labeled
+        in the generated visualization.
+        """
         history = self.output[version]['history']
         plot = self.metric_to_monitor.plot_labels
         display(show_history(history, figsize=figsize, plot=plot))