Addressed issues with an extremely imbalanced and small dataset by re…

…moving NaN values from the metrics. (This is a temporary fix.)

machine/learn/skl_utils.py

@@ -175,8 +175,8 @@ def get_column_names_from_ColumnTransformer(column_transformer, feature_names):
             new_feature_names += feature_columns
     return new_feature_names
 
-# decision rule for cross validation 2, 3, 4, 5, 6, 7, 8, 9, 10
-def decision_rule_cv_based_on_classes(each_class):
+#  decision rule for choosing number of folds based on the class distribution in the given dataset
+def decision_rule_fold_cv_based_on_classes(each_class):
     """
     Adjusts the number of cross-validation folds based on the class distribution.
     
@@ -188,23 +188,19 @@ def decision_rule_cv_based_on_classes(each_class):
     Returns
     -------
     cv : int
-        Adjusted number of cross-validation folds.
+        The suitable number of cross-validation folds ensuring that each fold can include instances of each class.
     """
-    # Find the class with the minimum number of samples based on the class sample counts
-    min_samples = min(each_class.values())
+    # Find the minimum class count to ensure every fold can contain at least one instance of every class.
+    min_class_count = min(each_class.values())
 
-    # Calculate the number of classes
-    n_classes = len(each_class)
+    # The maximum number of folds is determined by the smallest class to ensure representation in each fold.
+    # However, we cannot have more folds than the minimum class count.
+    n_folds = min(10, min_class_count)  # Starting with a default max of 10 folds
 
-    # Determine the appropriate number of cv folds based on the class with the minimum samples
-    if n_classes == 2:
-        # For binary classification, ensure at least one sample of each class is present in the folds, to the extent possible
-        n_split = min(max(2, min_samples), 10)
-    else:
-        # For multi-class, use more folds if possible to balance between classes
-        n_split = min(max(3, min_samples), 10)
+    # Ensure at least 2 folds for meaningful cross-validation.
+    n_folds = max(n_folds, 2)
 
-    return n_split
+    return n_folds
 
 def generate_results(model, input_data,
                      tmpdir, _id, target_name='class',
@@ -418,29 +414,17 @@ def generate_results(model, input_data,
                                 target, cv, return_times=True)
             model.fit(features, target)
 
-        # # plot learning curve
-        # plot_learning_curve(tmpdir,_id, model,features,target,cv,return_times=True)
-        # StratifiedKFold 
-        # stratified_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
-        # Initialize RepeatedStratifiedKFold
+
 
-        # n_splits = 2
-        # n_repeats = 2
-        # stratified_cv = RepeatedStratifiedKFold(n_splits=n_splits, n_repeats=n_repeats, random_state=42)
-
-        # print("stratified_cv", stratified_cv)
-        # computing cross-validated metrics
-
-        # Temporary fix to handle NaN values
-        stratified_cv = StratifiedKFold(n_splits=8)
 
 
         cv_scores = cross_validate(
             estimator=model,
             X=features,
             y=target,
             scoring=scoring,
-            cv = stratified_cv,
+            # cv = stratified_cv,
+            cv = cv,
             return_train_score=True,
             return_estimator=True
         )
@@ -784,9 +768,9 @@ def plot_confusion_matrix(
     None
     """
     pred_y = np.empty(y.shape)
-    # cv = StratifiedKFold(n_splits=10)
+    cv = StratifiedKFold(n_splits=10)
     # Temporary fix to handle NaN values
-    cv = StratifiedKFold(n_splits=8)
+    # cv = StratifiedKFold(n_splits=8)
     for cv_split, est in zip(cv.split(X, y), cv_scores['estimator']):
         train, test = cv_split
         pred_y[test] = est.predict(X[test])
@@ -1079,9 +1063,9 @@ def plot_roc_curve(tmpdir, _id, X, y, cv_scores, figure_export):
     """
     from scipy import interp
     from scipy.stats import sem, t
-    # cv = StratifiedKFold(n_splits=10)
+    cv = StratifiedKFold(n_splits=10)
     # Temporary fix to handle NaN values
-    cv = StratifiedKFold(n_splits=8)
+    # cv = StratifiedKFold(n_splits=8)
     tprs = []
     aucs = []
     mean_fpr = np.linspace(0, 1, 100)