Improve the feature selection method with validations etc.
Primoz
parent
0594993133
commit
259be708aa
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@ -29,9 +29,9 @@ class FeatureSelection:
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self.groups = groups
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def select_best_feature(self, features, method="remove", ml_type="classification", metric="recall", stored_features=[]):
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def select_best_feature(self, features, method="remove", ml_category="classification", ml_subcategory="bin", metric="recall", stored_features=[]):
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"""The method selects the best feature by testing the prediction on the feature set with or without the current feature.
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The "remove" method removes a particular feature and predicts on the test set without it. The "add" method adds a particulat
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The "remove" method removes a particular feature and predicts on the test set without it. The "add" method adds a particular
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feature to the previously established feature set (stored_features). The best feature is selected dependent on the metric
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specified as a parameter.
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@ -39,7 +39,11 @@ class FeatureSelection:
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df (DataFrame): Input data on which the predictions will be made.
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features (list): List of features to select the best/worst from
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method (str, optional): remove or add features. Defaults to "remove".
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ml_type (str, optional): Either classification or regression ml problem controls the ML algorithm and metric. Defaults to "classification".
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ml_category (str, optional): Either classification or regression ml problem controls the ML algorithm and metric.
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Defaults to "classification".
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ml_subcategory (str, optional): In case of classification '_bin' for binary classification
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and 'multi' for multiclass classification. For regression an empty string '' is sufficient.
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Defaults to "bin".
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metric (str, optional): Selected metric with which the best/worst feature will be determined. Defaults to "recall".
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stored_features (list, optional): In case if method is 'add', stored features refer to the features that had been previously added. Defaults to [].
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@ -53,9 +57,25 @@ class FeatureSelection:
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best_feature = None
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if ml_type == "classification" and metric not in ['accuracy', 'precision', 'recall', 'f1']:
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raise ValueError("Classification metric not recognized. Please choose 'accuracy', 'precision', 'recall' and/or 'f1'")
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elif ml_type == "regression" and metric not in ['r2']:
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# Validacije tipov ML in specificiranimi metrikami
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if ml_category == "classification":
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if ml_subcategory == "bin" and metric not in ['accuracy', 'precision', 'recall', 'f1']:
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raise ValueError("Classification metric not recognized. Please choose 'accuracy', 'precision', 'recall' and/or 'f1'")
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elif ml_subcategory == "multi":
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ml_subcategory_error = False
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if metric != "accuracy" and "_" in metric:
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metric_s, metric_t = metric.split("_")
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if metric_s not in ['accuracy', 'precision', 'recall', 'f1'] or metric_t not in ['micro', 'macro', 'weighted']:
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ml_subcategory_error = True
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else:
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ml_subcategory_error = True
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if ml_subcategory_error:
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raise ValueError(""""Classification metric for multi-class classification must be specified precisely.
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Available metric are: 'accuracy', 'precision', 'recall' and 'f1'.
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Only accuracy must be specified as 'accuracy'.
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For others please add appropriate suffixes: '_macro', '_micro', or '_weighted', e.g., 'f1_macro'""")
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elif ml_category == "regression" and metric not in ['r2']:
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raise ValueError("Regression metric not recognized. Please choose 'r2'")
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for feat in features:
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@ -73,7 +93,7 @@ class FeatureSelection:
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# See link about scoring for multiclassfication
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# http://iamirmasoud.com/2022/06/19/understanding-micro-macro-and-weighted-averages-for-scikit-learn-metrics-in-multi-class-classification-with-example/
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if ml_type == "classification":
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if ml_category == "classification":
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nb = GaussianNB()
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model_cv = cross_validate(
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nb,
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@ -85,18 +105,8 @@ class FeatureSelection:
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scoring=(metric)
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)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", message="Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.")
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metric_score = np.nanmean(model_cv[f'test_{metric}'])
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metric_score_std = np.nanstd(model_cv[f'test_{metric}'])
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if not best_feature or (metric_score > best_metric_score):
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best_feature = feat
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best_metric_score = metric_score
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best_metric_score_std = metric_score_std
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elif ml_type == "regression":
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elif ml_category == "regression":
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lass = Lasso()
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model_cv = cross_validate(
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lass,
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@ -108,16 +118,20 @@ class FeatureSelection:
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scoring=('r2')
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)
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if metric == "r2":
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r2 = np.mean(model_cv['test_r2'])
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r2_std = np.std(model_cv['test_r2'])
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if not best_feature or (r2 > best_metric_score):
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best_feature = feat
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best_metric_score = r2
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best_metric_score_std = r2_std
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else:
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raise ValueError("ML type not yet implemented!")
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# Section of metrics' scores comparison.
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", message="Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.")
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metric_score = np.nanmean(model_cv["test_score"])
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metric_score_std = np.nanstd(model_cv["test_score"])
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if not best_feature or (metric_score > best_metric_score):
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best_feature = feat
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best_metric_score = metric_score
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best_metric_score_std = metric_score_std
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return best_feature, best_metric_score, best_metric_score_std
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@ -137,9 +151,10 @@ class FeatureSelection:
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Args:
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n_min (int, optional): Minimal amount of features returned.
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n_max (int, optional): Maximal amount of features returned.
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k (int, optional): Determines the k in the k-best features method.
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k (int, optional): Determines the k in the k-best features method.
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If None, SelectKBest feature selection does not execute.
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ml_type(str, optional): Type of ML problem. Currently implemented options:
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classification_bin, classification_multi, and regression_
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'classification_bin', 'classification_multi', and 'regression_'
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method (str, optional): "remove" or "add" features. Defaults to "remove".
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n_tolerance (int, optional): If the best score is not improved in n that is specified by this parameter
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the method returns index of feature with current best score as a tipping point feature.
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@ -147,35 +162,38 @@ class FeatureSelection:
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Returns:
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list: list of selected features
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"""
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n_features = self.X.shape[1]
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if n_max >= n_features:
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n_max = n_features-1 # The algorithm removes at least one feature
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if k < n_max:
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raise ValueError("The k parameter needs to be lower than the n_max parameter.")
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if k is not None and k <= n_max:
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raise ValueError("The k parameter needs to be greater than the n_max parameter.")
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# Select k-best feature dependent on the type of ML task
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ml_type = ml_type.split("_")
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if ml_type[0] == "classification":
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if ml_type[1] == "bin":
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selector = SelectKBest(mutual_info_classif, k=k)
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elif ml_type[1] == "multi":
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selector = SelectKBest(f_classif, k=k)
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ml_category, ml_subcategory = ml_type.split("_")
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if k is not None:
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if ml_category == "classification":
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if ml_subcategory== "bin":
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selector = SelectKBest(mutual_info_classif, k=k)
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elif ml_subcategory== "multi":
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selector = SelectKBest(f_classif, k=k)
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else:
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raise ValueError("Unknown ML type: cannot recognize ML classification subtype.")
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elif ml_category == "regression":
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selector = SelectKBest(f_regression, k=k)
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else:
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raise ValueError("Unknown ML type: cannot recognize ML classification subtype.")
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elif ml_type[0] == "regression":
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selector = SelectKBest(f_regression, k=k)
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else:
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raise ValueError("Unknown ML type: cannot recognize ML type. Must be either classification or regression.")
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selector.fit(self.X, self.y)
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cols_idxs = selector.get_support(indices=True)
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self.X = self.X.iloc[:,cols_idxs]
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raise ValueError("Unknown ML type: cannot recognize ML type. Must be either classification or regression.")
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selector.fit(self.X, self.y)
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cols_idxs = selector.get_support(indices=True)
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self.X = self.X.iloc[:,cols_idxs]
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print("All columns (after SelectKBest method):")
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print(self.X.columns)
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# Sequential feature addition / removal
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n_features = self.X.shape[1]
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if n_max >= n_features:
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n_max = n_features-1 # The algorithm removes at least one feature
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if n_min > n_features:
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raise ValueError("The number of remaining features in the dataframe must be at least as n_min+1 parameter.")
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@ -190,17 +208,16 @@ class FeatureSelection:
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i_worse = 0
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for i in reversed(range(n_features)):
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print("Iteration:", i+1)
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if i+1 == n_min:
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break
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best_feature, best_metric_score, best_metric_score_std = \
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self.select_best_feature(features, method=method, ml_type=ml_type[0], metric=metric)
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self.select_best_feature(features, method=method, ml_category=ml_category, ml_subcategory=ml_subcategory, metric=metric)
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feature_importance.append((i+1, best_feature, best_metric_score, best_metric_score_std))
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features.remove(best_feature)
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print("Features left:", i)
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if i <= n_max:
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if best_metric_score >= best_score:
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@ -223,49 +240,6 @@ class FeatureSelection:
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selected_features = [feat for feat in self.X.columns.tolist() if feat not in features_to_remove]
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return selected_features
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"""
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# Selekcijski kriterij značilk v rangu max-min
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# Npr. izbira najboljšega score-a v tem rangu. Ali pa dokler se v tem rangu score zvišuje za 0.0X, ko se ne izberi tisti set značilk.
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# Set značilk se bo izbral od i=1 do i=index_izbrane_značilke
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# "Tipping point" značilka mora biti v rangu max-min
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selection_area = feature_importance_df[(feature_importance_df["i"] >= n_min+1) & (feature_importance_df["i"] <= n_max)]
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selection_area.set_index(["i", "name"], inplace=True)
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print(selection_area)
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diffrences = selection_area.diff()
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diffrences.dropna(how='any', inplace=True)
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print(diffrences)
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# Morda tudi komulativna sumacija? Kjer se preprosto index z najvišjo vrednostjo
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cumulative_sumation = diffrences.cumsum()
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print(cumulative_sumation)
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tipping_feature_indx_1 = cumulative_sumation.idxmax()["metric"]
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print(tipping_feature_indx_1)
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# Metoda, ki pusti n_tolerance značilkam, da premagajo dosedajno najboljši score
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tipping_feature_indx_2 = None
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best_score = 0
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i_worse = 0
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for indx, row in selection_area.iterrows():
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if row["metric"] > best_score:
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tipping_feature_indx_2 = indx
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best_score = row["metric"]
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i_worse = 0
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else:
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i_worse += 1
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if i_worse == n_tolerance:
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break
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print(tipping_feature_indx_2)
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selection_area.reset_index(inplace=True)
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features_to_remove = feature_importance_df[feature_importance_df["i"] >= tipping_feature_indx_2[0]]["name"].values.tolist()
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selected_features = [feat for feat in self.X.columns.tolist() if feat not in features_to_remove]
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"""
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else:
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raise ValueError("Method type not recognized: only the 'remove' method is currently implemented.")
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