Implement feature selection methods (WIP).

machine_learning/feature_selection.py

@@ -7,13 +7,12 @@ import pandas as pd
 
 from sklearn.feature_selection import SequentialFeatureSelector
 from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model import Lasso 
 
 
 """ Feature selection pipeline: a methods that can be used in the wrapper metod alongside other wrapper contents (hyperparameter tuning etc.).
 
-(1) Establish methods for each of the steps in feature selection protocol: 
+(1) Establish methods for each of the steps in feature selection protocol.
-    (a) feature selection inside specific sensors (sklearn method): returns most important features from all sensors 
-    (b) feature selection between "tuned" sensors: returns filtered sensors, containing most important features retured with (a)
 (2) Ensure that above methods are given only a part of data and use appropriate random seeds - to later simulate use case in production. 
 (3) Implement a method which gives graphical exploration of (1) (a) and (b) steps of the feature selection.
 (4) Prepare a core method that can be fit into a wrapper (see sklearn wrapper methods) and integrates methods from (1)
@@ -23,19 +22,196 @@ from sklearn.naive_bayes import GaussianNB
 class FeatureSelection:
 
     def __init__(self, X_train, X_test, y_train, y_test): # TODO: what about leave-one-subject-out CV?
-        pass
+        pass # TODO.... 
 
     
-    def within_sensors_feature_selection(estimator, scoring, tol):
+    def select_best_feature(df, features, method="remove", ml_type="classification", metric="recall", stored_features=[]):
-        features_list = []
+        """The method selects the best feature by testing the prediction on the feature set with or without the current feature.
+        The "remove" method removes a particular feature and predicts on the test set without it. The "add" method adds a particulat 
+        feature to the previously established feature set (stored_features). The best feature is selected dependent on the metric
+        specified as a parameter.
 
-        nb = GaussianNB()
+        Args:
-        sfs = SequentialFeatureSelector(nb, n_features_to_select='auto', tol=0.02) # Can set n_features to an absolute value -> then remove tol parameter.
+            df (DataFrame): Input data on which the predictions will be made.
+            features (list): List of features to select the best/worst from
+            method (str, optional): remove or add features.  Defaults to "remove".
+            ml_type (str, optional): Either classification or regression ml problem controls the ML algorithm and  metric. Defaults to "classification".
+            metric (str, optional): Selected metric with which the best/worst feature will be determined. Defaults to "recall".
+            stored_features (list, optional): In case if method is 'add', stored features refer to the features that had been previously added. Defaults to [].
+
+        Raises:
+            ValueError: Raises if classification or regression metrics are not recognised if a specific ml_type is selected.
+            ValueError: If unknown ml_type is chosen. 
+            
+        Returns:
+            tuple: name of the best feature, best feature score, best feature score standard deviation.
+        """
+        
+        best_feature = None
+        
+        if ml_type == "classification" and metric not in ['accuracy', 'precision', 'recall', 'f1']:
+            raise ValueError("Classification metric not recognized. Please choose 'accuracy', 'precision', 'recall' and/or 'f1'")
+        elif ml_type == "regression" and metric not in ['r2']:
+            raise ValueError("Regression metric not recognized. Please choose 'r2'")
+
+        for feat in features:
+            if method == "remove":
+                pred_features = [col for col in df.columns if feat != col] # All but feat
+            elif method == "add":
+                pred_features = [feat] + stored_features # Feat with stored features
+            
+            X, y  = df.drop(columns=['target', 'pid'])[pred_features], df['target']
+            
+            if ml_type == "classification":
+                nb = GaussianNB()
+                model_cv = cross_validate(
+                    nb,
+                    X=X,
+                    y=y,
+                    cv=StratifiedKFold(n_splits=5, shuffle=True),
+                    n_jobs=-1,
+                    scoring=('accuracy', 'precision', 'recall', 'f1')
+                )
+                
+                with warnings.catch_warnings():
+                    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.")
+
+                    if metric == "accuracy":
+                        acc = np.mean(model_cv['test_accuracy'])
+                        acc_std = np.std(model_cv['test_accuracy'])
+                        
+                        if not best_feature or (acc > best_metric_score):
+                            best_feature = feat
+                            best_metric_score = acc
+                            best_metric_score_std = acc_std
+                    
+                    elif metric == "precision":
+                        prec = np.mean(model_cv['test_precision'])
+                        prec_std = np.std(model_cv['test_precision'])
+                        
+                        if not best_feature or (prec > best_metric_score):
+                            best_feature = feat
+                            best_metric_score = prec
+                            best_metric_score_std = prec_std
+                    
+                    elif metric == "recall":
+                        rec = np.mean(model_cv['test_recall'])
+                        rec_std = np.std(model_cv['test_recall'])
+                        
+                        if not best_feature or (rec > best_metric_score):
+                            best_feature = feat
+                            best_metric_score = rec
+                            best_metric_score_std = rec_std
+                    
+                    else:
+                        f1 = np.mean(model_cv['test_f1'])
+                        f1_std = np.std(model_cv['test_f1'])
+                        
+                        if not best_feature or (f1 > best_metric_score):
+                            best_feature = feat
+                            best_metric_score = f1
+                            best_metric_score_std = f1_std 
+                                       
+            elif ml_type == "regression":
+                lass = Lasso()
+                model_cv = cross_validate(
+                    lass,
+                    X=X,
+                    y=y,
+                    cv=StratifiedKFold(n_splits=5, shuffle=True),
+                    n_jobs=-1,
+                    scoring=('r2')
+                )
+
+                if metric == "r2":
+                    r2 = np.mean(model_cv['test_r2'])
+                    r2_std = np.std(model_cv['test_r2'])
+                    
+                    if not best_feature or (r2 > best_metric_score):
+                        best_feature = feat
+                        best_metric_score = r2
+                        best_metric_score_std = r2_std
+            else:
+                raise ValueError("ML type not yet implemented!")
+                    
+        return best_feature, best_metric_score, best_metric_score_std
     
     
-        return features_list
+    def select_features(df, n_min=20, n_max=50, method="remove", n_not_improve=10):
         
-    def between_sensors_feature_selection():
+        n_features = df.shape[1] - 2 # -2 beacause pid and target are not considered
+        if n_max > n_features:
+            n_max = n_features
+        
+        if n_min > n_features:
+            raise ValueError("The number of features in the dataframe must be at least as n_min-1 parameter.")
+        
+        if n_max < n_min:
+            raise ValueError("n_max parameter needs to be greater than or equal to n_min parameter.")
+        
+        features = df.columns.tolist()
+        features.remove("pid")
+        features.remove("target")
+        feature_importance = []
+        if method == "remove":
+            for i in reversed(range(n_features)):
+                
+                best_feature, best_metric_score, best_metric_score_std = \
+                    self.select_best_feature(df, features, method=method, ml_type="classification", metric="recall")
+                feature_importance.append(tuple(i+1, best_feature, best_metric_score, best_metric_score_std))
+                
+                features.remove(best_feature)
+                
+            feature_importance_df = pd.DataFrame(feature_importance, columns=['i', 'name', 'metric', 'metric_sd'])
+            
+            # Selekcijski kriterij značilk v rangu max-min
+            # 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.
+            
+            # Set značilk se bo izbral od i=1 do i=index_izbrane_značilke
+            
+            # "Tipping point" značilka mora biti v rangu max-min
+            
+            selection_area = feature_importance_df[(feature_importance_df["i"] >= n_min+1) & (feature_importance_df["i"] <= n_max)]
+            selection_area.set_index(["i", "name"], inplace=True)
+            diffrences = selection_area.diff()
+            diffrences.dropna(how='any', inplace=True)
+            
+            # Morda tudi komulativna sumacija? Kjer se preprosto index z najvišjo vrednostjo 
+            cumulative_sumation = diffrences.cumsum()
+            tipping_feature_indx_1 = cumulative_sumation.idxmax()["metric"]
+
+            # Zelo konzervativna metoda, ki ob prvem neizboljšanjem rezultata preneha z iskanjem boljše alternative 
+            tipping_feature_indx_2 = None
+            for indx, row in diffrences.iterrows():
+                if row["metric"] > 0:
+                    tipping_feature_indx_2 = indx
+                else: 
+                    break
+                
+            # Metoda, ki pusti n_not_improve značilkam, da premagajo dosedajno najboljši score     
+            tipping_feature_indx_3 = None
+            cum_sum_score = 0
+            i_worse = 0
+            # TODO: morda bi bilo smisleno združiti diff, cumsum in scores stolpce ...
+            for indx, row in selection_area.iterrows():
+                if row["metric"] > 0:
+                    tipping_feature_indx_3 = indx
+                    cum_sum_score += row["metric"]
+                    i_worse = 0
+                else:
+                    i_worse += 1
+                
+                if i_worse == n_not_improve:
+                    break
+                    
+                    
+                
+                
+            
+            
+        
+
+    def make_predictions_with_features(df, groups_substrings, include_group=True, with_cols=[], print_flag=False):
         pass
 
     def vizualize_feature_selection_process():