finally made the fs work within class and without

UQ_LR_FS_p2.py

@@ -21,18 +21,25 @@ class ClfSwitcher(BaseEstimator):
     def __init__(
         self, 
         estimator = SGDClassifier(),
-        #feature = RFECV()
+        #feature = RFECV(SGDClassifier())
     ):
         """
         A Custom BaseEstimator that can switch between classifiers.
         :param estimator: sklearn object - The classifier
         """ 
         self.estimator = estimator
+        #self.feature = feature
     
     def fit(self, X, y=None, **kwargs):
         self.estimator.fit(X, y)
+        #self.feature.fit(X, y)
         return self
 
+    # def transform(self, X, y=None):
+    #     #self.estimator.transform(X, y)
+    #     self.feature.transform(X)
+    #     return self
+
     def predict(self, X, y=None):
         return self.estimator.predict(X)
     
@@ -42,23 +49,26 @@ class ClfSwitcher(BaseEstimator):
     def score(self, X, y):
         return self.estimator.score(X, y)
     
+#%%
 parameters = [
-    # {'feature__fs__estimator': LogisticRegression(**rs)
-    #  , 'feature__fs__cv': [10]
-    #  , 'feature__fs__scoring': ['matthews_corrcoef']
-    #  },
+    
+    # {'fs__feature__min_features_to_select': [1]
+    #  , 'fs__feature__scoring': ['matthews_corrcoef']
+    #  , 'fs__feature__cv': [skf_cv]},
+    
+    {'fs__min_features_to_select': [1]
+     #, 'fs__scoring': ['matthews_corrcoef']
+     , 'fs__cv': [skf_cv]},
     
     {
-        'clf__estimator': [LogisticRegression(**rs)],
-        'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
-        #'clf__estimator__C': np.logspace(0, 4, 10),
-        'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
-        'clf__estimator__max_iter': list(range(100,800,100)),
-        'clf__estimator__solver': ['saga']
+     'clf__estimator': [LogisticRegression(**rs)],
+      #'clf__estimator__C': np.logspace(0, 4, 10),
+      'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
+      'clf__estimator__max_iter': list(range(100,800,100)),
+      'clf__estimator__solver': ['saga']
     }#,
     # {
     #     'clf__estimator': [MODEL2(**rs)],
-    #     #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
     #     'clf__estimator__C': np.logspace(0, 4, 10),
     #     'clf__estimator__penalty': ['l2', 'none'],
     #     'clf__estimator__max_iter': list(range(100,800,100)),
@@ -68,13 +78,14 @@ parameters = [
 #%% Create pipeline
 pipeline = Pipeline([
     ('pre', MinMaxScaler())
-#   , ('fs',  RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
-   , ('selector', SelectKBest(mutual_info_classif, k=6))
-   , ('clf', ClfSwitcher())
-])
+    , ('fs', RFECV(LogisticRegression(**rs), scoring = 'matthews_corrcoef'))#cant be my mcc_fn
+#    , ('fs', ClfSwitcher())
+    , ('clf',  ClfSwitcher())
+    ])
 
-#%% Grid search i.e hyperparameter tuning and refitting on mcc
-mod_fs = GridSearchCV(pipeline
+#%%
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_lr = GridSearchCV(pipeline
                     , parameters
                     , scoring = mcc_score_fn, refit = 'mcc'
                     , cv = skf_cv
@@ -82,6 +93,66 @@ mod_fs = GridSearchCV(pipeline
                     , return_train_score = False
                     , verbose = 3)
 
+# Fit 
+gscv_lr.fit(X, y)
+
+####
+gscv_lr_fit = gscv_lr.fit(X, y)
+gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
+gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
+
+#%% Grid search i.e hyperparameter tuning and refitting on mcc
+
+param_grid2 = [
+    
+    {'fs__min_features_to_select': [1]
+    , 'fs__cv': [skf_cv]
+    },
+    
+    
+    { 
+        #'clf__estimator': [LogisticRegression(**rs)],
+        'clf__C': np.logspace(0, 4, 10),
+        'clf__penalty': ['l2'],
+        'clf__max_iter': list(range(100,200,100)),
+        #'clf__solver': ['newton-cg', 'lbfgs', 'sag']
+        'clf__solver': ['sag']
+
+    }, 
+    {
+        #'clf__estimator': [LogisticRegression(**rs)],
+        'clf__C': np.logspace(0, 4, 10),
+        'clf__penalty': ['l1', 'l2'],
+        'clf__max_iter': list(range(100,200,100)),
+        'clf__solver': ['liblinear']
+    }
+
+]
+# step 4: create  pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler())
+    #, ('fs', model_rfecv)
+    , ('fs', RFECV(LogisticRegression(**rs), scoring = 'matthews_corrcoef'))
+    , ('clf',  LogisticRegression(**rs))])
+   
+# step 5: Perform Gridsearch CV
+gs_final = GridSearchCV(pipeline
+                        , param_grid2
+                        , cv = skf_cv
+                        , scoring = mcc_score_fn, refit = 'mcc'
+                        , verbose = 1
+                        , return_train_score = False
+                        , **njobs)
+
+
+
+
+
+
+
+
+
+
 #%% Fit 
 mod_fs_fit = mod_fs.fit(X, y)
 mod_fs_fbm = mod_fs_fit.best_params_