moved UQ_RF older verison to earlier_versions dir

earlier_versions/UQ_RF.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        A Custom BaseEstimator that can switch between classifiers.
+        :param estimator: sklearn object - The classifier
+        """ 
+        self.estimator = estimator
+    
+    def fit(self, X, y=None, **kwargs):
+        self.estimator.fit(X, y)
+        return self
+
+    def predict(self, X, y=None):
+        return self.estimator.predict(X)
+    
+    def predict_proba(self, X):
+        return self.estimator.predict_proba(X)
+    
+    def score(self, X, y):
+        return self.estimator.score(X, y)
+    
+parameters = [
+    {
+        'clf__estimator': [RandomForestClassifier(**rs
+                                                  , **njobs
+                                                  , bootstrap = True
+                                                  , oob_score = True)],
+        'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
+        , 'clf__estimator__class_weight':['balanced','balanced_subsample']
+        , 'clf__estimator__n_estimators': [10, 25, 50, 100]
+        , 'clf__estimator__criterion': ['gini', 'entropy']#, 'log_loss']
+        #, 'clf__estimator__max_features': ['auto', 'sqrt']
+        , 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
+        , 'clf__estimator__min_samples_split': [2, 5, 15, 20]
+        }
+
+    # {
+    #     'clf__estimator': [RandomForestClassifier(**rs
+    #                                               , **njobs
+    #                                               , bootstrap = True
+    #                                               , oob_score = True)],
+    #     'clf__estimator__max_depth': [ 6, 8, 10 ]
+    #     , 'clf__estimator__class_weight':['balanced_subsample']
+    #     , 'clf__estimator__n_estimators': [10]
+    #     , 'clf__estimator__criterion': ['entropy']
+    #     #, 'clf__estimator__max_features': ['auto', 'sqrt']
+    #     , 'clf__estimator__min_samples_leaf': [2, 8]
+    #     , 'clf__estimator__min_samples_split': [20]
+    #     }
+   
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_rf = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_rf_fit_be  = gscv_rf.fit(X, y)
+
+print('Best model:\n', gscv_rf.best_params_)
+gscv_rf_fit_be.best_params_
+
+print('Best models score:\n', gscv_rf_fit_be.best_score_, ':' , round(gscv_rf_fit_be.best_score_, 2))
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_rf_fit_be.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+#gscv_rf_fit_be.score(test_predict, y_btsf)
+
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import matthews_corrcoef
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+print(matthews_corrcoef(test_predict, y_btsf))
+print(accuracy_score(test_predict, y_btsf))
+
+#check_score = f1_score(y, gscv_rf.predict(X))
+#check_score # should be the same as the best score when the same metric used!
+# mod_pred = gscv_rf.predict(X_test)
+# fscore  = f1_score(y_test, mod_pred)
+# fscore
+
+gscv_rf_be_mod = gscv_rf.best_params_
+print(gscv_rf_be_mod)
+gscv_rf_fit_be_res = gscv_rf_fit_be.cv_results_
+#print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_res['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_fit_be_res['mean_test_mcc']),2))
+
+
+# /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427
+# : FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3.
+#  To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter
+#  as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
+#   warn(
+
+# ALL
+# {'clf__estimator': RandomForestClassifier(class_weight='balanced_subsample', criterion='entropy',
+#                        max_depth=6, max_features='auto', min_samples_leaf=2,
+#                        min_samples_split=20, n_estimators=10, n_jobs=10,
+#                        oob_score=True, random_state=42)
+#  , 'clf__estimator__class_weight': 'balanced_subsample'
+#  , 'clf__estimator__criterion': 'entropy'
+#  , 'clf__estimator__max_depth': 6
+#  , 'clf__estimator__max_features': 'auto'
+#  , 'clf__estimator__min_samples_leaf': 2
+#  , 'clf__estimator__min_samples_split': 20
+#  , 'clf__estimator__n_estimators': 10}       
+
+#%%