added UQ scripts to do hyperparam ML models

UQ_RF.py

@@ -0,0 +1,140 @@
+#!/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]
+        }
+]
+
+# 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  = gscv_rf.fit(X, y)
+
+gscv_rf_fit_be_mod = gscv_rf_fit.best_params_
+gscv_rf_fit_be_res = gscv_rf_fit.cv_results_
+
+print('Best model:\n', gscv_rf_fit_be_mod)
+print('Best models score:\n', gscv_rf_fit.best_score_, ':' , round(gscv_rf_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_rf_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+rf_bts_dict = {#'best_model': list(gscv_rf_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+rf_bts_dict
+rf_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+rf_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+rf_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+rf_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+rf_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+rf_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+rf_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+rf_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(rf_bts_dict, orient = 'index', columns = 'best_model')
+
+rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
+rf_bts_df.columns = ['Logistic_Regression']
+print(rf_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_rf_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+rf_bts_df.columns
+rf_output = pd.concat([model_params_df, rf_bts_df], axis = 0)
+rf_output
+
+# Format the combined df
+# Drop the best_model_params row from rf_output
+rf_df = rf_output.drop([0], axis = 0)
+rf_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+