added sripts to try FS

UQ_FS_eg.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 21 02:52:36 2022
+
+@author: tanu
+"""
+# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
+import pandas as pd
+from sklearn.pipeline import Pipeline
+from sklearn.datasets import make_classification
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import GridSearchCV
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_selection import SelectKBest, mutual_info_classif
+#pd.options.plotting.backend = "plotly"
+X_eg, y_eg = make_classification(n_samples=1000,
+                           n_features=30,
+                           n_informative=5,
+                           n_redundant=5,
+                           n_classes=2,
+                           random_state=123)
+
+pipe = Pipeline([('scaler', StandardScaler()),
+                 ('selector', SelectKBest(mutual_info_classif, k=9)),
+                 ('classifier', LogisticRegression())])
+
+search_space = [{'selector__k': [5, 6, 7, 10]},
+                {'classifier': [LogisticRegression()],
+                 'classifier__C': [0.01,1.0],
+                 'classifier__solver': ['saga', 'lbfgs']},
+                {'classifier': [RandomForestClassifier(n_estimators=100)],
+                 'classifier__max_depth': [5, 10, None]},
+                {'classifier': [KNeighborsClassifier()],
+                 'classifier__n_neighbors': [3, 7, 11],
+                 'classifier__weights': ['uniform', 'distance']}]
+
+
+
+clf = GridSearchCV(pipe, search_space, cv=10, verbose=0)
+
+clf2 = clf.fit(X_eg, y_eg)
+clf2._check_feature_names
+clf2.best_estimator_.named_steps['selector'].n_features_in_
+
+clf2.best_estimator_ #n of best features
+clf2.best_params_ 
+clf2.best_estimator_.get_params
+clf2.get_feature_names()
+
+
+
+clf3 = clf2.best_estimator_ # 
+clf3._final_estimator
+clf3._final_estimator.C
+clf3._final_estimator.solver
+
+
+fs_bmod = clf2.best_estimator_
+print('\nbest model with feature selection:', fs_bmod)
+
+