added combined model FS code and run script

scripts/ml/untitled5.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Sep  2 11:11:49 2022
+
+@author: tanu
+"""
+# https://towardsdatascience.com/explain-feature-variation-employing-pca-in-scikit-learn-6711e0a5c0b7
+from sklearn.decomposition import PCA
+#import tensorflow as tf
+#from tensorflow import keras
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.metrics import matthews_corrcoef
+
+# pca = PCA().fit(X)
+# plt.plot(np.cumsum(pca.explained_variance_ratio_))
+# plt.xlabel(‘number of components’)
+# plt.ylabel(‘cumulative explained variance’)
+
+# from old scripts
+fooD = combined_DF_OS(combined_df)
+
+numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
+numerical_ix
+num_featuresL = list(numerical_ix)
+numerical_colind = fooD['X'].columns.get_indexer(list(numerical_ix) )
+numerical_colind
+
+numF = fooD['X'][numerical_ix]
+
+categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
+categorical_ix    
+categorical_colind = fooD['X'].columns.get_indexer(list(categorical_ix))
+categorical_colind
+
+##############
+
+X_train,X_test,y_train,y_test=train_test_split(numF,fooD['y'],test_size=0.2)
+
+pca=PCA(n_components=50) 
+X_train_new=pca.fit_transform(X_train)
+X_test_new=pca.transform(X_test)
+print(X_train.shape)
+print(X_train_new.shape)
+
+pca.explained_variance_ratio_
+clf=KNeighborsClassifier(n_neighbors=5)
+clf.fit(X_train_new,y_train)
+y_pred_new=clf.predict(X_test_new)
+matthews_corrcoef(y_test,y_pred_new)