added UQ_MultClassPipe4.py and UQ_imbalance.py

UQ_imbalance.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu May 26 05:19:25 2022
+
+@author: tanu
+"""
+#%% https://www.kite.com/blog/python/smote-python-imbalanced-learn-for-oversampling/
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn.svm import SVC
+from imblearn.over_sampling import SMOTE
+#%%############################################################################
+
+def train_SVM(df):
+   # select the feature columns
+   X = df.loc[:, df.columns != 'label']
+   # select the label column
+   y = df.label
+
+   # train an SVM with linear kernel
+   clf = SVC(kernel='linear')
+   clf.fit(X, y)
+
+   return clf
+
+
+def plot_svm_boundary(clf, df, title):
+   fig, ax = plt.subplots()
+   X0, X1 = df.iloc[:, 0], df.iloc[:, 1]
+
+   x_min, x_max = X0.min() - 1, X0.max() + 1
+   y_min, y_max = X1.min() - 1, X1.max() + 1
+   xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
+
+   Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+   Z = Z.reshape(xx.shape)
+   out = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
+
+   ax.scatter(X0, X1, c=df.label, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
+   ax.set_ylabel('y')
+   ax.set_xlabel('x')
+   ax.set_title(title)
+   plt.show()
+#%%############################################################################
+# SMOTE number of neighbors
+#k = 1 (pnca, extra trees baseline is 0.49,numerical only)
+k = 1
+
+sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k, **rs)
+X_sm, y_sm = sm.fit_resample(X, y)
+print(len(X_sm)) #228
+print(Counter(y))
+y_sm_df = y_sm.to_frame()
+y_sm_df.value_counts().plot(kind = 'bar')
+
+oversample = RandomOverSampler(sampling_strategy='minority')
+X_ros, y_ros = oversample.fit_resample(X, y)
+print(len(X_ros)) #228
+
+undersample = RandomUnderSampler(sampling_strategy='majority')
+X_rus, y_rus = undersample.fit_resample(X, y)
+print(len(X_rus)) #142
+
+sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all'))
+X_enn, y_enn = sm_enn.fit_resample(X, y)
+print(len(X_enn)) #53