Add FSS.py

code/FSS.py

+import numpy as np
+from math import log, e
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.feature_selection import mutual_info_classif
+
+
+def entropy(data):
+    # Computes entropy of label.
+
+    n_labels = len(data)
+
+    if n_labels <= 1:
+        return 0
+
+    value, counts = np.unique(data, return_counts=True)
+    probs = counts / n_labels
+    n_classes = np.count_nonzero(probs)
+
+    if n_classes <= 1:
+        return 0
+
+    ent = 0.
+
+    # Compute entropy
+    base = e
+    for i in probs:
+        ent -= i * log(i, base)
+
+    return ent
+
+
+if __name__ == '__main__':
+    feats = ["data/converted/feats.csv", "data/converted/featsCluster.csv"]
+    figs = ["data/FSS/gainRatio.svg", "data/FSS/gainRatio_cluster.svg"]
+    figs2 = ["data/FSS/corrMatrix.svg", "data/FSS/corrMatrix_cluster.svg"]
+    saves = ["data/FSS/featsGR.csv", "data/FSS/featsGR_cluster.csv"]
+
+    column = "Dropout_1"
+    y = pd.read_csv("data/converted/labels.csv")
+    for feat, fig, fig2, save in zip(feats, figs, figs2, saves):
+
+        xF = pd.read_csv(feat)
+
+        print(xF.shape)
+
+        # Mutual Information
+        entropyV = [entropy(xF[col].tolist()) for col in xF]
+        gr = mutual_info_classif(xF, y[column], random_state=1) / entropyV
+
+        indices = np.argsort(gr)
+
+        names = xF.columns
+        plt.figure(figsize=(25, 30))
+        plt.title(column)
+        plt.barh(range(len(indices)), gr[indices], color='g', align='center')
+        plt.yticks(range(len(indices)), [names[i] for i in indices])
+        plt.xlabel('Gain Ratio')
+        plt.savefig(fig, format='svg', dpi=1200)
+
+
+        grKeep = gr[indices][-20:]
+        namesKeep = names[indices][-20:]
+
+        xF1 = xF[namesKeep]
+
+        # Correlation Between Variables
+        correlation_matrix = xF1.corr()
+        plt.figure(figsize=(25, 25))
+        sns.heatmap(correlation_matrix, annot=True)
+        plt.savefig(fig2, format='svg', dpi=1200)
+        corr = []
+        for i in range(0, len(xF1.columns)):
+            for j in range(0, len(xF1.columns)):
+                if i != j:
+                    corr_1 = np.abs(xF1[xF1.columns[i]].corr(xF1[xF1.columns[j]]))
+                    if corr_1 > 0.75 and i < j:
+                        print(xF1.columns[i], "is highly  correlated  with", xF1.columns[j], "->", corr_1 )
+                        corr.append(xF1.columns[i])
+
+        xF2 = xF1.drop(columns=corr)
+        print(xF2.shape)
+        xF2.to_csv(save, index=False)