add correlation analysis

main.py

@@ -4,29 +4,22 @@ from sklearn.naive_bayes import CategoricalNB
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 from utils import *
+import statsmodels.graphics.api as smg
 
 K = 5
 random_seed = 0
 test_ratio = 0.2
 
-def read_data():
-    with open('./breast-cancer-wisconsin.data', 'r') as data_fid:
-        lines = data_fid.readlines()
-        records = []
-        for line in lines:
-            if '?' in line:
-                line = line.replace('?', '11')
-            line = line.split(',')
-            line = [int(item) for item in line][1:]
-            records.append(line)
-    records = np.array(records)
-    X = records[:, :-1]
-    y = records[:, -1]
-    return X, y
-
 X, y = read_data()
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_ratio, random_state=random_seed)
 
+# Correlation Analysis
+corr_matrix = np.corrcoef(X_train.T)
+smg.plot_corr(corr_matrix, xnames = ["Clump_Thickness", "Cell_Size", "Cell_Shape",
+                    "Marginal_Adhesion", "Single_Epithelial_Cell_Size", "Bare_Nuclei", "Bland_Chromatin",
+                    "Normal_Nucleoli", "Mitoses"])
+plt.show()
+
 # Naive Bayes
 clf = CategoricalNB()
 clf.fit(X_train, y_train)
@@ -35,6 +28,8 @@ acc = accuracy_score(y_test, y_pred)
 print('accuracy is: {}'.format(acc))
 save_cm_figs(y_test, y_pred, 'NB_ori')  # confusion matrix
 
+
+
 # X_2 = X[Y==2] #benign
 # X_4 = X[Y==4] #cancer
 

utils.py

@@ -3,6 +3,21 @@ import numpy as np
 from sklearn.metrics import classification_report, confusion_matrix
 import itertools
 
+def read_data():
+    with open('./breast-cancer-wisconsin.data', 'r') as data_fid:
+        lines = data_fid.readlines()
+        records = []
+        for line in lines:
+            if '?' in line:
+                line = line.replace('?', '11')
+            line = line.split(',')
+            line = [int(item) for item in line][1:]
+            records.append(line)
+    records = np.array(records)
+    X = records[:, :-1]
+    y = records[:, -1]
+    return X, y
+
 def plot_confusion_matrix(y_true, y_pred, classes = ['benign', 'cancer'],
                           normalize=False,
                           title='Confusion matrix',
@@ -60,4 +75,3 @@ def save_cm_figs(y_true, y_pred, arc, target_names = ['begign', 'cancer']):
                           title='Normalized confusion matrix')
     plt.savefig('./result/conf_norm_{}.png'.format(arc), bbox_inches='tight')
     plt.show()
-