增加解析tls message信息

Experiment/statFeature/mlAlgo.py

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+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.naive_bayes import GaussianNB
+from sklearn import tree
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import f1_score,recall_score,precision_score
+import random
+import matplotlib.pyplot as plt
+%matplotlib inline
+
+PREFIX_DIR = "/Users/Leo/Documents/github/GradProj/"
+
+
+def RF():
+    classifer = RandomForestClassifier()
+    classifer.fit(x_train, y_train)
+    y_pred = classifer.predict(x_test)
+    f1_score_list.append(f1_score(y_test, y_pred, average='micro'))
+    recall_score_list.append(recall_score(y_test, y_pred, average='micro'))
+    precision_score_list.append(precision_score(y_test, y_pred, average='micro'))
+    scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]
+    score_df.loc['RandomForest'] = scores
+    score_df.plot.bar()
+    print(scores)
+
+def main():
+    date = sys.argv[1]
+    example_csv_file = PREFIX_DIR + 'Experiment/statFeature/csvFile/' + date + '/examples.csv'
+    examples_df = pd.read_csv(example_csv_file)
+    class_counts = examples_df['label'].value_counts().plot.bar()
+    examples = examples_df.values.copy()
+    score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \
+                        columns = ['precision', 'recall', 'f1'])
+    f1_score_list = list()
+    recall_score_list = list()
+    precision_score_list = list()
+    for i in range(50):
+        np.random.shuffle(examples)
+        examples_train = examples[:int(len(examples)*0.75)]
+        examples_test = examples[int(len(examples)*0.75):]
+        x_train = examples_train[:,0:-1]
+        y_train = examples_train[:,-1]
+        x_test = examples_test[:,0:-1]
+        y_test = examples_test[:,-1]
+        RF(score_df, f1_score_list, recall_score_list, precision_score_list, \
+            x_train, y_train, x_test, y_test)
+
+
+
+if __name__ == '__main__':
+    main()