yapf code.py

chapter6/code.py

@@ -10,8 +10,6 @@ from scipy.interpolate import lagrange
 from sklearn.externals import joblib
 from sklearn.metrics import confusion_matrix, roc_curve
 from sklearn.tree import DecisionTreeClassifier
-
-
 """
 cm_plot-->自定义混淆矩阵可视化
 programmer_1-->使用拉格朗日插值法进行插值
@@ -19,6 +17,7 @@ programmer_2-->构建CART决策树模型，进行预测给出训练结果，并
 programmer_3-->使用神经网络模型，进行预测给出训练结果，并且绘制ROC曲线
 """
 
+
 def cm_plot(y, yp):
     cm = confusion_matrix(y, yp)
 
@@ -27,8 +26,11 @@ def cm_plot(y, yp):
 
     for x in range(len(cm)):
         for y in range(len(cm)):
-            plt.annotate(cm[x, y], xy=(
-                x, y), horizontalalignment='center', verticalalignment='center')
+            plt.annotate(
+                cm[x, y],
+                xy=(x, y),
+                horizontalalignment='center',
+                verticalalignment='center')
 
     plt.ylabel('True label')
     plt.xlabel('Predicted label')
@@ -56,6 +58,7 @@ def programmer_1():
 
     data.to_excel(outputfile, header=None, index=False)
 
+
 def programmer_2():
     datafile = 'data/model.xls'
     data = pd.read_excel(datafile)
@@ -63,7 +66,7 @@ def programmer_2():
     shuffle(data)  # 随机打乱数据
 
     # 设置训练数据比8:2
-    p = 0.8 
+    p = 0.8
     train = data[:int(len(data) * p), :]
     test = data[int(len(data) * p):, :]
 
@@ -79,17 +82,17 @@ def programmer_2():
 
     fpr, tpr, thresholds = roc_curve(
         test[:, 3], tree.predict_proba(test[:, :3])[:, 1], pos_label=1)
-    plt.plot(fpr, tpr, linewidth=2, label='ROC of CART',
-             color='green')
-    plt.xlabel('False Positive Rate') 
-    plt.ylabel('True Positive Rate') 
+    plt.plot(fpr, tpr, linewidth=2, label='ROC of CART', color='green')
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
     # 设定边界范围
-    plt.ylim(0, 1.05) 
-    plt.xlim(0, 1.05) 
-    plt.legend(loc=4) 
-    plt.show() 
+    plt.ylim(0, 1.05)
+    plt.xlim(0, 1.05)
+    plt.legend(loc=4)
+    plt.show()
     print(thresholds)
 
+
 def programmer_3():
     datafile = 'data/model.xls'
     data = pd.read_excel(datafile)
@@ -101,41 +104,43 @@ def programmer_3():
     test = data[int(len(data) * p):, :]
 
     # 构建LM神经网络模型
-    netfile = 'tmp/net.model' 
+    netfile = 'tmp/net.model'
 
     net = Sequential()  # 建立神经网络
-#    net.add(Dense(input_dim = 3, units = 10))
+    #    net.add(Dense(input_dim = 3, units = 10))
     # 添加输入层（3节点）到隐藏层（10节点）的连接
-    net.add(Dense(10, input_shape=(3,)))
+    net.add(Dense(10, input_shape=(3, )))
     net.add(Activation('relu'))  # 隐藏层使用relu激活函数
-#    net.add(Dense(input_dim = 10, units = 1)) 
+    #    net.add(Dense(input_dim = 10, units = 1))
     #添加隐藏层（10节点）到输出层（1节点）的连接
-    net.add(Dense(1, input_shape=(10,)))
+    net.add(Dense(1, input_shape=(10, )))
     net.add(Activation('sigmoid'))  # 输出层使用sigmoid激活函数
-    net.compile(loss='binary_crossentropy', optimizer='adam',
-                sample_weight_mode="binary")  # 编译模型，使用adam方法求解
+    net.compile(
+        loss='binary_crossentropy',
+        optimizer='adam',
+        sample_weight_mode="binary")  # 编译模型，使用adam方法求解
 
-    net.fit(train[:, :3], train[:, 3], epochs=100,
-            batch_size=1)
+    net.fit(train[:, :3], train[:, 3], epochs=100, batch_size=1)
     net.save_weights(netfile)
 
-    predict_result = net.predict_classes(
-        train[:, :3]).reshape(len(train))  # 预测结果变形
+    predict_result = net.predict_classes(train[:, :3]).reshape(
+        len(train))  # 预测结果变形
     '''这里要提醒的是，keras用predict给出预测概率，predict_classes才是给出预测类别，而且两者的预测结果都是n x 1维数组，而不是通常的 1 x n'''
 
     cm_plot(train[:, 3], predict_result).show()
 
     predict_result = net.predict(test[:, :3]).reshape(len(test))
     fpr, tpr, thresholds = roc_curve(test[:, 3], predict_result, pos_label=1)
-    plt.plot(fpr, tpr, linewidth=2, label='ROC of LM')  
-    plt.xlabel('False Positive Rate')  
-    plt.ylabel('True Positive Rate')  
-    plt.ylim(0, 1.05)  
-    plt.xlim(0, 1.05)  
-    plt.legend(loc=4)  
-    plt.show()  
+    plt.plot(fpr, tpr, linewidth=2, label='ROC of LM')
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    plt.ylim(0, 1.05)
+    plt.xlim(0, 1.05)
+    plt.legend(loc=4)
+    plt.show()
     print(thresholds)
 
+
 if __name__ == "__main__":
     # programmer_1()
     # programmer_2()