Chapter 05 section 06 completed.

05-Linear-Regression/05-Regression-Metrics-MSE-vs-MAE/playML/metrics.py

@@ -3,28 +3,28 @@
 
 
 def accuracy_score(y_true, y_predict):
-    '''计算y_true和y_predict之间的准确率'''
+    """计算y_true和y_predict之间的准确率"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum(y_true == y_predict) / len(y_true)
 
 
 def mean_squared_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的MSE'''
+    """计算y_true和y_predict之间的MSE"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum((y_true - y_predict)**2) / len(y_true)
 
 
 def root_mean_squared_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的RMSE'''
+    """计算y_true和y_predict之间的RMSE"""
 
     return sqrt(mean_squared_error(y_true, y_predict))
 
 
 def mean_absolute_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的RMSE'''
+    """计算y_true和y_predict之间的RMSE"""
 
     return np.sum(np.absolute(y_true - y_predict)) / len(y_true)

05-Linear-Regression/06-R-Squared/06-R-Squared.ipynb

@@ -132,18 +132,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.61293168039373225"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'y_test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-a7a5d5c1ca17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mplayML\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'y_test' is not defined"
+     ]
     }
    ],
    "source": [

05-Linear-Regression/06-R-Squared/playML/SimpleLinearRegression.py

@@ -37,10 +37,10 @@ def _predict(self, x_single):
         """给定单个待预测数据x，返回x的预测结果值"""
         return self.a_ * x_single + self.b_
 
-    def score(self, X_test, y_test):
-        """根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""
+    def score(self, x_test, y_test):
+        """根据测试数据集 x_test 和 y_test 确定当前模型的准确度"""
 
-        y_predict = self.predict(X_test)
+        y_predict = self.predict(x_test)
         return r2_score(y_test, y_predict)
 
     def __repr__(self):

05-Linear-Regression/06-R-Squared/playML/metrics.py

@@ -3,36 +3,36 @@
 
 
 def accuracy_score(y_true, y_predict):
-    '''计算y_true和y_predict之间的准确率'''
+    """计算y_true和y_predict之间的准确率"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum(y_true == y_predict) / len(y_true)
 
 
 def mean_squared_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的MSE'''
+    """计算y_true和y_predict之间的MSE"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum((y_true - y_predict)**2) / len(y_true)
 
 
 def root_mean_squared_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的RMSE'''
+    """计算y_true和y_predict之间的RMSE"""
 
     return sqrt(mean_squared_error(y_true, y_predict))
 
 
 def mean_absolute_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的RMSE'''
+    """计算y_true和y_predict之间的RMSE"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum(np.absolute(y_true - y_predict)) / len(y_true)
 
 
 def r2_score(y_true, y_predict):
-    '''计算y_true和y_predict之间的R Square'''
+    """计算y_true和y_predict之间的R Square"""
 
     return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)

05-Linear-Regression/08-Linear-Regression-in-scikit-learn/playML/SimpleLinearRegression.py

@@ -37,10 +37,10 @@ def _predict(self, x_single):
         """给定单个待预测数据x，返回x的预测结果值"""
         return self.a_ * x_single + self.b_
 
-    def score(self, X_test, y_test):
-        """根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""
+    def score(self, x_test, y_test):
+        """根据测试数据集 x_test 和 y_test 确定当前模型的准确度"""
 
-        y_predict = self.predict(X_test)
+        y_predict = self.predict(x_test)
         return r2_score(y_test, y_predict)
 
     def __repr__(self):

05-Linear-Regression/08-Linear-Regression-in-scikit-learn/playML/metrics.py

@@ -3,36 +3,36 @@
 
 
 def accuracy_score(y_true, y_predict):
-    '''计算y_true和y_predict之间的准确率'''
+    """计算y_true和y_predict之间的准确率"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum(y_true == y_predict) / len(y_true)
 
 
 def mean_squared_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的MSE'''
+    """计算y_true和y_predict之间的MSE"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum((y_true - y_predict)**2) / len(y_true)
 
 
 def root_mean_squared_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的RMSE'''
+    """计算y_true和y_predict之间的RMSE"""
 
     return sqrt(mean_squared_error(y_true, y_predict))
 
 
 def mean_absolute_error(y_true, y_predict):
-    '''计算y_true和y_predict之间的RMSE'''
+    """计算y_true和y_predict之间的RMSE"""
     assert len(y_true) == len(y_predict), \
         "the size of y_true must be equal to the size of y_predict"
 
     return np.sum(np.absolute(y_true - y_predict)) / len(y_true)
 
 
 def r2_score(y_true, y_predict):
-    '''计算y_true和y_predict之间的R Square'''
+    """计算y_true和y_predict之间的R Square"""
 
     return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)