Save predictions in diabetes_y_pred (scikit-learn#8241)

davidrobles · maskani-moh · commit 4b485804daed · 2017-11-15T12:11:36.000-05:00
- No need for `regr.predict(diabetes_X_test)` to run multiple times.
- Use `sklearn.metrics.mean_squared_error`.
- Use `sklearn.metrics.r2_score`, instead of `regr.score`, which
  runs `regr.predict` again.
diff --git a/examples/linear_model/plot_ols.py b/examples/linear_model/plot_ols.py
@@ -26,6 +26,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error, r2_score
 
 # Load the diabetes dataset
 diabetes = datasets.load_diabetes()
@@ -48,18 +49,20 @@
 # Train the model using the training sets
 regr.fit(diabetes_X_train, diabetes_y_train)
 
+# Make predictions using the testing set
+diabetes_y_pred = regr.predict(diabetes_X_test)
+
 # The coefficients
 print('Coefficients: \n', regr.coef_)
 # The mean squared error
 print("Mean squared error: %.2f"
-      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
+      % mean_squared_error(diabetes_y_test, diabetes_y_pred))
 # Explained variance score: 1 is perfect prediction
-print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
+print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
 
 # Plot outputs
 plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
-plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
-         linewidth=3)
+plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
 
 plt.xticks(())
 plt.yticks(())
