GridSearchCV cross validation

 1 # -*- coding: utf-8 -*-
 2 """
 3 Created on Sat Sep  1 11:54:48 2018
 4 
 5 @author: zhen
 6 
 7     Cross validation 8 """
 9 import numpy as np
10 from sklearn import datasets
11 from sklearn.linear_model import LogisticRegression
12 from sklearn.model_selection import GridSearchCV
13 import matplotlib.pyplot as plt
14 
15 iris = datasets.load_iris()
16 x = iris['data'][:, 3:]
17 y = iris['target']
18 
19 
20 def report(results, n_top=3):
21     for i in range(1, n_top + 1):
22         candidates = np.flatnonzero(results['rank_test_score'] == i)
23         for candidate in candidates:
24             print("Model with rank: {0}".format(i))
25             print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
26                    results['mean_test_score'][candidate],
27                    results['std_test_score'][candidate]))
28             print("Parameters: {0}".format(results['params'][candidate]))
29             print("")
30 
31 
32 param_grid = {"tol":[1e-4, 1e-3,1e-2], "C":[0.4, 0.6, 0.8]}
33 
34 log_reg = LogisticRegression(multi_class='ovr', solver='sag')
35 # 70 percent off cross validation.
36 grid_search = GridSearchCV(log_reg, param_grid=param_grid, cv=3)
37 grid_search.fit(x, y)
38 
39 report(grid_search.cv_results_)
40 
41 x_new = np.linspace(0, 3, 1000).reshape(-1, 1)
42 y_proba = grid_search.predict_proba(x_new)
43 y_hat = grid_search.predict(x_new)
44 
45 plt.plot(x_new, y_proba[:, 2], 'g-', label='Iris-Virginica')
46 plt.plot(x_new, y_proba[:, 1], 'r-', label='Iris-Versicolour')
47 plt.plot(x_new, y_proba[:, 0], 'b-', label='Iris-Setosa')
48 plt.show()
49 
50 print(grid_search.predict([[1.7], [1.5]]))