• API
• FAQ
• Tools
• Archive
daily pastebin goal
24%
SHARE
TWEET

# Untitled

a guest May 17th, 2018 94 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
1. for i in range(4):
2.         y_train= y[:,i]
3.         print('Train subject %d, class %s' % (subject, cols[i]))
4.         lr.fit(X_train[::sample,:],y_train[::sample])
5.         pred[:,i] = lr.predict_proba(X_test)[:,1]
6.
7. prediction = lr.predict(X_train)
8.
9. print(confusion_matrix(y_train, prediction))
10.
11. import numpy as np
12.
13.
14. def plot_confusion_matrix(cm,
15.                           target_names,
16.                           title='Confusion matrix',
17.                           cmap=None,
18.                           normalize=True):
19.     """
20.     given a sklearn confusion matrix (cm), make a nice plot
21.
22.     Arguments
23.     ---------
24.     cm:           confusion matrix from sklearn.metrics.confusion_matrix
25.
26.     target_names: given classification classes such as [0, 1, 2]
27.                   the class names, for example: ['high', 'medium', 'low']
28.
29.     title:        the text to display at the top of the matrix
30.
31.     cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
32.                   see http://matplotlib.org/examples/color/colormaps_reference.html
33.                   plt.get_cmap('jet') or plt.cm.Blues
34.
35.     normalize:    If False, plot the raw numbers
36.                   If True, plot the proportions
37.
38.     Usage
39.     -----
40.     plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
41.                                                               # sklearn.metrics.confusion_matrix
42.                           normalize    = True,                # show proportions
43.                           target_names = y_labels_vals,       # list of names of the classes
44.                           title        = best_estimator_name) # title of graph
45.
46.     Citiation
47.     ---------
48.     http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
49.
50.     """
51.     import matplotlib.pyplot as plt
52.     import numpy as np
53.     import itertools
54.
55.     accuracy = np.trace(cm) / float(np.sum(cm))
56.     misclass = 1 - accuracy
57.
58.     if cmap is None:
59.         cmap = plt.get_cmap('Blues')
60.
61.     plt.figure(figsize=(8, 6))
62.     plt.imshow(cm, interpolation='nearest', cmap=cmap)
63.     plt.title(title)
64.     plt.colorbar()
65.
66.     if target_names is not None:
67.         tick_marks = np.arange(len(target_names))
68.         plt.xticks(tick_marks, target_names, rotation=45)
69.         plt.yticks(tick_marks, target_names)
70.
71.     if normalize:
72.         cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
73.
74.
75.     thresh = cm.max() / 1.5 if normalize else cm.max() / 2
76.     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
77.         if normalize:
78.             plt.text(j, i, "{:0.4f}".format(cm[i, j]),
79.                      horizontalalignment="center",
80.                      color="white" if cm[i, j] > thresh else "black")
81.         else:
82.             plt.text(j, i, "{:,}".format(cm[i, j]),
83.                      horizontalalignment="center",
84.                      color="white" if cm[i, j] > thresh else "black")
85.
86.
87.     plt.tight_layout()
88.     plt.ylabel('True label')
89.     plt.xlabel('Predicted labelnaccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
90.     plt.show()
RAW Paste Data
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.

Top