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- for i in range(4):
- y_train= y[:,i]
- print('Train subject %d, class %s' % (subject, cols[i]))
- lr.fit(X_train[::sample,:],y_train[::sample])
- pred[:,i] = lr.predict_proba(X_test)[:,1]
- prediction = lr.predict(X_train)
- print(confusion_matrix(y_train, prediction))
- import numpy as np
- def plot_confusion_matrix(cm,
- target_names,
- title='Confusion matrix',
- cmap=None,
- normalize=True):
- """
- given a sklearn confusion matrix (cm), make a nice plot
- Arguments
- ---------
- cm: confusion matrix from sklearn.metrics.confusion_matrix
- target_names: given classification classes such as [0, 1, 2]
- the class names, for example: ['high', 'medium', 'low']
- title: the text to display at the top of the matrix
- cmap: the gradient of the values displayed from matplotlib.pyplot.cm
- see http://matplotlib.org/examples/color/colormaps_reference.html
- plt.get_cmap('jet') or plt.cm.Blues
- normalize: If False, plot the raw numbers
- If True, plot the proportions
- Usage
- -----
- plot_confusion_matrix(cm = cm, # confusion matrix created by
- # sklearn.metrics.confusion_matrix
- normalize = True, # show proportions
- target_names = y_labels_vals, # list of names of the classes
- title = best_estimator_name) # title of graph
- Citiation
- ---------
- http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
- """
- import matplotlib.pyplot as plt
- import numpy as np
- import itertools
- accuracy = np.trace(cm) / float(np.sum(cm))
- misclass = 1 - accuracy
- if cmap is None:
- cmap = plt.get_cmap('Blues')
- plt.figure(figsize=(8, 6))
- plt.imshow(cm, interpolation='nearest', cmap=cmap)
- plt.title(title)
- plt.colorbar()
- if target_names is not None:
- tick_marks = np.arange(len(target_names))
- plt.xticks(tick_marks, target_names, rotation=45)
- plt.yticks(tick_marks, target_names)
- if normalize:
- cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
- thresh = cm.max() / 1.5 if normalize else cm.max() / 2
- for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
- if normalize:
- plt.text(j, i, "{:0.4f}".format(cm[i, j]),
- horizontalalignment="center",
- color="white" if cm[i, j] > thresh else "black")
- else:
- plt.text(j, i, "{:,}".format(cm[i, j]),
- horizontalalignment="center",
- color="white" if cm[i, j] > thresh else "black")
- plt.tight_layout()
- plt.ylabel('True label')
- plt.xlabel('Predicted labelnaccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
- plt.show()
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