problem 3

hw3/plot_confusion_matrix.py

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+#!/usr/bin/env python
+import sys, os
+import itertools
+import numpy as np
+import matplotlib.pyplot as plt
+
+from keras.utils import to_categorical
+from keras.models import load_model
+from sklearn.metrics import confusion_matrix
+
+# Parameter
+height = width = 48
+num_classes = 7
+model_name = sys.argv[2]
+class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+option = sys.argv[3] # draw: draw confusion mastrix
+ # pick: pick a image fitting the type II error
+
+# Read the train data
+print('Read start')
+try:
+ X = np.load('X.npy')
+ Y = np.load('Y.npy')
+except:
+ with open(sys.argv[1], "r+") as f:
+ line = f.read().strip().replace(',', ' ').split('\n')[1:]
+ raw_data = ' '.join(line)
+ length = width*height+1 #1 is for label
+ data = np.array(raw_data.split()).astype('float').reshape(-1, length)
+ X = data[:, 1:]
+ Y = data[:, 0]
+ # Change data into CNN format
+ X = X.reshape(X.shape[0], height, width, 1)
+ Y = Y.reshape(Y.shape[0], 1)
+ print('Saving X.npy & Y.npy')
+ np.save('X.npy', X) # (-1, height, width, 1)
+ np.save('Y.npy', Y) # (-1, 1)
+
+X /= 255
+print('Read finished!')
+
+# Split the data
+valid_num = 3000
+X_train, Y_train = X[:-valid_num], Y[:-valid_num].astype('int')
+X_valid, Y_valid = X[-valid_num:], Y[-valid_num:].astype('int')
+# print(X_train.shape)
+# print(X_valid.shape)
+# print(Y_train.shape)
+# print(Y_valid.shape)
+
+# Load model
+model = load_model(model_name)
+print('Predicting')
+pred = model.predict(X_valid)
+# print(pred.shape)
+pred_label = np.argmax(pred, axis=1)
+# print(pred_label.shape)
+Y_valid = Y_valid.reshape(-1, )
+# print(Y_valid.shape)
+print('Predicting done!')
+
+def plot_confusion_matrix(cm, classes,
+ normalize=False,
+ title='Confusion matrix',
+ cmap=plt.cm.Blues):
+ """
+ This function prints and plots the confusion matrix.
+ Normalization can be applied by setting `normalize=True`.
+ """
+ plt.imshow(cm, interpolation='nearest', cmap=cmap)
+ plt.title(title)
+ plt.colorbar()
+ tick_marks = np.arange(len(classes))
+ plt.xticks(tick_marks, classes, rotation=45)
+ plt.yticks(tick_marks, classes)
+
+ if normalize:
+ cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+ print("Normalized confusion matrix")
+ else:
+ print('Confusion matrix, without normalization')
+
+ print(cm)
+
+ thresh = cm.max() / 2.
+ for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+ plt.text(j, i, '{:.3f}'.format(cm[i, j]),
+ horizontalalignment="center",
+ color="brown" if cm[i, j] > thresh else "black")
+
+ plt.tight_layout()
+ plt.ylabel('True label')
+ plt.xlabel('Predicted label')
+
+
+# Compute confusion matrix
+if option == 'draw':
+ cnf_matrix = confusion_matrix(Y_valid, pred_label)
+ np.set_printoptions(precision=3)
+
+ # Plot non-normalized confusion matrix
+ # plt.figure()
+ # plot_confusion_matrix(cnf_matrix, classes=class_names,
+ # title='Confusion matrix, without normalization')
+
+ # Plot normalized confusion matrix
+ plt.figure()
+ plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
+ title='Normalized confusion matrix')
+
+ plt.show()
+
+# Pick image
+elif option == 'pick':
+ base_dir = './'
+ img_dir = os.path.join(base_dir, 'cm_image')
+ if not os.path.exists(img_dir):
+ os.makedirs(img_dir)
+
+ true_label = np.argwhere(Y_valid == 3).squeeze()
+ # print(true_label)
+ picked_label = np.argwhere(pred_label[true_label] == 3).squeeze()
+ # print(picked_label)
+
+ idx = true_label[picked_label[3]]
+ print('Picking image number {}'.format(idx))
+ see = X_valid[idx].reshape(height, width)
+ # print(see.shape)
+ ans = ['{:.3f}'.format(i) for i in list(pred[idx])]
+ print('True label: {:d}; Predicted label: {}'.format(Y_valid[idx], pred_label[idx]))
+ print('Its percentage: {}'.format(' , '.join(ans)))
+
+ plt.figure()
+ plt.imshow(see,cmap='gray')
+ plt.colorbar()
+ plt.tight_layout()
+ fig = plt.gcf()
+ plt.draw()
+ fig.savefig(os.path.join(img_dir, '{}.png'.format(idx)), dpi=100)