updated testcase for class weights

test/test_classweights.py

@@ -2,7 +2,8 @@ from __future__ import absolute_import
 from __future__ import print_function
 from keras.datasets import mnist
 from keras.models import Sequential
-from keras.layers.core import Dense, Activation, Merge
+from keras.layers.core import Dense, Activation, Merge, Dropout
+from keras.optimizers import SGD
 from keras.utils import np_utils
 import numpy as np
 
@@ -49,9 +50,9 @@ model_train = createMNISTModel()
 high_weight = 100
 class_weight = {0:1,1:1,2:1,3:1,4:1,5:1,6:1,7:1,8:1,9:high_weight}
 
-########################
-#test different methods#
-########################
+############################
+# categorical crossentropy #
+############################
 
 print("Testing fit methods with and without classweights")
 # fit
@@ -78,4 +79,86 @@ for nb in range(nb_classes):
     if class_weight[nb] == high_weight and (score_cw[1] <= score[1] or score_cw_train[1] <= score_train[1]):
         raise Exception('Class weights are not implemented correctly')
 
+####################################################
+# test cases for all remaining objective functions #
+####################################################
+batch_size = 64
+nb_epoch = 10
+
+np.random.seed(1337) # for reproducibility
+
+def generateData(n_samples, n_dim):
+    A_feats = np.random.randn(n_samples, n_dim)
+    B_feats = np.random.randn(n_samples, n_dim)
+    A_label = np.zeros((n_samples,1))
+    B_label = np.ones((n_samples,1))
+    X = np.vstack((A_feats, B_feats))
+    y = np.vstack((A_label, B_label)).squeeze()
+    return X, y
+
+n_dim = 100
+X_train, y_train = generateData(1000, n_dim)
+X_test, y_test = generateData(5000, n_dim)
+
+def createModel(ls, n_dim, activation="sigmoid"):
+    model = Sequential()
+    model.add(Dense(n_dim, 50))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(50, 1))
+    model.add(Activation(activation))
+    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
+    model.compile(loss=ls, optimizer=sgd, class_mode="binary")
+    return model
+
+verbosity = 0
+cw = {0: 1.5, 1: 1}
+# binary crossentropy
+model = createModel('binary_crossentropy', n_dim)
+model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=verbosity, validation_data=(X_test, y_test), class_weight=cw)
+res = model.predict(X_test, verbose=verbosity).round()
+neg_preds, pos_preds = (1.0*np.sum(res == 0)/res.shape[0], 1.0*np.sum(res == 1)/res.shape[0])
+assert(neg_preds > pos_preds)
+print("binary crossentropy: %0.2f VS %0.2f" % (neg_preds, pos_preds))
+
+# MAE
+model = createModel('mean_absolute_error', n_dim)
+model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=verbosity, validation_data=(X_test, y_test), class_weight=cw)
+res = model.predict(X_test, verbose=verbosity).round()
+neg_preds, pos_preds = (1.0*np.sum(res == 0)/res.shape[0], 1.0*np.sum(res == 1)/res.shape[0])
+assert(neg_preds > pos_preds)
+print("MAE: %0.2f VS %0.2f" % (neg_preds, pos_preds))
+
+# MSE
+model = createModel('mean_squared_error', n_dim)
+model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=verbosity, validation_data=(X_test, y_test), class_weight=cw)
+res = model.predict(X_test, verbose=verbosity).round()
+neg_preds, pos_preds = (1.0*np.sum(res == 0)/res.shape[0], 1.0*np.sum(res == 1)/res.shape[0])
+assert(neg_preds > pos_preds)
+print("MSE: %0.2f VS %0.2f" % (neg_preds, pos_preds))
+
+# hinge losses, map labels
+y_train[y_train == 0] = -1
+y_test[y_test == 0] = -1
+cw = {-1: 1.5, 1: 1}
+
+# hinge
+model = createModel('hinge', n_dim, "tanh")
+model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=verbosity, validation_data=(X_test, y_test), class_weight=cw)
+res = model.predict(X_test, verbose=verbosity)
+res[res < 0] = -1
+res[res >= 0] = 1
+neg_preds, pos_preds = (1.0*np.sum(res == -1)/res.shape[0], 1.0*np.sum(res == 1)/res.shape[0])
+assert(neg_preds > pos_preds)
+print("hinge: %0.2f VS %0.2f" % (neg_preds, pos_preds))
+
+# squared hinge
+model = createModel('squared_hinge', n_dim, "tanh")
+model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=verbosity, validation_data=(X_test, y_test), class_weight=cw)
+res = model.predict(X_test, verbose=verbosity)
+res[res < 0] = -1
+res[res >= 0] = 1
+neg_preds, pos_preds = (1.0*np.sum(res == -1)/res.shape[0], 1.0*np.sum(res == 1)/res.shape[0])
+assert(neg_preds > pos_preds)
+print("sqr hinge: %0.2f VS %0.2f" % (neg_preds, pos_preds))