def test_grad(self):
my_cs = MulticlassSVM('crammer-singer')
norm_diff = np.linalg.norm(
my_cs.grad_student(self.W, self.X, self.y) -
np.array([[-2.30447458, -0.94740057], [2.62578591, 1.11242235],
[0.16124486, 1.71850243]]))
self.assertTrue(norm_diff < 5e-3)
def test_self_ovo_accuracy(self):
self_ovr = MulticlassSVM('ovo')
self_ovr.fit(self.X_train, self.y_train)
train_acc = metrics.accuracy_score(self.y_train,
self_ovr.predict(self.X_train))
test_acc = metrics.accuracy_score(self.y_test,
self_ovr.predict(self.X_test))
self.assertTrue(abs(train_acc - 1.0) < 5e-3)
self.assertTrue(abs(test_acc - 0.892625) < 5e-3)
def test_self_ovr_accuracy(self):
self_ovr = MulticlassSVM('ovr')
self_ovr.fit(self.X_train, self.y_train)
train_acc = metrics.accuracy_score(self.y_train,
self_ovr.predict(self.X_train))
test_acc = metrics.accuracy_score(self.y_test,
self_ovr.predict(self.X_test))
self.assertTrue(train_acc >= 1 - 1e-6)
# self.assertTrue(abs(train_acc - 1.0) < 1e-6)
self.assertTrue(test_acc >= 0.814875 - 1e-6)
def test_self_ovr_accuracy(self):
self_ovr = MulticlassSVM('ovr')
self_ovr.fit(self.X_train, self.y_train)
train_acc = metrics.accuracy_score(self.y_train,
self_ovr.predict(self.X_train))
test_acc = metrics.accuracy_score(self.y_test,
self_ovr.predict(self.X_test))
print("my3 train_acc:", abs(train_acc - 1.0))
print("my3 test_acc:", abs(test_acc - 0.814875))
self.assertTrue(abs(train_acc - 1.0) < 5e-3)
self.assertTrue(abs(test_acc - 0.814875) < 5e-3)
def test_loss(self):
my_cs = MulticlassSVM('crammer-singer')
loss_diff = abs(
my_cs.loss_student(self.W, self.X, self.y) - 7.441854144192854)
self.assertTrue(loss_diff < 5e-3)
def test_score_shape(self):
msvm = MulticlassSVM('ovr')
msvm.fit(self.X_train, self.y_train)
scores = msvm.scores_ovr_student(self.X_test)
print('socre shape', scores.shape)
self.assertTrue(scores.shape[0] == 5000 and scores.shape[1] == 10)
def main():
print('Loading data...')
mnist = np.loadtxt('data/mnist_test.csv', delimiter=',')
X_train = mnist[:len(mnist)//2, 1:]
y_train = mnist[:len(mnist)//2, 0].astype(np.int)
X_test = mnist[len(mnist)//2:, 1:]
y_test = mnist[len(mnist)//2:, 0].astype(np.int)
print('Training Sklearn OVR...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'ovr', X_train, y_train, X_test)
print('Sklearn OVR Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn OVR Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training Sklearn OVO...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'ovo', X_train, y_train, X_test)
print('Sklearn OVO Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn OVO Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training Sklearn Crammer-Singer...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'crammer', X_train, y_train, X_test)
print('Sklearn Crammer-Singer Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn Crammer-Singer Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training self OVR...')
self_ovr = MulticlassSVM('ovr')
self_ovr.fit(X_train, y_train)
print('Self OVR Accuracy (train):',
metrics.accuracy_score(y_train, self_ovr.predict(X_train)))
print('Self OVR Accuracy (test) :',
metrics.accuracy_score(y_test, self_ovr.predict(X_test)))
print('Training self OVO...')
self_ovo = MulticlassSVM('ovo')
self_ovo.fit(X_train, y_train)
print('Self OVO Accuracy (train):',
metrics.accuracy_score(y_train, self_ovo.predict(X_train)))
print('Self OVO Accuracy (test) :',
metrics.accuracy_score(y_test, self_ovo.predict(X_test)))
print('Training self Crammer-Singer...')
self_cs = MulticlassSVM('crammer-singer')
self_cs.fit(X_train, y_train)
print('Self Crammer-Singer Accuracy (train):',
metrics.accuracy_score(y_train, self_cs.predict(X_train)))
print('Self Crammer-Singer Accuracy (test) :',
metrics.accuracy_score(y_test, self_cs.predict(X_test)))
'ovo', X_train, y_train, X_test)
print('Sklearn OVO Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn OVO Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training Sklearn Crammer-Singer...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'crammer', X_train, y_train, X_test)
print('Sklearn Crammer-Singer Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn Crammer-Singer Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training self OVR...')
self_ovr = MulticlassSVM('ovr')
self_ovr.fit(X_train, y_train)
print('Self OVR Accuracy (train):',
metrics.accuracy_score(y_train, self_ovr.predict(X_train)))
print('Self OVR Accuracy (test) :',
metrics.accuracy_score(y_test, self_ovr.predict(X_test)))
print('Training self OVO...')
self_ovo = MulticlassSVM('ovo')
self_ovo.fit(X_train, y_train)
print('Self OVO Accuracy (train):',
metrics.accuracy_score(y_train, self_ovo.predict(X_train)))
print('Self OVO Accuracy (test) :',
metrics.accuracy_score(y_test, self_ovo.predict(X_test)))
print('Training self Crammer-Singer...')