def test_predict(self):
np.random.seed(100)
total_length = 6
features = np.random.uniform(0, 1, (total_length, 2))
label = (features).sum() + 0.4
predict_data = self.sc.parallelize(range(0, total_length)).map(
lambda i: Sample.from_ndarray(features[i], label))
model = Linear(2, 1).set_init_method(Xavier(), Zeros()) \
.set_name("linear1").set_seed(1234).reset()
predict_result = model.predict(predict_data)
p = predict_result.take(6)
ground_label = np.array([[-0.47596836], [-0.37598032], [-0.00492062],
[-0.5906958], [-0.12307882], [-0.77907401]], dtype="float32")
for i in range(0, total_length):
assert_allclose(p[i], ground_label[i], atol=1e-6, rtol=0)
predict_result_with_batch = model.predict(features=predict_data,
batch_size=4)
p_with_batch = predict_result_with_batch.take(6)
for i in range(0, total_length):
assert_allclose(p_with_batch[i], ground_label[i], atol=1e-6, rtol=0)
predict_class = model.predict_class(predict_data)
predict_labels = predict_class.take(6)
for i in range(0, total_length):
assert predict_labels[i] == 1
def test_predict(self):
np.random.seed(100)
total_length = 6
features = np.random.uniform(0, 1, (total_length, 2))
label = (features).sum() + 0.4
predict_data = self.sc.parallelize(range(0, total_length)).map(
lambda i: Sample.from_ndarray(features[i], label))
model = Linear(2, 1).set_init_method(Xavier(), Zeros()) \
.set_name("linear1").set_seed(1234).reset()
predict_result = model.predict(predict_data)
p = predict_result.take(6)
ground_label = np.array([[-0.47596836], [-0.37598032], [-0.00492062],
[-0.5906958], [-0.12307882], [-0.77907401]],
dtype="float32")
for i in range(0, total_length):
assert_allclose(p[i], ground_label[i], atol=1e-6, rtol=0)
predict_result_with_batch = model.predict(features=predict_data,
batch_size=4)
p_with_batch = predict_result_with_batch.take(6)
for i in range(0, total_length):
assert_allclose(p_with_batch[i],
ground_label[i],
atol=1e-6,
rtol=0)
predict_class = model.predict_class(predict_data)
predict_labels = predict_class.take(6)
for i in range(0, total_length):
assert predict_labels[i] == 1
def test_predict(self):
np.random.seed(100)
total_length = 6
features = np.random.uniform(0, 1, (total_length, 2))
label = (features).sum() + 0.4
predict_data = self.sc.parallelize(range(0, total_length)).map(
lambda i: Sample.from_ndarray(features[i], label))
model = Linear(2, 1,
"Xavier").set_name("linear1").set_seed(1234).reset()
predict_result = model.predict(predict_data)
p = predict_result.take(6)
ground_label = np.array([[-0.47596836], [-0.37598032], [-0.00492062],
[-0.5906958], [-0.12307882], [-0.77907401]],
dtype="float32")
for i in range(0, total_length):
self.assertTrue(
np.allclose(p[i], ground_label[i], atol=1e-6, rtol=0))
