def test_correct_cached_predict(self):
model = Classifier(**self.default_config())
train_sample = self.dataset.sample(n=self.n_sample)
valid_sample = self.dataset.sample(n=self.n_sample)
# Test with different sizes to make sure we handle cases where
# the data doesn't divide evenly into batches
half_sample = int(self.n_sample / 2)
quarter_sample = int(half_sample / 2)
model.fit(train_sample.Text.values, train_sample.Target.values)
# Predictions w/o cached predict
preds = [
model.predict_proba(valid_sample.Text.values[:half_sample]),
model.predict_proba(valid_sample.Text.values[half_sample:]),
model.predict_proba(valid_sample.Text.values[:quarter_sample]),
model.predict_proba(valid_sample.Text.values[quarter_sample:])
]
# Predictions w/ cached predict
with model.cached_predict():
cached_preds = [
model.predict_proba(valid_sample.Text.values[:half_sample]),
model.predict_proba(valid_sample.Text.values[half_sample:]),
model.predict_proba(valid_sample.Text.values[:quarter_sample]),
model.predict_proba(valid_sample.Text.values[quarter_sample:])
]
for batch_preds, batch_cached_preds in zip(preds, cached_preds):
for pred, cached_pred in zip(batch_preds, batch_cached_preds):
assert list(pred.keys()) == list(cached_pred.keys())
for pred_val, cached_pred_val in zip(pred.values(), cached_pred.values()):
np.testing.assert_almost_equal(pred_val, cached_pred_val, decimal=4)
def test_correct_cached_predict(self):
model = Classifier(**self.default_config())
train_sample = self.dataset.sample(n=self.n_sample)
valid_sample = self.dataset.sample(n=self.n_sample)
model.fit(train_sample.Text.values, train_sample.Target.values)
predictions = model.predict_proba(valid_sample.Text[:1].values)
predictions2 = model.predict_proba(valid_sample.Text[1:2].values)
with model.cached_predict():
np.testing.assert_allclose(list(
model.predict_proba(valid_sample.Text[:1].values)[0].values()),
list(predictions[0].values()),
rtol=1e-4)
np.testing.assert_allclose(list(
model.predict_proba(
valid_sample.Text[1:2].values)[0].values()),
list(predictions2[0].values()),
rtol=1e-4)
def test_cached_predict(self):
"""
Ensure second call to predict is faster than first
"""
model = Classifier(**self.default_config())
train_sample = self.dataset.sample(n=self.n_sample)
valid_sample = self.dataset.sample(n=self.n_sample)
model.fit(train_sample.Text.values, train_sample.Target.values)
with model.cached_predict():
start = time.time()
model.predict(valid_sample.Text[:1].values)
first = time.time()
model.predict(valid_sample.Text[:1].values)
second = time.time()
first_prediction_time = first - start
second_prediction_time = second - first
self.assertLess(second_prediction_time, first_prediction_time / 2.0)
