def test_sklearn_incremental_predictor_partial_fit_calls(
batch_size, num_epochs):
df = vaex.example()
df_train, df_test = df.ml.train_test_split(test_size=0.1, verbose=False)
features = df_train.column_names[:6]
target = 'FeH'
N_total = len(df_train)
num_batches = (N_total + batch_size - 1) // batch_size
# Create a mock model for counting the number of samples seen and partial_fit calls
class MockModel():
def __init__(self):
self.n_samples_ = 0
self.n_partial_fit_calls_ = 0
def partial_fit(self, X, y):
self.n_samples_ += X.shape[0]
self.n_partial_fit_calls_ += 1
incremental = IncrementalPredictor(model=MockModel(),
features=features,
batch_size=batch_size,
num_epochs=num_epochs,
shuffle=False,
prediction_name='pred')
incremental.fit(df=df_train, target=target)
assert incremental.model.n_samples_ == N_total * num_epochs
assert incremental.model.n_partial_fit_calls_ == num_batches * num_epochs
def test_sklearn_incremental_predictor_classification():
df = vaex.ml.datasets.load_iris_1e5()
df_train, df_test = df.ml.train_test_split(test_size=0.1, verbose=False)
features = df_train.column_names[:4]
target = 'class_'
incremental = IncrementalPredictor(
model=SGDClassifier(learning_rate='constant', eta0=0.01),
features=features,
batch_size=10_000,
num_epochs=3,
shuffle=False,
prediction_name='pred',
partial_fit_kwargs={'classes': [0, 1, 2]})
incremental.fit(df=df_train, target=target)
df_train = incremental.transform(df_train)
# State transfer
state = df_train.state_get()
df_test.state_set(state)
assert df_test.column_count() == 6
assert df_test.pred.values.shape == (10050, )
pred_in_memory = incremental.predict(df_test)
np.testing.assert_array_equal(pred_in_memory, df_test.pred.values)
def test_sklearn_incremental_predictor_serialize(tmpdir):
df = vaex.example()
df_train, df_test = df.ml.train_test_split(test_size=0.1, verbose=False)
features = df_train.column_names[:6]
target = 'FeH'
incremental = IncrementalPredictor(model=SGDRegressor(),
features=features,
batch_size=10 * 1000,
num_epochs=5,
shuffle=True,
prediction_name='pred')
incremental.fit(df=df_train, target=target)
df_train = incremental.transform(df_train)
# State transfer - serialization
df_train.state_write(str(tmpdir.join('test.json')))
df_test.state_load(str(tmpdir.join('test.json')))
assert df_train.column_count() == df_test.column_count()
assert df_test.pred.values.shape == (33000, )
pred_in_memory = incremental.predict(df_test)
np.testing.assert_array_almost_equal(pred_in_memory,
df_test.pred.values,
decimal=1)
def test_sklearn_incremental_predictor_regression(df_example):
df = df_example
df_train, df_test = df.ml.train_test_split(test_size=0.1, verbose=False)
features = df_train.column_names[:6]
target = 'FeH'
incremental = IncrementalPredictor(model=SGDRegressor(),
features=features,
target=target,
batch_size=10 * 1000,
num_epochs=5,
shuffle=True,
prediction_name='pred')
incremental.fit(df=df_train)
df_train = incremental.transform(df_train)
# State transfer
state = df_train.state_get()
df_test.state_set(state)
assert df_train.column_count() == df_test.column_count()
assert df_test.pred.values.shape == (33000, )
pred_in_memory = incremental.predict(df_test)
np.testing.assert_array_almost_equal(pred_in_memory,
df_test.pred.values,
decimal=1)
