def test_pipeline_setup_incrementally():
class SomeStepThatFits(NonTransformableMixin, BaseStep):
def __init__(self):
BaseStep.__init__(self)
self.has_fitted = False
def fit(self, data_inputs, expected_outputs=None) -> _FittableStep:
self.has_fitted = True
return self
class StepWithSensitiveSetup(Identity):
""" Asserts that step given in argument has fitted before performing setup"""
def __init__(self):
Identity.__init__(self)
def setup(self, context: ExecutionContext = None) -> BaseTransformer:
assert some_step.has_fitted is True
assert some_step2.has_fitted is False
return self
some_step = SomeStepThatFits()
some_step2 = SomeStepThatFits()
p = Pipeline([some_step, StepWithSensitiveSetup(), some_step2])
p.fit_transform(None, None)
def test_expectedoutputnull_is_fine_when_null(tmpdir):
data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
expected_outputs = None
p = Pipeline([SomeStep()])
p.fit_transform(data_inputs,expected_outputs)
def test_expectedoutputnull_raise_exception_when_notnull(tmpdir):
data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
expected_outputs = data_inputs * 2
p = Pipeline([AssertExpectedOutputIsNone()])
with pytest.raises(AssertionError) as error_info:
p.fit_transform(data_inputs, expected_outputs)
def test_forcehandleidentity_does_not_crash(tmpdir):
p = Pipeline([
ForceHandleIdentity()
])
data_inputs = np.array([0, 1, 2, 3])
expected_outputs = data_inputs * 2
p.fit(data_inputs, expected_outputs)
p.fit_transform(data_inputs, expected_outputs)
p.transform(data_inputs=data_inputs)
def test_load_full_dump_from_path(tmpdir):
# Given
tape_fit_callback_function = TapeCallbackFunction()
tape_transform_callback_function = TapeCallbackFunction()
pipeline = Pipeline(
[('step_a', Identity()),
('step_b',
OutputTransformerWrapper(
FitTransformCallbackStep(tape_fit_callback_function,
tape_transform_callback_function)))],
cache_folder=tmpdir).set_name(PIPELINE_NAME)
# When
pipeline, outputs = pipeline.fit_transform(DATA_INPUTS, EXPECTED_OUTPUTS)
pipeline.save(ExecutionContext(tmpdir), full_dump=True)
# Then
loaded_pipeline = ExecutionContext(tmpdir).load(
os.path.join(PIPELINE_NAME, 'step_b'))
assert isinstance(loaded_pipeline, OutputTransformerWrapper)
loaded_step_b_wrapped_step = loaded_pipeline.wrapped
assert np.array_equal(
loaded_step_b_wrapped_step.transform_callback_function.data[0],
EXPECTED_OUTPUTS)
assert np.array_equal(
loaded_step_b_wrapped_step.fit_callback_function.data[0][0],
EXPECTED_OUTPUTS)
assert np.array_equal(
loaded_step_b_wrapped_step.fit_callback_function.data[0][1],
[None] * len(EXPECTED_OUTPUTS))
def test_pipeline_nested_mutate_inverse_transform():
expected_tape = ["1", "2", "3", "4", "5", "6", "7", "7", "6", "5", "4", "3", "2", "1"]
tape = TapeCallbackFunction()
p = Pipeline([
Identity(),
TransformCallbackStep(tape.callback, ["1"]),
TransformCallbackStep(tape.callback, ["2"]),
Pipeline([
Identity(),
TransformCallbackStep(tape.callback, ["3"]),
TransformCallbackStep(tape.callback, ["4"]),
TransformCallbackStep(tape.callback, ["5"]),
Identity()
]),
TransformCallbackStep(tape.callback, ["6"]),
TransformCallbackStep(tape.callback, ["7"]),
Identity()
])
p, _ = p.fit_transform(np.ones((1, 1))) # will add range(1, 8) to tape.
print("[mutating]")
p = p.mutate(new_method="inverse_transform", method_to_assign_to="transform")
p.transform(np.ones((1, 1))) # will add reversed(range(1, 8)) to tape.
print(expected_tape)
print(tape.get_name_tape())
assert expected_tape == tape.get_name_tape()
def test_pipeline_nested_mutate_inverse_transform_without_identities():
"""
This test was required for a strange bug at the border of the pipelines
that happened when the identities were not used.
"""
expected_tape = ["1", "2", "3", "4", "5", "6", "7", "7", "6", "5", "4", "3", "2", "1"]
tape = TapeCallbackFunction()
p = Pipeline([
TransformCallbackStep(tape.callback, ["1"]),
TransformCallbackStep(tape.callback, ["2"]),
Pipeline([
TransformCallbackStep(tape.callback, ["3"]),
TransformCallbackStep(tape.callback, ["4"]),
TransformCallbackStep(tape.callback, ["5"]),
]),
TransformCallbackStep(tape.callback, ["6"]),
TransformCallbackStep(tape.callback, ["7"]),
])
p, _ = p.fit_transform(np.ones((1, 1))) # will add range(1, 8) to tape.
print("[mutating, inversing, and calling each inverse_transform]")
reversed(p).transform(np.ones((1, 1))) # will add reversed(range(1, 8)) to tape, calling inverse_transforms.
print(expected_tape)
print(tape.get_name_tape())
assert expected_tape == tape.get_name_tape()
def test_model_stacking_fit_transform():
model_stacking = Pipeline([
ModelStacking(
[
SKLearnWrapper(
GradientBoostingRegressor(),
HyperparameterSpace({
"n_estimators": RandInt(50, 600),
"max_depth": RandInt(1, 10),
"learning_rate": LogUniform(0.07, 0.7)
})),
SKLearnWrapper(
KMeans(),
HyperparameterSpace({"n_clusters": RandInt(5, 10)})),
],
joiner=NumpyTranspose(),
judge=SKLearnWrapper(
Ridge(),
HyperparameterSpace({
"alpha": LogUniform(0.7, 1.4),
"fit_intercept": Boolean()
})),
)
])
expected_outputs_shape = (379, 1)
data_inputs_shape = (379, 13)
data_inputs = _create_data(data_inputs_shape)
expected_outputs = _create_data(expected_outputs_shape)
model_stacking, outputs = model_stacking.fit_transform(
data_inputs, expected_outputs)
assert outputs.shape == expected_outputs_shape
def test_pipeline_fit_transform(steps_list, pipeline_runner):
data_input_ = [AN_INPUT]
expected_output_ = [AN_EXPECTED_OUTPUT]
p = Pipeline(steps_list, pipeline_runner=pipeline_runner())
p, result = p.fit_transform(data_input_, expected_output_)
assert tuple(result) == tuple(expected_output_)
def test_tape_callback():
expected_tape = ["1", "2", "3", "a", "b", "4"]
tape = TapeCallbackFunction()
p = Pipeline([
Identity(),
TransformCallbackStep(tape.callback, ["1"]),
TransformCallbackStep(tape.callback, ["2"]),
TransformCallbackStep(tape.callback, ["3"]),
AddFeatures([
TransformCallbackStep(tape.callback, ["a"]),
TransformCallbackStep(tape.callback, ["b"]),
]),
TransformCallbackStep(tape.callback, ["4"]),
Identity()
])
p.fit_transform(np.ones((1, 1)))
assert tape.get_name_tape() == expected_tape
def test_tensorflowv1_saver(tmpdir):
data_inputs = np.array([
3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042,
10.791, 5.313, 7.997, 5.654, 9.27, 3.1
])
expected_ouptuts = np.array([
1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827,
3.465, 1.65, 2.904, 2.42, 2.94, 1.3
])
model = Pipeline([create_model_step()])
for i in range(50):
model, outputs = model.fit_transform(data_inputs, expected_ouptuts)
model.save(ExecutionContext(root=tmpdir))
model = Pipeline([create_model_step()]).load(ExecutionContext(root=tmpdir))
model, outputs = model.fit_transform(data_inputs, expected_ouptuts)
assert ((outputs - expected_ouptuts)**2).mean() < 0.25
def test_fit_transform(steps: List[BaseStep], expected_tape: List[str]):
tape.data = []
tape.name_tape = []
pipeline = Pipeline(steps=steps)
actual_pipeline, actual_data_inputs = pipeline.fit_transform(
data_inputs, expected_outputs)
actual_tape = tape.get_name_tape()
assert isinstance(actual_pipeline, Pipeline)
assert actual_tape == expected_tape
assert np.array_equal(actual_data_inputs, data_inputs)
def test_feature_union_should_fit_transform_with_zip_features():
p = Pipeline(
[FeatureUnion([
Identity(),
Identity(),
], joiner=ZipFeatures())])
data_inputs = np.random.randint(low=0, high=100, size=(2, 20))
expected_outputs = None
p, outputs = p.fit_transform(data_inputs, expected_outputs)
assert np.array_equal(outputs, np.stack([data_inputs, data_inputs],
axis=1))
def test_feature_union_should_fit_transform_with_numpy_transpose():
p = Pipeline(
[FeatureUnion([
Identity(),
Identity(),
], joiner=NumpyTranspose())])
data_inputs = np.random.randint((1, 20))
expected_outputs = np.random.randint((1, 20))
p, outputs = p.fit_transform(data_inputs, expected_outputs)
assert np.array_equal(outputs,
np.array([data_inputs, data_inputs]).transpose())
def test_feature_union_should_fit_transform_with_concatenate_inner_features():
p = Pipeline([
FeatureUnion([
Identity(),
Identity(),
],
joiner=NumpyConcatenateInnerFeatures())
])
data_inputs = np.random.randint((1, 20))
expected_outputs = np.random.randint((1, 20))
p, outputs = p.fit_transform(data_inputs, expected_outputs)
assert np.array_equal(outputs, np.concatenate([data_inputs, data_inputs]))
def test_fit_transform_should_fit_then_use_cache(tmpdir):
tape_transform = TapeCallbackFunction()
tape_fit = TapeCallbackFunction()
p = Pipeline([
JoblibValueCachingWrapper(
LogFitTransformCallbackStep(tape_transform,
tape_fit,
transform_function=np.log), tmpdir)
])
p, outputs = p.fit_transform([1, 1, 2, 2], [2, 2, 4, 4])
assert outputs == EXPECTED_OUTPUTS
assert tape_transform.data == [[1], [2]]
assert tape_fit.data == [([1, 1, 2, 2], [2, 2, 4, 4])]
def test_should_flush_cache_on_every_fit(tmpdir):
tape_transform = TapeCallbackFunction()
tape_fit = TapeCallbackFunction()
wrapper = JoblibValueCachingWrapper(LogFitTransformCallbackStep(
tape_transform, tape_fit, transform_function=np.log),
cache_folder=tmpdir)
p = Pipeline([wrapper])
wrapper.create_checkpoint_path()
wrapper.write_cache(1, 10)
wrapper.write_cache(2, 20)
p, outputs = p.fit_transform([1, 1, 2, 2], [2, 2, 4, 4])
assert outputs == EXPECTED_OUTPUTS
assert tape_transform.data == [[1], [2]]
assert tape_fit.data == [([1, 1, 2, 2], [2, 2, 4, 4])]
def test_data_shuffling_should_shuffle_data_inputs_and_expected_outputs():
callback_fit = TapeCallbackFunction()
callback_transform = TapeCallbackFunction()
data_shuffler = Pipeline([
DataShuffler(seed=42, increment_seed_after_each_fit=True),
FitTransformCallbackStep(callback_transform, callback_fit)
])
data_inputs = np.array(range(10))
expected_outputs = np.array(range(10, 20))
outputs = data_shuffler.fit_transform(data_inputs, expected_outputs)
assert not np.array_equal(outputs, data_inputs)
assert not np.array_equal(callback_fit.data[0][0], data_inputs)
assert not np.array_equal(callback_fit.data[0][1], expected_outputs)
assert not np.array_equal(callback_transform.data, data_inputs)
def test_fit_transform_should_fit_transform_all_steps_for_each_data_inputs_expected_outputs():
tape = TapeCallbackFunction()
tape_fit = TapeCallbackFunction()
p = Pipeline([
ForEachDataInput(Pipeline([
FitTransformCallbackStep(tape.callback, tape_fit, ["1"]),
FitTransformCallbackStep(tape.callback, tape_fit, ["2"]),
]))
])
data_inputs = [[0, 1], [1, 2]]
expected_outputs = [[2, 3], [4, 5]]
p, outputs = p.fit_transform(data_inputs, expected_outputs)
assert tape.get_name_tape() == ["1", "2", "1", "2"]
assert tape_fit.get_name_tape() == ["1", "2", "1", "2"]
assert tape_fit.data == [([0, 1], [2, 3]), ([0, 1], [2, 3]), ([1, 2], [4, 5]), ([1, 2], [4, 5])]
def test_should_save_checkpoint_pickle(tmpdir: LocalPath):
tape = TapeCallbackFunction()
pickle_checkpoint_step = PickleCheckpointStep('1', tmpdir)
pipeline = Pipeline(steps=[
TransformCallbackStep(tape.callback, ["1"]), pickle_checkpoint_step,
TransformCallbackStep(tape.callback, ["2"]),
TransformCallbackStep(tape.callback, ["3"])
])
pipeline, actual_data_inputs = pipeline.fit_transform(
data_inputs, expected_outputs)
actual_tape = tape.get_name_tape()
assert actual_data_inputs == data_inputs
assert actual_tape == ["1", "2", "3"]
assert os.path.exists(
pickle_checkpoint_step.get_checkpoint_file_path(data_inputs))
def main():
np.random.seed(42)
X = np.random.randint(5, size=(100, 5))
# Create and fit the pipeline:
pipeline = Pipeline([
StandardScaler(),
Identity(),
Pipeline([
Identity(),
Identity(), # Note: an Identity step is a step that does nothing.
Identity(), # We use it here for demonstration purposes.
Pipeline([Identity(), PCA(n_components=2)])
])
])
pipeline, X_t = pipeline.fit_transform(X)
# Get the components:
pca_components = pipeline["Pipeline"]["Pipeline"][
-1].get_wrapped_sklearn_predictor().components_
assert pca_components.shape == (2, 5)
def test_pipeline_simple_mutate_inverse_transform():
expected_tape = ["1", "2", "3", "4", "4", "3", "2", "1"]
tape = TapeCallbackFunction()
p = Pipeline([
Identity(),
TransformCallbackStep(tape.callback, ["1"]),
TransformCallbackStep(tape.callback, ["2"]),
TransformCallbackStep(tape.callback, ["3"]),
TransformCallbackStep(tape.callback, ["4"]),
Identity()
])
p, _ = p.fit_transform(np.ones((1, 1)))
print("[mutating]")
p = p.mutate(new_method="inverse_transform", method_to_assign_to="transform")
p.transform(np.ones((1, 1)))
assert expected_tape == tape.get_name_tape()
def test_should_load_checkpoint_pickle(tmpdir: LocalPath):
tape = TapeCallbackFunction()
force_checkpoint_name = 'checkpoint_a'
pickle_checkpoint_step = PickleCheckpointStep(
force_checkpoint_name=force_checkpoint_name, cache_folder=tmpdir)
pickle_checkpoint_step.set_checkpoint_path(force_checkpoint_name)
with open(pickle_checkpoint_step.get_checkpoint_file_path(data_inputs),
'wb') as file:
pickle.dump(data_inputs, file)
pipeline = Pipeline(
steps=[('a', TransformCallbackStep(tape.callback, ["1"])
), ('b', TransformCallbackStep(tape.callback, ["2"])
), (force_checkpoint_name, pickle_checkpoint_step
), ('c', TransformCallbackStep(tape.callback, ["3"]))])
pipeline, actual_data_inputs = pipeline.fit_transform(
data_inputs, expected_outputs)
actual_tape = tape.get_name_tape()
assert actual_data_inputs == data_inputs
assert actual_tape == ["3"]
def test_expand_dim_fit_transform():
handle_fit_callback = TapeCallbackFunction()
handle_transform_callback = TapeCallbackFunction()
handle_fit_transform_callback = TapeCallbackFunction()
p = Pipeline([
ExpandDim(
HandleCallbackStep(handle_fit_callback, handle_transform_callback,
handle_fit_transform_callback))
])
p['ExpandDim'].hashers = [SomeSummaryHasher(fake_summary_id=SUMMARY_ID)]
p, outputs = p.fit_transform(np.array(range(10)), np.array(range(10)))
assert np.array_equal(outputs, np.array(range(10)))
assert handle_transform_callback.data == []
assert handle_fit_callback.data == []
assert handle_fit_transform_callback.data[0][0].current_ids == [SUMMARY_ID]
assert handle_fit_transform_callback.data[0][0].summary_id == SUMMARY_ID
assert np.array_equal(
np.array(handle_fit_transform_callback.data[0][0].data_inputs),
np.array([np.array(range(10))]))
assert np.array_equal(
np.array(handle_fit_transform_callback.data[0][0].expected_outputs),
np.array([np.array(range(10))]))
def test_expand_dim_fit_transform():
handle_fit_callback = TapeCallbackFunction()
handle_transform_callback = TapeCallbackFunction()
handle_fit_transform_callback = TapeCallbackFunction()
p = Pipeline([
ExpandDim(
HandleCallbackStep(handle_fit_callback, handle_transform_callback,
handle_fit_transform_callback))
])
p, outputs = p.fit_transform(np.array(range(10)), np.array(range(10)))
assert np.array_equal(outputs, np.array(range(10)))
assert handle_transform_callback.data == []
assert handle_fit_callback.data == []
assert handle_fit_transform_callback.data[0][0].current_ids == [
'781e5e245d69b566979b86e28d23f2c7'
]
assert np.array_equal(
np.array(handle_fit_transform_callback.data[0][0].data_inputs),
np.array([np.array(range(10))]))
assert np.array_equal(
np.array(handle_fit_transform_callback.data[0][0].expected_outputs),
np.array([np.array(range(10))]))
def main(chosen_device):
exercice_number = 1
print('exercice {}\n=================='.format(exercice_number))
data_inputs, expected_outputs = generate_data(
# See: https://github.com/guillaume-chevalier/seq2seq-signal-prediction/blob/master/datasets.py
exercice_number=exercice_number,
n_samples=None,
window_size_past=None,
window_size_future=None)
print('data_inputs shape: {} => (n_samples, window_size_past, input_dim)'.
format(data_inputs.shape))
print(
'expected_outputs shape: {} => (n_samples, window_size_future, output_dim)'
.format(expected_outputs.shape))
sequence_length = data_inputs.shape[1]
input_dim = data_inputs.shape[2]
output_dim = expected_outputs.shape[2]
batch_size = 100
epochs = 3
validation_size = 0.15
max_plotted_validation_predictions = 10
seq2seq_pipeline_hyperparams = HyperparameterSamples({
'hidden_dim':
100,
'layers_stacked_count':
2,
'lambda_loss_amount':
0.0003,
'learning_rate':
0.006,
'window_size_future':
sequence_length,
'output_dim':
output_dim,
'input_dim':
input_dim
})
feature_0_metric = metric_3d_to_2d_wrapper(mean_squared_error)
metrics = {'mse': feature_0_metric}
signal_prediction_pipeline = Pipeline([
ForEachDataInput(MeanStdNormalizer()),
ToNumpy(),
PlotPredictionsWrapper(
Tensorflow2ModelStep(
# See: https://github.com/Neuraxio/Neuraxle-TensorFlow
create_model=create_model,
create_loss=create_loss,
create_optimizer=create_optimizer,
expected_outputs_dtype=tf.dtypes.float32,
data_inputs_dtype=tf.dtypes.float32,
print_loss=True).set_hyperparams(seq2seq_pipeline_hyperparams))
]).set_name('SignalPrediction')
pipeline = Pipeline([
EpochRepeater(ValidationSplitWrapper(
MetricsWrapper(Pipeline([
TrainOnlyWrapper(DataShuffler()),
MiniBatchSequentialPipeline([
MetricsWrapper(signal_prediction_pipeline,
metrics=metrics,
name='batch_metrics')
],
batch_size=batch_size)
]),
metrics=metrics,
name='epoch_metrics',
print_metrics=True),
test_size=validation_size,
scoring_function=feature_0_metric),
epochs=epochs)
])
pipeline, outputs = pipeline.fit_transform(data_inputs, expected_outputs)
plot_metrics(pipeline=pipeline, exercice_number=exercice_number)
plot_predictions(data_inputs, expected_outputs, pipeline,
max_plotted_validation_predictions)
