def test_when_hyperparams_and_saved_no_pipeline_should_not_load_checkpoint_pickle(
tmpdir: LocalPath):
# Given
tape = TapeCallbackFunction()
pickle_checkpoint_step = DefaultCheckpoint()
# When
pipeline_save = create_pipeline(tmpdir=tmpdir,
pickle_checkpoint_step=DefaultCheckpoint(),
tape=TapeCallbackFunction(),
hyperparameters=HyperparameterSamples(
{"a__learning_rate": 1}),
different=True,
save_pipeline=False)
pipeline_save.fit_transform(data_inputs, expected_outputs)
pipeline_load = create_pipeline(
tmpdir=tmpdir,
pickle_checkpoint_step=pickle_checkpoint_step,
tape=tape,
hyperparameters=HyperparameterSamples({"a__learning_rate": 1}))
pipeline_load, actual_data_inputs = pipeline_load.fit_transform(
data_inputs, expected_outputs)
# Then
actual_tape = tape.get_name_tape()
assert np.array_equal(actual_data_inputs, data_inputs)
assert actual_tape == ["1", "2", "3"]
def test_when_hyperparams_and_saved_same_pipeline_should_load_checkpoint_pickle(tmpdir: LocalPath):
# Given
tape = TapeCallbackFunction()
# When
pipeline_save = create_pipeline(
tmpdir=tmpdir,
pickle_checkpoint_step=DefaultCheckpoint(),
tape=TapeCallbackFunction(),
hyperparameters=HyperparameterSamples({"a__learning_rate": 1})
)
pipeline_save.fit_transform(data_inputs, expected_outputs)
pipeline_load = create_pipeline(
tmpdir=tmpdir,
pickle_checkpoint_step=DefaultCheckpoint(),
tape=tape,
hyperparameters=HyperparameterSamples({"a__learning_rate": 1})
)
pipeline_load, actual_data_inputs = pipeline_load.fit_transform(data_inputs, expected_outputs)
# Then
actual_tape = tape.get_name_tape()
assert np.array_equal(actual_data_inputs, data_inputs)
assert actual_tape == EXPECTED_TAPE_AFTER_CHECKPOINT
def test_pickle_checkpoint_step_should_load_data_container(tmpdir: LocalPath):
initial_data_inputs = [1, 2]
initial_expected_outputs = [2, 3]
create_pipeline_output_transformer = lambda: ResumablePipeline([
('output_transformer_1', MultiplyBy2OutputTransformer()),
('pickle_checkpoint', DefaultCheckpoint()),
('output_transformer_2', MultiplyBy2OutputTransformer()),
],
cache_folder
=tmpdir)
create_pipeline_output_transformer().fit_transform(
data_inputs=initial_data_inputs,
expected_outputs=initial_expected_outputs)
transformer = create_pipeline_output_transformer()
actual_data_container = transformer.handle_transform(
DataContainer(current_ids=[0, 1],
data_inputs=initial_data_inputs,
expected_outputs=initial_expected_outputs),
ExecutionContext.create_from_root(transformer, ExecutionMode.TRANSFORM,
tmpdir))
assert np.array_equal(actual_data_container.data_inputs, [4, 8])
assert np.array_equal(actual_data_container.expected_outputs, [8, 12])
def test_when_hyperparams_should_save_checkpoint_pickle(tmpdir: LocalPath):
tape = TapeCallbackFunction()
pickle_checkpoint_step = DefaultCheckpoint()
pipeline = create_pipeline(tmpdir, pickle_checkpoint_step, tape,
HyperparameterSamples({"a__learning_rate": 1}))
pipeline, actual_data_inputs = pipeline.fit_transform(
data_inputs, expected_outputs)
actual_tape = tape.get_name_tape()
assert np.array_equal(actual_data_inputs, data_inputs)
assert actual_tape == ["1", "2", "3"]
assert os.path.exists(
os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'di',
'44f9d6dd8b6ccae571ca04525c3eaffa.pickle'))
assert os.path.exists(
os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'di',
'898a67b2f5eeae6393ca4b3162ba8e3d.pickle'))
assert os.path.exists(
os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'eo',
'44f9d6dd8b6ccae571ca04525c3eaffa.pickle'))
assert os.path.exists(
os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'eo',
'898a67b2f5eeae6393ca4b3162ba8e3d.pickle'))
def create_pipeline_output_transformer(tmpdir):
return ResumablePipeline([
('output_transformer_1', MultiplyBy2OutputTransformer()),
('joblib_checkpoint', DefaultCheckpoint()),
('output_transformer_2', MultiplyBy2OutputTransformer()),
],
cache_folder=tmpdir)
def test_resumable_pipeline_fit_transform_should_save_all_fitted_pipeline_steps(
tmpdir: LocalPath):
p = ResumablePipeline(
[(SOME_STEP_1, MultiplyByN(multiply_by=2)),
(PIPELINE_2,
ResumablePipeline([(SOME_STEP_2, MultiplyByN(multiply_by=4)),
(CHECKPOINT, DefaultCheckpoint()),
(SOME_STEP_3, MultiplyByN(multiply_by=6))]))],
cache_folder=tmpdir)
p.name = ROOT
p, outputs = p.fit_transform(np.array(range(10)), np.array(range(10)))
not_saved_paths = [create_some_step3_path(tmpdir)]
saved_paths = [
create_root_path(tmpdir),
create_pipeline2_path(tmpdir),
create_some_step1_path(tmpdir),
create_some_step2_path(tmpdir),
create_some_checkpoint_path(tmpdir)
]
assert np.array_equal(outputs, EXPECTED_OUTPUTS)
for p in saved_paths:
assert os.path.exists(p)
for p in not_saved_paths:
assert not os.path.exists(p)
def given_saved_pipeline(tmpdir: LocalPath):
step_savers = [(SOME_STEP_1, []),
(PIPELINE_2, [TruncableJoblibStepSaver()])]
path = create_root_path(tmpdir, True)
root = ResumablePipeline([], cache_folder=tmpdir)
root.sub_steps_savers = step_savers
root.name = ROOT
dump(root, path)
pipeline_2 = ResumablePipeline([], cache_folder=tmpdir)
pipeline_2.name = 'pipeline2'
pipeline_2.sub_steps_savers = [
(SOME_STEP_2, []),
(CHECKPOINT, []),
(SOME_STEP_3, []),
]
dump(pipeline_2, create_pipeline2_path(tmpdir, True))
given_saved_some_step(multiply_by=2,
name=SOME_STEP_1,
path=create_some_step1_path(tmpdir, True))
given_saved_some_step(multiply_by=4,
name=SOME_STEP_2,
path=create_some_step2_path(tmpdir, True))
given_saved_some_step(multiply_by=6,
name=SOME_STEP_3,
path=create_some_step3_path(tmpdir, True))
checkpoint = DefaultCheckpoint()
checkpoint.name = CHECKPOINT
dump(checkpoint, create_some_checkpoint_path(tmpdir, True))
p = ResumablePipeline(
[(SOME_STEP_1, MultiplyByN(multiply_by=1)),
(PIPELINE_2,
ResumablePipeline([(SOME_STEP_2, MultiplyByN(multiply_by=1)),
(CHECKPOINT, DefaultCheckpoint()),
(SOME_STEP_3, MultiplyByN(multiply_by=1))]))],
cache_folder=tmpdir)
p.name = ROOT
return p
def create_checkpoint_test_case(tmpdir):
tape_transform_1 = TapeCallbackFunction()
tape_fit_1 = TapeCallbackFunction()
tape_transform_2 = TapeCallbackFunction()
tape_fit_2 = TapeCallbackFunction()
pipeline = ResumablePipeline(
[('step1', FitTransformCallbackStep(tape_transform_1, tape_fit_1)),
('checkpoint', DefaultCheckpoint()),
('step2', FitTransformCallbackStep(tape_transform_2, tape_fit_2))],
cache_folder=tmpdir)
return CheckpointTest(tape_transform_1, tape_fit_1, tape_transform_2,
tape_fit_2, pipeline)
def test_when_no_hyperparams_should_save_checkpoint_pickle(tmpdir: LocalPath):
tape = TapeCallbackFunction()
pickle_checkpoint_step = DefaultCheckpoint()
pipeline = create_pipeline(tmpdir, pickle_checkpoint_step, tape)
pipeline, actual_data_inputs = pipeline.fit_transform(data_inputs, expected_outputs)
actual_tape = tape.get_name_tape()
assert np.array_equal(actual_data_inputs, data_inputs)
assert actual_tape == ["1", "2", "3"]
assert os.path.exists(os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'di', '0.pickle'))
assert os.path.exists(os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'di', '1.pickle'))
assert os.path.exists(os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'eo', '0.pickle'))
assert os.path.exists(os.path.join(tmpdir, 'ResumablePipeline', 'pickle_checkpoint', 'eo', '1.pickle'))
def main(tmpdir, sleep_time: float = 0.001, n_iter: int = 10):
DATA_INPUTS = np.array(range(100))
EXPECTED_OUTPUTS = np.array(range(100, 200))
HYPERPARAMETER_SPACE = HyperparameterSpace({
'multiplication_1__multiply_by': RandInt(1, 2),
'multiplication_2__multiply_by': RandInt(1, 2),
'multiplication_3__multiply_by': RandInt(1, 2),
})
print('Classic Pipeline:')
classic_pipeline_folder = os.path.join(str(tmpdir), 'classic')
pipeline = Pipeline([
('multiplication_1', MultiplyByN()),
('sleep_1', ForEachDataInput(Sleep(sleep_time))),
('multiplication_2', MultiplyByN()),
('sleep_2', ForEachDataInput(Sleep(sleep_time))),
('multiplication_3', MultiplyByN()),
], cache_folder=classic_pipeline_folder).set_hyperparams_space(HYPERPARAMETER_SPACE)
time_a = time.time()
auto_ml = AutoML(
pipeline,
refit_trial=True,
n_trials=n_iter,
cache_folder_when_no_handle=classic_pipeline_folder,
validation_splitter=ValidationSplitter(0.2),
hyperparams_optimizer=RandomSearchHyperparameterSelectionStrategy(),
scoring_callback=ScoringCallback(mean_squared_error, higher_score_is_better=False),
callbacks=[
MetricCallback('mse', metric_function=mean_squared_error, higher_score_is_better=False)
],
)
auto_ml = auto_ml.fit(DATA_INPUTS, EXPECTED_OUTPUTS)
outputs = auto_ml.get_best_model().predict(DATA_INPUTS)
time_b = time.time()
actual_score = mean_squared_error(EXPECTED_OUTPUTS, outputs)
print('{0} seconds'.format(time_b - time_a))
print('output: {0}'.format(outputs))
print('smallest mse: {0}'.format(actual_score))
print('best hyperparams: {0}'.format(pipeline.get_hyperparams()))
assert isinstance(actual_score, float)
print('Resumable Pipeline:')
resumable_pipeline_folder = os.path.join(str(tmpdir), 'resumable')
pipeline = ResumablePipeline([
('multiplication_1', MultiplyByN()),
('ForEach(sleep_1)', ForEachDataInput(Sleep(sleep_time))),
('checkpoint1', ExpandDim(DefaultCheckpoint())),
('multiplication_2', MultiplyByN()),
('sleep_2', ForEachDataInput(Sleep(sleep_time))),
('checkpoint2', ExpandDim(DefaultCheckpoint())),
('multiplication_3', MultiplyByN())
], cache_folder=resumable_pipeline_folder).set_hyperparams_space(HYPERPARAMETER_SPACE)
time_a = time.time()
auto_ml = AutoML(
pipeline,
refit_trial=True,
n_trials=n_iter,
cache_folder_when_no_handle=resumable_pipeline_folder,
validation_splitter=ValidationSplitter(0.2),
hyperparams_optimizer=RandomSearchHyperparameterSelectionStrategy(),
scoring_callback=ScoringCallback(mean_squared_error, higher_score_is_better=False),
callbacks=[
MetricCallback('mse', metric_function=mean_squared_error, higher_score_is_better=False)
]
)
auto_ml = auto_ml.fit(DATA_INPUTS, EXPECTED_OUTPUTS)
outputs = auto_ml.get_best_model().predict(DATA_INPUTS)
time_b = time.time()
pipeline.flush_all_cache()
actual_score = mean_squared_error(EXPECTED_OUTPUTS, outputs)
print('{0} seconds'.format(time_b - time_a))
print('output: {0}'.format(outputs))
print('smallest mse: {0}'.format(actual_score))
print('best hyperparams: {0}'.format(pipeline.get_hyperparams()))
assert isinstance(actual_score, float)
def main(tmpdir, sleep_time: float = 0, n_iter: int = 10):
DATA_INPUTS = np.array(range(100))
EXPECTED_OUTPUTS = np.array(range(100, 200))
HYPERPARAMETER_SPACE = HyperparameterSpace({
'multiplication_1__multiply_by':
RandInt(1, 2),
'multiplication_2__multiply_by':
RandInt(1, 2),
'multiplication_3__multiply_by':
RandInt(1, 2),
})
print('Classic Pipeline:')
pipeline = Pipeline([
('multiplication_1', MultiplyByN()),
('sleep_1', ForEachDataInput(Sleep(sleep_time))),
('multiplication_2', MultiplyByN()),
('sleep_2', ForEachDataInput(Sleep(sleep_time))),
('multiplication_3', MultiplyByN()),
]).set_hyperparams_space(HYPERPARAMETER_SPACE)
time_a = time.time()
best_model = RandomSearch(pipeline,
n_iter=n_iter,
higher_score_is_better=True).fit(
DATA_INPUTS, EXPECTED_OUTPUTS)
outputs = best_model.transform(DATA_INPUTS)
time_b = time.time()
actual_score = mean_squared_error(EXPECTED_OUTPUTS, outputs)
print('{0} seconds'.format(time_b - time_a))
print('output: {0}'.format(outputs))
print('smallest mse: {0}'.format(actual_score))
print('best hyperparams: {0}'.format(pipeline.get_hyperparams()))
assert isinstance(actual_score, float)
print('Resumable Pipeline:')
pipeline = ResumablePipeline(
[('multiplication_1', MultiplyByN()),
('ForEach(sleep_1)', ForEachDataInput(Sleep(sleep_time))),
('checkpoint1', ExpandDim(DefaultCheckpoint())),
('multiplication_2', MultiplyByN()),
('sleep_2', ForEachDataInput(Sleep(sleep_time))),
('checkpoint2', ExpandDim(DefaultCheckpoint())),
('multiplication_3', MultiplyByN())],
cache_folder=tmpdir).set_hyperparams_space(HYPERPARAMETER_SPACE)
time_a = time.time()
best_model = RandomSearch(pipeline,
n_iter=n_iter,
higher_score_is_better=True).fit(
DATA_INPUTS, EXPECTED_OUTPUTS)
outputs = best_model.transform(DATA_INPUTS)
time_b = time.time()
pipeline.flush_all_cache()
actual_score = mean_squared_error(EXPECTED_OUTPUTS, outputs)
print('{0} seconds'.format(time_b - time_a))
print('output: {0}'.format(outputs))
print('smallest mse: {0}'.format(actual_score))
print('best hyperparams: {0}'.format(pipeline.get_hyperparams()))
assert isinstance(actual_score, float)