def __init__(self, brothers):
ModelStacking.__init__(
self,
brothers,
SKLearnWrapper(
Ridge(),
HyperparameterSpace({"alpha": LogUniform(0.1, 10.0), "fit_intercept": Boolean()})
),
joiner=NumpyTranspose()
)
def test_hyperparam_space():
p = Pipeline([
AddFeatures([
SomeStep(hyperparams_space=HyperparameterSpace({"n_components": RandInt(1, 5)})),
SomeStep(hyperparams_space=HyperparameterSpace({"n_components": RandInt(1, 5)}))
]),
ModelStacking([
SomeStep(hyperparams_space=HyperparameterSpace({"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace({"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace({"max_depth": RandInt(1, 100)})),
SomeStep(hyperparams_space=HyperparameterSpace({"max_depth": RandInt(1, 100)}))
],
joiner=NumpyTranspose(),
judge=SomeStep(hyperparams_space=HyperparameterSpace({"alpha": LogUniform(0.1, 10.0)}))
)
])
rvsed = p.get_hyperparams_space()
p.set_hyperparams(rvsed)
hyperparams = p.get_hyperparams()
assert "AddFeatures" in hyperparams.keys()
assert "SomeStep" in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep"]
assert "SomeStep1" in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep1"]
assert "SomeStep" in hyperparams["ModelStacking"]
assert "n_estimators" in hyperparams["ModelStacking"]["SomeStep"]
assert "SomeStep1" in hyperparams["ModelStacking"]
assert "max_depth" in hyperparams["ModelStacking"]["SomeStep2"]
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_hyperparam_space():
p = Pipeline([
AddFeatures([
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_components": RandInt(1, 5)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_components": RandInt(1, 5)}))
]),
ModelStacking([
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"max_depth": RandInt(1, 100)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"max_depth": RandInt(1, 100)}))
],
joiner=NumpyTranspose(),
judge=SomeStep(hyperparams_space=HyperparameterSpace(
{"alpha": LogUniform(0.1, 10.0)})))
])
rvsed = p.get_hyperparams_space()
p.set_hyperparams(rvsed)
hyperparams = p.get_hyperparams()
flat_hyperparams_keys = hyperparams.to_flat_dict().keys()
assert 'AddFeatures' in hyperparams
assert 'SomeStep' in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep"]
assert 'SomeStep1' in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep1"]
assert 'ModelStacking' in hyperparams
assert 'SomeStep' in hyperparams["ModelStacking"]
assert 'n_estimators' in hyperparams["ModelStacking"]["SomeStep"]
assert 'SomeStep1' in hyperparams["ModelStacking"]
assert 'n_estimators' in hyperparams["ModelStacking"]["SomeStep1"]
assert 'SomeStep2' in hyperparams["ModelStacking"]
assert 'max_depth' in hyperparams["ModelStacking"]["SomeStep2"]
assert 'SomeStep3' in hyperparams["ModelStacking"]
assert 'max_depth' in hyperparams["ModelStacking"]["SomeStep3"]
assert 'AddFeatures__SomeStep1__n_components' in flat_hyperparams_keys
assert 'AddFeatures__SomeStep__n_components' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep__n_estimators' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep1__n_estimators' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep2__max_depth' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep3__max_depth' in flat_hyperparams_keys
ModelStacking(
[
SKLearnWrapper(
GradientBoostingRegressor(),
HyperparameterSpace({
"n_estimators": RandInt(50, 600),
"max_depth": RandInt(1, 10),
"learning_rate": LogUniform(0.07, 0.7)
})),
SKLearnWrapper(
GradientBoostingRegressor(),
HyperparameterSpace({
"n_estimators": RandInt(50, 600),
"max_depth": RandInt(1, 10),
"learning_rate": LogUniform(0.07, 0.7)
})),
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()
})),
)
def main():
boston = load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
shuffle=False)
# Note that the hyperparameter spaces are defined here during the pipeline definition, but it could be already set
# within the classes ar their definition if using custom classes, or also it could be defined after declaring the
# pipeline using a flat dict or a nested dict.
p = Pipeline([
AddFeatures([
SKLearnWrapper(
PCA(n_components=2),
HyperparameterSpace({"n_components": RandInt(1, 3)})),
SKLearnWrapper(
FastICA(n_components=2),
HyperparameterSpace({"n_components": RandInt(1, 3)})),
]),
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()
})),
)
])
print("Meta-fitting on train:")
p = p.meta_fit(X_train,
y_train,
metastep=RandomSearch(
n_iter=10,
higher_score_is_better=True,
validation_technique=KFoldCrossValidationWrapper(
scoring_function=r2_score, k_fold=10)))
# Here is an alternative way to do it, more "pipeliney":
# p = RandomSearch(
# p,
# n_iter=15,
# higher_score_is_better=True,
# validation_technique=KFoldCrossValidation(scoring_function=r2_score, k_fold=3)
# ).fit(X_train, y_train)
print("")
print("Transforming train and test:")
y_train_predicted = p.predict(X_train)
y_test_predicted = p.predict(X_test)
print("")
print("Evaluating transformed train:")
score_transform = r2_score(y_train_predicted, y_train)
print('R2 regression score:', score_transform)
print("")
print("Evaluating transformed test:")
score_test = r2_score(y_test_predicted, y_test)
print('R2 regression score:', score_test)
def main(tmpdir):
boston = load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, shuffle=False)
# Note that the hyperparameter spaces are defined here during the pipeline definition, but it could be already set
# within the classes ar their definition if using custom classes, or also it could be defined after declaring the
# pipeline using a flat dict or a nested dict.
p = Pipeline([
AddFeatures([
SKLearnWrapper(
PCA(n_components=2),
HyperparameterSpace({"n_components": RandInt(1, 3)})
),
SKLearnWrapper(
FastICA(n_components=2),
HyperparameterSpace({"n_components": RandInt(1, 3)})
),
]),
ModelStacking([
SKLearnWrapper(
GradientBoostingRegressor(),
HyperparameterSpace({
"n_estimators": RandInt(50, 300), "max_depth": RandInt(1, 4),
"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()})
),
)
])
print("Meta-fitting on train:")
auto_ml = AutoML(
p,
validation_splitter=ValidationSplitter(0.20),
refit_trial=True,
n_trials=10,
epochs=1, # 1 epoc here due to using sklearn models that just fit once.
cache_folder_when_no_handle=str(tmpdir),
scoring_callback=ScoringCallback(mean_squared_error, higher_score_is_better=False),
callbacks=[MetricCallback('mse', metric_function=mean_squared_error, higher_score_is_better=False)],
hyperparams_repository=InMemoryHyperparamsRepository(cache_folder=str(tmpdir))
)
random_search = auto_ml.fit(X_train, y_train)
p = random_search.get_best_model()
print("")
print("Transforming train and test:")
y_train_predicted = p.predict(X_train)
y_test_predicted = p.predict(X_test)
print("")
print("Evaluating transformed train:")
score_transform = r2_score(y_train_predicted, y_train)
print('R2 regression score:', score_transform)
print("")
print("Evaluating transformed test:")
score_test = r2_score(y_test_predicted, y_test)
print('R2 regression score:', score_test)
def test_deep_learning_pipeline():
# Given
boston = load_boston()
data_inputs, expected_outputs = shuffle(boston.data,
boston.target,
random_state=13)
expected_outputs = expected_outputs.astype(np.float32)
data_inputs = data_inputs.astype(np.float32)
pipeline = Pipeline([
AddFeatures([
SKLearnWrapper(
PCA(n_components=2),
HyperparameterSpace({"n_components": RandInt(1, 3)})),
SKLearnWrapper(
FastICA(n_components=2),
HyperparameterSpace({"n_components": RandInt(1, 3)})),
]),
ModelStacking(
[
SKLearnWrapper(
GradientBoostingRegressor(),
HyperparameterSpace({
"n_estimators": RandInt(50, 600),
"max_depth": RandInt(1, 10),
"learning_rate": LogUniform(0.07, 0.7)
})),
SKLearnWrapper(
KMeans(n_clusters=7),
HyperparameterSpace({"n_clusters": RandInt(5, 10)})),
],
joiner=NumpyTranspose(),
judge=SKLearnWrapper(
Ridge(),
HyperparameterSpace({
"alpha": LogUniform(0.7, 1.4),
"fit_intercept": Boolean()
})),
)
])
p = DeepLearningPipeline(
pipeline,
validation_size=VALIDATION_SIZE,
batch_size=BATCH_SIZE,
batch_metrics={'mse': to_numpy_metric_wrapper(mean_squared_error)},
shuffle_in_each_epoch_at_train=True,
n_epochs=N_EPOCHS,
epochs_metrics={'mse': to_numpy_metric_wrapper(mean_squared_error)},
scoring_function=to_numpy_metric_wrapper(mean_squared_error),
)
# When
p, outputs = p.fit_transform(data_inputs, expected_outputs)
# Then
batch_mse_train = p.get_batch_metric_train('mse')
epoch_mse_train = p.get_epoch_metric_train('mse')
batch_mse_validation = p.get_batch_metric_validation('mse')
epoch_mse_validation = p.get_epoch_metric_validation('mse')
assert len(epoch_mse_train) == N_EPOCHS
assert len(epoch_mse_validation) == N_EPOCHS
expected_len_batch_mse_train = math.ceil(
(len(data_inputs) / BATCH_SIZE) * (1 - VALIDATION_SIZE)) * N_EPOCHS
expected_len_batch_mse_validation = math.ceil(
(len(data_inputs) / BATCH_SIZE) * VALIDATION_SIZE) * N_EPOCHS
assert len(batch_mse_train) == expected_len_batch_mse_train
assert len(batch_mse_validation) == expected_len_batch_mse_validation
last_batch_mse_validation = batch_mse_validation[-1]
last_batch_mse_train = batch_mse_train[-1]
last_epoch_mse_train = epoch_mse_train[-1]
last_epoch_mse_validation = epoch_mse_validation[-1]
assert last_batch_mse_train < last_batch_mse_validation
assert last_epoch_mse_train < last_epoch_mse_validation
assert last_batch_mse_train < 1
assert last_epoch_mse_train < 1