def test_additional_objectives(X_y_binary):
X, y = X_y_binary
objective = FraudCost(retry_percentage=.5,
interchange_fee=.02,
fraud_payout_percentage=.75,
amount_col=10)
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective='F1',
max_iterations=2,
additional_objectives=[objective],
n_jobs=1)
automl.search()
results = automl.describe_pipeline(0, return_dict=True)
assert 'Fraud Cost' in list(
results["cv_data"][0]["all_objective_scores"].keys())
def test_random_seed(X_y_regression):
X, y = X_y_regression
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='regression', objective="R2", max_iterations=5, random_seed=0,
n_jobs=1)
automl.search()
automl_1 = AutoMLSearch(X_train=X, y_train=y, problem_type='regression', objective="R2", max_iterations=5, random_seed=0,
n_jobs=1)
automl_1.search()
# need to use assert_frame_equal as R2 could be different at the 10+ decimal
assert pd.testing.assert_frame_equal(automl.rankings, automl_1.rankings) is None
def test_recall_error(X_y_binary):
X, y = X_y_binary
# Recall is a valid objective but it's not allowed in AutoML so a ValueError is expected
error_msg = 'recall is not allowed in AutoML!'
with pytest.raises(ValueError, match=error_msg):
AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective='recall',
max_iterations=1)
def test_automl_time_series_classification_threshold(
mock_binary_fit, mock_binary_score, mock_predict_proba,
mock_encode_targets, mock_optimize_threshold, mock_split_data,
optimize, objective, X_y_binary):
X, y = X_y_binary
mock_binary_score.return_value = {objective: 0.4}
problem_type = 'time series binary'
configuration = {
"gap": 0,
"max_delay": 0,
'delay_target': False,
'delay_features': True
}
mock_optimize_threshold.return_value = 0.62
mock_split_data.return_value = split_data(X,
y,
problem_type,
test_size=0.2,
random_seed=0)
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type=problem_type,
problem_configuration=configuration,
objective=objective,
optimize_thresholds=optimize,
max_batches=2)
automl.search()
assert isinstance(automl.data_splitter, TimeSeriesSplit)
if objective == 'Log Loss Binary':
mock_optimize_threshold.assert_not_called()
assert automl.best_pipeline.threshold is None
mock_split_data.assert_not_called()
elif optimize and objective == 'F1':
mock_optimize_threshold.assert_called()
assert automl.best_pipeline.threshold == 0.62
mock_split_data.assert_called()
assert str(mock_split_data.call_args[0][2]) == problem_type
elif not optimize and objective == 'F1':
mock_optimize_threshold.assert_not_called()
assert automl.best_pipeline.threshold == 0.5
mock_split_data.assert_not_called()
def test_non_optimizable_threshold(mock_fit, mock_score, X_y_binary):
mock_score.return_value = {"AUC": 1.0}
X, y = X_y_binary
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective='AUC',
optimize_thresholds=False,
max_iterations=1)
automl.search()
mock_fit.assert_called()
mock_score.assert_called()
assert automl.best_pipeline.threshold is None
assert automl.results['pipeline_results'][0]['cv_data'][0].get(
'binary_classification_threshold') is None
assert automl.results['pipeline_results'][0]['cv_data'][1].get(
'binary_classification_threshold') is None
assert automl.results['pipeline_results'][0]['cv_data'][2].get(
'binary_classification_threshold') is None
def test_automl_allowed_pipelines_specified_allowed_pipelines(
mock_fit, mock_score, dummy_regression_pipeline_class, X_y_regression):
X, y = X_y_regression
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
allowed_pipelines=[dummy_regression_pipeline_class],
allowed_model_families=None)
mock_score.return_value = {automl.objective.name: 1.0}
expected_pipelines = [dummy_regression_pipeline_class]
mock_score.return_value = {automl.objective.name: 1.0}
assert automl.allowed_pipelines == expected_pipelines
assert automl.allowed_model_families == [ModelFamily.NONE]
automl.search()
mock_fit.assert_called()
mock_score.assert_called()
assert automl.allowed_pipelines == expected_pipelines
assert automl.allowed_model_families == [ModelFamily.NONE]
def test_automl_allowed_pipelines_init_allowed_both_specified(
mock_fit, mock_score, dummy_regression_pipeline_class, X_y_regression,
assert_allowed_pipelines_equal_helper):
X, y = X_y_regression
automl = AutoMLSearch(
X_train=X,
y_train=y,
problem_type='regression',
allowed_pipelines=[dummy_regression_pipeline_class({})],
allowed_model_families=[ModelFamily.RANDOM_FOREST])
mock_score.return_value = {automl.objective.name: 1.0}
expected_pipelines = [dummy_regression_pipeline_class({})]
assert_allowed_pipelines_equal_helper(automl.allowed_pipelines,
expected_pipelines)
assert set(automl.allowed_model_families) == set(
[p.model_family for p in expected_pipelines])
automl.search()
mock_fit.assert_called()
mock_score.assert_called()
def test_fraud_objective(X_y_binary):
X, y = X_y_binary
objective = FraudCost(retry_percentage=.5,
interchange_fee=.02,
fraud_payout_percentage=.75,
amount_col=10)
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective=objective,
max_iterations=1)
automl.search()
pipeline = automl.best_pipeline
pipeline.fit(X, y)
pipeline.predict(X, objective)
pipeline.predict_proba(X)
pipeline.score(X, y, [objective])
def test_automl_regression_nonlinear_pipeline_search(
nonlinear_regression_pipeline_class, X_y_regression):
X, y = X_y_regression
allowed_pipelines = [nonlinear_regression_pipeline_class]
start_iteration_callback = MagicMock()
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
max_iterations=2,
start_iteration_callback=start_iteration_callback,
allowed_pipelines=allowed_pipelines,
n_jobs=1)
automl.search()
assert start_iteration_callback.call_count == 2
assert start_iteration_callback.call_args_list[0][0][
0] == MeanBaselineRegressionPipeline
assert start_iteration_callback.call_args_list[1][0][
0] == nonlinear_regression_pipeline_class
def test_automl_binary_nonlinear_pipeline_search(
nonlinear_binary_pipeline_class, X_y_binary):
X, y = X_y_binary
allowed_pipelines = [nonlinear_binary_pipeline_class]
start_iteration_callback = MagicMock()
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
max_iterations=2,
start_iteration_callback=start_iteration_callback,
allowed_pipelines=allowed_pipelines,
n_jobs=1)
automl.search()
assert start_iteration_callback.call_count == 2
assert start_iteration_callback.call_args_list[0][0][
0] == ModeBaselineBinaryPipeline
assert start_iteration_callback.call_args_list[1][0][
0] == nonlinear_binary_pipeline_class
def test_automl_allowed_pipelines_no_allowed_pipelines(automl_type, X_y_binary,
X_y_multi):
is_multiclass = automl_type == ProblemTypes.MULTICLASS
X, y = X_y_multi if is_multiclass else X_y_binary
problem_type = 'multiclass' if is_multiclass else 'binary'
with pytest.raises(ValueError, match="No allowed pipelines to search"):
AutoMLSearch(X_train=X,
y_train=y,
problem_type=problem_type,
allowed_pipelines=None,
allowed_model_families=[])
def test_plot_iterations_ipython_mock(mock_ipython_display, X_y_binary):
pytest.importorskip(
'IPython.display',
reason='Skipping plotting test because ipywidgets not installed')
pytest.importorskip(
'plotly.graph_objects',
reason='Skipping plotting test because plotly not installed')
X, y = X_y_binary
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective="f1",
max_iterations=3,
n_jobs=1)
automl.search()
plot = automl.plot.search_iteration_plot(interactive_plot=True)
assert isinstance(plot, SearchIterationPlot)
assert isinstance(plot.data, AutoMLSearch)
mock_ipython_display.assert_called_with(plot.best_score_by_iter_fig)
def test_cbm_objective_automl(optimize_thresholds, X_y_binary):
X, y = X_y_binary
cbm = CostBenefitMatrix(true_positive=10,
true_negative=-1,
false_positive=-7,
false_negative=-2)
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective=cbm,
max_iterations=2,
optimize_thresholds=optimize_thresholds)
automl.search()
pipeline = automl.best_pipeline
pipeline.fit(X, y)
predictions = pipeline.predict(X, cbm)
assert not np.isnan(predictions.to_series()).values.any()
assert not np.isnan(pipeline.predict_proba(X).to_dataframe()).values.any()
assert not np.isnan(pipeline.score(X, y, [cbm])['Cost Benefit Matrix'])
def test_categorical_hyperparam(X_y_multi):
X, y = X_y_multi
class CustomPipeline(MulticlassClassificationPipeline):
component_graph = [
'Imputer', 'One Hot Encoder', 'Standard Scaler',
'Logistic Regression Classifier'
]
custom_hyperparameters = {
'Simple Imputer': {
'impute_strategy': Categorical(['mean', 'most_frequent'])
}
}
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type="multiclass",
allowed_pipelines=[CustomPipeline],
n_jobs=1)
automl.search()
def test_plot_iterations_ipython_mock_import_failure(mock_ipython_display, X_y_binary):
pytest.importorskip('IPython.display', reason='Skipping plotting test because ipywidgets not installed')
go = pytest.importorskip('plotly.graph_objects', reason='Skipping plotting test because plotly not installed')
X, y = X_y_binary
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', objective="f1", max_iterations=3, n_jobs=1)
automl.search()
mock_ipython_display.side_effect = ImportError('KABOOOOOOMMMM')
plot = automl.plot.search_iteration_plot(interactive_plot=True)
mock_ipython_display.assert_called_once()
assert isinstance(plot, go.Figure)
assert isinstance(plot.data, tuple)
plot_data = plot.data[0]
x = pd.Series(plot_data['x'])
y = pd.Series(plot_data['y'])
assert x.is_monotonic_increasing
assert y.is_monotonic_increasing
assert len(x) == 3
assert len(y) == 3
def test_automl_allowed_pipelines_search(mock_fit, mock_score,
dummy_binary_pipeline_class,
X_y_binary):
X, y = X_y_binary
mock_score.return_value = {'Log Loss Binary': 1.0}
allowed_pipelines = [dummy_binary_pipeline_class]
start_iteration_callback = MagicMock()
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
max_iterations=2,
start_iteration_callback=start_iteration_callback,
allowed_pipelines=allowed_pipelines)
automl.search()
assert start_iteration_callback.call_count == 2
assert start_iteration_callback.call_args_list[0][0][
0] == ModeBaselineBinaryPipeline
assert start_iteration_callback.call_args_list[1][0][
0] == dummy_binary_pipeline_class
def test_log_metrics_only_passed_directly(X_y_regression):
X, y = X_y_regression
with pytest.raises(
ObjectiveNotFoundError,
match="RootMeanSquaredLogError is not a valid Objective!"):
AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
additional_objectives=[
'RootMeanSquaredLogError', 'MeanSquaredLogError'
])
ar = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
additional_objectives=[
RootMeanSquaredLogError(),
MeanSquaredLogError()
])
assert ar.additional_objectives[0].name == 'Root Mean Squared Log Error'
assert ar.additional_objectives[1].name == 'Mean Squared Log Error'
def test_automl_allowed_pipelines_search(mock_fit, mock_score,
dummy_regression_pipeline_class,
X_y_regression):
X, y = X_y_regression
mock_score.return_value = {'R2': 1.0}
allowed_pipelines = [dummy_regression_pipeline_class]
start_iteration_callback = MagicMock()
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
max_iterations=2,
start_iteration_callback=start_iteration_callback,
allowed_pipelines=allowed_pipelines)
automl.search()
assert start_iteration_callback.call_count == 2
assert start_iteration_callback.call_args_list[0][0][
0] == MeanBaselineRegressionPipeline
assert start_iteration_callback.call_args_list[1][0][
0] == dummy_regression_pipeline_class
def test_automl_multiclass_nonlinear_pipeline_search_more_iterations(
nonlinear_multiclass_pipeline_class, X_y_multi):
X, y = X_y_multi
allowed_pipelines = [nonlinear_multiclass_pipeline_class]
start_iteration_callback = MagicMock()
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='multiclass',
max_iterations=5,
start_iteration_callback=start_iteration_callback,
allowed_pipelines=allowed_pipelines,
n_jobs=1)
automl.search()
assert start_iteration_callback.call_args_list[0][0][
0] == ModeBaselineMulticlassPipeline
assert start_iteration_callback.call_args_list[1][0][
0] == nonlinear_multiclass_pipeline_class
assert start_iteration_callback.call_args_list[4][0][
0] == nonlinear_multiclass_pipeline_class
def test_multi_objective(X_y_multi):
X, y = X_y_multi
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective="Log Loss Binary")
assert automl.problem_type == ProblemTypes.BINARY
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='multiclass',
objective="Log Loss Multiclass")
assert automl.problem_type == ProblemTypes.MULTICLASS
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='multiclass',
objective='AUC Micro')
assert automl.problem_type == ProblemTypes.MULTICLASS
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective='AUC')
assert automl.problem_type == ProblemTypes.BINARY
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='multiclass')
assert automl.problem_type == ProblemTypes.MULTICLASS
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='binary')
assert automl.problem_type == ProblemTypes.BINARY
def test_optimizable_threshold_enabled(mock_fit, mock_score,
mock_predict_proba, mock_encode_targets,
mock_optimize_threshold, X_y_binary,
caplog):
mock_optimize_threshold.return_value = 0.8
X, y = X_y_binary
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective='precision',
max_iterations=1,
optimize_thresholds=True)
mock_score.return_value = {'precision': 1.0}
automl.search()
mock_fit.assert_called()
mock_score.assert_called()
mock_predict_proba.assert_called()
mock_optimize_threshold.assert_called()
assert automl.best_pipeline.threshold == 0.8
assert automl.results['pipeline_results'][0]['cv_data'][0].get(
'binary_classification_threshold') == 0.8
assert automl.results['pipeline_results'][0]['cv_data'][1].get(
'binary_classification_threshold') == 0.8
assert automl.results['pipeline_results'][0]['cv_data'][2].get(
'binary_classification_threshold') == 0.8
automl.describe_pipeline(0)
out = caplog.text
assert "Objective to optimize binary classification pipeline thresholds for" in out
def test_automl_time_series_classification_pickle_generated_pipeline(
mock_binary_fit, mock_multi_fit, mock_binary_score,
mock_multiclass_score, problem_type, X_y_binary, X_y_multi):
if problem_type == ProblemTypes.TIME_SERIES_BINARY:
X, y = X_y_binary
pipeline = GeneratedPipelineTimeSeriesBinary
else:
X, y = X_y_multi
pipeline = GeneratedPipelineTimeSeriesMulticlass
configuration = {
"gap": 0,
"max_delay": 0,
'delay_target': False,
'delay_features': True
}
a = AutoMLSearch(X_train=X,
y_train=y,
problem_type=problem_type,
problem_configuration=configuration)
a.search()
for i, row in a.rankings.iterrows():
assert a.get_pipeline(row['id']).__class__ == pipeline
assert pickle.loads(pickle.dumps(a.get_pipeline(row['id'])))
def test_plot_iterations_max_iterations(X_y_regression):
go = pytest.importorskip(
'plotly.graph_objects',
reason='Skipping plotting test because plotly not installed')
X, y = X_y_regression
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
max_iterations=3,
n_jobs=1)
automl.search()
plot = automl.plot.search_iteration_plot()
plot_data = plot.data[0]
x = pd.Series(plot_data['x'])
y = pd.Series(plot_data['y'])
assert isinstance(plot, go.Figure)
assert x.is_monotonic_increasing
assert y.is_monotonic_increasing
assert len(x) == 3
assert len(y) == 3
def test_plot_disabled_missing_dependency(X_y_regression,
has_minimal_dependencies):
X, y = X_y_regression
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
max_iterations=3)
if has_minimal_dependencies:
with pytest.raises(AttributeError):
automl.plot.search_iteration_plot
else:
automl.plot.search_iteration_plot
def test_callback(X_y_regression):
X, y = X_y_regression
counts = {
"start_iteration_callback": 0,
"add_result_callback": 0,
}
def start_iteration_callback(pipeline_class, parameters, automl_obj, counts=counts):
counts["start_iteration_callback"] += 1
def add_result_callback(results, trained_pipeline, automl_obj, counts=counts):
counts["add_result_callback"] += 1
max_iterations = 3
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='regression', objective="R2", max_iterations=max_iterations,
start_iteration_callback=start_iteration_callback,
add_result_callback=add_result_callback, n_jobs=1)
automl.search()
assert counts["start_iteration_callback"] == max_iterations
assert counts["add_result_callback"] == max_iterations
def test_automl_allowed_pipelines_init_allowed_both_not_specified(
mock_fit, mock_score, X_y_regression,
assert_allowed_pipelines_equal_helper):
X, y = X_y_regression
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='regression',
allowed_pipelines=None,
allowed_model_families=None)
mock_score.return_value = {automl.objective.name: 1.0}
expected_pipelines = [
make_pipeline(X, y, estimator, ProblemTypes.REGRESSION)
for estimator in get_estimators(ProblemTypes.REGRESSION,
model_families=None)
]
assert_allowed_pipelines_equal_helper(automl.allowed_pipelines,
expected_pipelines)
assert set(automl.allowed_model_families) == set(
[p.model_family for p in expected_pipelines])
automl.search()
mock_fit.assert_called()
mock_score.assert_called()
def test_automl_allowed_pipelines_init_allowed_both_specified_multi(
mock_fit, mock_score, dummy_multiclass_pipeline_class, X_y_multi,
assert_allowed_pipelines_equal_helper):
X, y = X_y_multi
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='multiclass',
allowed_pipelines=[dummy_multiclass_pipeline_class],
allowed_model_families=[ModelFamily.RANDOM_FOREST])
mock_score.return_value = {automl.objective.name: 1.0}
expected_pipelines = [dummy_multiclass_pipeline_class]
assert automl.allowed_pipelines == expected_pipelines
# the dummy multiclass pipeline estimator has model family NONE
assert set(automl.allowed_model_families) == set([ModelFamily.NONE])
automl.search()
assert_allowed_pipelines_equal_helper(automl.allowed_pipelines,
expected_pipelines)
assert set(automl.allowed_model_families) == set(
[p.model_family for p in expected_pipelines])
mock_fit.assert_called()
mock_score.assert_called()
def test_early_stopping(caplog, linear_regression_pipeline_class, X_y_regression):
X, y = X_y_regression
tolerance = 0.005
patience = 2
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='regression', objective='mse', max_time='60 seconds',
patience=patience, tolerance=tolerance,
allowed_model_families=['linear_model'], random_seed=0, n_jobs=1)
mock_results = {
'search_order': [0, 1, 2],
'pipeline_results': {}
}
scores = [150, 200, 195]
for id in mock_results['search_order']:
mock_results['pipeline_results'][id] = {}
mock_results['pipeline_results'][id]['score'] = scores[id]
mock_results['pipeline_results'][id]['pipeline_class'] = linear_regression_pipeline_class
automl._results = mock_results
automl._check_stopping_condition(time.time())
out = caplog.text
assert "2 iterations without improvement. Stopping search early." in out
def test_plot_iterations_max_time(X_y_binary):
go = pytest.importorskip(
'plotly.graph_objects',
reason='Skipping plotting test because plotly not installed')
X, y = X_y_binary
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective="f1",
max_time=10,
n_jobs=1)
automl.search(show_iteration_plot=False)
plot = automl.plot.search_iteration_plot()
plot_data = plot.data[0]
x = pd.Series(plot_data['x'])
y = pd.Series(plot_data['y'])
assert isinstance(plot, go.Figure)
assert x.is_monotonic_increasing
assert y.is_monotonic_increasing
assert len(x) > 0
assert len(y) > 0
def test_data_splitter(X_y_binary):
X, y = X_y_binary
cv_folds = 5
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', data_splitter=BalancedClassificationDataCVSplit(n_splits=cv_folds), max_iterations=1,
n_jobs=1)
automl.search()
assert isinstance(automl.rankings, pd.DataFrame)
assert len(automl.results['pipeline_results'][0]["cv_data"]) == cv_folds
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', data_splitter=TimeSeriesSplit(n_splits=cv_folds),
max_iterations=1, n_jobs=1)
automl.search()
assert isinstance(automl.rankings, pd.DataFrame)
assert len(automl.results['pipeline_results'][0]["cv_data"]) == cv_folds