def test_make_pipeline_datetime_no_categorical(input_type, problem_type):
X = pd.DataFrame({"numerical": [1, 2, 3, 1, 2],
"some dates": pd.date_range('2000-02-03', periods=5, freq='W')})
y = pd.Series([0, 1, 1, 0, 0])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
pipeline = make_pipeline(X, y, estimator_class, problem_type)
assert isinstance(pipeline, type(pipeline_class))
assert pipeline.custom_hyperparameters is None
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
else:
delayed_features = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [StandardScaler, estimator_class]
elif estimator_class.model_family == ModelFamily.CATBOOST:
estimator_components = [estimator_class]
else:
estimator_components = [estimator_class]
assert pipeline.component_graph == [Imputer, DateTimeFeaturizer] + delayed_features + estimator_components
def test_make_pipeline_all_nan_no_categoricals(input_type, problem_type):
# testing that all_null column is not considered categorical
X = pd.DataFrame({"all_null": [np.nan, np.nan, np.nan, np.nan, np.nan],
"num": [1, 2, 3, 4, 5]})
y = pd.Series([0, 0, 1, 1, 0])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
pipeline = make_pipeline(X, y, estimator_class, problem_type)
assert isinstance(pipeline, type(pipeline_class))
assert pipeline.custom_hyperparameters is None
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
else:
delayed_features = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [StandardScaler, estimator_class]
elif estimator_class.model_family == ModelFamily.CATBOOST:
estimator_components = [estimator_class]
else:
estimator_components = [estimator_class]
assert pipeline.component_graph == [DropNullColumns, Imputer] + delayed_features + estimator_components
def test_make_pipeline_no_column_names(input_type, problem_type):
X = pd.DataFrame([[1, "a", np.nan], [2, "b", np.nan], [5, "b", np.nan]])
y = pd.Series([0, 0, 1])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
pipeline = make_pipeline(X, y, estimator_class, problem_type)
assert isinstance(pipeline, type(pipeline_class))
assert pipeline.custom_hyperparameters is None
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
else:
delayed_features = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [OneHotEncoder, StandardScaler, estimator_class]
elif estimator_class.model_family == ModelFamily.CATBOOST:
estimator_components = [estimator_class]
else:
estimator_components = [OneHotEncoder, estimator_class]
assert pipeline.component_graph == [DropNullColumns, Imputer] + delayed_features + estimator_components
def test_make_pipeline_text_columns(input_type, problem_type):
X = pd.DataFrame({"numerical": [1, 2, 3, 1, 2],
"categorical": ["a", "b", "a", "c", "c"],
"text": ["string one", "another", "text for a column, this should be a text column!!", "text string", "hello world"]})
y = pd.Series([0, 0, 1, 1, 0])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
pipeline = make_pipeline(X, y, estimator_class, problem_type)
assert isinstance(pipeline, type(pipeline_class))
assert pipeline.custom_hyperparameters is None
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
else:
delayed_features = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [OneHotEncoder, StandardScaler, estimator_class]
elif estimator_class.model_family == ModelFamily.CATBOOST:
estimator_components = [estimator_class]
else:
estimator_components = [OneHotEncoder, estimator_class]
assert pipeline.component_graph == [Imputer, TextFeaturizer] + delayed_features + estimator_components
def test_make_pipeline_numpy_input(problem_type):
X = np.array([[1, 2, 0, np.nan], [2, 2, 1, np.nan], [5, 1, np.nan,
np.nan]])
y = np.array([0, 0, 1, 0])
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
pipeline = make_pipeline(X, y, estimator_class, problem_type)
assert isinstance(pipeline, type(pipeline_class))
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
else:
delayed_features = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [StandardScaler, estimator_class]
else:
estimator_components = [estimator_class]
assert pipeline.component_graph == [
DropNullColumns, Imputer
] + delayed_features + estimator_components
def test_make_pipeline_only_text_columns(input_type, problem_type):
X = pd.DataFrame({"text": ["string one", "the evalml team is full of wonderful people", "text for a column, this should be a text column!!", "text string", "hello world"],
"another text": ["ladidididididida", "cats are great", "text for a column, this should be a text column!!", "text string", "goodbye world"]})
y = pd.Series([0, 0, 1, 1, 0])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
pipeline = make_pipeline(X, y, estimator_class, problem_type)
assert isinstance(pipeline, type(pipeline_class))
assert pipeline.custom_hyperparameters is None
delayed_features = []
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
standard_scaler = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
standard_scaler = [StandardScaler]
assert pipeline.component_graph == [TextFeaturizer] + delayed_features + standard_scaler + [estimator_class]
def test_make_pipeline_no_nulls(input_type, problem_type):
X = pd.DataFrame({
"numerical": [1, 2, 3, 1, 2],
"categorical": ["a", "b", "a", "c", "c"],
"some dates":
pd.date_range('2000-02-03', periods=5, freq='W')
})
y = pd.Series([0, 1, 1, 0, 0])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
parameters = {}
if is_time_series(problem_type):
parameters = {
"pipeline": {
"date_index": "some dates",
"gap": 1,
"max_delay": 1
},
"Time Series Baseline Estimator": {
"date_index": "some dates",
"gap": 1,
"max_delay": 1
}
}
pipeline = make_pipeline(X, y, estimator_class, problem_type,
parameters)
assert isinstance(pipeline, pipeline_class)
assert pipeline.custom_hyperparameters is None
delayed_features = []
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [
OneHotEncoder, StandardScaler, estimator_class
]
elif estimator_class.model_family == ModelFamily.CATBOOST:
estimator_components = [estimator_class]
else:
estimator_components = [OneHotEncoder, estimator_class]
if estimator_class.model_family == ModelFamily.ARIMA:
assert pipeline.component_graph == [Imputer
] + estimator_components
else:
assert pipeline.component_graph == [
Imputer, DateTimeFeaturizer
] + delayed_features + estimator_components
def test_make_pipeline_only_datetime_columns(input_type, problem_type):
X = pd.DataFrame({
"some dates":
pd.date_range('2000-02-03', periods=5, freq='W'),
"some other dates":
pd.date_range('2000-05-19', periods=5, freq='W')
})
y = pd.Series([0, 0, 1, 1, 0])
if input_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
parameters = {}
if is_time_series(problem_type):
parameters = {
"pipeline": {
"date_index": "some dates",
"gap": 1,
"max_delay": 1
},
"Time Series Baseline Estimator": {
"date_index": "some dates",
"gap": 1,
"max_delay": 1
}
}
pipeline = make_pipeline(X, y, estimator_class, problem_type,
parameters)
assert isinstance(pipeline, pipeline_class)
assert pipeline.custom_hyperparameters is None
delayed_features = []
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
standard_scaler = []
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
standard_scaler = [StandardScaler]
if estimator_class.model_family == ModelFamily.ARIMA:
assert pipeline.component_graph == standard_scaler + [
estimator_class
]
else:
assert pipeline.component_graph == [
DateTimeFeaturizer
] + delayed_features + standard_scaler + [estimator_class]
def test_make_pipeline_numpy_input(problem_type):
X = np.array([[1, 2, 0, np.nan], [2, 2, 1, np.nan], [5, 1, np.nan,
np.nan]])
y = np.array([0, 0, 1, 0])
estimators = get_estimators(problem_type=problem_type)
pipeline_class = _get_pipeline_base_class(problem_type)
if problem_type == ProblemTypes.MULTICLASS:
y = pd.Series([0, 2, 1, 2])
for estimator_class in estimators:
if problem_type in estimator_class.supported_problem_types:
parameters = {}
if is_time_series(problem_type):
parameters = {
"pipeline": {
"date_index": None,
"gap": 1,
"max_delay": 1
},
"Time Series Baseline Estimator": {
"date_index": None,
"gap": 1,
"max_delay": 1
}
}
pipeline = make_pipeline(X, y, estimator_class, problem_type,
parameters)
assert isinstance(pipeline, pipeline_class)
delayed_features = []
if is_time_series(problem_type):
delayed_features = [DelayedFeatureTransformer]
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
estimator_components = [StandardScaler, estimator_class]
else:
estimator_components = [estimator_class]
if estimator_class.model_family == ModelFamily.ARIMA:
assert pipeline.component_graph == [DropNullColumns, Imputer
] + estimator_components
else:
assert pipeline.component_graph == [
DropNullColumns, Imputer
] + delayed_features + estimator_components
