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test_time_series_engines.py
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test_time_series_engines.py
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"""Module to test setting of engines in time series
"""
from daal4py.sklearn.linear_model._linear import (
LinearRegression as SklearnexLinearRegression,
)
from sklearn.linear_model import LinearRegression as SklearnLinearRegression
from sktime.forecasting.arima import AutoARIMA as PmdAutoARIMA
from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
from pycaret.time_series import TSForecastingExperiment
##############################
# Functions Start Here ####
##############################
# NOTE: Fixtures can not be used to parameterize tests
# https://stackoverflow.com/questions/52764279/pytest-how-to-parametrize-a-test-with-a-list-that-is-returned-from-a-fixture
# Hence, we have to create functions and create the parameterized list first
# (must happen during collect phase) before passing it to mark.parameterize.
############################
# Functions End Here ####
############################
##########################
# Tests Start Here ####
##########################
def test_engines_setup_global_args(load_pos_and_neg_data):
"""Tests the setting of engines using global arguments in setup.
We test for both statistical models and regression models.
"""
exp = TSForecastingExperiment()
data = load_pos_and_neg_data
exp.setup(
data=data,
fold=2,
fh=12,
fold_strategy="sliding",
verbose=False,
engine={"auto_arima": "statsforecast", "lr_cds_dt": "sklearnex"},
)
# Default Model Engine ----
# A. Statistical Models
assert exp.get_engine("auto_arima") == "statsforecast"
model = exp.create_model("auto_arima", cross_validation=False)
assert isinstance(model, StatsForecastAutoARIMA)
# Original engine should remain the same
assert exp.get_engine("auto_arima") == "statsforecast"
# B. Regression Models
assert exp.get_engine("lr_cds_dt") == "sklearnex"
model = exp.create_model("lr_cds_dt", cross_validation=False)
assert isinstance(model.regressor, SklearnexLinearRegression)
# Original engine should remain the same
assert exp.get_engine("lr_cds_dt") == "sklearnex"
def test_engines_global_methods(load_pos_and_neg_data):
"""Tests the setting of engines using methods like set_engine (global changes).
We test for both statistical models and regression models.
"""
exp = TSForecastingExperiment()
data = load_pos_and_neg_data
exp.setup(
data=data,
fold=2,
fh=12,
fold_strategy="sliding",
verbose=False,
engine={"auto_arima": "statsforecast", "lr_cds_dt": "sklearnex"},
)
# Globally reset engine ----
# A. Statistical Models
assert exp.get_engine("auto_arima") == "statsforecast"
exp._set_engine("auto_arima", "pmdarima")
assert exp.get_engine("auto_arima") == "pmdarima"
model = exp.create_model("auto_arima", cross_validation=False)
assert isinstance(model, PmdAutoARIMA)
# B. Regression Models
assert exp.get_engine("lr_cds_dt") == "sklearnex"
exp._set_engine("lr_cds_dt", "sklearn")
assert exp.get_engine("lr_cds_dt") == "sklearn"
model = exp.create_model("lr_cds_dt", cross_validation=False)
assert isinstance(model.regressor, SklearnLinearRegression)
def test_create_model_engines_local_args(load_pos_and_neg_data):
"""Tests the setting of engines for create_model using local args.
We test for both statistical models and regression models.
"""
exp = TSForecastingExperiment()
data = load_pos_and_neg_data
exp.setup(
data=data,
fold=2,
fh=12,
fold_strategy="sliding",
verbose=False,
)
# Default Model Engine ----
# A. Statistical Models
assert exp.get_engine("auto_arima") == "pmdarima"
model = exp.create_model("auto_arima", cross_validation=False)
assert isinstance(model, PmdAutoARIMA)
# Original engine should remain the same
assert exp.get_engine("auto_arima") == "pmdarima"
# B. Regression Models
assert exp.get_engine("lr_cds_dt") == "sklearn"
model = exp.create_model("lr_cds_dt", cross_validation=False)
assert isinstance(model.regressor, SklearnLinearRegression)
# Original engine should remain the same
assert exp.get_engine("lr_cds_dt") == "sklearn"
# Override model engine locally ----
# A. Statistical Models
model = exp.create_model(
"auto_arima", engine="statsforecast", cross_validation=False
)
assert isinstance(model, StatsForecastAutoARIMA)
# Original engine should remain the same
assert exp.get_engine("auto_arima") == "pmdarima"
model = exp.create_model("auto_arima")
assert isinstance(model, PmdAutoARIMA)
# B. Regression Models
model = exp.create_model("lr_cds_dt", engine="sklearnex", cross_validation=False)
assert isinstance(model.regressor, SklearnexLinearRegression)
# Original engine should remain the same
assert exp.get_engine("lr_cds_dt") == "sklearn"
model = exp.create_model("lr_cds_dt")
assert isinstance(model.regressor, SklearnLinearRegression)
def test_compare_models_engines_local_args(load_pos_and_neg_data):
"""Tests the setting of engines for compare_models using local args.
We test for both statistical models and regression models.
"""
exp = TSForecastingExperiment()
data = load_pos_and_neg_data
exp.setup(
data=data,
fold=2,
fh=12,
fold_strategy="sliding",
verbose=False,
)
# Default Model Engine ----
# A. Statistical Models
assert exp.get_engine("auto_arima") == "pmdarima"
model = exp.compare_models(include=["auto_arima"])
assert isinstance(model, PmdAutoARIMA)
# Original engine should remain the same
assert exp.get_engine("auto_arima") == "pmdarima"
# B. Regression Models
assert exp.get_engine("lr_cds_dt") == "sklearn"
model = exp.compare_models(include=["lr_cds_dt"])
assert isinstance(model.regressor, SklearnLinearRegression)
# Original engine should remain the same
assert exp.get_engine("lr_cds_dt") == "sklearn"
# Override model engine locally ----
# A. Statistical Models
model = exp.compare_models(
include=["auto_arima"], engine={"auto_arima": "statsforecast"}
)
assert isinstance(model, StatsForecastAutoARIMA)
# Original engine should remain the same
assert exp.get_engine("auto_arima") == "pmdarima"
model = exp.compare_models(include=["auto_arima"])
assert isinstance(model, PmdAutoARIMA)
# B. Regression Models
model = exp.compare_models(include=["lr_cds_dt"], engine={"lr_cds_dt": "sklearnex"})
assert isinstance(model.regressor, SklearnexLinearRegression)
# Original engine should remain the same
assert exp.get_engine("lr_cds_dt") == "sklearn"
model = exp.compare_models(include=["lr_cds_dt"])
assert isinstance(model.regressor, SklearnLinearRegression)