def test():
# loading dataset
data = pycaret.datasets.get_data('boston')
assert isinstance(data, pd.core.frame.DataFrame)
# init setup
reg = pycaret.regression.setup(data, target='medv', train_size=0.99, fold=2, silent=True, html=False, session_id=123, n_jobs=1)
models = pycaret.regression.compare_models(turbo=False, n_select=100)
models.append(pycaret.regression.stack_models(models[:3]))
models.append(pycaret.regression.ensemble_model(models[0]))
for model in models:
print(f"Testing model {model}")
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='scikit-learn', search_algorithm='random', early_stopping=False)
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='scikit-optimize', search_algorithm='bayesian', early_stopping=False)
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='optuna', search_algorithm='tpe', early_stopping=False)
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='tune-sklearn', search_algorithm='random', early_stopping=False)
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='optuna', search_algorithm='tpe', early_stopping="asha")
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='tune-sklearn', search_algorithm='hyperopt', early_stopping="asha")
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='tune-sklearn', search_algorithm='bayesian', early_stopping="asha")
if can_early_stop(model, True, True, True, {}):
pycaret.regression.tune_model(model, fold=2, n_iter=2, search_library='tune-sklearn', search_algorithm='bohb', early_stopping=True)
assert 1 == 1
def test():
# loading dataset
data = pycaret.datasets.get_data("juice")
assert isinstance(data, pd.core.frame.DataFrame)
# init setup
clf1 = pycaret.classification.setup(
data,
target="Purchase",
train_size=0.99,
fold=2,
silent=True,
html=False,
session_id=123,
n_jobs=1,
)
models = pycaret.classification.compare_models(turbo=False, n_select=100)
models.append(pycaret.classification.stack_models(models[:3]))
models.append(pycaret.classification.ensemble_model(models[0]))
for model in models:
print(f"Testing model {model}")
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="scikit-learn",
search_algorithm="random",
early_stopping=False,
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="scikit-optimize",
search_algorithm="bayesian",
early_stopping=False,
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="optuna",
search_algorithm="tpe",
early_stopping=False,
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="tune-sklearn",
search_algorithm="random",
early_stopping=False,
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="tune-sklearn",
search_algorithm="optuna",
early_stopping=False,
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="optuna",
search_algorithm="tpe",
early_stopping="asha",
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="tune-sklearn",
search_algorithm="hyperopt",
early_stopping="asha",
)
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="tune-sklearn",
search_algorithm="bayesian",
early_stopping="asha",
)
if can_early_stop(model, True, True, True, {}):
pycaret.classification.tune_model(
model,
fold=2,
n_iter=2,
search_library="tune-sklearn",
search_algorithm="bohb",
early_stopping=True,
)
assert 1 == 1
