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test_regression.py
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test_regression.py
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import uuid
import numpy as np
import pandas as pd
import pytest
from mlflow.tracking import MlflowClient
import pycaret.datasets
import pycaret.regression
@pytest.fixture(scope="module")
def boston_dataframe():
return pycaret.datasets.get_data("boston")
@pytest.mark.parametrize("return_train_score", [True, False])
def test_regression(boston_dataframe, return_train_score):
# loading dataset
assert isinstance(boston_dataframe, pd.DataFrame)
# init setup
pycaret.regression.setup(
boston_dataframe,
target="medv",
remove_multicollinearity=True,
multicollinearity_threshold=0.95,
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
experiment_name=uuid.uuid4().hex,
)
# compare models
top3 = pycaret.regression.compare_models(
n_select=100,
exclude=["catboost"],
errors="raise",
)[:3]
assert isinstance(top3, list)
metrics = pycaret.regression.pull()
# no metric should be 0
assert (
(
metrics.loc[[i for i in metrics.index if i not in ("dummy")]][
[c for c in metrics.columns if c not in ("Model", "TT (Sec)")]
]
!= 0
)
.all()
.all()
)
# tune model
tuned_top3 = [
pycaret.regression.tune_model(
i, n_iter=3, return_train_score=return_train_score
)
for i in top3
]
assert isinstance(tuned_top3, list)
pycaret.regression.tune_model(
top3[0], n_iter=3, choose_better=True, return_train_score=return_train_score
)
# ensemble model
bagged_top3 = [
pycaret.regression.ensemble_model(i, return_train_score=return_train_score)
for i in tuned_top3
]
assert isinstance(bagged_top3, list)
# blend models
pycaret.regression.blend_models(top3, return_train_score=return_train_score)
# stack models
pycaret.regression.stack_models(
estimator_list=top3[1:],
meta_model=top3[0],
return_train_score=return_train_score,
)
# plot model
lr = pycaret.regression.create_model("lr", return_train_score=return_train_score)
pycaret.regression.plot_model(
lr, save=True
) # scale removed because build failed due to large image size
# select best model
pycaret.regression.automl(optimize="MAPE", use_holdout=True)
best = pycaret.regression.automl(optimize="MAPE")
# hold out predictions
predict_holdout = pycaret.regression.predict_model(best)
assert isinstance(predict_holdout, pd.DataFrame)
# predictions on new dataset
predict_holdout = pycaret.regression.predict_model(best, data=boston_dataframe)
assert isinstance(predict_holdout, pd.DataFrame)
# finalize model
pycaret.regression.finalize_model(best)
# save model
pycaret.regression.save_model(best, "best_model_23122019")
# load model
pycaret.regression.load_model("best_model_23122019")
# returns table of models
all_models = pycaret.regression.models()
assert isinstance(all_models, pd.DataFrame)
# get config
X_train = pycaret.regression.get_config("X_train")
X_test = pycaret.regression.get_config("X_test")
y_train = pycaret.regression.get_config("y_train")
y_test = pycaret.regression.get_config("y_test")
assert isinstance(X_train, pd.DataFrame)
assert isinstance(X_test, pd.DataFrame)
assert isinstance(y_train, pd.Series)
assert isinstance(y_test, pd.Series)
# set config
pycaret.regression.set_config("seed", 124)
seed = pycaret.regression.get_config("seed")
assert seed == 124
assert 1 == 1
def test_regression_predict_on_unseen(boston_dataframe):
exp = pycaret.regression.RegressionExperiment()
# init setup
exp.setup(
boston_dataframe,
target="medv",
remove_multicollinearity=True,
multicollinearity_threshold=0.95,
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
experiment_name=uuid.uuid4().hex,
)
model = exp.create_model("dt", cross_validation=False)
# save model
exp.save_model(model, "best_model_23122019")
exp = pycaret.regression.RegressionExperiment()
# load model
model = exp.load_model("best_model_23122019")
exp.predict_model(model, boston_dataframe)
def test_regression_target_transformation(boston_dataframe):
exp = pycaret.regression.RegressionExperiment()
# init setup
exp.setup(
boston_dataframe,
target="medv",
transform_target=True,
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
experiment_name=uuid.uuid4().hex,
)
model = exp.create_model("dt", cross_validation=False)
preds = exp.predict_model(model)
assert np.isclose(preds["prediction_label"].iloc[0], 49.999989)
class TestRegressionExperimentCustomTags:
def test_regression_setup_fails_with_experiment_custom_tags(self, boston_dataframe):
with pytest.raises(Exception):
# init setup
_ = pycaret.regression.setup(
boston_dataframe,
target="medv",
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
experiment_name=uuid.uuid4().hex,
experiment_custom_tags="custom_tag",
)
@pytest.mark.parametrize("custom_tag", [1, ("pytest", "True"), True, 1000.0])
def test_regression_setup_fails_with_experiment_custom_multiples_inputs(
self, custom_tag
):
with pytest.raises(Exception):
# init setup
_ = pycaret.regression.setup(
pycaret.datasets.get_data("boston"),
target="medv",
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
experiment_name=uuid.uuid4().hex,
experiment_custom_tags=custom_tag,
)
def test_regression_models_with_experiment_custom_tags(self, boston_dataframe):
# init setup
experiment_name = uuid.uuid4().hex
_ = pycaret.regression.setup(
boston_dataframe,
target="medv",
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
experiment_name=experiment_name,
)
_ = pycaret.regression.compare_models(
n_select=100, experiment_custom_tags={"pytest": "testing"}
)[:2]
# get experiment data
tracking_api = MlflowClient()
experiment = tracking_api.get_experiment_by_name(experiment_name)
experiment_id = experiment.experiment_id
# get run's info
experiment_run = tracking_api.search_runs(experiment_id)[0]
# get run id
run_id = experiment_run.info.run_id
# get run data
run_data = tracking_api.get_run(run_id)
# assert that custom tag was inserted
assert "testing" == run_data.to_dictionary().get("data").get("tags").get(
"pytest"
)
if __name__ == "__main__":
test_regression()
test_regression_predict_on_unseen()
TestRegressionExperimentCustomTags()