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test_multiclass.py
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test_multiclass.py
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import pandas as pd
import pytest
import pycaret.classification
import pycaret.datasets
@pytest.fixture(scope="module")
def iris_dataframe():
# loading dataset
return pycaret.datasets.get_data("iris")
@pytest.mark.parametrize("return_train_score", [True, False])
def test_multiclass(iris_dataframe, return_train_score):
# loading dataset
assert isinstance(iris_dataframe, pd.DataFrame)
# init setup
pycaret.classification.setup(
iris_dataframe,
target="species",
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
)
# compare models
top3 = pycaret.classification.compare_models(errors="raise", n_select=100)[:3]
assert isinstance(top3, list)
# tune model
tuned_top3 = [
pycaret.classification.tune_model(i, return_train_score=return_train_score)
for i in top3
]
assert isinstance(tuned_top3, list)
# ensemble model
bagged_top3 = [
pycaret.classification.ensemble_model(i, return_train_score=return_train_score)
for i in tuned_top3
]
assert isinstance(bagged_top3, list)
# blend models
pycaret.classification.blend_models(top3, return_train_score=return_train_score)
# stack models
stacker = pycaret.classification.stack_models(
estimator_list=top3, return_train_score=return_train_score
)
pycaret.classification.predict_model(stacker)
# plot model
lr = pycaret.classification.create_model(
"lr", return_train_score=return_train_score
)
pycaret.classification.plot_model(lr, save=True, scale=5)
# select best model
pycaret.classification.automl(optimize="MCC", use_holdout=True)
best = pycaret.classification.automl(optimize="MCC")
# hold out predictions
predict_holdout = pycaret.classification.predict_model(best)
assert isinstance(predict_holdout, pd.DataFrame)
# predictions on new dataset
predict_holdout = pycaret.classification.predict_model(
best, data=iris_dataframe.drop("species", axis=1)
)
assert isinstance(predict_holdout, pd.DataFrame)
# calibrate model
pycaret.classification.calibrate_model(best, return_train_score=return_train_score)
# finalize model
pycaret.classification.finalize_model(best)
# save model
pycaret.classification.save_model(best, "best_model_23122019")
# load model
pycaret.classification.load_model("best_model_23122019")
# returns table of models
all_models = pycaret.classification.models()
assert isinstance(all_models, pd.DataFrame)
# get config
X_train = pycaret.classification.get_config("X_train")
X_test = pycaret.classification.get_config("X_test")
y_train = pycaret.classification.get_config("y_train")
y_test = pycaret.classification.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.classification.set_config("seed", 124)
seed = pycaret.classification.get_config("seed")
assert seed == 124
assert 1 == 1
def test_multiclass_predict_on_unseen(iris_dataframe):
exp = pycaret.classification.ClassificationExperiment()
# init setup
exp.setup(
iris_dataframe,
target="species",
log_experiment=True,
html=False,
session_id=123,
n_jobs=1,
)
model = exp.create_model("dt", cross_validation=False)
# save model
exp.save_model(model, "best_model_23122019")
exp = pycaret.classification.ClassificationExperiment()
# load model
model = exp.load_model("best_model_23122019")
exp.predict_model(model, iris_dataframe)
if __name__ == "__main__":
test_multiclass()
test_multiclass_predict_on_unseen()