test_pipeline.py

import pytest
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

@pytest.mark.skip(reason="Not implemented yet")
def test_pipeline():
    # Load the iris dataset
    X, y = load_iris(return_X_y=True)

    # Create a pipeline with a logistic regression model
    model = Pipeline([
        ('lr', LogisticRegression())
    ])

    # Fit the model on the training data
    model.fit(X, y)

    # Use the model to make predictions on the test data
    y_pred = model.predict(X)

    # Assert that the model has a high accuracy
    assert accuracy_score(y, y_pred) >= 0.9

# this will not run by pytest    
if __name__ == "__main__":
    import sys

    # Add --cov flag if not already present
    if "--cov" not in sys.argv:
        sys.argv.append("--cov")

    # Run the tests
    pytest.main(sys.argv)