def test_delete_default():
"""Assert that the whole pipeline is deleted as default."""
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
atom.run(["LR", "LDA"])
atom.delete()
assert not (atom.models or atom.metric)
assert atom.results.empty
def test_delete_models_is_sequence():
"""Assert that for a sequence, multiple models are deleted."""
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
atom.run(["LR", "Tree", "RF"])
atom.delete(["Tree", "RF"])
assert atom.models == "LR"
assert atom.winner is atom.LR
assert len(atom.results) == 1
def test_plot_partial_dependence(features):
"""Assert that the plot_partial_dependence method work as intended."""
# For binary classification tasks
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
pytest.raises(NotFittedError, atom.plot_partial_dependence)
atom.run(["Tree", "LGB"], metric="f1")
# More than 3 features
with pytest.raises(ValueError, match=r".*Maximum 3 allowed.*"):
atom.plot_partial_dependence(features=[0, 1, 2, 3], display=False)
# Triple feature
with pytest.raises(ValueError, match=r".*should be single or in pairs.*"):
atom.lgb.plot_partial_dependence(features=[(0, 1, 2), 2],
display=False)
# Pair for multi-model
with pytest.raises(ValueError, match=r".*when plotting multiple models.*"):
atom.plot_partial_dependence(features=[(0, 2), 2], display=False)
# Unknown feature
with pytest.raises(ValueError, match=r".*not found in the dataset.*"):
atom.plot_partial_dependence(features=["test", 2], display=False)
# Invalid index
with pytest.raises(ValueError, match=r".*got index.*"):
atom.plot_partial_dependence(features=[120, 2], display=False)
# Different features for multiple models
atom.branch = "branch_2"
atom.feature_selection(strategy="pca", n_features=5)
atom.run(["tree2"])
with pytest.raises(ValueError, match=r".*models use the same features.*"):
atom.plot_partial_dependence(features=(0, 1), display=False)
atom.delete("Tree2") # Drop model created for test
atom.branch.delete() # Drop branch created for test
atom.plot_partial_dependence(display=False)
atom.lgb.plot_feature_importance(show=5, display=False)
atom.lgb.plot_partial_dependence(display=False)
# For multiclass classification tasks
atom = ATOMClassifier(X_class, y_class, random_state=1)
atom.run(["Tree", "LGB"], metric="f1_macro")
# Invalid target int
with pytest.raises(ValueError, match=r".*classes, got .*"):
atom.plot_partial_dependence(target=5, display=False)
# Invalid target str
with pytest.raises(ValueError, match=r".*not found in the mapping.*"):
atom.plot_partial_dependence(target="Yes", display=False)
atom.lgb.plot_partial_dependence(features,
target=2,
title="title",
display=False)
def test_delete_models_is_str():
"""Assert that for a string, a single model is deleted."""
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
atom.run(["LR", "Tree"])
atom.delete("winner")
assert atom.models == "Tree"
assert atom.winner is atom.Tree
assert len(atom.results) == 1
assert not hasattr(atom, "LR")
def test_delete_duplicates():
"""Assert that duplicate models are ignored."""
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
atom.run("LR")
atom.delete(["LR", "LR"])
assert not atom.models
def test_delete_general_number():
"""Assert that the general number selects all models with that number."""
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
atom.run(["LR0", "RF0"])
atom.delete("0")
assert not atom.models
def test_delete_general_name():
"""Assert that the general name selects all models from that acronym."""
atom = ATOMClassifier(X_bin, y_bin, random_state=1)
atom.run(["LR1", "LR2"])
atom.delete("LR")
assert not atom.models