mirror of
https://github.com/Doctorado-ML/bayesclass.git
synced 2025-08-16 16:15:57 +00:00
95 lines
2.4 KiB
Python
95 lines
2.4 KiB
Python
import pytest
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import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import KBinsDiscretizer
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from matplotlib.testing.decorators import image_comparison
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from matplotlib.testing.conftest import mpl_test_settings
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from bayesclass import KDB
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from .._version import __version__
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@pytest.fixture
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def data():
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X, y = load_iris(return_X_y=True)
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enc = KBinsDiscretizer(encode="ordinal")
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return enc.fit_transform(X), y
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@pytest.fixture
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def clf():
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return KDB(k=3)
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def test_KDB_default_hyperparameters(data, clf):
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# Test default values of hyperparameters
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assert not clf.show_progress
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assert clf.random_state is None
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assert clf.theta == 0.03
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clf = KDB(show_progress=True, random_state=17, k=3)
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assert clf.show_progress
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assert clf.random_state == 17
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assert clf.k == 3
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clf.fit(*data)
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assert clf.class_name_ == "class"
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assert clf.features_ == [
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"feature_0",
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"feature_1",
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"feature_2",
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"feature_3",
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]
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def test_KDB_version(clf):
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"""Check KDB version."""
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assert __version__ == clf.version()
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def test_KDB_nodes_leaves(clf):
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assert clf.nodes_leaves() == (0, 0)
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def test_KDB_classifier(data, clf):
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clf.fit(*data)
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attribs = ["classes_", "X_", "y_", "features_", "class_name_"]
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for attr in attribs:
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assert hasattr(clf, attr)
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X = data[0]
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y = data[1]
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y_pred = clf.predict(X)
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assert y_pred.shape == (X.shape[0],)
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assert sum(y == y_pred) == 148
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@image_comparison(
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baseline_images=["line_dashes_KDB"], remove_text=True, extensions=["png"]
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)
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def test_KDB_plot(data, clf):
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# mpl_test_settings will automatically clean these internal side effects
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mpl_test_settings
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dataset = load_iris(as_frame=True)
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clf.fit(*data, features=dataset["feature_names"])
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clf.plot("KDB Iris")
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def test_KDB_wrong_num_features(data, clf):
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with pytest.raises(
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ValueError,
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match="Number of features does not match the number of columns in X",
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):
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clf.fit(*data, features=["feature_1", "feature_2"])
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def test_KDB_wrong_hyperparam(data, clf):
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with pytest.raises(ValueError, match="Unexpected argument: wrong_param"):
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clf.fit(*data, wrong_param="wrong_param")
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def test_KDB_error_size_predict(data, clf):
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X, y = data
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clf.fit(X, y)
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with pytest.raises(ValueError):
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X_diff_size = np.ones((10, X.shape[1] + 1))
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clf.predict(X_diff_size)
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