mirror of
https://github.com/Doctorado-ML/bayesclass.git
synced 2025-08-16 16:15:57 +00:00
Ricardo Montañana
f9b35f61f0
Fix local discretization Refactor tests Unifiy iris dataset from sklearn with iris.arff
121 lines
3.0 KiB
Python
121 lines
3.0 KiB
Python
import pytest
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import numpy as np
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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.clfs import TANNew
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from .._version import __version__
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@pytest.fixture
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def clf():
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return TANNew(random_state=17)
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def test_TANNew_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 == 17
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clf = TANNew(show_progress=True)
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assert clf.show_progress
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assert clf.random_state is None
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clf.fit(*data)
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assert clf.head_ == 0
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assert clf.class_name_ == "class"
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assert clf.feature_names_in_ == [
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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_TANNew_version(clf):
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"""Check TANNew version."""
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assert __version__ == clf.version()
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def test_TANNew_nodes_edges(clf, data):
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assert clf.nodes_edges() == (0, 0)
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clf.fit(*data, head="random")
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assert clf.nodes_leaves() == (5, 7)
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def test_TANNew_states(clf, data):
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assert clf.states_ == 0
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clf.fit(*data)
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assert clf.states_ == 18
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assert clf.depth_ == clf.states_
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def test_TANNew_random_head(clf, data):
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clf.fit(*data, head="random")
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assert clf.head_ == 3
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def test_TANNew_local_discretization(clf, data):
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expected = [-1, [0, -1], [0, -1], [1, -1]]
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clf.fit(*data)
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for feature in range(4):
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assert (
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expected[feature] == clf.estimator_.discretizer_.target_[feature]
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)
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def test_TANNew_classifier(data, clf):
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clf.fit(*data)
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attribs = [
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"classes_",
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"X_",
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"y_",
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"head_",
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"feature_names_in_",
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"class_name_",
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]
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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) == 146
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@image_comparison(
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baseline_images=["line_dashes_TANNew"],
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remove_text=True,
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extensions=["png"],
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)
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def test_TANNew_plot(data, features, 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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clf.fit(*data, features=features, head=0)
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clf.plot("TANNew Iris head=0")
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def test_TANNew_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_TANNew_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_TANNew_head_out_of_range(data, clf):
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with pytest.raises(ValueError, match="Head index out of range"):
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clf.fit(*data, head=4)
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def test_TANNew_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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