Exemplo n.º 1
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def test_predict():
    """Assert that the TPOT predict function raises a ValueError when no optimized pipeline exists"""

    tpot_obj = TPOT()

    try:
        tpot_obj.predict(testing_features)
        assert False  # Should be unreachable
    except ValueError:
        pass
Exemplo n.º 2
0
def test_predict():
    """Ensure that the TPOT predict function raises a ValueError when no optimized pipeline exists"""

    tpot_obj = TPOT()

    try:
        tpot_obj.predict(testing_features)
        assert False  # Should be unreachable
    except ValueError:
        pass
Exemplo n.º 3
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def test_predict_2():
    """Ensure that the TPOT predict function returns a DataFrame of shape (num_testing_rows,)"""

    tpot_obj = TPOT()
    tpot_obj._training_classes = training_classes
    tpot_obj._training_features = training_features
    tpot_obj._optimized_pipeline = creator.Individual.\
        from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset)

    result = tpot_obj.predict(testing_features)

    assert result.shape == (testing_features.shape[0],)
Exemplo n.º 4
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def test_predict_2():
    """Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""

    tpot_obj = TPOT()
    tpot_obj._optimized_pipeline = creator.Individual.\
        from_string('DecisionTreeClassifier(input_matrix)', tpot_obj._pset)
    tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
    tpot_obj._fitted_pipeline.fit(training_features, training_classes)

    result = tpot_obj.predict(testing_features)

    assert result.shape == (testing_features.shape[0],)
Exemplo n.º 5
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def test_predict_2():
    """Ensure that the TPOT predict function returns a DataFrame of shape (num_testing_rows,)"""

    tpot_obj = TPOT()
    tpot_obj._training_classes = training_classes
    tpot_obj._training_features = training_features
    tpot_obj._optimized_pipeline = creator.Individual.\
        from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset)

    result = tpot_obj.predict(testing_features)

    assert result.shape == (testing_features.shape[0], )
Exemplo n.º 6
0
def test_predict_2():
    """Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""

    tpot_obj = TPOT()
    tpot_obj._optimized_pipeline = creator.Individual.\
        from_string('DecisionTreeClassifier(input_matrix)', tpot_obj._pset)
    tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(
        expr=tpot_obj._optimized_pipeline)
    tpot_obj._fitted_pipeline.fit(training_features, training_classes)

    result = tpot_obj.predict(testing_features)

    assert result.shape == (testing_features.shape[0], )