Python Pipeline.set_params方法代码示例

# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import set_params [as 别名]
def test_pipeline_init():
    # Test the various init parameters of the pipeline.
    assert_raises(TypeError, Pipeline)
    # Check that we can't instantiate pipelines with objects without fit
    # method
    pipe = assert_raises(TypeError, Pipeline, [('svc', IncorrectT)])
    # Smoke test with only an estimator
    clf = T()
    pipe = Pipeline([('svc', clf)])
    assert_equal(
        pipe.get_params(deep=True),
        dict(
            svc__a=None, svc__b=None, svc=clf, **pipe.get_params(deep=False)))

    # Check that params are set
    pipe.set_params(svc__a=0.1)
    assert_equal(clf.a, 0.1)
    assert_equal(clf.b, None)
    # Smoke test the repr:
    repr(pipe)

    # Test with two objects
    clf = SVC()
    filter1 = SelectKBest(f_classif)
    pipe = Pipeline([('anova', filter1), ('svc', clf)])

    # Check that we can't use the same stage name twice
    assert_raises(ValueError, Pipeline, [('svc', SVC()), ('svc', SVC())])

    # Check that params are set
    pipe.set_params(svc__C=0.1)
    assert_equal(clf.C, 0.1)
    # Smoke test the repr:
    repr(pipe)

    # Check that params are not set when naming them wrong
    assert_raises(ValueError, pipe.set_params, anova__C=0.1)

    # Test clone
    pipe2 = clone(pipe)
    assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])

    # Check that apart from estimators, the parameters are the same
    params = pipe.get_params(deep=True)
    params2 = pipe2.get_params(deep=True)

    for x in pipe.get_params(deep=False):
        params.pop(x)

    for x in pipe2.get_params(deep=False):
        params2.pop(x)

    # Remove estimators that where copied
    params.pop('svc')
    params.pop('anova')
    params2.pop('svc')
    params2.pop('anova')
    assert_equal(params, params2)

# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import set_params [as 别名]
def test_pipeline_raise_set_params_error():
    # Test pipeline raises set params error message for nested models.
    pipe = Pipeline([('cls', LinearRegression())])
    with raises(ValueError, match="Invalid parameter"):
        pipe.set_params(fake='nope')

    # nested model check
    with raises(ValueError, match="Invalid parameter"):
        pipe.set_params(fake__estimator='nope')

# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import set_params [as 别名]
def test_set_pipeline_steps():
    transf1 = Transf()
    transf2 = Transf()
    pipeline = Pipeline([('mock', transf1)])
    assert pipeline.named_steps['mock'] is transf1

    # Directly setting attr
    pipeline.steps = [('mock2', transf2)]
    assert 'mock' not in pipeline.named_steps
    assert pipeline.named_steps['mock2'] is transf2
    assert [('mock2', transf2)] == pipeline.steps

    # Using set_params
    pipeline.set_params(steps=[('mock', transf1)])
    assert [('mock', transf1)] == pipeline.steps

    # Using set_params to replace single step
    pipeline.set_params(mock=transf2)
    assert [('mock', transf2)] == pipeline.steps

    # With invalid data
    pipeline.set_params(steps=[('junk', ())])
    with raises(TypeError):
        pipeline.fit([[1]], [1])
    with raises(TypeError):
        pipeline.fit_transform([[1]], [1])

# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import set_params [as 别名]
def test_pipeline_init():
    # Test the various init parameters of the pipeline.
    with raises(TypeError):
        Pipeline()
    # Check that we can't instantiate pipelines with objects without fit
    # method
    error_regex = 'Last step of Pipeline should implement fit. .*NoFit.*'
    with raises(TypeError, match=error_regex):
        Pipeline([('clf', NoFit())])
    # Smoke test with only an estimator
    clf = NoTrans()
    pipe = Pipeline([('svc', clf)])
    expected = dict(svc__a=None, svc__b=None, svc=clf,
                    **pipe.get_params(deep=False))
    assert pipe.get_params(deep=True) == expected

    # Check that params are set
    pipe.set_params(svc__a=0.1)
    assert clf.a == 0.1
    assert clf.b is None
    # Smoke test the repr:
    repr(pipe)

    # Test with two objects
    clf = SVC()
    filter1 = SelectKBest(f_classif)
    pipe = Pipeline([('anova', filter1), ('svc', clf)])

    # Check that we can't instantiate with non-transformers on the way
    # Note that NoTrans implements fit, but not transform
    error_regex = 'implement fit and transform or sample'
    with raises(TypeError, match=error_regex):
        Pipeline([('t', NoTrans()), ('svc', clf)])

    # Check that params are set
    pipe.set_params(svc__C=0.1)
    assert clf.C == 0.1
    # Smoke test the repr:
    repr(pipe)

    # Check that params are not set when naming them wrong
    with raises(ValueError):
        pipe.set_params(anova__C=0.1)

    # Test clone
    pipe2 = clone(pipe)
    assert not pipe.named_steps['svc'] is pipe2.named_steps['svc']

    # Check that apart from estimators, the parameters are the same
    params = pipe.get_params(deep=True)
    params2 = pipe2.get_params(deep=True)

    for x in pipe.get_params(deep=False):
        params.pop(x)

    for x in pipe2.get_params(deep=False):
        params2.pop(x)

    # Remove estimators that where copied
    params.pop('svc')
    params.pop('anova')
    params2.pop('svc')
    params2.pop('anova')
    assert params == params2