コード例 #1
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def test_particles_no_parameters(transition: Transition):
    df = pd.DataFrame(index=[0, 1, 2, 3])
    assert len(df) == 4
    w = np.array([1, 1, 1, 1]) / 4
    transition.fit(df, w)
    with pytest.raises(NotEnoughParticles):
        transition.required_nr_samples(.1)
コード例 #2
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def test_variance_no_side_effect(transition: Transition):
    df, w = data(60)
    transition.fit(df, w)
    # very intrusive test. touches internals of m. not very nice.
    X_orig_id = id(transition.X)
    transition.mean_cv()  # this has to be called here
    assert id(transition.X) == X_orig_id
コード例 #3
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def test_pdf_return_types(transition: Transition):
    df, w = data(20)
    transition.fit(df, w)
    single = transition.pdf(df.iloc[0])
    multiple = transition.pdf(df)
    assert isinstance(single, float)
    assert multiple.shape == (20,)
コード例 #4
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def test_variance_estimate(transition: Transition):
    var_list = []
    for n in [20, 250]:
        df, w = data(n)
        transition.fit(df, w)
        var = transition.mean_cv()
        var_list.append(var)

    assert var_list[0] >= var_list[1]
コード例 #5
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def test_mean_coefficient_of_variation_sample_not_full_rank(
        transition: Transition):
    """
    This is a test created after I encountered this kind of bug
    """
    n = 13
    df = pd.DataFrame({"a": np.ones(n) * 2, "b": np.ones(n)})
    w = np.ones(len(df)) / len(df)
    transition.fit(df, w)
    transition.mean_cv()
コード例 #6
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def test_variance_estimate_higher_n_than_sample(transition: Transition):
    n = 100
    df, w = data(n)
    transition.fit(df, w)

    var_list = []
    for n_test in [n, n*4, n*10]:
        var = transition.mean_cv(n_test)
        var_list.append(var)

    for lower, upper in zip(var_list[:-1], var_list[1:]):
        assert lower >= upper
コード例 #7
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def test_argument_order(transition: Transition):
    """
    Dataframes passed to the transition kernels are generated from dicts.
    Order of parameter names is no guaranteed.
    The Transition kernel has to take care of the correct sorting.
    """
    df, w = data(20)
    transition.fit(df, w)
    test = df.iloc[0]
    reversed = test[::-1]
    # works b/c of even nr of parameters
    assert (np.array(test) != np.array(reversed)).all()
    assert transition.pdf(test) == transition.pdf(reversed)
コード例 #8
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def test_variance_estimate_higher_n_than_sample(transition: Transition):
    n = 100
    df, w = data(n)
    transition.fit(df, w)

    var_list = []
    for n_test in [n, n * 4, n * 10]:
        var = transition.mean_cv(n_test)
        var_list.append(var)

    for lower, upper in zip(var_list[:-1], var_list[1:]):
        # add a little buffer to overcome slight random fluctuations
        assert lower + 1e-2 >= upper
コード例 #9
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def test_many_particles_single_par(transition: Transition):
    df, w = data_single(20)
    transition.fit(df, w)
    transition.required_nr_samples(.1)
コード例 #10
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def test_rvs_return_type(transition: Transition):
    df, w = data(20)
    transition.fit(df, w)
    sample = transition.rvs()
    assert (sample.index == pd.Index(["a", "b"])).all()
コード例 #11
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def test_many_particles(transition: Transition):
    df, w = data(20)
    transition.fit(df, w)
    transition.required_nr_samples(0.1)
コード例 #12
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def test_two_particles_required_nr_samples(transition: Transition):
    # TODO define proper behavior
    df, w = data(2)
    transition.fit(df, w)
    transition.required_nr_samples(.1)
コード例 #13
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def test_two_particles_fit(transition: Transition):
    # TODO define proper behavior
    df, w = data(2)
    transition.fit(df, w)
コード例 #14
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def test_single_particle_fit(transition: Transition):
    # TODO define proper behavior
    df, w = data(1)
    transition.fit(df, w)
コード例 #15
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def test_score(transition: Transition):
    df, w = data(20)
    transition.fit(df, w)
    transition.score(df, w)  # just call it
コード例 #16
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def test_0_particles_fit(transition: Transition):
    # TODO define proper behavior
    df, w = data(0)
    with pytest.raises(NotEnoughParticles):
        transition.fit(df, w)
コード例 #17
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def test_single_particle_required_nr_samples(transition: Transition):
    # TODO define proper behavior
    df, w = data(1)
    transition.fit(df, w)
    transition.required_nr_samples(0.1)