Python testing.assert_raises方法代碼示例

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_predict():
    dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
    endog = pd.Series([1,2], index=dates)
    mod = MLEModel(endog, **kwargs)
    res = mod.filter([])

    # Test that predict with start=None, end=None does prediction with full
    # dataset
    predict = res.predict()
    assert_equal(predict.shape, (mod.nobs,))
    assert_allclose(res.get_prediction().predicted_mean, predict)

    # Test a string value to the dynamic option
    assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())

    # Test an invalid date string value to the dynamic option
    # assert_raises(ValueError, res.predict, dynamic='1982-01-01')

    # Test for passing a string to predict when dates are not set
    mod = MLEModel([1,2], **kwargs)
    res = mod.filter([])
    assert_raises(KeyError, res.predict, dynamic='string') 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_erlang_runtimewarning():
    # erlang should generate a RuntimeWarning if a non-integer
    # shape parameter is used.
    with warnings.catch_warnings():
        warnings.simplefilter("error", RuntimeWarning)

        # The non-integer shape parameter 1.3 should trigger a RuntimeWarning
        npt.assert_raises(RuntimeWarning,
                          stats.erlang.rvs, 1.3, loc=0, scale=1, size=4)

        # Calling the fit method with `f0` set to an integer should
        # *not* trigger a RuntimeWarning.  It should return the same
        # values as gamma.fit(...).
        data = [0.5, 1.0, 2.0, 4.0]
        result_erlang = stats.erlang.fit(data, f0=1)
        result_gamma = stats.gamma.fit(data, f0=1)
        npt.assert_allclose(result_erlang, result_gamma, rtol=1e-3) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_valid_origins():
    """Regression test for #1311."""
    func = lambda x: np.mean(x)
    data = np.array([1,2,3,4,5], dtype=np.float64)
    assert_raises(ValueError, sndi.generic_filter, data, func, size=3,
                  origin=2)
    func2 = lambda x, y: np.mean(x + y)
    assert_raises(ValueError, sndi.generic_filter1d, data, func,
                  filter_size=3, origin=2)
    assert_raises(ValueError, sndi.percentile_filter, data, 0.2, size=3,
                  origin=2)

    for filter in [sndi.uniform_filter, sndi.minimum_filter,
                   sndi.maximum_filter, sndi.maximum_filter1d,
                   sndi.median_filter, sndi.minimum_filter1d]:
        # This should work, since for size == 3, the valid range for origin is
        # -1 to 1.
        list(filter(data, 3, origin=-1))
        list(filter(data, 3, origin=1))
        # Just check this raises an error instead of silently accepting or
        # segfaulting.
        assert_raises(ValueError, filter, data, 3, origin=2) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_pow_freq_bands():
    expected = np.array([0, 0.005, 0, 0, 0.00125]) / 0.00625
    assert_almost_equal(compute_pow_freq_bands(sfreq, data_sin,
                                               psd_method='fft'), expected)
    # Ratios of power in bands:
    # For data_sin, only the usual theta (4Hz - 8Hz) and low gamma
    # (30Hz - 70Hz) bands contain non-zero power.
    fb = np.array([[4., 8.], [30., 70.]])
    expected_pow = np.array([0.005, 0.00125]) / 0.00625
    expected_ratios = np.array([4., 0.25])
    assert_almost_equal(compute_pow_freq_bands(sfreq, data_sin, freq_bands=fb,
                                               ratios='all', psd_method='fft'),
                        np.r_[expected_pow, expected_ratios])
    assert_almost_equal(compute_pow_freq_bands(sfreq, data_sin, freq_bands=fb,
                                               ratios='only',
                                               psd_method='fft'),
                        expected_ratios)
    with assert_raises(ValueError):
        # Invalid `ratios` parameter
        compute_pow_freq_bands(sfreq, data_sin, ratios=['alpha', 'beta']) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_user_defined_feature_function():
    # User-defined feature function
    @nb.jit()
    def top_feature(arr, gamma=3.14):
        return np.sum(np.power(gamma * arr, 3) - np.power(arr / gamma, 2),
                      axis=-1)
    # Valid feature extraction
    selected_funcs = ['mean', ('top_feature', top_feature)]
    feat = extract_features(data, sfreq, selected_funcs)
    assert_equal(feat.shape, (n_epochs, 2 * n_channels))
    # Changing optional parameter ``gamma`` of ``top_feature``
    feat2 = extract_features(data, sfreq, selected_funcs,
                             funcs_params={'top_feature__gamma': 1.41})
    assert_equal(feat2.shape, (n_epochs, 2 * n_channels))
    # Invalid feature extractions
    with assert_raises(ValueError):
        # Alias is already used
        extract_features(data, sfreq, ['variance', ('mean', top_feature)])
        # Tuple is not of length 2
        extract_features(data, sfreq, ['variance', ('top_feature', top_feature,
                                                    data[:, ::2])])
        # Invalid type
        extract_features(data, sfreq, ['mean', top_feature]) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_dataset_evaluators():
    X = theano.tensor.matrix('X')
    brick = TestBrick(name='test_brick')
    Y = brick.apply(X)
    graph = ComputationGraph([Y])
    monitor_variables = [v for v in graph.auxiliary_variables]
    validator = DatasetEvaluator(monitor_variables)

    data = [numpy.arange(1, 5, dtype=theano.config.floatX).reshape(2, 2),
            numpy.arange(10, 16, dtype=theano.config.floatX).reshape(3, 2)]
    data_stream = IterableDataset(dict(X=data)).get_example_stream()

    values = validator.evaluate(data_stream)
    assert values['test_brick_apply_V_squared'] == 4
    numpy.testing.assert_allclose(
        values['test_brick_apply_mean_row_mean'], numpy.vstack(data).mean())
    per_batch_mean = numpy.mean([batch.mean() for batch in data])
    numpy.testing.assert_allclose(
        values['test_brick_apply_mean_batch_element'], per_batch_mean)

    with assert_raises(Exception) as ar:
        data_stream = IterableDataset(dict(X2=data)).get_example_stream()
        validator.evaluate(data_stream)
    assert "Not all data sources" in ar.exception.args[0] 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_raise_exception_spatial():
    """Test that SpatialBatchNormalization raises an expected exception."""
    # Work around a stupid bug in nose2 that unpacks the tuple into
    # separate arguments.
    sbn1 = SpatialBatchNormalization((5,))
    yield assert_raises, (ValueError, sbn1.allocate)
    sbn2 = SpatialBatchNormalization(3)
    yield assert_raises, (ValueError, sbn2.allocate)

    def do_not_fail(*input_dim):
        try:
            sbn = SpatialBatchNormalization(input_dim)
            sbn.allocate()
        except ValueError:
            assert False

    # Work around a stupid bug in nose2 by passing as *args.
    yield do_not_fail, 5, 4, 3
    yield do_not_fail, 7, 6
    yield do_not_fail, 3, 9, 2, 3 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_autoconversion(self):
        # Tests autoconversion
        adtype = [('A', int), ('B', bool), ('C', float)]
        a = ma.array([(1, 2, 3)], mask=[(0, 1, 0)], dtype=adtype)
        bdtype = [('A', int), ('B', float), ('C', float)]
        b = ma.array([(4, 5, 6)], dtype=bdtype)
        control = ma.array([(1, 2, 3), (4, 5, 6)], mask=[(0, 1, 0), (0, 0, 0)],
                           dtype=bdtype)
        test = stack_arrays((a, b), autoconvert=True)
        assert_equal(test, control)
        assert_equal(test.mask, control.mask)
        with assert_raises(TypeError):
            stack_arrays((a, b), autoconvert=False) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_duplicate_keys(self):
        a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u1')])
        b = np.ones(3, dtype=[('c', 'u1'), ('b', 'f4'), ('a', 'i4')])
        assert_raises(ValueError, join_by, ['a', 'b', 'b'], a, b) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_no_postfix(self):
        assert_raises(ValueError, join_by, 'a', self.a, self.b,
                      r1postfix='', r2postfix='') 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_opt_out(self):

        class OptOut(object):
            """Object that opts out of __array_ufunc__."""
            __array_ufunc__ = None

            def __add__(self, other):
                return self

            def __radd__(self, other):
                return self

        array_like = ArrayLike(1)
        opt_out = OptOut()

        # supported operations
        assert_(array_like + opt_out is opt_out)
        assert_(opt_out + array_like is opt_out)

        # not supported
        with assert_raises(TypeError):
            # don't use the Python default, array_like = array_like + opt_out
            array_like += opt_out
        with assert_raises(TypeError):
            array_like - opt_out
        with assert_raises(TypeError):
            opt_out - array_like 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_object(self):
        x = ArrayLike(0)
        obj = object()
        with assert_raises(TypeError):
            x + obj
        with assert_raises(TypeError):
            obj + x
        with assert_raises(TypeError):
            x += obj 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def check_function(self, t):
        if t.__doc__.split()[0] in ['t0', 't4', 's0', 's4']:
            err = 1e-5
        else:
            err = 0.0
        assert_(abs(t(234) - 234.0) <= err)
        assert_(abs(t(234.6) - 234.6) <= err)
        assert_(abs(t(long(234)) - 234.0) <= err)
        assert_(abs(t('234') - 234) <= err)
        assert_(abs(t('234.6') - 234.6) <= err)
        assert_(abs(t(-234) + 234) <= err)
        assert_(abs(t([234]) - 234) <= err)
        assert_(abs(t((234,)) - 234.) <= err)
        assert_(abs(t(array(234)) - 234.) <= err)
        assert_(abs(t(array([234])) - 234.) <= err)
        assert_(abs(t(array([[234]])) - 234.) <= err)
        assert_(abs(t(array([234], 'b')) + 22) <= err)
        assert_(abs(t(array([234], 'h')) - 234.) <= err)
        assert_(abs(t(array([234], 'i')) - 234.) <= err)
        assert_(abs(t(array([234], 'l')) - 234.) <= err)
        assert_(abs(t(array([234], 'B')) - 234.) <= err)
        assert_(abs(t(array([234], 'f')) - 234.) <= err)
        assert_(abs(t(array([234], 'd')) - 234.) <= err)
        if t.__doc__.split()[0] in ['t0', 't4', 's0', 's4']:
            assert_(t(1e200) == t(1e300))  # inf

        #assert_raises(ValueError, t, array([234], 'S1'))
        assert_raises(ValueError, t, 'abc')

        assert_raises(IndexError, t, [])
        assert_raises(IndexError, t, ())

        assert_raises(Exception, t, t)
        assert_raises(Exception, t, {})

        try:
            r = t(10 ** 400)
            assert_(repr(r) in ['inf', 'Infinity'], repr(r))
        except OverflowError:
            pass 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def check_function(self, t):
        assert_(t(123) == 123, repr(t(123)))
        assert_(t(123.6) == 123)
        assert_(t(long(123)) == 123)
        assert_(t('123') == 123)
        assert_(t(-123) == -123)
        assert_(t([123]) == 123)
        assert_(t((123,)) == 123)
        assert_(t(array(123)) == 123)
        assert_(t(array([123])) == 123)
        assert_(t(array([[123]])) == 123)
        assert_(t(array([123], 'b')) == 123)
        assert_(t(array([123], 'h')) == 123)
        assert_(t(array([123], 'i')) == 123)
        assert_(t(array([123], 'l')) == 123)
        assert_(t(array([123], 'B')) == 123)
        assert_(t(array([123], 'f')) == 123)
        assert_(t(array([123], 'd')) == 123)

        #assert_raises(ValueError, t, array([123],'S3'))
        assert_raises(ValueError, t, 'abc')

        assert_raises(IndexError, t, [])
        assert_raises(IndexError, t, ())

        assert_raises(Exception, t, t)
        assert_raises(Exception, t, {})

        if t.__doc__.split()[0] in ['t8', 's8']:
            assert_raises(OverflowError, t, 100000000000000000000000)
            assert_raises(OverflowError, t, 10000000011111111111111.23) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_raises [as 別名]
def test_invalid(self):
        with np.errstate(all='raise', under='ignore'):
            a = -np.arange(3)
            # This should work
            with np.errstate(invalid='ignore'):
                np.sqrt(a)
            # While this should fail!
            with assert_raises(FloatingPointError):
                np.sqrt(a)