How to use everything_except method in hypothesis

Best Python code snippet using hypothesis

test_LivePlot.py

Source:test_LivePlot.py

...27                lambda x: len(set(x)) != len(x)28            )29        ),30        dict(31            metrics=cst.everything_except((str, Sequence))32            | st.lists(cst.everything_except(str))33        ),34        dict(35            nrows=cst.everything_except((Integral, type(None)))36            | st.integers(max_value=0)37        ),38        dict(39            ncols=cst.everything_except((Integral, type(None)))40            | st.integers(max_value=0)41        ),42        dict(43            max_fraction_spent_plotting=cst.everything_except((float, int))44            | st.floats().filter(lambda x: not 0 <= x <= 1)45        ),46        dict(47            last_n_batches=cst.everything_except((int, type(None)))48            | st.integers(max_value=0)49        ),50    ],51)52@given(data=st.data())53def test_input_validation(bad_input: dict, data: st.DataObject):54    defaults = dict(metrics=["a"])55    defaults.update(56        {k: cst.draw_if_strategy(data, v, label=k) for k, v in bad_input.items()}57    )58    with pytest.raises((ValueError, TypeError)):59        LivePlot(**defaults)60@pytest.mark.parametrize(61    ("num_metrics", "fig_layout", "outer_type", "shape"),62    [63        (1, dict(), Axes, tuple()),64        (1, dict(nrows=1), Axes, tuple()),65        (1, dict(ncols=1), Axes, tuple()),66        (3, dict(nrows=2, ncols=2), ndarray, (2, 2)),67        (3, dict(), ndarray, (3,)),68        (3, dict(nrows=3), ndarray, (3,)),69        (3, dict(ncols=3), ndarray, (3,)),70    ],71)72@pytest.mark.usefixtures("killplots")73def test_plot_grid(num_metrics, fig_layout, outer_type, shape):74    """Ensure that axes have the right type/shape for a given grid spec"""75    metric_names = list(ascii_letters[:num_metrics])76    fig, ax = LivePlot(metric_names, **fig_layout).plot_objects77    assert isinstance(fig, Figure)78    assert isinstance(ax, outer_type)79    if shape:80        assert ax.shape == shape81@pytest.mark.usefixtures("killplots")82def test_adaptive_plot_grid():83    plotter = LivePlot(list(ascii_letters[:5]), ncols=2)84    assert plotter._pltkwargs["nrows"] == 385    assert plotter._pltkwargs["ncols"] == 286@pytest.mark.usefixtures("killplots")87@given(88    num_metrics=st.integers(1, 12), nrows=st.integers(1, 12), ncols=st.integers(1, 12)89)90def test_fuzz_plot_grid(num_metrics: int, nrows: int, ncols: int):91    plotter = LivePlot(list(ascii_letters[:num_metrics]), nrows=nrows, ncols=ncols)92    assert plotter._pltkwargs["nrows"] * plotter._pltkwargs["ncols"] >= num_metrics93def test_unregister_metric_warns():94    plotter = LivePlot(metrics=["a"])95    with pytest.warns(UserWarning):96        plotter.set_train_batch(dict(a=1, b=1), batch_size=1)97    with pytest.warns(UserWarning):98        plotter.set_test_batch(dict(a=1, c=1), batch_size=1)99def test_trivial_case():100    """ Perform a trivial sanity check on live plotter"""101    plotter = LivePlot("a")102    plotter.set_train_batch(dict(a=1.0), batch_size=1, plot=False)103    plotter.set_train_batch(dict(a=3.0), batch_size=1, plot=False)104    plotter.set_train_epoch()105    assert_array_equal(plotter.train_metrics["a"]["batch_data"], np.array([1.0, 3.0]))106    assert_array_equal(plotter.train_metrics["a"]["epoch_domain"], np.array([2]))107    assert_array_equal(108        plotter.train_metrics["a"]["epoch_data"], np.array([1.0 / 2.0 + 3.0 / 2.0])109    )110@given(111    plotter=cst.plotters(),112    bad_size=(113        cst.everything_except(Sequence)114        | st.tuples(*[cst.everything_except(Real)] * 2)115        | st.lists(st.floats(max_value=10)).filter(116            lambda x: len(x) != 2 or any(i <= 0 for i in x)117        )118    ),119)120def test_bad_figsize(plotter: LivePlot, bad_size):121    with pytest.raises(ValueError):122        plotter.figsize = bad_size123@given(colors=st.lists(cst.matplotlib_colors(), min_size=1, max_size=4))124def test_flat_color_syntax(colors: list):125    metric_names = ascii_letters[: len(colors)]126    p = LivePlot({n: c for n, c in zip(metric_names, colors)})127    assert p.metric_colors == {n: dict(train=c) for n, c in zip(metric_names, colors)}128@settings(deadline=None)129@given(plotter=cst.plotters(), data=st.data())130def test_setting_color_for_non_metric_is_silent(plotter: LivePlot, data: st.DataObject):131    color = {132        data.draw(st.text(), label="non_metric"): data.draw(133            cst.matplotlib_colors(), label="color"134        )135    }136    original_colors = plotter.metric_colors137    plotter.metric_colors = color138    assert plotter.metric_colors == original_colors139@settings(deadline=None)140@given(plotter=cst.plotters(), bad_colors=cst.everything_except(dict))141def test_color_setter_validation(plotter: LivePlot, bad_colors):142    with pytest.raises(TypeError):143        plotter.metric_colors = bad_colors144@settings(deadline=None)145@given(146    plotter=cst.plotters(),147    colors=st.fixed_dictionaries(148        {"train": cst.matplotlib_colors(), "test": cst.matplotlib_colors()}149    ),150    data=st.data(),151)152def test_set_color(plotter: LivePlot, colors: dict, data: st.DataObject):153    metric = data.draw(st.sampled_from(plotter.metrics), label="metric")154    plotter.metric_colors = {metric: colors}...

test_LiveMetric.py

Source:test_LiveMetric.py

...36            getattr(metric, name),37            err_msg=name + " does not map to the correct value in the metric-dict",38        )39@pytest.mark.parametrize(40    "bad_input", [cst.everything_except(dict), st.just(dict(a_bad_key=1))]41)42@settings(suppress_health_check=[HealthCheck.too_slow])43@given(data=st.data())44def test_from_dict_input_validation(bad_input: st.SearchStrategy, data: st.DataObject):45    bad_input = data.draw(bad_input, label="bad_input")46    with pytest.raises((ValueError, TypeError)):47        LiveMetric.from_dict(bad_input)48static_logger_dict = cst.live_metrics(min_num_metrics=1).example()  # type: dict49@pytest.mark.parametrize(50    "bad_input",51    [52        dict(batch_data=np.arange(9).reshape(3, 3)),53        dict(epoch_data=np.arange(9).reshape(3, 3)),54        dict(cnt_since_epoch=-1),55        dict(epoch_domain=np.arange(9).reshape(3, 3)),56        dict(batch_data=cst.everything_except(np.ndarray)),57        dict(epoch_data=cst.everything_except(np.ndarray)),58        dict(epoch_domain=cst.everything_except(np.ndarray)),59        dict(cnt_since_epoch=cst.everything_except(Real)),60        dict(total_weighting=cst.everything_except(Real)),61        dict(running_weighted_sum=cst.everything_except(Real)),62        dict(name=cst.everything_except(str)),63    ],64)65@settings(suppress_health_check=[HealthCheck.too_slow])66@given(data=st.data())67def test_from_dict_input_validation2(bad_input: dict, data: st.DataObject):68    input_dict = {}69    bad_input = {70        k: data.draw(v, label=k) if isinstance(v, st.SearchStrategy) else v71        for k, v in bad_input.items()72    }73    for name, metrics in static_logger_dict.items():74        input_dict = metrics.copy()75        input_dict.update(bad_input)76        break...

test_field.py

Source:test_field.py

...10    actual = Field.empty(name=name)11    _, ok = actual.default12    assert actual.name == name13    assert ok == False14@given(everything_except(str, type(None)))15@settings(max_examples=settings().max_examples * 50)16def test_empty_failure(name):17    with pytest.raises(ValueError):18        actual = Field.empty(name=name)19@given(20    st.text(printable),21    everything_except(type(None)),22    st.functions(like=(lambda: None), returns=everything_except()),23)24@settings(max_examples=settings().max_examples * 25)25def test_bad_defaults(name, default, factory):26    with pytest.raises(ValueError):...

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