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How to use floating_dtypes method in hypothesis

Best Python code snippet using hypothesis

test_conveniences.py

Source:test_conveniences.py

...148            st.dictionaries(st.text(), st.floats(allow_infinity=False, allow_nan=False), min_size=10),149            float,150            dict,151        ),152        value_with_type(xps.arrays(xps.floating_dtypes(), 10, unique=True), float, list),153        value_with_type(xps.arrays(xps.integer_dtypes(), 10, unique=True), int, list),154        value_with_type(xps.arrays(xps.boolean_dtypes(), 10, unique=True), int, list),155    ),156)157def test_conv_sequences(value_and_types: tuple[Any, type, type]):158    values, type_, container_type = value_and_types159    assume(inf not in values)160    converted_list = conv(values)161    assert isinstance(converted_list, container_type)162    # Check if size of converted value doesn't change163    assert len(converted_list) == len(values)164    # Check if type of each item from converted value is correct165    if isinstance(converted_list, dict):166        iterator = converted_list.values()167        original = values.values()168    else:169        iterator = converted_list170        original = values171    for converted_value, value in zip(sorted(iterator), sorted(original)):172        assert isinstance(converted_value, type_)173        conv_value = conv(value)174        # check if converted values are the same or both are nan175        assert converted_value == conv_value or (converted_value != converted_value and conv_value != conv_value)176@pytest.mark.unit177@pytest.mark.parametrize("EPSILON", [0.1])178@given(value=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False))179def test_epsilon_on_fp_conv(value, EPSILON):180    converted_value = conv(value)181    assert value - converted_value < EPSILON182    assert converted_value - value < EPSILON183@pytest.mark.unit184@given(185    value_and_type=st.one_of(186        value_with_type(187            elements=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False),188            expected_type=float,189        ),190        value_with_type(191            elements=numpy_value(xps.integer_dtypes(), allow_infinity=False, allow_nan=False),192            expected_type=int,193        ),194        value_with_type(195            elements=st.datetimes(timezones=xptz.timezones()),196            expected_type=int,197        ),198    ),199)200def test_conv(value_and_type):201    value, expected_type = value_and_type202    converted_value = conv(value)203    assert isinstance(converted_value, expected_type)204@pytest.mark.unit205@pytest.mark.parametrize(206    "sysmax, maxint",207    [208        (numpy.int64(sys.maxsize), numpy.int64(9223372036854775807)),209    ],210)211def test_maxint_conv(sysmax, maxint):212    # Robustness213    assert conv(sysmax) == maxint214@pytest.mark.unit215@given(216    value_and_types=st.one_of(217        value_with_type(218            st.integers(min_value=numpy.iinfo("int64").min, max_value=numpy.iinfo("int64").max),219            int,220            xps.integer_dtypes(endianness="=", sizes=(64,)),221        ),222        value_with_type(223            st.integers(min_value=numpy.iinfo("int32").min, max_value=numpy.iinfo("int32").max),224            int,225            xps.integer_dtypes(endianness="=", sizes=(32,)),226        ),227        value_with_type(228            st.integers(min_value=numpy.iinfo("int16").min, max_value=numpy.iinfo("int16").max),229            int,230            xps.integer_dtypes(endianness="=", sizes=(16,)),231        ),232        value_with_type(233            st.integers(min_value=numpy.iinfo("int8").min, max_value=numpy.iinfo("int8").max),234            int,235            xps.integer_dtypes(endianness="=", sizes=(8,)),236        ),237        value_with_type(st.floats(width=16), float, xps.floating_dtypes(endianness="=", sizes=(16,))),238        value_with_type(st.floats(width=32), float, xps.floating_dtypes(endianness="=", sizes=(32,))),239        value_with_type(st.floats(width=64), float, xps.floating_dtypes(endianness="=", sizes=(64,))),240    ),241)242def test_reverse_conv(value_and_types):243    value, current_type, data_type = value_and_types244    # verify if the current data type is as expected (int or float)245    assert isinstance(value, current_type)246    # convert value to given data type (int64, int32, float64 etc .. )247    converted_value = reverse_conv(data_type, value)248    # check if conversion is performed according to given data (int -> numpy.int64, float -> numpy.float64)249    assert numpy.issubdtype(type(converted_value), data_type)250    # check if converted data type is changed and not match with old one251    assert type(converted_value) != current_type252@pytest.mark.unit253@given(254    value_and_type=st.one_of(255        value_with_type(256            elements=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False),257            expected_type=float,258        ),259        value_with_type(260            elements=numpy_value(xps.integer_dtypes(), allow_infinity=False, allow_nan=False),261            expected_type=int,262        ),263        value_with_type(264            elements=st.datetimes(),265            expected_type=int,266        ),267    ),268)269@settings(suppress_health_check=(HealthCheck.function_scoped_fixture,))270def test_hypotesis_with_dask(dask_client_all, value_and_type):...

test_operator.py

Source:test_operator.py

...40@pytest.mark.parametrize("toep_cls", OPERATOR_LIST)41@given(42    arrays(43        shared(44            floating_dtypes(sizes=FLOAT_SIZES, endianness="="),45            key="dtype"46        ),47        shared(integers(min_value=1, max_value=MAX_ARRAY), key="nrows"),48        elements=floats(allow_infinity=False, allow_nan=False, width=32)49    ),50    arrays(51        shared(52            floating_dtypes(sizes=FLOAT_SIZES, endianness="="),53            key="dtype"54        ),55        shared(integers(min_value=1, max_value=MAX_ARRAY), key="ncols"),56        elements=floats(allow_infinity=False, allow_nan=False, width=32)57    ),58    arrays(59        shared(60            floating_dtypes(sizes=FLOAT_SIZES, endianness="="),61            key="dtype"62        ),63        tuples(64            shared(integers(min_value=1, max_value=MAX_ARRAY), key="ncols"),65            integers(min_value=1, max_value=MAX_ARRAY)66        ),67        elements=floats(allow_infinity=False, allow_nan=False, width=32)68    ),69)70def test_toeplitz_real_mat(toep_cls, first_col, first_row, test):71    """Test toeplitz for real inputs."""72    full_mat = toeplitz(first_col, first_row)73    toeplitz_op = toep_cls(first_col, first_row)74    if first_col.dtype == np.float16:75        atol_frac = 1e-276    elif first_col.dtype == np.float32:77        atol_frac = 1e-578    elif first_col.dtype == np.float64:79        atol_frac = 1e-1480    elif first_col.dtype == np.float128:81        atol_frac = 1.1e-1582        if toep_cls == FFTToeplitz:83            atol_frac = 1e-1484    max_el = np.max(np.abs(first_col))85    if len(first_row) > 1:86        max_el = max(max_el, np.max(np.abs(first_row[1:])))87    max_test = np.max(np.abs(test))88    if max_el != 0 and max_test != 0:89        max_el *= max_test90    mat_result = full_mat.dot(test)91    if first_col.dtype == np.float32:92        # Apparently `np.dot` uses an extended-precision accumulator93        assume(np.all(np.isfinite(mat_result)))94    op_result = toeplitz_op.dot(test)95    if toep_cls == FFTToeplitz:96        assume(np.all(np.isfinite(op_result)))97    np_tst.assert_allclose(98        op_result,99        mat_result,100        atol=(atol_frac * max_el +101              ATOL_MIN * (len(test) + toeplitz_op.shape[0])),102        rtol=atol_frac103    )104@pytest.mark.parametrize("toep_cls", OPERATOR_LIST)105@given(106    arrays(107        shared(108            integer_dtypes(sizes=INTEGER_SIZES, endianness="="),109            key="dtype"110        ),111        shared(integers(min_value=1, max_value=MAX_ARRAY), key="nrows"),112    ),113    arrays(114        shared(115            integer_dtypes(sizes=INTEGER_SIZES, endianness="="),116            key="dtype"117        ),118        shared(integers(min_value=1, max_value=MAX_ARRAY), key="ncols"),119    ),120    arrays(121        shared(122            integer_dtypes(sizes=INTEGER_SIZES, endianness="="),123            key="dtype"124        ),125        tuples(126            shared(integers(min_value=1, max_value=MAX_ARRAY), key="ncols"),127            integers(min_value=1, max_value=MAX_ARRAY)128        ),129    ),130)131def test_toeplitz_int_mat(toep_cls, first_col, first_row, test):132    """Test toeplitz for integer inputs."""133    full_mat = toeplitz(first_col, first_row)134    toeplitz_op = toep_cls(first_col, first_row)135    mat_result = full_mat.dot(test)136    if toep_cls in (ConvolveToeplitz, FFTToeplitz):137        rtol = 2e-6138        max_el = np.max(np.abs(first_col))139        if len(first_row) > 1:140            max_el = max(max_el, np.max(np.abs(first_row[1:])))141        # if max_el != 0:142        #     max_el *= np.max(np.abs(test))143        # assume(np.array(max_el, first_col.dtype)  == max_el)144        atol = abs(rtol * max_el * np.max(np.abs(test)))145        mat_result_long = toeplitz(146            first_col.astype(float),147            first_row.astype(float)148        ).dot(149            test.astype(float)150        )151        assume(np.allclose(152            mat_result,153            mat_result_long.astype(first_col.dtype)154        ))155    else:156        rtol = 0157        atol = 0158    np_tst.assert_allclose(159        toeplitz_op.dot(test),160        mat_result,161        rtol=rtol, atol=atol162    )163@pytest.mark.parametrize("toep_cls", OPERATOR_LIST)164@given(165    arrays(166        shared(167            complex_number_dtypes(sizes=COMPLEX_SIZES, endianness="="),168            key="dtype"169        ),170        shared(integers(min_value=1, max_value=MAX_ARRAY), key="nrows"),171        elements=builds(172            complex,173            floats(allow_infinity=False, allow_nan=False, width=32),174            floats(allow_infinity=False, allow_nan=False, width=32),175        ),176    ).filter(lambda x: np.all(np.isfinite(x))),177    arrays(178        shared(179            complex_number_dtypes(sizes=COMPLEX_SIZES, endianness="="),180            key="dtype"181        ),182        shared(integers(min_value=1, max_value=MAX_ARRAY), key="ncols"),183        elements=builds(184            complex,185            floats(allow_infinity=False, allow_nan=False, width=32),186            floats(allow_infinity=False, allow_nan=False, width=32),187        ),188    ).filter(lambda x: np.all(np.isfinite(x))),189    arrays(190        shared(191            complex_number_dtypes(sizes=COMPLEX_SIZES, endianness="="),192            key="dtype"193        ),194        tuples(195            shared(integers(min_value=1, max_value=MAX_ARRAY), key="ncols"),196            integers(min_value=1, max_value=MAX_ARRAY)197        ),198        elements=builds(199            complex,200            floats(allow_infinity=False, allow_nan=False, width=32),201            floats(allow_infinity=False, allow_nan=False, width=32),202        ),203    ).filter(lambda x: np.all(np.isfinite(x))),204)205def test_toeplitz_complex_mat(toep_cls, first_col, first_row, test):206    """Test toeplitz for complex inputs."""207    full_mat = toeplitz(first_col, first_row)208    toeplitz_op = toep_cls(first_col, first_row)209    if first_col.dtype == np.complex64:210        atol_frac = 1e-5211    elif first_col.dtype == np.complex128:212        atol_frac = 1e-14213    elif first_col.dtype == np.complex256:214        atol_frac = 1e-15215        if toep_cls == FFTToeplitz:216            atol_frac = 1e-14217    max_el = np.max(np.abs(first_col))218    if len(first_row) > 1:219        max_el = max(max_el, np.max(np.abs(first_row[1:])))220    max_test = np.max(np.abs(test))221    if max_el != 0 and max_test != 0:222        max_el *= max_test223    mat_result = full_mat.dot(test)224    # Apparently `np.dot` uses an extended-precision accumulator225    assume(np.all(np.isfinite(mat_result)))226    op_result = toeplitz_op.dot(test)227    # np.dot may give nan or zero depending on array rank.228    assume(~np.any(np.isnan(op_result)))229    assume(np.all(np.isfinite(np.abs(op_result))))230    atol = atol_frac * max_el + ATOL_MIN * (len(test) + toeplitz_op.shape[0])231    assume(atol < np.inf)232    assume(atol != np.inf)233    np_tst.assert_allclose(234        op_result,235        mat_result,236        atol=atol,237        rtol=atol_frac238    )239@pytest.mark.parametrize("toep_cls", OPERATOR_LIST)240@given(241    arrays(242        shared(243            floating_dtypes(sizes=FLOAT_SIZES, endianness="="),244            key="dtype"245        ),246        shared(integers(min_value=1, max_value=MAX_ARRAY), key="nrows"),247        elements=floats(allow_infinity=False, allow_nan=False, width=32)248    ),249    arrays(250        shared(251            floating_dtypes(sizes=FLOAT_SIZES, endianness="="),252            key="dtype"253        ),254        tuples(255            shared(integers(min_value=1, max_value=MAX_ARRAY), key="nrows"),256            integers(min_value=1, max_value=MAX_ARRAY)257        ),258        elements=floats(allow_infinity=False, allow_nan=False, width=32)259    ),260)261def test_toeplitz_only_col(toep_cls, first_col, test):262    """Test toeplitz for real inputs."""263    full_mat = toeplitz(first_col)264    toeplitz_op = toep_cls(first_col)265    if first_col.dtype == np.float16:...

common.py

Source:common.py

2from hypothesis.extra.numpy import arrays as hy_arrays3from hypothesis.extra.numpy import floating_dtypes, integer_dtypes4from hypothesis.strategies import one_of5hy_array_gen = hy_arrays(6    dtype=one_of(integer_dtypes(sizes=(32, 64)), floating_dtypes(sizes=(32, 64))),7    shape=array_shapes(),8)9hy_int_array_gen = hy_arrays(10    dtype=integer_dtypes(sizes=(32, 64)),11    shape=array_shapes(),...

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