reconnect moved files to git repo
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from collections import OrderedDict
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import numpy as np
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import pandas as pd
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import pytest
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from statsmodels.tools.validation import (
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array_like,
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PandasWrapper,
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bool_like,
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dict_like,
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float_like,
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int_like,
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string_like,
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)
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from statsmodels.tools.validation.validation import _right_squeeze
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@pytest.fixture(params=[True, False])
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def use_pandas(request):
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return request.param
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def gen_data(dim, use_pandas):
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if dim == 1:
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out = np.empty(10,)
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if use_pandas:
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out = pd.Series(out)
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elif dim == 2:
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out = np.empty((20, 10))
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if use_pandas:
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out = pd.DataFrame(out)
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else:
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out = np.empty(np.arange(5, 5 + dim))
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return out
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class TestArrayLike:
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def test_1d(self, use_pandas):
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data = gen_data(1, use_pandas)
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a = array_like(data, "a")
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assert a.ndim == 1
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assert a.shape == (10,)
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assert type(a) is np.ndarray
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a = array_like(data, "a", ndim=1)
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assert a.ndim == 1
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a = array_like(data, "a", shape=(10,))
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assert a.shape == (10,)
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a = array_like(data, "a", ndim=1, shape=(None,))
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assert a.ndim == 1
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a = array_like(data, "a", ndim=2, shape=(10, 1))
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assert a.ndim == 2
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assert a.shape == (10, 1)
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with pytest.raises(ValueError, match="a is required to have shape"):
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array_like(data, "a", shape=(5,))
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def test_2d(self, use_pandas):
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data = gen_data(2, use_pandas)
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a = array_like(data, "a", ndim=2)
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assert a.ndim == 2
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assert a.shape == (20, 10)
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assert type(a) is np.ndarray
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a = array_like(data, "a", ndim=2)
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assert a.ndim == 2
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a = array_like(data, "a", ndim=2, shape=(20, None))
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assert a.shape == (20, 10)
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a = array_like(data, "a", ndim=2, shape=(20,))
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assert a.shape == (20, 10)
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a = array_like(data, "a", ndim=2, shape=(None, 10))
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assert a.shape == (20, 10)
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a = array_like(data, "a", ndim=2, shape=(None, None))
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assert a.ndim == 2
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a = array_like(data, "a", ndim=3)
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assert a.ndim == 3
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assert a.shape == (20, 10, 1)
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with pytest.raises(ValueError, match="a is required to have shape"):
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array_like(data, "a", ndim=2, shape=(10,))
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with pytest.raises(ValueError, match="a is required to have shape"):
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array_like(data, "a", ndim=2, shape=(20, 20))
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with pytest.raises(ValueError, match="a is required to have shape"):
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array_like(data, "a", ndim=2, shape=(None, 20))
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match = "a is required to have ndim 1 but has ndim 2"
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with pytest.raises(ValueError, match=match):
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array_like(data, "a", ndim=1)
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match = "a must have ndim <= 1"
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with pytest.raises(ValueError, match=match):
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array_like(data, "a", maxdim=1)
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def test_3d(self):
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data = gen_data(3, False)
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a = array_like(data, "a", ndim=3)
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assert a.shape == (5, 6, 7)
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assert a.ndim == 3
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assert type(a) is np.ndarray
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a = array_like(data, "a", ndim=3, shape=(5, None, 7))
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assert a.shape == (5, 6, 7)
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a = array_like(data, "a", ndim=3, shape=(None, None, 7))
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assert a.shape == (5, 6, 7)
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a = array_like(data, "a", ndim=5)
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assert a.shape == (5, 6, 7, 1, 1)
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with pytest.raises(ValueError, match="a is required to have shape"):
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array_like(data, "a", ndim=3, shape=(10,))
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with pytest.raises(ValueError, match="a is required to have shape"):
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array_like(data, "a", ndim=3, shape=(None, None, 5))
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match = "a is required to have ndim 2 but has ndim 3"
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with pytest.raises(ValueError, match=match):
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array_like(data, "a", ndim=2)
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match = "a must have ndim <= 1"
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with pytest.raises(ValueError, match=match):
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array_like(data, "a", maxdim=1)
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match = "a must have ndim <= 2"
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with pytest.raises(ValueError, match=match):
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array_like(data, "a", maxdim=2)
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def test_right_squeeze_and_pad(self):
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data = np.empty((2, 1, 2))
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a = array_like(data, "a", ndim=3)
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assert a.shape == (2, 1, 2)
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data = np.empty(2)
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a = array_like(data, "a", ndim=3)
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assert a.shape == (2, 1, 1)
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data = np.empty((2, 1))
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a = array_like(data, "a", ndim=3)
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assert a.shape == (2, 1, 1)
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data = np.empty((2, 1, 1, 1))
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a = array_like(data, "a", ndim=3)
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assert a.shape == (2, 1, 1)
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data = np.empty((2, 1, 1, 2, 1, 1))
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with pytest.raises(ValueError):
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array_like(data, "a", ndim=3)
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def test_contiguous(self):
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x = np.arange(10)
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y = x[::2]
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a = array_like(y, "a", contiguous=True)
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assert not y.flags["C_CONTIGUOUS"]
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assert a.flags["C_CONTIGUOUS"]
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def test_dtype(self):
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x = np.arange(10)
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a = array_like(x, "a", dtype=np.float32)
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assert a.dtype == np.float32
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a = array_like(x, "a", dtype=np.uint8)
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assert a.dtype == np.uint8
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@pytest.mark.xfail(reason="Failing for now")
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def test_dot(self, use_pandas):
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data = gen_data(2, use_pandas)
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a = array_like(data, "a")
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assert not isinstance(a.T.dot(data), array_like)
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assert not isinstance(a.T.dot(a), array_like)
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def test_slice(self, use_pandas):
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data = gen_data(2, use_pandas)
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a = array_like(data, "a", ndim=2)
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assert type(a[1:]) is np.ndarray
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def test_right_squeeze():
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x = np.empty((10, 1, 10))
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y = _right_squeeze(x)
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assert y.shape == (10, 1, 10)
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x = np.empty((10, 10, 1))
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y = _right_squeeze(x)
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assert y.shape == (10, 10)
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x = np.empty((10, 10, 1, 1, 1, 1, 1))
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y = _right_squeeze(x)
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assert y.shape == (10, 10)
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x = np.empty((10, 1, 10, 1, 1, 1, 1, 1))
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y = _right_squeeze(x)
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assert y.shape == (10, 1, 10)
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def test_wrap_pandas(use_pandas):
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a = gen_data(1, use_pandas)
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b = gen_data(1, False)
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wrapped = PandasWrapper(a).wrap(b)
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expected_type = pd.Series if use_pandas else np.ndarray
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assert isinstance(wrapped, expected_type)
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assert not use_pandas or wrapped.name is None
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wrapped = PandasWrapper(a).wrap(b, columns="name")
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assert isinstance(wrapped, expected_type)
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assert not use_pandas or wrapped.name == "name"
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wrapped = PandasWrapper(a).wrap(b, columns=["name"])
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assert isinstance(wrapped, expected_type)
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assert not use_pandas or wrapped.name == "name"
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expected_type = pd.DataFrame if use_pandas else np.ndarray
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wrapped = PandasWrapper(a).wrap(b[:, None])
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assert isinstance(wrapped, expected_type)
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assert not use_pandas or wrapped.columns[0] == 0
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wrapped = PandasWrapper(a).wrap(b[:, None], columns=["name"])
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assert isinstance(wrapped, expected_type)
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assert not use_pandas or wrapped.columns == ["name"]
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if use_pandas:
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match = "Can only wrap 1 or 2-d array_like"
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with pytest.raises(ValueError, match=match):
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PandasWrapper(a).wrap(b[:, None, None])
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match = "obj must have the same number of elements in axis 0 as"
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with pytest.raises(ValueError, match=match):
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PandasWrapper(a).wrap(b[: b.shape[0] // 2])
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def test_wrap_pandas_append():
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a = gen_data(1, True)
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a.name = "apple"
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b = gen_data(1, False)
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wrapped = PandasWrapper(a).wrap(b, append="appended")
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expected = "apple_appended"
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assert wrapped.name == expected
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a = gen_data(2, True)
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a.columns = ["apple_" + str(i) for i in range(a.shape[1])]
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b = gen_data(2, False)
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wrapped = PandasWrapper(a).wrap(b, append="appended")
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expected = [c + "_appended" for c in a.columns]
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assert list(wrapped.columns) == expected
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def test_wrap_pandas_append_non_string():
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# GH 6826
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a = gen_data(1, True)
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a.name = 7
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b = gen_data(1, False)
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wrapped = PandasWrapper(a).wrap(b, append="appended")
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expected = "7_appended"
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assert wrapped.name == expected
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a = gen_data(2, True)
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a.columns = [i for i in range(a.shape[1])]
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b = gen_data(2, False)
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wrapped = PandasWrapper(a).wrap(b, append="appended")
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expected = [f"{c}_appended" for c in a.columns]
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assert list(wrapped.columns) == expected
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class CustomDict(dict):
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pass
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@pytest.fixture(params=(dict, OrderedDict, CustomDict, None))
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def dict_type(request):
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return request.param
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def test_optional_dict_like(dict_type):
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val = dict_type() if dict_type is not None else dict_type
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out = dict_like(val, "value", optional=True)
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assert isinstance(out, type(val))
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def test_optional_dict_like_error():
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match = r"value must be a dict or dict_like \(i.e., a Mapping\)"
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with pytest.raises(TypeError, match=match):
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dict_like([], "value", optional=True)
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with pytest.raises(TypeError, match=match):
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dict_like({"a"}, "value", optional=True)
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with pytest.raises(TypeError, match=match):
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dict_like("a", "value", optional=True)
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def test_string():
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out = string_like("apple", "value")
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assert out == "apple"
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out = string_like("apple", "value", options=("apple", "banana", "cherry"))
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assert out == "apple"
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with pytest.raises(TypeError, match="value must be a string"):
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string_like(1, "value")
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with pytest.raises(TypeError, match="value must be a string"):
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string_like(b"4", "value")
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with pytest.raises(
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ValueError,
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match="value must be one of: 'apple'," " 'banana', 'cherry'",
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):
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string_like("date", "value", options=("apple", "banana", "cherry"))
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def test_optional_string():
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out = string_like("apple", "value")
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assert out == "apple"
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out = string_like("apple", "value", options=("apple", "banana", "cherry"))
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assert out == "apple"
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out = string_like(None, "value", optional=True)
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assert out is None
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out = string_like(
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None, "value", optional=True, options=("apple", "banana", "cherry")
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)
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assert out is None
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with pytest.raises(TypeError, match="value must be a string"):
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string_like(1, "value", optional=True)
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with pytest.raises(TypeError, match="value must be a string"):
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string_like(b"4", "value", optional=True)
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@pytest.fixture(params=(1.0, 1.1, np.float32(1.2), np.array([1.2]), 1.2 + 0j))
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def floating(request):
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return request.param
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@pytest.fixture(params=(np.empty(2), 1.2 + 1j, True, "3.2", None))
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def not_floating(request):
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return request.param
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def test_float_like(floating):
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assert isinstance(float_like(floating, "floating"), float)
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assert isinstance(float_like(floating, "floating", optional=True), float)
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assert float_like(None, "floating", optional=True) is None
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if isinstance(floating, (int, np.integer, float, np.inexact)):
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assert isinstance(float_like(floating, "floating", strict=True), float)
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assert float_like(None, "floating", optional=True, strict=True) is None
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def test_not_float_like(not_floating):
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with pytest.raises(TypeError):
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float_like(not_floating, "floating")
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@pytest.fixture(params=(1.0, 2, np.float32(3.0), np.array([4.0])))
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def integer(request):
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return request.param
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@pytest.fixture(
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params=(
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3.2,
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np.float32(3.2),
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3 + 2j,
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complex(2.3 + 0j),
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"apple",
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1.0 + 0j,
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np.timedelta64(2),
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)
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)
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def not_integer(request):
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return request.param
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def test_int_like(integer):
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assert isinstance(int_like(integer, "integer"), int)
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assert isinstance(int_like(integer, "integer", optional=True), int)
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assert int_like(None, "floating", optional=True) is None
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if isinstance(integer, (int, np.integer)):
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assert isinstance(int_like(integer, "integer", strict=True), int)
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assert int_like(None, "floating", optional=True, strict=True) is None
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def test_not_int_like(not_integer):
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with pytest.raises(TypeError):
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int_like(not_integer, "integer")
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@pytest.fixture(params=[True, False, 1, 1.2, "a", ""])
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def boolean(request):
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return request.param
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def test_bool_like(boolean):
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assert isinstance(bool_like(boolean, "boolean"), bool)
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assert bool_like(None, "boolean", optional=True) is None
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if isinstance(boolean, bool):
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assert isinstance(bool_like(boolean, "boolean", strict=True), bool)
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else:
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with pytest.raises(TypeError):
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bool_like(boolean, "boolean", strict=True)
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def test_not_bool_like():
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with pytest.raises(TypeError):
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bool_like(np.array([True, True]), boolean)
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