843 lines
24 KiB
Python
843 lines
24 KiB
Python
import warnings
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import unittest
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import os
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import atexit
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import numpy as np
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from scipy import sparse
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import pytest
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from sklearn.utils.deprecation import deprecated
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from sklearn.utils.metaestimators import available_if, if_delegate_has_method
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from sklearn.utils._readonly_array_wrapper import _test_sum
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from sklearn.utils._testing import (
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assert_raises,
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assert_no_warnings,
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set_random_state,
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assert_raise_message,
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ignore_warnings,
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check_docstring_parameters,
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assert_allclose_dense_sparse,
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assert_raises_regex,
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TempMemmap,
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create_memmap_backed_data,
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_delete_folder,
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_convert_container,
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raises,
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assert_allclose,
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)
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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def test_set_random_state():
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lda = LinearDiscriminantAnalysis()
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tree = DecisionTreeClassifier()
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# Linear Discriminant Analysis doesn't have random state: smoke test
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set_random_state(lda, 3)
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set_random_state(tree, 3)
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assert tree.random_state == 3
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def test_assert_allclose_dense_sparse():
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x = np.arange(9).reshape(3, 3)
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msg = "Not equal to tolerance "
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y = sparse.csc_matrix(x)
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for X in [x, y]:
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# basic compare
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with pytest.raises(AssertionError, match=msg):
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assert_allclose_dense_sparse(X, X * 2)
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assert_allclose_dense_sparse(X, X)
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with pytest.raises(ValueError, match="Can only compare two sparse"):
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assert_allclose_dense_sparse(x, y)
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A = sparse.diags(np.ones(5), offsets=0).tocsr()
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B = sparse.csr_matrix(np.ones((1, 5)))
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with pytest.raises(AssertionError, match="Arrays are not equal"):
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assert_allclose_dense_sparse(B, A)
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def test_assert_raises_msg():
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with assert_raises_regex(AssertionError, "Hello world"):
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with assert_raises(ValueError, msg="Hello world"):
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pass
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def test_assert_raise_message():
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def _raise_ValueError(message):
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raise ValueError(message)
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def _no_raise():
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pass
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assert_raise_message(ValueError, "test", _raise_ValueError, "test")
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assert_raises(
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AssertionError,
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assert_raise_message,
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ValueError,
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"something else",
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_raise_ValueError,
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"test",
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)
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assert_raises(
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ValueError,
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assert_raise_message,
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TypeError,
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"something else",
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_raise_ValueError,
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"test",
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)
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assert_raises(AssertionError, assert_raise_message, ValueError, "test", _no_raise)
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# multiple exceptions in a tuple
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assert_raises(
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AssertionError,
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assert_raise_message,
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(ValueError, AttributeError),
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"test",
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_no_raise,
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)
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def test_ignore_warning():
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# This check that ignore_warning decorator and context manager are working
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# as expected
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def _warning_function():
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warnings.warn("deprecation warning", DeprecationWarning)
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def _multiple_warning_function():
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warnings.warn("deprecation warning", DeprecationWarning)
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warnings.warn("deprecation warning")
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# Check the function directly
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assert_no_warnings(ignore_warnings(_warning_function))
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assert_no_warnings(ignore_warnings(_warning_function, category=DeprecationWarning))
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with pytest.warns(DeprecationWarning):
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ignore_warnings(_warning_function, category=UserWarning)()
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with pytest.warns(UserWarning):
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ignore_warnings(_multiple_warning_function, category=FutureWarning)()
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with pytest.warns(DeprecationWarning):
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ignore_warnings(_multiple_warning_function, category=UserWarning)()
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assert_no_warnings(
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ignore_warnings(_warning_function, category=(DeprecationWarning, UserWarning))
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)
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# Check the decorator
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@ignore_warnings
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def decorator_no_warning():
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_warning_function()
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_multiple_warning_function()
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@ignore_warnings(category=(DeprecationWarning, UserWarning))
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def decorator_no_warning_multiple():
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_multiple_warning_function()
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@ignore_warnings(category=DeprecationWarning)
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def decorator_no_deprecation_warning():
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_warning_function()
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@ignore_warnings(category=UserWarning)
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def decorator_no_user_warning():
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_warning_function()
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@ignore_warnings(category=DeprecationWarning)
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def decorator_no_deprecation_multiple_warning():
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_multiple_warning_function()
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@ignore_warnings(category=UserWarning)
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def decorator_no_user_multiple_warning():
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_multiple_warning_function()
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assert_no_warnings(decorator_no_warning)
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assert_no_warnings(decorator_no_warning_multiple)
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assert_no_warnings(decorator_no_deprecation_warning)
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with pytest.warns(DeprecationWarning):
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decorator_no_user_warning()
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with pytest.warns(UserWarning):
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decorator_no_deprecation_multiple_warning()
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with pytest.warns(DeprecationWarning):
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decorator_no_user_multiple_warning()
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# Check the context manager
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def context_manager_no_warning():
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with ignore_warnings():
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_warning_function()
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def context_manager_no_warning_multiple():
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with ignore_warnings(category=(DeprecationWarning, UserWarning)):
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_multiple_warning_function()
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def context_manager_no_deprecation_warning():
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with ignore_warnings(category=DeprecationWarning):
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_warning_function()
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def context_manager_no_user_warning():
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with ignore_warnings(category=UserWarning):
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_warning_function()
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def context_manager_no_deprecation_multiple_warning():
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with ignore_warnings(category=DeprecationWarning):
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_multiple_warning_function()
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def context_manager_no_user_multiple_warning():
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with ignore_warnings(category=UserWarning):
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_multiple_warning_function()
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assert_no_warnings(context_manager_no_warning)
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assert_no_warnings(context_manager_no_warning_multiple)
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assert_no_warnings(context_manager_no_deprecation_warning)
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with pytest.warns(DeprecationWarning):
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context_manager_no_user_warning()
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with pytest.warns(UserWarning):
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context_manager_no_deprecation_multiple_warning()
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with pytest.warns(DeprecationWarning):
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context_manager_no_user_multiple_warning()
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# Check that passing warning class as first positional argument
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warning_class = UserWarning
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match = "'obj' should be a callable.+you should use 'category=UserWarning'"
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with pytest.raises(ValueError, match=match):
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silence_warnings_func = ignore_warnings(warning_class)(_warning_function)
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silence_warnings_func()
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with pytest.raises(ValueError, match=match):
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@ignore_warnings(warning_class)
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def test():
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pass
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class TestWarns(unittest.TestCase):
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def test_warn(self):
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def f():
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warnings.warn("yo")
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return 3
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with pytest.raises(AssertionError):
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assert_no_warnings(f)
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assert assert_no_warnings(lambda x: x, 1) == 1
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# Tests for docstrings:
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def f_ok(a, b):
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"""Function f
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Parameters
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----------
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a : int
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Parameter a
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b : float
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Parameter b
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Returns
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-------
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c : list
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Parameter c
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"""
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c = a + b
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return c
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def f_bad_sections(a, b):
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"""Function f
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Parameters
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----------
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a : int
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Parameter a
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b : float
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Parameter b
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Results
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-------
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c : list
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Parameter c
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"""
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c = a + b
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return c
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def f_bad_order(b, a):
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"""Function f
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Parameters
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----------
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a : int
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Parameter a
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b : float
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Parameter b
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Returns
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-------
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c : list
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Parameter c
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"""
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c = a + b
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return c
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def f_too_many_param_docstring(a, b):
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"""Function f
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Parameters
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----------
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a : int
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Parameter a
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b : int
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Parameter b
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c : int
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Parameter c
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Returns
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-------
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d : list
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Parameter c
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"""
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d = a + b
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return d
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def f_missing(a, b):
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"""Function f
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Parameters
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----------
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a : int
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Parameter a
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Returns
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-------
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c : list
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Parameter c
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"""
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c = a + b
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return c
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def f_check_param_definition(a, b, c, d, e):
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"""Function f
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Parameters
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----------
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a: int
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Parameter a
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b:
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Parameter b
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c :
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This is parsed correctly in numpydoc 1.2
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d:int
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Parameter d
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e
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No typespec is allowed without colon
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"""
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return a + b + c + d
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class Klass:
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def f_missing(self, X, y):
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pass
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def f_bad_sections(self, X, y):
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"""Function f
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Parameter
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---------
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a : int
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Parameter a
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b : float
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Parameter b
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Results
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-------
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c : list
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Parameter c
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"""
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pass
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class MockEst:
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def __init__(self):
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"""MockEstimator"""
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def fit(self, X, y):
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return X
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def predict(self, X):
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return X
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def predict_proba(self, X):
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return X
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def score(self, X):
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return 1.0
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class MockMetaEstimator:
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def __init__(self, delegate):
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"""MetaEstimator to check if doctest on delegated methods work.
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Parameters
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---------
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delegate : estimator
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Delegated estimator.
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"""
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self.delegate = delegate
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@available_if(lambda self: hasattr(self.delegate, "predict"))
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def predict(self, X):
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"""This is available only if delegate has predict.
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Parameters
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----------
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y : ndarray
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Parameter y
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"""
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return self.delegate.predict(X)
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@available_if(lambda self: hasattr(self.delegate, "score"))
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@deprecated("Testing a deprecated delegated method")
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def score(self, X):
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"""This is available only if delegate has score.
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Parameters
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---------
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y : ndarray
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Parameter y
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"""
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@available_if(lambda self: hasattr(self.delegate, "predict_proba"))
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def predict_proba(self, X):
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"""This is available only if delegate has predict_proba.
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Parameters
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---------
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X : ndarray
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Parameter X
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"""
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return X
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@deprecated("Testing deprecated function with wrong params")
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def fit(self, X, y):
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"""Incorrect docstring but should not be tested"""
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class MockMetaEstimatorDeprecatedDelegation:
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def __init__(self, delegate):
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"""MetaEstimator to check if doctest on delegated methods work.
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Parameters
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---------
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delegate : estimator
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Delegated estimator.
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"""
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self.delegate = delegate
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@if_delegate_has_method(delegate="delegate")
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def predict(self, X):
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"""This is available only if delegate has predict.
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Parameters
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----------
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y : ndarray
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Parameter y
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"""
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return self.delegate.predict(X)
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@if_delegate_has_method(delegate="delegate")
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@deprecated("Testing a deprecated delegated method")
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def score(self, X):
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"""This is available only if delegate has score.
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Parameters
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---------
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y : ndarray
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Parameter y
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"""
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@if_delegate_has_method(delegate="delegate")
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def predict_proba(self, X):
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"""This is available only if delegate has predict_proba.
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Parameters
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---------
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X : ndarray
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Parameter X
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"""
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return X
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@deprecated("Testing deprecated function with wrong params")
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def fit(self, X, y):
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"""Incorrect docstring but should not be tested"""
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@pytest.mark.filterwarnings("ignore:if_delegate_has_method was deprecated")
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@pytest.mark.parametrize(
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"mock_meta",
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[
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MockMetaEstimator(delegate=MockEst()),
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MockMetaEstimatorDeprecatedDelegation(delegate=MockEst()),
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],
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)
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def test_check_docstring_parameters(mock_meta):
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pytest.importorskip(
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"numpydoc",
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reason="numpydoc is required to test the docstrings",
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minversion="1.2.0",
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)
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incorrect = check_docstring_parameters(f_ok)
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assert incorrect == []
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incorrect = check_docstring_parameters(f_ok, ignore=["b"])
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assert incorrect == []
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incorrect = check_docstring_parameters(f_missing, ignore=["b"])
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assert incorrect == []
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with pytest.raises(RuntimeError, match="Unknown section Results"):
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check_docstring_parameters(f_bad_sections)
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with pytest.raises(RuntimeError, match="Unknown section Parameter"):
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check_docstring_parameters(Klass.f_bad_sections)
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incorrect = check_docstring_parameters(f_check_param_definition)
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mock_meta_name = mock_meta.__class__.__name__
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assert incorrect == [
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"sklearn.utils.tests.test_testing.f_check_param_definition There "
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"was no space between the param name and colon ('a: int')",
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"sklearn.utils.tests.test_testing.f_check_param_definition There "
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"was no space between the param name and colon ('b:')",
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"sklearn.utils.tests.test_testing.f_check_param_definition There "
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"was no space between the param name and colon ('d:int')",
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]
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messages = [
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[
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"In function: sklearn.utils.tests.test_testing.f_bad_order",
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"There's a parameter name mismatch in function docstring w.r.t."
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" function signature, at index 0 diff: 'b' != 'a'",
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"Full diff:",
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"- ['b', 'a']",
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"+ ['a', 'b']",
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],
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[
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"In function: "
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+ "sklearn.utils.tests.test_testing.f_too_many_param_docstring",
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"Parameters in function docstring have more items w.r.t. function"
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" signature, first extra item: c",
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"Full diff:",
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"- ['a', 'b']",
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"+ ['a', 'b', 'c']",
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"? +++++",
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],
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[
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"In function: sklearn.utils.tests.test_testing.f_missing",
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"Parameters in function docstring have less items w.r.t. function"
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" signature, first missing item: b",
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"Full diff:",
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"- ['a', 'b']",
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"+ ['a']",
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],
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[
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"In function: sklearn.utils.tests.test_testing.Klass.f_missing",
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"Parameters in function docstring have less items w.r.t. function"
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" signature, first missing item: X",
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"Full diff:",
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"- ['X', 'y']",
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"+ []",
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],
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[
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"In function: "
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+ f"sklearn.utils.tests.test_testing.{mock_meta_name}.predict",
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"There's a parameter name mismatch in function docstring w.r.t."
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" function signature, at index 0 diff: 'X' != 'y'",
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"Full diff:",
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"- ['X']",
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"? ^",
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"+ ['y']",
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"? ^",
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],
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[
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"In function: "
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+ f"sklearn.utils.tests.test_testing.{mock_meta_name}."
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+ "predict_proba",
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"potentially wrong underline length... ",
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"Parameters ",
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"--------- in ",
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],
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[
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"In function: "
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+ f"sklearn.utils.tests.test_testing.{mock_meta_name}.score",
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"potentially wrong underline length... ",
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"Parameters ",
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"--------- in ",
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],
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[
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"In function: " + f"sklearn.utils.tests.test_testing.{mock_meta_name}.fit",
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"Parameters in function docstring have less items w.r.t. function"
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" signature, first missing item: X",
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"Full diff:",
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"- ['X', 'y']",
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"+ []",
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],
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]
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for msg, f in zip(
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messages,
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[
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f_bad_order,
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f_too_many_param_docstring,
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f_missing,
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Klass.f_missing,
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mock_meta.predict,
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mock_meta.predict_proba,
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mock_meta.score,
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mock_meta.fit,
|
|
],
|
|
):
|
|
incorrect = check_docstring_parameters(f)
|
|
assert msg == incorrect, '\n"%s"\n not in \n"%s"' % (msg, incorrect)
|
|
|
|
|
|
class RegistrationCounter:
|
|
def __init__(self):
|
|
self.nb_calls = 0
|
|
|
|
def __call__(self, to_register_func):
|
|
self.nb_calls += 1
|
|
assert to_register_func.func is _delete_folder
|
|
|
|
|
|
def check_memmap(input_array, mmap_data, mmap_mode="r"):
|
|
assert isinstance(mmap_data, np.memmap)
|
|
writeable = mmap_mode != "r"
|
|
assert mmap_data.flags.writeable is writeable
|
|
np.testing.assert_array_equal(input_array, mmap_data)
|
|
|
|
|
|
def test_tempmemmap(monkeypatch):
|
|
registration_counter = RegistrationCounter()
|
|
monkeypatch.setattr(atexit, "register", registration_counter)
|
|
|
|
input_array = np.ones(3)
|
|
with TempMemmap(input_array) as data:
|
|
check_memmap(input_array, data)
|
|
temp_folder = os.path.dirname(data.filename)
|
|
if os.name != "nt":
|
|
assert not os.path.exists(temp_folder)
|
|
assert registration_counter.nb_calls == 1
|
|
|
|
mmap_mode = "r+"
|
|
with TempMemmap(input_array, mmap_mode=mmap_mode) as data:
|
|
check_memmap(input_array, data, mmap_mode=mmap_mode)
|
|
temp_folder = os.path.dirname(data.filename)
|
|
if os.name != "nt":
|
|
assert not os.path.exists(temp_folder)
|
|
assert registration_counter.nb_calls == 2
|
|
|
|
|
|
@pytest.mark.parametrize("aligned", [False, True])
|
|
def test_create_memmap_backed_data(monkeypatch, aligned):
|
|
registration_counter = RegistrationCounter()
|
|
monkeypatch.setattr(atexit, "register", registration_counter)
|
|
|
|
input_array = np.ones(3)
|
|
data = create_memmap_backed_data(input_array, aligned=aligned)
|
|
check_memmap(input_array, data)
|
|
assert registration_counter.nb_calls == 1
|
|
|
|
data, folder = create_memmap_backed_data(
|
|
input_array, return_folder=True, aligned=aligned
|
|
)
|
|
check_memmap(input_array, data)
|
|
assert folder == os.path.dirname(data.filename)
|
|
assert registration_counter.nb_calls == 2
|
|
|
|
mmap_mode = "r+"
|
|
data = create_memmap_backed_data(input_array, mmap_mode=mmap_mode, aligned=aligned)
|
|
check_memmap(input_array, data, mmap_mode)
|
|
assert registration_counter.nb_calls == 3
|
|
|
|
input_list = [input_array, input_array + 1, input_array + 2]
|
|
mmap_data_list = create_memmap_backed_data(input_list, aligned=aligned)
|
|
for input_array, data in zip(input_list, mmap_data_list):
|
|
check_memmap(input_array, data)
|
|
assert registration_counter.nb_calls == 4
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=(
|
|
"When creating aligned memmap-backed arrays, input must be a single array"
|
|
" or a sequence of arrays"
|
|
),
|
|
):
|
|
create_memmap_backed_data([input_array, "not-an-array"], aligned=True)
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.int32, np.int64])
|
|
def test_memmap_on_contiguous_data(dtype):
|
|
"""Test memory mapped array on contiguous memoryview."""
|
|
x = np.arange(10).astype(dtype)
|
|
assert x.flags["C_CONTIGUOUS"]
|
|
assert x.flags["ALIGNED"]
|
|
|
|
# _test_sum consumes contiguous arrays
|
|
# def _test_sum(NUM_TYPES[::1] x):
|
|
sum_origin = _test_sum(x)
|
|
|
|
# now on memory mapped data
|
|
# aligned=True so avoid https://github.com/joblib/joblib/issues/563
|
|
# without alignment, this can produce segmentation faults, see
|
|
# https://github.com/scikit-learn/scikit-learn/pull/21654
|
|
x_mmap = create_memmap_backed_data(x, mmap_mode="r+", aligned=True)
|
|
sum_mmap = _test_sum(x_mmap)
|
|
assert sum_mmap == pytest.approx(sum_origin, rel=1e-11)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"constructor_name, container_type",
|
|
[
|
|
("list", list),
|
|
("tuple", tuple),
|
|
("array", np.ndarray),
|
|
("sparse", sparse.csr_matrix),
|
|
("sparse_csr", sparse.csr_matrix),
|
|
("sparse_csc", sparse.csc_matrix),
|
|
("dataframe", lambda: pytest.importorskip("pandas").DataFrame),
|
|
("series", lambda: pytest.importorskip("pandas").Series),
|
|
("index", lambda: pytest.importorskip("pandas").Index),
|
|
("slice", slice),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"dtype, superdtype",
|
|
[
|
|
(np.int32, np.integer),
|
|
(np.int64, np.integer),
|
|
(np.float32, np.floating),
|
|
(np.float64, np.floating),
|
|
],
|
|
)
|
|
def test_convert_container(
|
|
constructor_name,
|
|
container_type,
|
|
dtype,
|
|
superdtype,
|
|
):
|
|
"""Check that we convert the container to the right type of array with the
|
|
right data type."""
|
|
if constructor_name in ("dataframe", "series", "index"):
|
|
# delay the import of pandas within the function to only skip this test
|
|
# instead of the whole file
|
|
container_type = container_type()
|
|
container = [0, 1]
|
|
container_converted = _convert_container(
|
|
container,
|
|
constructor_name,
|
|
dtype=dtype,
|
|
)
|
|
assert isinstance(container_converted, container_type)
|
|
|
|
if constructor_name in ("list", "tuple", "index"):
|
|
# list and tuple will use Python class dtype: int, float
|
|
# pandas index will always use high precision: np.int64 and np.float64
|
|
assert np.issubdtype(type(container_converted[0]), superdtype)
|
|
elif hasattr(container_converted, "dtype"):
|
|
assert container_converted.dtype == dtype
|
|
elif hasattr(container_converted, "dtypes"):
|
|
assert container_converted.dtypes[0] == dtype
|
|
|
|
|
|
def test_raises():
|
|
# Tests for the raises context manager
|
|
|
|
# Proper type, no match
|
|
with raises(TypeError):
|
|
raise TypeError()
|
|
|
|
# Proper type, proper match
|
|
with raises(TypeError, match="how are you") as cm:
|
|
raise TypeError("hello how are you")
|
|
assert cm.raised_and_matched
|
|
|
|
# Proper type, proper match with multiple patterns
|
|
with raises(TypeError, match=["not this one", "how are you"]) as cm:
|
|
raise TypeError("hello how are you")
|
|
assert cm.raised_and_matched
|
|
|
|
# bad type, no match
|
|
with pytest.raises(ValueError, match="this will be raised"):
|
|
with raises(TypeError) as cm:
|
|
raise ValueError("this will be raised")
|
|
assert not cm.raised_and_matched
|
|
|
|
# Bad type, no match, with a err_msg
|
|
with pytest.raises(AssertionError, match="the failure message"):
|
|
with raises(TypeError, err_msg="the failure message") as cm:
|
|
raise ValueError()
|
|
assert not cm.raised_and_matched
|
|
|
|
# bad type, with match (is ignored anyway)
|
|
with pytest.raises(ValueError, match="this will be raised"):
|
|
with raises(TypeError, match="this is ignored") as cm:
|
|
raise ValueError("this will be raised")
|
|
assert not cm.raised_and_matched
|
|
|
|
# proper type but bad match
|
|
with pytest.raises(
|
|
AssertionError, match="should contain one of the following patterns"
|
|
):
|
|
with raises(TypeError, match="hello") as cm:
|
|
raise TypeError("Bad message")
|
|
assert not cm.raised_and_matched
|
|
|
|
# proper type but bad match, with err_msg
|
|
with pytest.raises(AssertionError, match="the failure message"):
|
|
with raises(TypeError, match="hello", err_msg="the failure message") as cm:
|
|
raise TypeError("Bad message")
|
|
assert not cm.raised_and_matched
|
|
|
|
# no raise with default may_pass=False
|
|
with pytest.raises(AssertionError, match="Did not raise"):
|
|
with raises(TypeError) as cm:
|
|
pass
|
|
assert not cm.raised_and_matched
|
|
|
|
# no raise with may_pass=True
|
|
with raises(TypeError, match="hello", may_pass=True) as cm:
|
|
pass # still OK
|
|
assert not cm.raised_and_matched
|
|
|
|
# Multiple exception types:
|
|
with raises((TypeError, ValueError)):
|
|
raise TypeError()
|
|
with raises((TypeError, ValueError)):
|
|
raise ValueError()
|
|
with pytest.raises(AssertionError):
|
|
with raises((TypeError, ValueError)):
|
|
pass
|
|
|
|
|
|
def test_float32_aware_assert_allclose():
|
|
# The relative tolerance for float32 inputs is 1e-4
|
|
assert_allclose(np.array([1.0 + 2e-5], dtype=np.float32), 1.0)
|
|
with pytest.raises(AssertionError):
|
|
assert_allclose(np.array([1.0 + 2e-4], dtype=np.float32), 1.0)
|
|
|
|
# The relative tolerance for other inputs is left to 1e-7 as in
|
|
# the original numpy version.
|
|
assert_allclose(np.array([1.0 + 2e-8], dtype=np.float64), 1.0)
|
|
with pytest.raises(AssertionError):
|
|
assert_allclose(np.array([1.0 + 2e-7], dtype=np.float64), 1.0)
|
|
|
|
# atol is left to 0.0 by default, even for float32
|
|
with pytest.raises(AssertionError):
|
|
assert_allclose(np.array([1e-5], dtype=np.float32), 0.0)
|
|
assert_allclose(np.array([1e-5], dtype=np.float32), 0.0, atol=2e-5)
|