168 lines
5.1 KiB
Python
168 lines
5.1 KiB
Python
# Authors: Lars Buitinck
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# Dan Blanchard <dblanchard@ets.org>
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# License: BSD 3 clause
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from random import Random
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import numpy as np
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import scipy.sparse as sp
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from numpy.testing import assert_array_equal
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import pytest
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from sklearn.feature_extraction import DictVectorizer
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from sklearn.feature_selection import SelectKBest, chi2
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@pytest.mark.parametrize('sparse', (True, False))
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@pytest.mark.parametrize('dtype', (int, np.float32, np.int16))
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@pytest.mark.parametrize('sort', (True, False))
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@pytest.mark.parametrize('iterable', (True, False))
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def test_dictvectorizer(sparse, dtype, sort, iterable):
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D = [{"foo": 1, "bar": 3},
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{"bar": 4, "baz": 2},
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{"bar": 1, "quux": 1, "quuux": 2}]
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v = DictVectorizer(sparse=sparse, dtype=dtype, sort=sort)
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X = v.fit_transform(iter(D) if iterable else D)
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assert sp.issparse(X) == sparse
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assert X.shape == (3, 5)
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assert X.sum() == 14
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assert v.inverse_transform(X) == D
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if sparse:
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# CSR matrices can't be compared for equality
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assert_array_equal(X.A, v.transform(iter(D) if iterable
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else D).A)
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else:
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assert_array_equal(X, v.transform(iter(D) if iterable
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else D))
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if sort:
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assert (v.feature_names_ ==
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sorted(v.feature_names_))
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def test_feature_selection():
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# make two feature dicts with two useful features and a bunch of useless
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# ones, in terms of chi2
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d1 = dict([("useless%d" % i, 10) for i in range(20)],
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useful1=1, useful2=20)
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d2 = dict([("useless%d" % i, 10) for i in range(20)],
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useful1=20, useful2=1)
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for indices in (True, False):
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v = DictVectorizer().fit([d1, d2])
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X = v.transform([d1, d2])
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sel = SelectKBest(chi2, k=2).fit(X, [0, 1])
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v.restrict(sel.get_support(indices=indices), indices=indices)
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assert v.get_feature_names() == ["useful1", "useful2"]
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def test_one_of_k():
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D_in = [{"version": "1", "ham": 2},
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{"version": "2", "spam": .3},
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{"version=3": True, "spam": -1}]
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v = DictVectorizer()
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X = v.fit_transform(D_in)
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assert X.shape == (3, 5)
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D_out = v.inverse_transform(X)
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assert D_out[0] == {"version=1": 1, "ham": 2}
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names = v.get_feature_names()
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assert "version=2" in names
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assert "version" not in names
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def test_iterable_value():
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D_names = ['ham', 'spam', 'version=1', 'version=2', 'version=3']
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X_expected = [[2.0, 0.0, 2.0, 1.0, 0.0],
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[0.0, 0.3, 0.0, 1.0, 0.0],
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[0.0, -1.0, 0.0, 0.0, 1.0]]
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D_in = [{"version": ["1", "2", "1"], "ham": 2},
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{"version": "2", "spam": .3},
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{"version=3": True, "spam": -1}]
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v = DictVectorizer()
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X = v.fit_transform(D_in)
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X = X.toarray()
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assert_array_equal(X, X_expected)
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D_out = v.inverse_transform(X)
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assert D_out[0] == {"version=1": 2, "version=2": 1, "ham": 2}
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names = v.get_feature_names()
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assert names == D_names
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def test_iterable_not_string_error():
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error_value = ("Unsupported type <class 'int'> in iterable value. "
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"Only iterables of string are supported.")
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D2 = [{'foo': '1', 'bar': '2'},
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{'foo': '3', 'baz': '1'},
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{'foo': [1, 'three']}]
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v = DictVectorizer(sparse=False)
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with pytest.raises(TypeError) as error:
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v.fit(D2)
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assert str(error.value) == error_value
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def test_mapping_error():
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error_value = ("Unsupported value type <class 'dict'> "
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"for foo: {'one': 1, 'three': 3}.\n"
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"Mapping objects are not supported.")
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D2 = [{'foo': '1', 'bar': '2'},
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{'foo': '3', 'baz': '1'},
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{'foo': {'one': 1, 'three': 3}}]
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v = DictVectorizer(sparse=False)
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with pytest.raises(TypeError) as error:
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v.fit(D2)
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assert str(error.value) == error_value
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def test_unseen_or_no_features():
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D = [{"camelot": 0, "spamalot": 1}]
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for sparse in [True, False]:
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v = DictVectorizer(sparse=sparse).fit(D)
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X = v.transform({"push the pram a lot": 2})
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if sparse:
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X = X.toarray()
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assert_array_equal(X, np.zeros((1, 2)))
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X = v.transform({})
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if sparse:
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X = X.toarray()
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assert_array_equal(X, np.zeros((1, 2)))
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try:
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v.transform([])
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except ValueError as e:
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assert "empty" in str(e)
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def test_deterministic_vocabulary():
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# Generate equal dictionaries with different memory layouts
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items = [("%03d" % i, i) for i in range(1000)]
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rng = Random(42)
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d_sorted = dict(items)
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rng.shuffle(items)
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d_shuffled = dict(items)
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# check that the memory layout does not impact the resulting vocabulary
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v_1 = DictVectorizer().fit([d_sorted])
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v_2 = DictVectorizer().fit([d_shuffled])
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assert v_1.vocabulary_ == v_2.vocabulary_
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def test_n_features_in():
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# For vectorizers, n_features_in_ does not make sense and does not exist.
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dv = DictVectorizer()
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assert not hasattr(dv, 'n_features_in_')
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d = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
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dv.fit(d)
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assert not hasattr(dv, 'n_features_in_')
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