691 lines
26 KiB
Python
691 lines
26 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import division
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import re
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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.exceptions import NotFittedError
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raises_regex
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from sklearn.utils.testing import assert_allclose
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.testing import assert_warns_message
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from sklearn.utils.testing import assert_no_warnings
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.preprocessing import OrdinalEncoder
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def toarray(a):
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if hasattr(a, "toarray"):
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a = a.toarray()
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return a
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def test_one_hot_encoder_sparse():
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# Test OneHotEncoder's fit and transform.
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X = [[3, 2, 1], [0, 1, 1]]
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enc = OneHotEncoder()
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with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
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# discover max values automatically
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X_trans = enc.fit_transform(X).toarray()
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assert_equal(X_trans.shape, (2, 5))
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assert_array_equal(enc.active_features_,
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np.where([1, 0, 0, 1, 0, 1, 1, 0, 1])[0])
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assert_array_equal(enc.feature_indices_, [0, 4, 7, 9])
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# check outcome
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assert_array_equal(X_trans,
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[[0., 1., 0., 1., 1.],
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[1., 0., 1., 0., 1.]])
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# max value given as 3
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# enc = assert_warns(DeprecationWarning, OneHotEncoder, n_values=4)
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enc = OneHotEncoder(n_values=4)
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with ignore_warnings(category=DeprecationWarning):
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X_trans = enc.fit_transform(X)
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assert_equal(X_trans.shape, (2, 4 * 3))
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assert_array_equal(enc.feature_indices_, [0, 4, 8, 12])
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# max value given per feature
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# enc = assert_warns(DeprecationWarning, OneHotEncoder, n_values=[3, 2, 2])
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enc = OneHotEncoder(n_values=[3, 2, 2])
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with ignore_warnings(category=DeprecationWarning):
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X = [[1, 0, 1], [0, 1, 1]]
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X_trans = enc.fit_transform(X)
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assert_equal(X_trans.shape, (2, 3 + 2 + 2))
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assert_array_equal(enc.n_values_, [3, 2, 2])
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# check that testing with larger feature works:
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X = np.array([[2, 0, 1], [0, 1, 1]])
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enc.transform(X)
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# test that an error is raised when out of bounds:
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X_too_large = [[0, 2, 1], [0, 1, 1]]
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assert_raises(ValueError, enc.transform, X_too_large)
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error_msg = r"unknown categorical feature present \[2\] during transform"
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assert_raises_regex(ValueError, error_msg, enc.transform, X_too_large)
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with ignore_warnings(category=DeprecationWarning):
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assert_raises(
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ValueError,
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OneHotEncoder(n_values=2).fit_transform, X)
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# test that error is raised when wrong number of features
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assert_raises(ValueError, enc.transform, X[:, :-1])
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# test that error is raised when wrong number of features in fit
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# with prespecified n_values
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with ignore_warnings(category=DeprecationWarning):
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assert_raises(ValueError, enc.fit, X[:, :-1])
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# test exception on wrong init param
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with ignore_warnings(category=DeprecationWarning):
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assert_raises(
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TypeError, OneHotEncoder(n_values=np.int).fit, X)
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enc = OneHotEncoder()
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# test negative input to fit
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with ignore_warnings(category=FutureWarning):
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assert_raises(ValueError, enc.fit, [[0], [-1]])
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# test negative input to transform
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with ignore_warnings(category=FutureWarning):
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enc.fit([[0], [1]])
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assert_raises(ValueError, enc.transform, [[0], [-1]])
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def test_one_hot_encoder_dense():
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# check for sparse=False
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X = [[3, 2, 1], [0, 1, 1]]
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enc = OneHotEncoder(sparse=False)
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with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
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# discover max values automatically
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X_trans = enc.fit_transform(X)
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assert_equal(X_trans.shape, (2, 5))
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assert_array_equal(enc.active_features_,
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np.where([1, 0, 0, 1, 0, 1, 1, 0, 1])[0])
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assert_array_equal(enc.feature_indices_, [0, 4, 7, 9])
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# check outcome
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assert_array_equal(X_trans,
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np.array([[0., 1., 0., 1., 1.],
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[1., 0., 1., 0., 1.]]))
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def test_one_hot_encoder_deprecationwarnings():
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for X in [[[3, 2, 1], [0, 1, 1]],
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[[3., 2., 1.], [0., 1., 1.]]]:
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enc = OneHotEncoder()
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assert_warns_message(FutureWarning, "handling of integer",
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enc.fit, X)
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enc = OneHotEncoder()
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assert_warns_message(FutureWarning, "handling of integer",
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enc.fit_transform, X)
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# check it still works correctly as well
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with ignore_warnings(category=FutureWarning):
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X_trans = enc.fit_transform(X).toarray()
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res = [[0., 1., 0., 1., 1.],
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[1., 0., 1., 0., 1.]]
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assert_array_equal(X_trans, res)
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# check deprecated attributes
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assert_warns(DeprecationWarning, lambda: enc.active_features_)
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assert_warns(DeprecationWarning, lambda: enc.feature_indices_)
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assert_warns(DeprecationWarning, lambda: enc.n_values_)
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# check no warning is raised if keyword is specified
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enc = OneHotEncoder(categories='auto')
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assert_no_warnings(enc.fit, X)
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enc = OneHotEncoder(categories='auto')
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assert_no_warnings(enc.fit_transform, X)
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X_trans = enc.fit_transform(X).toarray()
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assert_array_equal(X_trans, res)
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# check there is also a warning if the default is passed
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enc = OneHotEncoder(n_values='auto', handle_unknown='ignore')
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assert_warns(DeprecationWarning, enc.fit, X)
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X = np.array([['cat1', 'cat2']], dtype=object).T
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enc = OneHotEncoder(categorical_features='all')
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assert_warns(DeprecationWarning, enc.fit, X)
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def test_one_hot_encoder_force_new_behaviour():
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# ambiguous integer case (non secutive range of categories)
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X = np.array([[1, 2]]).T
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X2 = np.array([[0, 1]]).T
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# without argument -> by default using legacy behaviour with warnings
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enc = OneHotEncoder()
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with ignore_warnings(category=FutureWarning):
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enc.fit(X)
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res = enc.transform(X2)
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exp = np.array([[0, 0], [1, 0]])
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assert_array_equal(res.toarray(), exp)
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# with explicit auto argument -> don't use legacy behaviour
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# (so will raise an error on unseen value within range)
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enc = OneHotEncoder(categories='auto')
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enc.fit(X)
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assert_raises(ValueError, enc.transform, X2)
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def _run_one_hot(X, X2, cat):
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# enc = assert_warns(
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# DeprecationWarning,
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# OneHotEncoder, categorical_features=cat)
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enc = OneHotEncoder(categorical_features=cat)
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with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
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Xtr = enc.fit_transform(X)
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with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
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X2tr = enc.fit(X).transform(X2)
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return Xtr, X2tr
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def _check_one_hot(X, X2, cat, n_features):
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ind = np.where(cat)[0]
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# With mask
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A, B = _run_one_hot(X, X2, cat)
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# With indices
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C, D = _run_one_hot(X, X2, ind)
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# Check shape
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assert_equal(A.shape, (2, n_features))
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assert_equal(B.shape, (1, n_features))
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assert_equal(C.shape, (2, n_features))
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assert_equal(D.shape, (1, n_features))
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# Check that mask and indices give the same results
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assert_array_equal(toarray(A), toarray(C))
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assert_array_equal(toarray(B), toarray(D))
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def test_one_hot_encoder_categorical_features():
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X = np.array([[3, 2, 1], [0, 1, 1]])
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X2 = np.array([[1, 1, 1]])
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cat = [True, False, False]
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_check_one_hot(X, X2, cat, 4)
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# Edge case: all non-categorical
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cat = [False, False, False]
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_check_one_hot(X, X2, cat, 3)
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# Edge case: all categorical
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cat = [True, True, True]
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_check_one_hot(X, X2, cat, 5)
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# check error raised if also specifying categories
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oh = OneHotEncoder(categories=[range(3)],
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categorical_features=[True, False, False])
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assert_raises(ValueError, oh.fit, X)
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def test_one_hot_encoder_categorical_features_ignore_unknown():
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# GH12881 bug in combination of categorical_features with ignore
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X = np.array([[1, 2, 3], [4, 5, 6], [2, 3, 2]]).T
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oh = OneHotEncoder(categorical_features=[2], handle_unknown='ignore')
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with ignore_warnings(category=DeprecationWarning):
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res = oh.fit_transform(X)
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expected = np.array([[1, 0, 1], [0, 1, 0], [1, 2, 3], [4, 5, 6]]).T
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assert_array_equal(res.toarray(), expected)
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def test_one_hot_encoder_handle_unknown():
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X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
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X2 = np.array([[4, 1, 1]])
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# Test that one hot encoder raises error for unknown features
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# present during transform.
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oh = OneHotEncoder(handle_unknown='error')
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assert_warns(FutureWarning, oh.fit, X)
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assert_raises(ValueError, oh.transform, X2)
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# Test the ignore option, ignores unknown features (giving all 0's)
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oh = OneHotEncoder(handle_unknown='ignore')
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oh.fit(X)
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X2_passed = X2.copy()
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assert_array_equal(
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oh.transform(X2_passed).toarray(),
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np.array([[0., 0., 0., 0., 1., 0., 0.]]))
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# ensure transformed data was not modified in place
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assert_allclose(X2, X2_passed)
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# Raise error if handle_unknown is neither ignore or error.
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oh = OneHotEncoder(handle_unknown='42')
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assert_raises(ValueError, oh.fit, X)
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def test_one_hot_encoder_not_fitted():
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X = np.array([['a'], ['b']])
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enc = OneHotEncoder(categories=['a', 'b'])
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msg = ("This OneHotEncoder instance is not fitted yet. "
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"Call 'fit' with appropriate arguments before using this method.")
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with pytest.raises(NotFittedError, match=msg):
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enc.transform(X)
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def test_one_hot_encoder_no_categorical_features():
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X = np.array([[3, 2, 1], [0, 1, 1]], dtype='float64')
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cat = [False, False, False]
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enc = OneHotEncoder(categorical_features=cat)
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with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
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X_tr = enc.fit_transform(X)
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expected_features = np.array(list(), dtype='object')
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assert_array_equal(X, X_tr)
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assert_array_equal(enc.get_feature_names(), expected_features)
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assert enc.categories_ == []
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def test_one_hot_encoder_handle_unknown_strings():
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X = np.array(['11111111', '22', '333', '4444']).reshape((-1, 1))
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X2 = np.array(['55555', '22']).reshape((-1, 1))
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# Non Regression test for the issue #12470
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# Test the ignore option, when categories are numpy string dtype
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# particularly when the known category strings are larger
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# than the unknown category strings
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oh = OneHotEncoder(handle_unknown='ignore')
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oh.fit(X)
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X2_passed = X2.copy()
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assert_array_equal(
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oh.transform(X2_passed).toarray(),
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np.array([[0., 0., 0., 0.], [0., 1., 0., 0.]]))
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# ensure transformed data was not modified in place
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assert_array_equal(X2, X2_passed)
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@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
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@pytest.mark.parametrize("input_dtype", [np.int32, np.float32, np.float64])
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def test_one_hot_encoder_dtype(input_dtype, output_dtype):
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X = np.asarray([[0, 1]], dtype=input_dtype).T
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X_expected = np.asarray([[1, 0], [0, 1]], dtype=output_dtype)
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oh = OneHotEncoder(categories='auto', dtype=output_dtype)
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assert_array_equal(oh.fit_transform(X).toarray(), X_expected)
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assert_array_equal(oh.fit(X).transform(X).toarray(), X_expected)
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oh = OneHotEncoder(categories='auto', dtype=output_dtype, sparse=False)
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assert_array_equal(oh.fit_transform(X), X_expected)
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assert_array_equal(oh.fit(X).transform(X), X_expected)
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@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
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def test_one_hot_encoder_dtype_pandas(output_dtype):
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pd = pytest.importorskip('pandas')
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X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
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X_expected = np.array([[1, 0, 1, 0], [0, 1, 0, 1]], dtype=output_dtype)
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oh = OneHotEncoder(dtype=output_dtype)
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assert_array_equal(oh.fit_transform(X_df).toarray(), X_expected)
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assert_array_equal(oh.fit(X_df).transform(X_df).toarray(), X_expected)
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oh = OneHotEncoder(dtype=output_dtype, sparse=False)
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assert_array_equal(oh.fit_transform(X_df), X_expected)
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assert_array_equal(oh.fit(X_df).transform(X_df), X_expected)
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def test_one_hot_encoder_set_params():
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X = np.array([[1, 2]]).T
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oh = OneHotEncoder()
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# set params on not yet fitted object
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oh.set_params(categories=[[0, 1, 2, 3]])
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assert oh.get_params()['categories'] == [[0, 1, 2, 3]]
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assert oh.fit_transform(X).toarray().shape == (2, 4)
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# set params on already fitted object
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oh.set_params(categories=[[0, 1, 2, 3, 4]])
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assert oh.fit_transform(X).toarray().shape == (2, 5)
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def check_categorical_onehot(X):
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enc = OneHotEncoder(categories='auto')
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Xtr1 = enc.fit_transform(X)
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enc = OneHotEncoder(categories='auto', sparse=False)
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Xtr2 = enc.fit_transform(X)
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assert_allclose(Xtr1.toarray(), Xtr2)
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assert sparse.isspmatrix_csr(Xtr1)
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return Xtr1.toarray()
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@pytest.mark.parametrize("X", [
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[['def', 1, 55], ['abc', 2, 55]],
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np.array([[10, 1, 55], [5, 2, 55]]),
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np.array([['b', 'A', 'cat'], ['a', 'B', 'cat']], dtype=object)
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], ids=['mixed', 'numeric', 'object'])
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def test_one_hot_encoder(X):
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Xtr = check_categorical_onehot(np.array(X)[:, [0]])
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assert_allclose(Xtr, [[0, 1], [1, 0]])
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Xtr = check_categorical_onehot(np.array(X)[:, [0, 1]])
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assert_allclose(Xtr, [[0, 1, 1, 0], [1, 0, 0, 1]])
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Xtr = OneHotEncoder(categories='auto').fit_transform(X)
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assert_allclose(Xtr.toarray(), [[0, 1, 1, 0, 1], [1, 0, 0, 1, 1]])
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def test_one_hot_encoder_inverse():
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for sparse_ in [True, False]:
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X = [['abc', 2, 55], ['def', 1, 55], ['abc', 3, 55]]
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enc = OneHotEncoder(sparse=sparse_)
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X_tr = enc.fit_transform(X)
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exp = np.array(X, dtype=object)
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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X = [[2, 55], [1, 55], [3, 55]]
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enc = OneHotEncoder(sparse=sparse_, categories='auto')
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X_tr = enc.fit_transform(X)
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exp = np.array(X)
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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# with unknown categories
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X = [['abc', 2, 55], ['def', 1, 55], ['abc', 3, 55]]
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enc = OneHotEncoder(sparse=sparse_, handle_unknown='ignore',
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categories=[['abc', 'def'], [1, 2],
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[54, 55, 56]])
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X_tr = enc.fit_transform(X)
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exp = np.array(X, dtype=object)
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exp[2, 1] = None
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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# with an otherwise numerical output, still object if unknown
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X = [[2, 55], [1, 55], [3, 55]]
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enc = OneHotEncoder(sparse=sparse_, categories=[[1, 2], [54, 56]],
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handle_unknown='ignore')
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X_tr = enc.fit_transform(X)
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exp = np.array(X, dtype=object)
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exp[2, 0] = None
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exp[:, 1] = None
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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# incorrect shape raises
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X_tr = np.array([[0, 1, 1], [1, 0, 1]])
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msg = re.escape('Shape of the passed X data is not correct')
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assert_raises_regex(ValueError, msg, enc.inverse_transform, X_tr)
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@pytest.mark.parametrize("X, cat_exp, cat_dtype", [
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([['abc', 55], ['def', 55]], [['abc', 'def'], [55]], np.object_),
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(np.array([[1, 2], [3, 2]]), [[1, 3], [2]], np.integer),
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(np.array([['A', 'cat'], ['B', 'cat']], dtype=object),
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[['A', 'B'], ['cat']], np.object_),
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(np.array([['A', 'cat'], ['B', 'cat']]),
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[['A', 'B'], ['cat']], np.str_)
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], ids=['mixed', 'numeric', 'object', 'string'])
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def test_one_hot_encoder_categories(X, cat_exp, cat_dtype):
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# order of categories should not depend on order of samples
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for Xi in [X, X[::-1]]:
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enc = OneHotEncoder(categories='auto')
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enc.fit(Xi)
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# assert enc.categories == 'auto'
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assert isinstance(enc.categories_, list)
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for res, exp in zip(enc.categories_, cat_exp):
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assert res.tolist() == exp
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assert np.issubdtype(res.dtype, cat_dtype)
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|
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@pytest.mark.parametrize("X, X2, cats, cat_dtype", [
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(np.array([['a', 'b']], dtype=object).T,
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np.array([['a', 'd']], dtype=object).T,
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[['a', 'b', 'c']], np.object_),
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(np.array([[1, 2]], dtype='int64').T,
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np.array([[1, 4]], dtype='int64').T,
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[[1, 2, 3]], np.int64),
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(np.array([['a', 'b']], dtype=object).T,
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np.array([['a', 'd']], dtype=object).T,
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[np.array(['a', 'b', 'c'])], np.object_),
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], ids=['object', 'numeric', 'object-string-cat'])
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def test_one_hot_encoder_specified_categories(X, X2, cats, cat_dtype):
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enc = OneHotEncoder(categories=cats)
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exp = np.array([[1., 0., 0.],
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[0., 1., 0.]])
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assert_array_equal(enc.fit_transform(X).toarray(), exp)
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assert list(enc.categories[0]) == list(cats[0])
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assert enc.categories_[0].tolist() == list(cats[0])
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# manually specified categories should have same dtype as
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# the data when coerced from lists
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assert enc.categories_[0].dtype == cat_dtype
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|
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# when specifying categories manually, unknown categories should already
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# raise when fitting
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enc = OneHotEncoder(categories=cats)
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with pytest.raises(ValueError, match="Found unknown categories"):
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enc.fit(X2)
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enc = OneHotEncoder(categories=cats, handle_unknown='ignore')
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exp = np.array([[1., 0., 0.], [0., 0., 0.]])
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assert_array_equal(enc.fit(X2).transform(X2).toarray(), exp)
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def test_one_hot_encoder_unsorted_categories():
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X = np.array([['a', 'b']], dtype=object).T
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enc = OneHotEncoder(categories=[['b', 'a', 'c']])
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exp = np.array([[0., 1., 0.],
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[1., 0., 0.]])
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assert_array_equal(enc.fit(X).transform(X).toarray(), exp)
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assert_array_equal(enc.fit_transform(X).toarray(), exp)
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assert enc.categories_[0].tolist() == ['b', 'a', 'c']
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assert np.issubdtype(enc.categories_[0].dtype, np.object_)
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# unsorted passed categories still raise for numerical values
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X = np.array([[1, 2]]).T
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enc = OneHotEncoder(categories=[[2, 1, 3]])
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msg = 'Unsorted categories are not supported'
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with pytest.raises(ValueError, match=msg):
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enc.fit_transform(X)
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|
|
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def test_one_hot_encoder_specified_categories_mixed_columns():
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# multiple columns
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X = np.array([['a', 'b'], [0, 2]], dtype=object).T
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enc = OneHotEncoder(categories=[['a', 'b', 'c'], [0, 1, 2]])
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exp = np.array([[1., 0., 0., 1., 0., 0.],
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[0., 1., 0., 0., 0., 1.]])
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assert_array_equal(enc.fit_transform(X).toarray(), exp)
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assert enc.categories_[0].tolist() == ['a', 'b', 'c']
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assert np.issubdtype(enc.categories_[0].dtype, np.object_)
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assert enc.categories_[1].tolist() == [0, 1, 2]
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# integer categories but from object dtype data
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assert np.issubdtype(enc.categories_[1].dtype, np.object_)
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|
|
|
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def test_one_hot_encoder_pandas():
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pd = pytest.importorskip('pandas')
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|
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X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
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|
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Xtr = check_categorical_onehot(X_df)
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assert_allclose(Xtr, [[1, 0, 1, 0], [0, 1, 0, 1]])
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|
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|
def test_one_hot_encoder_feature_names():
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enc = OneHotEncoder()
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X = [['Male', 1, 'girl', 2, 3],
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['Female', 41, 'girl', 1, 10],
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['Male', 51, 'boy', 12, 3],
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['Male', 91, 'girl', 21, 30]]
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|
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enc.fit(X)
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feature_names = enc.get_feature_names()
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assert isinstance(feature_names, np.ndarray)
|
|
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|
assert_array_equal(['x0_Female', 'x0_Male',
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'x1_1', 'x1_41', 'x1_51', 'x1_91',
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'x2_boy', 'x2_girl',
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'x3_1', 'x3_2', 'x3_12', 'x3_21',
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'x4_3',
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'x4_10', 'x4_30'], feature_names)
|
|
|
|
feature_names2 = enc.get_feature_names(['one', 'two',
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'three', 'four', 'five'])
|
|
|
|
assert_array_equal(['one_Female', 'one_Male',
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|
'two_1', 'two_41', 'two_51', 'two_91',
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|
'three_boy', 'three_girl',
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|
'four_1', 'four_2', 'four_12', 'four_21',
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'five_3', 'five_10', 'five_30'], feature_names2)
|
|
|
|
with pytest.raises(ValueError, match="input_features should have length"):
|
|
enc.get_feature_names(['one', 'two'])
|
|
|
|
|
|
def test_one_hot_encoder_feature_names_unicode():
|
|
enc = OneHotEncoder()
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|
X = np.array([[u'c❤t1', u'dat2']], dtype=object).T
|
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enc.fit(X)
|
|
feature_names = enc.get_feature_names()
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|
assert_array_equal([u'x0_c❤t1', u'x0_dat2'], feature_names)
|
|
feature_names = enc.get_feature_names(input_features=[u'n👍me'])
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|
assert_array_equal([u'n👍me_c❤t1', u'n👍me_dat2'], feature_names)
|
|
|
|
|
|
@pytest.mark.parametrize("X", [np.array([[1, np.nan]]).T,
|
|
np.array([['a', np.nan]], dtype=object).T],
|
|
ids=['numeric', 'object'])
|
|
@pytest.mark.parametrize("handle_unknown", ['error', 'ignore'])
|
|
def test_one_hot_encoder_raise_missing(X, handle_unknown):
|
|
ohe = OneHotEncoder(categories='auto', handle_unknown=handle_unknown)
|
|
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
|
ohe.fit(X)
|
|
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
|
ohe.fit_transform(X)
|
|
|
|
ohe.fit(X[:1, :])
|
|
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
|
ohe.transform(X)
|
|
|
|
|
|
@pytest.mark.parametrize("X", [
|
|
[['abc', 2, 55], ['def', 1, 55]],
|
|
np.array([[10, 2, 55], [20, 1, 55]]),
|
|
np.array([['a', 'B', 'cat'], ['b', 'A', 'cat']], dtype=object)
|
|
], ids=['mixed', 'numeric', 'object'])
|
|
def test_ordinal_encoder(X):
|
|
enc = OrdinalEncoder()
|
|
exp = np.array([[0, 1, 0],
|
|
[1, 0, 0]], dtype='int64')
|
|
assert_array_equal(enc.fit_transform(X), exp.astype('float64'))
|
|
enc = OrdinalEncoder(dtype='int64')
|
|
assert_array_equal(enc.fit_transform(X), exp)
|
|
|
|
|
|
@pytest.mark.parametrize("X, X2, cats, cat_dtype", [
|
|
(np.array([['a', 'b']], dtype=object).T,
|
|
np.array([['a', 'd']], dtype=object).T,
|
|
[['a', 'b', 'c']], np.object_),
|
|
(np.array([[1, 2]], dtype='int64').T,
|
|
np.array([[1, 4]], dtype='int64').T,
|
|
[[1, 2, 3]], np.int64),
|
|
(np.array([['a', 'b']], dtype=object).T,
|
|
np.array([['a', 'd']], dtype=object).T,
|
|
[np.array(['a', 'b', 'c'])], np.object_),
|
|
], ids=['object', 'numeric', 'object-string-cat'])
|
|
def test_ordinal_encoder_specified_categories(X, X2, cats, cat_dtype):
|
|
enc = OrdinalEncoder(categories=cats)
|
|
exp = np.array([[0.], [1.]])
|
|
assert_array_equal(enc.fit_transform(X), exp)
|
|
assert list(enc.categories[0]) == list(cats[0])
|
|
assert enc.categories_[0].tolist() == list(cats[0])
|
|
# manually specified categories should have same dtype as
|
|
# the data when coerced from lists
|
|
assert enc.categories_[0].dtype == cat_dtype
|
|
|
|
# when specifying categories manually, unknown categories should already
|
|
# raise when fitting
|
|
enc = OrdinalEncoder(categories=cats)
|
|
with pytest.raises(ValueError, match="Found unknown categories"):
|
|
enc.fit(X2)
|
|
|
|
|
|
def test_ordinal_encoder_inverse():
|
|
X = [['abc', 2, 55], ['def', 1, 55]]
|
|
enc = OrdinalEncoder()
|
|
X_tr = enc.fit_transform(X)
|
|
exp = np.array(X, dtype=object)
|
|
assert_array_equal(enc.inverse_transform(X_tr), exp)
|
|
|
|
# incorrect shape raises
|
|
X_tr = np.array([[0, 1, 1, 2], [1, 0, 1, 0]])
|
|
msg = re.escape('Shape of the passed X data is not correct')
|
|
assert_raises_regex(ValueError, msg, enc.inverse_transform, X_tr)
|
|
|
|
|
|
@pytest.mark.parametrize("X", [np.array([[1, np.nan]]).T,
|
|
np.array([['a', np.nan]], dtype=object).T],
|
|
ids=['numeric', 'object'])
|
|
def test_ordinal_encoder_raise_missing(X):
|
|
ohe = OrdinalEncoder()
|
|
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
|
ohe.fit(X)
|
|
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
|
ohe.fit_transform(X)
|
|
|
|
ohe.fit(X[:1, :])
|
|
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
|
ohe.transform(X)
|
|
|
|
|
|
def test_encoder_dtypes():
|
|
# check that dtypes are preserved when determining categories
|
|
enc = OneHotEncoder(categories='auto')
|
|
exp = np.array([[1., 0., 1., 0.], [0., 1., 0., 1.]], dtype='float64')
|
|
|
|
for X in [np.array([[1, 2], [3, 4]], dtype='int64'),
|
|
np.array([[1, 2], [3, 4]], dtype='float64'),
|
|
np.array([['a', 'b'], ['c', 'd']]), # string dtype
|
|
np.array([[1, 'a'], [3, 'b']], dtype='object')]:
|
|
enc.fit(X)
|
|
assert all([enc.categories_[i].dtype == X.dtype for i in range(2)])
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
|
|
|
X = [[1, 2], [3, 4]]
|
|
enc.fit(X)
|
|
assert all([np.issubdtype(enc.categories_[i].dtype, np.integer)
|
|
for i in range(2)])
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
|
|
|
X = [[1, 'a'], [3, 'b']]
|
|
enc.fit(X)
|
|
assert all([enc.categories_[i].dtype == 'object' for i in range(2)])
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
|
|
|
|
|
def test_encoder_dtypes_pandas():
|
|
# check dtype (similar to test_categorical_encoder_dtypes for dataframes)
|
|
pd = pytest.importorskip('pandas')
|
|
|
|
enc = OneHotEncoder(categories='auto')
|
|
exp = np.array([[1., 0., 1., 0.], [0., 1., 0., 1.]], dtype='float64')
|
|
|
|
X = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, dtype='int64')
|
|
enc.fit(X)
|
|
assert all([enc.categories_[i].dtype == 'int64' for i in range(2)])
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
|
|
|
X = pd.DataFrame({'A': [1, 2], 'B': ['a', 'b']})
|
|
enc.fit(X)
|
|
assert all([enc.categories_[i].dtype == 'object' for i in range(2)])
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
|
|
|
|
|
def test_one_hot_encoder_warning():
|
|
enc = OneHotEncoder()
|
|
X = [['Male', 1], ['Female', 3]]
|
|
np.testing.assert_no_warnings(enc.fit_transform, X)
|