# -*- coding: utf-8 -*-
import re
import numpy as np
from scipy import sparse
import pytest
from sklearn.exceptions import NotFittedError
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_allclose
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
def test_one_hot_encoder_sparse_dense():
# check that sparse and dense will give the same results
X = np.array([[3, 2, 1], [0, 1, 1]])
enc_sparse = OneHotEncoder()
enc_dense = OneHotEncoder(sparse=False)
X_trans_sparse = enc_sparse.fit_transform(X)
X_trans_dense = enc_dense.fit_transform(X)
assert X_trans_sparse.shape == (2, 5)
assert X_trans_dense.shape == (2, 5)
assert sparse.issparse(X_trans_sparse)
assert not sparse.issparse(X_trans_dense)
# check outcome
assert_array_equal(X_trans_sparse.toarray(), [[0., 1., 0., 1., 1.],
[1., 0., 1., 0., 1.]])
assert_array_equal(X_trans_sparse.toarray(), X_trans_dense)
def test_one_hot_encoder_diff_n_features():
X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
X2 = np.array([[1, 0]])
enc = OneHotEncoder()
enc.fit(X)
err_msg = ("The number of features in X is different to the number of "
"features of the fitted data.")
with pytest.raises(ValueError, match=err_msg):
enc.transform(X2)
def test_one_hot_encoder_handle_unknown():
X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
X2 = np.array([[4, 1, 1]])
# Test that one hot encoder raises error for unknown features
# present during transform.
oh = OneHotEncoder(handle_unknown='error')
oh.fit(X)
with pytest.raises(ValueError, match='Found unknown categories'):
oh.transform(X2)
# Test the ignore option, ignores unknown features (giving all 0's)
oh = OneHotEncoder(handle_unknown='ignore')
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0., 0., 0., 0., 1., 0., 0.]]))
# ensure transformed data was not modified in place
assert_allclose(X2, X2_passed)
# Raise error if handle_unknown is neither ignore or error.
oh = OneHotEncoder(handle_unknown='42')
with pytest.raises(ValueError, match='handle_unknown should be either'):
oh.fit(X)
def test_one_hot_encoder_not_fitted():
X = np.array([['a'], ['b']])
enc = OneHotEncoder(categories=['a', 'b'])
msg = ("This OneHotEncoder instance is not fitted yet. "
"Call 'fit' with appropriate arguments before using this "
"estimator.")
with pytest.raises(NotFittedError, match=msg):
enc.transform(X)
def test_one_hot_encoder_handle_unknown_strings():
X = np.array(['11111111', '22', '333', '4444']).reshape((-1, 1))
X2 = np.array(['55555', '22']).reshape((-1, 1))
# Non Regression test for the issue #12470
# Test the ignore option, when categories are numpy string dtype
# particularly when the known category strings are larger
# than the unknown category strings
oh = OneHotEncoder(handle_unknown='ignore')
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0., 0., 0., 0.], [0., 1., 0., 0.]]))
# ensure transformed data was not modified in place
assert_array_equal(X2, X2_passed)
@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
@pytest.mark.parametrize("input_dtype", [np.int32, np.float32, np.float64])
def test_one_hot_encoder_dtype(input_dtype, output_dtype):
X = np.asarray([[0, 1]], dtype=input_dtype).T
X_expected = np.asarray([[1, 0], [0, 1]], dtype=output_dtype)
oh = OneHotEncoder(categories='auto', dtype=output_dtype)
assert_array_equal(oh.fit_transform(X).toarray(), X_expected)
assert_array_equal(oh.fit(X).transform(X).toarray(), X_expected)
oh = OneHotEncoder(categories='auto', dtype=output_dtype, sparse=False)
assert_array_equal(oh.fit_transform(X), X_expected)
assert_array_equal(oh.fit(X).transform(X), X_expected)
@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
def test_one_hot_encoder_dtype_pandas(output_dtype):
pd = pytest.importorskip('pandas')
X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
X_expected = np.array([[1, 0, 1, 0], [0, 1, 0, 1]], dtype=output_dtype)
oh = OneHotEncoder(dtype=output_dtype)
assert_array_equal(oh.fit_transform(X_df).toarray(), X_expected)
assert_array_equal(oh.fit(X_df).transform(X_df).toarray(), X_expected)
oh = OneHotEncoder(dtype=output_dtype, sparse=False)
assert_array_equal(oh.fit_transform(X_df), X_expected)
assert_array_equal(oh.fit(X_df).transform(X_df), X_expected)
def test_one_hot_encoder_feature_names():
enc = OneHotEncoder()
X = [['Male', 1, 'girl', 2, 3],
['Female', 41, 'girl', 1, 10],
['Male', 51, 'boy', 12, 3],
['Male', 91, 'girl', 21, 30]]
enc.fit(X)
feature_names = enc.get_feature_names()
assert isinstance(feature_names, np.ndarray)
assert_array_equal(['x0_Female', 'x0_Male',
'x1_1', 'x1_41', 'x1_51', 'x1_91',
'x2_boy', 'x2_girl',
'x3_1', 'x3_2', 'x3_12', 'x3_21',
'x4_3',
'x4_10', 'x4_30'], feature_names)
feature_names2 = enc.get_feature_names(['one', 'two',
'three', 'four', 'five'])
assert_array_equal(['one_Female', 'one_Male',
'two_1', 'two_41', 'two_51', 'two_91',
'three_boy', 'three_girl',
'four_1', 'four_2', 'four_12', 'four_21',
'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()
X = np.array([['c❤t1', 'dat2']], dtype=object).T
enc.fit(X)
feature_names = enc.get_feature_names()
assert_array_equal(['x0_c❤t1', 'x0_dat2'], feature_names)
feature_names = enc.get_feature_names(input_features=['n👍me'])
assert_array_equal(['n👍me_c❤t1', 'n👍me_dat2'], feature_names)
def test_one_hot_encoder_set_params():
X = np.array([[1, 2]]).T
oh = OneHotEncoder()
# set params on not yet fitted object
oh.set_params(categories=[[0, 1, 2, 3]])
assert oh.get_params()['categories'] == [[0, 1, 2, 3]]
assert oh.fit_transform(X).toarray().shape == (2, 4)
# set params on already fitted object
oh.set_params(categories=[[0, 1, 2, 3, 4]])
assert oh.fit_transform(X).toarray().shape == (2, 5)
def check_categorical_onehot(X):
enc = OneHotEncoder(categories='auto')
Xtr1 = enc.fit_transform(X)
enc = OneHotEncoder(categories='auto', sparse=False)
Xtr2 = enc.fit_transform(X)
assert_allclose(Xtr1.toarray(), Xtr2)
assert sparse.isspmatrix_csr(Xtr1)
return Xtr1.toarray()
@pytest.mark.parametrize("X", [
[['def', 1, 55], ['abc', 2, 55]],
np.array([[10, 1, 55], [5, 2, 55]]),
np.array([['b', 'A', 'cat'], ['a', 'B', 'cat']], dtype=object)
], ids=['mixed', 'numeric', 'object'])
def test_one_hot_encoder(X):
Xtr = check_categorical_onehot(np.array(X)[:, [0]])
assert_allclose(Xtr, [[0, 1], [1, 0]])
Xtr = check_categorical_onehot(np.array(X)[:, [0, 1]])
assert_allclose(Xtr, [[0, 1, 1, 0], [1, 0, 0, 1]])
Xtr = OneHotEncoder(categories='auto').fit_transform(X)
assert_allclose(Xtr.toarray(), [[0, 1, 1, 0, 1], [1, 0, 0, 1, 1]])
@pytest.mark.parametrize('sparse_', [False, True])
@pytest.mark.parametrize('drop', [None, 'first'])
def test_one_hot_encoder_inverse(sparse_, drop):
X = [['abc', 2, 55], ['def', 1, 55], ['abc', 3, 55]]
enc = OneHotEncoder(sparse=sparse_, drop=drop)
X_tr = enc.fit_transform(X)
exp = np.array(X, dtype=object)
assert_array_equal(enc.inverse_transform(X_tr), exp)
X = [[2, 55], [1, 55], [3, 55]]
enc = OneHotEncoder(sparse=sparse_, categories='auto',
drop=drop)
X_tr = enc.fit_transform(X)
exp = np.array(X)
assert_array_equal(enc.inverse_transform(X_tr), exp)
if drop is None:
# with unknown categories
# drop is incompatible with handle_unknown=ignore
X = [['abc', 2, 55], ['def', 1, 55], ['abc', 3, 55]]
enc = OneHotEncoder(sparse=sparse_, handle_unknown='ignore',
categories=[['abc', 'def'], [1, 2],
[54, 55, 56]])
X_tr = enc.fit_transform(X)
exp = np.array(X, dtype=object)
exp[2, 1] = None
assert_array_equal(enc.inverse_transform(X_tr), exp)
# with an otherwise numerical output, still object if unknown
X = [[2, 55], [1, 55], [3, 55]]
enc = OneHotEncoder(sparse=sparse_, categories=[[1, 2], [54, 56]],
handle_unknown='ignore')
X_tr = enc.fit_transform(X)
exp = np.array(X, dtype=object)
exp[2, 0] = None
exp[:, 1] = None
assert_array_equal(enc.inverse_transform(X_tr), exp)
# incorrect shape raises
X_tr = np.array([[0, 1, 1], [1, 0, 1]])
msg = re.escape('Shape of the passed X data is not correct')
with pytest.raises(ValueError, match=msg):
enc.inverse_transform(X_tr)
def test_one_hot_encoder_inverse_if_binary():
X = np.array([['Male', 1],
['Female', 3],
['Female', 2]], dtype=object)
ohe = OneHotEncoder(drop='if_binary', sparse=False)
X_tr = ohe.fit_transform(X)
assert_array_equal(ohe.inverse_transform(X_tr), X)
# check that resetting drop option without refitting does not throw an error
@pytest.mark.parametrize('drop', ['if_binary', 'first', None])
@pytest.mark.parametrize('reset_drop', ['if_binary', 'first', None])
def test_one_hot_encoder_drop_reset(drop, reset_drop):
X = np.array([['Male', 1],
['Female', 3],
['Female', 2]], dtype=object)
ohe = OneHotEncoder(drop=drop, sparse=False)
ohe.fit(X)
X_tr = ohe.transform(X)
feature_names = ohe.get_feature_names()
ohe.set_params(drop=reset_drop)
assert_array_equal(ohe.inverse_transform(X_tr), X)
assert_allclose(ohe.transform(X), X_tr)
assert_array_equal(ohe.get_feature_names(), feature_names)
@pytest.mark.parametrize("method", ['fit', 'fit_transform'])
@pytest.mark.parametrize("X", [
[1, 2],
np.array([3., 4.])
])
def test_X_is_not_1D(X, method):
oh = OneHotEncoder()
msg = ("Expected 2D array, got 1D array instead")
with pytest.raises(ValueError, match=msg):
getattr(oh, method)(X)
@pytest.mark.parametrize("method", ['fit', 'fit_transform'])
def test_X_is_not_1D_pandas(method):
pd = pytest.importorskip('pandas')
X = pd.Series([6, 3, 4, 6])
oh = OneHotEncoder()
msg = ("Expected 2D array, got 1D array instead")
with pytest.raises(ValueError, match=msg):
getattr(oh, method)(X)
@pytest.mark.parametrize("X, cat_exp, cat_dtype", [
([['abc', 55], ['def', 55]], [['abc', 'def'], [55]], np.object_),
(np.array([[1, 2], [3, 2]]), [[1, 3], [2]], np.integer),
(np.array([['A', 'cat'], ['B', 'cat']], dtype=object),
[['A', 'B'], ['cat']], np.object_),
(np.array([['A', 'cat'], ['B', 'cat']]),
[['A', 'B'], ['cat']], np.str_)
], ids=['mixed', 'numeric', 'object', 'string'])
def test_one_hot_encoder_categories(X, cat_exp, cat_dtype):
# order of categories should not depend on order of samples
for Xi in [X, X[::-1]]:
enc = OneHotEncoder(categories='auto')
enc.fit(Xi)
# assert enc.categories == 'auto'
assert isinstance(enc.categories_, list)
for res, exp in zip(enc.categories_, cat_exp):
assert res.tolist() == exp
assert np.issubdtype(res.dtype, cat_dtype)
@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_one_hot_encoder_specified_categories(X, X2, cats, cat_dtype):
enc = OneHotEncoder(categories=cats)
exp = np.array([[1., 0., 0.],
[0., 1., 0.]])
assert_array_equal(enc.fit_transform(X).toarray(), 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 = OneHotEncoder(categories=cats)
with pytest.raises(ValueError, match="Found unknown categories"):
enc.fit(X2)
enc = OneHotEncoder(categories=cats, handle_unknown='ignore')
exp = np.array([[1., 0., 0.], [0., 0., 0.]])
assert_array_equal(enc.fit(X2).transform(X2).toarray(), exp)
def test_one_hot_encoder_unsorted_categories():
X = np.array([['a', 'b']], dtype=object).T
enc = OneHotEncoder(categories=[['b', 'a', 'c']])
exp = np.array([[0., 1., 0.],
[1., 0., 0.]])
assert_array_equal(enc.fit(X).transform(X).toarray(), exp)
assert_array_equal(enc.fit_transform(X).toarray(), exp)
assert enc.categories_[0].tolist() == ['b', 'a', 'c']
assert np.issubdtype(enc.categories_[0].dtype, np.object_)
# unsorted passed categories still raise for numerical values
X = np.array([[1, 2]]).T
enc = OneHotEncoder(categories=[[2, 1, 3]])
msg = 'Unsorted categories are not supported'
with pytest.raises(ValueError, match=msg):
enc.fit_transform(X)
def test_one_hot_encoder_specified_categories_mixed_columns():
# multiple columns
X = np.array([['a', 'b'], [0, 2]], dtype=object).T
enc = OneHotEncoder(categories=[['a', 'b', 'c'], [0, 1, 2]])
exp = np.array([[1., 0., 0., 1., 0., 0.],
[0., 1., 0., 0., 0., 1.]])
assert_array_equal(enc.fit_transform(X).toarray(), exp)
assert enc.categories_[0].tolist() == ['a', 'b', 'c']
assert np.issubdtype(enc.categories_[0].dtype, np.object_)
assert enc.categories_[1].tolist() == [0, 1, 2]
# integer categories but from object dtype data
assert np.issubdtype(enc.categories_[1].dtype, np.object_)
def test_one_hot_encoder_pandas():
pd = pytest.importorskip('pandas')
X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
Xtr = check_categorical_onehot(X_df)
assert_allclose(Xtr, [[1, 0, 1, 0], [0, 1, 0, 1]])
@pytest.mark.parametrize("drop, expected_names",
[('first', ['x0_c', 'x2_b']),
('if_binary', ['x0_c', 'x1_2', 'x2_b']),
(['c', 2, 'b'], ['x0_b', 'x2_a'])],
ids=['first', 'binary', 'manual'])
def test_one_hot_encoder_feature_names_drop(drop, expected_names):
X = [['c', 2, 'a'],
['b', 2, 'b']]
ohe = OneHotEncoder(drop=drop)
ohe.fit(X)
feature_names = ohe.get_feature_names()
assert isinstance(feature_names, np.ndarray)
assert_array_equal(expected_names, feature_names)
def test_one_hot_encoder_drop_equals_if_binary():
# Canonical case
X = [[10, 'yes'],
[20, 'no'],
[30, 'yes']]
expected = np.array([[1., 0., 0., 1.],
[0., 1., 0., 0.],
[0., 0., 1., 1.]])
expected_drop_idx = np.array([None, 0])
ohe = OneHotEncoder(drop='if_binary', sparse=False)
result = ohe.fit_transform(X)
assert_array_equal(ohe.drop_idx_, expected_drop_idx)
assert_allclose(result, expected)
# with only one cat, the behaviour is equivalent to drop=None
X = [['true', 'a'],
['false', 'a'],
['false', 'a']]
expected = np.array([[1., 1.],
[0., 1.],
[0., 1.]])
expected_drop_idx = np.array([0, None])
ohe = OneHotEncoder(drop='if_binary', sparse=False)
result = ohe.fit_transform(X)
assert_array_equal(ohe.drop_idx_, expected_drop_idx)
assert_allclose(result, expected)
@pytest.mark.parametrize("X", [np.array([[1, np.nan]]).T,
np.array([['a', np.nan]], dtype=object).T],
ids=['numeric', 'object'])
@pytest.mark.parametrize("as_data_frame", [False, True],
ids=['array', 'dataframe'])
@pytest.mark.parametrize("handle_unknown", ['error', 'ignore'])
def test_one_hot_encoder_raise_missing(X, as_data_frame, handle_unknown):
if as_data_frame:
pd = pytest.importorskip('pandas')
X = pd.DataFrame(X)
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)
if as_data_frame:
X_partial = X.iloc[:1, :]
else:
X_partial = X[:1, :]
ohe.fit(X_partial)
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')
with pytest.raises(ValueError, match=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_ordinal_encoder_raise_categories_shape():
X = np.array([['Low', 'Medium', 'High', 'Medium', 'Low']], dtype=object).T
cats = ['Low', 'Medium', 'High']
enc = OrdinalEncoder(categories=cats)
msg = ("Shape mismatch: if categories is an array,")
with pytest.raises(ValueError, match=msg):
enc.fit(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., 1., 0.],
[0., 1., 0., 1., 0., 1.]], dtype='float64')
X = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}, 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'], 'C': [3., 4.]})
X_type = [X['A'].dtype, X['B'].dtype, X['C'].dtype]
enc.fit(X)
assert all([enc.categories_[i].dtype == X_type[i] for i in range(3)])
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)
def test_one_hot_encoder_drop_manual():
cats_to_drop = ['def', 12, 3, 56]
enc = OneHotEncoder(drop=cats_to_drop)
X = [['abc', 12, 2, 55],
['def', 12, 1, 55],
['def', 12, 3, 56]]
trans = enc.fit_transform(X).toarray()
exp = [[1, 0, 1, 1],
[0, 1, 0, 1],
[0, 0, 0, 0]]
assert_array_equal(trans, exp)
dropped_cats = [cat[feature]
for cat, feature in zip(enc.categories_,
enc.drop_idx_)]
assert_array_equal(dropped_cats, cats_to_drop)
assert_array_equal(np.array(X, dtype=object),
enc.inverse_transform(trans))
@pytest.mark.parametrize(
"X_fit, params, err_msg",
[([["Male"], ["Female"]], {'drop': 'second'},
"Wrong input for parameter `drop`"),
([["Male"], ["Female"]], {'drop': 'first', 'handle_unknown': 'ignore'},
"`handle_unknown` must be 'error'"),
([['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]],
{'drop': np.asarray('b', dtype=object)},
"Wrong input for parameter `drop`"),
([['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]],
{'drop': ['ghi', 3, 59]},
"The following categories were supposed")]
)
def test_one_hot_encoder_invalid_params(X_fit, params, err_msg):
enc = OneHotEncoder(**params)
with pytest.raises(ValueError, match=err_msg):
enc.fit(X_fit)
@pytest.mark.parametrize('drop', [['abc', 3], ['abc', 3, 41, 'a']])
def test_invalid_drop_length(drop):
enc = OneHotEncoder(drop=drop)
err_msg = "`drop` should have length equal to the number"
with pytest.raises(ValueError, match=err_msg):
enc.fit([['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]])
@pytest.mark.parametrize("density", [True, False],
ids=['sparse', 'dense'])
@pytest.mark.parametrize("drop", ['first',
['a', 2, 'b']],
ids=['first', 'manual'])
def test_categories(density, drop):
ohe_base = OneHotEncoder(sparse=density)
ohe_test = OneHotEncoder(sparse=density, drop=drop)
X = [['c', 1, 'a'],
['a', 2, 'b']]
ohe_base.fit(X)
ohe_test.fit(X)
assert_array_equal(ohe_base.categories_, ohe_test.categories_)
if drop == 'first':
assert_array_equal(ohe_test.drop_idx_, 0)
else:
for drop_cat, drop_idx, cat_list in zip(drop,
ohe_test.drop_idx_,
ohe_test.categories_):
assert cat_list[int(drop_idx)] == drop_cat
assert isinstance(ohe_test.drop_idx_, np.ndarray)
assert ohe_test.drop_idx_.dtype == np.object
@pytest.mark.parametrize('Encoder', [OneHotEncoder, OrdinalEncoder])
def test_encoders_has_categorical_tags(Encoder):
assert 'categorical' in Encoder()._get_tags()['X_types']
@pytest.mark.parametrize('Encoder', [OneHotEncoder, OrdinalEncoder])
def test_encoders_does_not_support_none_values(Encoder):
values = [["a"], [None]]
with pytest.raises(TypeError, match="Encoders require their input to be "
"uniformly strings or numbers."):
Encoder().fit(values)