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ai-content-maker/.venv/Lib/site-packages/sklearn/preprocessing/tests/test_encoders.py

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first commit
2024-05-03 04:18:51 +03:00
import re
import numpy as np
import pytest
from scipy import sparse
from sklearn.exceptions import NotFittedError
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.utils import is_scalar_nan
from sklearn.utils._testing import (
_convert_container,
assert_allclose,
assert_array_equal,
)
from sklearn.utils.fixes import CSR_CONTAINERS
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_output=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(), [[0.0, 1.0, 0.0, 1.0, 1.0], [1.0, 0.0, 1.0, 0.0, 1.0]]
)
assert_array_equal(X_trans_sparse.toarray(), X_trans_dense)
@pytest.mark.parametrize("handle_unknown", ["ignore", "infrequent_if_exist"])
def test_one_hot_encoder_handle_unknown(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=handle_unknown)
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]]),
)
# ensure transformed data was not modified in place
assert_allclose(X2, X2_passed)
@pytest.mark.parametrize("handle_unknown", ["ignore", "infrequent_if_exist"])
def test_one_hot_encoder_handle_unknown_strings(handle_unknown):
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=handle_unknown)
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 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_output=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_output=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_out()
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_out(["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_out(["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_out()
assert_array_equal(["x0_c❤t1", "x0_dat2"], feature_names)
feature_names = enc.get_feature_names_out(input_features=["n👍me"])
assert_array_equal(["n👍me_c❤t1", "n👍me_dat2"], feature_names)
def test_one_hot_encoder_custom_feature_name_combiner():
"""Check the behaviour of `feature_name_combiner` as a callable."""
def name_combiner(feature, category):
return feature + "_" + repr(category)
enc = OneHotEncoder(feature_name_combiner=name_combiner)
X = np.array([["None", None]], dtype=object).T
enc.fit(X)
feature_names = enc.get_feature_names_out()
assert_array_equal(["x0_'None'", "x0_None"], feature_names)
feature_names = enc.get_feature_names_out(input_features=["a"])
assert_array_equal(["a_'None'", "a_None"], feature_names)
def wrong_combiner(feature, category):
# we should be returning a Python string
return 0
enc = OneHotEncoder(feature_name_combiner=wrong_combiner).fit(X)
err_msg = (
"When `feature_name_combiner` is a callable, it should return a Python string."
)
with pytest.raises(TypeError, match=err_msg):
enc.get_feature_names_out()
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_output=False)
Xtr2 = enc.fit_transform(X)
assert_allclose(Xtr1.toarray(), Xtr2)
assert sparse.issparse(Xtr1) and Xtr1.format == "csr"
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),
np.array([["b", 1, "cat"], ["a", np.nan, "cat"]], dtype=object),
np.array([["b", 1, "cat"], ["a", float("nan"), "cat"]], dtype=object),
np.array([[None, 1, "cat"], ["a", 2, "cat"]], dtype=object),
np.array([[None, 1, None], ["a", np.nan, None]], dtype=object),
np.array([[None, 1, None], ["a", float("nan"), None]], dtype=object),
],
ids=[
"mixed",
"numeric",
"object",
"mixed-nan",
"mixed-float-nan",
"mixed-None",
"mixed-None-nan",
"mixed-None-float-nan",
],
)
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("handle_unknown", ["ignore", "infrequent_if_exist"])
@pytest.mark.parametrize("sparse_", [False, True])
@pytest.mark.parametrize("drop", [None, "first"])
def test_one_hot_encoder_inverse(handle_unknown, sparse_, drop):
X = [["abc", 2, 55], ["def", 1, 55], ["abc", 3, 55]]
enc = OneHotEncoder(sparse_output=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_output=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_output=sparse_,
handle_unknown=handle_unknown,
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_output=sparse_,
categories=[[1, 2], [54, 56]],
handle_unknown=handle_unknown,
)
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)
@pytest.mark.parametrize("sparse_", [False, True])
@pytest.mark.parametrize(
"X, X_trans",
[
([[2, 55], [1, 55], [2, 55]], [[0, 1, 1], [0, 0, 0], [0, 1, 1]]),
(
[["one", "a"], ["two", "a"], ["three", "b"], ["two", "a"]],
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 1], [0, 1, 0, 0, 0]],
),
],
)
def test_one_hot_encoder_inverse_transform_raise_error_with_unknown(
X, X_trans, sparse_
):
"""Check that `inverse_transform` raise an error with unknown samples, no
dropped feature, and `handle_unknow="error`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/14934
"""
enc = OneHotEncoder(sparse_output=sparse_).fit(X)
msg = (
r"Samples \[(\d )*\d\] can not be inverted when drop=None and "
r"handle_unknown='error' because they contain all zeros"
)
if sparse_:
# emulate sparse data transform by a one-hot encoder sparse.
X_trans = _convert_container(X_trans, "sparse")
with pytest.raises(ValueError, match=msg):
enc.inverse_transform(X_trans)
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_output=False)
X_tr = ohe.fit_transform(X)
assert_array_equal(ohe.inverse_transform(X_tr), X)
@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):
# check that resetting drop option without refitting does not throw an error
X = np.array([["Male", 1], ["Female", 3], ["Female", 2]], dtype=object)
ohe = OneHotEncoder(drop=drop, sparse_output=False)
ohe.fit(X)
X_tr = ohe.transform(X)
feature_names = ohe.get_feature_names_out()
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_out(), feature_names)
@pytest.mark.parametrize("method", ["fit", "fit_transform"])
@pytest.mark.parametrize("X", [[1, 2], np.array([3.0, 4.0])])
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 = f"Expected a 2-dimensional container but got {type(X)} 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_),
(np.array([[1, 2], [np.nan, 2]]), [[1, np.nan], [2]], np.float64),
(
np.array([["A", np.nan], [None, np.nan]], dtype=object),
[["A", None], [np.nan]],
np.object_,
),
(
np.array([["A", float("nan")], [None, float("nan")]], dtype=object),
[["A", None], [float("nan")]],
np.object_,
),
],
ids=[
"mixed",
"numeric",
"object",
"string",
"missing-float",
"missing-np.nan-object",
"missing-float-nan-object",
],
)
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):
res_list = res.tolist()
if is_scalar_nan(exp[-1]):
assert is_scalar_nan(res_list[-1])
assert res_list[:-1] == exp[:-1]
else:
assert res.tolist() == exp
assert np.issubdtype(res.dtype, cat_dtype)
@pytest.mark.parametrize("handle_unknown", ["ignore", "infrequent_if_exist"])
@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_,
),
(
np.array([[None, "a"]], dtype=object).T,
np.array([[None, "b"]], dtype=object).T,
[[None, "a", "z"]],
object,
),
(
np.array([["a", "b"]], dtype=object).T,
np.array([["a", np.nan]], dtype=object).T,
[["a", "b", "z"]],
object,
),
(
np.array([["a", None]], dtype=object).T,
np.array([["a", np.nan]], dtype=object).T,
[["a", None, "z"]],
object,
),
],
ids=[
"object",
"numeric",
"object-string",
"object-string-none",
"object-string-nan",
"object-None-and-nan",
],
)
def test_one_hot_encoder_specified_categories(X, X2, cats, cat_dtype, handle_unknown):
enc = OneHotEncoder(categories=cats)
exp = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
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=handle_unknown)
exp = np.array([[1.0, 0.0, 0.0], [0.0, 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([[0.0, 1.0, 0.0], [1.0, 0.0, 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)
@pytest.mark.parametrize("Encoder", [OneHotEncoder, OrdinalEncoder])
def test_encoder_nan_ending_specified_categories(Encoder):
"""Test encoder for specified categories that nan is at the end.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27088
"""
cats = [np.array([0, np.nan, 1])]
enc = Encoder(categories=cats)
X = np.array([[0, 1]], dtype=object).T
with pytest.raises(ValueError, match="Nan should be the last element"):
enc.fit(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, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 1.0]])
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_out()
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, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]
)
expected_drop_idx = np.array([None, 0])
ohe = OneHotEncoder(drop="if_binary", sparse_output=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([[1.0, 1.0], [0.0, 1.0], [0.0, 1.0]])
expected_drop_idx = np.array([0, None])
ohe = OneHotEncoder(drop="if_binary", sparse_output=False)
result = ohe.fit_transform(X)
assert_array_equal(ohe.drop_idx_, expected_drop_idx)
assert_allclose(result, expected)
@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([[0.0], [1.0]])
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)
def test_ordinal_encoder_handle_unknowns_string():
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-2)
X_fit = np.array([["a", "x"], ["b", "y"], ["c", "z"]], dtype=object)
X_trans = np.array([["c", "xy"], ["bla", "y"], ["a", "x"]], dtype=object)
enc.fit(X_fit)
X_trans_enc = enc.transform(X_trans)
exp = np.array([[2, -2], [-2, 1], [0, 0]], dtype="int64")
assert_array_equal(X_trans_enc, exp)
X_trans_inv = enc.inverse_transform(X_trans_enc)
inv_exp = np.array([["c", None], [None, "y"], ["a", "x"]], dtype=object)
assert_array_equal(X_trans_inv, inv_exp)
@pytest.mark.parametrize("dtype", [float, int])
def test_ordinal_encoder_handle_unknowns_numeric(dtype):
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-999)
X_fit = np.array([[1, 7], [2, 8], [3, 9]], dtype=dtype)
X_trans = np.array([[3, 12], [23, 8], [1, 7]], dtype=dtype)
enc.fit(X_fit)
X_trans_enc = enc.transform(X_trans)
exp = np.array([[2, -999], [-999, 1], [0, 0]], dtype="int64")
assert_array_equal(X_trans_enc, exp)
X_trans_inv = enc.inverse_transform(X_trans_enc)
inv_exp = np.array([[3, None], [None, 8], [1, 7]], dtype=object)
assert_array_equal(X_trans_inv, inv_exp)
def test_ordinal_encoder_handle_unknowns_nan():
# Make sure unknown_value=np.nan properly works
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=np.nan)
X_fit = np.array([[1], [2], [3]])
enc.fit(X_fit)
X_trans = enc.transform([[1], [2], [4]])
assert_array_equal(X_trans, [[0], [1], [np.nan]])
def test_ordinal_encoder_handle_unknowns_nan_non_float_dtype():
# Make sure an error is raised when unknown_value=np.nan and the dtype
# isn't a float dtype
enc = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=np.nan, dtype=int
)
X_fit = np.array([[1], [2], [3]])
with pytest.raises(ValueError, match="dtype parameter should be a float dtype"):
enc.fit(X_fit)
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, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0]], 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"]]), # str dtype
np.array([[b"a", b"b"], [b"c", b"d"]]), # bytes 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, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0, 1.0]],
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": [3.0, 4.0]})
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)
@pytest.mark.parametrize("missing_value", [np.nan, None, float("nan")])
def test_one_hot_encoder_drop_manual(missing_value):
cats_to_drop = ["def", 12, 3, 56, missing_value]
enc = OneHotEncoder(drop=cats_to_drop)
X = [
["abc", 12, 2, 55, "a"],
["def", 12, 1, 55, "a"],
["def", 12, 3, 56, missing_value],
]
trans = enc.fit_transform(X).toarray()
exp = [[1, 0, 1, 1, 1], [0, 1, 0, 1, 1], [0, 0, 0, 0, 0]]
assert_array_equal(trans, exp)
assert enc.drop is cats_to_drop
dropped_cats = [
cat[feature] for cat, feature in zip(enc.categories_, enc.drop_idx_)
]
X_inv_trans = enc.inverse_transform(trans)
X_array = np.array(X, dtype=object)
# last value is np.nan
if is_scalar_nan(cats_to_drop[-1]):
assert_array_equal(dropped_cats[:-1], cats_to_drop[:-1])
assert is_scalar_nan(dropped_cats[-1])
assert is_scalar_nan(cats_to_drop[-1])
# do not include the last column which includes missing values
assert_array_equal(X_array[:, :-1], X_inv_trans[:, :-1])
# check last column is the missing value
assert_array_equal(X_array[-1, :-1], X_inv_trans[-1, :-1])
assert is_scalar_nan(X_array[-1, -1])
assert is_scalar_nan(X_inv_trans[-1, -1])
else:
assert_array_equal(dropped_cats, cats_to_drop)
assert_array_equal(X_array, X_inv_trans)
@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_output=density)
ohe_test = OneHotEncoder(sparse_output=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 == 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(
"kwargs",
[
{"max_categories": 2},
{"min_frequency": 11},
{"min_frequency": 0.29},
{"max_categories": 2, "min_frequency": 6},
{"max_categories": 4, "min_frequency": 12},
],
)
@pytest.mark.parametrize("categories", ["auto", [["a", "b", "c", "d"]]])
def test_ohe_infrequent_two_levels(kwargs, categories):
"""Test that different parameters for combine 'a', 'c', and 'd' into
the infrequent category works as expected."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
categories=categories,
handle_unknown="infrequent_if_exist",
sparse_output=False,
**kwargs,
).fit(X_train)
assert_array_equal(ohe.infrequent_categories_, [["a", "c", "d"]])
X_test = [["b"], ["a"], ["c"], ["d"], ["e"]]
expected = np.array([[1, 0], [0, 1], [0, 1], [0, 1], [0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
expected_inv = [[col] for col in ["b"] + ["infrequent_sklearn"] * 4]
X_inv = ohe.inverse_transform(X_trans)
assert_array_equal(expected_inv, X_inv)
feature_names = ohe.get_feature_names_out()
assert_array_equal(["x0_b", "x0_infrequent_sklearn"], feature_names)
@pytest.mark.parametrize("drop", ["if_binary", "first", ["b"]])
def test_ohe_infrequent_two_levels_drop_frequent(drop):
"""Test two levels and dropping the frequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max_categories=2,
drop=drop,
).fit(X_train)
assert ohe.categories_[0][ohe.drop_idx_[0]] == "b"
X_test = np.array([["b"], ["c"]])
X_trans = ohe.transform(X_test)
assert_allclose([[0], [1]], X_trans)
feature_names = ohe.get_feature_names_out()
assert_array_equal(["x0_infrequent_sklearn"], feature_names)
X_inverse = ohe.inverse_transform(X_trans)
assert_array_equal([["b"], ["infrequent_sklearn"]], X_inverse)
@pytest.mark.parametrize("drop", [["a"], ["d"]])
def test_ohe_infrequent_two_levels_drop_infrequent_errors(drop):
"""Test two levels and dropping any infrequent category removes the
whole infrequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max_categories=2,
drop=drop,
)
msg = f"Unable to drop category {drop[0]!r} from feature 0 because it is infrequent"
with pytest.raises(ValueError, match=msg):
ohe.fit(X_train)
@pytest.mark.parametrize(
"kwargs",
[
{"max_categories": 3},
{"min_frequency": 6},
{"min_frequency": 9},
{"min_frequency": 0.24},
{"min_frequency": 0.16},
{"max_categories": 3, "min_frequency": 8},
{"max_categories": 4, "min_frequency": 6},
],
)
def test_ohe_infrequent_three_levels(kwargs):
"""Test that different parameters for combing 'a', and 'd' into
the infrequent category works as expected."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist", sparse_output=False, **kwargs
).fit(X_train)
assert_array_equal(ohe.infrequent_categories_, [["a", "d"]])
X_test = [["b"], ["a"], ["c"], ["d"], ["e"]]
expected = np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 1], [0, 0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
expected_inv = [
["b"],
["infrequent_sklearn"],
["c"],
["infrequent_sklearn"],
["infrequent_sklearn"],
]
X_inv = ohe.inverse_transform(X_trans)
assert_array_equal(expected_inv, X_inv)
feature_names = ohe.get_feature_names_out()
assert_array_equal(["x0_b", "x0_c", "x0_infrequent_sklearn"], feature_names)
@pytest.mark.parametrize("drop", ["first", ["b"]])
def test_ohe_infrequent_three_levels_drop_frequent(drop):
"""Test three levels and dropping the frequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max_categories=3,
drop=drop,
).fit(X_train)
X_test = np.array([["b"], ["c"], ["d"]])
assert_allclose([[0, 0], [1, 0], [0, 1]], ohe.transform(X_test))
# Check handle_unknown="ignore"
ohe.set_params(handle_unknown="ignore").fit(X_train)
msg = "Found unknown categories"
with pytest.warns(UserWarning, match=msg):
X_trans = ohe.transform([["b"], ["e"]])
assert_allclose([[0, 0], [0, 0]], X_trans)
@pytest.mark.parametrize("drop", [["a"], ["d"]])
def test_ohe_infrequent_three_levels_drop_infrequent_errors(drop):
"""Test three levels and dropping the infrequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max_categories=3,
drop=drop,
)
msg = f"Unable to drop category {drop[0]!r} from feature 0 because it is infrequent"
with pytest.raises(ValueError, match=msg):
ohe.fit(X_train)
def test_ohe_infrequent_handle_unknown_error():
"""Test that different parameters for combining 'a', and 'd' into
the infrequent category works as expected."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="error", sparse_output=False, max_categories=3
).fit(X_train)
assert_array_equal(ohe.infrequent_categories_, [["a", "d"]])
# all categories are known
X_test = [["b"], ["a"], ["c"], ["d"]]
expected = np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
# 'bad' is not known and will error
X_test = [["bad"]]
msg = r"Found unknown categories \['bad'\] in column 0"
with pytest.raises(ValueError, match=msg):
ohe.transform(X_test)
@pytest.mark.parametrize(
"kwargs", [{"max_categories": 3, "min_frequency": 1}, {"min_frequency": 4}]
)
def test_ohe_infrequent_two_levels_user_cats_one_frequent(kwargs):
"""'a' is the only frequent category, all other categories are infrequent."""
X_train = np.array([["a"] * 5 + ["e"] * 30], dtype=object).T
ohe = OneHotEncoder(
categories=[["c", "d", "a", "b"]],
sparse_output=False,
handle_unknown="infrequent_if_exist",
**kwargs,
).fit(X_train)
X_test = [["a"], ["b"], ["c"], ["d"], ["e"]]
expected = np.array([[1, 0], [0, 1], [0, 1], [0, 1], [0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
# 'a' is dropped
drops = ["first", "if_binary", ["a"]]
X_test = [["a"], ["c"]]
for drop in drops:
ohe.set_params(drop=drop).fit(X_train)
assert_allclose([[0], [1]], ohe.transform(X_test))
def test_ohe_infrequent_two_levels_user_cats():
"""Test that the order of the categories provided by a user is respected."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
ohe = OneHotEncoder(
categories=[["c", "d", "a", "b"]],
sparse_output=False,
handle_unknown="infrequent_if_exist",
max_categories=2,
).fit(X_train)
assert_array_equal(ohe.infrequent_categories_, [["c", "d", "a"]])
X_test = [["b"], ["a"], ["c"], ["d"], ["e"]]
expected = np.array([[1, 0], [0, 1], [0, 1], [0, 1], [0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
# 'infrequent' is used to denote the infrequent categories for
# `inverse_transform`
expected_inv = [[col] for col in ["b"] + ["infrequent_sklearn"] * 4]
X_inv = ohe.inverse_transform(X_trans)
assert_array_equal(expected_inv, X_inv)
def test_ohe_infrequent_three_levels_user_cats():
"""Test that the order of the categories provided by a user is respected.
In this case 'c' is encoded as the first category and 'b' is encoded
as the second one."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
ohe = OneHotEncoder(
categories=[["c", "d", "b", "a"]],
sparse_output=False,
handle_unknown="infrequent_if_exist",
max_categories=3,
).fit(X_train)
assert_array_equal(ohe.infrequent_categories_, [["d", "a"]])
X_test = [["b"], ["a"], ["c"], ["d"], ["e"]]
expected = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
# 'infrequent' is used to denote the infrequent categories for
# `inverse_transform`
expected_inv = [
["b"],
["infrequent_sklearn"],
["c"],
["infrequent_sklearn"],
["infrequent_sklearn"],
]
X_inv = ohe.inverse_transform(X_trans)
assert_array_equal(expected_inv, X_inv)
def test_ohe_infrequent_mixed():
"""Test infrequent categories where feature 0 has infrequent categories,
and feature 1 does not."""
# X[:, 0] 1 and 2 are infrequent
# X[:, 1] nothing is infrequent
X = np.c_[[0, 1, 3, 3, 3, 3, 2, 0, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1]]
ohe = OneHotEncoder(max_categories=3, drop="if_binary", sparse_output=False)
ohe.fit(X)
X_test = [[3, 0], [1, 1]]
X_trans = ohe.transform(X_test)
# feature 1 is binary so it drops a category 0
assert_allclose(X_trans, [[0, 1, 0, 0], [0, 0, 1, 1]])
def test_ohe_infrequent_multiple_categories():
"""Test infrequent categories with feature matrix with 3 features."""
X = np.c_[
[0, 1, 3, 3, 3, 3, 2, 0, 3],
[0, 0, 5, 1, 1, 10, 5, 5, 0],
[1, 0, 1, 0, 1, 0, 1, 0, 1],
]
ohe = OneHotEncoder(
categories="auto", max_categories=3, handle_unknown="infrequent_if_exist"
)
# X[:, 0] 1 and 2 are infrequent
# X[:, 1] 1 and 10 are infrequent
# X[:, 2] nothing is infrequent
X_trans = ohe.fit_transform(X).toarray()
assert_array_equal(ohe.infrequent_categories_[0], [1, 2])
assert_array_equal(ohe.infrequent_categories_[1], [1, 10])
assert_array_equal(ohe.infrequent_categories_[2], None)
# 'infrequent' is used to denote the infrequent categories
# For the first column, 1 and 2 have the same frequency. In this case,
# 1 will be chosen to be the feature name because is smaller lexiconically
feature_names = ohe.get_feature_names_out()
assert_array_equal(
[
"x0_0",
"x0_3",
"x0_infrequent_sklearn",
"x1_0",
"x1_5",
"x1_infrequent_sklearn",
"x2_0",
"x2_1",
],
feature_names,
)
expected = [
[1, 0, 0, 1, 0, 0, 0, 1],
[0, 0, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 0, 0, 1],
[0, 1, 0, 0, 0, 1, 1, 0],
[0, 1, 0, 0, 0, 1, 0, 1],
[0, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 0, 0, 1],
]
assert_allclose(expected, X_trans)
X_test = [[3, 1, 2], [4, 0, 3]]
X_test_trans = ohe.transform(X_test)
# X[:, 2] does not have an infrequent category, thus it is encoded as all
# zeros
expected = [[0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0]]
assert_allclose(expected, X_test_trans.toarray())
X_inv = ohe.inverse_transform(X_test_trans)
expected_inv = np.array(
[[3, "infrequent_sklearn", None], ["infrequent_sklearn", 0, None]], dtype=object
)
assert_array_equal(expected_inv, X_inv)
# error for unknown categories
ohe = OneHotEncoder(
categories="auto", max_categories=3, handle_unknown="error"
).fit(X)
with pytest.raises(ValueError, match="Found unknown categories"):
ohe.transform(X_test)
# only infrequent or known categories
X_test = [[1, 1, 1], [3, 10, 0]]
X_test_trans = ohe.transform(X_test)
expected = [[0, 0, 1, 0, 0, 1, 0, 1], [0, 1, 0, 0, 0, 1, 1, 0]]
assert_allclose(expected, X_test_trans.toarray())
X_inv = ohe.inverse_transform(X_test_trans)
expected_inv = np.array(
[["infrequent_sklearn", "infrequent_sklearn", 1], [3, "infrequent_sklearn", 0]],
dtype=object,
)
assert_array_equal(expected_inv, X_inv)
def test_ohe_infrequent_multiple_categories_dtypes():
"""Test infrequent categories with a pandas dataframe with multiple dtypes."""
pd = pytest.importorskip("pandas")
X = pd.DataFrame(
{
"str": ["a", "f", "c", "f", "f", "a", "c", "b", "b"],
"int": [5, 3, 0, 10, 10, 12, 0, 3, 5],
},
columns=["str", "int"],
)
ohe = OneHotEncoder(
categories="auto", max_categories=3, handle_unknown="infrequent_if_exist"
)
# X[:, 0] 'a', 'b', 'c' have the same frequency. 'a' and 'b' will be
# considered infrequent because they are greater
# X[:, 1] 0, 3, 5, 10 has frequency 2 and 12 has frequency 1.
# 0, 3, 12 will be considered infrequent
X_trans = ohe.fit_transform(X).toarray()
assert_array_equal(ohe.infrequent_categories_[0], ["a", "b"])
assert_array_equal(ohe.infrequent_categories_[1], [0, 3, 12])
expected = [
[0, 0, 1, 1, 0, 0],
[0, 1, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 0, 1],
[0, 0, 1, 0, 0, 1],
[0, 0, 1, 1, 0, 0],
]
assert_allclose(expected, X_trans)
X_test = pd.DataFrame({"str": ["b", "f"], "int": [14, 12]}, columns=["str", "int"])
expected = [[0, 0, 1, 0, 0, 1], [0, 1, 0, 0, 0, 1]]
X_test_trans = ohe.transform(X_test)
assert_allclose(expected, X_test_trans.toarray())
X_inv = ohe.inverse_transform(X_test_trans)
expected_inv = np.array(
[["infrequent_sklearn", "infrequent_sklearn"], ["f", "infrequent_sklearn"]],
dtype=object,
)
assert_array_equal(expected_inv, X_inv)
# only infrequent or known categories
X_test = pd.DataFrame({"str": ["c", "b"], "int": [12, 5]}, columns=["str", "int"])
X_test_trans = ohe.transform(X_test).toarray()
expected = [[1, 0, 0, 0, 0, 1], [0, 0, 1, 1, 0, 0]]
assert_allclose(expected, X_test_trans)
X_inv = ohe.inverse_transform(X_test_trans)
expected_inv = np.array(
[["c", "infrequent_sklearn"], ["infrequent_sklearn", 5]], dtype=object
)
assert_array_equal(expected_inv, X_inv)
@pytest.mark.parametrize("kwargs", [{"min_frequency": 21, "max_categories": 1}])
def test_ohe_infrequent_one_level_errors(kwargs):
"""All user provided categories are infrequent."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 2]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist", sparse_output=False, **kwargs
)
ohe.fit(X_train)
X_trans = ohe.transform([["a"]])
assert_allclose(X_trans, [[1]])
@pytest.mark.parametrize("kwargs", [{"min_frequency": 2, "max_categories": 3}])
def test_ohe_infrequent_user_cats_unknown_training_errors(kwargs):
"""All user provided categories are infrequent."""
X_train = np.array([["e"] * 3], dtype=object).T
ohe = OneHotEncoder(
categories=[["c", "d", "a", "b"]],
sparse_output=False,
handle_unknown="infrequent_if_exist",
**kwargs,
).fit(X_train)
X_trans = ohe.transform([["a"], ["e"]])
assert_allclose(X_trans, [[1], [1]])
# deliberately omit 'OS' as an invalid combo
@pytest.mark.parametrize(
"input_dtype, category_dtype", ["OO", "OU", "UO", "UU", "SO", "SU", "SS"]
)
@pytest.mark.parametrize("array_type", ["list", "array", "dataframe"])
def test_encoders_string_categories(input_dtype, category_dtype, array_type):
"""Check that encoding work with object, unicode, and byte string dtypes.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/15616
https://github.com/scikit-learn/scikit-learn/issues/15726
https://github.com/scikit-learn/scikit-learn/issues/19677
"""
X = np.array([["b"], ["a"]], dtype=input_dtype)
categories = [np.array(["b", "a"], dtype=category_dtype)]
ohe = OneHotEncoder(categories=categories, sparse_output=False).fit(X)
X_test = _convert_container(
[["a"], ["a"], ["b"], ["a"]], array_type, dtype=input_dtype
)
X_trans = ohe.transform(X_test)
expected = np.array([[0, 1], [0, 1], [1, 0], [0, 1]])
assert_allclose(X_trans, expected)
oe = OrdinalEncoder(categories=categories).fit(X)
X_trans = oe.transform(X_test)
expected = np.array([[1], [1], [0], [1]])
assert_array_equal(X_trans, expected)
def test_mixed_string_bytes_categoricals():
"""Check that this mixture of predefined categories and X raises an error.
Categories defined as bytes can not easily be compared to data that is
a string.
"""
# data as unicode
X = np.array([["b"], ["a"]], dtype="U")
# predefined categories as bytes
categories = [np.array(["b", "a"], dtype="S")]
ohe = OneHotEncoder(categories=categories, sparse_output=False)
msg = re.escape(
"In column 0, the predefined categories have type 'bytes' which is incompatible"
" with values of type 'str_'."
)
with pytest.raises(ValueError, match=msg):
ohe.fit(X)
@pytest.mark.parametrize("missing_value", [np.nan, None])
def test_ohe_missing_values_get_feature_names(missing_value):
# encoder with missing values with object dtypes
X = np.array([["a", "b", missing_value, "a", missing_value]], dtype=object).T
ohe = OneHotEncoder(sparse_output=False, handle_unknown="ignore").fit(X)
names = ohe.get_feature_names_out()
assert_array_equal(names, ["x0_a", "x0_b", f"x0_{missing_value}"])
def test_ohe_missing_value_support_pandas():
# check support for pandas with mixed dtypes and missing values
pd = pytest.importorskip("pandas")
df = pd.DataFrame(
{
"col1": ["dog", "cat", None, "cat"],
"col2": np.array([3, 0, 4, np.nan], dtype=float),
},
columns=["col1", "col2"],
)
expected_df_trans = np.array(
[
[0, 1, 0, 0, 1, 0, 0],
[1, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1],
]
)
Xtr = check_categorical_onehot(df)
assert_allclose(Xtr, expected_df_trans)
@pytest.mark.parametrize("handle_unknown", ["infrequent_if_exist", "ignore"])
@pytest.mark.parametrize("pd_nan_type", ["pd.NA", "np.nan"])
def test_ohe_missing_value_support_pandas_categorical(pd_nan_type, handle_unknown):
# checks pandas dataframe with categorical features
pd = pytest.importorskip("pandas")
pd_missing_value = pd.NA if pd_nan_type == "pd.NA" else np.nan
df = pd.DataFrame(
{
"col1": pd.Series(["c", "a", pd_missing_value, "b", "a"], dtype="category"),
}
)
expected_df_trans = np.array(
[
[0, 0, 1, 0],
[1, 0, 0, 0],
[0, 0, 0, 1],
[0, 1, 0, 0],
[1, 0, 0, 0],
]
)
ohe = OneHotEncoder(sparse_output=False, handle_unknown=handle_unknown)
df_trans = ohe.fit_transform(df)
assert_allclose(expected_df_trans, df_trans)
assert len(ohe.categories_) == 1
assert_array_equal(ohe.categories_[0][:-1], ["a", "b", "c"])
assert np.isnan(ohe.categories_[0][-1])
@pytest.mark.parametrize("handle_unknown", ["ignore", "infrequent_if_exist"])
def test_ohe_drop_first_handle_unknown_ignore_warns(handle_unknown):
"""Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'
during transform."""
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="first", sparse_output=False, handle_unknown=handle_unknown
)
X_trans = ohe.fit_transform(X)
X_expected = np.array(
[
[0, 0, 0],
[1, 0, 1],
[1, 1, 0],
]
)
assert_allclose(X_trans, X_expected)
# Both categories are unknown
X_test = [["c", 3]]
X_expected = np.array([[0, 0, 0]])
warn_msg = (
r"Found unknown categories in columns \[0, 1\] during "
"transform. These unknown categories will be encoded as all "
"zeros"
)
with pytest.warns(UserWarning, match=warn_msg):
X_trans = ohe.transform(X_test)
assert_allclose(X_trans, X_expected)
# inverse_transform maps to None
X_inv = ohe.inverse_transform(X_expected)
assert_array_equal(X_inv, np.array([["a", 0]], dtype=object))
@pytest.mark.parametrize("handle_unknown", ["ignore", "infrequent_if_exist"])
def test_ohe_drop_if_binary_handle_unknown_ignore_warns(handle_unknown):
"""Check drop='if_binary' and handle_unknown='ignore' during transform."""
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="if_binary", sparse_output=False, handle_unknown=handle_unknown
)
X_trans = ohe.fit_transform(X)
X_expected = np.array(
[
[0, 1, 0, 0],
[1, 0, 0, 1],
[1, 0, 1, 0],
]
)
assert_allclose(X_trans, X_expected)
# Both categories are unknown
X_test = [["c", 3]]
X_expected = np.array([[0, 0, 0, 0]])
warn_msg = (
r"Found unknown categories in columns \[0, 1\] during "
"transform. These unknown categories will be encoded as all "
"zeros"
)
with pytest.warns(UserWarning, match=warn_msg):
X_trans = ohe.transform(X_test)
assert_allclose(X_trans, X_expected)
# inverse_transform maps to None
X_inv = ohe.inverse_transform(X_expected)
assert_array_equal(X_inv, np.array([["a", None]], dtype=object))
@pytest.mark.parametrize("handle_unknown", ["ignore", "infrequent_if_exist"])
def test_ohe_drop_first_explicit_categories(handle_unknown):
"""Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'
during fit with categories passed in."""
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="first",
sparse_output=False,
handle_unknown=handle_unknown,
categories=[["b", "a"], [1, 2]],
)
ohe.fit(X)
X_test = [["c", 1]]
X_expected = np.array([[0, 0]])
warn_msg = (
r"Found unknown categories in columns \[0\] during transform. "
r"These unknown categories will be encoded as all zeros"
)
with pytest.warns(UserWarning, match=warn_msg):
X_trans = ohe.transform(X_test)
assert_allclose(X_trans, X_expected)
def test_ohe_more_informative_error_message():
"""Raise informative error message when pandas output and sparse_output=True."""
pd = pytest.importorskip("pandas")
df = pd.DataFrame({"a": [1, 2, 3], "b": ["z", "b", "b"]}, columns=["a", "b"])
ohe = OneHotEncoder(sparse_output=True)
ohe.set_output(transform="pandas")
msg = (
"Pandas output does not support sparse data. Set "
"sparse_output=False to output pandas dataframes or disable Pandas output"
)
with pytest.raises(ValueError, match=msg):
ohe.fit_transform(df)
ohe.fit(df)
with pytest.raises(ValueError, match=msg):
ohe.transform(df)
def test_ordinal_encoder_passthrough_missing_values_float_errors_dtype():
"""Test ordinal encoder with nan passthrough fails when dtype=np.int32."""
X = np.array([[np.nan, 3.0, 1.0, 3.0]]).T
oe = OrdinalEncoder(dtype=np.int32)
msg = (
r"There are missing values in features \[0\]. For OrdinalEncoder "
f"to encode missing values with dtype: {np.int32}"
)
with pytest.raises(ValueError, match=msg):
oe.fit(X)
@pytest.mark.parametrize("encoded_missing_value", [np.nan, -2])
def test_ordinal_encoder_passthrough_missing_values_float(encoded_missing_value):
"""Test ordinal encoder with nan on float dtypes."""
X = np.array([[np.nan, 3.0, 1.0, 3.0]], dtype=np.float64).T
oe = OrdinalEncoder(encoded_missing_value=encoded_missing_value).fit(X)
assert len(oe.categories_) == 1
assert_allclose(oe.categories_[0], [1.0, 3.0, np.nan])
X_trans = oe.transform(X)
assert_allclose(X_trans, [[encoded_missing_value], [1.0], [0.0], [1.0]])
X_inverse = oe.inverse_transform(X_trans)
assert_allclose(X_inverse, X)
@pytest.mark.parametrize("pd_nan_type", ["pd.NA", "np.nan"])
@pytest.mark.parametrize("encoded_missing_value", [np.nan, -2])
def test_ordinal_encoder_missing_value_support_pandas_categorical(
pd_nan_type, encoded_missing_value
):
"""Check ordinal encoder is compatible with pandas."""
# checks pandas dataframe with categorical features
pd = pytest.importorskip("pandas")
pd_missing_value = pd.NA if pd_nan_type == "pd.NA" else np.nan
df = pd.DataFrame(
{
"col1": pd.Series(["c", "a", pd_missing_value, "b", "a"], dtype="category"),
}
)
oe = OrdinalEncoder(encoded_missing_value=encoded_missing_value).fit(df)
assert len(oe.categories_) == 1
assert_array_equal(oe.categories_[0][:3], ["a", "b", "c"])
assert np.isnan(oe.categories_[0][-1])
df_trans = oe.transform(df)
assert_allclose(df_trans, [[2.0], [0.0], [encoded_missing_value], [1.0], [0.0]])
X_inverse = oe.inverse_transform(df_trans)
assert X_inverse.shape == (5, 1)
assert_array_equal(X_inverse[:2, 0], ["c", "a"])
assert_array_equal(X_inverse[3:, 0], ["b", "a"])
assert np.isnan(X_inverse[2, 0])
@pytest.mark.parametrize(
"X, X2, cats, cat_dtype",
[
(
(
np.array([["a", np.nan]], dtype=object).T,
np.array([["a", "b"]], dtype=object).T,
[np.array(["a", "d", np.nan], dtype=object)],
np.object_,
)
),
(
(
np.array([["a", np.nan]], dtype=object).T,
np.array([["a", "b"]], dtype=object).T,
[np.array(["a", "d", np.nan], dtype=object)],
np.object_,
)
),
(
(
np.array([[2.0, np.nan]], dtype=np.float64).T,
np.array([[3.0]], dtype=np.float64).T,
[np.array([2.0, 4.0, np.nan])],
np.float64,
)
),
],
ids=[
"object-None-missing-value",
"object-nan-missing_value",
"numeric-missing-value",
],
)
def test_ordinal_encoder_specified_categories_missing_passthrough(
X, X2, cats, cat_dtype
):
"""Test ordinal encoder for specified categories."""
oe = OrdinalEncoder(categories=cats)
exp = np.array([[0.0], [np.nan]])
assert_array_equal(oe.fit_transform(X), exp)
# manually specified categories should have same dtype as
# the data when coerced from lists
assert oe.categories_[0].dtype == cat_dtype
# when specifying categories manually, unknown categories should already
# raise when fitting
oe = OrdinalEncoder(categories=cats)
with pytest.raises(ValueError, match="Found unknown categories"):
oe.fit(X2)
@pytest.mark.parametrize("Encoder", [OneHotEncoder, OrdinalEncoder])
def test_encoder_duplicate_specified_categories(Encoder):
"""Test encoder for specified categories have duplicate values.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27088
"""
cats = [np.array(["a", "b", "a"], dtype=object)]
enc = Encoder(categories=cats)
X = np.array([["a", "b"]], dtype=object).T
with pytest.raises(
ValueError, match="the predefined categories contain duplicate elements."
):
enc.fit(X)
@pytest.mark.parametrize(
"X, expected_X_trans, X_test",
[
(
np.array([[1.0, np.nan, 3.0]]).T,
np.array([[0.0, np.nan, 1.0]]).T,
np.array([[4.0]]),
),
(
np.array([[1.0, 4.0, 3.0]]).T,
np.array([[0.0, 2.0, 1.0]]).T,
np.array([[np.nan]]),
),
(
np.array([["c", np.nan, "b"]], dtype=object).T,
np.array([[1.0, np.nan, 0.0]]).T,
np.array([["d"]], dtype=object),
),
(
np.array([["c", "a", "b"]], dtype=object).T,
np.array([[2.0, 0.0, 1.0]]).T,
np.array([[np.nan]], dtype=object),
),
],
)
def test_ordinal_encoder_handle_missing_and_unknown(X, expected_X_trans, X_test):
"""Test the interaction between missing values and handle_unknown"""
oe = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)
X_trans = oe.fit_transform(X)
assert_allclose(X_trans, expected_X_trans)
assert_allclose(oe.transform(X_test), [[-1.0]])
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_ordinal_encoder_sparse(csr_container):
"""Check that we raise proper error with sparse input in OrdinalEncoder.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19878
"""
X = np.array([[3, 2, 1], [0, 1, 1]])
X_sparse = csr_container(X)
encoder = OrdinalEncoder()
err_msg = "Sparse data was passed, but dense data is required"
with pytest.raises(TypeError, match=err_msg):
encoder.fit(X_sparse)
with pytest.raises(TypeError, match=err_msg):
encoder.fit_transform(X_sparse)
X_trans = encoder.fit_transform(X)
X_trans_sparse = csr_container(X_trans)
with pytest.raises(TypeError, match=err_msg):
encoder.inverse_transform(X_trans_sparse)
def test_ordinal_encoder_fit_with_unseen_category():
"""Check OrdinalEncoder.fit works with unseen category when
`handle_unknown="use_encoded_value"`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19872
"""
X = np.array([0, 0, 1, 0, 2, 5])[:, np.newaxis]
oe = OrdinalEncoder(
categories=[[-1, 0, 1]], handle_unknown="use_encoded_value", unknown_value=-999
)
oe.fit(X)
oe = OrdinalEncoder(categories=[[-1, 0, 1]], handle_unknown="error")
with pytest.raises(ValueError, match="Found unknown categories"):
oe.fit(X)
@pytest.mark.parametrize(
"X_train",
[
[["AA", "B"]],
np.array([["AA", "B"]], dtype="O"),
np.array([["AA", "B"]], dtype="U"),
],
)
@pytest.mark.parametrize(
"X_test",
[
[["A", "B"]],
np.array([["A", "B"]], dtype="O"),
np.array([["A", "B"]], dtype="U"),
],
)
def test_ordinal_encoder_handle_unknown_string_dtypes(X_train, X_test):
"""Checks that `OrdinalEncoder` transforms string dtypes.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19872
"""
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-9)
enc.fit(X_train)
X_trans = enc.transform(X_test)
assert_allclose(X_trans, [[-9, 0]])
def test_ordinal_encoder_python_integer():
"""Check that `OrdinalEncoder` accepts Python integers that are potentially
larger than 64 bits.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20721
"""
X = np.array(
[
44253463435747313673,
9867966753463435747313673,
44253462342215747313673,
442534634357764313673,
]
).reshape(-1, 1)
encoder = OrdinalEncoder().fit(X)
assert_array_equal(encoder.categories_, np.sort(X, axis=0).T)
X_trans = encoder.transform(X)
assert_array_equal(X_trans, [[0], [3], [2], [1]])
def test_ordinal_encoder_features_names_out_pandas():
"""Check feature names out is same as the input."""
pd = pytest.importorskip("pandas")
names = ["b", "c", "a"]
X = pd.DataFrame([[1, 2, 3]], columns=names)
enc = OrdinalEncoder().fit(X)
feature_names_out = enc.get_feature_names_out()
assert_array_equal(names, feature_names_out)
def test_ordinal_encoder_unknown_missing_interaction():
"""Check interactions between encode_unknown and missing value encoding."""
X = np.array([["a"], ["b"], [np.nan]], dtype=object)
oe = OrdinalEncoder(
handle_unknown="use_encoded_value",
unknown_value=np.nan,
encoded_missing_value=-3,
).fit(X)
X_trans = oe.transform(X)
assert_allclose(X_trans, [[0], [1], [-3]])
# "c" is unknown and is mapped to np.nan
# "None" is a missing value and is set to -3
X_test = np.array([["c"], [np.nan]], dtype=object)
X_test_trans = oe.transform(X_test)
assert_allclose(X_test_trans, [[np.nan], [-3]])
# Non-regression test for #24082
X_roundtrip = oe.inverse_transform(X_test_trans)
# np.nan is unknown so it maps to None
assert X_roundtrip[0][0] is None
# -3 is the encoded missing value so it maps back to nan
assert np.isnan(X_roundtrip[1][0])
@pytest.mark.parametrize("with_pandas", [True, False])
def test_ordinal_encoder_encoded_missing_value_error(with_pandas):
"""Check OrdinalEncoder errors when encoded_missing_value is used by
an known category."""
X = np.array([["a", "dog"], ["b", "cat"], ["c", np.nan]], dtype=object)
# The 0-th feature has no missing values so it is not included in the list of
# features
error_msg = (
r"encoded_missing_value \(1\) is already used to encode a known category "
r"in features: "
)
if with_pandas:
pd = pytest.importorskip("pandas")
X = pd.DataFrame(X, columns=["letter", "pet"])
error_msg = error_msg + r"\['pet'\]"
else:
error_msg = error_msg + r"\[1\]"
oe = OrdinalEncoder(encoded_missing_value=1)
with pytest.raises(ValueError, match=error_msg):
oe.fit(X)
@pytest.mark.parametrize(
"X_train, X_test_trans_expected, X_roundtrip_expected",
[
(
# missing value is not in training set
# inverse transform will considering encoded nan as unknown
np.array([["a"], ["1"]], dtype=object),
[[0], [np.nan], [np.nan]],
np.asarray([["1"], [None], [None]], dtype=object),
),
(
# missing value in training set,
# inverse transform will considering encoded nan as missing
np.array([[np.nan], ["1"], ["a"]], dtype=object),
[[0], [np.nan], [np.nan]],
np.asarray([["1"], [np.nan], [np.nan]], dtype=object),
),
],
)
def test_ordinal_encoder_unknown_missing_interaction_both_nan(
X_train, X_test_trans_expected, X_roundtrip_expected
):
"""Check transform when unknown_value and encoded_missing_value is nan.
Non-regression test for #24082.
"""
oe = OrdinalEncoder(
handle_unknown="use_encoded_value",
unknown_value=np.nan,
encoded_missing_value=np.nan,
).fit(X_train)
X_test = np.array([["1"], [np.nan], ["b"]])
X_test_trans = oe.transform(X_test)
# both nan and unknown are encoded as nan
assert_allclose(X_test_trans, X_test_trans_expected)
X_roundtrip = oe.inverse_transform(X_test_trans)
n_samples = X_roundtrip_expected.shape[0]
for i in range(n_samples):
expected_val = X_roundtrip_expected[i, 0]
val = X_roundtrip[i, 0]
if expected_val is None:
assert val is None
elif is_scalar_nan(expected_val):
assert np.isnan(val)
else:
assert val == expected_val
def test_one_hot_encoder_set_output():
"""Check OneHotEncoder works with set_output."""
pd = pytest.importorskip("pandas")
X_df = pd.DataFrame({"A": ["a", "b"], "B": [1, 2]})
ohe = OneHotEncoder()
ohe.set_output(transform="pandas")
match = "Pandas output does not support sparse data. Set sparse_output=False"
with pytest.raises(ValueError, match=match):
ohe.fit_transform(X_df)
ohe_default = OneHotEncoder(sparse_output=False).set_output(transform="default")
ohe_pandas = OneHotEncoder(sparse_output=False).set_output(transform="pandas")
X_default = ohe_default.fit_transform(X_df)
X_pandas = ohe_pandas.fit_transform(X_df)
assert_allclose(X_pandas.to_numpy(), X_default)
assert_array_equal(ohe_pandas.get_feature_names_out(), X_pandas.columns)
def test_ordinal_set_output():
"""Check OrdinalEncoder works with set_output."""
pd = pytest.importorskip("pandas")
X_df = pd.DataFrame({"A": ["a", "b"], "B": [1, 2]})
ord_default = OrdinalEncoder().set_output(transform="default")
ord_pandas = OrdinalEncoder().set_output(transform="pandas")
X_default = ord_default.fit_transform(X_df)
X_pandas = ord_pandas.fit_transform(X_df)
assert_allclose(X_pandas.to_numpy(), X_default)
assert_array_equal(ord_pandas.get_feature_names_out(), X_pandas.columns)
def test_predefined_categories_dtype():
"""Check that the categories_ dtype is `object` for string categories
Regression test for gh-25171.
"""
categories = [["as", "mmas", "eas", "ras", "acs"], ["1", "2"]]
enc = OneHotEncoder(categories=categories)
enc.fit([["as", "1"]])
assert len(categories) == len(enc.categories_)
for n, cat in enumerate(enc.categories_):
assert cat.dtype == object
assert_array_equal(categories[n], cat)
def test_ordinal_encoder_missing_unknown_encoding_max():
"""Check missing value or unknown encoding can equal the cardinality."""
X = np.array([["dog"], ["cat"], [np.nan]], dtype=object)
X_trans = OrdinalEncoder(encoded_missing_value=2).fit_transform(X)
assert_allclose(X_trans, [[1], [0], [2]])
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=2).fit(X)
X_test = np.array([["snake"]])
X_trans = enc.transform(X_test)
assert_allclose(X_trans, [[2]])
def test_drop_idx_infrequent_categories():
"""Check drop_idx is defined correctly with infrequent categories.
Non-regression test for gh-25550.
"""
X = np.array(
[["a"] * 2 + ["b"] * 4 + ["c"] * 4 + ["d"] * 4 + ["e"] * 4], dtype=object
).T
ohe = OneHotEncoder(min_frequency=4, sparse_output=False, drop="first").fit(X)
assert_array_equal(
ohe.get_feature_names_out(), ["x0_c", "x0_d", "x0_e", "x0_infrequent_sklearn"]
)
assert ohe.categories_[0][ohe.drop_idx_[0]] == "b"
X = np.array([["a"] * 2 + ["b"] * 2 + ["c"] * 10], dtype=object).T
ohe = OneHotEncoder(min_frequency=4, sparse_output=False, drop="if_binary").fit(X)
assert_array_equal(ohe.get_feature_names_out(), ["x0_infrequent_sklearn"])
assert ohe.categories_[0][ohe.drop_idx_[0]] == "c"
X = np.array(
[["a"] * 2 + ["b"] * 4 + ["c"] * 4 + ["d"] * 4 + ["e"] * 4], dtype=object
).T
ohe = OneHotEncoder(min_frequency=4, sparse_output=False, drop=["d"]).fit(X)
assert_array_equal(
ohe.get_feature_names_out(), ["x0_b", "x0_c", "x0_e", "x0_infrequent_sklearn"]
)
assert ohe.categories_[0][ohe.drop_idx_[0]] == "d"
ohe = OneHotEncoder(min_frequency=4, sparse_output=False, drop=None).fit(X)
assert_array_equal(
ohe.get_feature_names_out(),
["x0_b", "x0_c", "x0_d", "x0_e", "x0_infrequent_sklearn"],
)
assert ohe.drop_idx_ is None
@pytest.mark.parametrize(
"kwargs",
[
{"max_categories": 3},
{"min_frequency": 6},
{"min_frequency": 9},
{"min_frequency": 0.24},
{"min_frequency": 0.16},
{"max_categories": 3, "min_frequency": 8},
{"max_categories": 4, "min_frequency": 6},
],
)
def test_ordinal_encoder_infrequent_three_levels(kwargs):
"""Test parameters for grouping 'a', and 'd' into the infrequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ordinal = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1, **kwargs
).fit(X_train)
assert_array_equal(ordinal.categories_, [["a", "b", "c", "d"]])
assert_array_equal(ordinal.infrequent_categories_, [["a", "d"]])
X_test = [["a"], ["b"], ["c"], ["d"], ["z"]]
expected_trans = [[2], [0], [1], [2], [-1]]
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, expected_trans)
X_inverse = ordinal.inverse_transform(X_trans)
expected_inverse = [
["infrequent_sklearn"],
["b"],
["c"],
["infrequent_sklearn"],
[None],
]
assert_array_equal(X_inverse, expected_inverse)
def test_ordinal_encoder_infrequent_three_levels_user_cats():
"""Test that the order of the categories provided by a user is respected.
In this case 'c' is encoded as the first category and 'b' is encoded
as the second one.
"""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
ordinal = OrdinalEncoder(
categories=[["c", "d", "b", "a"]],
max_categories=3,
handle_unknown="use_encoded_value",
unknown_value=-1,
).fit(X_train)
assert_array_equal(ordinal.categories_, [["c", "d", "b", "a"]])
assert_array_equal(ordinal.infrequent_categories_, [["d", "a"]])
X_test = [["a"], ["b"], ["c"], ["d"], ["z"]]
expected_trans = [[2], [1], [0], [2], [-1]]
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, expected_trans)
X_inverse = ordinal.inverse_transform(X_trans)
expected_inverse = [
["infrequent_sklearn"],
["b"],
["c"],
["infrequent_sklearn"],
[None],
]
assert_array_equal(X_inverse, expected_inverse)
def test_ordinal_encoder_infrequent_mixed():
"""Test when feature 0 has infrequent categories and feature 1 does not."""
X = np.column_stack(([0, 1, 3, 3, 3, 3, 2, 0, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1]))
ordinal = OrdinalEncoder(max_categories=3).fit(X)
assert_array_equal(ordinal.infrequent_categories_[0], [1, 2])
assert ordinal.infrequent_categories_[1] is None
X_test = [[3, 0], [1, 1]]
expected_trans = [[1, 0], [2, 1]]
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, expected_trans)
X_inverse = ordinal.inverse_transform(X_trans)
expected_inverse = np.array([[3, 0], ["infrequent_sklearn", 1]], dtype=object)
assert_array_equal(X_inverse, expected_inverse)
def test_ordinal_encoder_infrequent_multiple_categories_dtypes():
"""Test infrequent categories with a pandas DataFrame with multiple dtypes."""
pd = pytest.importorskip("pandas")
categorical_dtype = pd.CategoricalDtype(["bird", "cat", "dog", "snake"])
X = pd.DataFrame(
{
"str": ["a", "f", "c", "f", "f", "a", "c", "b", "b"],
"int": [5, 3, 0, 10, 10, 12, 0, 3, 5],
"categorical": pd.Series(
["dog"] * 4 + ["cat"] * 3 + ["snake"] + ["bird"],
dtype=categorical_dtype,
),
},
columns=["str", "int", "categorical"],
)
ordinal = OrdinalEncoder(max_categories=3).fit(X)
# X[:, 0] 'a', 'b', 'c' have the same frequency. 'a' and 'b' will be
# considered infrequent because they appear first when sorted
# X[:, 1] 0, 3, 5, 10 has frequency 2 and 12 has frequency 1.
# 0, 3, 12 will be considered infrequent because they appear first when
# sorted.
# X[:, 2] "snake" and "bird" or infrequent
assert_array_equal(ordinal.infrequent_categories_[0], ["a", "b"])
assert_array_equal(ordinal.infrequent_categories_[1], [0, 3, 12])
assert_array_equal(ordinal.infrequent_categories_[2], ["bird", "snake"])
X_test = pd.DataFrame(
{
"str": ["a", "b", "f", "c"],
"int": [12, 0, 10, 5],
"categorical": pd.Series(
["cat"] + ["snake"] + ["bird"] + ["dog"],
dtype=categorical_dtype,
),
},
columns=["str", "int", "categorical"],
)
expected_trans = [[2, 2, 0], [2, 2, 2], [1, 1, 2], [0, 0, 1]]
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, expected_trans)
def test_ordinal_encoder_infrequent_custom_mapping():
"""Check behavior of unknown_value and encoded_missing_value with infrequent."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]], dtype=object
).T
ordinal = OrdinalEncoder(
handle_unknown="use_encoded_value",
unknown_value=2,
max_categories=2,
encoded_missing_value=3,
).fit(X_train)
assert_array_equal(ordinal.infrequent_categories_, [["a", "c", "d"]])
X_test = np.array([["a"], ["b"], ["c"], ["d"], ["e"], [np.nan]], dtype=object)
expected_trans = [[1], [0], [1], [1], [2], [3]]
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, expected_trans)
@pytest.mark.parametrize(
"kwargs",
[
{"max_categories": 6},
{"min_frequency": 2},
],
)
def test_ordinal_encoder_all_frequent(kwargs):
"""All categories are considered frequent have same encoding as default encoder."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
adjusted_encoder = OrdinalEncoder(
**kwargs, handle_unknown="use_encoded_value", unknown_value=-1
).fit(X_train)
default_encoder = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1
).fit(X_train)
X_test = [["a"], ["b"], ["c"], ["d"], ["e"]]
assert_allclose(
adjusted_encoder.transform(X_test), default_encoder.transform(X_test)
)
@pytest.mark.parametrize(
"kwargs",
[
{"max_categories": 1},
{"min_frequency": 100},
],
)
def test_ordinal_encoder_all_infrequent(kwargs):
"""When all categories are infrequent, they are all encoded as zero."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
encoder = OrdinalEncoder(
**kwargs, handle_unknown="use_encoded_value", unknown_value=-1
).fit(X_train)
X_test = [["a"], ["b"], ["c"], ["d"], ["e"]]
assert_allclose(encoder.transform(X_test), [[0], [0], [0], [0], [-1]])
def test_ordinal_encoder_missing_appears_frequent():
"""Check behavior when missing value appears frequently."""
X = np.array(
[[np.nan] * 20 + ["dog"] * 10 + ["cat"] * 5 + ["snake"] + ["deer"]],
dtype=object,
).T
ordinal = OrdinalEncoder(max_categories=3).fit(X)
X_test = np.array([["snake", "cat", "dog", np.nan]], dtype=object).T
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, [[2], [0], [1], [np.nan]])
def test_ordinal_encoder_missing_appears_infrequent():
"""Check behavior when missing value appears infrequently."""
# feature 0 has infrequent categories
# feature 1 has no infrequent categories
X = np.array(
[
[np.nan] + ["dog"] * 10 + ["cat"] * 5 + ["snake"] + ["deer"],
["red"] * 9 + ["green"] * 9,
],
dtype=object,
).T
ordinal = OrdinalEncoder(min_frequency=4).fit(X)
X_test = np.array(
[
["snake", "red"],
["deer", "green"],
[np.nan, "green"],
["dog", "green"],
["cat", "red"],
],
dtype=object,
)
X_trans = ordinal.transform(X_test)
assert_allclose(X_trans, [[2, 1], [2, 0], [np.nan, 0], [1, 0], [0, 1]])
@pytest.mark.parametrize("Encoder", [OneHotEncoder, OrdinalEncoder])
def test_encoder_not_fitted(Encoder):
"""Check that we raise a `NotFittedError` by calling transform before fit with
the encoders.
One could expect that the passing the `categories` argument to the encoder
would make it stateless. However, `fit` is making a couple of check, such as the
position of `np.nan`.
"""
X = np.array([["A"], ["B"], ["C"]], dtype=object)
encoder = Encoder(categories=[["A", "B", "C"]])
with pytest.raises(NotFittedError):
encoder.transform(X)