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test_set_output.py
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test_set_output.py
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import pytest
import numpy as np
from scipy.sparse import csr_matrix
from numpy.testing import assert_array_equal
from sklearn._config import config_context, get_config
from sklearn.utils._set_output import _wrap_in_pandas_container
from sklearn.utils._set_output import _safe_set_output
from sklearn.utils._set_output import _SetOutputMixin
from sklearn.utils._set_output import _get_output_config
def test__wrap_in_pandas_container_dense():
"""Check _wrap_in_pandas_container for dense data."""
pd = pytest.importorskip("pandas")
X = np.asarray([[1, 0, 3], [0, 0, 1]])
columns = np.asarray(["f0", "f1", "f2"], dtype=object)
index = np.asarray([0, 1])
dense_named = _wrap_in_pandas_container(X, columns=lambda: columns, index=index)
assert isinstance(dense_named, pd.DataFrame)
assert_array_equal(dense_named.columns, columns)
assert_array_equal(dense_named.index, index)
def test__wrap_in_pandas_container_dense_update_columns_and_index():
"""Check that _wrap_in_pandas_container overrides columns and index."""
pd = pytest.importorskip("pandas")
X_df = pd.DataFrame([[1, 0, 3], [0, 0, 1]], columns=["a", "b", "c"])
new_columns = np.asarray(["f0", "f1", "f2"], dtype=object)
new_index = [10, 12]
new_df = _wrap_in_pandas_container(X_df, columns=new_columns, index=new_index)
assert_array_equal(new_df.columns, new_columns)
# Index does not change when the input is a DataFrame
assert_array_equal(new_df.index, X_df.index)
def test__wrap_in_pandas_container_error_validation():
"""Check errors in _wrap_in_pandas_container."""
X = np.asarray([[1, 0, 3], [0, 0, 1]])
X_csr = csr_matrix(X)
match = "Pandas output does not support sparse data"
with pytest.raises(ValueError, match=match):
_wrap_in_pandas_container(X_csr, columns=["a", "b", "c"])
class EstimatorWithoutSetOutputAndWithoutTransform:
pass
class EstimatorNoSetOutputWithTransform:
def transform(self, X, y=None):
return X # pragma: no cover
class EstimatorWithSetOutput(_SetOutputMixin):
def fit(self, X, y=None):
self.n_features_in_ = X.shape[1]
return self
def transform(self, X, y=None):
return X
def get_feature_names_out(self, input_features=None):
return np.asarray([f"X{i}" for i in range(self.n_features_in_)], dtype=object)
def test__safe_set_output():
"""Check _safe_set_output works as expected."""
# Estimator without transform will not raise when setting set_output for transform.
est = EstimatorWithoutSetOutputAndWithoutTransform()
_safe_set_output(est, transform="pandas")
# Estimator with transform but without set_output will raise
est = EstimatorNoSetOutputWithTransform()
with pytest.raises(ValueError, match="Unable to configure output"):
_safe_set_output(est, transform="pandas")
est = EstimatorWithSetOutput().fit(np.asarray([[1, 2, 3]]))
_safe_set_output(est, transform="pandas")
config = _get_output_config("transform", est)
assert config["dense"] == "pandas"
_safe_set_output(est, transform="default")
config = _get_output_config("transform", est)
assert config["dense"] == "default"
# transform is None is a no-op, so the config remains "default"
_safe_set_output(est, transform=None)
config = _get_output_config("transform", est)
assert config["dense"] == "default"
class EstimatorNoSetOutputWithTransformNoFeatureNamesOut(_SetOutputMixin):
def transform(self, X, y=None):
return X # pragma: no cover
def test_set_output_mixin():
"""Estimator without get_feature_names_out does not define `set_output`."""
est = EstimatorNoSetOutputWithTransformNoFeatureNamesOut()
assert not hasattr(est, "set_output")
def test__safe_set_output_error():
"""Check transform with invalid config."""
X = np.asarray([[1, 0, 3], [0, 0, 1]])
est = EstimatorWithSetOutput()
_safe_set_output(est, transform="bad")
msg = "output config must be 'default'"
with pytest.raises(ValueError, match=msg):
est.transform(X)
def test_set_output_method():
"""Check that the output is pandas."""
pd = pytest.importorskip("pandas")
X = np.asarray([[1, 0, 3], [0, 0, 1]])
est = EstimatorWithSetOutput().fit(X)
# transform=None is a no-op
est2 = est.set_output(transform=None)
assert est2 is est
X_trans_np = est2.transform(X)
assert isinstance(X_trans_np, np.ndarray)
est.set_output(transform="pandas")
X_trans_pd = est.transform(X)
assert isinstance(X_trans_pd, pd.DataFrame)
def test_set_output_method_error():
"""Check transform fails with invalid transform."""
X = np.asarray([[1, 0, 3], [0, 0, 1]])
est = EstimatorWithSetOutput().fit(X)
est.set_output(transform="bad")
msg = "output config must be 'default'"
with pytest.raises(ValueError, match=msg):
est.transform(X)
def test__get_output_config():
"""Check _get_output_config works as expected."""
# Without a configuration set, the global config is used
global_config = get_config()["transform_output"]
config = _get_output_config("transform")
assert config["dense"] == global_config
with config_context(transform_output="pandas"):
# with estimator=None, the global config is used
config = _get_output_config("transform")
assert config["dense"] == "pandas"
est = EstimatorNoSetOutputWithTransform()
config = _get_output_config("transform", est)
assert config["dense"] == "pandas"
est = EstimatorWithSetOutput()
# If estimator has not config, use global config
config = _get_output_config("transform", est)
assert config["dense"] == "pandas"
# If estimator has a config, use local config
est.set_output(transform="default")
config = _get_output_config("transform", est)
assert config["dense"] == "default"
est.set_output(transform="pandas")
config = _get_output_config("transform", est)
assert config["dense"] == "pandas"
class EstimatorWithSetOutputNoAutoWrap(_SetOutputMixin, auto_wrap_output_keys=None):
def transform(self, X, y=None):
return X
def test_get_output_auto_wrap_false():
"""Check that auto_wrap_output_keys=None does not wrap."""
est = EstimatorWithSetOutputNoAutoWrap()
assert not hasattr(est, "set_output")
X = np.asarray([[1, 0, 3], [0, 0, 1]])
assert X is est.transform(X)
def test_auto_wrap_output_keys_errors_with_incorrect_input():
msg = "auto_wrap_output_keys must be None or a tuple of keys."
with pytest.raises(ValueError, match=msg):
class BadEstimator(_SetOutputMixin, auto_wrap_output_keys="bad_parameter"):
pass
class AnotherMixin:
def __init_subclass__(cls, custom_parameter, **kwargs):
super().__init_subclass__(**kwargs)
cls.custom_parameter = custom_parameter
def test_set_output_mixin_custom_mixin():
"""Check that multiple init_subclasses passes parameters up."""
class BothMixinEstimator(_SetOutputMixin, AnotherMixin, custom_parameter=123):
def transform(self, X, y=None):
return X
def get_feature_names_out(self, input_features=None):
return input_features
est = BothMixinEstimator()
assert est.custom_parameter == 123
assert hasattr(est, "set_output")
def test__wrap_in_pandas_container_column_errors():
"""If a callable `columns` errors, it has the same semantics as columns=None."""
pd = pytest.importorskip("pandas")
def get_columns():
raise ValueError("No feature names defined")
X_df = pd.DataFrame({"feat1": [1, 2, 3], "feat2": [3, 4, 5]})
X_wrapped = _wrap_in_pandas_container(X_df, columns=get_columns)
assert_array_equal(X_wrapped.columns, X_df.columns)
X_np = np.asarray([[1, 3], [2, 4], [3, 5]])
X_wrapped = _wrap_in_pandas_container(X_np, columns=get_columns)
assert_array_equal(X_wrapped.columns, range(X_np.shape[1]))
def test_set_output_mro():
"""Check that multi-inheritance resolves to the correct class method.
Non-regression test gh-25293.
"""
class Base(_SetOutputMixin):
def transform(self, X):
return "Base" # noqa
class A(Base):
pass
class B(Base):
def transform(self, X):
return "B"
class C(A, B):
pass
assert C().transform(None) == "B"
class EstimatorWithSetOutputIndex(_SetOutputMixin):
def fit(self, X, y=None):
self.n_features_in_ = X.shape[1]
return self
def transform(self, X, y=None):
import pandas as pd
# transform by giving output a new index.
return pd.DataFrame(X.to_numpy(), index=[f"s{i}" for i in range(X.shape[0])])
def get_feature_names_out(self, input_features=None):
return np.asarray([f"X{i}" for i in range(self.n_features_in_)], dtype=object)
def test_set_output_pandas_keep_index():
"""Check that set_output does not override index.
Non-regression test for gh-25730.
"""
pd = pytest.importorskip("pandas")
X = pd.DataFrame([[1, 2, 3], [4, 5, 6]], index=[0, 1])
est = EstimatorWithSetOutputIndex().set_output(transform="pandas")
est.fit(X)
X_trans = est.transform(X)
assert_array_equal(X_trans.index, ["s0", "s1"])