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_column_transformer.py
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_column_transformer.py
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"""
The :mod:`sklearn.compose._column_transformer` module implements utilities
to work with heterogeneous data and to apply different transformers to
different columns.
"""
# Author: Andreas Mueller
# Joris Van den Bossche
# License: BSD
from numbers import Integral, Real
from itertools import chain
from collections import Counter
import numpy as np
from scipy import sparse
from ..base import clone, TransformerMixin
from ..utils._estimator_html_repr import _VisualBlock
from ..pipeline import _fit_transform_one, _transform_one, _name_estimators
from ..preprocessing import FunctionTransformer
from ..utils import Bunch
from ..utils import _safe_indexing
from ..utils import _get_column_indices
from ..utils._param_validation import HasMethods, Interval, StrOptions, Hidden
from ..utils._set_output import _get_output_config, _safe_set_output
from ..utils import check_pandas_support
from ..utils.metaestimators import _BaseComposition
from ..utils.validation import check_array, check_is_fitted, _check_feature_names_in
from ..utils.parallel import delayed, Parallel
__all__ = ["ColumnTransformer", "make_column_transformer", "make_column_selector"]
_ERR_MSG_1DCOLUMN = (
"1D data passed to a transformer that expects 2D data. "
"Try to specify the column selection as a list of one "
"item instead of a scalar."
)
class ColumnTransformer(TransformerMixin, _BaseComposition):
"""Applies transformers to columns of an array or pandas DataFrame.
This estimator allows different columns or column subsets of the input
to be transformed separately and the features generated by each transformer
will be concatenated to form a single feature space.
This is useful for heterogeneous or columnar data, to combine several
feature extraction mechanisms or transformations into a single transformer.
Read more in the :ref:`User Guide <column_transformer>`.
.. versionadded:: 0.20
Parameters
----------
transformers : list of tuples
List of (name, transformer, columns) tuples specifying the
transformer objects to be applied to subsets of the data.
name : str
Like in Pipeline and FeatureUnion, this allows the transformer and
its parameters to be set using ``set_params`` and searched in grid
search.
transformer : {'drop', 'passthrough'} or estimator
Estimator must support :term:`fit` and :term:`transform`.
Special-cased strings 'drop' and 'passthrough' are accepted as
well, to indicate to drop the columns or to pass them through
untransformed, respectively.
columns : str, array-like of str, int, array-like of int, \
array-like of bool, slice or callable
Indexes the data on its second axis. Integers are interpreted as
positional columns, while strings can reference DataFrame columns
by name. A scalar string or int should be used where
``transformer`` expects X to be a 1d array-like (vector),
otherwise a 2d array will be passed to the transformer.
A callable is passed the input data `X` and can return any of the
above. To select multiple columns by name or dtype, you can use
:obj:`make_column_selector`.
remainder : {'drop', 'passthrough'} or estimator, default='drop'
By default, only the specified columns in `transformers` are
transformed and combined in the output, and the non-specified
columns are dropped. (default of ``'drop'``).
By specifying ``remainder='passthrough'``, all remaining columns that
were not specified in `transformers` will be automatically passed
through. This subset of columns is concatenated with the output of
the transformers.
By setting ``remainder`` to be an estimator, the remaining
non-specified columns will use the ``remainder`` estimator. The
estimator must support :term:`fit` and :term:`transform`.
Note that using this feature requires that the DataFrame columns
input at :term:`fit` and :term:`transform` have identical order.
sparse_threshold : float, default=0.3
If the output of the different transformers contains sparse matrices,
these will be stacked as a sparse matrix if the overall density is
lower than this value. Use ``sparse_threshold=0`` to always return
dense. When the transformed output consists of all dense data, the
stacked result will be dense, and this keyword will be ignored.
n_jobs : int, default=None
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
transformer_weights : dict, default=None
Multiplicative weights for features per transformer. The output of the
transformer is multiplied by these weights. Keys are transformer names,
values the weights.
verbose : bool, default=False
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
verbose_feature_names_out : bool, default=True
If True, :meth:`get_feature_names_out` will prefix all feature names
with the name of the transformer that generated that feature.
If False, :meth:`get_feature_names_out` will not prefix any feature
names and will error if feature names are not unique.
.. versionadded:: 1.0
Attributes
----------
transformers_ : list
The collection of fitted transformers as tuples of
(name, fitted_transformer, column). `fitted_transformer` can be an
estimator, 'drop', or 'passthrough'. In case there were no columns
selected, this will be the unfitted transformer.
If there are remaining columns, the final element is a tuple of the
form:
('remainder', transformer, remaining_columns) corresponding to the
``remainder`` parameter. If there are remaining columns, then
``len(transformers_)==len(transformers)+1``, otherwise
``len(transformers_)==len(transformers)``.
named_transformers_ : :class:`~sklearn.utils.Bunch`
Read-only attribute to access any transformer by given name.
Keys are transformer names and values are the fitted transformer
objects.
sparse_output_ : bool
Boolean flag indicating whether the output of ``transform`` is a
sparse matrix or a dense numpy array, which depends on the output
of the individual transformers and the `sparse_threshold` keyword.
output_indices_ : dict
A dictionary from each transformer name to a slice, where the slice
corresponds to indices in the transformed output. This is useful to
inspect which transformer is responsible for which transformed
feature(s).
.. versionadded:: 1.0
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying transformers expose such an attribute when fit.
.. versionadded:: 0.24
See Also
--------
make_column_transformer : Convenience function for
combining the outputs of multiple transformer objects applied to
column subsets of the original feature space.
make_column_selector : Convenience function for selecting
columns based on datatype or the columns name with a regex pattern.
Notes
-----
The order of the columns in the transformed feature matrix follows the
order of how the columns are specified in the `transformers` list.
Columns of the original feature matrix that are not specified are
dropped from the resulting transformed feature matrix, unless specified
in the `passthrough` keyword. Those columns specified with `passthrough`
are added at the right to the output of the transformers.
Examples
--------
>>> import numpy as np
>>> from sklearn.compose import ColumnTransformer
>>> from sklearn.preprocessing import Normalizer
>>> ct = ColumnTransformer(
... [("norm1", Normalizer(norm='l1'), [0, 1]),
... ("norm2", Normalizer(norm='l1'), slice(2, 4))])
>>> X = np.array([[0., 1., 2., 2.],
... [1., 1., 0., 1.]])
>>> # Normalizer scales each row of X to unit norm. A separate scaling
>>> # is applied for the two first and two last elements of each
>>> # row independently.
>>> ct.fit_transform(X)
array([[0. , 1. , 0.5, 0.5],
[0.5, 0.5, 0. , 1. ]])
:class:`ColumnTransformer` can be configured with a transformer that requires
a 1d array by setting the column to a string:
>>> from sklearn.feature_extraction import FeatureHasher
>>> from sklearn.preprocessing import MinMaxScaler
>>> import pandas as pd # doctest: +SKIP
>>> X = pd.DataFrame({
... "documents": ["First item", "second one here", "Is this the last?"],
... "width": [3, 4, 5],
... }) # doctest: +SKIP
>>> # "documents" is a string which configures ColumnTransformer to
>>> # pass the documents column as a 1d array to the FeatureHasher
>>> ct = ColumnTransformer(
... [("text_preprocess", FeatureHasher(input_type="string"), "documents"),
... ("num_preprocess", MinMaxScaler(), ["width"])])
>>> X_trans = ct.fit_transform(X) # doctest: +SKIP
"""
_required_parameters = ["transformers"]
_parameter_constraints: dict = {
"transformers": [list, Hidden(tuple)],
"remainder": [
StrOptions({"drop", "passthrough"}),
HasMethods(["fit", "transform"]),
HasMethods(["fit_transform", "transform"]),
],
"sparse_threshold": [Interval(Real, 0, 1, closed="both")],
"n_jobs": [Integral, None],
"transformer_weights": [dict, None],
"verbose": ["verbose"],
"verbose_feature_names_out": ["boolean"],
}
def __init__(
self,
transformers,
*,
remainder="drop",
sparse_threshold=0.3,
n_jobs=None,
transformer_weights=None,
verbose=False,
verbose_feature_names_out=True,
):
self.transformers = transformers
self.remainder = remainder
self.sparse_threshold = sparse_threshold
self.n_jobs = n_jobs
self.transformer_weights = transformer_weights
self.verbose = verbose
self.verbose_feature_names_out = verbose_feature_names_out
@property
def _transformers(self):
"""
Internal list of transformer only containing the name and
transformers, dropping the columns. This is for the implementation
of get_params via BaseComposition._get_params which expects lists
of tuples of len 2.
"""
try:
return [(name, trans) for name, trans, _ in self.transformers]
except (TypeError, ValueError):
return self.transformers
@_transformers.setter
def _transformers(self, value):
try:
self.transformers = [
(name, trans, col)
for ((name, trans), (_, _, col)) in zip(value, self.transformers)
]
except (TypeError, ValueError):
self.transformers = value
def set_output(self, *, transform=None):
"""Set the output container when `"transform"` and `"fit_transform"` are called.
Calling `set_output` will set the output of all estimators in `transformers`
and `transformers_`.
Parameters
----------
transform : {"default", "pandas"}, default=None
Configure output of `transform` and `fit_transform`.
- `"default"`: Default output format of a transformer
- `"pandas"`: DataFrame output
- `None`: Transform configuration is unchanged
Returns
-------
self : estimator instance
Estimator instance.
"""
super().set_output(transform=transform)
transformers = (
trans
for _, trans, _ in chain(
self.transformers, getattr(self, "transformers_", [])
)
if trans not in {"passthrough", "drop"}
)
for trans in transformers:
_safe_set_output(trans, transform=transform)
return self
def get_params(self, deep=True):
"""Get parameters for this estimator.
Returns the parameters given in the constructor as well as the
estimators contained within the `transformers` of the
`ColumnTransformer`.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
return self._get_params("_transformers", deep=deep)
def set_params(self, **kwargs):
"""Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``. Note that you
can directly set the parameters of the estimators contained in
`transformers` of `ColumnTransformer`.
Parameters
----------
**kwargs : dict
Estimator parameters.
Returns
-------
self : ColumnTransformer
This estimator.
"""
self._set_params("_transformers", **kwargs)
return self
def _iter(self, fitted=False, replace_strings=False, column_as_strings=False):
"""
Generate (name, trans, column, weight) tuples.
If fitted=True, use the fitted transformers, else use the
user specified transformers updated with converted column names
and potentially appended with transformer for remainder.
"""
if fitted:
if replace_strings:
# Replace "passthrough" with the fitted version in
# _name_to_fitted_passthrough
def replace_passthrough(name, trans, columns):
if name not in self._name_to_fitted_passthrough:
return name, trans, columns
return name, self._name_to_fitted_passthrough[name], columns
transformers = [
replace_passthrough(*trans) for trans in self.transformers_
]
else:
transformers = self.transformers_
else:
# interleave the validated column specifiers
transformers = [
(name, trans, column)
for (name, trans, _), column in zip(self.transformers, self._columns)
]
# add transformer tuple for remainder
if self._remainder[2]:
transformers = chain(transformers, [self._remainder])
get_weight = (self.transformer_weights or {}).get
output_config = _get_output_config("transform", self)
for name, trans, columns in transformers:
if replace_strings:
# replace 'passthrough' with identity transformer and
# skip in case of 'drop'
if trans == "passthrough":
trans = FunctionTransformer(
accept_sparse=True,
check_inverse=False,
feature_names_out="one-to-one",
).set_output(transform=output_config["dense"])
elif trans == "drop":
continue
elif _is_empty_column_selection(columns):
continue
if column_as_strings:
# Convert all columns to using their string labels
columns_is_scalar = np.isscalar(columns)
indices = self._transformer_to_input_indices[name]
columns = self.feature_names_in_[indices]
if columns_is_scalar:
# selection is done with one dimension
columns = columns[0]
yield (name, trans, columns, get_weight(name))
def _validate_transformers(self):
if not self.transformers:
return
names, transformers, _ = zip(*self.transformers)
# validate names
self._validate_names(names)
# validate estimators
for t in transformers:
if t in ("drop", "passthrough"):
continue
if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(
t, "transform"
):
# Used to validate the transformers in the `transformers` list
raise TypeError(
"All estimators should implement fit and "
"transform, or can be 'drop' or 'passthrough' "
"specifiers. '%s' (type %s) doesn't." % (t, type(t))
)
def _validate_column_callables(self, X):
"""
Converts callable column specifications.
"""
all_columns = []
transformer_to_input_indices = {}
for name, _, columns in self.transformers:
if callable(columns):
columns = columns(X)
all_columns.append(columns)
transformer_to_input_indices[name] = _get_column_indices(X, columns)
self._columns = all_columns
self._transformer_to_input_indices = transformer_to_input_indices
def _validate_remainder(self, X):
"""
Validates ``remainder`` and defines ``_remainder`` targeting
the remaining columns.
"""
self._n_features = X.shape[1]
cols = set(chain(*self._transformer_to_input_indices.values()))
remaining = sorted(set(range(self._n_features)) - cols)
self._remainder = ("remainder", self.remainder, remaining)
self._transformer_to_input_indices["remainder"] = remaining
@property
def named_transformers_(self):
"""Access the fitted transformer by name.
Read-only attribute to access any transformer by given name.
Keys are transformer names and values are the fitted transformer
objects.
"""
# Use Bunch object to improve autocomplete
return Bunch(**{name: trans for name, trans, _ in self.transformers_})
def _get_feature_name_out_for_transformer(
self, name, trans, column, feature_names_in
):
"""Gets feature names of transformer.
Used in conjunction with self._iter(fitted=True) in get_feature_names_out.
"""
column_indices = self._transformer_to_input_indices[name]
names = feature_names_in[column_indices]
if trans == "drop" or _is_empty_column_selection(column):
return
elif trans == "passthrough":
return names
# An actual transformer
if not hasattr(trans, "get_feature_names_out"):
raise AttributeError(
f"Transformer {name} (type {type(trans).__name__}) does "
"not provide get_feature_names_out."
)
return trans.get_feature_names_out(names)
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
- If `input_features` is `None`, then `feature_names_in_` is
used as feature names in. If `feature_names_in_` is not defined,
then the following input feature names are generated:
`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
- If `input_features` is an array-like, then `input_features` must
match `feature_names_in_` if `feature_names_in_` is defined.
Returns
-------
feature_names_out : ndarray of str objects
Transformed feature names.
"""
check_is_fitted(self)
input_features = _check_feature_names_in(self, input_features)
# List of tuples (name, feature_names_out)
transformer_with_feature_names_out = []
for name, trans, column, _ in self._iter(fitted=True):
feature_names_out = self._get_feature_name_out_for_transformer(
name, trans, column, input_features
)
if feature_names_out is None:
continue
transformer_with_feature_names_out.append((name, feature_names_out))
if not transformer_with_feature_names_out:
# No feature names
return np.array([], dtype=object)
return self._add_prefix_for_feature_names_out(
transformer_with_feature_names_out
)
def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):
"""Add prefix for feature names out that includes the transformer names.
Parameters
----------
transformer_with_feature_names_out : list of tuples of (str, array-like of str)
The tuple consistent of the transformer's name and its feature names out.
Returns
-------
feature_names_out : ndarray of shape (n_features,), dtype=str
Transformed feature names.
"""
if self.verbose_feature_names_out:
# Prefix the feature names out with the transformers name
names = list(
chain.from_iterable(
(f"{name}__{i}" for i in feature_names_out)
for name, feature_names_out in transformer_with_feature_names_out
)
)
return np.asarray(names, dtype=object)
# verbose_feature_names_out is False
# Check that names are all unique without a prefix
feature_names_count = Counter(
chain.from_iterable(s for _, s in transformer_with_feature_names_out)
)
top_6_overlap = [
name for name, count in feature_names_count.most_common(6) if count > 1
]
top_6_overlap.sort()
if top_6_overlap:
if len(top_6_overlap) == 6:
# There are more than 5 overlapping names, we only show the 5
# of the feature names
names_repr = str(top_6_overlap[:5])[:-1] + ", ...]"
else:
names_repr = str(top_6_overlap)
raise ValueError(
f"Output feature names: {names_repr} are not unique. Please set "
"verbose_feature_names_out=True to add prefixes to feature names"
)
return np.concatenate(
[name for _, name in transformer_with_feature_names_out],
)
def _update_fitted_transformers(self, transformers):
# transformers are fitted; excludes 'drop' cases
fitted_transformers = iter(transformers)
transformers_ = []
self._name_to_fitted_passthrough = {}
for name, old, column, _ in self._iter():
if old == "drop":
trans = "drop"
elif old == "passthrough":
# FunctionTransformer is present in list of transformers,
# so get next transformer, but save original string
func_transformer = next(fitted_transformers)
trans = "passthrough"
# The fitted FunctionTransformer is saved in another attribute,
# so it can be used during transform for set_output.
self._name_to_fitted_passthrough[name] = func_transformer
elif _is_empty_column_selection(column):
trans = old
else:
trans = next(fitted_transformers)
transformers_.append((name, trans, column))
# sanity check that transformers is exhausted
assert not list(fitted_transformers)
self.transformers_ = transformers_
def _validate_output(self, result):
"""
Ensure that the output of each transformer is 2D. Otherwise
hstack can raise an error or produce incorrect results.
"""
names = [
name for name, _, _, _ in self._iter(fitted=True, replace_strings=True)
]
for Xs, name in zip(result, names):
if not getattr(Xs, "ndim", 0) == 2:
raise ValueError(
"The output of the '{0}' transformer should be 2D (scipy "
"matrix, array, or pandas DataFrame).".format(name)
)
def _record_output_indices(self, Xs):
"""
Record which transformer produced which column.
"""
idx = 0
self.output_indices_ = {}
for transformer_idx, (name, _, _, _) in enumerate(
self._iter(fitted=True, replace_strings=True)
):
n_columns = Xs[transformer_idx].shape[1]
self.output_indices_[name] = slice(idx, idx + n_columns)
idx += n_columns
# `_iter` only generates transformers that have a non empty
# selection. Here we set empty slices for transformers that
# generate no output, which are safe for indexing
all_names = [t[0] for t in self.transformers] + ["remainder"]
for name in all_names:
if name not in self.output_indices_:
self.output_indices_[name] = slice(0, 0)
def _log_message(self, name, idx, total):
if not self.verbose:
return None
return "(%d of %d) Processing %s" % (idx, total, name)
def _fit_transform(self, X, y, func, fitted=False, column_as_strings=False):
"""
Private function to fit and/or transform on demand.
Return value (transformers and/or transformed X data) depends
on the passed function.
``fitted=True`` ensures the fitted transformers are used.
"""
transformers = list(
self._iter(
fitted=fitted, replace_strings=True, column_as_strings=column_as_strings
)
)
try:
return Parallel(n_jobs=self.n_jobs)(
delayed(func)(
transformer=clone(trans) if not fitted else trans,
X=_safe_indexing(X, column, axis=1),
y=y,
weight=weight,
message_clsname="ColumnTransformer",
message=self._log_message(name, idx, len(transformers)),
)
for idx, (name, trans, column, weight) in enumerate(transformers, 1)
)
except ValueError as e:
if "Expected 2D array, got 1D array instead" in str(e):
raise ValueError(_ERR_MSG_1DCOLUMN) from e
else:
raise
def fit(self, X, y=None):
"""Fit all transformers using X.
Parameters
----------
X : {array-like, dataframe} of shape (n_samples, n_features)
Input data, of which specified subsets are used to fit the
transformers.
y : array-like of shape (n_samples,...), default=None
Targets for supervised learning.
Returns
-------
self : ColumnTransformer
This estimator.
"""
self._validate_params()
# we use fit_transform to make sure to set sparse_output_ (for which we
# need the transformed data) to have consistent output type in predict
self.fit_transform(X, y=y)
return self
def fit_transform(self, X, y=None):
"""Fit all transformers, transform the data and concatenate results.
Parameters
----------
X : {array-like, dataframe} of shape (n_samples, n_features)
Input data, of which specified subsets are used to fit the
transformers.
y : array-like of shape (n_samples,), default=None
Targets for supervised learning.
Returns
-------
X_t : {array-like, sparse matrix} of \
shape (n_samples, sum_n_components)
Horizontally stacked results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.
"""
self._validate_params()
self._check_feature_names(X, reset=True)
X = _check_X(X)
# set n_features_in_ attribute
self._check_n_features(X, reset=True)
self._validate_transformers()
self._validate_column_callables(X)
self._validate_remainder(X)
result = self._fit_transform(X, y, _fit_transform_one)
if not result:
self._update_fitted_transformers([])
# All transformers are None
return np.zeros((X.shape[0], 0))
Xs, transformers = zip(*result)
# determine if concatenated output will be sparse or not
if any(sparse.issparse(X) for X in Xs):
nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs)
total = sum(
X.shape[0] * X.shape[1] if sparse.issparse(X) else X.size for X in Xs
)
density = nnz / total
self.sparse_output_ = density < self.sparse_threshold
else:
self.sparse_output_ = False
self._update_fitted_transformers(transformers)
self._validate_output(Xs)
self._record_output_indices(Xs)
return self._hstack(list(Xs))
def transform(self, X):
"""Transform X separately by each transformer, concatenate results.
Parameters
----------
X : {array-like, dataframe} of shape (n_samples, n_features)
The data to be transformed by subset.
Returns
-------
X_t : {array-like, sparse matrix} of \
shape (n_samples, sum_n_components)
Horizontally stacked results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.
"""
check_is_fitted(self)
X = _check_X(X)
fit_dataframe_and_transform_dataframe = hasattr(
self, "feature_names_in_"
) and hasattr(X, "columns")
if fit_dataframe_and_transform_dataframe:
named_transformers = self.named_transformers_
# check that all names seen in fit are in transform, unless
# they were dropped
non_dropped_indices = [
ind
for name, ind in self._transformer_to_input_indices.items()
if name in named_transformers
and isinstance(named_transformers[name], str)
and named_transformers[name] != "drop"
]
all_indices = set(chain(*non_dropped_indices))
all_names = set(self.feature_names_in_[ind] for ind in all_indices)
diff = all_names - set(X.columns)
if diff:
raise ValueError(f"columns are missing: {diff}")
else:
# ndarray was used for fitting or transforming, thus we only
# check that n_features_in_ is consistent
self._check_n_features(X, reset=False)
Xs = self._fit_transform(
X,
None,
_transform_one,
fitted=True,
column_as_strings=fit_dataframe_and_transform_dataframe,
)
self._validate_output(Xs)
if not Xs:
# All transformers are None
return np.zeros((X.shape[0], 0))
return self._hstack(list(Xs))
def _hstack(self, Xs):
"""Stacks Xs horizontally.
This allows subclasses to control the stacking behavior, while reusing
everything else from ColumnTransformer.
Parameters
----------
Xs : list of {array-like, sparse matrix, dataframe}
"""
if self.sparse_output_:
try:
# since all columns should be numeric before stacking them
# in a sparse matrix, `check_array` is used for the
# dtype conversion if necessary.
converted_Xs = [
check_array(X, accept_sparse=True, force_all_finite=False)
for X in Xs
]
except ValueError as e:
raise ValueError(
"For a sparse output, all columns should "
"be a numeric or convertible to a numeric."
) from e
return sparse.hstack(converted_Xs).tocsr()
else:
Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs]
config = _get_output_config("transform", self)
if config["dense"] == "pandas" and all(hasattr(X, "iloc") for X in Xs):
pd = check_pandas_support("transform")
output = pd.concat(Xs, axis=1)
# If all transformers define `get_feature_names_out`, then transform
# will adjust the column names to be consistent with
# verbose_feature_names_out. Here we prefix the feature names if
# verbose_feature_names_out=True.
if not self.verbose_feature_names_out:
return output
transformer_names = [
t[0] for t in self._iter(fitted=True, replace_strings=True)
]
feature_names_outs = [X.columns for X in Xs]
names_out = self._add_prefix_for_feature_names_out(
list(zip(transformer_names, feature_names_outs))
)
output.columns = names_out
return output
return np.hstack(Xs)
def _sk_visual_block_(self):
if isinstance(self.remainder, str) and self.remainder == "drop":
transformers = self.transformers
elif hasattr(self, "_remainder"):
remainder_columns = self._remainder[2]
if (
hasattr(self, "feature_names_in_")
and remainder_columns
and not all(isinstance(col, str) for col in remainder_columns)
):
remainder_columns = self.feature_names_in_[remainder_columns].tolist()
transformers = chain(
self.transformers, [("remainder", self.remainder, remainder_columns)]
)
else:
transformers = chain(self.transformers, [("remainder", self.remainder, "")])
names, transformers, name_details = zip(*transformers)
return _VisualBlock(
"parallel", transformers, names=names, name_details=name_details
)
def _check_X(X):
"""Use check_array only on lists and other non-array-likes / sparse"""
if hasattr(X, "__array__") or sparse.issparse(X):
return X
return check_array(X, force_all_finite="allow-nan", dtype=object)
def _is_empty_column_selection(column):
"""
Return True if the column selection is empty (empty list or all-False
boolean array).
"""
if hasattr(column, "dtype") and np.issubdtype(column.dtype, np.bool_):
return not column.any()
elif hasattr(column, "__len__"):
return (
len(column) == 0
or all(isinstance(col, bool) for col in column)
and not any(column)
)
else:
return False
def _get_transformer_list(estimators):
"""
Construct (name, trans, column) tuples from list
"""
transformers, columns = zip(*estimators)
names, _ = zip(*_name_estimators(transformers))
transformer_list = list(zip(names, transformers, columns))
return transformer_list
def make_column_transformer(
*transformers,
remainder="drop",
sparse_threshold=0.3,
n_jobs=None,
verbose=False,
verbose_feature_names_out=True,
):
"""Construct a ColumnTransformer from the given transformers.
This is a shorthand for the ColumnTransformer constructor; it does not
require, and does not permit, naming the transformers. Instead, they will
be given names automatically based on their types. It also does not allow
weighting with ``transformer_weights``.
Read more in the :ref:`User Guide <make_column_transformer>`.
Parameters
----------
*transformers : tuples
Tuples of the form (transformer, columns) specifying the
transformer objects to be applied to subsets of the data.
transformer : {'drop', 'passthrough'} or estimator
Estimator must support :term:`fit` and :term:`transform`.
Special-cased strings 'drop' and 'passthrough' are accepted as
well, to indicate to drop the columns or to pass them through
untransformed, respectively.
columns : str, array-like of str, int, array-like of int, slice, \
array-like of bool or callable
Indexes the data on its second axis. Integers are interpreted as
positional columns, while strings can reference DataFrame columns
by name. A scalar string or int should be used where
``transformer`` expects X to be a 1d array-like (vector),
otherwise a 2d array will be passed to the transformer.
A callable is passed the input data `X` and can return any of the
above. To select multiple columns by name or dtype, you can use
:obj:`make_column_selector`.
remainder : {'drop', 'passthrough'} or estimator, default='drop'
By default, only the specified columns in `transformers` are
transformed and combined in the output, and the non-specified
columns are dropped. (default of ``'drop'``).
By specifying ``remainder='passthrough'``, all remaining columns that
were not specified in `transformers` will be automatically passed
through. This subset of columns is concatenated with the output of
the transformers.
By setting ``remainder`` to be an estimator, the remaining
non-specified columns will use the ``remainder`` estimator. The
estimator must support :term:`fit` and :term:`transform`.
sparse_threshold : float, default=0.3
If the transformed output consists of a mix of sparse and dense data,
it will be stacked as a sparse matrix if the density is lower than this
value. Use ``sparse_threshold=0`` to always return dense.
When the transformed output consists of all sparse or all dense data,
the stacked result will be sparse or dense, respectively, and this
keyword will be ignored.
n_jobs : int, default=None
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
verbose : bool, default=False