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3RNN/Lib/site-packages/sklearn/compose/_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
import warnings
from collections import Counter, UserList
from itertools import chain
from numbers import Integral, Real
import numpy as np
from scipy import sparse
from ..base import TransformerMixin, _fit_context, clone
from ..pipeline import _fit_transform_one, _name_estimators, _transform_one
from ..preprocessing import FunctionTransformer
from ..utils import Bunch
from ..utils._estimator_html_repr import _VisualBlock
from ..utils._indexing import _determine_key_type, _get_column_indices
from ..utils._metadata_requests import METHODS
from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions
from ..utils._set_output import (
_get_container_adapter,
_get_output_config,
_safe_set_output,
)
from ..utils.metadata_routing import (
MetadataRouter,
MethodMapping,
_raise_for_params,
_routing_enabled,
process_routing,
)
from ..utils.metaestimators import _BaseComposition
from ..utils.parallel import Parallel, delayed
from ..utils.validation import (
_check_feature_names_in,
_get_feature_names,
_is_pandas_df,
_num_samples,
check_array,
check_is_fitted,
)
__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`, but present in the data passed
to `fit` will be automatically passed through. This subset of columns
is concatenated with the output of the transformers. For dataframes,
extra columns not seen during `fit` will be excluded from the output
of `transform`.
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:`ColumnTransformer.get_feature_names_out` will prefix
all feature names with the name of the transformer that generated that
feature.
If False, :meth:`ColumnTransformer.get_feature_names_out` will not
prefix any feature names and will error if feature names are not
unique.
.. versionadded:: 1.0
force_int_remainder_cols : bool, default=True
Force the columns of the last entry of `transformers_`, which
corresponds to the "remainder" transformer, to always be stored as
indices (int) rather than column names (str). See description of the
`transformers_` attribute for details.
.. note::
If you do not access the list of columns for the remainder columns
in the `transformers_` fitted attribute, you do not need to set
this parameter.
.. versionadded:: 1.5
.. versionchanged:: 1.7
The default value for `force_int_remainder_cols` will change from
`True` to `False` in version 1.7.
Attributes
----------
transformers_ : list
The collection of fitted transformers as tuples of (name,
fitted_transformer, column). `fitted_transformer` can be an estimator,
or `'drop'`; `'passthrough'` is replaced with an equivalent
:class:`~sklearn.preprocessing.FunctionTransformer`. 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)``.
.. versionchanged:: 1.5
If there are remaining columns and `force_int_remainder_cols` is
True, the remaining columns are always represented by their
positional indices in the input `X` (as in older versions). If
`force_int_remainder_cols` is False, the format attempts to match
that of the other transformers: if all columns were provided as
column names (`str`), the remaining columns are stored as column
names; if all columns were provided as mask arrays (`bool`), so are
the remaining columns; in all other cases the remaining columns are
stored as indices (`int`).
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
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
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.text import CountVectorizer
>>> 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 CountVectorizer
>>> ct = ColumnTransformer(
... [("text_preprocess", CountVectorizer(), "documents"),
... ("num_preprocess", MinMaxScaler(), ["width"])])
>>> X_trans = ct.fit_transform(X) # doctest: +SKIP
For a more detailed example of usage, see
:ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`.
"""
_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"],
"force_int_remainder_cols": ["boolean"],
}
def __init__(
self,
transformers,
*,
remainder="drop",
sparse_threshold=0.3,
n_jobs=None,
transformer_weights=None,
verbose=False,
verbose_feature_names_out=True,
force_int_remainder_cols=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
self.force_int_remainder_cols = force_int_remainder_cols
@property
def _transformers(self):
"""
Internal list of transformer only containing the name and
transformers, dropping the columns.
DO NOT USE: This is for the implementation of get_params via
BaseComposition._get_params which expects lists of tuples of len 2.
To iterate through the transformers, use ``self._iter`` instead.
"""
try:
return [(name, trans) for name, trans, _ in self.transformers]
except (TypeError, ValueError):
return self.transformers
@_transformers.setter
def _transformers(self, value):
"""DO NOT USE: This is for the implementation of set_params via
BaseComposition._get_params which gives lists of tuples of len 2.
"""
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", "polars"}, default=None
Configure output of `transform` and `fit_transform`.
- `"default"`: Default output format of a transformer
- `"pandas"`: DataFrame output
- `"polars"`: Polars output
- `None`: Transform configuration is unchanged
.. versionadded:: 1.4
`"polars"` option was added.
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)
if self.remainder not in {"passthrough", "drop"}:
_safe_set_output(self.remainder, 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, column_as_labels, skip_drop, skip_empty_columns):
"""
Generate (name, trans, columns, weight) tuples.
Parameters
----------
fitted : bool
If True, use the fitted transformers (``self.transformers_``) to
iterate through transformers, else use the transformers passed by
the user (``self.transformers``).
column_as_labels : bool
If True, columns are returned as string labels. If False, columns
are returned as they were given by the user. This can only be True
if the ``ColumnTransformer`` is already fitted.
skip_drop : bool
If True, 'drop' transformers are filtered out.
skip_empty_columns : bool
If True, transformers with empty selected columns are filtered out.
Yields
------
A generator of tuples containing:
- name : the name of the transformer
- transformer : the transformer object
- columns : the columns for that transformer
- weight : the weight of the transformer
"""
if fitted:
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])
# We want the warning about the future change of the remainder
# columns dtype to be shown only when a user accesses them
# directly, not when they are used by the ColumnTransformer itself.
# We disable warnings here; they are enabled when setting
# self.transformers_.
transformers = _with_dtype_warning_enabled_set_to(False, transformers)
get_weight = (self.transformer_weights or {}).get
for name, trans, columns in transformers:
if skip_drop and trans == "drop":
continue
if skip_empty_columns and _is_empty_column_selection(columns):
continue
if column_as_labels:
# 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):
"""Validate names of transformers and the transformers themselves.
This checks whether given transformers have the required methods, i.e.
`fit` or `fit_transform` and `transform` implemented.
"""
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.
This stores a dictionary of the form `{step_name: column_indices}` and
calls the `columns` on `X` if `columns` is a callable for a given
transformer.
The results are then stored in `self._transformer_to_input_indices`.
"""
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.
"""
cols = set(chain(*self._transformer_to_input_indices.values()))
remaining = sorted(set(range(self.n_features_in_)) - cols)
self._transformer_to_input_indices["remainder"] = remaining
remainder_cols = self._get_remainder_cols(remaining)
self._remainder = ("remainder", self.remainder, remainder_cols)
def _get_remainder_cols_dtype(self):
try:
all_dtypes = {_determine_key_type(c) for (*_, c) in self.transformers}
if len(all_dtypes) == 1:
return next(iter(all_dtypes))
except ValueError:
# _determine_key_type raises a ValueError if some transformer
# columns are Callables
return "int"
return "int"
def _get_remainder_cols(self, indices):
dtype = self._get_remainder_cols_dtype()
if self.force_int_remainder_cols and dtype != "int":
return _RemainderColsList(indices, future_dtype=dtype)
if dtype == "str":
return list(self.feature_names_in_[indices])
if dtype == "bool":
return [i in indices for i in range(self.n_features_in_)]
return indices
@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, 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]
# 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, *_ in self._iter(
fitted=True,
column_as_labels=False,
skip_empty_columns=True,
skip_drop=True,
):
feature_names_out = self._get_feature_name_out_for_transformer(
name, trans, 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):
"""Set self.transformers_ from given transformers.
Parameters
----------
transformers : list of estimators
The fitted estimators as the output of
`self._call_func_on_transformers(func=_fit_transform_one, ...)`.
That function doesn't include 'drop' or transformers for which no
column is selected. 'drop' is kept as is, and for the no-column
transformers the unfitted transformer is put in
`self.transformers_`.
"""
# transformers are fitted; excludes 'drop' cases
fitted_transformers = iter(transformers)
transformers_ = []
for name, old, column, _ in self._iter(
fitted=False,
column_as_labels=False,
skip_drop=False,
skip_empty_columns=False,
):
if old == "drop":
trans = "drop"
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_ = _with_dtype_warning_enabled_set_to(True, 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,
column_as_labels=False,
skip_drop=True,
skip_empty_columns=True,
)
]
for Xs, name in zip(result, names):
if not getattr(Xs, "ndim", 0) == 2 and not hasattr(Xs, "__dataframe__"):
raise ValueError(
"The output of the '{0}' transformer should be 2D (numpy array, "
"scipy sparse array, dataframe).".format(name)
)
if _get_output_config("transform", self)["dense"] == "pandas":
return
try:
import pandas as pd
except ImportError:
return
for Xs, name in zip(result, names):
if not _is_pandas_df(Xs):
continue
for col_name, dtype in Xs.dtypes.to_dict().items():
if getattr(dtype, "na_value", None) is not pd.NA:
continue
if pd.NA not in Xs[col_name].values:
continue
class_name = self.__class__.__name__
# TODO(1.6): replace warning with ValueError
warnings.warn(
(
f"The output of the '{name}' transformer for column"
f" '{col_name}' has dtype {dtype} and uses pandas.NA to"
" represent null values. Storing this output in a numpy array"
" can cause errors in downstream scikit-learn estimators, and"
" inefficiencies. Starting with scikit-learn version 1.6, this"
" will raise a ValueError. To avoid this problem you can (i)"
" store the output in a pandas DataFrame by using"
f" {class_name}.set_output(transform='pandas') or (ii) modify"
f" the input data or the '{name}' transformer to avoid the"
" presence of pandas.NA (for example by using"
" pandas.DataFrame.astype)."
),
FutureWarning,
)
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,
column_as_labels=False,
skip_drop=True,
skip_empty_columns=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 _call_func_on_transformers(self, X, y, func, column_as_labels, routed_params):
"""
Private function to fit and/or transform on demand.
Parameters
----------
X : {array-like, dataframe} of shape (n_samples, n_features)
The data to be used in fit and/or transform.
y : array-like of shape (n_samples,)
Targets.
func : callable
Function to call, which can be _fit_transform_one or
_transform_one.
column_as_labels : bool
Used to iterate through transformers. If True, columns are returned
as strings. If False, columns are returned as they were given by
the user. Can be True only if the ``ColumnTransformer`` is already
fitted.
routed_params : dict
The routed parameters as the output from ``process_routing``.
Returns
-------
Return value (transformers and/or transformed X data) depends
on the passed function.
"""
if func is _fit_transform_one:
fitted = False
else: # func is _transform_one
fitted = True
transformers = list(
self._iter(
fitted=fitted,
column_as_labels=column_as_labels,
skip_drop=True,
skip_empty_columns=True,
)
)
try:
jobs = []
for idx, (name, trans, columns, weight) in enumerate(transformers, start=1):
if func is _fit_transform_one:
if trans == "passthrough":
output_config = _get_output_config("transform", self)
trans = FunctionTransformer(
accept_sparse=True,
check_inverse=False,
feature_names_out="one-to-one",
).set_output(transform=output_config["dense"])
extra_args = dict(
message_clsname="ColumnTransformer",
message=self._log_message(name, idx, len(transformers)),
)
else: # func is _transform_one
extra_args = {}
jobs.append(
delayed(func)(
transformer=clone(trans) if not fitted else trans,
X=X,
y=y,
weight=weight,
columns=columns,
**extra_args,
params=routed_params[name],
)
)
return Parallel(n_jobs=self.n_jobs)(jobs)
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, **params):
"""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.
**params : dict, default=None
Parameters to be passed to the underlying transformers' ``fit`` and
``transform`` methods.
You can only pass this if metadata routing is enabled, which you
can enable using ``sklearn.set_config(enable_metadata_routing=True)``.
.. versionadded:: 1.4
Returns
-------
self : ColumnTransformer
This estimator.
"""
_raise_for_params(params, self, "fit")
# 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, **params)
return self
@_fit_context(
# estimators in ColumnTransformer.transformers are not validated yet
prefer_skip_nested_validation=False
)
def fit_transform(self, X, y=None, **params):
"""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.
**params : dict, default=None
Parameters to be passed to the underlying transformers' ``fit`` and
``transform`` methods.
You can only pass this if metadata routing is enabled, which you
can enable using ``sklearn.set_config(enable_metadata_routing=True)``.
.. versionadded:: 1.4
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.
"""
_raise_for_params(params, self, "fit_transform")
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()
n_samples = _num_samples(X)
self._validate_column_callables(X)
self._validate_remainder(X)
if _routing_enabled():
routed_params = process_routing(self, "fit_transform", **params)
else:
routed_params = self._get_empty_routing()
result = self._call_func_on_transformers(
X,
y,
_fit_transform_one,
column_as_labels=False,
routed_params=routed_params,
)
if not result:
self._update_fitted_transformers([])
# All transformers are None
return np.zeros((n_samples, 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), n_samples=n_samples)
def transform(self, X, **params):
"""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.
**params : dict, default=None
Parameters to be passed to the underlying transformers' ``transform``
method.
You can only pass this if metadata routing is enabled, which you
can enable using ``sklearn.set_config(enable_metadata_routing=True)``.
.. versionadded:: 1.4
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.
"""
_raise_for_params(params, self, "transform")
check_is_fitted(self)
X = _check_X(X)
# If ColumnTransformer is fit using a dataframe, and now a dataframe is
# passed to be transformed, we select columns by name instead. This
# enables the user to pass X at transform time with extra columns which
# were not present in fit time, and the order of the columns doesn't
# matter.
fit_dataframe_and_transform_dataframe = hasattr(self, "feature_names_in_") and (
_is_pandas_df(X) or hasattr(X, "__dataframe__")
)
n_samples = _num_samples(X)
column_names = _get_feature_names(X)
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 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(column_names)
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)
if _routing_enabled():
routed_params = process_routing(self, "transform", **params)
else:
routed_params = self._get_empty_routing()
Xs = self._call_func_on_transformers(
X,
None,
_transform_one,
column_as_labels=fit_dataframe_and_transform_dataframe,
routed_params=routed_params,
)
self._validate_output(Xs)
if not Xs:
# All transformers are None
return np.zeros((n_samples, 0))
return self._hstack(list(Xs), n_samples=n_samples)
def _hstack(self, Xs, *, n_samples):
"""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}
The container to concatenate.
n_samples : int
The number of samples in the input data to checking the transformation
consistency.
"""
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]
adapter = _get_container_adapter("transform", self)
if adapter and all(adapter.is_supported_container(X) for X in Xs):
# rename before stacking as it avoids to error on temporary duplicated
# columns
transformer_names = [
t[0]
for t in self._iter(
fitted=True,
column_as_labels=False,
skip_drop=True,
skip_empty_columns=True,
)
]
feature_names_outs = [X.columns for X in Xs if X.shape[1] != 0]
if self.verbose_feature_names_out:
# `_add_prefix_for_feature_names_out` takes care about raising
# an error if there are duplicated columns.
feature_names_outs = self._add_prefix_for_feature_names_out(
list(zip(transformer_names, feature_names_outs))
)
else:
# check for duplicated columns and raise if any
feature_names_outs = list(chain.from_iterable(feature_names_outs))
feature_names_count = Counter(feature_names_outs)
if any(count > 1 for count in feature_names_count.values()):
duplicated_feature_names = sorted(
name
for name, count in feature_names_count.items()
if count > 1
)
err_msg = (
"Duplicated feature names found before concatenating the"
" outputs of the transformers:"
f" {duplicated_feature_names}.\n"
)
for transformer_name, X in zip(transformer_names, Xs):
if X.shape[1] == 0:
continue
dup_cols_in_transformer = sorted(
set(X.columns).intersection(duplicated_feature_names)
)
if len(dup_cols_in_transformer):
err_msg += (
f"Transformer {transformer_name} has conflicting "
f"columns names: {dup_cols_in_transformer}.\n"
)
raise ValueError(
err_msg
+ "Either make sure that the transformers named above "
"do not generate columns with conflicting names or set "
"verbose_feature_names_out=True to automatically "
"prefix to the output feature names with the name "
"of the transformer to prevent any conflicting "
"names."
)
names_idx = 0
for X in Xs:
if X.shape[1] == 0:
continue
names_out = feature_names_outs[names_idx : names_idx + X.shape[1]]
adapter.rename_columns(X, names_out)
names_idx += X.shape[1]
output = adapter.hstack(Xs)
output_samples = output.shape[0]
if output_samples != n_samples:
raise ValueError(
"Concatenating DataFrames from the transformer's output lead to"
" an inconsistent number of samples. The output may have Pandas"
" Indexes that do not match, or that transformers are returning"
" number of samples which are not the same as the number input"
" samples."
)
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 __getitem__(self, key):
try:
return self.named_transformers_[key]
except AttributeError as e:
raise TypeError(
"ColumnTransformer is subscriptable after it is fitted"
) from e
except KeyError as e:
raise KeyError(f"'{key}' is not a valid transformer name") from e
def _get_empty_routing(self):
"""Return empty routing.
Used while routing can be disabled.
TODO: Remove when ``set_config(enable_metadata_routing=False)`` is no
more an option.
"""
return Bunch(
**{
name: Bunch(**{method: {} for method in METHODS})
for name, step, _, _ in self._iter(
fitted=False,
column_as_labels=False,
skip_drop=True,
skip_empty_columns=True,
)
}
)
def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
.. versionadded:: 1.4
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
routing information.
"""
router = MetadataRouter(owner=self.__class__.__name__)
# Here we don't care about which columns are used for which
# transformers, and whether or not a transformer is used at all, which
# might happen if no columns are selected for that transformer. We
# request all metadata requested by all transformers.
transformers = chain(self.transformers, [("remainder", self.remainder, None)])
for name, step, _ in transformers:
method_mapping = MethodMapping()
if hasattr(step, "fit_transform"):
(
method_mapping.add(caller="fit", callee="fit_transform").add(
caller="fit_transform", callee="fit_transform"
)
)
else:
(
method_mapping.add(caller="fit", callee="fit")
.add(caller="fit", callee="transform")
.add(caller="fit_transform", callee="fit")
.add(caller="fit_transform", callee="transform")
)
method_mapping.add(caller="transform", callee="transform")
router.add(method_mapping=method_mapping, **{name: step})
return router
def _check_X(X):
"""Use check_array only when necessary, e.g. on lists and other non-array-likes."""
if hasattr(X, "__array__") or hasattr(X, "__dataframe__") 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
# This function is not validated using validate_params because
# it's just a factory for ColumnTransformer.
def make_column_transformer(
*transformers,
remainder="drop",
sparse_threshold=0.3,
n_jobs=None,
verbose=False,
verbose_feature_names_out=True,
force_int_remainder_cols=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
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:`ColumnTransformer.get_feature_names_out` will prefix
all feature names with the name of the transformer that generated that
feature.
If False, :meth:`ColumnTransformer.get_feature_names_out` will not
prefix any feature names and will error if feature names are not
unique.
.. versionadded:: 1.0
force_int_remainder_cols : bool, default=True
Force the columns of the last entry of `transformers_`, which
corresponds to the "remainder" transformer, to always be stored as
indices (int) rather than column names (str). See description of the
:attr:`ColumnTransformer.transformers_` attribute for details.
.. note::
If you do not access the list of columns for the remainder columns
in the :attr:`ColumnTransformer.transformers_` fitted attribute,
you do not need to set this parameter.
.. versionadded:: 1.5
.. versionchanged:: 1.7
The default value for `force_int_remainder_cols` will change from
`True` to `False` in version 1.7.
Returns
-------
ct : ColumnTransformer
Returns a :class:`ColumnTransformer` object.
See Also
--------
ColumnTransformer : Class that allows combining the
outputs of multiple transformer objects used on column subsets
of the data into a single feature space.
Examples
--------
>>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
>>> from sklearn.compose import make_column_transformer
>>> make_column_transformer(
... (StandardScaler(), ['numerical_column']),
... (OneHotEncoder(), ['categorical_column']))
ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),
['numerical_column']),
('onehotencoder', OneHotEncoder(...),
['categorical_column'])])
"""
# transformer_weights keyword is not passed through because the user
# would need to know the automatically generated names of the transformers
transformer_list = _get_transformer_list(transformers)
return ColumnTransformer(
transformer_list,
n_jobs=n_jobs,
remainder=remainder,
sparse_threshold=sparse_threshold,
verbose=verbose,
verbose_feature_names_out=verbose_feature_names_out,
force_int_remainder_cols=force_int_remainder_cols,
)
class make_column_selector:
"""Create a callable to select columns to be used with
:class:`ColumnTransformer`.
:func:`make_column_selector` can select columns based on datatype or the
columns name with a regex. When using multiple selection criteria, **all**
criteria must match for a column to be selected.
For an example of how to use :func:`make_column_selector` within a
:class:`ColumnTransformer` to select columns based on data type (i.e.
`dtype`), refer to
:ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`.
Parameters
----------
pattern : str, default=None
Name of columns containing this regex pattern will be included. If
None, column selection will not be selected based on pattern.
dtype_include : column dtype or list of column dtypes, default=None
A selection of dtypes to include. For more details, see
:meth:`pandas.DataFrame.select_dtypes`.
dtype_exclude : column dtype or list of column dtypes, default=None
A selection of dtypes to exclude. For more details, see
:meth:`pandas.DataFrame.select_dtypes`.
Returns
-------
selector : callable
Callable for column selection to be used by a
:class:`ColumnTransformer`.
See Also
--------
ColumnTransformer : Class that allows combining the
outputs of multiple transformer objects used on column subsets
of the data into a single feature space.
Examples
--------
>>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
>>> from sklearn.compose import make_column_transformer
>>> from sklearn.compose import make_column_selector
>>> import numpy as np
>>> import pandas as pd # doctest: +SKIP
>>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
... 'rating': [5, 3, 4, 5]}) # doctest: +SKIP
>>> ct = make_column_transformer(
... (StandardScaler(),
... make_column_selector(dtype_include=np.number)), # rating
... (OneHotEncoder(),
... make_column_selector(dtype_include=object))) # city
>>> ct.fit_transform(X) # doctest: +SKIP
array([[ 0.90453403, 1. , 0. , 0. ],
[-1.50755672, 1. , 0. , 0. ],
[-0.30151134, 0. , 1. , 0. ],
[ 0.90453403, 0. , 0. , 1. ]])
"""
def __init__(self, pattern=None, *, dtype_include=None, dtype_exclude=None):
self.pattern = pattern
self.dtype_include = dtype_include
self.dtype_exclude = dtype_exclude
def __call__(self, df):
"""Callable for column selection to be used by a
:class:`ColumnTransformer`.
Parameters
----------
df : dataframe of shape (n_features, n_samples)
DataFrame to select columns from.
"""
if not hasattr(df, "iloc"):
raise ValueError(
"make_column_selector can only be applied to pandas dataframes"
)
df_row = df.iloc[:1]
if self.dtype_include is not None or self.dtype_exclude is not None:
df_row = df_row.select_dtypes(
include=self.dtype_include, exclude=self.dtype_exclude
)
cols = df_row.columns
if self.pattern is not None:
cols = cols[cols.str.contains(self.pattern, regex=True)]
return cols.tolist()
class _RemainderColsList(UserList):
"""A list that raises a warning whenever items are accessed.
It is used to store the columns handled by the "remainder" entry of
``ColumnTransformer.transformers_``, ie ``transformers_[-1][-1]``.
For some values of the ``ColumnTransformer`` ``transformers`` parameter,
this list of indices will be replaced by either a list of column names or a
boolean mask; in those cases we emit a ``FutureWarning`` the first time an
element is accessed.
Parameters
----------
columns : list of int
The remainder columns.
future_dtype : {'str', 'bool'}, default=None
The dtype that will be used by a ColumnTransformer with the same inputs
in a future release. There is a default value because providing a
constructor that takes a single argument is a requirement for
subclasses of UserList, but we do not use it in practice. It would only
be used if a user called methods that return a new list such are
copying or concatenating `_RemainderColsList`.
warning_was_emitted : bool, default=False
Whether the warning for that particular list was already shown, so we
only emit it once.
warning_enabled : bool, default=True
When False, the list never emits the warning nor updates
`warning_was_emitted``. This is used to obtain a quiet copy of the list
for use by the `ColumnTransformer` itself, so that the warning is only
shown when a user accesses it directly.
"""
def __init__(
self,
columns,
*,
future_dtype=None,
warning_was_emitted=False,
warning_enabled=True,
):
super().__init__(columns)
self.future_dtype = future_dtype
self.warning_was_emitted = warning_was_emitted
self.warning_enabled = warning_enabled
def __getitem__(self, index):
self._show_remainder_cols_warning()
return super().__getitem__(index)
def _show_remainder_cols_warning(self):
if self.warning_was_emitted or not self.warning_enabled:
return
self.warning_was_emitted = True
future_dtype_description = {
"str": "column names (of type str)",
"bool": "a mask array (of type bool)",
# shouldn't happen because we always initialize it with a
# non-default future_dtype
None: "a different type depending on the ColumnTransformer inputs",
}.get(self.future_dtype, self.future_dtype)
# TODO(1.7) Update the warning to say that the old behavior will be
# removed in 1.9.
warnings.warn(
(
"\nThe format of the columns of the 'remainder' transformer in"
" ColumnTransformer.transformers_ will change in version 1.7 to"
" match the format of the other transformers.\nAt the moment the"
" remainder columns are stored as indices (of type int). With the same"
" ColumnTransformer configuration, in the future they will be stored"
f" as {future_dtype_description}.\nTo use the new behavior now and"
" suppress this warning, use"
" ColumnTransformer(force_int_remainder_cols=False).\n"
),
category=FutureWarning,
)
def _repr_pretty_(self, printer, *_):
"""Override display in ipython console, otherwise the class name is shown."""
printer.text(repr(self.data))
def _with_dtype_warning_enabled_set_to(warning_enabled, transformers):
result = []
for name, trans, columns in transformers:
if isinstance(columns, _RemainderColsList):
columns = _RemainderColsList(
columns.data,
future_dtype=columns.future_dtype,
warning_was_emitted=columns.warning_was_emitted,
warning_enabled=warning_enabled,
)
result.append((name, trans, columns))
return result
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