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Traktor/myenv/Lib/site-packages/sklearn/preprocessing/_encoders.py

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2024-05-23 01:57:24 +02:00
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Joris Van den Bossche <jorisvandenbossche@gmail.com>
# License: BSD 3 clause
import numbers
import warnings
from numbers import Integral
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils import _safe_indexing, check_array
from ..utils._encode import _check_unknown, _encode, _get_counts, _unique
from ..utils._mask import _get_mask
from ..utils._missing import is_scalar_nan
from ..utils._param_validation import Interval, RealNotInt, StrOptions
from ..utils._set_output import _get_output_config
from ..utils.validation import _check_feature_names_in, check_is_fitted
__all__ = ["OneHotEncoder", "OrdinalEncoder"]
class _BaseEncoder(TransformerMixin, BaseEstimator):
"""
Base class for encoders that includes the code to categorize and
transform the input features.
"""
def _check_X(self, X, force_all_finite=True):
"""
Perform custom check_array:
- convert list of strings to object dtype
- check for missing values for object dtype data (check_array does
not do that)
- return list of features (arrays): this list of features is
constructed feature by feature to preserve the data types
of pandas DataFrame columns, as otherwise information is lost
and cannot be used, e.g. for the `categories_` attribute.
"""
if not (hasattr(X, "iloc") and getattr(X, "ndim", 0) == 2):
# if not a dataframe, do normal check_array validation
X_temp = check_array(X, dtype=None, force_all_finite=force_all_finite)
if not hasattr(X, "dtype") and np.issubdtype(X_temp.dtype, np.str_):
X = check_array(X, dtype=object, force_all_finite=force_all_finite)
else:
X = X_temp
needs_validation = False
else:
# pandas dataframe, do validation later column by column, in order
# to keep the dtype information to be used in the encoder.
needs_validation = force_all_finite
n_samples, n_features = X.shape
X_columns = []
for i in range(n_features):
Xi = _safe_indexing(X, indices=i, axis=1)
Xi = check_array(
Xi, ensure_2d=False, dtype=None, force_all_finite=needs_validation
)
X_columns.append(Xi)
return X_columns, n_samples, n_features
def _fit(
self,
X,
handle_unknown="error",
force_all_finite=True,
return_counts=False,
return_and_ignore_missing_for_infrequent=False,
):
self._check_infrequent_enabled()
self._check_n_features(X, reset=True)
self._check_feature_names(X, reset=True)
X_list, n_samples, n_features = self._check_X(
X, force_all_finite=force_all_finite
)
self.n_features_in_ = n_features
if self.categories != "auto":
if len(self.categories) != n_features:
raise ValueError(
"Shape mismatch: if categories is an array,"
" it has to be of shape (n_features,)."
)
self.categories_ = []
category_counts = []
compute_counts = return_counts or self._infrequent_enabled
for i in range(n_features):
Xi = X_list[i]
if self.categories == "auto":
result = _unique(Xi, return_counts=compute_counts)
if compute_counts:
cats, counts = result
category_counts.append(counts)
else:
cats = result
else:
if np.issubdtype(Xi.dtype, np.str_):
# Always convert string categories to objects to avoid
# unexpected string truncation for longer category labels
# passed in the constructor.
Xi_dtype = object
else:
Xi_dtype = Xi.dtype
cats = np.array(self.categories[i], dtype=Xi_dtype)
if (
cats.dtype == object
and isinstance(cats[0], bytes)
and Xi.dtype.kind != "S"
):
msg = (
f"In column {i}, the predefined categories have type 'bytes'"
" which is incompatible with values of type"
f" '{type(Xi[0]).__name__}'."
)
raise ValueError(msg)
# `nan` must be the last stated category
for category in cats[:-1]:
if is_scalar_nan(category):
raise ValueError(
"Nan should be the last element in user"
f" provided categories, see categories {cats}"
f" in column #{i}"
)
if cats.size != len(_unique(cats)):
msg = (
f"In column {i}, the predefined categories"
" contain duplicate elements."
)
raise ValueError(msg)
if Xi.dtype.kind not in "OUS":
sorted_cats = np.sort(cats)
error_msg = (
"Unsorted categories are not supported for numerical categories"
)
# if there are nans, nan should be the last element
stop_idx = -1 if np.isnan(sorted_cats[-1]) else None
if np.any(sorted_cats[:stop_idx] != cats[:stop_idx]):
raise ValueError(error_msg)
if handle_unknown == "error":
diff = _check_unknown(Xi, cats)
if diff:
msg = (
"Found unknown categories {0} in column {1}"
" during fit".format(diff, i)
)
raise ValueError(msg)
if compute_counts:
category_counts.append(_get_counts(Xi, cats))
self.categories_.append(cats)
output = {"n_samples": n_samples}
if return_counts:
output["category_counts"] = category_counts
missing_indices = {}
if return_and_ignore_missing_for_infrequent:
for feature_idx, categories_for_idx in enumerate(self.categories_):
if is_scalar_nan(categories_for_idx[-1]):
# `nan` values can only be placed in the latest position
missing_indices[feature_idx] = categories_for_idx.size - 1
output["missing_indices"] = missing_indices
if self._infrequent_enabled:
self._fit_infrequent_category_mapping(
n_samples,
category_counts,
missing_indices,
)
return output
def _transform(
self,
X,
handle_unknown="error",
force_all_finite=True,
warn_on_unknown=False,
ignore_category_indices=None,
):
X_list, n_samples, n_features = self._check_X(
X, force_all_finite=force_all_finite
)
self._check_feature_names(X, reset=False)
self._check_n_features(X, reset=False)
X_int = np.zeros((n_samples, n_features), dtype=int)
X_mask = np.ones((n_samples, n_features), dtype=bool)
columns_with_unknown = []
for i in range(n_features):
Xi = X_list[i]
diff, valid_mask = _check_unknown(Xi, self.categories_[i], return_mask=True)
if not np.all(valid_mask):
if handle_unknown == "error":
msg = (
"Found unknown categories {0} in column {1}"
" during transform".format(diff, i)
)
raise ValueError(msg)
else:
if warn_on_unknown:
columns_with_unknown.append(i)
# Set the problematic rows to an acceptable value and
# continue `The rows are marked `X_mask` and will be
# removed later.
X_mask[:, i] = valid_mask
# cast Xi into the largest string type necessary
# to handle different lengths of numpy strings
if (
self.categories_[i].dtype.kind in ("U", "S")
and self.categories_[i].itemsize > Xi.itemsize
):
Xi = Xi.astype(self.categories_[i].dtype)
elif self.categories_[i].dtype.kind == "O" and Xi.dtype.kind == "U":
# categories are objects and Xi are numpy strings.
# Cast Xi to an object dtype to prevent truncation
# when setting invalid values.
Xi = Xi.astype("O")
else:
Xi = Xi.copy()
Xi[~valid_mask] = self.categories_[i][0]
# We use check_unknown=False, since _check_unknown was
# already called above.
X_int[:, i] = _encode(Xi, uniques=self.categories_[i], check_unknown=False)
if columns_with_unknown:
warnings.warn(
(
"Found unknown categories in columns "
f"{columns_with_unknown} during transform. These "
"unknown categories will be encoded as all zeros"
),
UserWarning,
)
self._map_infrequent_categories(X_int, X_mask, ignore_category_indices)
return X_int, X_mask
@property
def infrequent_categories_(self):
"""Infrequent categories for each feature."""
# raises an AttributeError if `_infrequent_indices` is not defined
infrequent_indices = self._infrequent_indices
return [
None if indices is None else category[indices]
for category, indices in zip(self.categories_, infrequent_indices)
]
def _check_infrequent_enabled(self):
"""
This functions checks whether _infrequent_enabled is True or False.
This has to be called after parameter validation in the fit function.
"""
max_categories = getattr(self, "max_categories", None)
min_frequency = getattr(self, "min_frequency", None)
self._infrequent_enabled = (
max_categories is not None and max_categories >= 1
) or min_frequency is not None
def _identify_infrequent(self, category_count, n_samples, col_idx):
"""Compute the infrequent indices.
Parameters
----------
category_count : ndarray of shape (n_cardinality,)
Category counts.
n_samples : int
Number of samples.
col_idx : int
Index of the current category. Only used for the error message.
Returns
-------
output : ndarray of shape (n_infrequent_categories,) or None
If there are infrequent categories, indices of infrequent
categories. Otherwise None.
"""
if isinstance(self.min_frequency, numbers.Integral):
infrequent_mask = category_count < self.min_frequency
elif isinstance(self.min_frequency, numbers.Real):
min_frequency_abs = n_samples * self.min_frequency
infrequent_mask = category_count < min_frequency_abs
else:
infrequent_mask = np.zeros(category_count.shape[0], dtype=bool)
n_current_features = category_count.size - infrequent_mask.sum() + 1
if self.max_categories is not None and self.max_categories < n_current_features:
# max_categories includes the one infrequent category
frequent_category_count = self.max_categories - 1
if frequent_category_count == 0:
# All categories are infrequent
infrequent_mask[:] = True
else:
# stable sort to preserve original count order
smallest_levels = np.argsort(category_count, kind="mergesort")[
:-frequent_category_count
]
infrequent_mask[smallest_levels] = True
output = np.flatnonzero(infrequent_mask)
return output if output.size > 0 else None
def _fit_infrequent_category_mapping(
self, n_samples, category_counts, missing_indices
):
"""Fit infrequent categories.
Defines the private attribute: `_default_to_infrequent_mappings`. For
feature `i`, `_default_to_infrequent_mappings[i]` defines the mapping
from the integer encoding returned by `super().transform()` into
infrequent categories. If `_default_to_infrequent_mappings[i]` is None,
there were no infrequent categories in the training set.
For example if categories 0, 2 and 4 were frequent, while categories
1, 3, 5 were infrequent for feature 7, then these categories are mapped
to a single output:
`_default_to_infrequent_mappings[7] = array([0, 3, 1, 3, 2, 3])`
Defines private attribute: `_infrequent_indices`. `_infrequent_indices[i]`
is an array of indices such that
`categories_[i][_infrequent_indices[i]]` are all the infrequent category
labels. If the feature `i` has no infrequent categories
`_infrequent_indices[i]` is None.
.. versionadded:: 1.1
Parameters
----------
n_samples : int
Number of samples in training set.
category_counts: list of ndarray
`category_counts[i]` is the category counts corresponding to
`self.categories_[i]`.
missing_indices : dict
Dict mapping from feature_idx to category index with a missing value.
"""
# Remove missing value from counts, so it is not considered as infrequent
if missing_indices:
category_counts_ = []
for feature_idx, count in enumerate(category_counts):
if feature_idx in missing_indices:
category_counts_.append(
np.delete(count, missing_indices[feature_idx])
)
else:
category_counts_.append(count)
else:
category_counts_ = category_counts
self._infrequent_indices = [
self._identify_infrequent(category_count, n_samples, col_idx)
for col_idx, category_count in enumerate(category_counts_)
]
# compute mapping from default mapping to infrequent mapping
self._default_to_infrequent_mappings = []
for feature_idx, infreq_idx in enumerate(self._infrequent_indices):
cats = self.categories_[feature_idx]
# no infrequent categories
if infreq_idx is None:
self._default_to_infrequent_mappings.append(None)
continue
n_cats = len(cats)
if feature_idx in missing_indices:
# Missing index was removed from this category when computing
# infrequent indices, thus we need to decrease the number of
# total categories when considering the infrequent mapping.
n_cats -= 1
# infrequent indices exist
mapping = np.empty(n_cats, dtype=np.int64)
n_infrequent_cats = infreq_idx.size
# infrequent categories are mapped to the last element.
n_frequent_cats = n_cats - n_infrequent_cats
mapping[infreq_idx] = n_frequent_cats
frequent_indices = np.setdiff1d(np.arange(n_cats), infreq_idx)
mapping[frequent_indices] = np.arange(n_frequent_cats)
self._default_to_infrequent_mappings.append(mapping)
def _map_infrequent_categories(self, X_int, X_mask, ignore_category_indices):
"""Map infrequent categories to integer representing the infrequent category.
This modifies X_int in-place. Values that were invalid based on `X_mask`
are mapped to the infrequent category if there was an infrequent
category for that feature.
Parameters
----------
X_int: ndarray of shape (n_samples, n_features)
Integer encoded categories.
X_mask: ndarray of shape (n_samples, n_features)
Bool mask for valid values in `X_int`.
ignore_category_indices : dict
Dictionary mapping from feature_idx to category index to ignore.
Ignored indexes will not be grouped and the original ordinal encoding
will remain.
"""
if not self._infrequent_enabled:
return
ignore_category_indices = ignore_category_indices or {}
for col_idx in range(X_int.shape[1]):
infrequent_idx = self._infrequent_indices[col_idx]
if infrequent_idx is None:
continue
X_int[~X_mask[:, col_idx], col_idx] = infrequent_idx[0]
if self.handle_unknown == "infrequent_if_exist":
# All the unknown values are now mapped to the
# infrequent_idx[0], which makes the unknown values valid
# This is needed in `transform` when the encoding is formed
# using `X_mask`.
X_mask[:, col_idx] = True
# Remaps encoding in `X_int` where the infrequent categories are
# grouped together.
for i, mapping in enumerate(self._default_to_infrequent_mappings):
if mapping is None:
continue
if i in ignore_category_indices:
# Update rows that are **not** ignored
rows_to_update = X_int[:, i] != ignore_category_indices[i]
else:
rows_to_update = slice(None)
X_int[rows_to_update, i] = np.take(mapping, X_int[rows_to_update, i])
def _more_tags(self):
return {"X_types": ["2darray", "categorical"], "allow_nan": True}
class OneHotEncoder(_BaseEncoder):
"""
Encode categorical features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
encoding scheme. This creates a binary column for each category and
returns a sparse matrix or dense array (depending on the ``sparse_output``
parameter).
By default, the encoder derives the categories based on the unique values
in each feature. Alternatively, you can also specify the `categories`
manually.
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Note: a one-hot encoding of y labels should use a LabelBinarizer
instead.
Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
For a comparison of different encoders, refer to:
:ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`.
Parameters
----------
categories : 'auto' or a list of array-like, default='auto'
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data.
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories should not mix strings and numeric
values within a single feature, and should be sorted in case of
numeric values.
The used categories can be found in the ``categories_`` attribute.
.. versionadded:: 0.20
drop : {'first', 'if_binary'} or an array-like of shape (n_features,), \
default=None
Specifies a methodology to use to drop one of the categories per
feature. This is useful in situations where perfectly collinear
features cause problems, such as when feeding the resulting data
into an unregularized linear regression model.
However, dropping one category breaks the symmetry of the original
representation and can therefore induce a bias in downstream models,
for instance for penalized linear classification or regression models.
- None : retain all features (the default).
- 'first' : drop the first category in each feature. If only one
category is present, the feature will be dropped entirely.
- 'if_binary' : drop the first category in each feature with two
categories. Features with 1 or more than 2 categories are
left intact.
- array : ``drop[i]`` is the category in feature ``X[:, i]`` that
should be dropped.
When `max_categories` or `min_frequency` is configured to group
infrequent categories, the dropping behavior is handled after the
grouping.
.. versionadded:: 0.21
The parameter `drop` was added in 0.21.
.. versionchanged:: 0.23
The option `drop='if_binary'` was added in 0.23.
.. versionchanged:: 1.1
Support for dropping infrequent categories.
sparse_output : bool, default=True
When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,
i.e. a sparse matrix in "Compressed Sparse Row" (CSR) format.
.. versionadded:: 1.2
`sparse` was renamed to `sparse_output`
dtype : number type, default=np.float64
Desired dtype of output.
handle_unknown : {'error', 'ignore', 'infrequent_if_exist'}, \
default='error'
Specifies the way unknown categories are handled during :meth:`transform`.
- 'error' : Raise an error if an unknown category is present during transform.
- 'ignore' : When an unknown category is encountered during
transform, the resulting one-hot encoded columns for this feature
will be all zeros. In the inverse transform, an unknown category
will be denoted as None.
- 'infrequent_if_exist' : When an unknown category is encountered
during transform, the resulting one-hot encoded columns for this
feature will map to the infrequent category if it exists. The
infrequent category will be mapped to the last position in the
encoding. During inverse transform, an unknown category will be
mapped to the category denoted `'infrequent'` if it exists. If the
`'infrequent'` category does not exist, then :meth:`transform` and
:meth:`inverse_transform` will handle an unknown category as with
`handle_unknown='ignore'`. Infrequent categories exist based on
`min_frequency` and `max_categories`. Read more in the
:ref:`User Guide <encoder_infrequent_categories>`.
.. versionchanged:: 1.1
`'infrequent_if_exist'` was added to automatically handle unknown
categories and infrequent categories.
min_frequency : int or float, default=None
Specifies the minimum frequency below which a category will be
considered infrequent.
- If `int`, categories with a smaller cardinality will be considered
infrequent.
- If `float`, categories with a smaller cardinality than
`min_frequency * n_samples` will be considered infrequent.
.. versionadded:: 1.1
Read more in the :ref:`User Guide <encoder_infrequent_categories>`.
max_categories : int, default=None
Specifies an upper limit to the number of output features for each input
feature when considering infrequent categories. If there are infrequent
categories, `max_categories` includes the category representing the
infrequent categories along with the frequent categories. If `None`,
there is no limit to the number of output features.
.. versionadded:: 1.1
Read more in the :ref:`User Guide <encoder_infrequent_categories>`.
feature_name_combiner : "concat" or callable, default="concat"
Callable with signature `def callable(input_feature, category)` that returns a
string. This is used to create feature names to be returned by
:meth:`get_feature_names_out`.
`"concat"` concatenates encoded feature name and category with
`feature + "_" + str(category)`.E.g. feature X with values 1, 6, 7 create
feature names `X_1, X_6, X_7`.
.. versionadded:: 1.3
Attributes
----------
categories_ : list of arrays
The categories of each feature determined during fitting
(in order of the features in X and corresponding with the output
of ``transform``). This includes the category specified in ``drop``
(if any).
drop_idx_ : array of shape (n_features,)
- ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category
to be dropped for each feature.
- ``drop_idx_[i] = None`` if no category is to be dropped from the
feature with index ``i``, e.g. when `drop='if_binary'` and the
feature isn't binary.
- ``drop_idx_ = None`` if all the transformed features will be
retained.
If infrequent categories are enabled by setting `min_frequency` or
`max_categories` to a non-default value and `drop_idx[i]` corresponds
to a infrequent category, then the entire infrequent category is
dropped.
.. versionchanged:: 0.23
Added the possibility to contain `None` values.
infrequent_categories_ : list of ndarray
Defined only if infrequent categories are enabled by setting
`min_frequency` or `max_categories` to a non-default value.
`infrequent_categories_[i]` are the infrequent categories for feature
`i`. If the feature `i` has no infrequent categories
`infrequent_categories_[i]` is None.
.. versionadded:: 1.1
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 1.0
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
feature_name_combiner : callable or None
Callable with signature `def callable(input_feature, category)` that returns a
string. This is used to create feature names to be returned by
:meth:`get_feature_names_out`.
.. versionadded:: 1.3
See Also
--------
OrdinalEncoder : Performs an ordinal (integer)
encoding of the categorical features.
TargetEncoder : Encodes categorical features using the target.
sklearn.feature_extraction.DictVectorizer : Performs a one-hot encoding of
dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : Performs an approximate one-hot
encoding of dictionary items or strings.
LabelBinarizer : Binarizes labels in a one-vs-all
fashion.
MultiLabelBinarizer : Transforms between iterable of
iterables and a multilabel format, e.g. a (samples x classes) binary
matrix indicating the presence of a class label.
Examples
--------
Given a dataset with two features, we let the encoder find the unique
values per feature and transform the data to a binary one-hot encoding.
>>> from sklearn.preprocessing import OneHotEncoder
One can discard categories not seen during `fit`:
>>> enc = OneHotEncoder(handle_unknown='ignore')
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
OneHotEncoder(handle_unknown='ignore')
>>> enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> enc.transform([['Female', 1], ['Male', 4]]).toarray()
array([[1., 0., 1., 0., 0.],
[0., 1., 0., 0., 0.]])
>>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])
array([['Male', 1],
[None, 2]], dtype=object)
>>> enc.get_feature_names_out(['gender', 'group'])
array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'], ...)
One can always drop the first column for each feature:
>>> drop_enc = OneHotEncoder(drop='first').fit(X)
>>> drop_enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> drop_enc.transform([['Female', 1], ['Male', 2]]).toarray()
array([[0., 0., 0.],
[1., 1., 0.]])
Or drop a column for feature only having 2 categories:
>>> drop_binary_enc = OneHotEncoder(drop='if_binary').fit(X)
>>> drop_binary_enc.transform([['Female', 1], ['Male', 2]]).toarray()
array([[0., 1., 0., 0.],
[1., 0., 1., 0.]])
One can change the way feature names are created.
>>> def custom_combiner(feature, category):
... return str(feature) + "_" + type(category).__name__ + "_" + str(category)
>>> custom_fnames_enc = OneHotEncoder(feature_name_combiner=custom_combiner).fit(X)
>>> custom_fnames_enc.get_feature_names_out()
array(['x0_str_Female', 'x0_str_Male', 'x1_int_1', 'x1_int_2', 'x1_int_3'],
dtype=object)
Infrequent categories are enabled by setting `max_categories` or `min_frequency`.
>>> import numpy as np
>>> X = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object).T
>>> ohe = OneHotEncoder(max_categories=3, sparse_output=False).fit(X)
>>> ohe.infrequent_categories_
[array(['a', 'd'], dtype=object)]
>>> ohe.transform([["a"], ["b"]])
array([[0., 0., 1.],
[1., 0., 0.]])
"""
_parameter_constraints: dict = {
"categories": [StrOptions({"auto"}), list],
"drop": [StrOptions({"first", "if_binary"}), "array-like", None],
"dtype": "no_validation", # validation delegated to numpy
"handle_unknown": [StrOptions({"error", "ignore", "infrequent_if_exist"})],
"max_categories": [Interval(Integral, 1, None, closed="left"), None],
"min_frequency": [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0, 1, closed="neither"),
None,
],
"sparse_output": ["boolean"],
"feature_name_combiner": [StrOptions({"concat"}), callable],
}
def __init__(
self,
*,
categories="auto",
drop=None,
sparse_output=True,
dtype=np.float64,
handle_unknown="error",
min_frequency=None,
max_categories=None,
feature_name_combiner="concat",
):
self.categories = categories
self.sparse_output = sparse_output
self.dtype = dtype
self.handle_unknown = handle_unknown
self.drop = drop
self.min_frequency = min_frequency
self.max_categories = max_categories
self.feature_name_combiner = feature_name_combiner
def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx):
"""Convert `drop_idx` into the index for infrequent categories.
If there are no infrequent categories, then `drop_idx` is
returned. This method is called in `_set_drop_idx` when the `drop`
parameter is an array-like.
"""
if not self._infrequent_enabled:
return drop_idx
default_to_infrequent = self._default_to_infrequent_mappings[feature_idx]
if default_to_infrequent is None:
return drop_idx
# Raise error when explicitly dropping a category that is infrequent
infrequent_indices = self._infrequent_indices[feature_idx]
if infrequent_indices is not None and drop_idx in infrequent_indices:
categories = self.categories_[feature_idx]
raise ValueError(
f"Unable to drop category {categories[drop_idx].item()!r} from"
f" feature {feature_idx} because it is infrequent"
)
return default_to_infrequent[drop_idx]
def _set_drop_idx(self):
"""Compute the drop indices associated with `self.categories_`.
If `self.drop` is:
- `None`, No categories have been dropped.
- `'first'`, All zeros to drop the first category.
- `'if_binary'`, All zeros if the category is binary and `None`
otherwise.
- array-like, The indices of the categories that match the
categories in `self.drop`. If the dropped category is an infrequent
category, then the index for the infrequent category is used. This
means that the entire infrequent category is dropped.
This methods defines a public `drop_idx_` and a private
`_drop_idx_after_grouping`.
- `drop_idx_`: Public facing API that references the drop category in
`self.categories_`.
- `_drop_idx_after_grouping`: Used internally to drop categories *after* the
infrequent categories are grouped together.
If there are no infrequent categories or drop is `None`, then
`drop_idx_=_drop_idx_after_grouping`.
"""
if self.drop is None:
drop_idx_after_grouping = None
elif isinstance(self.drop, str):
if self.drop == "first":
drop_idx_after_grouping = np.zeros(len(self.categories_), dtype=object)
elif self.drop == "if_binary":
n_features_out_no_drop = [len(cat) for cat in self.categories_]
if self._infrequent_enabled:
for i, infreq_idx in enumerate(self._infrequent_indices):
if infreq_idx is None:
continue
n_features_out_no_drop[i] -= infreq_idx.size - 1
drop_idx_after_grouping = np.array(
[
0 if n_features_out == 2 else None
for n_features_out in n_features_out_no_drop
],
dtype=object,
)
else:
drop_array = np.asarray(self.drop, dtype=object)
droplen = len(drop_array)
if droplen != len(self.categories_):
msg = (
"`drop` should have length equal to the number "
"of features ({}), got {}"
)
raise ValueError(msg.format(len(self.categories_), droplen))
missing_drops = []
drop_indices = []
for feature_idx, (drop_val, cat_list) in enumerate(
zip(drop_array, self.categories_)
):
if not is_scalar_nan(drop_val):
drop_idx = np.where(cat_list == drop_val)[0]
if drop_idx.size: # found drop idx
drop_indices.append(
self._map_drop_idx_to_infrequent(feature_idx, drop_idx[0])
)
else:
missing_drops.append((feature_idx, drop_val))
continue
# drop_val is nan, find nan in categories manually
if is_scalar_nan(cat_list[-1]):
drop_indices.append(
self._map_drop_idx_to_infrequent(feature_idx, cat_list.size - 1)
)
else: # nan is missing
missing_drops.append((feature_idx, drop_val))
if any(missing_drops):
msg = (
"The following categories were supposed to be "
"dropped, but were not found in the training "
"data.\n{}".format(
"\n".join(
[
"Category: {}, Feature: {}".format(c, v)
for c, v in missing_drops
]
)
)
)
raise ValueError(msg)
drop_idx_after_grouping = np.array(drop_indices, dtype=object)
# `_drop_idx_after_grouping` are the categories to drop *after* the infrequent
# categories are grouped together. If needed, we remap `drop_idx` back
# to the categories seen in `self.categories_`.
self._drop_idx_after_grouping = drop_idx_after_grouping
if not self._infrequent_enabled or drop_idx_after_grouping is None:
self.drop_idx_ = self._drop_idx_after_grouping
else:
drop_idx_ = []
for feature_idx, drop_idx in enumerate(drop_idx_after_grouping):
default_to_infrequent = self._default_to_infrequent_mappings[
feature_idx
]
if drop_idx is None or default_to_infrequent is None:
orig_drop_idx = drop_idx
else:
orig_drop_idx = np.flatnonzero(default_to_infrequent == drop_idx)[0]
drop_idx_.append(orig_drop_idx)
self.drop_idx_ = np.asarray(drop_idx_, dtype=object)
def _compute_transformed_categories(self, i, remove_dropped=True):
"""Compute the transformed categories used for column `i`.
1. If there are infrequent categories, the category is named
'infrequent_sklearn'.
2. Dropped columns are removed when remove_dropped=True.
"""
cats = self.categories_[i]
if self._infrequent_enabled:
infreq_map = self._default_to_infrequent_mappings[i]
if infreq_map is not None:
frequent_mask = infreq_map < infreq_map.max()
infrequent_cat = "infrequent_sklearn"
# infrequent category is always at the end
cats = np.concatenate(
(cats[frequent_mask], np.array([infrequent_cat], dtype=object))
)
if remove_dropped:
cats = self._remove_dropped_categories(cats, i)
return cats
def _remove_dropped_categories(self, categories, i):
"""Remove dropped categories."""
if (
self._drop_idx_after_grouping is not None
and self._drop_idx_after_grouping[i] is not None
):
return np.delete(categories, self._drop_idx_after_grouping[i])
return categories
def _compute_n_features_outs(self):
"""Compute the n_features_out for each input feature."""
output = [len(cats) for cats in self.categories_]
if self._drop_idx_after_grouping is not None:
for i, drop_idx in enumerate(self._drop_idx_after_grouping):
if drop_idx is not None:
output[i] -= 1
if not self._infrequent_enabled:
return output
# infrequent is enabled, the number of features out are reduced
# because the infrequent categories are grouped together
for i, infreq_idx in enumerate(self._infrequent_indices):
if infreq_idx is None:
continue
output[i] -= infreq_idx.size - 1
return output
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X, y=None):
"""
Fit OneHotEncoder to X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data to determine the categories of each feature.
y : None
Ignored. This parameter exists only for compatibility with
:class:`~sklearn.pipeline.Pipeline`.
Returns
-------
self
Fitted encoder.
"""
self._fit(
X,
handle_unknown=self.handle_unknown,
force_all_finite="allow-nan",
)
self._set_drop_idx()
self._n_features_outs = self._compute_n_features_outs()
return self
def transform(self, X):
"""
Transform X using one-hot encoding.
If `sparse_output=True` (default), it returns an instance of
:class:`scipy.sparse._csr.csr_matrix` (CSR format).
If there are infrequent categories for a feature, set by specifying
`max_categories` or `min_frequency`, the infrequent categories are
grouped into a single category.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data to encode.
Returns
-------
X_out : {ndarray, sparse matrix} of shape \
(n_samples, n_encoded_features)
Transformed input. If `sparse_output=True`, a sparse matrix will be
returned.
"""
check_is_fitted(self)
transform_output = _get_output_config("transform", estimator=self)["dense"]
if transform_output != "default" and self.sparse_output:
capitalize_transform_output = transform_output.capitalize()
raise ValueError(
f"{capitalize_transform_output} output does not support sparse data."
f" Set sparse_output=False to output {transform_output} dataframes or"
f" disable {capitalize_transform_output} output via"
'` ohe.set_output(transform="default").'
)
# validation of X happens in _check_X called by _transform
warn_on_unknown = self.drop is not None and self.handle_unknown in {
"ignore",
"infrequent_if_exist",
}
X_int, X_mask = self._transform(
X,
handle_unknown=self.handle_unknown,
force_all_finite="allow-nan",
warn_on_unknown=warn_on_unknown,
)
n_samples, n_features = X_int.shape
if self._drop_idx_after_grouping is not None:
to_drop = self._drop_idx_after_grouping.copy()
# We remove all the dropped categories from mask, and decrement all
# categories that occur after them to avoid an empty column.
keep_cells = X_int != to_drop
for i, cats in enumerate(self.categories_):
# drop='if_binary' but feature isn't binary
if to_drop[i] is None:
# set to cardinality to not drop from X_int
to_drop[i] = len(cats)
to_drop = to_drop.reshape(1, -1)
X_int[X_int > to_drop] -= 1
X_mask &= keep_cells
mask = X_mask.ravel()
feature_indices = np.cumsum([0] + self._n_features_outs)
indices = (X_int + feature_indices[:-1]).ravel()[mask]
indptr = np.empty(n_samples + 1, dtype=int)
indptr[0] = 0
np.sum(X_mask, axis=1, out=indptr[1:], dtype=indptr.dtype)
np.cumsum(indptr[1:], out=indptr[1:])
data = np.ones(indptr[-1])
out = sparse.csr_matrix(
(data, indices, indptr),
shape=(n_samples, feature_indices[-1]),
dtype=self.dtype,
)
if not self.sparse_output:
return out.toarray()
else:
return out
def inverse_transform(self, X):
"""
Convert the data back to the original representation.
When unknown categories are encountered (all zeros in the
one-hot encoding), ``None`` is used to represent this category. If the
feature with the unknown category has a dropped category, the dropped
category will be its inverse.
For a given input feature, if there is an infrequent category,
'infrequent_sklearn' will be used to represent the infrequent category.
Parameters
----------
X : {array-like, sparse matrix} of shape \
(n_samples, n_encoded_features)
The transformed data.
Returns
-------
X_tr : ndarray of shape (n_samples, n_features)
Inverse transformed array.
"""
check_is_fitted(self)
X = check_array(X, accept_sparse="csr")
n_samples, _ = X.shape
n_features = len(self.categories_)
n_features_out = np.sum(self._n_features_outs)
# validate shape of passed X
msg = (
"Shape of the passed X data is not correct. Expected {0} columns, got {1}."
)
if X.shape[1] != n_features_out:
raise ValueError(msg.format(n_features_out, X.shape[1]))
transformed_features = [
self._compute_transformed_categories(i, remove_dropped=False)
for i, _ in enumerate(self.categories_)
]
# create resulting array of appropriate dtype
dt = np.result_type(*[cat.dtype for cat in transformed_features])
X_tr = np.empty((n_samples, n_features), dtype=dt)
j = 0
found_unknown = {}
if self._infrequent_enabled:
infrequent_indices = self._infrequent_indices
else:
infrequent_indices = [None] * n_features
for i in range(n_features):
cats_wo_dropped = self._remove_dropped_categories(
transformed_features[i], i
)
n_categories = cats_wo_dropped.shape[0]
# Only happens if there was a column with a unique
# category. In this case we just fill the column with this
# unique category value.
if n_categories == 0:
X_tr[:, i] = self.categories_[i][self._drop_idx_after_grouping[i]]
j += n_categories
continue
sub = X[:, j : j + n_categories]
# for sparse X argmax returns 2D matrix, ensure 1D array
labels = np.asarray(sub.argmax(axis=1)).flatten()
X_tr[:, i] = cats_wo_dropped[labels]
if self.handle_unknown == "ignore" or (
self.handle_unknown == "infrequent_if_exist"
and infrequent_indices[i] is None
):
unknown = np.asarray(sub.sum(axis=1) == 0).flatten()
# ignored unknown categories: we have a row of all zero
if unknown.any():
# if categories were dropped then unknown categories will
# be mapped to the dropped category
if (
self._drop_idx_after_grouping is None
or self._drop_idx_after_grouping[i] is None
):
found_unknown[i] = unknown
else:
X_tr[unknown, i] = self.categories_[i][
self._drop_idx_after_grouping[i]
]
else:
dropped = np.asarray(sub.sum(axis=1) == 0).flatten()
if dropped.any():
if self._drop_idx_after_grouping is None:
all_zero_samples = np.flatnonzero(dropped)
raise ValueError(
f"Samples {all_zero_samples} can not be inverted "
"when drop=None and handle_unknown='error' "
"because they contain all zeros"
)
# we can safely assume that all of the nulls in each column
# are the dropped value
drop_idx = self._drop_idx_after_grouping[i]
X_tr[dropped, i] = transformed_features[i][drop_idx]
j += n_categories
# if ignored are found: potentially need to upcast result to
# insert None values
if found_unknown:
if X_tr.dtype != object:
X_tr = X_tr.astype(object)
for idx, mask in found_unknown.items():
X_tr[mask, idx] = None
return X_tr
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)
cats = [
self._compute_transformed_categories(i)
for i, _ in enumerate(self.categories_)
]
name_combiner = self._check_get_feature_name_combiner()
feature_names = []
for i in range(len(cats)):
names = [name_combiner(input_features[i], t) for t in cats[i]]
feature_names.extend(names)
return np.array(feature_names, dtype=object)
def _check_get_feature_name_combiner(self):
if self.feature_name_combiner == "concat":
return lambda feature, category: feature + "_" + str(category)
else: # callable
dry_run_combiner = self.feature_name_combiner("feature", "category")
if not isinstance(dry_run_combiner, str):
raise TypeError(
"When `feature_name_combiner` is a callable, it should return a "
f"Python string. Got {type(dry_run_combiner)} instead."
)
return self.feature_name_combiner
class OrdinalEncoder(OneToOneFeatureMixin, _BaseEncoder):
"""
Encode categorical features as an integer array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are converted to ordinal integers. This results in
a single column of integers (0 to n_categories - 1) per feature.
Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
For a comparison of different encoders, refer to:
:ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`.
.. versionadded:: 0.20
Parameters
----------
categories : 'auto' or a list of array-like, default='auto'
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data.
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories should not mix strings and numeric
values, and should be sorted in case of numeric values.
The used categories can be found in the ``categories_`` attribute.
dtype : number type, default=np.float64
Desired dtype of output.
handle_unknown : {'error', 'use_encoded_value'}, default='error'
When set to 'error' an error will be raised in case an unknown
categorical feature is present during transform. When set to
'use_encoded_value', the encoded value of unknown categories will be
set to the value given for the parameter `unknown_value`. In
:meth:`inverse_transform`, an unknown category will be denoted as None.
.. versionadded:: 0.24
unknown_value : int or np.nan, default=None
When the parameter handle_unknown is set to 'use_encoded_value', this
parameter is required and will set the encoded value of unknown
categories. It has to be distinct from the values used to encode any of
the categories in `fit`. If set to np.nan, the `dtype` parameter must
be a float dtype.
.. versionadded:: 0.24
encoded_missing_value : int or np.nan, default=np.nan
Encoded value of missing categories. If set to `np.nan`, then the `dtype`
parameter must be a float dtype.
.. versionadded:: 1.1
min_frequency : int or float, default=None
Specifies the minimum frequency below which a category will be
considered infrequent.
- If `int`, categories with a smaller cardinality will be considered
infrequent.
- If `float`, categories with a smaller cardinality than
`min_frequency * n_samples` will be considered infrequent.
.. versionadded:: 1.3
Read more in the :ref:`User Guide <encoder_infrequent_categories>`.
max_categories : int, default=None
Specifies an upper limit to the number of output categories for each input
feature when considering infrequent categories. If there are infrequent
categories, `max_categories` includes the category representing the
infrequent categories along with the frequent categories. If `None`,
there is no limit to the number of output features.
`max_categories` do **not** take into account missing or unknown
categories. Setting `unknown_value` or `encoded_missing_value` to an
integer will increase the number of unique integer codes by one each.
This can result in up to `max_categories + 2` integer codes.
.. versionadded:: 1.3
Read more in the :ref:`User Guide <encoder_infrequent_categories>`.
Attributes
----------
categories_ : list of arrays
The categories of each feature determined during ``fit`` (in order of
the features in X and corresponding with the output of ``transform``).
This does not include categories that weren't seen during ``fit``.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 1.0
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
infrequent_categories_ : list of ndarray
Defined only if infrequent categories are enabled by setting
`min_frequency` or `max_categories` to a non-default value.
`infrequent_categories_[i]` are the infrequent categories for feature
`i`. If the feature `i` has no infrequent categories
`infrequent_categories_[i]` is None.
.. versionadded:: 1.3
See Also
--------
OneHotEncoder : Performs a one-hot encoding of categorical features. This encoding
is suitable for low to medium cardinality categorical variables, both in
supervised and unsupervised settings.
TargetEncoder : Encodes categorical features using supervised signal
in a classification or regression pipeline. This encoding is typically
suitable for high cardinality categorical variables.
LabelEncoder : Encodes target labels with values between 0 and
``n_classes-1``.
Notes
-----
With a high proportion of `nan` values, inferring categories becomes slow with
Python versions before 3.10. The handling of `nan` values was improved
from Python 3.10 onwards, (c.f.
`bpo-43475 <https://github.com/python/cpython/issues/87641>`_).
Examples
--------
Given a dataset with two features, we let the encoder find the unique
values per feature and transform the data to an ordinal encoding.
>>> from sklearn.preprocessing import OrdinalEncoder
>>> enc = OrdinalEncoder()
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
OrdinalEncoder()
>>> enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> enc.transform([['Female', 3], ['Male', 1]])
array([[0., 2.],
[1., 0.]])
>>> enc.inverse_transform([[1, 0], [0, 1]])
array([['Male', 1],
['Female', 2]], dtype=object)
By default, :class:`OrdinalEncoder` is lenient towards missing values by
propagating them.
>>> import numpy as np
>>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]]
>>> enc.fit_transform(X)
array([[ 1., 0.],
[ 0., 1.],
[ 0., nan]])
You can use the parameter `encoded_missing_value` to encode missing values.
>>> enc.set_params(encoded_missing_value=-1).fit_transform(X)
array([[ 1., 0.],
[ 0., 1.],
[ 0., -1.]])
Infrequent categories are enabled by setting `max_categories` or `min_frequency`.
In the following example, "a" and "d" are considered infrequent and grouped
together into a single category, "b" and "c" are their own categories, unknown
values are encoded as 3 and missing values are encoded as 4.
>>> X_train = np.array(
... [["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]],
... dtype=object).T
>>> enc = OrdinalEncoder(
... handle_unknown="use_encoded_value", unknown_value=3,
... max_categories=3, encoded_missing_value=4)
>>> _ = enc.fit(X_train)
>>> X_test = np.array([["a"], ["b"], ["c"], ["d"], ["e"], [np.nan]], dtype=object)
>>> enc.transform(X_test)
array([[2.],
[0.],
[1.],
[2.],
[3.],
[4.]])
"""
_parameter_constraints: dict = {
"categories": [StrOptions({"auto"}), list],
"dtype": "no_validation", # validation delegated to numpy
"encoded_missing_value": [Integral, type(np.nan)],
"handle_unknown": [StrOptions({"error", "use_encoded_value"})],
"unknown_value": [Integral, type(np.nan), None],
"max_categories": [Interval(Integral, 1, None, closed="left"), None],
"min_frequency": [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0, 1, closed="neither"),
None,
],
}
def __init__(
self,
*,
categories="auto",
dtype=np.float64,
handle_unknown="error",
unknown_value=None,
encoded_missing_value=np.nan,
min_frequency=None,
max_categories=None,
):
self.categories = categories
self.dtype = dtype
self.handle_unknown = handle_unknown
self.unknown_value = unknown_value
self.encoded_missing_value = encoded_missing_value
self.min_frequency = min_frequency
self.max_categories = max_categories
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X, y=None):
"""
Fit the OrdinalEncoder to X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data to determine the categories of each feature.
y : None
Ignored. This parameter exists only for compatibility with
:class:`~sklearn.pipeline.Pipeline`.
Returns
-------
self : object
Fitted encoder.
"""
if self.handle_unknown == "use_encoded_value":
if is_scalar_nan(self.unknown_value):
if np.dtype(self.dtype).kind != "f":
raise ValueError(
"When unknown_value is np.nan, the dtype "
"parameter should be "
f"a float dtype. Got {self.dtype}."
)
elif not isinstance(self.unknown_value, numbers.Integral):
raise TypeError(
"unknown_value should be an integer or "
"np.nan when "
"handle_unknown is 'use_encoded_value', "
f"got {self.unknown_value}."
)
elif self.unknown_value is not None:
raise TypeError(
"unknown_value should only be set when "
"handle_unknown is 'use_encoded_value', "
f"got {self.unknown_value}."
)
# `_fit` will only raise an error when `self.handle_unknown="error"`
fit_results = self._fit(
X,
handle_unknown=self.handle_unknown,
force_all_finite="allow-nan",
return_and_ignore_missing_for_infrequent=True,
)
self._missing_indices = fit_results["missing_indices"]
cardinalities = [len(categories) for categories in self.categories_]
if self._infrequent_enabled:
# Cardinality decreases because the infrequent categories are grouped
# together
for feature_idx, infrequent in enumerate(self.infrequent_categories_):
if infrequent is not None:
cardinalities[feature_idx] -= len(infrequent)
# missing values are not considered part of the cardinality
# when considering unknown categories or encoded_missing_value
for cat_idx, categories_for_idx in enumerate(self.categories_):
if is_scalar_nan(categories_for_idx[-1]):
cardinalities[cat_idx] -= 1
if self.handle_unknown == "use_encoded_value":
for cardinality in cardinalities:
if 0 <= self.unknown_value < cardinality:
raise ValueError(
"The used value for unknown_value "
f"{self.unknown_value} is one of the "
"values already used for encoding the "
"seen categories."
)
if self._missing_indices:
if np.dtype(self.dtype).kind != "f" and is_scalar_nan(
self.encoded_missing_value
):
raise ValueError(
"There are missing values in features "
f"{list(self._missing_indices)}. For OrdinalEncoder to "
f"encode missing values with dtype: {self.dtype}, set "
"encoded_missing_value to a non-nan value, or "
"set dtype to a float"
)
if not is_scalar_nan(self.encoded_missing_value):
# Features are invalid when they contain a missing category
# and encoded_missing_value was already used to encode a
# known category
invalid_features = [
cat_idx
for cat_idx, cardinality in enumerate(cardinalities)
if cat_idx in self._missing_indices
and 0 <= self.encoded_missing_value < cardinality
]
if invalid_features:
# Use feature names if they are available
if hasattr(self, "feature_names_in_"):
invalid_features = self.feature_names_in_[invalid_features]
raise ValueError(
f"encoded_missing_value ({self.encoded_missing_value}) "
"is already used to encode a known category in features: "
f"{invalid_features}"
)
return self
def transform(self, X):
"""
Transform X to ordinal codes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data to encode.
Returns
-------
X_out : ndarray of shape (n_samples, n_features)
Transformed input.
"""
check_is_fitted(self, "categories_")
X_int, X_mask = self._transform(
X,
handle_unknown=self.handle_unknown,
force_all_finite="allow-nan",
ignore_category_indices=self._missing_indices,
)
X_trans = X_int.astype(self.dtype, copy=False)
for cat_idx, missing_idx in self._missing_indices.items():
X_missing_mask = X_int[:, cat_idx] == missing_idx
X_trans[X_missing_mask, cat_idx] = self.encoded_missing_value
# create separate category for unknown values
if self.handle_unknown == "use_encoded_value":
X_trans[~X_mask] = self.unknown_value
return X_trans
def inverse_transform(self, X):
"""
Convert the data back to the original representation.
Parameters
----------
X : array-like of shape (n_samples, n_encoded_features)
The transformed data.
Returns
-------
X_tr : ndarray of shape (n_samples, n_features)
Inverse transformed array.
"""
check_is_fitted(self)
X = check_array(X, force_all_finite="allow-nan")
n_samples, _ = X.shape
n_features = len(self.categories_)
# validate shape of passed X
msg = (
"Shape of the passed X data is not correct. Expected {0} columns, got {1}."
)
if X.shape[1] != n_features:
raise ValueError(msg.format(n_features, X.shape[1]))
# create resulting array of appropriate dtype
dt = np.result_type(*[cat.dtype for cat in self.categories_])
X_tr = np.empty((n_samples, n_features), dtype=dt)
found_unknown = {}
infrequent_masks = {}
infrequent_indices = getattr(self, "_infrequent_indices", None)
for i in range(n_features):
labels = X[:, i]
# replace values of X[:, i] that were nan with actual indices
if i in self._missing_indices:
X_i_mask = _get_mask(labels, self.encoded_missing_value)
labels[X_i_mask] = self._missing_indices[i]
rows_to_update = slice(None)
categories = self.categories_[i]
if infrequent_indices is not None and infrequent_indices[i] is not None:
# Compute mask for frequent categories
infrequent_encoding_value = len(categories) - len(infrequent_indices[i])
infrequent_masks[i] = labels == infrequent_encoding_value
rows_to_update = ~infrequent_masks[i]
# Remap categories to be only frequent categories. The infrequent
# categories will be mapped to "infrequent_sklearn" later
frequent_categories_mask = np.ones_like(categories, dtype=bool)
frequent_categories_mask[infrequent_indices[i]] = False
categories = categories[frequent_categories_mask]
if self.handle_unknown == "use_encoded_value":
unknown_labels = _get_mask(labels, self.unknown_value)
found_unknown[i] = unknown_labels
known_labels = ~unknown_labels
if isinstance(rows_to_update, np.ndarray):
rows_to_update &= known_labels
else:
rows_to_update = known_labels
labels_int = labels[rows_to_update].astype("int64", copy=False)
X_tr[rows_to_update, i] = categories[labels_int]
if found_unknown or infrequent_masks:
X_tr = X_tr.astype(object, copy=False)
# insert None values for unknown values
if found_unknown:
for idx, mask in found_unknown.items():
X_tr[mask, idx] = None
if infrequent_masks:
for idx, mask in infrequent_masks.items():
X_tr[mask, idx] = "infrequent_sklearn"
return X_tr
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