863 lines
33 KiB
Python
863 lines
33 KiB
Python
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
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# Joris Van den Bossche <jorisvandenbossche@gmail.com>
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# License: BSD 3 clause
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import numpy as np
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from scipy import sparse
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import numbers
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from ..base import BaseEstimator, TransformerMixin
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from ..utils import check_array, is_scalar_nan
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from ..utils.validation import check_is_fitted
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from ..utils.validation import _deprecate_positional_args
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from ..utils._encode import _encode, _check_unknown, _unique
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__all__ = [
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'OneHotEncoder',
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'OrdinalEncoder'
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]
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class _BaseEncoder(TransformerMixin, BaseEstimator):
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"""
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Base class for encoders that includes the code to categorize and
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transform the input features.
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"""
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def _check_X(self, X, force_all_finite=True):
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"""
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Perform custom check_array:
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- convert list of strings to object dtype
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- check for missing values for object dtype data (check_array does
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not do that)
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- return list of features (arrays): this list of features is
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constructed feature by feature to preserve the data types
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of pandas DataFrame columns, as otherwise information is lost
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and cannot be used, eg for the `categories_` attribute.
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"""
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if not (hasattr(X, 'iloc') and getattr(X, 'ndim', 0) == 2):
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# if not a dataframe, do normal check_array validation
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X_temp = check_array(X, dtype=None,
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force_all_finite=force_all_finite)
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if (not hasattr(X, 'dtype')
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and np.issubdtype(X_temp.dtype, np.str_)):
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X = check_array(X, dtype=object,
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force_all_finite=force_all_finite)
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else:
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X = X_temp
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needs_validation = False
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else:
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# pandas dataframe, do validation later column by column, in order
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# to keep the dtype information to be used in the encoder.
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needs_validation = force_all_finite
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n_samples, n_features = X.shape
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X_columns = []
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for i in range(n_features):
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Xi = self._get_feature(X, feature_idx=i)
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Xi = check_array(Xi, ensure_2d=False, dtype=None,
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force_all_finite=needs_validation)
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X_columns.append(Xi)
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return X_columns, n_samples, n_features
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def _get_feature(self, X, feature_idx):
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if hasattr(X, 'iloc'):
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# pandas dataframes
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return X.iloc[:, feature_idx]
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# numpy arrays, sparse arrays
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return X[:, feature_idx]
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def _fit(self, X, handle_unknown='error', force_all_finite=True):
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X_list, n_samples, n_features = self._check_X(
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X, force_all_finite=force_all_finite)
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if self.categories != 'auto':
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if len(self.categories) != n_features:
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raise ValueError("Shape mismatch: if categories is an array,"
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" it has to be of shape (n_features,).")
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self.categories_ = []
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for i in range(n_features):
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Xi = X_list[i]
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if self.categories == 'auto':
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cats = _unique(Xi)
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else:
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cats = np.array(self.categories[i], dtype=Xi.dtype)
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if Xi.dtype.kind not in 'OUS':
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sorted_cats = np.sort(cats)
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error_msg = ("Unsorted categories are not "
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"supported for numerical categories")
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# if there are nans, nan should be the last element
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stop_idx = -1 if np.isnan(sorted_cats[-1]) else None
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if (np.any(sorted_cats[:stop_idx] != cats[:stop_idx]) or
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(np.isnan(sorted_cats[-1]) and
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not np.isnan(sorted_cats[-1]))):
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raise ValueError(error_msg)
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if handle_unknown == 'error':
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diff = _check_unknown(Xi, cats)
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if diff:
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msg = ("Found unknown categories {0} in column {1}"
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" during fit".format(diff, i))
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raise ValueError(msg)
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self.categories_.append(cats)
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def _transform(self, X, handle_unknown='error', force_all_finite=True):
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X_list, n_samples, n_features = self._check_X(
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X, force_all_finite=force_all_finite)
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X_int = np.zeros((n_samples, n_features), dtype=int)
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X_mask = np.ones((n_samples, n_features), dtype=bool)
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if n_features != len(self.categories_):
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raise ValueError(
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"The number of features in X is different to the number of "
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"features of the fitted data. The fitted data had {} features "
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"and the X has {} features."
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.format(len(self.categories_,), n_features)
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)
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for i in range(n_features):
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Xi = X_list[i]
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diff, valid_mask = _check_unknown(Xi, self.categories_[i],
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return_mask=True)
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if not np.all(valid_mask):
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if handle_unknown == 'error':
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msg = ("Found unknown categories {0} in column {1}"
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" during transform".format(diff, i))
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raise ValueError(msg)
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else:
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# Set the problematic rows to an acceptable value and
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# continue `The rows are marked `X_mask` and will be
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# removed later.
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X_mask[:, i] = valid_mask
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# cast Xi into the largest string type necessary
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# to handle different lengths of numpy strings
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if (self.categories_[i].dtype.kind in ('U', 'S')
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and self.categories_[i].itemsize > Xi.itemsize):
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Xi = Xi.astype(self.categories_[i].dtype)
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elif (self.categories_[i].dtype.kind == 'O' and
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Xi.dtype.kind == 'U'):
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# categories are objects and Xi are numpy strings.
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# Cast Xi to an object dtype to prevent truncation
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# when setting invalid values.
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Xi = Xi.astype('O')
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else:
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Xi = Xi.copy()
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Xi[~valid_mask] = self.categories_[i][0]
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# We use check_unknown=False, since _check_unknown was
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# already called above.
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X_int[:, i] = _encode(Xi, uniques=self.categories_[i],
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check_unknown=False)
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return X_int, X_mask
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def _more_tags(self):
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return {'X_types': ['categorical']}
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class OneHotEncoder(_BaseEncoder):
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"""
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Encode categorical features as a one-hot numeric array.
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The input to this transformer should be an array-like of integers or
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strings, denoting the values taken on by categorical (discrete) features.
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The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
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encoding scheme. This creates a binary column for each category and
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returns a sparse matrix or dense array (depending on the ``sparse``
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parameter)
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By default, the encoder derives the categories based on the unique values
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in each feature. Alternatively, you can also specify the `categories`
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manually.
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This encoding is needed for feeding categorical data to many scikit-learn
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estimators, notably linear models and SVMs with the standard kernels.
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Note: a one-hot encoding of y labels should use a LabelBinarizer
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instead.
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Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
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Parameters
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----------
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categories : 'auto' or a list of array-like, default='auto'
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Categories (unique values) per feature:
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- 'auto' : Determine categories automatically from the training data.
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- list : ``categories[i]`` holds the categories expected in the ith
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column. The passed categories should not mix strings and numeric
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values within a single feature, and should be sorted in case of
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numeric values.
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The used categories can be found in the ``categories_`` attribute.
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.. versionadded:: 0.20
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drop : {'first', 'if_binary'} or a array-like of shape (n_features,), \
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default=None
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Specifies a methodology to use to drop one of the categories per
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feature. This is useful in situations where perfectly collinear
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features cause problems, such as when feeding the resulting data
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into a neural network or an unregularized regression.
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However, dropping one category breaks the symmetry of the original
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representation and can therefore induce a bias in downstream models,
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for instance for penalized linear classification or regression models.
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- None : retain all features (the default).
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- 'first' : drop the first category in each feature. If only one
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category is present, the feature will be dropped entirely.
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- 'if_binary' : drop the first category in each feature with two
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categories. Features with 1 or more than 2 categories are
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left intact.
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- array : ``drop[i]`` is the category in feature ``X[:, i]`` that
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should be dropped.
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.. versionadded:: 0.21
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The parameter `drop` was added in 0.21.
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.. versionchanged:: 0.23
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The option `drop='if_binary'` was added in 0.23.
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sparse : bool, default=True
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Will return sparse matrix if set True else will return an array.
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dtype : number type, default=float
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Desired dtype of output.
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handle_unknown : {'error', 'ignore'}, default='error'
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Whether to raise an error or ignore if an unknown categorical feature
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is present during transform (default is to raise). When this parameter
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is set to 'ignore' and an unknown category is encountered during
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transform, the resulting one-hot encoded columns for this feature
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will be all zeros. In the inverse transform, an unknown category
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will be denoted as None.
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Attributes
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----------
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categories_ : list of arrays
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The categories of each feature determined during fitting
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(in order of the features in X and corresponding with the output
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of ``transform``). This includes the category specified in ``drop``
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(if any).
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drop_idx_ : array of shape (n_features,)
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- ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category
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to be dropped for each feature.
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- ``drop_idx_[i] = None`` if no category is to be dropped from the
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feature with index ``i``, e.g. when `drop='if_binary'` and the
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feature isn't binary.
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- ``drop_idx_ = None`` if all the transformed features will be
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retained.
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.. versionchanged:: 0.23
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Added the possibility to contain `None` values.
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See Also
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--------
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OrdinalEncoder : Performs an ordinal (integer)
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encoding of the categorical features.
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sklearn.feature_extraction.DictVectorizer : Performs a one-hot encoding of
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dictionary items (also handles string-valued features).
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sklearn.feature_extraction.FeatureHasher : Performs an approximate one-hot
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encoding of dictionary items or strings.
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LabelBinarizer : Binarizes labels in a one-vs-all
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fashion.
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MultiLabelBinarizer : Transforms between iterable of
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iterables and a multilabel format, e.g. a (samples x classes) binary
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matrix indicating the presence of a class label.
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Examples
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--------
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Given a dataset with two features, we let the encoder find the unique
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values per feature and transform the data to a binary one-hot encoding.
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>>> from sklearn.preprocessing import OneHotEncoder
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One can discard categories not seen during `fit`:
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>>> enc = OneHotEncoder(handle_unknown='ignore')
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>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
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>>> enc.fit(X)
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OneHotEncoder(handle_unknown='ignore')
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>>> enc.categories_
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[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
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>>> enc.transform([['Female', 1], ['Male', 4]]).toarray()
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array([[1., 0., 1., 0., 0.],
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[0., 1., 0., 0., 0.]])
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>>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])
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array([['Male', 1],
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[None, 2]], dtype=object)
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>>> enc.get_feature_names(['gender', 'group'])
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array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'],
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dtype=object)
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One can always drop the first column for each feature:
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>>> drop_enc = OneHotEncoder(drop='first').fit(X)
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>>> drop_enc.categories_
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[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
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>>> drop_enc.transform([['Female', 1], ['Male', 2]]).toarray()
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array([[0., 0., 0.],
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[1., 1., 0.]])
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Or drop a column for feature only having 2 categories:
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>>> drop_binary_enc = OneHotEncoder(drop='if_binary').fit(X)
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>>> drop_binary_enc.transform([['Female', 1], ['Male', 2]]).toarray()
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array([[0., 1., 0., 0.],
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[1., 0., 1., 0.]])
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"""
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@_deprecate_positional_args
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def __init__(self, *, categories='auto', drop=None, sparse=True,
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dtype=np.float64, handle_unknown='error'):
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self.categories = categories
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self.sparse = sparse
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self.dtype = dtype
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self.handle_unknown = handle_unknown
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self.drop = drop
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def _validate_keywords(self):
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if self.handle_unknown not in ('error', 'ignore'):
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msg = ("handle_unknown should be either 'error' or 'ignore', "
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"got {0}.".format(self.handle_unknown))
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raise ValueError(msg)
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# If we have both dropped columns and ignored unknown
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# values, there will be ambiguous cells. This creates difficulties
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# in interpreting the model.
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if self.drop is not None and self.handle_unknown != 'error':
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raise ValueError(
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"`handle_unknown` must be 'error' when the drop parameter is "
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"specified, as both would create categories that are all "
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"zero.")
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def _compute_drop_idx(self):
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if self.drop is None:
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return None
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elif isinstance(self.drop, str):
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if self.drop == 'first':
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return np.zeros(len(self.categories_), dtype=object)
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elif self.drop == 'if_binary':
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return np.array([0 if len(cats) == 2 else None
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for cats in self.categories_], dtype=object)
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else:
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msg = (
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"Wrong input for parameter `drop`. Expected "
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"'first', 'if_binary', None or array of objects, got {}"
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)
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raise ValueError(msg.format(type(self.drop)))
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else:
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try:
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drop_array = np.asarray(self.drop, dtype=object)
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droplen = len(drop_array)
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except (ValueError, TypeError):
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msg = (
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"Wrong input for parameter `drop`. Expected "
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"'first', 'if_binary', None or array of objects, got {}"
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)
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raise ValueError(msg.format(type(drop_array)))
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if droplen != len(self.categories_):
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msg = ("`drop` should have length equal to the number "
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"of features ({}), got {}")
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raise ValueError(msg.format(len(self.categories_), droplen))
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missing_drops = []
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drop_indices = []
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for col_idx, (val, cat_list) in enumerate(zip(drop_array,
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self.categories_)):
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if not is_scalar_nan(val):
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drop_idx = np.where(cat_list == val)[0]
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if drop_idx.size: # found drop idx
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drop_indices.append(drop_idx[0])
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else:
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missing_drops.append((col_idx, val))
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continue
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# val is nan, find nan in categories manually
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for cat_idx, cat in enumerate(cat_list):
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if is_scalar_nan(cat):
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drop_indices.append(cat_idx)
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break
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else: # loop did not break thus drop is missing
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missing_drops.append((col_idx, val))
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if any(missing_drops):
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msg = ("The following categories were supposed to be "
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"dropped, but were not found in the training "
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"data.\n{}".format(
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"\n".join(
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["Category: {}, Feature: {}".format(c, v)
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for c, v in missing_drops])))
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raise ValueError(msg)
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return np.array(drop_indices, dtype=object)
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def fit(self, X, y=None):
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"""
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Fit OneHotEncoder to X.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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The data to determine the categories of each feature.
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y : None
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Ignored. This parameter exists only for compatibility with
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:class:`~sklearn.pipeline.Pipeline`.
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Returns
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-------
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self
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"""
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self._validate_keywords()
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self._fit(X, handle_unknown=self.handle_unknown,
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force_all_finite='allow-nan')
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self.drop_idx_ = self._compute_drop_idx()
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return self
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def fit_transform(self, X, y=None):
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"""
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Fit OneHotEncoder to X, then transform X.
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Equivalent to fit(X).transform(X) but more convenient.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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The data to encode.
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y : None
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Ignored. This parameter exists only for compatibility with
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:class:`~sklearn.pipeline.Pipeline`.
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Returns
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-------
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X_out : {ndarray, sparse matrix} of shape \
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(n_samples, n_encoded_features)
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Transformed input. If `sparse=True`, a sparse matrix will be
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returned.
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"""
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self._validate_keywords()
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return super().fit_transform(X, y)
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def transform(self, X):
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"""
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Transform X using one-hot encoding.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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The data to encode.
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Returns
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-------
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X_out : {ndarray, sparse matrix} of shape \
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(n_samples, n_encoded_features)
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Transformed input. If `sparse=True`, a sparse matrix will be
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returned.
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"""
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check_is_fitted(self)
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# validation of X happens in _check_X called by _transform
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X_int, X_mask = self._transform(X, handle_unknown=self.handle_unknown,
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force_all_finite='allow-nan')
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n_samples, n_features = X_int.shape
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if self.drop_idx_ is not None:
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to_drop = self.drop_idx_.copy()
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# We remove all the dropped categories from mask, and decrement all
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# categories that occur after them to avoid an empty column.
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keep_cells = X_int != to_drop
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n_values = []
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for i, cats in enumerate(self.categories_):
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n_cats = len(cats)
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# drop='if_binary' but feature isn't binary
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if to_drop[i] is None:
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# set to cardinality to not drop from X_int
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to_drop[i] = n_cats
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n_values.append(n_cats)
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else: # dropped
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n_values.append(n_cats - 1)
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to_drop = to_drop.reshape(1, -1)
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X_int[X_int > to_drop] -= 1
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X_mask &= keep_cells
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else:
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n_values = [len(cats) for cats in self.categories_]
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mask = X_mask.ravel()
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feature_indices = np.cumsum([0] + n_values)
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indices = (X_int + feature_indices[:-1]).ravel()[mask]
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||
|
||
indptr = np.empty(n_samples + 1, dtype=int)
|
||
indptr[0] = 0
|
||
np.sum(X_mask, axis=1, out=indptr[1:])
|
||
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:
|
||
return out.toarray()
|
||
else:
|
||
return out
|
||
|
||
def inverse_transform(self, X):
|
||
"""
|
||
Convert the data back to the original representation.
|
||
|
||
In case unknown categories are encountered (all zeros in the
|
||
one-hot encoding), ``None`` is used to represent this 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_)
|
||
if self.drop_idx_ is None:
|
||
n_transformed_features = sum(len(cats)
|
||
for cats in self.categories_)
|
||
else:
|
||
n_transformed_features = sum(
|
||
len(cats) - 1 if to_drop is not None else len(cats)
|
||
for cats, to_drop in zip(self.categories_, self.drop_idx_)
|
||
)
|
||
|
||
# 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_transformed_features:
|
||
raise ValueError(msg.format(n_transformed_features, X.shape[1]))
|
||
|
||
# create resulting array of appropriate dtype
|
||
dt = np.find_common_type([cat.dtype for cat in self.categories_], [])
|
||
X_tr = np.empty((n_samples, n_features), dtype=dt)
|
||
|
||
j = 0
|
||
found_unknown = {}
|
||
|
||
for i in range(n_features):
|
||
if self.drop_idx_ is None or self.drop_idx_[i] is None:
|
||
cats = self.categories_[i]
|
||
else:
|
||
cats = np.delete(self.categories_[i], self.drop_idx_[i])
|
||
n_categories = len(cats)
|
||
|
||
# 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_[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[labels]
|
||
if self.handle_unknown == 'ignore':
|
||
unknown = np.asarray(sub.sum(axis=1) == 0).flatten()
|
||
# ignored unknown categories: we have a row of all zero
|
||
if unknown.any():
|
||
found_unknown[i] = unknown
|
||
else:
|
||
dropped = np.asarray(sub.sum(axis=1) == 0).flatten()
|
||
if dropped.any():
|
||
if self.drop_idx_ 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
|
||
X_tr[dropped, i] = self.categories_[i][
|
||
self.drop_idx_[i]
|
||
]
|
||
|
||
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(self, input_features=None):
|
||
"""
|
||
Return feature names for output features.
|
||
|
||
Parameters
|
||
----------
|
||
input_features : list of str of shape (n_features,)
|
||
String names for input features if available. By default,
|
||
"x0", "x1", ... "xn_features" is used.
|
||
|
||
Returns
|
||
-------
|
||
output_feature_names : ndarray of shape (n_output_features,)
|
||
Array of feature names.
|
||
"""
|
||
check_is_fitted(self)
|
||
cats = self.categories_
|
||
if input_features is None:
|
||
input_features = ['x%d' % i for i in range(len(cats))]
|
||
elif len(input_features) != len(self.categories_):
|
||
raise ValueError(
|
||
"input_features should have length equal to number of "
|
||
"features ({}), got {}".format(len(self.categories_),
|
||
len(input_features)))
|
||
|
||
feature_names = []
|
||
for i in range(len(cats)):
|
||
names = [
|
||
input_features[i] + '_' + str(t) for t in cats[i]]
|
||
if self.drop_idx_ is not None and self.drop_idx_[i] is not None:
|
||
names.pop(self.drop_idx_[i])
|
||
feature_names.extend(names)
|
||
|
||
return np.array(feature_names, dtype=object)
|
||
|
||
|
||
class OrdinalEncoder(_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>`.
|
||
|
||
.. 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
|
||
|
||
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``.
|
||
|
||
See Also
|
||
--------
|
||
OneHotEncoder : Performs a one-hot encoding of categorical features.
|
||
LabelEncoder : Encodes target labels with values between 0 and
|
||
``n_classes-1``.
|
||
|
||
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)
|
||
"""
|
||
|
||
@_deprecate_positional_args
|
||
def __init__(self, *, categories='auto', dtype=np.float64,
|
||
handle_unknown='error', unknown_value=None):
|
||
self.categories = categories
|
||
self.dtype = dtype
|
||
self.handle_unknown = handle_unknown
|
||
self.unknown_value = unknown_value
|
||
|
||
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
|
||
"""
|
||
handle_unknown_strategies = ("error", "use_encoded_value")
|
||
if self.handle_unknown not in handle_unknown_strategies:
|
||
raise ValueError(
|
||
f"handle_unknown should be either 'error' or "
|
||
f"'use_encoded_value', got {self.handle_unknown}."
|
||
)
|
||
|
||
if self.handle_unknown == 'use_encoded_value':
|
||
if is_scalar_nan(self.unknown_value):
|
||
if np.dtype(self.dtype).kind != 'f':
|
||
raise ValueError(
|
||
f"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(f"unknown_value should be an integer or "
|
||
f"np.nan when "
|
||
f"handle_unknown is 'use_encoded_value', "
|
||
f"got {self.unknown_value}.")
|
||
elif self.unknown_value is not None:
|
||
raise TypeError(f"unknown_value should only be set when "
|
||
f"handle_unknown is 'use_encoded_value', "
|
||
f"got {self.unknown_value}.")
|
||
|
||
self._fit(X)
|
||
|
||
if self.handle_unknown == 'use_encoded_value':
|
||
for feature_cats in self.categories_:
|
||
if 0 <= self.unknown_value < len(feature_cats):
|
||
raise ValueError(f"The used value for unknown_value "
|
||
f"{self.unknown_value} is one of the "
|
||
f"values already used for encoding the "
|
||
f"seen categories.")
|
||
|
||
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.
|
||
"""
|
||
X_int, X_mask = self._transform(X, handle_unknown=self.handle_unknown)
|
||
X_trans = X_int.astype(self.dtype, copy=False)
|
||
|
||
# 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, sparse matrix} of shape (n_samples, n_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_)
|
||
|
||
# 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.find_common_type([cat.dtype for cat in self.categories_], [])
|
||
X_tr = np.empty((n_samples, n_features), dtype=dt)
|
||
|
||
found_unknown = {}
|
||
|
||
for i in range(n_features):
|
||
labels = X[:, i].astype('int64', copy=False)
|
||
if self.handle_unknown == 'use_encoded_value':
|
||
unknown_labels = labels == self.unknown_value
|
||
X_tr[:, i] = self.categories_[i][np.where(
|
||
unknown_labels, 0, labels)]
|
||
found_unknown[i] = unknown_labels
|
||
else:
|
||
X_tr[:, i] = self.categories_[i][labels]
|
||
|
||
# insert None values for unknown values
|
||
if found_unknown:
|
||
X_tr = X_tr.astype(object, copy=False)
|
||
|
||
for idx, mask in found_unknown.items():
|
||
X_tr[mask, idx] = None
|
||
|
||
return X_tr
|