1452 lines
56 KiB
Python
1452 lines
56 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 numbers
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from numbers import Integral, Real
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import warnings
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import numpy as np
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from scipy import sparse
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from ..base import BaseEstimator, TransformerMixin, OneToOneFeatureMixin
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from ..utils import check_array, is_scalar_nan, _safe_indexing
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from ..utils.validation import check_is_fitted
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from ..utils.validation import _check_feature_names_in
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from ..utils._param_validation import Interval, StrOptions, Hidden
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from ..utils._mask import _get_mask
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from ..utils._encode import _encode, _check_unknown, _unique, _get_counts
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__all__ = ["OneHotEncoder", "OrdinalEncoder"]
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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, e.g. 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, force_all_finite=force_all_finite)
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if not hasattr(X, "dtype") and np.issubdtype(X_temp.dtype, np.str_):
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X = check_array(X, dtype=object, 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 = _safe_indexing(X, indices=i, axis=1)
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Xi = check_array(
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Xi, ensure_2d=False, dtype=None, force_all_finite=needs_validation
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)
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X_columns.append(Xi)
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return X_columns, n_samples, n_features
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def _fit(
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self, X, handle_unknown="error", force_all_finite=True, return_counts=False
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):
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self._check_n_features(X, reset=True)
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self._check_feature_names(X, reset=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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)
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self.n_features_in_ = n_features
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if self.categories != "auto":
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if len(self.categories) != n_features:
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raise ValueError(
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"Shape mismatch: if categories is an array,"
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" it has to be of shape (n_features,)."
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)
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self.categories_ = []
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category_counts = []
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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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result = _unique(Xi, return_counts=return_counts)
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if return_counts:
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cats, counts = result
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category_counts.append(counts)
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else:
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cats = result
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else:
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if np.issubdtype(Xi.dtype, np.str_):
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# Always convert string categories to objects to avoid
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# unexpected string truncation for longer category labels
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# passed in the constructor.
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Xi_dtype = object
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else:
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Xi_dtype = Xi.dtype
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cats = np.array(self.categories[i], dtype=Xi_dtype)
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if (
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cats.dtype == object
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and isinstance(cats[0], bytes)
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and Xi.dtype.kind != "S"
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):
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msg = (
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f"In column {i}, the predefined categories have type 'bytes'"
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" which is incompatible with values of type"
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f" '{type(Xi[0]).__name__}'."
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)
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raise ValueError(msg)
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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 = (
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"Unsorted categories are not supported for numerical categories"
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)
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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 not np.isnan(sorted_cats[-1])
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):
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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 = (
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"Found unknown categories {0} in column {1}"
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" during fit".format(diff, i)
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)
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raise ValueError(msg)
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if return_counts:
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category_counts.append(_get_counts(Xi, cats))
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self.categories_.append(cats)
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output = {"n_samples": n_samples}
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if return_counts:
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output["category_counts"] = category_counts
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return output
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def _transform(
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self, X, handle_unknown="error", force_all_finite=True, warn_on_unknown=False
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):
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self._check_feature_names(X, reset=False)
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self._check_n_features(X, reset=False)
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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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)
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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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columns_with_unknown = []
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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], 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 = (
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"Found unknown categories {0} in column {1}"
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" during transform".format(diff, i)
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)
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raise ValueError(msg)
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else:
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if warn_on_unknown:
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columns_with_unknown.append(i)
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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 (
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self.categories_[i].dtype.kind in ("U", "S")
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and self.categories_[i].itemsize > Xi.itemsize
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):
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Xi = Xi.astype(self.categories_[i].dtype)
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elif self.categories_[i].dtype.kind == "O" and 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], check_unknown=False)
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if columns_with_unknown:
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warnings.warn(
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"Found unknown categories in columns "
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f"{columns_with_unknown} during transform. These "
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"unknown categories will be encoded as all zeros",
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UserWarning,
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)
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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_output``
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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 an 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 an unregularized linear regression model.
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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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When `max_categories` or `min_frequency` is configured to group
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infrequent categories, the dropping behavior is handled after the
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grouping.
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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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.. versionchanged:: 1.1
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Support for dropping infrequent categories.
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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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.. deprecated:: 1.2
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`sparse` is deprecated in 1.2 and will be removed in 1.4. Use
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`sparse_output` instead.
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sparse_output : bool, default=True
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Will return sparse matrix if set True else will return an array.
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.. versionadded:: 1.2
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`sparse` was renamed to `sparse_output`
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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', 'infrequent_if_exist'}, \
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default='error'
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Specifies the way unknown categories are handled during :meth:`transform`.
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- 'error' : Raise an error if an unknown category is present during transform.
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- 'ignore' : When 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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- 'infrequent_if_exist' : When an unknown category is encountered
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during transform, the resulting one-hot encoded columns for this
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feature will map to the infrequent category if it exists. The
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infrequent category will be mapped to the last position in the
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encoding. During inverse transform, an unknown category will be
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mapped to the category denoted `'infrequent'` if it exists. If the
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`'infrequent'` category does not exist, then :meth:`transform` and
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:meth:`inverse_transform` will handle an unknown category as with
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`handle_unknown='ignore'`. Infrequent categories exist based on
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`min_frequency` and `max_categories`. Read more in the
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:ref:`User Guide <one_hot_encoder_infrequent_categories>`.
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.. versionchanged:: 1.1
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`'infrequent_if_exist'` was added to automatically handle unknown
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categories and infrequent categories.
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min_frequency : int or float, default=None
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Specifies the minimum frequency below which a category will be
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considered infrequent.
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- If `int`, categories with a smaller cardinality will be considered
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infrequent.
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- If `float`, categories with a smaller cardinality than
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`min_frequency * n_samples` will be considered infrequent.
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.. versionadded:: 1.1
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Read more in the :ref:`User Guide <one_hot_encoder_infrequent_categories>`.
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max_categories : int, default=None
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Specifies an upper limit to the number of output features for each input
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feature when considering infrequent categories. If there are infrequent
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categories, `max_categories` includes the category representing the
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infrequent categories along with the frequent categories. If `None`,
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there is no limit to the number of output features.
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.. versionadded:: 1.1
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Read more in the :ref:`User Guide <one_hot_encoder_infrequent_categories>`.
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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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If infrequent categories are enabled by setting `min_frequency` or
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`max_categories` to a non-default value and `drop_idx[i]` corresponds
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to a infrequent category, then the entire infrequent category is
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dropped.
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.. versionchanged:: 0.23
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Added the possibility to contain `None` values.
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infrequent_categories_ : list of ndarray
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Defined only if infrequent categories are enabled by setting
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`min_frequency` or `max_categories` to a non-default value.
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`infrequent_categories_[i]` are the infrequent categories for feature
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`i`. If the feature `i` has no infrequent categories
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`infrequent_categories_[i]` is None.
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.. versionadded:: 1.1
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 1.0
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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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_out(['gender', 'group'])
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array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'], ...)
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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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Infrequent categories are enabled by setting `max_categories` or `min_frequency`.
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>>> import numpy as np
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>>> X = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object).T
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>>> ohe = OneHotEncoder(max_categories=3, sparse_output=False).fit(X)
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>>> ohe.infrequent_categories_
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[array(['a', 'd'], dtype=object)]
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>>> ohe.transform([["a"], ["b"]])
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array([[0., 0., 1.],
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[1., 0., 0.]])
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"""
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_parameter_constraints: dict = {
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"categories": [StrOptions({"auto"}), list],
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"drop": [StrOptions({"first", "if_binary"}), "array-like", None],
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"dtype": "no_validation", # validation delegated to numpy
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"handle_unknown": [StrOptions({"error", "ignore", "infrequent_if_exist"})],
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"max_categories": [Interval(Integral, 1, None, closed="left"), None],
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"min_frequency": [
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Interval(Integral, 1, None, closed="left"),
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Interval(Real, 0, 1, closed="neither"),
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None,
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],
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"sparse": [Hidden(StrOptions({"deprecated"})), "boolean"], # deprecated
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"sparse_output": ["boolean"],
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}
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def __init__(
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self,
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*,
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categories="auto",
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drop=None,
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sparse="deprecated",
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sparse_output=True,
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dtype=np.float64,
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handle_unknown="error",
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min_frequency=None,
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max_categories=None,
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):
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self.categories = categories
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# TODO(1.4): Remove self.sparse
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self.sparse = sparse
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self.sparse_output = sparse_output
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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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self.min_frequency = min_frequency
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self.max_categories = max_categories
|
|
|
|
@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.
|
|
"""
|
|
self._infrequent_enabled = (
|
|
self.max_categories is not None and self.max_categories >= 1
|
|
) or self.min_frequency is not None
|
|
|
|
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]!r} from feature"
|
|
f" {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
|
|
for cat_idx, cat in enumerate(cat_list):
|
|
if is_scalar_nan(cat):
|
|
drop_indices.append(
|
|
self._map_drop_idx_to_infrequent(feature_idx, cat_idx)
|
|
)
|
|
break
|
|
else: # loop did not break thus drop 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 _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:
|
|
# stable sort to preserve original count order
|
|
smallest_levels = np.argsort(category_count, kind="mergesort")[
|
|
: -self.max_categories + 1
|
|
]
|
|
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):
|
|
"""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]`.
|
|
"""
|
|
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 cats, infreq_idx in zip(self.categories_, self._infrequent_indices):
|
|
# no infrequent categories
|
|
if infreq_idx is None:
|
|
self._default_to_infrequent_mappings.append(None)
|
|
continue
|
|
|
|
n_cats = len(cats)
|
|
# 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):
|
|
"""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`.
|
|
"""
|
|
if not self._infrequent_enabled:
|
|
return
|
|
|
|
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
|
|
X_int[:, i] = np.take(mapping, X_int[:, i])
|
|
|
|
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
|
|
|
|
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._validate_params()
|
|
|
|
if self.sparse != "deprecated":
|
|
warnings.warn(
|
|
"`sparse` was renamed to `sparse_output` in version 1.2 and "
|
|
"will be removed in 1.4. `sparse_output` is ignored unless you "
|
|
"leave `sparse` to its default value.",
|
|
FutureWarning,
|
|
)
|
|
self.sparse_output = self.sparse
|
|
|
|
self._check_infrequent_enabled()
|
|
|
|
fit_results = self._fit(
|
|
X,
|
|
handle_unknown=self.handle_unknown,
|
|
force_all_finite="allow-nan",
|
|
return_counts=self._infrequent_enabled,
|
|
)
|
|
if self._infrequent_enabled:
|
|
self._fit_infrequent_category_mapping(
|
|
fit_results["n_samples"], fit_results["category_counts"]
|
|
)
|
|
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 there are infrequent categories for a feature, the infrequent
|
|
categories will be 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)
|
|
# 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,
|
|
)
|
|
self._map_infrequent_categories(X_int, X_mask)
|
|
|
|
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_)
|
|
]
|
|
|
|
feature_names = []
|
|
for i in range(len(cats)):
|
|
names = [input_features[i] + "_" + str(t) for t in cats[i]]
|
|
feature_names.extend(names)
|
|
|
|
return np.array(feature_names, dtype=object)
|
|
|
|
|
|
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>`.
|
|
|
|
.. 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
|
|
|
|
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
|
|
|
|
See Also
|
|
--------
|
|
OneHotEncoder : Performs a one-hot encoding of categorical features.
|
|
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.]])
|
|
"""
|
|
|
|
_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],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
categories="auto",
|
|
dtype=np.float64,
|
|
handle_unknown="error",
|
|
unknown_value=None,
|
|
encoded_missing_value=np.nan,
|
|
):
|
|
self.categories = categories
|
|
self.dtype = dtype
|
|
self.handle_unknown = handle_unknown
|
|
self.unknown_value = unknown_value
|
|
self.encoded_missing_value = encoded_missing_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 : object
|
|
Fitted encoder.
|
|
"""
|
|
self._validate_params()
|
|
|
|
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"`
|
|
self._fit(X, handle_unknown=self.handle_unknown, force_all_finite="allow-nan")
|
|
|
|
cardinalities = [len(categories) for categories in self.categories_]
|
|
|
|
# stores the missing indices per category
|
|
self._missing_indices = {}
|
|
for cat_idx, categories_for_idx in enumerate(self.categories_):
|
|
for i, cat in enumerate(categories_for_idx):
|
|
if is_scalar_nan(cat):
|
|
self._missing_indices[cat_idx] = i
|
|
|
|
# missing values are not considered part of the cardinality
|
|
# when considering unknown categories or encoded_missing_value
|
|
cardinalities[cat_idx] -= 1
|
|
continue
|
|
|
|
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 avaliable
|
|
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.
|
|
"""
|
|
X_int, X_mask = self._transform(
|
|
X, handle_unknown=self.handle_unknown, force_all_finite="allow-nan"
|
|
)
|
|
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 = {}
|
|
|
|
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]
|
|
|
|
if self.handle_unknown == "use_encoded_value":
|
|
unknown_labels = _get_mask(labels, self.unknown_value)
|
|
|
|
known_labels = ~unknown_labels
|
|
X_tr[known_labels, i] = self.categories_[i][
|
|
labels[known_labels].astype("int64", copy=False)
|
|
]
|
|
found_unknown[i] = unknown_labels
|
|
else:
|
|
X_tr[:, i] = self.categories_[i][labels.astype("int64", copy=False)]
|
|
|
|
# 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
|