466 lines
16 KiB
Python
466 lines
16 KiB
Python
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# Author: Henry Lin <hlin117@gmail.com>
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# Tom Dupré la Tour
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# License: BSD
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import warnings
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from numbers import Integral
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import numpy as np
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from ..base import BaseEstimator, TransformerMixin, _fit_context
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from ..utils import resample
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from ..utils._param_validation import Interval, Options, StrOptions
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from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
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from ..utils.stats import _weighted_percentile
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from ..utils.validation import (
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_check_feature_names_in,
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_check_sample_weight,
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check_array,
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check_is_fitted,
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)
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from ._encoders import OneHotEncoder
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class KBinsDiscretizer(TransformerMixin, BaseEstimator):
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"""
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Bin continuous data into intervals.
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Read more in the :ref:`User Guide <preprocessing_discretization>`.
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.. versionadded:: 0.20
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Parameters
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----------
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n_bins : int or array-like of shape (n_features,), default=5
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The number of bins to produce. Raises ValueError if ``n_bins < 2``.
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encode : {'onehot', 'onehot-dense', 'ordinal'}, default='onehot'
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Method used to encode the transformed result.
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- 'onehot': Encode the transformed result with one-hot encoding
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and return a sparse matrix. Ignored features are always
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stacked to the right.
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- 'onehot-dense': Encode the transformed result with one-hot encoding
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and return a dense array. Ignored features are always
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stacked to the right.
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- 'ordinal': Return the bin identifier encoded as an integer value.
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strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
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Strategy used to define the widths of the bins.
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- 'uniform': All bins in each feature have identical widths.
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- 'quantile': All bins in each feature have the same number of points.
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- 'kmeans': Values in each bin have the same nearest center of a 1D
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k-means cluster.
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For an example of the different strategies see:
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:ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`.
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dtype : {np.float32, np.float64}, default=None
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The desired data-type for the output. If None, output dtype is
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consistent with input dtype. Only np.float32 and np.float64 are
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supported.
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.. versionadded:: 0.24
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subsample : int or None, default=200_000
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Maximum number of samples, used to fit the model, for computational
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efficiency.
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`subsample=None` means that all the training samples are used when
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computing the quantiles that determine the binning thresholds.
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Since quantile computation relies on sorting each column of `X` and
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that sorting has an `n log(n)` time complexity,
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it is recommended to use subsampling on datasets with a
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very large number of samples.
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.. versionchanged:: 1.3
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The default value of `subsample` changed from `None` to `200_000` when
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`strategy="quantile"`.
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.. versionchanged:: 1.5
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The default value of `subsample` changed from `None` to `200_000` when
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`strategy="uniform"` or `strategy="kmeans"`.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for subsampling.
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Pass an int for reproducible results across multiple function calls.
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See the `subsample` parameter for more details.
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See :term:`Glossary <random_state>`.
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.. versionadded:: 1.1
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Attributes
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----------
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bin_edges_ : ndarray of ndarray of shape (n_features,)
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The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )``
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Ignored features will have empty arrays.
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n_bins_ : ndarray of shape (n_features,), dtype=np.int64
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Number of bins per feature. Bins whose width are too small
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(i.e., <= 1e-8) are removed with a warning.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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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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Binarizer : Class used to bin values as ``0`` or
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``1`` based on a parameter ``threshold``.
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Notes
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-----
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For a visualization of discretization on different datasets refer to
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:ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`.
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On the effect of discretization on linear models see:
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:ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`.
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In bin edges for feature ``i``, the first and last values are used only for
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``inverse_transform``. During transform, bin edges are extended to::
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np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf])
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You can combine ``KBinsDiscretizer`` with
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:class:`~sklearn.compose.ColumnTransformer` if you only want to preprocess
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part of the features.
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``KBinsDiscretizer`` might produce constant features (e.g., when
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``encode = 'onehot'`` and certain bins do not contain any data).
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These features can be removed with feature selection algorithms
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(e.g., :class:`~sklearn.feature_selection.VarianceThreshold`).
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Examples
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--------
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>>> from sklearn.preprocessing import KBinsDiscretizer
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>>> X = [[-2, 1, -4, -1],
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... [-1, 2, -3, -0.5],
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... [ 0, 3, -2, 0.5],
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... [ 1, 4, -1, 2]]
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>>> est = KBinsDiscretizer(
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... n_bins=3, encode='ordinal', strategy='uniform'
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... )
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>>> est.fit(X)
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KBinsDiscretizer(...)
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>>> Xt = est.transform(X)
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>>> Xt # doctest: +SKIP
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array([[ 0., 0., 0., 0.],
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[ 1., 1., 1., 0.],
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[ 2., 2., 2., 1.],
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[ 2., 2., 2., 2.]])
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Sometimes it may be useful to convert the data back into the original
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feature space. The ``inverse_transform`` function converts the binned
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data into the original feature space. Each value will be equal to the mean
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of the two bin edges.
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>>> est.bin_edges_[0]
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array([-2., -1., 0., 1.])
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>>> est.inverse_transform(Xt)
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array([[-1.5, 1.5, -3.5, -0.5],
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[-0.5, 2.5, -2.5, -0.5],
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[ 0.5, 3.5, -1.5, 0.5],
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[ 0.5, 3.5, -1.5, 1.5]])
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"""
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_parameter_constraints: dict = {
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"n_bins": [Interval(Integral, 2, None, closed="left"), "array-like"],
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"encode": [StrOptions({"onehot", "onehot-dense", "ordinal"})],
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"strategy": [StrOptions({"uniform", "quantile", "kmeans"})],
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"dtype": [Options(type, {np.float64, np.float32}), None],
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"subsample": [Interval(Integral, 1, None, closed="left"), None],
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"random_state": ["random_state"],
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}
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def __init__(
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self,
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n_bins=5,
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*,
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encode="onehot",
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strategy="quantile",
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dtype=None,
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subsample=200_000,
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random_state=None,
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):
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self.n_bins = n_bins
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self.encode = encode
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self.strategy = strategy
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self.dtype = dtype
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self.subsample = subsample
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self.random_state = random_state
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@_fit_context(prefer_skip_nested_validation=True)
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def fit(self, X, y=None, sample_weight=None):
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"""
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Fit the estimator.
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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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Data to be discretized.
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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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sample_weight : ndarray of shape (n_samples,)
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Contains weight values to be associated with each sample.
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Cannot be used when `strategy` is set to `"uniform"`.
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.. versionadded:: 1.3
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Returns
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-------
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self : object
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Returns the instance itself.
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"""
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X = self._validate_data(X, dtype="numeric")
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if self.dtype in (np.float64, np.float32):
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output_dtype = self.dtype
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else: # self.dtype is None
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output_dtype = X.dtype
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n_samples, n_features = X.shape
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if sample_weight is not None and self.strategy == "uniform":
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raise ValueError(
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"`sample_weight` was provided but it cannot be "
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"used with strategy='uniform'. Got strategy="
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f"{self.strategy!r} instead."
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)
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if self.subsample is not None and n_samples > self.subsample:
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# Take a subsample of `X`
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X = resample(
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X,
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replace=False,
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n_samples=self.subsample,
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random_state=self.random_state,
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)
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n_features = X.shape[1]
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n_bins = self._validate_n_bins(n_features)
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if sample_weight is not None:
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sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
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bin_edges = np.zeros(n_features, dtype=object)
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for jj in range(n_features):
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column = X[:, jj]
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col_min, col_max = column.min(), column.max()
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if col_min == col_max:
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warnings.warn(
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"Feature %d is constant and will be replaced with 0." % jj
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)
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n_bins[jj] = 1
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bin_edges[jj] = np.array([-np.inf, np.inf])
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continue
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if self.strategy == "uniform":
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bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1)
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elif self.strategy == "quantile":
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quantiles = np.linspace(0, 100, n_bins[jj] + 1)
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if sample_weight is None:
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bin_edges[jj] = np.asarray(np.percentile(column, quantiles))
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else:
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bin_edges[jj] = np.asarray(
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[
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_weighted_percentile(column, sample_weight, q)
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for q in quantiles
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],
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dtype=np.float64,
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)
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elif self.strategy == "kmeans":
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from ..cluster import KMeans # fixes import loops
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# Deterministic initialization with uniform spacing
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uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1)
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init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5
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# 1D k-means procedure
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km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)
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centers = km.fit(
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column[:, None], sample_weight=sample_weight
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).cluster_centers_[:, 0]
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# Must sort, centers may be unsorted even with sorted init
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centers.sort()
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bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5
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bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max]
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# Remove bins whose width are too small (i.e., <= 1e-8)
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if self.strategy in ("quantile", "kmeans"):
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mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8
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bin_edges[jj] = bin_edges[jj][mask]
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if len(bin_edges[jj]) - 1 != n_bins[jj]:
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warnings.warn(
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"Bins whose width are too small (i.e., <= "
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"1e-8) in feature %d are removed. Consider "
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"decreasing the number of bins." % jj
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)
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n_bins[jj] = len(bin_edges[jj]) - 1
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self.bin_edges_ = bin_edges
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self.n_bins_ = n_bins
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if "onehot" in self.encode:
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self._encoder = OneHotEncoder(
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categories=[np.arange(i) for i in self.n_bins_],
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sparse_output=self.encode == "onehot",
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dtype=output_dtype,
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)
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# Fit the OneHotEncoder with toy datasets
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# so that it's ready for use after the KBinsDiscretizer is fitted
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self._encoder.fit(np.zeros((1, len(self.n_bins_))))
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return self
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def _validate_n_bins(self, n_features):
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"""Returns n_bins_, the number of bins per feature."""
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orig_bins = self.n_bins
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if isinstance(orig_bins, Integral):
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return np.full(n_features, orig_bins, dtype=int)
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n_bins = check_array(orig_bins, dtype=int, copy=True, ensure_2d=False)
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if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
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raise ValueError("n_bins must be a scalar or array of shape (n_features,).")
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bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
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violating_indices = np.where(bad_nbins_value)[0]
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if violating_indices.shape[0] > 0:
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indices = ", ".join(str(i) for i in violating_indices)
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raise ValueError(
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"{} received an invalid number "
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"of bins at indices {}. Number of bins "
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"must be at least 2, and must be an int.".format(
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KBinsDiscretizer.__name__, indices
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)
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)
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return n_bins
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def transform(self, X):
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"""
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Discretize the data.
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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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Data to be discretized.
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Returns
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-------
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Xt : {ndarray, sparse matrix}, dtype={np.float32, np.float64}
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||
|
|
Data in the binned space. Will be a sparse matrix if
|
||
|
|
`self.encode='onehot'` and ndarray otherwise.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
# check input and attribute dtypes
|
||
|
|
dtype = (np.float64, np.float32) if self.dtype is None else self.dtype
|
||
|
|
Xt = self._validate_data(X, copy=True, dtype=dtype, reset=False)
|
||
|
|
|
||
|
|
bin_edges = self.bin_edges_
|
||
|
|
for jj in range(Xt.shape[1]):
|
||
|
|
Xt[:, jj] = np.searchsorted(bin_edges[jj][1:-1], Xt[:, jj], side="right")
|
||
|
|
|
||
|
|
if self.encode == "ordinal":
|
||
|
|
return Xt
|
||
|
|
|
||
|
|
dtype_init = None
|
||
|
|
if "onehot" in self.encode:
|
||
|
|
dtype_init = self._encoder.dtype
|
||
|
|
self._encoder.dtype = Xt.dtype
|
||
|
|
try:
|
||
|
|
Xt_enc = self._encoder.transform(Xt)
|
||
|
|
finally:
|
||
|
|
# revert the initial dtype to avoid modifying self.
|
||
|
|
self._encoder.dtype = dtype_init
|
||
|
|
return Xt_enc
|
||
|
|
|
||
|
|
def inverse_transform(self, X=None, *, Xt=None):
|
||
|
|
"""
|
||
|
|
Transform discretized data back to original feature space.
|
||
|
|
|
||
|
|
Note that this function does not regenerate the original data
|
||
|
|
due to discretization rounding.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Transformed data in the binned space.
|
||
|
|
|
||
|
|
Xt : array-like of shape (n_samples, n_features)
|
||
|
|
Transformed data in the binned space.
|
||
|
|
|
||
|
|
.. deprecated:: 1.5
|
||
|
|
`Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Xinv : ndarray, dtype={np.float32, np.float64}
|
||
|
|
Data in the original feature space.
|
||
|
|
"""
|
||
|
|
X = _deprecate_Xt_in_inverse_transform(X, Xt)
|
||
|
|
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
if "onehot" in self.encode:
|
||
|
|
X = self._encoder.inverse_transform(X)
|
||
|
|
|
||
|
|
Xinv = check_array(X, copy=True, dtype=(np.float64, np.float32))
|
||
|
|
n_features = self.n_bins_.shape[0]
|
||
|
|
if Xinv.shape[1] != n_features:
|
||
|
|
raise ValueError(
|
||
|
|
"Incorrect number of features. Expecting {}, received {}.".format(
|
||
|
|
n_features, Xinv.shape[1]
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
for jj in range(n_features):
|
||
|
|
bin_edges = self.bin_edges_[jj]
|
||
|
|
bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5
|
||
|
|
Xinv[:, jj] = bin_centers[(Xinv[:, jj]).astype(np.int64)]
|
||
|
|
|
||
|
|
return Xinv
|
||
|
|
|
||
|
|
def get_feature_names_out(self, input_features=None):
|
||
|
|
"""Get output feature names.
|
||
|
|
|
||
|
|
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, "n_features_in_")
|
||
|
|
input_features = _check_feature_names_in(self, input_features)
|
||
|
|
if hasattr(self, "_encoder"):
|
||
|
|
return self._encoder.get_feature_names_out(input_features)
|
||
|
|
|
||
|
|
# ordinal encoding
|
||
|
|
return input_features
|