325 lines
12 KiB
Python
325 lines
12 KiB
Python
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# -*- coding: utf-8 -*-
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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 numbers
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import numpy as np
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import warnings
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from . import OneHotEncoder
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from ..base import BaseEstimator, TransformerMixin
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from ..utils.validation import check_array
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from ..utils.validation import check_is_fitted
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from ..utils.validation import FLOAT_DTYPES
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from ..utils.validation import _deprecate_positional_args
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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, 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
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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
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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
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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
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All bins in each feature have identical widths.
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quantile
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All bins in each feature have the same number of points.
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kmeans
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Values in each bin have the same nearest center of a 1D k-means
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cluster.
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Attributes
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----------
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n_bins_ : int array, shape (n_features,)
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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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bin_edges_ : array of arrays, 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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See Also
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--------
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sklearn.preprocessing.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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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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>>> 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(n_bins=3, encode='ordinal', strategy='uniform')
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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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@_deprecate_positional_args
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def __init__(self, n_bins=5, *, encode='onehot', strategy='quantile'):
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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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def fit(self, X, y=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 : numeric array-like, 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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Returns
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-------
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self
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"""
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X = self._validate_data(X, dtype='numeric')
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valid_encode = ('onehot', 'onehot-dense', 'ordinal')
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if self.encode not in valid_encode:
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raise ValueError("Valid options for 'encode' are {}. "
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"Got encode={!r} instead."
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.format(valid_encode, self.encode))
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valid_strategy = ('uniform', 'quantile', 'kmeans')
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if self.strategy not in valid_strategy:
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raise ValueError("Valid options for 'strategy' are {}. "
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"Got strategy={!r} instead."
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.format(valid_strategy, self.strategy))
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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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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("Feature %d is constant and will be "
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"replaced with 0." % jj)
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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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bin_edges[jj] = np.asarray(np.percentile(column, quantiles))
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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(column[:, None]).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('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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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=self.encode == 'onehot')
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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_)), dtype=int))
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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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"""
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orig_bins = self.n_bins
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if isinstance(orig_bins, numbers.Number):
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if not isinstance(orig_bins, numbers.Integral):
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raise ValueError("{} received an invalid n_bins type. "
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"Received {}, expected int."
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.format(KBinsDiscretizer.__name__,
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type(orig_bins).__name__))
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if orig_bins < 2:
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raise ValueError("{} received an invalid number "
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"of bins. Received {}, expected at least 2."
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.format(KBinsDiscretizer.__name__, orig_bins))
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return np.full(n_features, orig_bins, dtype=np.int)
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n_bins = check_array(orig_bins, dtype=np.int, copy=True,
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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 "
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"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("{} 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."
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.format(KBinsDiscretizer.__name__, indices))
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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 : numeric array-like, 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 : numeric array-like or sparse matrix
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Data in the binned space.
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"""
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check_is_fitted(self)
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Xt = check_array(X, copy=True, dtype=FLOAT_DTYPES)
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n_features = self.n_bins_.shape[0]
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if Xt.shape[1] != n_features:
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raise ValueError("Incorrect number of features. Expecting {}, "
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"received {}.".format(n_features, Xt.shape[1]))
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bin_edges = self.bin_edges_
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for jj in range(Xt.shape[1]):
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# Values which are close to a bin edge are susceptible to numeric
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# instability. Add eps to X so these values are binned correctly
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# with respect to their decimal truncation. See documentation of
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# numpy.isclose for an explanation of ``rtol`` and ``atol``.
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rtol = 1.e-5
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atol = 1.e-8
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eps = atol + rtol * np.abs(Xt[:, jj])
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Xt[:, jj] = np.digitize(Xt[:, jj] + eps, bin_edges[jj][1:])
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np.clip(Xt, 0, self.n_bins_ - 1, out=Xt)
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if self.encode == 'ordinal':
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return Xt
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return self._encoder.transform(Xt)
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def inverse_transform(self, Xt):
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"""
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Transform discretized data back to original feature space.
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Note that this function does not regenerate the original data
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due to discretization rounding.
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Parameters
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----------
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Xt : numeric array-like, shape (n_sample, n_features)
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Transformed data in the binned space.
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Returns
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-------
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Xinv : numeric array-like
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Data in the original feature space.
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"""
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check_is_fitted(self)
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if 'onehot' in self.encode:
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Xt = self._encoder.inverse_transform(Xt)
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Xinv = check_array(Xt, copy=True, dtype=FLOAT_DTYPES)
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n_features = self.n_bins_.shape[0]
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if Xinv.shape[1] != n_features:
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raise ValueError("Incorrect number of features. Expecting {}, "
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"received {}.".format(n_features, Xinv.shape[1]))
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for jj in range(n_features):
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bin_edges = self.bin_edges_[jj]
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bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5
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Xinv[:, jj] = bin_centers[np.int_(Xinv[:, jj])]
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return Xinv
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