473 lines
17 KiB
Python
473 lines
17 KiB
Python
|
# Author: Henry Lin <hlin117@gmail.com>
|
||
|
|
# Tom Dupré la Tour
|
||
|
|
|
||
|
|
# License: BSD
|
||
|
|
|
||
|
|
|
||
|
|
import warnings
|
||
|
|
from numbers import Integral
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from ..base import BaseEstimator, TransformerMixin, _fit_context
|
||
|
|
from ..utils import _safe_indexing
|
||
|
|
from ..utils._param_validation import Hidden, Interval, Options, StrOptions
|
||
|
|
from ..utils.stats import _weighted_percentile
|
||
|
|
from ..utils.validation import (
|
||
|
|
_check_feature_names_in,
|
||
|
|
_check_sample_weight,
|
||
|
|
check_array,
|
||
|
|
check_is_fitted,
|
||
|
|
check_random_state,
|
||
|
|
)
|
||
|
|
from ._encoders import OneHotEncoder
|
||
|
|
|
||
|
|
|
||
|
|
class KBinsDiscretizer(TransformerMixin, BaseEstimator):
|
||
|
|
"""
|
||
|
|
Bin continuous data into intervals.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <preprocessing_discretization>`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n_bins : int or array-like of shape (n_features,), default=5
|
||
|
|
The number of bins to produce. Raises ValueError if ``n_bins < 2``.
|
||
|
|
|
||
|
|
encode : {'onehot', 'onehot-dense', 'ordinal'}, default='onehot'
|
||
|
|
Method used to encode the transformed result.
|
||
|
|
|
||
|
|
- 'onehot': Encode the transformed result with one-hot encoding
|
||
|
|
and return a sparse matrix. Ignored features are always
|
||
|
|
stacked to the right.
|
||
|
|
- 'onehot-dense': Encode the transformed result with one-hot encoding
|
||
|
|
and return a dense array. Ignored features are always
|
||
|
|
stacked to the right.
|
||
|
|
- 'ordinal': Return the bin identifier encoded as an integer value.
|
||
|
|
|
||
|
|
strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
|
||
|
|
Strategy used to define the widths of the bins.
|
||
|
|
|
||
|
|
- 'uniform': All bins in each feature have identical widths.
|
||
|
|
- 'quantile': All bins in each feature have the same number of points.
|
||
|
|
- 'kmeans': Values in each bin have the same nearest center of a 1D
|
||
|
|
k-means cluster.
|
||
|
|
|
||
|
|
For an example of the different strategies see:
|
||
|
|
:ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`.
|
||
|
|
|
||
|
|
dtype : {np.float32, np.float64}, default=None
|
||
|
|
The desired data-type for the output. If None, output dtype is
|
||
|
|
consistent with input dtype. Only np.float32 and np.float64 are
|
||
|
|
supported.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24
|
||
|
|
|
||
|
|
subsample : int or None, default='warn'
|
||
|
|
Maximum number of samples, used to fit the model, for computational
|
||
|
|
efficiency. Defaults to 200_000 when `strategy='quantile'` and to `None`
|
||
|
|
when `strategy='uniform'` or `strategy='kmeans'`.
|
||
|
|
`subsample=None` means that all the training samples are used when
|
||
|
|
computing the quantiles that determine the binning thresholds.
|
||
|
|
Since quantile computation relies on sorting each column of `X` and
|
||
|
|
that sorting has an `n log(n)` time complexity,
|
||
|
|
it is recommended to use subsampling on datasets with a
|
||
|
|
very large number of samples.
|
||
|
|
|
||
|
|
.. versionchanged:: 1.3
|
||
|
|
The default value of `subsample` changed from `None` to `200_000` when
|
||
|
|
`strategy="quantile"`.
|
||
|
|
|
||
|
|
.. versionchanged:: 1.5
|
||
|
|
The default value of `subsample` changed from `None` to `200_000` when
|
||
|
|
`strategy="uniform"` or `strategy="kmeans"`.
|
||
|
|
|
||
|
|
random_state : int, RandomState instance or None, default=None
|
||
|
|
Determines random number generation for subsampling.
|
||
|
|
Pass an int for reproducible results across multiple function calls.
|
||
|
|
See the `subsample` parameter for more details.
|
||
|
|
See :term:`Glossary <random_state>`.
|
||
|
|
|
||
|
|
.. versionadded:: 1.1
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
bin_edges_ : ndarray of ndarray of shape (n_features,)
|
||
|
|
The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )``
|
||
|
|
Ignored features will have empty arrays.
|
||
|
|
|
||
|
|
n_bins_ : ndarray of shape (n_features,), dtype=np.int64
|
||
|
|
Number of bins per feature. Bins whose width are too small
|
||
|
|
(i.e., <= 1e-8) are removed with a warning.
|
||
|
|
|
||
|
|
n_features_in_ : int
|
||
|
|
Number of features seen during :term:`fit`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24
|
||
|
|
|
||
|
|
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
|
||
|
|
--------
|
||
|
|
Binarizer : Class used to bin values as ``0`` or
|
||
|
|
``1`` based on a parameter ``threshold``.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
For a visualization of discretization on different datasets refer to
|
||
|
|
:ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`.
|
||
|
|
On the effect of discretization on linear models see:
|
||
|
|
:ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`.
|
||
|
|
|
||
|
|
In bin edges for feature ``i``, the first and last values are used only for
|
||
|
|
``inverse_transform``. During transform, bin edges are extended to::
|
||
|
|
|
||
|
|
np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf])
|
||
|
|
|
||
|
|
You can combine ``KBinsDiscretizer`` with
|
||
|
|
:class:`~sklearn.compose.ColumnTransformer` if you only want to preprocess
|
||
|
|
part of the features.
|
||
|
|
|
||
|
|
``KBinsDiscretizer`` might produce constant features (e.g., when
|
||
|
|
``encode = 'onehot'`` and certain bins do not contain any data).
|
||
|
|
These features can be removed with feature selection algorithms
|
||
|
|
(e.g., :class:`~sklearn.feature_selection.VarianceThreshold`).
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from sklearn.preprocessing import KBinsDiscretizer
|
||
|
|
>>> X = [[-2, 1, -4, -1],
|
||
|
|
... [-1, 2, -3, -0.5],
|
||
|
|
... [ 0, 3, -2, 0.5],
|
||
|
|
... [ 1, 4, -1, 2]]
|
||
|
|
>>> est = KBinsDiscretizer(
|
||
|
|
... n_bins=3, encode='ordinal', strategy='uniform', subsample=None
|
||
|
|
... )
|
||
|
|
>>> est.fit(X)
|
||
|
|
KBinsDiscretizer(...)
|
||
|
|
>>> Xt = est.transform(X)
|
||
|
|
>>> Xt # doctest: +SKIP
|
||
|
|
array([[ 0., 0., 0., 0.],
|
||
|
|
[ 1., 1., 1., 0.],
|
||
|
|
[ 2., 2., 2., 1.],
|
||
|
|
[ 2., 2., 2., 2.]])
|
||
|
|
|
||
|
|
Sometimes it may be useful to convert the data back into the original
|
||
|
|
feature space. The ``inverse_transform`` function converts the binned
|
||
|
|
data into the original feature space. Each value will be equal to the mean
|
||
|
|
of the two bin edges.
|
||
|
|
|
||
|
|
>>> est.bin_edges_[0]
|
||
|
|
array([-2., -1., 0., 1.])
|
||
|
|
>>> est.inverse_transform(Xt)
|
||
|
|
array([[-1.5, 1.5, -3.5, -0.5],
|
||
|
|
[-0.5, 2.5, -2.5, -0.5],
|
||
|
|
[ 0.5, 3.5, -1.5, 0.5],
|
||
|
|
[ 0.5, 3.5, -1.5, 1.5]])
|
||
|
|
"""
|
||
|
|
|
||
|
|
_parameter_constraints: dict = {
|
||
|
|
"n_bins": [Interval(Integral, 2, None, closed="left"), "array-like"],
|
||
|
|
"encode": [StrOptions({"onehot", "onehot-dense", "ordinal"})],
|
||
|
|
"strategy": [StrOptions({"uniform", "quantile", "kmeans"})],
|
||
|
|
"dtype": [Options(type, {np.float64, np.float32}), None],
|
||
|
|
"subsample": [
|
||
|
|
Interval(Integral, 1, None, closed="left"),
|
||
|
|
None,
|
||
|
|
Hidden(StrOptions({"warn"})),
|
||
|
|
],
|
||
|
|
"random_state": ["random_state"],
|
||
|
|
}
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
n_bins=5,
|
||
|
|
*,
|
||
|
|
encode="onehot",
|
||
|
|
strategy="quantile",
|
||
|
|
dtype=None,
|
||
|
|
subsample="warn",
|
||
|
|
random_state=None,
|
||
|
|
):
|
||
|
|
self.n_bins = n_bins
|
||
|
|
self.encode = encode
|
||
|
|
self.strategy = strategy
|
||
|
|
self.dtype = dtype
|
||
|
|
self.subsample = subsample
|
||
|
|
self.random_state = random_state
|
||
|
|
|
||
|
|
@_fit_context(prefer_skip_nested_validation=True)
|
||
|
|
def fit(self, X, y=None, sample_weight=None):
|
||
|
|
"""
|
||
|
|
Fit the estimator.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Data to be discretized.
|
||
|
|
|
||
|
|
y : None
|
||
|
|
Ignored. This parameter exists only for compatibility with
|
||
|
|
:class:`~sklearn.pipeline.Pipeline`.
|
||
|
|
|
||
|
|
sample_weight : ndarray of shape (n_samples,)
|
||
|
|
Contains weight values to be associated with each sample.
|
||
|
|
Only possible when `strategy` is set to `"quantile"`.
|
||
|
|
|
||
|
|
.. versionadded:: 1.3
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : object
|
||
|
|
Returns the instance itself.
|
||
|
|
"""
|
||
|
|
X = self._validate_data(X, dtype="numeric")
|
||
|
|
|
||
|
|
if self.dtype in (np.float64, np.float32):
|
||
|
|
output_dtype = self.dtype
|
||
|
|
else: # self.dtype is None
|
||
|
|
output_dtype = X.dtype
|
||
|
|
|
||
|
|
n_samples, n_features = X.shape
|
||
|
|
|
||
|
|
if sample_weight is not None and self.strategy == "uniform":
|
||
|
|
raise ValueError(
|
||
|
|
"`sample_weight` was provided but it cannot be "
|
||
|
|
"used with strategy='uniform'. Got strategy="
|
||
|
|
f"{self.strategy!r} instead."
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.strategy in ("uniform", "kmeans") and self.subsample == "warn":
|
||
|
|
warnings.warn(
|
||
|
|
(
|
||
|
|
"In version 1.5 onwards, subsample=200_000 "
|
||
|
|
"will be used by default. Set subsample explicitly to "
|
||
|
|
"silence this warning in the mean time. Set "
|
||
|
|
"subsample=None to disable subsampling explicitly."
|
||
|
|
),
|
||
|
|
FutureWarning,
|
||
|
|
)
|
||
|
|
|
||
|
|
subsample = self.subsample
|
||
|
|
if subsample == "warn":
|
||
|
|
subsample = 200000 if self.strategy == "quantile" else None
|
||
|
|
if subsample is not None and n_samples > subsample:
|
||
|
|
rng = check_random_state(self.random_state)
|
||
|
|
subsample_idx = rng.choice(n_samples, size=subsample, replace=False)
|
||
|
|
X = _safe_indexing(X, subsample_idx)
|
||
|
|
|
||
|
|
n_features = X.shape[1]
|
||
|
|
n_bins = self._validate_n_bins(n_features)
|
||
|
|
|
||
|
|
if sample_weight is not None:
|
||
|
|
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
|
||
|
|
|
||
|
|
bin_edges = np.zeros(n_features, dtype=object)
|
||
|
|
for jj in range(n_features):
|
||
|
|
column = X[:, jj]
|
||
|
|
col_min, col_max = column.min(), column.max()
|
||
|
|
|
||
|
|
if col_min == col_max:
|
||
|
|
warnings.warn(
|
||
|
|
"Feature %d is constant and will be replaced with 0." % jj
|
||
|
|
)
|
||
|
|
n_bins[jj] = 1
|
||
|
|
bin_edges[jj] = np.array([-np.inf, np.inf])
|
||
|
|
continue
|
||
|
|
|
||
|
|
if self.strategy == "uniform":
|
||
|
|
bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1)
|
||
|
|
|
||
|
|
elif self.strategy == "quantile":
|
||
|
|
quantiles = np.linspace(0, 100, n_bins[jj] + 1)
|
||
|
|
if sample_weight is None:
|
||
|
|
bin_edges[jj] = np.asarray(np.percentile(column, quantiles))
|
||
|
|
else:
|
||
|
|
bin_edges[jj] = np.asarray(
|
||
|
|
[
|
||
|
|
_weighted_percentile(column, sample_weight, q)
|
||
|
|
for q in quantiles
|
||
|
|
],
|
||
|
|
dtype=np.float64,
|
||
|
|
)
|
||
|
|
elif self.strategy == "kmeans":
|
||
|
|
from ..cluster import KMeans # fixes import loops
|
||
|
|
|
||
|
|
# Deterministic initialization with uniform spacing
|
||
|
|
uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1)
|
||
|
|
init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5
|
||
|
|
|
||
|
|
# 1D k-means procedure
|
||
|
|
km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)
|
||
|
|
centers = km.fit(
|
||
|
|
column[:, None], sample_weight=sample_weight
|
||
|
|
).cluster_centers_[:, 0]
|
||
|
|
# Must sort, centers may be unsorted even with sorted init
|
||
|
|
centers.sort()
|
||
|
|
bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5
|
||
|
|
bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max]
|
||
|
|
|
||
|
|
# Remove bins whose width are too small (i.e., <= 1e-8)
|
||
|
|
if self.strategy in ("quantile", "kmeans"):
|
||
|
|
mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8
|
||
|
|
bin_edges[jj] = bin_edges[jj][mask]
|
||
|
|
if len(bin_edges[jj]) - 1 != n_bins[jj]:
|
||
|
|
warnings.warn(
|
||
|
|
"Bins whose width are too small (i.e., <= "
|
||
|
|
"1e-8) in feature %d are removed. Consider "
|
||
|
|
"decreasing the number of bins." % jj
|
||
|
|
)
|
||
|
|
n_bins[jj] = len(bin_edges[jj]) - 1
|
||
|
|
|
||
|
|
self.bin_edges_ = bin_edges
|
||
|
|
self.n_bins_ = n_bins
|
||
|
|
|
||
|
|
if "onehot" in self.encode:
|
||
|
|
self._encoder = OneHotEncoder(
|
||
|
|
categories=[np.arange(i) for i in self.n_bins_],
|
||
|
|
sparse_output=self.encode == "onehot",
|
||
|
|
dtype=output_dtype,
|
||
|
|
)
|
||
|
|
# Fit the OneHotEncoder with toy datasets
|
||
|
|
# so that it's ready for use after the KBinsDiscretizer is fitted
|
||
|
|
self._encoder.fit(np.zeros((1, len(self.n_bins_))))
|
||
|
|
|
||
|
|
return self
|
||
|
|
|
||
|
|
def _validate_n_bins(self, n_features):
|
||
|
|
"""Returns n_bins_, the number of bins per feature."""
|
||
|
|
orig_bins = self.n_bins
|
||
|
|
if isinstance(orig_bins, Integral):
|
||
|
|
return np.full(n_features, orig_bins, dtype=int)
|
||
|
|
|
||
|
|
n_bins = check_array(orig_bins, dtype=int, copy=True, ensure_2d=False)
|
||
|
|
|
||
|
|
if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
|
||
|
|
raise ValueError("n_bins must be a scalar or array of shape (n_features,).")
|
||
|
|
|
||
|
|
bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
|
||
|
|
|
||
|
|
violating_indices = np.where(bad_nbins_value)[0]
|
||
|
|
if violating_indices.shape[0] > 0:
|
||
|
|
indices = ", ".join(str(i) for i in violating_indices)
|
||
|
|
raise ValueError(
|
||
|
|
"{} received an invalid number "
|
||
|
|
"of bins at indices {}. Number of bins "
|
||
|
|
"must be at least 2, and must be an int.".format(
|
||
|
|
KBinsDiscretizer.__name__, indices
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return n_bins
|
||
|
|
|
||
|
|
def transform(self, X):
|
||
|
|
"""
|
||
|
|
Discretize the data.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Data to be discretized.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Xt : {ndarray, sparse matrix}, dtype={np.float32, np.float64}
|
||
|
|
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, Xt):
|
||
|
|
"""
|
||
|
|
Transform discretized data back to original feature space.
|
||
|
|
|
||
|
|
Note that this function does not regenerate the original data
|
||
|
|
due to discretization rounding.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
Xt : array-like of shape (n_samples, n_features)
|
||
|
|
Transformed data in the binned space.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Xinv : ndarray, dtype={np.float32, np.float64}
|
||
|
|
Data in the original feature space.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
if "onehot" in self.encode:
|
||
|
|
Xt = self._encoder.inverse_transform(Xt)
|
||
|
|
|
||
|
|
Xinv = check_array(Xt, 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
|