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Traktor/myenv/Lib/site-packages/sklearn/random_projection.py

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losowanie zdjec
2024-05-26 05:12:46 +02:00
"""Random Projection transformers.
Random Projections are a simple and computationally efficient way to
reduce the dimensionality of the data by trading a controlled amount
of accuracy (as additional variance) for faster processing times and
smaller model sizes.
The dimensions and distribution of Random Projections matrices are
controlled so as to preserve the pairwise distances between any two
samples of the dataset.
The main theoretical result behind the efficiency of random projection is the
`Johnson-Lindenstrauss lemma (quoting Wikipedia)
<https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma>`_:
In mathematics, the Johnson-Lindenstrauss lemma is a result
concerning low-distortion embeddings of points from high-dimensional
into low-dimensional Euclidean space. The lemma states that a small set
of points in a high-dimensional space can be embedded into a space of
much lower dimension in such a way that distances between the points are
nearly preserved. The map used for the embedding is at least Lipschitz,
and can even be taken to be an orthogonal projection.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>,
# Arnaud Joly <a.joly@ulg.ac.be>
# License: BSD 3 clause
import warnings
from abc import ABCMeta, abstractmethod
from numbers import Integral, Real
import numpy as np
import scipy.sparse as sp
from scipy import linalg
from .base import (
BaseEstimator,
ClassNamePrefixFeaturesOutMixin,
TransformerMixin,
_fit_context,
)
from .exceptions import DataDimensionalityWarning
from .utils import check_random_state
from .utils._param_validation import Interval, StrOptions, validate_params
from .utils.extmath import safe_sparse_dot
from .utils.random import sample_without_replacement
from .utils.validation import check_array, check_is_fitted
__all__ = [
"SparseRandomProjection",
"GaussianRandomProjection",
"johnson_lindenstrauss_min_dim",
]
@validate_params(
{
"n_samples": ["array-like", Interval(Real, 1, None, closed="left")],
"eps": ["array-like", Interval(Real, 0, 1, closed="neither")],
},
prefer_skip_nested_validation=True,
)
def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1):
"""Find a 'safe' number of components to randomly project to.
The distortion introduced by a random projection `p` only changes the
distance between two points by a factor (1 +- eps) in a euclidean space
with good probability. The projection `p` is an eps-embedding as defined
by:
(1 - eps) ||u - v||^2 < ||p(u) - p(v)||^2 < (1 + eps) ||u - v||^2
Where u and v are any rows taken from a dataset of shape (n_samples,
n_features), eps is in ]0, 1[ and p is a projection by a random Gaussian
N(0, 1) matrix of shape (n_components, n_features) (or a sparse
Achlioptas matrix).
The minimum number of components to guarantee the eps-embedding is
given by:
n_components >= 4 log(n_samples) / (eps^2 / 2 - eps^3 / 3)
Note that the number of dimensions is independent of the original
number of features but instead depends on the size of the dataset:
the larger the dataset, the higher is the minimal dimensionality of
an eps-embedding.
Read more in the :ref:`User Guide <johnson_lindenstrauss>`.
Parameters
----------
n_samples : int or array-like of int
Number of samples that should be an integer greater than 0. If an array
is given, it will compute a safe number of components array-wise.
eps : float or array-like of shape (n_components,), dtype=float, \
default=0.1
Maximum distortion rate in the range (0, 1) as defined by the
Johnson-Lindenstrauss lemma. If an array is given, it will compute a
safe number of components array-wise.
Returns
-------
n_components : int or ndarray of int
The minimal number of components to guarantee with good probability
an eps-embedding with n_samples.
References
----------
.. [1] https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
.. [2] `Sanjoy Dasgupta and Anupam Gupta, 1999,
"An elementary proof of the Johnson-Lindenstrauss Lemma."
<https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_
Examples
--------
>>> from sklearn.random_projection import johnson_lindenstrauss_min_dim
>>> johnson_lindenstrauss_min_dim(1e6, eps=0.5)
663
>>> johnson_lindenstrauss_min_dim(1e6, eps=[0.5, 0.1, 0.01])
array([ 663, 11841, 1112658])
>>> johnson_lindenstrauss_min_dim([1e4, 1e5, 1e6], eps=0.1)
array([ 7894, 9868, 11841])
"""
eps = np.asarray(eps)
n_samples = np.asarray(n_samples)
if np.any(eps <= 0.0) or np.any(eps >= 1):
raise ValueError("The JL bound is defined for eps in ]0, 1[, got %r" % eps)
if np.any(n_samples <= 0):
raise ValueError(
"The JL bound is defined for n_samples greater than zero, got %r"
% n_samples
)
denominator = (eps**2 / 2) - (eps**3 / 3)
return (4 * np.log(n_samples) / denominator).astype(np.int64)
def _check_density(density, n_features):
"""Factorize density check according to Li et al."""
if density == "auto":
density = 1 / np.sqrt(n_features)
elif density <= 0 or density > 1:
raise ValueError("Expected density in range ]0, 1], got: %r" % density)
return density
def _check_input_size(n_components, n_features):
"""Factorize argument checking for random matrix generation."""
if n_components <= 0:
raise ValueError(
"n_components must be strictly positive, got %d" % n_components
)
if n_features <= 0:
raise ValueError("n_features must be strictly positive, got %d" % n_features)
def _gaussian_random_matrix(n_components, n_features, random_state=None):
"""Generate a dense Gaussian random matrix.
The components of the random matrix are drawn from
N(0, 1.0 / n_components).
Read more in the :ref:`User Guide <gaussian_random_matrix>`.
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
random_state : int, RandomState instance or None, default=None
Controls the pseudo random number generator used to generate the matrix
at fit time.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Returns
-------
components : ndarray of shape (n_components, n_features)
The generated Gaussian random matrix.
See Also
--------
GaussianRandomProjection
"""
_check_input_size(n_components, n_features)
rng = check_random_state(random_state)
components = rng.normal(
loc=0.0, scale=1.0 / np.sqrt(n_components), size=(n_components, n_features)
)
return components
def _sparse_random_matrix(n_components, n_features, density="auto", random_state=None):
"""Generalized Achlioptas random sparse matrix for random projection.
Setting density to 1 / 3 will yield the original matrix by Dimitris
Achlioptas while setting a lower value will yield the generalization
by Ping Li et al.
If we note :math:`s = 1 / density`, the components of the random matrix are
drawn from:
- -sqrt(s) / sqrt(n_components) with probability 1 / 2s
- 0 with probability 1 - 1 / s
- +sqrt(s) / sqrt(n_components) with probability 1 / 2s
Read more in the :ref:`User Guide <sparse_random_matrix>`.
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
density : float or 'auto', default='auto'
Ratio of non-zero component in the random projection matrix in the
range `(0, 1]`
If density = 'auto', the value is set to the minimum density
as recommended by Ping Li et al.: 1 / sqrt(n_features).
Use density = 1 / 3.0 if you want to reproduce the results from
Achlioptas, 2001.
random_state : int, RandomState instance or None, default=None
Controls the pseudo random number generator used to generate the matrix
at fit time.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Returns
-------
components : {ndarray, sparse matrix} of shape (n_components, n_features)
The generated Gaussian random matrix. Sparse matrix will be of CSR
format.
See Also
--------
SparseRandomProjection
References
----------
.. [1] Ping Li, T. Hastie and K. W. Church, 2006,
"Very Sparse Random Projections".
https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
.. [2] D. Achlioptas, 2001, "Database-friendly random projections",
https://cgi.di.uoa.gr/~optas/papers/jl.pdf
"""
_check_input_size(n_components, n_features)
density = _check_density(density, n_features)
rng = check_random_state(random_state)
if density == 1:
# skip index generation if totally dense
components = rng.binomial(1, 0.5, (n_components, n_features)) * 2 - 1
return 1 / np.sqrt(n_components) * components
else:
# Generate location of non zero elements
indices = []
offset = 0
indptr = [offset]
for _ in range(n_components):
# find the indices of the non-zero components for row i
n_nonzero_i = rng.binomial(n_features, density)
indices_i = sample_without_replacement(
n_features, n_nonzero_i, random_state=rng
)
indices.append(indices_i)
offset += n_nonzero_i
indptr.append(offset)
indices = np.concatenate(indices)
# Among non zero components the probability of the sign is 50%/50%
data = rng.binomial(1, 0.5, size=np.size(indices)) * 2 - 1
# build the CSR structure by concatenating the rows
components = sp.csr_matrix(
(data, indices, indptr), shape=(n_components, n_features)
)
return np.sqrt(1 / density) / np.sqrt(n_components) * components
class BaseRandomProjection(
TransformerMixin, BaseEstimator, ClassNamePrefixFeaturesOutMixin, metaclass=ABCMeta
):
"""Base class for random projections.
Warning: This class should not be used directly.
Use derived classes instead.
"""
_parameter_constraints: dict = {
"n_components": [
Interval(Integral, 1, None, closed="left"),
StrOptions({"auto"}),
],
"eps": [Interval(Real, 0, None, closed="neither")],
"compute_inverse_components": ["boolean"],
"random_state": ["random_state"],
}
@abstractmethod
def __init__(
self,
n_components="auto",
*,
eps=0.1,
compute_inverse_components=False,
random_state=None,
):
self.n_components = n_components
self.eps = eps
self.compute_inverse_components = compute_inverse_components
self.random_state = random_state
@abstractmethod
def _make_random_matrix(self, n_components, n_features):
"""Generate the random projection matrix.
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
Returns
-------
components : {ndarray, sparse matrix} of shape (n_components, n_features)
The generated random matrix. Sparse matrix will be of CSR format.
"""
def _compute_inverse_components(self):
"""Compute the pseudo-inverse of the (densified) components."""
components = self.components_
if sp.issparse(components):
components = components.toarray()
return linalg.pinv(components, check_finite=False)
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X, y=None):
"""Generate a sparse random projection matrix.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
Training set: only the shape is used to find optimal random
matrix dimensions based on the theory referenced in the
afore mentioned papers.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self : object
BaseRandomProjection class instance.
"""
X = self._validate_data(
X, accept_sparse=["csr", "csc"], dtype=[np.float64, np.float32]
)
n_samples, n_features = X.shape
if self.n_components == "auto":
self.n_components_ = johnson_lindenstrauss_min_dim(
n_samples=n_samples, eps=self.eps
)
if self.n_components_ <= 0:
raise ValueError(
"eps=%f and n_samples=%d lead to a target dimension of "
"%d which is invalid" % (self.eps, n_samples, self.n_components_)
)
elif self.n_components_ > n_features:
raise ValueError(
"eps=%f and n_samples=%d lead to a target dimension of "
"%d which is larger than the original space with "
"n_features=%d"
% (self.eps, n_samples, self.n_components_, n_features)
)
else:
if self.n_components > n_features:
warnings.warn(
"The number of components is higher than the number of"
" features: n_features < n_components (%s < %s)."
"The dimensionality of the problem will not be reduced."
% (n_features, self.n_components),
DataDimensionalityWarning,
)
self.n_components_ = self.n_components
# Generate a projection matrix of size [n_components, n_features]
self.components_ = self._make_random_matrix(
self.n_components_, n_features
).astype(X.dtype, copy=False)
if self.compute_inverse_components:
self.inverse_components_ = self._compute_inverse_components()
# Required by ClassNamePrefixFeaturesOutMixin.get_feature_names_out.
self._n_features_out = self.n_components
return self
def inverse_transform(self, X):
"""Project data back to its original space.
Returns an array X_original whose transform would be X. Note that even
if X is sparse, X_original is dense: this may use a lot of RAM.
If `compute_inverse_components` is False, the inverse of the components is
computed during each call to `inverse_transform` which can be costly.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_components)
Data to be transformed back.
Returns
-------
X_original : ndarray of shape (n_samples, n_features)
Reconstructed data.
"""
check_is_fitted(self)
X = check_array(X, dtype=[np.float64, np.float32], accept_sparse=("csr", "csc"))
if self.compute_inverse_components:
return X @ self.inverse_components_.T
inverse_components = self._compute_inverse_components()
return X @ inverse_components.T
def _more_tags(self):
return {
"preserves_dtype": [np.float64, np.float32],
}
class GaussianRandomProjection(BaseRandomProjection):
"""Reduce dimensionality through Gaussian random projection.
The components of the random matrix are drawn from N(0, 1 / n_components).
Read more in the :ref:`User Guide <gaussian_random_matrix>`.
.. versionadded:: 0.13
Parameters
----------
n_components : int or 'auto', default='auto'
Dimensionality of the target projection space.
n_components can be automatically adjusted according to the
number of samples in the dataset and the bound given by the
Johnson-Lindenstrauss lemma. In that case the quality of the
embedding is controlled by the ``eps`` parameter.
It should be noted that Johnson-Lindenstrauss lemma can yield
very conservative estimated of the required number of components
as it makes no assumption on the structure of the dataset.
eps : float, default=0.1
Parameter to control the quality of the embedding according to
the Johnson-Lindenstrauss lemma when `n_components` is set to
'auto'. The value should be strictly positive.
Smaller values lead to better embedding and higher number of
dimensions (n_components) in the target projection space.
compute_inverse_components : bool, default=False
Learn the inverse transform by computing the pseudo-inverse of the
components during fit. Note that computing the pseudo-inverse does not
scale well to large matrices.
random_state : int, RandomState instance or None, default=None
Controls the pseudo random number generator used to generate the
projection matrix at fit time.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Attributes
----------
n_components_ : int
Concrete number of components computed when n_components="auto".
components_ : ndarray of shape (n_components, n_features)
Random matrix used for the projection.
inverse_components_ : ndarray of shape (n_features, n_components)
Pseudo-inverse of the components, only computed if
`compute_inverse_components` is True.
.. versionadded:: 1.1
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
--------
SparseRandomProjection : Reduce dimensionality through sparse
random projection.
Examples
--------
>>> import numpy as np
>>> from sklearn.random_projection import GaussianRandomProjection
>>> rng = np.random.RandomState(42)
>>> X = rng.rand(25, 3000)
>>> transformer = GaussianRandomProjection(random_state=rng)
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(25, 2759)
"""
def __init__(
self,
n_components="auto",
*,
eps=0.1,
compute_inverse_components=False,
random_state=None,
):
super().__init__(
n_components=n_components,
eps=eps,
compute_inverse_components=compute_inverse_components,
random_state=random_state,
)
def _make_random_matrix(self, n_components, n_features):
"""Generate the random projection matrix.
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
Returns
-------
components : ndarray of shape (n_components, n_features)
The generated random matrix.
"""
random_state = check_random_state(self.random_state)
return _gaussian_random_matrix(
n_components, n_features, random_state=random_state
)
def transform(self, X):
"""Project the data by using matrix product with the random matrix.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input data to project into a smaller dimensional space.
Returns
-------
X_new : ndarray of shape (n_samples, n_components)
Projected array.
"""
check_is_fitted(self)
X = self._validate_data(
X, accept_sparse=["csr", "csc"], reset=False, dtype=[np.float64, np.float32]
)
return X @ self.components_.T
class SparseRandomProjection(BaseRandomProjection):
"""Reduce dimensionality through sparse random projection.
Sparse random matrix is an alternative to dense random
projection matrix that guarantees similar embedding quality while being
much more memory efficient and allowing faster computation of the
projected data.
If we note `s = 1 / density` the components of the random matrix are
drawn from:
- -sqrt(s) / sqrt(n_components) with probability 1 / 2s
- 0 with probability 1 - 1 / s
- +sqrt(s) / sqrt(n_components) with probability 1 / 2s
Read more in the :ref:`User Guide <sparse_random_matrix>`.
.. versionadded:: 0.13
Parameters
----------
n_components : int or 'auto', default='auto'
Dimensionality of the target projection space.
n_components can be automatically adjusted according to the
number of samples in the dataset and the bound given by the
Johnson-Lindenstrauss lemma. In that case the quality of the
embedding is controlled by the ``eps`` parameter.
It should be noted that Johnson-Lindenstrauss lemma can yield
very conservative estimated of the required number of components
as it makes no assumption on the structure of the dataset.
density : float or 'auto', default='auto'
Ratio in the range (0, 1] of non-zero component in the random
projection matrix.
If density = 'auto', the value is set to the minimum density
as recommended by Ping Li et al.: 1 / sqrt(n_features).
Use density = 1 / 3.0 if you want to reproduce the results from
Achlioptas, 2001.
eps : float, default=0.1
Parameter to control the quality of the embedding according to
the Johnson-Lindenstrauss lemma when n_components is set to
'auto'. This value should be strictly positive.
Smaller values lead to better embedding and higher number of
dimensions (n_components) in the target projection space.
dense_output : bool, default=False
If True, ensure that the output of the random projection is a
dense numpy array even if the input and random projection matrix
are both sparse. In practice, if the number of components is
small the number of zero components in the projected data will
be very small and it will be more CPU and memory efficient to
use a dense representation.
If False, the projected data uses a sparse representation if
the input is sparse.
compute_inverse_components : bool, default=False
Learn the inverse transform by computing the pseudo-inverse of the
components during fit. Note that the pseudo-inverse is always a dense
array, even if the training data was sparse. This means that it might be
necessary to call `inverse_transform` on a small batch of samples at a
time to avoid exhausting the available memory on the host. Moreover,
computing the pseudo-inverse does not scale well to large matrices.
random_state : int, RandomState instance or None, default=None
Controls the pseudo random number generator used to generate the
projection matrix at fit time.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Attributes
----------
n_components_ : int
Concrete number of components computed when n_components="auto".
components_ : sparse matrix of shape (n_components, n_features)
Random matrix used for the projection. Sparse matrix will be of CSR
format.
inverse_components_ : ndarray of shape (n_features, n_components)
Pseudo-inverse of the components, only computed if
`compute_inverse_components` is True.
.. versionadded:: 1.1
density_ : float in range 0.0 - 1.0
Concrete density computed from when density = "auto".
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
--------
GaussianRandomProjection : Reduce dimensionality through Gaussian
random projection.
References
----------
.. [1] Ping Li, T. Hastie and K. W. Church, 2006,
"Very Sparse Random Projections".
https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
.. [2] D. Achlioptas, 2001, "Database-friendly random projections",
https://cgi.di.uoa.gr/~optas/papers/jl.pdf
Examples
--------
>>> import numpy as np
>>> from sklearn.random_projection import SparseRandomProjection
>>> rng = np.random.RandomState(42)
>>> X = rng.rand(25, 3000)
>>> transformer = SparseRandomProjection(random_state=rng)
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(25, 2759)
>>> # very few components are non-zero
>>> np.mean(transformer.components_ != 0)
0.0182...
"""
_parameter_constraints: dict = {
**BaseRandomProjection._parameter_constraints,
"density": [Interval(Real, 0.0, 1.0, closed="right"), StrOptions({"auto"})],
"dense_output": ["boolean"],
}
def __init__(
self,
n_components="auto",
*,
density="auto",
eps=0.1,
dense_output=False,
compute_inverse_components=False,
random_state=None,
):
super().__init__(
n_components=n_components,
eps=eps,
compute_inverse_components=compute_inverse_components,
random_state=random_state,
)
self.dense_output = dense_output
self.density = density
def _make_random_matrix(self, n_components, n_features):
"""Generate the random projection matrix
Parameters
----------
n_components : int
Dimensionality of the target projection space.
n_features : int
Dimensionality of the original source space.
Returns
-------
components : sparse matrix of shape (n_components, n_features)
The generated random matrix in CSR format.
"""
random_state = check_random_state(self.random_state)
self.density_ = _check_density(self.density, n_features)
return _sparse_random_matrix(
n_components, n_features, density=self.density_, random_state=random_state
)
def transform(self, X):
"""Project the data by using matrix product with the random matrix.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input data to project into a smaller dimensional space.
Returns
-------
X_new : {ndarray, sparse matrix} of shape (n_samples, n_components)
Projected array. It is a sparse matrix only when the input is sparse and
`dense_output = False`.
"""
check_is_fitted(self)
X = self._validate_data(
X, accept_sparse=["csr", "csc"], reset=False, dtype=[np.float64, np.float32]
)
return safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)
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