"""
Linear Discriminant Analysis and Quadratic Discriminant Analysis
"""
# Authors: Clemens Brunner
# Martin Billinger
# Matthieu Perrot
# Mathieu Blondel
# License: BSD 3-Clause
import warnings
import numpy as np
from scipy import linalg
from scipy.special import expit
from .base import BaseEstimator, TransformerMixin, ClassifierMixin
from .linear_model._base import LinearClassifierMixin
from .covariance import ledoit_wolf, empirical_covariance, shrunk_covariance
from .utils.multiclass import unique_labels
from .utils import check_array
from .utils.validation import check_is_fitted
from .utils.multiclass import check_classification_targets
from .utils.extmath import softmax
from .preprocessing import StandardScaler
from .utils.validation import _deprecate_positional_args
__all__ = ['LinearDiscriminantAnalysis', 'QuadraticDiscriminantAnalysis']
def _cov(X, shrinkage=None):
"""Estimate covariance matrix (using optional shrinkage).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter, possible values:
- None or 'empirical': no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Returns
-------
s : ndarray of shape (n_features, n_features)
Estimated covariance matrix.
"""
shrinkage = "empirical" if shrinkage is None else shrinkage
if isinstance(shrinkage, str):
if shrinkage == 'auto':
sc = StandardScaler() # standardize features
X = sc.fit_transform(X)
s = ledoit_wolf(X)[0]
# rescale
s = sc.scale_[:, np.newaxis] * s * sc.scale_[np.newaxis, :]
elif shrinkage == 'empirical':
s = empirical_covariance(X)
else:
raise ValueError('unknown shrinkage parameter')
elif isinstance(shrinkage, float) or isinstance(shrinkage, int):
if shrinkage < 0 or shrinkage > 1:
raise ValueError('shrinkage parameter must be between 0 and 1')
s = shrunk_covariance(empirical_covariance(X), shrinkage)
else:
raise TypeError('shrinkage must be of string or int type')
return s
def _class_means(X, y):
"""Compute class means.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Returns
-------
means : array-like of shape (n_classes, n_features)
Class means.
"""
classes, y = np.unique(y, return_inverse=True)
cnt = np.bincount(y)
means = np.zeros(shape=(len(classes), X.shape[1]))
np.add.at(means, y, X)
means /= cnt[:, None]
return means
def _class_cov(X, y, priors, shrinkage=None):
"""Compute weighted within-class covariance matrix.
The per-class covariance are weighted by the class priors.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
priors : array-like of shape (n_classes,)
Class priors.
shrinkage : 'auto' or float, default=None
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Returns
-------
cov : array-like of shape (n_features, n_features)
Weighted within-class covariance matrix
"""
classes = np.unique(y)
cov = np.zeros(shape=(X.shape[1], X.shape[1]))
for idx, group in enumerate(classes):
Xg = X[y == group, :]
cov += priors[idx] * np.atleast_2d(_cov(Xg, shrinkage))
return cov
class LinearDiscriminantAnalysis(BaseEstimator, LinearClassifierMixin,
TransformerMixin):
"""Linear Discriminant Analysis
A classifier with a linear decision boundary, generated by fitting class
conditional densities to the data and using Bayes' rule.
The model fits a Gaussian density to each class, assuming that all classes
share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality of the input
by projecting it to the most discriminative directions, using the
`transform` method.
.. versionadded:: 0.17
*LinearDiscriminantAnalysis*.
Read more in the :ref:`User Guide <lda_qda>`.
Parameters
----------
solver : {'svd', 'lsqr', 'eigen'}, default='svd'
Solver to use, possible values:
- 'svd': Singular value decomposition (default).
Does not compute the covariance matrix, therefore this solver is
recommended for data with a large number of features.
- 'lsqr': Least squares solution, can be combined with shrinkage.
- 'eigen': Eigenvalue decomposition, can be combined with shrinkage.
shrinkage : 'auto' or float, default=None
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Note that shrinkage works only with 'lsqr' and 'eigen' solvers.
priors : array-like of shape (n_classes,), default=None
The class prior probabilities. By default, the class proportions are
inferred from the training data.
n_components : int, default=None
Number of components (<= min(n_classes - 1, n_features)) for
dimensionality reduction. If None, will be set to
min(n_classes - 1, n_features). This parameter only affects the
`transform` method.
store_covariance : bool, default=False
If True, explicitely compute the weighted within-class covariance
matrix when solver is 'svd'. The matrix is always computed
and stored for the other solvers.
.. versionadded:: 0.17
tol : float, default=1.0e-4
Absolute threshold for a singular value of X to be considered
significant, used to estimate the rank of X. Dimensions whose
singular values are non-significant are discarded. Only used if
solver is 'svd'.
.. versionadded:: 0.17
Attributes
----------
coef_ : ndarray of shape (n_features,) or (n_classes, n_features)
Weight vector(s).
intercept_ : ndarray of shape (n_classes,)
Intercept term.
covariance_ : array-like of shape (n_features, n_features)
Weighted within-class covariance matrix. It corresponds to
`sum_k prior_k * C_k` where `C_k` is the covariance matrix of the
samples in class `k`. The `C_k` are estimated using the (potentially
shrunk) biased estimator of covariance. If solver is 'svd', only
exists when `store_covariance` is True.
explained_variance_ratio_ : ndarray of shape (n_components,)
Percentage of variance explained by each of the selected components.
If ``n_components`` is not set then all components are stored and the
sum of explained variances is equal to 1.0. Only available when eigen
or svd solver is used.
means_ : array-like of shape (n_classes, n_features)
Class-wise means.
priors_ : array-like of shape (n_classes,)
Class priors (sum to 1).
scalings_ : array-like of shape (rank, n_classes - 1)
Scaling of the features in the space spanned by the class centroids.
Only available for 'svd' and 'eigen' solvers.
xbar_ : array-like of shape (n_features,)
Overall mean. Only present if solver is 'svd'.
classes_ : array-like of shape (n_classes,)
Unique class labels.
See also
--------
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis: Quadratic
Discriminant Analysis
Examples
--------
>>> import numpy as np
>>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = LinearDiscriminantAnalysis()
>>> clf.fit(X, y)
LinearDiscriminantAnalysis()
>>> print(clf.predict([[-0.8, -1]]))
[1]
"""
@_deprecate_positional_args
def __init__(self, *, solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False, tol=1e-4):
self.solver = solver
self.shrinkage = shrinkage
self.priors = priors
self.n_components = n_components
self.store_covariance = store_covariance # used only in svd solver
self.tol = tol # used only in svd solver
def _solve_lsqr(self, X, y, shrinkage):
"""Least squares solver.
The least squares solver computes a straightforward solution of the
optimal decision rule based directly on the discriminant functions. It
can only be used for classification (with optional shrinkage), because
estimation of eigenvectors is not performed. Therefore, dimensionality
reduction with the transform is not supported.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_classes)
Target values.
shrinkage : 'auto', float or None
Shrinkage parameter, possible values:
- None: no shrinkage.
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Notes
-----
This solver is based on [1]_, section 2.6.2, pp. 39-41.
References
----------
.. [1] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification
(Second Edition). John Wiley & Sons, Inc., New York, 2001. ISBN
0-471-05669-3.
"""
self.means_ = _class_means(X, y)
self.covariance_ = _class_cov(X, y, self.priors_, shrinkage)
self.coef_ = linalg.lstsq(self.covariance_, self.means_.T)[0].T
self.intercept_ = (-0.5 * np.diag(np.dot(self.means_, self.coef_.T)) +
np.log(self.priors_))
def _solve_eigen(self, X, y, shrinkage):
"""Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
dimensionality reduction (with optional shrinkage).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
shrinkage : 'auto', float or None
Shrinkage parameter, possible values:
- None: no shrinkage.
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage constant.
Notes
-----
This solver is based on [1]_, section 3.8.3, pp. 121-124.
References
----------
.. [1] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification
(Second Edition). John Wiley & Sons, Inc., New York, 2001. ISBN
0-471-05669-3.
"""
self.means_ = _class_means(X, y)
self.covariance_ = _class_cov(X, y, self.priors_, shrinkage)
Sw = self.covariance_ # within scatter
St = _cov(X, shrinkage) # total scatter
Sb = St - Sw # between scatter
evals, evecs = linalg.eigh(Sb, Sw)
self.explained_variance_ratio_ = np.sort(evals / np.sum(evals)
)[::-1][:self._max_components]
evecs = evecs[:, np.argsort(evals)[::-1]] # sort eigenvectors
self.scalings_ = evecs
self.coef_ = np.dot(self.means_, evecs).dot(evecs.T)
self.intercept_ = (-0.5 * np.diag(np.dot(self.means_, self.coef_.T)) +
np.log(self.priors_))
def _solve_svd(self, X, y):
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