# Source code for sklearn.decomposition.factor_analysis

"""Factor Analysis.

A latent linear variable model.

FactorAnalysis is similar to probabilistic PCA implemented by PCA.score
While PCA assumes Gaussian noise with the same variance for each
feature, the FactorAnalysis model assumes different variances for
each of them.

This implementation is based on David Barber's Book,
Bayesian Reasoning and Machine Learning,
http://www.cs.ucl.ac.uk/staff/d.barber/brml,
Algorithm 21.1
"""

# Author: Christian Osendorfer <osendorf@gmail.com>
#         Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Denis A. Engemann <denis-alexander.engemann@inria.fr>

import warnings
from math import sqrt, log
import numpy as np
from scipy import linalg

from ..base import BaseEstimator, TransformerMixin
from ..externals.six.moves import xrange
from ..utils import check_array, check_random_state
from ..utils.extmath import fast_logdet, randomized_svd, squared_norm
from ..utils.validation import check_is_fitted
from ..exceptions import ConvergenceWarning

class FactorAnalysis(BaseEstimator, TransformerMixin):
"""Factor Analysis (FA)

A simple linear generative model with Gaussian latent variables.

The observations are assumed to be caused by a linear transformation of
lower dimensional latent factors and added Gaussian noise.
Without loss of generality the factors are distributed according to a
Gaussian with zero mean and unit covariance. The noise is also zero mean
and has an arbitrary diagonal covariance matrix.

If we would restrict the model further, by assuming that the Gaussian
noise is even isotropic (all diagonal entries are the same) we would obtain
:class:PPCA.

FactorAnalysis performs a maximum likelihood estimate of the so-called
loading matrix, the transformation of the latent variables to the
observed ones, using expectation-maximization (EM).

Read more in the :ref:User Guide <FA>.

Parameters
----------
n_components : int | None
Dimensionality of latent space, the number of components
of X that are obtained after transform.
If None, n_components is set to the number of features.

tol : float
Stopping tolerance for EM algorithm.

copy : bool
Whether to make a copy of X. If False, the input X gets overwritten
during fitting.

max_iter : int
Maximum number of iterations.

noise_variance_init : None | array, shape=(n_features,)
The initial guess of the noise variance for each feature.
If None, it defaults to np.ones(n_features)

svd_method : {'lapack', 'randomized'}
Which SVD method to use. If 'lapack' use standard SVD from
scipy.linalg, if 'randomized' use fast randomized_svd function.
Defaults to 'randomized'. For most applications 'randomized' will
be sufficiently precise while providing significant speed gains.
Accuracy can also be improved by setting higher values for
iterated_power. If this is not sufficient, for maximum precision
you should choose 'lapack'.

iterated_power : int, optional
Number of iterations for the power method. 3 by default. Only used
if svd_method equals 'randomized'

random_state : int, RandomState instance or None, optional (default=0)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by np.random. Only used when svd_method equals 'randomized'.

Attributes
----------
components_ : array, [n_components, n_features]
Components with maximum variance.

loglike_ : list, [n_iterations]
The log likelihood at each iteration.

noise_variance_ : array, shape=(n_features,)
The estimated noise variance for each feature.

n_iter_ : int
Number of iterations run.

References
----------
.. David Barber, Bayesian Reasoning and Machine Learning,
Algorithm 21.1

.. Christopher M. Bishop: Pattern Recognition and Machine Learning,
Chapter 12.2.4

--------
PCA: Principal component analysis is also a latent linear variable model
which however assumes equal noise variance for each feature.
This extra assumption makes probabilistic PCA faster as it can be
computed in closed form.
FastICA: Independent component analysis, a latent variable model with
non-Gaussian latent variables.
"""
def __init__(self, n_components=None, tol=1e-2, copy=True, max_iter=1000,
noise_variance_init=None, svd_method='randomized',
iterated_power=3, random_state=0):
self.n_components = n_components
self.copy = copy
self.tol = tol
self.max_iter = max_iter
if svd_method not in ['lapack', 'randomized']:
raise ValueError('SVD method %s is not supported. Please consider'
' the documentation' % svd_method)
self.svd_method = svd_method

self.noise_variance_init = noise_variance_init
self.iterated_power = iterated_power
self.random_state = random_state

[docs]    def fit(self, X, y=None):
"""Fit the FactorAnalysis model to X using EM

Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.

y : Ignored.

Returns
-------
self
"""
X = check_array(X, copy=self.copy, dtype=np.float64)

n_samples, n_features = X.shape
n_components = self.n_components
if n_components is None:
n_components = n_features
self.mean_ = np.mean(X, axis=0)
X -= self.mean_

# some constant terms
nsqrt = sqrt(n_samples)
llconst = n_features * log(2. * np.pi) + n_components
var = np.var(X, axis=0)

if self.noise_variance_init is None:
psi = np.ones(n_features, dtype=X.dtype)
else:
if len(self.noise_variance_init) != n_features:
raise ValueError("noise_variance_init dimension does not "
"with number of features : %d != %d" %
(len(self.noise_variance_init), n_features))
psi = np.array(self.noise_variance_init)

loglike = []
old_ll = -np.inf
SMALL = 1e-12

# we'll modify svd outputs to return unexplained variance
# to allow for unified computation of loglikelihood
if self.svd_method == 'lapack':
def my_svd(X):
_, s, V = linalg.svd(X, full_matrices=False)
return (s[:n_components], V[:n_components],
squared_norm(s[n_components:]))
elif self.svd_method == 'randomized':
random_state = check_random_state(self.random_state)

def my_svd(X):
_, s, V = randomized_svd(X, n_components,
random_state=random_state,
n_iter=self.iterated_power)
return s, V, squared_norm(X) - squared_norm(s)
else:
raise ValueError('SVD method %s is not supported. Please consider'
' the documentation' % self.svd_method)

for i in xrange(self.max_iter):
# SMALL helps numerics
sqrt_psi = np.sqrt(psi) + SMALL
s, V, unexp_var = my_svd(X / (sqrt_psi * nsqrt))
s **= 2
# Use 'maximum' here to avoid sqrt problems.
W = np.sqrt(np.maximum(s - 1., 0.))[:, np.newaxis] * V
del V
W *= sqrt_psi

# loglikelihood
ll = llconst + np.sum(np.log(s))
ll += unexp_var + np.sum(np.log(psi))
ll *= -n_samples / 2.
loglike.append(ll)
if (ll - old_ll) < self.tol:
break
old_ll = ll

psi = np.maximum(var - np.sum(W ** 2, axis=0), SMALL)
else:
warnings.warn('FactorAnalysis did not converge.' +
' You might want' +
' to increase the number of iterations.',
ConvergenceWarning)

self.components_ = W
self.noise_variance_ = psi
self.loglike_ = loglike
self.n_iter_ = i + 1
return self

[docs]    def transform(self, X):
"""Apply dimensionality reduction to X using the model.

Compute the expected mean of the latent variables.
See Barber, 21.2.33 (or Bishop, 12.66).

Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.

Returns
-------
X_new : array-like, shape (n_samples, n_components)
The latent variables of X.
"""
check_is_fitted(self, 'components_')

X = check_array(X)
Ih = np.eye(len(self.components_))

X_transformed = X - self.mean_

Wpsi = self.components_ / self.noise_variance_
cov_z = linalg.inv(Ih + np.dot(Wpsi, self.components_.T))
tmp = np.dot(X_transformed, Wpsi.T)
X_transformed = np.dot(tmp, cov_z)

return X_transformed

def get_covariance(self):
"""Compute data covariance with the FactorAnalysis model.

cov = components_.T * components_ + diag(noise_variance)

Returns
-------
cov : array, shape (n_features, n_features)
Estimated covariance of data.
"""
check_is_fitted(self, 'components_')

cov = np.dot(self.components_.T, self.components_)
cov.flat[::len(cov) + 1] += self.noise_variance_  # modify diag inplace
return cov

def get_precision(self):
"""Compute data precision matrix with the FactorAnalysis model.

Returns
-------
precision : array, shape (n_features, n_features)
Estimated precision of data.
"""
check_is_fitted(self, 'components_')

n_features = self.components_.shape

# handle corner cases first
if self.n_components == 0:
return np.diag(1. / self.noise_variance_)
if self.n_components == n_features:
return linalg.inv(self.get_covariance())

# Get precision using matrix inversion lemma
components_ = self.components_
precision = np.dot(components_ / self.noise_variance_, components_.T)
precision.flat[::len(precision) + 1] += 1.
precision = np.dot(components_.T,
np.dot(linalg.inv(precision), components_))
precision /= self.noise_variance_[:, np.newaxis]
precision /= -self.noise_variance_[np.newaxis, :]
precision.flat[::len(precision) + 1] += 1. / self.noise_variance_
return precision

[docs]    def score_samples(self, X):
"""Compute the log-likelihood of each sample

Parameters
----------
X : array, shape (n_samples, n_features)
The data

Returns
-------
ll : array, shape (n_samples,)
Log-likelihood of each sample under the current model
"""
check_is_fitted(self, 'components_')

Xr = X - self.mean_
precision = self.get_precision()
n_features = X.shape
log_like = np.zeros(X.shape)
log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
log_like -= .5 * (n_features * log(2. * np.pi)
- fast_logdet(precision))
return log_like

[docs]    def score(self, X, y=None):
"""Compute the average log-likelihood of the samples

Parameters
----------
X : array, shape (n_samples, n_features)
The data

y : Ignored.

Returns
-------
ll : float
Average log-likelihood of the samples under the current model
"""
return np.mean(self.score_samples(X))