365 lines
12 KiB
Python
365 lines
12 KiB
Python
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"""Factor Analysis.
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A latent linear variable model.
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FactorAnalysis is similar to probabilistic PCA implemented by PCA.score
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While PCA assumes Gaussian noise with the same variance for each
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feature, the FactorAnalysis model assumes different variances for
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each of them.
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This implementation is based on David Barber's Book,
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Bayesian Reasoning and Machine Learning,
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http://www.cs.ucl.ac.uk/staff/d.barber/brml,
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Algorithm 21.1
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"""
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# Author: Christian Osendorfer <osendorf@gmail.com>
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# Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Denis A. Engemann <denis-alexander.engemann@inria.fr>
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# License: BSD3
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import warnings
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from math import sqrt, log
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import numpy as np
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from scipy import linalg
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from ..base import BaseEstimator, TransformerMixin
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from ..utils import check_array, check_random_state
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from ..utils.extmath import fast_logdet, randomized_svd, squared_norm
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from ..utils.validation import check_is_fitted, _deprecate_positional_args
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from ..exceptions import ConvergenceWarning
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class FactorAnalysis(TransformerMixin, BaseEstimator):
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"""Factor Analysis (FA)
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A simple linear generative model with Gaussian latent variables.
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The observations are assumed to be caused by a linear transformation of
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lower dimensional latent factors and added Gaussian noise.
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Without loss of generality the factors are distributed according to a
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Gaussian with zero mean and unit covariance. The noise is also zero mean
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and has an arbitrary diagonal covariance matrix.
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If we would restrict the model further, by assuming that the Gaussian
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noise is even isotropic (all diagonal entries are the same) we would obtain
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:class:`PPCA`.
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FactorAnalysis performs a maximum likelihood estimate of the so-called
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`loading` matrix, the transformation of the latent variables to the
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observed ones, using SVD based approach.
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Read more in the :ref:`User Guide <FA>`.
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.. versionadded:: 0.13
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Parameters
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----------
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n_components : int | None
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Dimensionality of latent space, the number of components
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of ``X`` that are obtained after ``transform``.
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If None, n_components is set to the number of features.
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tol : float
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Stopping tolerance for log-likelihood increase.
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copy : bool
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Whether to make a copy of X. If ``False``, the input X gets overwritten
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during fitting.
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max_iter : int
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Maximum number of iterations.
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noise_variance_init : None | array, shape=(n_features,)
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The initial guess of the noise variance for each feature.
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If None, it defaults to np.ones(n_features)
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svd_method : {'lapack', 'randomized'}
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Which SVD method to use. If 'lapack' use standard SVD from
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scipy.linalg, if 'randomized' use fast ``randomized_svd`` function.
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Defaults to 'randomized'. For most applications 'randomized' will
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be sufficiently precise while providing significant speed gains.
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Accuracy can also be improved by setting higher values for
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`iterated_power`. If this is not sufficient, for maximum precision
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you should choose 'lapack'.
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iterated_power : int, optional
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Number of iterations for the power method. 3 by default. Only used
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if ``svd_method`` equals 'randomized'
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random_state : int, RandomState instance, default=0
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Only used when ``svd_method`` equals 'randomized'. Pass an int for
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reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
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components_ : array, [n_components, n_features]
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Components with maximum variance.
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loglike_ : list, [n_iterations]
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The log likelihood at each iteration.
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noise_variance_ : array, shape=(n_features,)
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The estimated noise variance for each feature.
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n_iter_ : int
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Number of iterations run.
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mean_ : array, shape (n_features,)
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Per-feature empirical mean, estimated from the training set.
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Examples
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--------
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>>> from sklearn.datasets import load_digits
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>>> from sklearn.decomposition import FactorAnalysis
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>>> X, _ = load_digits(return_X_y=True)
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>>> transformer = FactorAnalysis(n_components=7, random_state=0)
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>>> X_transformed = transformer.fit_transform(X)
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>>> X_transformed.shape
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(1797, 7)
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References
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----------
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.. David Barber, Bayesian Reasoning and Machine Learning,
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Algorithm 21.1
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.. Christopher M. Bishop: Pattern Recognition and Machine Learning,
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Chapter 12.2.4
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See also
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--------
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PCA: Principal component analysis is also a latent linear variable model
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which however assumes equal noise variance for each feature.
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This extra assumption makes probabilistic PCA faster as it can be
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computed in closed form.
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FastICA: Independent component analysis, a latent variable model with
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non-Gaussian latent variables.
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"""
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@_deprecate_positional_args
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def __init__(self, n_components=None, *, tol=1e-2, copy=True,
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max_iter=1000,
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noise_variance_init=None, svd_method='randomized',
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iterated_power=3, random_state=0):
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self.n_components = n_components
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self.copy = copy
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self.tol = tol
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self.max_iter = max_iter
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if svd_method not in ['lapack', 'randomized']:
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raise ValueError('SVD method %s is not supported. Please consider'
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' the documentation' % svd_method)
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self.svd_method = svd_method
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self.noise_variance_init = noise_variance_init
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self.iterated_power = iterated_power
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self.random_state = random_state
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def fit(self, X, y=None):
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"""Fit the FactorAnalysis model to X using SVD based approach
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Training data.
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y : Ignored
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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, copy=self.copy, dtype=np.float64)
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n_samples, n_features = X.shape
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n_components = self.n_components
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if n_components is None:
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n_components = n_features
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self.mean_ = np.mean(X, axis=0)
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X -= self.mean_
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# some constant terms
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nsqrt = sqrt(n_samples)
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llconst = n_features * log(2. * np.pi) + n_components
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var = np.var(X, axis=0)
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if self.noise_variance_init is None:
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psi = np.ones(n_features, dtype=X.dtype)
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else:
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if len(self.noise_variance_init) != n_features:
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raise ValueError("noise_variance_init dimension does not "
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"with number of features : %d != %d" %
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(len(self.noise_variance_init), n_features))
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psi = np.array(self.noise_variance_init)
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loglike = []
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old_ll = -np.inf
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SMALL = 1e-12
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# we'll modify svd outputs to return unexplained variance
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# to allow for unified computation of loglikelihood
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if self.svd_method == 'lapack':
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def my_svd(X):
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_, s, V = linalg.svd(X, full_matrices=False)
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return (s[:n_components], V[:n_components],
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squared_norm(s[n_components:]))
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elif self.svd_method == 'randomized':
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random_state = check_random_state(self.random_state)
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def my_svd(X):
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_, s, V = randomized_svd(X, n_components,
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random_state=random_state,
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n_iter=self.iterated_power)
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return s, V, squared_norm(X) - squared_norm(s)
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else:
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raise ValueError('SVD method %s is not supported. Please consider'
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' the documentation' % self.svd_method)
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for i in range(self.max_iter):
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# SMALL helps numerics
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sqrt_psi = np.sqrt(psi) + SMALL
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s, V, unexp_var = my_svd(X / (sqrt_psi * nsqrt))
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s **= 2
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# Use 'maximum' here to avoid sqrt problems.
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W = np.sqrt(np.maximum(s - 1., 0.))[:, np.newaxis] * V
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del V
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W *= sqrt_psi
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# loglikelihood
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ll = llconst + np.sum(np.log(s))
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ll += unexp_var + np.sum(np.log(psi))
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ll *= -n_samples / 2.
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loglike.append(ll)
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if (ll - old_ll) < self.tol:
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break
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old_ll = ll
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psi = np.maximum(var - np.sum(W ** 2, axis=0), SMALL)
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else:
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warnings.warn('FactorAnalysis did not converge.' +
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' You might want' +
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' to increase the number of iterations.',
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ConvergenceWarning)
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self.components_ = W
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self.noise_variance_ = psi
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self.loglike_ = loglike
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self.n_iter_ = i + 1
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return self
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def transform(self, X):
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"""Apply dimensionality reduction to X using the model.
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Compute the expected mean of the latent variables.
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See Barber, 21.2.33 (or Bishop, 12.66).
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Training data.
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Returns
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-------
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X_new : array-like, shape (n_samples, n_components)
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The latent variables of X.
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"""
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check_is_fitted(self)
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X = check_array(X)
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Ih = np.eye(len(self.components_))
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X_transformed = X - self.mean_
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Wpsi = self.components_ / self.noise_variance_
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cov_z = linalg.inv(Ih + np.dot(Wpsi, self.components_.T))
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tmp = np.dot(X_transformed, Wpsi.T)
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X_transformed = np.dot(tmp, cov_z)
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return X_transformed
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def get_covariance(self):
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"""Compute data covariance with the FactorAnalysis model.
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``cov = components_.T * components_ + diag(noise_variance)``
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Returns
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-------
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cov : array, shape (n_features, n_features)
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Estimated covariance of data.
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"""
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check_is_fitted(self)
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cov = np.dot(self.components_.T, self.components_)
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cov.flat[::len(cov) + 1] += self.noise_variance_ # modify diag inplace
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return cov
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def get_precision(self):
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"""Compute data precision matrix with the FactorAnalysis model.
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Returns
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-------
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precision : array, shape (n_features, n_features)
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Estimated precision of data.
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"""
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check_is_fitted(self)
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n_features = self.components_.shape[1]
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# handle corner cases first
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if self.n_components == 0:
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return np.diag(1. / self.noise_variance_)
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if self.n_components == n_features:
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return linalg.inv(self.get_covariance())
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# Get precision using matrix inversion lemma
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components_ = self.components_
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precision = np.dot(components_ / self.noise_variance_, components_.T)
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precision.flat[::len(precision) + 1] += 1.
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precision = np.dot(components_.T,
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np.dot(linalg.inv(precision), components_))
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precision /= self.noise_variance_[:, np.newaxis]
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precision /= -self.noise_variance_[np.newaxis, :]
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precision.flat[::len(precision) + 1] += 1. / self.noise_variance_
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return precision
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def score_samples(self, X):
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"""Compute the log-likelihood of each sample
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Parameters
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----------
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X : array, shape (n_samples, n_features)
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The data
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Returns
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-------
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ll : array, shape (n_samples,)
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Log-likelihood of each sample under the current model
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"""
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check_is_fitted(self)
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Xr = X - self.mean_
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precision = self.get_precision()
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n_features = X.shape[1]
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log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
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log_like -= .5 * (n_features * log(2. * np.pi)
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- fast_logdet(precision))
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return log_like
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def score(self, X, y=None):
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"""Compute the average log-likelihood of the samples
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Parameters
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----------
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X : array, shape (n_samples, n_features)
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The data
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y : Ignored
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Returns
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-------
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ll : float
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Average log-likelihood of the samples under the current model
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"""
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return np.mean(self.score_samples(X))
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