913 lines
32 KiB
Python
913 lines
32 KiB
Python
"""Orthogonal matching pursuit algorithms
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"""
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# Author: Vlad Niculae
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#
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# License: BSD 3 clause
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import warnings
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from math import sqrt
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import numpy as np
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from scipy import linalg
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from scipy.linalg.lapack import get_lapack_funcs
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from joblib import Parallel, delayed
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from ._base import LinearModel, _pre_fit
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from ..base import RegressorMixin, MultiOutputMixin
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from ..utils import as_float_array, check_array
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from ..utils.validation import _deprecate_positional_args
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from ..model_selection import check_cv
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premature = """ Orthogonal matching pursuit ended prematurely due to linear
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dependence in the dictionary. The requested precision might not have been met.
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"""
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def _cholesky_omp(X, y, n_nonzero_coefs, tol=None, copy_X=True,
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return_path=False):
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"""Orthogonal Matching Pursuit step using the Cholesky decomposition.
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Parameters
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----------
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X : array, shape (n_samples, n_features)
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Input dictionary. Columns are assumed to have unit norm.
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y : array, shape (n_samples,)
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Input targets
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n_nonzero_coefs : int
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Targeted number of non-zero elements
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tol : float
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Targeted squared error, if not None overrides n_nonzero_coefs.
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copy_X : bool, optional
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Whether the design matrix X must be copied by the algorithm. A false
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value is only helpful if X is already Fortran-ordered, otherwise a
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copy is made anyway.
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return_path : bool, optional. Default: False
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Whether to return every value of the nonzero coefficients along the
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forward path. Useful for cross-validation.
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Returns
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-------
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gamma : array, shape (n_nonzero_coefs,)
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Non-zero elements of the solution
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idx : array, shape (n_nonzero_coefs,)
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Indices of the positions of the elements in gamma within the solution
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vector
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coef : array, shape (n_features, n_nonzero_coefs)
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The first k values of column k correspond to the coefficient value
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for the active features at that step. The lower left triangle contains
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garbage. Only returned if ``return_path=True``.
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n_active : int
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Number of active features at convergence.
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"""
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if copy_X:
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X = X.copy('F')
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else: # even if we are allowed to overwrite, still copy it if bad order
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X = np.asfortranarray(X)
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min_float = np.finfo(X.dtype).eps
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nrm2, swap = linalg.get_blas_funcs(('nrm2', 'swap'), (X,))
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potrs, = get_lapack_funcs(('potrs',), (X,))
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alpha = np.dot(X.T, y)
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residual = y
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gamma = np.empty(0)
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n_active = 0
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indices = np.arange(X.shape[1]) # keeping track of swapping
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max_features = X.shape[1] if tol is not None else n_nonzero_coefs
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L = np.empty((max_features, max_features), dtype=X.dtype)
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if return_path:
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coefs = np.empty_like(L)
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while True:
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lam = np.argmax(np.abs(np.dot(X.T, residual)))
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if lam < n_active or alpha[lam] ** 2 < min_float:
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# atom already selected or inner product too small
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warnings.warn(premature, RuntimeWarning, stacklevel=2)
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break
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if n_active > 0:
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# Updates the Cholesky decomposition of X' X
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L[n_active, :n_active] = np.dot(X[:, :n_active].T, X[:, lam])
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linalg.solve_triangular(L[:n_active, :n_active],
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L[n_active, :n_active],
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trans=0, lower=1,
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overwrite_b=True,
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check_finite=False)
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v = nrm2(L[n_active, :n_active]) ** 2
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Lkk = linalg.norm(X[:, lam]) ** 2 - v
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if Lkk <= min_float: # selected atoms are dependent
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warnings.warn(premature, RuntimeWarning, stacklevel=2)
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break
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L[n_active, n_active] = sqrt(Lkk)
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else:
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L[0, 0] = linalg.norm(X[:, lam])
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X.T[n_active], X.T[lam] = swap(X.T[n_active], X.T[lam])
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alpha[n_active], alpha[lam] = alpha[lam], alpha[n_active]
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indices[n_active], indices[lam] = indices[lam], indices[n_active]
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n_active += 1
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# solves LL'x = X'y as a composition of two triangular systems
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gamma, _ = potrs(L[:n_active, :n_active], alpha[:n_active], lower=True,
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overwrite_b=False)
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if return_path:
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coefs[:n_active, n_active - 1] = gamma
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residual = y - np.dot(X[:, :n_active], gamma)
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if tol is not None and nrm2(residual) ** 2 <= tol:
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break
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elif n_active == max_features:
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break
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if return_path:
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return gamma, indices[:n_active], coefs[:, :n_active], n_active
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else:
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return gamma, indices[:n_active], n_active
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def _gram_omp(Gram, Xy, n_nonzero_coefs, tol_0=None, tol=None,
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copy_Gram=True, copy_Xy=True, return_path=False):
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"""Orthogonal Matching Pursuit step on a precomputed Gram matrix.
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This function uses the Cholesky decomposition method.
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Parameters
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----------
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Gram : array, shape (n_features, n_features)
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Gram matrix of the input data matrix
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Xy : array, shape (n_features,)
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Input targets
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n_nonzero_coefs : int
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Targeted number of non-zero elements
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tol_0 : float
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Squared norm of y, required if tol is not None.
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tol : float
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Targeted squared error, if not None overrides n_nonzero_coefs.
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copy_Gram : bool, optional
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Whether the gram matrix must be copied by the algorithm. A false
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value is only helpful if it is already Fortran-ordered, otherwise a
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copy is made anyway.
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copy_Xy : bool, optional
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Whether the covariance vector Xy must be copied by the algorithm.
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If False, it may be overwritten.
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return_path : bool, optional. Default: False
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Whether to return every value of the nonzero coefficients along the
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forward path. Useful for cross-validation.
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Returns
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-------
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gamma : array, shape (n_nonzero_coefs,)
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Non-zero elements of the solution
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idx : array, shape (n_nonzero_coefs,)
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Indices of the positions of the elements in gamma within the solution
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vector
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coefs : array, shape (n_features, n_nonzero_coefs)
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The first k values of column k correspond to the coefficient value
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for the active features at that step. The lower left triangle contains
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garbage. Only returned if ``return_path=True``.
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n_active : int
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Number of active features at convergence.
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"""
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Gram = Gram.copy('F') if copy_Gram else np.asfortranarray(Gram)
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if copy_Xy or not Xy.flags.writeable:
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Xy = Xy.copy()
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min_float = np.finfo(Gram.dtype).eps
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nrm2, swap = linalg.get_blas_funcs(('nrm2', 'swap'), (Gram,))
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potrs, = get_lapack_funcs(('potrs',), (Gram,))
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indices = np.arange(len(Gram)) # keeping track of swapping
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alpha = Xy
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tol_curr = tol_0
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delta = 0
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gamma = np.empty(0)
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n_active = 0
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max_features = len(Gram) if tol is not None else n_nonzero_coefs
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L = np.empty((max_features, max_features), dtype=Gram.dtype)
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L[0, 0] = 1.
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if return_path:
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coefs = np.empty_like(L)
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while True:
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lam = np.argmax(np.abs(alpha))
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if lam < n_active or alpha[lam] ** 2 < min_float:
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# selected same atom twice, or inner product too small
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warnings.warn(premature, RuntimeWarning, stacklevel=3)
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break
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if n_active > 0:
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L[n_active, :n_active] = Gram[lam, :n_active]
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linalg.solve_triangular(L[:n_active, :n_active],
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L[n_active, :n_active],
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trans=0, lower=1,
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overwrite_b=True,
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check_finite=False)
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v = nrm2(L[n_active, :n_active]) ** 2
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Lkk = Gram[lam, lam] - v
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if Lkk <= min_float: # selected atoms are dependent
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warnings.warn(premature, RuntimeWarning, stacklevel=3)
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break
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L[n_active, n_active] = sqrt(Lkk)
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else:
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L[0, 0] = sqrt(Gram[lam, lam])
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Gram[n_active], Gram[lam] = swap(Gram[n_active], Gram[lam])
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Gram.T[n_active], Gram.T[lam] = swap(Gram.T[n_active], Gram.T[lam])
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indices[n_active], indices[lam] = indices[lam], indices[n_active]
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Xy[n_active], Xy[lam] = Xy[lam], Xy[n_active]
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n_active += 1
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# solves LL'x = X'y as a composition of two triangular systems
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gamma, _ = potrs(L[:n_active, :n_active], Xy[:n_active], lower=True,
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overwrite_b=False)
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if return_path:
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coefs[:n_active, n_active - 1] = gamma
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beta = np.dot(Gram[:, :n_active], gamma)
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alpha = Xy - beta
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if tol is not None:
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tol_curr += delta
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delta = np.inner(gamma, beta[:n_active])
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tol_curr -= delta
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if abs(tol_curr) <= tol:
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break
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elif n_active == max_features:
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break
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if return_path:
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return gamma, indices[:n_active], coefs[:, :n_active], n_active
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else:
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return gamma, indices[:n_active], n_active
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@_deprecate_positional_args
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def orthogonal_mp(X, y, *, n_nonzero_coefs=None, tol=None, precompute=False,
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copy_X=True, return_path=False,
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return_n_iter=False):
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r"""Orthogonal Matching Pursuit (OMP)
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Solves n_targets Orthogonal Matching Pursuit problems.
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An instance of the problem has the form:
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When parametrized by the number of non-zero coefficients using
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`n_nonzero_coefs`:
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argmin ||y - X\gamma||^2 subject to ||\gamma||_0 <= n_{nonzero coefs}
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When parametrized by error using the parameter `tol`:
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argmin ||\gamma||_0 subject to ||y - X\gamma||^2 <= tol
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Read more in the :ref:`User Guide <omp>`.
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Parameters
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----------
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X : array, shape (n_samples, n_features)
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Input data. Columns are assumed to have unit norm.
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y : array, shape (n_samples,) or (n_samples, n_targets)
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Input targets
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n_nonzero_coefs : int
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Desired number of non-zero entries in the solution. If None (by
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default) this value is set to 10% of n_features.
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tol : float
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Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
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precompute : {True, False, 'auto'},
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Whether to perform precomputations. Improves performance when n_targets
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or n_samples is very large.
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copy_X : bool, optional
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Whether the design matrix X must be copied by the algorithm. A false
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value is only helpful if X is already Fortran-ordered, otherwise a
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copy is made anyway.
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return_path : bool, optional. Default: False
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Whether to return every value of the nonzero coefficients along the
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forward path. Useful for cross-validation.
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return_n_iter : bool, optional default False
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Whether or not to return the number of iterations.
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Returns
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-------
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coef : array, shape (n_features,) or (n_features, n_targets)
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Coefficients of the OMP solution. If `return_path=True`, this contains
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the whole coefficient path. In this case its shape is
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(n_features, n_features) or (n_features, n_targets, n_features) and
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iterating over the last axis yields coefficients in increasing order
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of active features.
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n_iters : array-like or int
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Number of active features across every target. Returned only if
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`return_n_iter` is set to True.
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See also
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--------
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OrthogonalMatchingPursuit
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orthogonal_mp_gram
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lars_path
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decomposition.sparse_encode
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Notes
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-----
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Orthogonal matching pursuit was introduced in S. Mallat, Z. Zhang,
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Matching pursuits with time-frequency dictionaries, IEEE Transactions on
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Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
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(http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)
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This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
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M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
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Matching Pursuit Technical Report - CS Technion, April 2008.
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https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
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"""
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X = check_array(X, order='F', copy=copy_X)
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copy_X = False
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if y.ndim == 1:
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y = y.reshape(-1, 1)
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y = check_array(y)
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if y.shape[1] > 1: # subsequent targets will be affected
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copy_X = True
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if n_nonzero_coefs is None and tol is None:
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# default for n_nonzero_coefs is 0.1 * n_features
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# but at least one.
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n_nonzero_coefs = max(int(0.1 * X.shape[1]), 1)
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if tol is not None and tol < 0:
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raise ValueError("Epsilon cannot be negative")
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if tol is None and n_nonzero_coefs <= 0:
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raise ValueError("The number of atoms must be positive")
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if tol is None and n_nonzero_coefs > X.shape[1]:
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raise ValueError("The number of atoms cannot be more than the number "
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"of features")
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if precompute == 'auto':
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precompute = X.shape[0] > X.shape[1]
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if precompute:
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G = np.dot(X.T, X)
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G = np.asfortranarray(G)
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Xy = np.dot(X.T, y)
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if tol is not None:
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norms_squared = np.sum((y ** 2), axis=0)
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else:
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norms_squared = None
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return orthogonal_mp_gram(G, Xy, n_nonzero_coefs=n_nonzero_coefs,
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tol=tol, norms_squared=norms_squared,
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copy_Gram=copy_X, copy_Xy=False,
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return_path=return_path)
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if return_path:
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coef = np.zeros((X.shape[1], y.shape[1], X.shape[1]))
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else:
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coef = np.zeros((X.shape[1], y.shape[1]))
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n_iters = []
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for k in range(y.shape[1]):
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out = _cholesky_omp(
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X, y[:, k], n_nonzero_coefs, tol,
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copy_X=copy_X, return_path=return_path)
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if return_path:
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_, idx, coefs, n_iter = out
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coef = coef[:, :, :len(idx)]
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for n_active, x in enumerate(coefs.T):
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coef[idx[:n_active + 1], k, n_active] = x[:n_active + 1]
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else:
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x, idx, n_iter = out
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coef[idx, k] = x
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n_iters.append(n_iter)
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if y.shape[1] == 1:
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n_iters = n_iters[0]
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if return_n_iter:
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return np.squeeze(coef), n_iters
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else:
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return np.squeeze(coef)
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@_deprecate_positional_args
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def orthogonal_mp_gram(Gram, Xy, *, n_nonzero_coefs=None, tol=None,
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norms_squared=None, copy_Gram=True,
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copy_Xy=True, return_path=False,
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return_n_iter=False):
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"""Gram Orthogonal Matching Pursuit (OMP)
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Solves n_targets Orthogonal Matching Pursuit problems using only
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the Gram matrix X.T * X and the product X.T * y.
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Read more in the :ref:`User Guide <omp>`.
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Parameters
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----------
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Gram : array, shape (n_features, n_features)
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Gram matrix of the input data: X.T * X
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Xy : array, shape (n_features,) or (n_features, n_targets)
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Input targets multiplied by X: X.T * y
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n_nonzero_coefs : int
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Desired number of non-zero entries in the solution. If None (by
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default) this value is set to 10% of n_features.
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tol : float
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Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
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norms_squared : array-like, shape (n_targets,)
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Squared L2 norms of the lines of y. Required if tol is not None.
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copy_Gram : bool, optional
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Whether the gram matrix must be copied by the algorithm. A false
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value is only helpful if it is already Fortran-ordered, otherwise a
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copy is made anyway.
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copy_Xy : bool, optional
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Whether the covariance vector Xy must be copied by the algorithm.
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If False, it may be overwritten.
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return_path : bool, optional. Default: False
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Whether to return every value of the nonzero coefficients along the
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forward path. Useful for cross-validation.
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return_n_iter : bool, optional default False
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Whether or not to return the number of iterations.
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Returns
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-------
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coef : array, shape (n_features,) or (n_features, n_targets)
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Coefficients of the OMP solution. If `return_path=True`, this contains
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the whole coefficient path. In this case its shape is
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(n_features, n_features) or (n_features, n_targets, n_features) and
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iterating over the last axis yields coefficients in increasing order
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of active features.
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n_iters : array-like or int
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Number of active features across every target. Returned only if
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`return_n_iter` is set to True.
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|
See also
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|
--------
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OrthogonalMatchingPursuit
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orthogonal_mp
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lars_path
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decomposition.sparse_encode
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|
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|
Notes
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|
-----
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|
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
|
|
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
|
|
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
|
|
(http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)
|
|
|
|
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
|
|
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
|
|
Matching Pursuit Technical Report - CS Technion, April 2008.
|
|
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
|
|
|
|
"""
|
|
Gram = check_array(Gram, order='F', copy=copy_Gram)
|
|
Xy = np.asarray(Xy)
|
|
if Xy.ndim > 1 and Xy.shape[1] > 1:
|
|
# or subsequent target will be affected
|
|
copy_Gram = True
|
|
if Xy.ndim == 1:
|
|
Xy = Xy[:, np.newaxis]
|
|
if tol is not None:
|
|
norms_squared = [norms_squared]
|
|
if copy_Xy or not Xy.flags.writeable:
|
|
# Make the copy once instead of many times in _gram_omp itself.
|
|
Xy = Xy.copy()
|
|
|
|
if n_nonzero_coefs is None and tol is None:
|
|
n_nonzero_coefs = int(0.1 * len(Gram))
|
|
if tol is not None and norms_squared is None:
|
|
raise ValueError('Gram OMP needs the precomputed norms in order '
|
|
'to evaluate the error sum of squares.')
|
|
if tol is not None and tol < 0:
|
|
raise ValueError("Epsilon cannot be negative")
|
|
if tol is None and n_nonzero_coefs <= 0:
|
|
raise ValueError("The number of atoms must be positive")
|
|
if tol is None and n_nonzero_coefs > len(Gram):
|
|
raise ValueError("The number of atoms cannot be more than the number "
|
|
"of features")
|
|
|
|
if return_path:
|
|
coef = np.zeros((len(Gram), Xy.shape[1], len(Gram)))
|
|
else:
|
|
coef = np.zeros((len(Gram), Xy.shape[1]))
|
|
|
|
n_iters = []
|
|
for k in range(Xy.shape[1]):
|
|
out = _gram_omp(
|
|
Gram, Xy[:, k], n_nonzero_coefs,
|
|
norms_squared[k] if tol is not None else None, tol,
|
|
copy_Gram=copy_Gram, copy_Xy=False,
|
|
return_path=return_path)
|
|
if return_path:
|
|
_, idx, coefs, n_iter = out
|
|
coef = coef[:, :, :len(idx)]
|
|
for n_active, x in enumerate(coefs.T):
|
|
coef[idx[:n_active + 1], k, n_active] = x[:n_active + 1]
|
|
else:
|
|
x, idx, n_iter = out
|
|
coef[idx, k] = x
|
|
n_iters.append(n_iter)
|
|
|
|
if Xy.shape[1] == 1:
|
|
n_iters = n_iters[0]
|
|
|
|
if return_n_iter:
|
|
return np.squeeze(coef), n_iters
|
|
else:
|
|
return np.squeeze(coef)
|
|
|
|
|
|
class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):
|
|
"""Orthogonal Matching Pursuit model (OMP)
|
|
|
|
Read more in the :ref:`User Guide <omp>`.
|
|
|
|
Parameters
|
|
----------
|
|
n_nonzero_coefs : int, optional
|
|
Desired number of non-zero entries in the solution. If None (by
|
|
default) this value is set to 10% of n_features.
|
|
|
|
tol : float, optional
|
|
Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
|
|
|
|
fit_intercept : boolean, optional
|
|
whether to calculate the intercept for this model. If set
|
|
to false, no intercept will be used in calculations
|
|
(i.e. data is expected to be centered).
|
|
|
|
normalize : boolean, optional, default True
|
|
This parameter is ignored when ``fit_intercept`` is set to False.
|
|
If True, the regressors X will be normalized before regression by
|
|
subtracting the mean and dividing by the l2-norm.
|
|
If you wish to standardize, please use
|
|
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
|
|
on an estimator with ``normalize=False``.
|
|
|
|
precompute : {True, False, 'auto'}, default 'auto'
|
|
Whether to use a precomputed Gram and Xy matrix to speed up
|
|
calculations. Improves performance when :term:`n_targets` or
|
|
:term:`n_samples` is very large. Note that if you already have such
|
|
matrices, you can pass them directly to the fit method.
|
|
|
|
Attributes
|
|
----------
|
|
coef_ : array, shape (n_features,) or (n_targets, n_features)
|
|
parameter vector (w in the formula)
|
|
|
|
intercept_ : float or array, shape (n_targets,)
|
|
independent term in decision function.
|
|
|
|
n_iter_ : int or array-like
|
|
Number of active features across every target.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.linear_model import OrthogonalMatchingPursuit
|
|
>>> from sklearn.datasets import make_regression
|
|
>>> X, y = make_regression(noise=4, random_state=0)
|
|
>>> reg = OrthogonalMatchingPursuit().fit(X, y)
|
|
>>> reg.score(X, y)
|
|
0.9991...
|
|
>>> reg.predict(X[:1,])
|
|
array([-78.3854...])
|
|
|
|
Notes
|
|
-----
|
|
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
|
|
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
|
|
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
|
|
(http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)
|
|
|
|
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
|
|
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
|
|
Matching Pursuit Technical Report - CS Technion, April 2008.
|
|
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
|
|
|
|
See also
|
|
--------
|
|
orthogonal_mp
|
|
orthogonal_mp_gram
|
|
lars_path
|
|
Lars
|
|
LassoLars
|
|
decomposition.sparse_encode
|
|
OrthogonalMatchingPursuitCV
|
|
"""
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, n_nonzero_coefs=None, tol=None, fit_intercept=True,
|
|
normalize=True, precompute='auto'):
|
|
self.n_nonzero_coefs = n_nonzero_coefs
|
|
self.tol = tol
|
|
self.fit_intercept = fit_intercept
|
|
self.normalize = normalize
|
|
self.precompute = precompute
|
|
|
|
def fit(self, X, y):
|
|
"""Fit the model using X, y as training data.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like, shape (n_samples, n_features)
|
|
Training data.
|
|
|
|
y : array-like, shape (n_samples,) or (n_samples, n_targets)
|
|
Target values. Will be cast to X's dtype if necessary
|
|
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
returns an instance of self.
|
|
"""
|
|
X, y = self._validate_data(X, y, multi_output=True, y_numeric=True)
|
|
n_features = X.shape[1]
|
|
|
|
X, y, X_offset, y_offset, X_scale, Gram, Xy = \
|
|
_pre_fit(X, y, None, self.precompute, self.normalize,
|
|
self.fit_intercept, copy=True)
|
|
|
|
if y.ndim == 1:
|
|
y = y[:, np.newaxis]
|
|
|
|
if self.n_nonzero_coefs is None and self.tol is None:
|
|
# default for n_nonzero_coefs is 0.1 * n_features
|
|
# but at least one.
|
|
self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1)
|
|
else:
|
|
self.n_nonzero_coefs_ = self.n_nonzero_coefs
|
|
|
|
if Gram is False:
|
|
coef_, self.n_iter_ = orthogonal_mp(
|
|
X, y, n_nonzero_coefs=self.n_nonzero_coefs_, tol=self.tol,
|
|
precompute=False, copy_X=True,
|
|
return_n_iter=True)
|
|
else:
|
|
norms_sq = np.sum(y ** 2, axis=0) if self.tol is not None else None
|
|
|
|
coef_, self.n_iter_ = orthogonal_mp_gram(
|
|
Gram, Xy=Xy, n_nonzero_coefs=self.n_nonzero_coefs_,
|
|
tol=self.tol, norms_squared=norms_sq,
|
|
copy_Gram=True, copy_Xy=True,
|
|
return_n_iter=True)
|
|
self.coef_ = coef_.T
|
|
self._set_intercept(X_offset, y_offset, X_scale)
|
|
return self
|
|
|
|
|
|
def _omp_path_residues(X_train, y_train, X_test, y_test, copy=True,
|
|
fit_intercept=True, normalize=True, max_iter=100):
|
|
"""Compute the residues on left-out data for a full LARS path
|
|
|
|
Parameters
|
|
----------
|
|
X_train : array, shape (n_samples, n_features)
|
|
The data to fit the LARS on
|
|
|
|
y_train : array, shape (n_samples)
|
|
The target variable to fit LARS on
|
|
|
|
X_test : array, shape (n_samples, n_features)
|
|
The data to compute the residues on
|
|
|
|
y_test : array, shape (n_samples)
|
|
The target variable to compute the residues on
|
|
|
|
copy : boolean, optional
|
|
Whether X_train, X_test, y_train and y_test should be copied. If
|
|
False, they may be overwritten.
|
|
|
|
fit_intercept : boolean
|
|
whether to calculate the intercept for this model. If set
|
|
to false, no intercept will be used in calculations
|
|
(i.e. data is expected to be centered).
|
|
|
|
normalize : boolean, optional, default True
|
|
This parameter is ignored when ``fit_intercept`` is set to False.
|
|
If True, the regressors X will be normalized before regression by
|
|
subtracting the mean and dividing by the l2-norm.
|
|
If you wish to standardize, please use
|
|
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
|
|
on an estimator with ``normalize=False``.
|
|
|
|
max_iter : integer, optional
|
|
Maximum numbers of iterations to perform, therefore maximum features
|
|
to include. 100 by default.
|
|
|
|
Returns
|
|
-------
|
|
residues : array, shape (n_samples, max_features)
|
|
Residues of the prediction on the test data
|
|
"""
|
|
|
|
if copy:
|
|
X_train = X_train.copy()
|
|
y_train = y_train.copy()
|
|
X_test = X_test.copy()
|
|
y_test = y_test.copy()
|
|
|
|
if fit_intercept:
|
|
X_mean = X_train.mean(axis=0)
|
|
X_train -= X_mean
|
|
X_test -= X_mean
|
|
y_mean = y_train.mean(axis=0)
|
|
y_train = as_float_array(y_train, copy=False)
|
|
y_train -= y_mean
|
|
y_test = as_float_array(y_test, copy=False)
|
|
y_test -= y_mean
|
|
|
|
if normalize:
|
|
norms = np.sqrt(np.sum(X_train ** 2, axis=0))
|
|
nonzeros = np.flatnonzero(norms)
|
|
X_train[:, nonzeros] /= norms[nonzeros]
|
|
|
|
coefs = orthogonal_mp(X_train, y_train, n_nonzero_coefs=max_iter, tol=None,
|
|
precompute=False, copy_X=False,
|
|
return_path=True)
|
|
if coefs.ndim == 1:
|
|
coefs = coefs[:, np.newaxis]
|
|
if normalize:
|
|
coefs[nonzeros] /= norms[nonzeros][:, np.newaxis]
|
|
|
|
return np.dot(coefs.T, X_test.T) - y_test
|
|
|
|
|
|
class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):
|
|
"""Cross-validated Orthogonal Matching Pursuit model (OMP).
|
|
|
|
See glossary entry for :term:`cross-validation estimator`.
|
|
|
|
Read more in the :ref:`User Guide <omp>`.
|
|
|
|
Parameters
|
|
----------
|
|
copy : bool, optional
|
|
Whether the design matrix X must be copied by the algorithm. A false
|
|
value is only helpful if X is already Fortran-ordered, otherwise a
|
|
copy is made anyway.
|
|
|
|
fit_intercept : boolean, optional
|
|
whether to calculate the intercept for this model. If set
|
|
to false, no intercept will be used in calculations
|
|
(i.e. data is expected to be centered).
|
|
|
|
normalize : boolean, optional, default True
|
|
This parameter is ignored when ``fit_intercept`` is set to False.
|
|
If True, the regressors X will be normalized before regression by
|
|
subtracting the mean and dividing by the l2-norm.
|
|
If you wish to standardize, please use
|
|
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
|
|
on an estimator with ``normalize=False``.
|
|
|
|
max_iter : integer, optional
|
|
Maximum numbers of iterations to perform, therefore maximum features
|
|
to include. 10% of ``n_features`` but at least 5 if available.
|
|
|
|
cv : int, cross-validation generator or an iterable, optional
|
|
Determines the cross-validation splitting strategy.
|
|
Possible inputs for cv are:
|
|
|
|
- None, to use the default 5-fold cross-validation,
|
|
- integer, to specify the number of folds.
|
|
- :term:`CV splitter`,
|
|
- An iterable yielding (train, test) splits as arrays of indices.
|
|
|
|
For integer/None inputs, :class:`KFold` is used.
|
|
|
|
Refer :ref:`User Guide <cross_validation>` for the various
|
|
cross-validation strategies that can be used here.
|
|
|
|
.. versionchanged:: 0.22
|
|
``cv`` default value if None changed from 3-fold to 5-fold.
|
|
|
|
n_jobs : int or None, optional (default=None)
|
|
Number of CPUs to use during the cross validation.
|
|
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
|
|
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
|
|
for more details.
|
|
|
|
verbose : boolean or integer, optional
|
|
Sets the verbosity amount
|
|
|
|
Attributes
|
|
----------
|
|
intercept_ : float or array, shape (n_targets,)
|
|
Independent term in decision function.
|
|
|
|
coef_ : array, shape (n_features,) or (n_targets, n_features)
|
|
Parameter vector (w in the problem formulation).
|
|
|
|
n_nonzero_coefs_ : int
|
|
Estimated number of non-zero coefficients giving the best mean squared
|
|
error over the cross-validation folds.
|
|
|
|
n_iter_ : int or array-like
|
|
Number of active features across every target for the model refit with
|
|
the best hyperparameters got by cross-validating across all folds.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.linear_model import OrthogonalMatchingPursuitCV
|
|
>>> from sklearn.datasets import make_regression
|
|
>>> X, y = make_regression(n_features=100, n_informative=10,
|
|
... noise=4, random_state=0)
|
|
>>> reg = OrthogonalMatchingPursuitCV(cv=5).fit(X, y)
|
|
>>> reg.score(X, y)
|
|
0.9991...
|
|
>>> reg.n_nonzero_coefs_
|
|
10
|
|
>>> reg.predict(X[:1,])
|
|
array([-78.3854...])
|
|
|
|
See also
|
|
--------
|
|
orthogonal_mp
|
|
orthogonal_mp_gram
|
|
lars_path
|
|
Lars
|
|
LassoLars
|
|
OrthogonalMatchingPursuit
|
|
LarsCV
|
|
LassoLarsCV
|
|
decomposition.sparse_encode
|
|
|
|
"""
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, copy=True, fit_intercept=True, normalize=True,
|
|
max_iter=None, cv=None, n_jobs=None, verbose=False):
|
|
self.copy = copy
|
|
self.fit_intercept = fit_intercept
|
|
self.normalize = normalize
|
|
self.max_iter = max_iter
|
|
self.cv = cv
|
|
self.n_jobs = n_jobs
|
|
self.verbose = verbose
|
|
|
|
def fit(self, X, y):
|
|
"""Fit the model using X, y as training data.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like, shape [n_samples, n_features]
|
|
Training data.
|
|
|
|
y : array-like, shape [n_samples]
|
|
Target values. Will be cast to X's dtype if necessary
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
returns an instance of self.
|
|
"""
|
|
X, y = self._validate_data(X, y, y_numeric=True, ensure_min_features=2,
|
|
estimator=self)
|
|
X = as_float_array(X, copy=False, force_all_finite=False)
|
|
cv = check_cv(self.cv, classifier=False)
|
|
max_iter = (min(max(int(0.1 * X.shape[1]), 5), X.shape[1])
|
|
if not self.max_iter
|
|
else self.max_iter)
|
|
cv_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
|
|
delayed(_omp_path_residues)(
|
|
X[train], y[train], X[test], y[test], self.copy,
|
|
self.fit_intercept, self.normalize, max_iter)
|
|
for train, test in cv.split(X))
|
|
|
|
min_early_stop = min(fold.shape[0] for fold in cv_paths)
|
|
mse_folds = np.array([(fold[:min_early_stop] ** 2).mean(axis=1)
|
|
for fold in cv_paths])
|
|
best_n_nonzero_coefs = np.argmin(mse_folds.mean(axis=0)) + 1
|
|
self.n_nonzero_coefs_ = best_n_nonzero_coefs
|
|
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=best_n_nonzero_coefs,
|
|
fit_intercept=self.fit_intercept,
|
|
normalize=self.normalize)
|
|
omp.fit(X, y)
|
|
self.coef_ = omp.coef_
|
|
self.intercept_ = omp.intercept_
|
|
self.n_iter_ = omp.n_iter_
|
|
return self
|