Source code for tensorly.regression.cp_regression

import numpy as np
from ..base import partial_tensor_to_vec, partial_unfold
from ..tenalg import khatri_rao
from ..cp_tensor import cp_to_tensor, cp_to_vec
from .. import backend as T
from ..utils import DefineDeprecated

# Author: Jean Kossaifi

# License: BSD 3 clause

[docs]class CPRegressor(): """CP tensor regression Learns a low rank CP tensor weight Parameters ---------- weight_rank : int rank of the CP decomposition of the regression weights tol : float convergence value reg_W : int, optional, default is 1 regularisation on the weights n_iter_max : int, optional, default is 100 maximum number of iteration random_state : None, int or RandomState, optional, default is None verbose : int, default is 1 level of verbosity """ def __init__(self, weight_rank, tol=10e-7, reg_W=1, n_iter_max=100, random_state=None, verbose=1): self.weight_rank = weight_rank self.tol = tol self.reg_W = reg_W self.n_iter_max = n_iter_max self.random_state = random_state self.verbose = verbose
[docs] def get_params(self, **kwargs): """Returns a dictionary of parameters """ params = ['weight_rank', 'tol', 'reg_W', 'n_iter_max', 'random_state', 'verbose'] return {param_name: getattr(self, param_name) for param_name in params}
[docs] def set_params(self, **parameters): """Sets the value of the provided parameters""" for parameter, value in parameters.items(): setattr(self, parameter, value) return self
[docs] def fit(self, X, y): """Fits the model to the data (X, y) Parameters ---------- X : ndarray tensor data of shape (n_samples, N1, ..., NS) y : 1D-array of shape (n_samples, ) labels associated with each sample Returns ------- self """ rng = T.check_random_state(self.random_state) # Initialise randomly the weights W = [] for i in range(1, T.ndim(X)): # The first dimension of X is the number of samples W.append(T.tensor(rng.randn(X.shape[i], self.weight_rank), **T.context(X))) # Norm of the weight tensor at each iteration norm_W = [] weights = T.ones(self.weight_rank, **T.context(X)) for iteration in range(self.n_iter_max): # Optimise each factor of W for i in range(len(W)): phi = T.reshape(, i, skip_begin=1), khatri_rao(W, skip_matrix=i)), (X.shape[0], -1)) inv_term =, phi) + self.reg_W*T.tensor(np.eye(phi.shape[1]), **T.context(X)) W[i] = T.reshape(T.solve(inv_term,, y)), (X.shape[i + 1], self.weight_rank)) weight_tensor_ = cp_to_tensor((weights, W)) norm_W.append(T.norm(weight_tensor_, 2)) # Convergence check if iteration > 1: weight_evolution = abs(norm_W[-1] - norm_W[-2]) / norm_W[-1] if (weight_evolution <= self.tol): if self.verbose: print('\nConverged in {} iterations'.format(iteration)) break self.weight_tensor_ = weight_tensor_ self.cp_weight_ = (weights, W) self.vec_W_ = cp_to_vec((weights, W)) self.n_iterations_ = iteration + 1 self.norm_W_ = norm_W return self
[docs] def predict(self, X): """Returns the predicted labels for a new data tensor Parameters ---------- X : ndarray tensor data of shape (n_samples, N1, ..., NS) """ return, self.vec_W_)
KruskalRegressor = DefineDeprecated('KruskalRegressor', CPRegressor)