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# (C) Mathieu Blondel 2012
# License: BSD 3 clause
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils.extmath import safe_sparse_dot
class LbfgsNNLS(BaseEstimator, RegressorMixin):
def __init__(self, tol=1e-3, callback=None):
self.tol = tol
self.callback = callback
def fit(self, X, y):
n_features = X.shape[1]
def f(w, *args):
return np.sum((safe_sparse_dot(X, w) - y) ** 2)
def fprime(w, *args):
if self.callback is not None:
self.coef_ = w
return 2 * safe_sparse_dot(X.T, safe_sparse_dot(X, w) - y)
coef0 = np.zeros(n_features, dtype=np.float64)
w, f, d = fmin_l_bfgs_b(f, x0=coef0, fprime=fprime, pgtol=self.tol,
bounds=[(0, None)] * n_features)
self.coef_ = w
return self
def n_nonzero(self, percentage=False):
nz = np.sum(self.coef_ != 0)
if percentage:
nz /= float(self.coef_.shape[0])
return nz
def predict(self, X):
return safe_sparse_dot(X, self.coef_)

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@vene vene commented Jan 1, 2013

I added L1 regularization:
Can this be made to support two-dimensional Y or is the loop unavoidable?


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@mblondel mblondel commented Jan 19, 2013

Strange, I didn't receive any notification for your comment. If you decompose the objective value and the gradient as explained in scikit-learn/scikit-learn#1359 (comment) I guess you can pre-compute the parts which are independent of y. But other than that, the loop seems unavoidable to me.


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@artemyk artemyk commented Feb 21, 2013

Hi! I found some errors when passing in sparse matrices. Fixed it and added test cases at .

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