from benchopt.base import BaseSolver
from benchopt.util import safe_import
with safe_import() as solver_import:
from cyanure import Regression
class Solver(BaseSolver):
name = 'Cyanure'
install_cmd = 'pip'
requirements = ['cyanure-mkl']
requirements_import = ['cyanure']
def set_objective(self, X, y, lmbd):
self.X, self.y, self.lmbd = X, y, lmbd
n_samples = self.X.shape[0]
self.solver = Regression(loss='square',
penalty='l1',
fit_intercept=False)
self.solver_parameter = dict(lambd=self.lmbd / n_samples,
solver='auto',
tol=1e-12,
verbose=False)
def run(self, n_iter):
self.solver.fit(self.X,
self.y,
max_epochs=n_iter,
import os
import numpy as np
from benchopt.base import BaseDataset
from benchopt.config import get_global_setting
from benchopt.util import safe_import
with safe_import():
# Dependencies of download_libsvm are scikit-learn, download and tqdm
from benchopt.utils.datasets.libsvm import download_libsvm
from scipy import sparse
DATA_DIR = get_global_setting('data_dir')
class Dataset(BaseDataset):
# TODO call the dataset log1p_train to harmonize with libsvm naming?
name = "finance"
install_cmd = 'pip'
requirements = ['scikit-learn', 'scipy', 'download', 'tqdm']
requirements_import = ['sklearn', 'scipy', 'download', 'tqdm']
def get_data(self):
X_path = os.path.join(DATA_DIR, self.name, "X.npz")
y_path = os.path.join(DATA_DIR, self.name, "y.npy")
try:
X = sparse.load_npz(X_path)
y = np.load(y_path)
