# Source code for lightning.impl.svrg

# Author: Mathieu Blondel

import numpy as np

from .base import BaseClassifier, BaseRegressor
from .dataset_fast import get_dataset
from .svrg_fast import _svrg_fit

from .sgd_fast import ModifiedHuber
from .sgd_fast import SmoothHinge
from .sgd_fast import SquaredHinge
from .sgd_fast import Log
from .sgd_fast import SquaredLoss

class _BaseSVRG(object):

def _finalize_coef(self):
self.coef_ *= self.coef_scale_
self.coef_scale_.fill(1.0)

def _fit(self, X, Y):
n_samples, n_features = X.shape
rng = self._get_random_state()
loss = self._get_loss()
n_vectors = Y.shape[1]
n_inner = int(self.n_inner * n_samples)
ds = get_dataset(X, order="c")

self.coef_ = np.zeros((n_vectors, n_features), dtype=np.float64)
grad = np.zeros((n_vectors, n_samples), dtype=np.float64)
self.coef_scale_ = np.ones(n_vectors, dtype=np.float64)

for i in range(n_vectors):
y = Y[:, i]

_svrg_fit(self, ds, y, self.coef_[i], self.coef_scale_[i:],
self.max_iter, n_inner, self.tol, self.verbose,
self.callback, rng)

return self

[docs]class SVRGClassifier(BaseClassifier, _BaseSVRG):
r"""
Estimator for learning linear classifiers by SVRG.

Solves the following objective:

.. code-block::

minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
+ alpha * 0.5 * ||w||^2_2
"""

def __init__(self, eta=1.0, alpha=1.0, loss="smooth_hinge", gamma=1.0,
max_iter=10, n_inner=1.0, tol=1e-3, verbose=0,
callback=None, random_state=None):
self.eta = eta
self.alpha = alpha
self.loss = loss
self.gamma = gamma
self.max_iter = max_iter
self.n_inner = n_inner
self.tol = tol
self.verbose = verbose
self.callback = callback
self.random_state = random_state

def _get_loss(self):
losses = {
"modified_huber": ModifiedHuber(),
"smooth_hinge": SmoothHinge(self.gamma),
"squared_hinge": SquaredHinge(1.0),
"log": Log(),
"squared": SquaredLoss(),
}
return losses[self.loss]

[docs]    def fit(self, X, y):
self._set_label_transformers(y)
Y = np.asfortranarray(self.label_binarizer_.transform(y),
dtype=np.float64)
return self._fit(X, Y)

[docs]class SVRGRegressor(BaseRegressor, _BaseSVRG):
r"""
Estimator for learning linear regressors by SVRG.

Solves the following objective:

.. code-block::

minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
+ alpha * 0.5 * ||w||^2_2
"""

def __init__(self, eta=1.0, alpha=1.0, loss="squared", gamma=1.0,
max_iter=10, n_inner=1.0, tol=1e-3, verbose=0,
callback=None, random_state=None):
self.eta = eta
self.alpha = alpha
self.loss = loss
self.gamma = gamma
self.max_iter = max_iter
self.n_inner = n_inner
self.tol = tol
self.verbose = verbose
self.callback = callback
self.random_state = random_state

def _get_loss(self):
losses = {
"squared": SquaredLoss(),
}
return losses[self.loss]

[docs]    def fit(self, X, y):
self.outputs_2d_ = len(y.shape) > 1
Y = y.reshape(-1, 1) if not self.outputs_2d_ else y
Y = Y.astype(np.float64)
return self._fit(X, Y)