def learn_parameters(self, data, num_iterations):
"""Logistic Psdd's parameter learning is reduced to logistic regression.
We use mini-batch SGD to optimize the parameters."""
model = LogisticRegression(solver='saga',
fit_intercept=False,
max_iter=num_iterations,
C=1e8,
warm_start=True,
coef_=[self._parameters])
images, features, labels = data.balanced_all()
model.fit(features, labels.ravel())
self._record_learned_parameters(model.coef_[0])
def learn_parameters(self, data, num_iterations, num_cores=-1):
"""Logistic Psdd's parameter learning is reduced to logistic regression.
We use mini-batch SGD to optimize the parameters."""
model = LogisticRegression(
solver="saga",
fit_intercept=False,
multi_class="ovr",
max_iter=num_iterations,
C=10.0,
warm_start=True,
tol=1e-5,
coef_=self._parameters,
n_jobs=num_cores,
)
model.fit(data.features, data.labels)
self._record_learned_parameters(model.coef_)
gc.collect()