def evaluate(self, dataset):
rows, cols, vals = dataset.find(self._app)
u = self.U[:, rows]
v = self.V[:, cols]
predictions = u * v
q = nps.sum(predictions, axis=0)
return mean_squared_error(q.get(), vals.get(), squared=False)
def example(max_iters, batch_size):
app = am.instance()
model = LogisticRegression(app=app,
cluster_shape=(1, 1),
fit_intercept=False)
X, y = sample(app, sample_size=8)
model.init(X)
for i in range(max_iters):
# Take a step.
X, y = sample(app, batch_size)
model.partial_fit(X, y)
print("train accuracy",
(nps.sum(y == model.predict(X)) / X.shape[0]).get())
import nums
import nums.numpy as nps
from nums.models.glms import LogisticRegression
nums.init()
# Make dataset.
X1 = nps.random.randn(500, 1) + 5.0
y1 = nps.zeros(shape=(500, ), dtype=bool)
X2 = nps.random.randn(500, 1) + 10.0
y2 = nps.ones(shape=(500, ), dtype=bool)
X = nps.concatenate([X1, X2], axis=0)
y = nps.concatenate([y1, y2], axis=0)
# Train Logistic Regression Model.
model = LogisticRegression(solver="newton", tol=1e-8, max_iter=1)
model.fit(X, y)
y_pred = model.predict(X)
print("accuracy", (nps.sum(y == y_pred) / X.shape[0]).get())