class classifier_Vowpalwabbit(object):
def __init__(self):
self.name = "Vowpalwabbit"
self.model = VWClassifier()
return
# ----------------------------------------------------------------------------------------------------------------------
def maybe_reshape(self, X):
if numpy.ndim(X) == 2:
return X
else:
return numpy.reshape(X, (-1, X.shape[0]))
# ----------------------------------------------------------------------------------------------------------------------
def learn(self, data_train, target_train):
yyy = target_train.astype(float)
yyy[yyy > 0] = 1
yyy[yyy <= 0] = -1
self.model.fit(self.maybe_reshape(data_train).astype(numpy.float), yyy)
return
# ----------------------------------------------------------------------------------------------------------------------
def predict(self, array):
xxx = self.maybe_reshape(array).astype(numpy.float)
res = numpy.array(self.model.decision_function(xxx))
#res = numpy.array(self.model.predict(xxx))
return numpy.vstack((numpy.zeros_like(res), res)).T
# ----------------------------------------------------------------------------------------------------------------------
def sanitycheck(self):
X, y = datasets.make_hastie_10_2(n_samples=1000, random_state=1)
X = X.astype(numpy.float32)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=256)
model = VWClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
score_train = model.score(X_train, y_train)
scoer_test = model.score(X_test, y_test)
return
# ----------------------------------------------------------------------------------------------------------------------
def test_decision_function(self, data):
model = VWClassifier()
model.fit(data.x, data.y)
actual = model.decision_function(data.x)
assert actual.shape[0] == 100
assert np.isclose(actual[0], 0.4069, atol=1e-4)
