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
# ----------------------------------------------------------------------------------------------------------------------
from vowpalwabbit.sklearn_vw import VWClassifier X = [[1, 2], [3, 4], [5, 6], [7, 8]] y = [-1, -1, 1, 1] model = VWClassifier(loss_function='logistic', l=0.01, l2=0.1) model.fit(X, y) print(model.predict(X)) print(model.score(X, y))
# from vowpalwabbit import pyvw
#
# vw = pyvw.vw(quiet=True)
# ex = vw.example('1 | a b c')
# vw.learn(ex)
# vw.predict(ex)
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from vowpalwabbit.pyvw import vw
from vowpalwabbit.sklearn_vw import VWClassifier
# generate some data
X, y = datasets.make_hastie_10_2(n_samples=10000, random_state=1)
X = X.astype(np.float32)
# split train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=256)
# build model
model = VWClassifier()
model.fit(X_train, y_train)
# predict model
y_pred = model.predict(X_test)
print(y_pred)
# evaluate model
model.score(X_train, y_train)
model.score(X_test, y_test)
