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)