def test_model_datastream(self):
model_nimbusml = Pipeline(
steps=[
('cat',
OneHotVectorizer() << categorical_columns),
('linear',
FastLinearBinaryClassifier(
shuffle=False,
number_of_threads=1))])
model_nimbusml.fit(train, label)
# Save with pickle
pickle_filename = get_temp_file(suffix='.p')
with open(pickle_filename, 'wb') as f:
pickle.dump(model_nimbusml, f)
with open(pickle_filename, "rb") as f:
model_nimbusml_pickle = pickle.load(f)
os.remove(pickle_filename)
score1 = model_nimbusml.predict(test).head(5)
score2 = model_nimbusml_pickle.predict(test).head(5)
metrics, score = model_nimbusml.test(test, test_label, output_scores=True)
metrics_pickle, score_pickle = model_nimbusml_pickle.test(
test, test_label, output_scores=True)
assert_almost_equal(score1.sum().sum(), score2.sum().sum(), decimal=2)
assert_almost_equal(
metrics.sum().sum(),
metrics_pickle.sum().sum(),
decimal=2)
# Save load with pipeline methods
model_filename = get_temp_file(suffix='.m')
model_nimbusml.save_model(model_filename)
model_nimbusml_load = Pipeline()
model_nimbusml_load.load_model(model_filename)
score1 = model_nimbusml.predict(test).head(5)
score2 = model_nimbusml_load.predict(test).head(5)
metrics, score = model_nimbusml.test(test, test_label, output_scores=True)
model_nimbusml_load, score_load = model_nimbusml_load.test(
test, test_label, evaltype='binary', output_scores=True)
assert_almost_equal(score1.sum().sum(), score2.sum().sum(), decimal=2)
assert_almost_equal(
metrics.sum().sum(),
model_nimbusml_load.sum().sum(),
decimal=2)
os.remove(model_filename)
def test_model_datastream(self):
model_nimbusml = Pipeline(
steps=[('cat', OneHotVectorizer() << categorical_columns),
('linear',
FastLinearBinaryClassifier(shuffle=False, train_threads=1)
)])
model_nimbusml.fit(train, label)
# Save with pickle
pickle.dump(model_nimbusml, open('nimbusml_model.p', 'wb'))
model_nimbusml_pickle = pickle.load(open("nimbusml_model.p", "rb"))
score1 = model_nimbusml.predict(test).head(5)
score2 = model_nimbusml_pickle.predict(test).head(5)
metrics, score = model_nimbusml.test(test,
test_label,
output_scores=True)
metrics_pickle, score_pickle = model_nimbusml_pickle.test(
test, test_label, output_scores=True)
assert_almost_equal(score1.sum().sum(), score2.sum().sum(), decimal=2)
assert_almost_equal(metrics.sum().sum(),
metrics_pickle.sum().sum(),
decimal=2)
# Save load with pipeline methods
model_nimbusml.save_model('model.nimbusml.m')
model_nimbusml_load = Pipeline()
model_nimbusml_load.load_model('model.nimbusml.m')
score1 = model_nimbusml.predict(test).head(5)
score2 = model_nimbusml_load.predict(test).head(5)
metrics, score = model_nimbusml.test(test,
test_label,
output_scores=True)
model_nimbusml_load, score_load = model_nimbusml_load.test(
test, test_label, evaltype='binary', output_scores=True)
assert_almost_equal(score1.sum().sum(), score2.sum().sum(), decimal=2)
assert_almost_equal(metrics.sum().sum(),
model_nimbusml_load.sum().sum(),
decimal=2)
