def test_learn_predict_multiline():
model = Workspace(quiet=True, cb_adf=True)
ex = model.parse(["| a:1 b:0.5", "0:0.1:0.75 | a:0.5 b:1 c:2"])
assert model.predict(ex) == [0.0, 0.0]
model.finish_example(ex)
ex = ["| a", "| b"]
model.learn(ex)
assert model.predict(ex) == [0.0, 0.0]
def test_scalar_prediction_type():
model = Workspace(quiet=True)
model.learn("1 | a b c")
assert model.get_prediction_type() == model.pSCALAR
prediction = model.predict(" | a b c")
assert isinstance(prediction, float)
del model
def test_multilabel_prediction_type():
model = Workspace(multilabel_oaa=4, quiet=True)
model.learn("1 | a b c")
assert model.get_prediction_type() == model.pMULTILABELS
prediction = model.predict(" | a b c")
assert isinstance(prediction, list)
del model
def test_keys_with_list_of_values():
# No exception in creating and executing model with a key/list pair
model = Workspace(quiet=True, q=["fa", "fb"])
model.learn("1 | a b c")
prediction = model.predict(" | a b c")
assert isinstance(prediction, float)
del model
def test_action_probs_prediction_type():
model = Workspace(cb_explore=2, ngram=2, quiet=True)
model.learn("1 | a b c")
assert model.get_prediction_type() == model.pACTION_PROBS
prediction = model.predict(" | a b c")
assert isinstance(prediction, list)
del model
def test_multiclass_prediction_type():
n = 3
model = Workspace(loss_function="logistic", oaa=n, quiet=True)
model.learn("1 | a b c")
assert model.get_prediction_type(
) == vowpalwabbit.PredictionType.MULTICLASS
prediction = model.predict(" | a b c")
assert isinstance(prediction, int)
del model
def test_action_scores_prediction_type():
model = Workspace(loss_function="logistic", csoaa_ldf="m", quiet=True)
multi_ex = [model.example("1:1 | a b c"), model.example("2:-1 | a b c")]
model.learn(multi_ex)
assert model.get_prediction_type() == model.pMULTICLASS
multi_ex = [model.example("1 | a b c"), model.example("2 | a b c")]
prediction = model.predict(multi_ex)
assert isinstance(prediction, int)
del model
def test_scalars_prediction_type():
n = 3
model = Workspace(loss_function="logistic",
oaa=n,
probabilities=True,
quiet=True)
model.learn("1 | a b c")
assert model.get_prediction_type() == vowpalwabbit.PredictionType.SCALARS
prediction = model.predict(" | a b c")
assert isinstance(prediction, list)
assert len(prediction) == n
del model
def test_command_line_with_space_and_escape_kwargs():
# load and parse external data file
test_file_dir = Path(__file__).resolve().parent
data_file = test_file_dir / "resources" / "train file.dat"
model = Workspace(oaa=3,
data=str(data_file),
final_regressor="test model.vw")
assert model.predict("| feature1:2.5") == 1
del model
model_file = Path("test model.vw")
assert model_file.is_file()
model_file.unlink()
def test_regressor_args():
# load and parse external data file
data_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"resources", "train.dat")
model = Workspace(oaa=3, data=data_file, passes=30, c=True, k=True)
assert model.predict("| feature1:2.5") == 1
# update model in memory
for _ in range(10):
model.learn("3 | feature1:2.5")
assert model.predict("| feature1:2.5") == 3
# save model
model.save("tmp.model")
del model
# load initial regressor and confirm updated prediction
new_model = Workspace(i="tmp.model", quiet=True)
assert new_model.predict("| feature1:2.5") == 3
del new_model
# clean up
os.remove("{}.cache".format(data_file))
os.remove("tmp.model")
def test_command_line_using_arg_list():
# load and parse external data file
test_file_dir = Path(__file__).resolve().parent
data_file = test_file_dir / "resources" / "train file.dat"
args = [
"--oaa", "3", "--data",
str(data_file), "--final_regressor", "test model2.vw"
]
model = Workspace(arg_list=args)
assert model.predict("| feature1:2.5") == 1
del model
model_file = Path("test model2.vw")
assert model_file.is_file()
model_file.unlink()
def test_prob_prediction_type():
model = Workspace(
loss_function="logistic",
csoaa_ldf="mc",
probabilities=True,
quiet=True,
)
multi_ex = [
model.example("1:0.2 | a b c"),
model.example("2:0.8 | a b c"),
]
model.learn(multi_ex)
assert model.get_prediction_type() == vowpalwabbit.PredictionType.PROB
multi_ex = [model.example("1 | a b c"), model.example("2 | a b c")]
prediction = model.predict(multi_ex)
assert isinstance(prediction, float)
del model