def test_ftrl_set_wrong_nepochs_type():
ft = Ftrl()
with pytest.raises(TypeError) as e:
ft.nepochs = "-10.0"
assert ("Expected an integer, instead got <class 'str'>" == str(e.value))
def test_ftrl_construct_wrong_lambda2_value():
with pytest.raises(ValueError) as e:
noop(Ftrl(lambda2=-1.0))
assert ("Argument `lambda2` in Ftrl() constructor should be greater than "
"or equal to zero: -1.0" == str(e.value))
def test_ftrl_construct_wrong_nepochs_value():
with pytest.raises(ValueError) as e:
noop(Ftrl(nepochs=-1))
assert ("Argument `nepochs` in Ftrl() constructor cannot be negative: -1"
== str(e.value))
def test_ftrl_construct_wrong_double_precision_type():
with pytest.raises(TypeError) as e:
noop(Ftrl(double_precision=2))
assert (
"Argument `double_precision` in Ftrl() constructor should be a boolean, "
"instead got <class 'int'>" == str(e.value))
def test_ftrl_construct_wrong_params_type():
params = tparams._replace(alpha="1.0")
with pytest.raises(TypeError) as e:
Ftrl(params)
assert ("Expected a float, instead got <class 'str'>" == str(e.value))
def test_ftrl_pickling_empty_model():
ft_pickled = pickle.dumps(Ftrl())
ft_unpickled = pickle.loads(ft_pickled)
assert ft_unpickled.model == None
assert ft_unpickled.feature_importances == None
assert ft_unpickled.params == Ftrl().params
def test_ftrl_construct_wrong_nepochs_type():
with pytest.raises(TypeError) as e:
noop(Ftrl(nepochs=10.0))
assert ("Argument `nepochs` in Ftrl() constructor should be an integer, "
"instead got <class 'float'>" == str(e.value))
def test_ftrl_model_untrained():
ft = Ftrl()
assert ft.model == None
def test_ftrl_set_negative_n_model():
ft = Ftrl(tparams)
with pytest.raises(ValueError) as e:
ft.model = (tmodel[:, {'z': f.z, 'n': -f.n}][:, ['z', 'n']], )
assert ("Element 0: Values in column `n` cannot be negative" == str(
e.value))
def test_ftrl_set_wrong_nbins_value():
ft = Ftrl()
with pytest.raises(ValueError) as e:
ft.nbins = 0
assert ("Value should be positive: 0" == str(e.value))
def test_ftrl_set_wrong_nepochs_value():
ft = Ftrl()
with pytest.raises(ValueError) as e:
ft.nepochs = -10
assert ("Integer value cannot be negative" == str(e.value))
def test_ftrl_set_bad_lambda2_value(value):
ft = Ftrl()
with pytest.raises(ValueError) as e:
ft.lambda2 = value
assert ("Value should be greater than or equal to zero: %s" %
str(value) == str(e.value))
def test_ftrl_set_bad_alpha_value(value):
ft = Ftrl()
with pytest.raises(ValueError) as e:
ft.alpha = value
assert ("Value should be positive: %s" % str(value) == str(e.value))
def test_ftrl_set_wrong_interactions_type():
ft = Ftrl()
with pytest.raises(TypeError) as e:
ft.interactions = 2
assert ("Expected a boolean, instead got <class 'int'>" == str(e.value))
def test_ftrl_fit_predict_multinomial_vs_binomial():
ft1 = Ftrl(nbins=10, nepochs=2)
df_train1 = dt.Frame(range(ft1.nbins))
df_target1 = dt.Frame([True, False] * 5)
ft1.fit(df_train1, df_target1)
p1 = ft1.predict(df_train1)
ft2 = Ftrl(nbins=10, nepochs=2)
ft2.labels = ["target", "target2"]
df_train2 = dt.Frame(range(ft2.nbins))
df_target2 = dt.Frame(ft2.labels * 5)
ft2.fit(df_train2, df_target2)
p2 = ft2.predict(df_train2)
assert_equals(ft1.model[0], ft2.model[0])
assert_equals(p1, p2[:, 0])
def test_ftrl_get_set_model():
ft = Ftrl(tparams)
ft.model = (tmodel, )
assert_equals(ft.model[0], tmodel)
def test_ftrl_construct_wrong_beta_type():
with pytest.raises(TypeError) as e:
noop(Ftrl(beta="1.0"))
assert ("Argument `beta` in Ftrl() constructor should be a float, instead "
"got <class 'str'>" == str(e.value))
def test_ftrl_reset_model():
ft = Ftrl(tparams)
ft.model = (tmodel, )
ft.reset()
assert ft.model == None
def test_ftrl_construct_wrong_lambda2_type():
with pytest.raises(TypeError) as e:
noop(Ftrl(lambda2="1.0"))
assert ("Argument `lambda2` in Ftrl() constructor should be a float, "
"instead got <class 'str'>" == str(e.value))
def test_ftrl_none_model():
ft = Ftrl(tparams)
ft.model = None
assert ft.model == None
def test_ftrl_construct_wrong_interactions_type():
with pytest.raises(TypeError) as e:
noop(Ftrl(interactions=2))
assert (
"Argument `interactions` in Ftrl() constructor should be a boolean, "
"instead got <class 'int'>" == str(e.value))
def test_ftrl_fit_no_frame():
ft = Ftrl()
with pytest.raises(ValueError) as e:
ft.fit()
assert ("Training frame parameter is missing" == str(e.value))
def test_ftrl_construct_unknown_arg():
with pytest.raises(TypeError) as e:
noop(Ftrl(c=1.0))
assert ("Ftrl() constructor got an unexpected keyword argument `c`" == str(
e.value))
def test_ftrl_fit_no_target():
ft = Ftrl()
with pytest.raises(ValueError) as e:
ft.fit(None)
assert ("Target frame parameter is missing" == str(e.value))
def test_ftrl_construct_wrong_alpha_value():
with pytest.raises(ValueError) as e:
noop(Ftrl(alpha=0.0))
assert ("Argument `alpha` in Ftrl() constructor should be positive: 0.0" ==
str(e.value))
def test_ftrl_fit_predict_nones():
ft = Ftrl()
ft.fit(None, None)
df_target = ft.predict(None)
assert df_target == None
def test_ftrl_construct_wrong_nbins_value():
with pytest.raises(ValueError) as e:
noop(Ftrl(nbins=0))
assert (
"Argument `nbins` in Ftrl() constructor should be positive: 0" == str(
e.value))
def test_ftrl_fit_predict_from_setters():
ft = Ftrl(nbins=10)
df_train = dt.Frame(range(ft.nbins))
df_target = dt.Frame([True] * ft.nbins)
# Train `ft` to get a model
ft.fit(df_train, df_target)
# Set this model and parameters to `ft2`
ft2 = Ftrl()
ft2.params = ft.params
ft2.model = ft.model
# Train `ft2` and make predictions
ft2.fit(df_train, df_target)
target2 = ft2.predict(df_train)
# Train `ft` and make predictions
ft.fit(df_train, df_target)
target1 = ft.predict(df_train)
assert_equals(ft.model[0], ft2.model[0])
assert_equals(target1, target2)
def test_ftrl_create_default():
ft = Ftrl()
assert ft.params == default_params
def test_ftrl_set_wrong_lambda2_type():
ft = Ftrl()
with pytest.raises(TypeError) as e:
ft.lambda2 = "-1.0"
assert ("Expected a float, instead got <class 'str'>" == str(e.value))
