def test_GroupNormalizer(kwargs, groups):
data = pd.DataFrame(dict(a=[1, 1, 2, 2, 3], b=[1.1, 1.1, 1.0, 5.0, 1.1]))
defaults = dict(method="standard",
transformation=None,
center=True,
scale_by_group=False)
defaults.update(kwargs)
kwargs = defaults
kwargs["groups"] = groups
kwargs["scale_by_group"] = kwargs["scale_by_group"] and len(
kwargs["groups"]) > 0
if kwargs.get("transformation") in ["relu", "softplus"]:
data.b = data.b - 2.0
normalizer = GroupNormalizer(**kwargs)
encoded = normalizer.fit_transform(data["b"], data)
test_data = dict(
prediction=torch.tensor([encoded[0]]),
target_scale=torch.tensor(normalizer.get_parameters([1])).unsqueeze(0),
)
if kwargs.get("transformation") in ["relu", "softplus", "log1p"]:
assert (normalizer(test_data) >=
0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer(test_data), torch.tensor(data.b.iloc[0]),
atol=1e-5).all(), "Inverse transform should reverse transform"
def dataloaders_with_covariates(data_with_covariates):
training_cutoff = "2016-09-01"
max_encoder_length = 36
max_prediction_length = 6
training = TimeSeriesDataSet(
data_with_covariates[lambda x: x.date < training_cutoff],
time_idx="time_idx",
target="volume",
# weight="weight",
group_ids=["agency", "sku"],
time_varying_known_reals=["discount"],
time_varying_unknown_reals=["volume"],
static_categoricals=["agency"],
max_encoder_length=max_encoder_length,
max_prediction_length=max_prediction_length,
add_relative_time_idx=True,
target_normalizer=GroupNormalizer(groups=["agency", "sku"],
coerce_positive=False),
)
validation = TimeSeriesDataSet.from_dataset(
training,
data_with_covariates,
min_prediction_idx=training.index.time.max() + 1)
batch_size = 4
train_dataloader = training.to_dataloader(train=True,
batch_size=batch_size,
num_workers=0)
val_dataloader = validation.to_dataloader(train=False,
batch_size=batch_size,
num_workers=0)
return dict(train=train_dataloader, val=val_dataloader)
def dataloaders_with_covariates(data_with_covariates):
return make_dataloaders(
data_with_covariates,
time_varying_known_reals=["discount"],
time_varying_unknown_reals=["volume"],
static_categoricals=["agency"],
add_relative_time_idx=True,
target_normalizer=GroupNormalizer(groups=["agency", "sku"]),
)
def dataloaders_with_covariates(data_with_covariates):
return make_dataloaders(
data_with_covariates.copy(),
target="target",
time_varying_known_reals=["discount"],
time_varying_unknown_reals=["target"],
static_categoricals=["agency"],
add_relative_time_idx=False,
target_normalizer=GroupNormalizer(groups=["agency", "sku"],
center=False),
)
def dataloaders_with_covariates(data_with_covariates):
data_with_covariates["target"] = data_with_covariates["volume"].clip(1e-3, 1.0)
return make_dataloaders(
data_with_covariates,
target="target",
time_varying_known_reals=["discount"],
time_varying_unknown_reals=["target"],
static_categoricals=["agency"],
add_relative_time_idx=True,
target_normalizer=GroupNormalizer(groups=["agency", "sku"], center=False),
)
def test_GroupNormalizer(kwargs, groups):
data = pd.DataFrame(dict(a=[1, 1, 2, 2, 3], b=[1.1, 1.1, 1.0, 5.0, 1.1]))
defaults = dict(
method="standard", log_scale=False, coerce_positive=False, center=True, log_zero_value=0.0, scale_by_group=False
)
defaults.update(kwargs)
kwargs = defaults
kwargs["groups"] = groups
kwargs["scale_by_group"] = kwargs["scale_by_group"] and len(kwargs["groups"]) > 0
if kwargs["coerce_positive"] and kwargs["log_scale"]:
with pytest.raises(AssertionError):
normalizer = GroupNormalizer(**kwargs)
else:
if kwargs["coerce_positive"]:
data.b = data.b - 2.0
normalizer = GroupNormalizer(**kwargs)
encoded = normalizer.fit_transform(data["b"], data)
test_data = dict(
prediction=torch.tensor([encoded.iloc[0]]),
target_scale=torch.tensor(normalizer.get_parameters([1])).unsqueeze(0),
)
if kwargs["coerce_positive"]:
assert (normalizer(test_data) >= 0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer(test_data), torch.tensor(data.b.iloc[0]), atol=1e-5
).all(), "Inverse transform should reverse transform"
def create_data_spec(self) -> dict:
cfg = self.cfg
data_spec = dict(
time_idx=cfg.get("time_index"),
target=cfg.get("target"),
group_ids=cfg.get("target_keys"),
target_normalizer=GroupNormalizer(
groups=cfg.get("target_keys"), coerce_positive=1.0
), # use softplus with beta=1.0 and normalize by group
static_categoricals=cfg.get("static").categorical,
static_reals=cfg.get("static").numerical,
time_varying_known_categoricals=cfg.get("known").categorical,
variable_groups=cfg.get("variable_groups"),
time_varying_known_reals=cfg.get("known").numerical,
time_varying_unknown_categoricals=cfg.get("unknown").categorical,
time_varying_unknown_reals=cfg.get("unknown").numerical,
)
return data_spec
def test_MultiNormalizer_fitted():
data = pd.DataFrame(
dict(a=[1, 1, 2, 2, 3],
b=[1.1, 1.1, 1.0, 5.0, 1.1],
c=[1.1, 1.1, 1.0, 5.0, 1.1]))
normalizer = MultiNormalizer(
[GroupNormalizer(groups=["a"]),
TorchNormalizer()])
with pytest.raises(NotFittedError):
check_is_fitted(normalizer)
normalizer.fit(data, data)
try:
check_is_fitted(normalizer.normalizers[0])
check_is_fitted(normalizer.normalizers[1])
check_is_fitted(normalizer)
except NotFittedError:
pytest.fail(f"{NotFittedError}")
time_varying_known_reals=[
"time_idx", "price_regular", "price_actual", "discount",
"discount_in_percent"
],
time_varying_unknown_categoricals=[],
time_varying_unknown_reals=[
"volume", "log_volume", "industry_volume", "soda_volume",
"avg_max_temp"
],
constant_fill_strategy={"volume": 0},
categorical_encoders={"sku": NaNLabelEncoder(add_nan=True)},
),
dict(static_categoricals=["agency", "sku"]),
dict(randomize_length=True, min_encoder_length=2),
dict(target_normalizer=EncoderNormalizer(), min_encoder_length=2),
dict(target_normalizer=GroupNormalizer(transformation="log1p")),
dict(target_normalizer=GroupNormalizer(groups=["agency", "sku"],
transformation="softplus",
center=False)),
dict(target="agency"),
# test multiple targets
dict(target=["industry_volume", "volume"]),
dict(target=["agency", "volume"]),
dict(target=["agency", "volume"],
min_encoder_length=1,
min_prediction_length=1),
dict(target=["agency", "volume"], weight="volume"),
# test weights
dict(target="volume", weight="volume"),
],
scope="session",
"labor_day",
"independence_day",
"revolution_day_memorial",
"regional_games",
"fifa_u_17_world_cup",
"football_gold_cup",
"beer_capital",
"music_fest",
]
),
),
dict(time_varying_known_reals=["time_idx", "price_regular", "discount_in_percent"]),
dict(time_varying_unknown_reals=["volume", "log_volume", "industry_volume", "soda_volume", "avg_max_temp"]),
dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], log_scale=True, scale_by_group=True, log_zero_value=1.0
)
),
dict(target_normalizer=EncoderNormalizer(), min_encoder_length=2),
dict(randomize_length=True, min_encoder_length=2, min_prediction_length=1),
dict(predict_mode=True),
dict(add_target_scales=True),
dict(add_encoder_length=True),
dict(add_encoder_length=True),
dict(add_relative_time_idx=True),
dict(weight="volume"),
dict(
scalers=dict(time_idx=GroupNormalizer(), price_regular=StandardScaler()),
categorical_encoders=dict(month=NaNLabelEncoder()),
time_varying_known_categoricals=["month"],
time_varying_known_reals=["time_idx", "price_regular"],
"fifa_u_17_world_cup",
"football_gold_cup",
"beer_capital",
"music_fest",
]),
),
dict(time_varying_known_reals=[
"time_idx", "price_regular", "discount_in_percent"
]),
dict(time_varying_unknown_reals=[
"volume", "log_volume", "industry_volume", "soda_volume",
"avg_max_temp"
]),
dict(target_normalizer=GroupNormalizer(
groups=["agency", "sku"],
transformation="log1p",
scale_by_group=True,
)),
dict(target_normalizer=EncoderNormalizer(), min_encoder_length=2),
dict(randomize_length=True,
min_encoder_length=2,
min_prediction_length=1),
dict(predict_mode=True),
dict(add_target_scales=True),
dict(add_encoder_length=True),
dict(add_encoder_length=True),
dict(add_relative_time_idx=True),
dict(weight="volume"),
dict(
scalers=dict(time_idx=GroupNormalizer(),
price_regular=StandardScaler()),
time_varying_known_reals=[
"time_idx", "price_regular", "price_actual", "discount",
"discount_in_percent"
],
time_varying_unknown_categoricals=[],
time_varying_unknown_reals=[
"volume", "log_volume", "industry_volume", "soda_volume",
"avg_max_temp"
],
constant_fill_strategy={"volume": 0},
dropout_categoricals=["sku"],
),
dict(static_categoricals=["agency", "sku"]),
dict(randomize_length=True, min_encoder_length=2),
dict(target_normalizer=EncoderNormalizer(), min_encoder_length=2),
dict(target_normalizer=GroupNormalizer(log_scale=True)),
dict(target_normalizer=GroupNormalizer(groups=["agency", "sku"],
coerce_positive=1.0)),
dict(target="agency"),
])
def multiple_dataloaders_with_covariates(data_with_covariates, request):
training_cutoff = "2016-09-01"
max_encoder_length = 36
max_prediction_length = 6
params = request.param
params.setdefault("target", "volume")
training = TimeSeriesDataSet(
data_with_covariates[lambda x: x.date < training_cutoff],
time_idx="time_idx",
"independence_day",
"revolution_day_memorial",
"regional_games",
"fifa_u_17_world_cup",
"football_gold_cup",
"beer_capital",
"music_fest",
]
),
),
dict(time_varying_known_reals=["time_idx", "price_regular", "discount_in_percent"]),
dict(time_varying_unknown_reals=["volume", "log_volume", "industry_volume", "soda_volume", "avg_max_temp"]),
dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"],
transformation="log1p",
scale_by_group=True,
)
),
dict(target_normalizer=EncoderNormalizer(), min_encoder_length=2),
dict(randomize_length=True, min_encoder_length=2, min_prediction_length=1),
dict(predict_mode=True),
dict(add_target_scales=True),
dict(add_encoder_length=True),
dict(add_encoder_length=True),
dict(add_relative_time_idx=True),
dict(weight="volume"),
dict(
scalers=dict(time_idx=GroupNormalizer(), price_regular=StandardScaler()),
categorical_encoders=dict(month=NaNLabelEncoder()),
time_varying_known_categoricals=["month"],
"regional_games",
"fifa_u_17_world_cup",
"football_gold_cup",
"beer_capital",
"music_fest",
]),
),
dict(time_varying_known_reals=[
"time_idx", "price_regular", "discount_in_percent"
]),
dict(time_varying_unknown_reals=[
"volume", "log_volume", "industry_volume", "soda_volume",
"avg_max_temp"
]),
dict(target_normalizer=GroupNormalizer(groups=["agency", "sku"],
log_scale=True,
scale_by_group=True,
log_zero_value=1.0)),
dict(target_normalizer=EncoderNormalizer(), min_encoder_length=2),
dict(randomize_length=True,
min_encoder_length=2,
min_prediction_length=1),
dict(predict_mode=True),
dict(add_target_scales=True),
dict(add_encoder_length=True),
dict(add_encoder_length=True),
dict(add_relative_time_idx=True),
dict(weight="volume"),
dict(
scalers=dict(time_idx=GroupNormalizer(),
price_regular=StandardScaler()),
categorical_encoders=dict(month=NaNLabelEncoder()),
variable_groups={"special_days": special_days},
time_varying_known_reals=[
"time_idx", "price_regular", "discount_in_percent"
],
time_varying_unknown_categoricals=[],
time_varying_unknown_reals=[
"volume",
"log_volume",
"industry_volume",
"soda_volume",
"avg_max_temp",
"avg_volume_by_agency",
"avg_volume_by_sku",
],
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], coerce_positive=1.0
), # use softplus with beta=1.0 and normalize by group
add_relative_time_idx=True, # add as feature
add_target_scales=True, # add as feature
add_encoder_length=True, # add as feature
)
# create validation set (predict=True) which means to predict the
# last max_prediction_length points in time for each series
validation = TimeSeriesDataSet.from_dataset(training,
data,
predict=True,
stop_randomization=True)
# create dataloaders for model
batch_size = 128
train_dataloader = training.to_dataloader(train=True,
