diff --git a/src/gluonts/core/component.py b/src/gluonts/core/component.py
index 8fcdc07f8f..c5f18d011a 100644
--- a/src/gluonts/core/component.py
+++ b/src/gluonts/core/component.py
@@ -296,12 +296,16 @@ def validator(init):
init_params = inspect.signature(init).parameters
init_fields = {
param.name: (
- param.annotation
- if param.annotation != inspect.Parameter.empty
- else Any,
- param.default
- if param.default != inspect.Parameter.empty
- else ...,
+ (
+ param.annotation
+ if param.annotation != inspect.Parameter.empty
+ else Any
+ ),
+ (
+ param.default
+ if param.default != inspect.Parameter.empty
+ else ...
+ ),
)
for param in init_params.values()
if param.name != "self"
diff --git a/src/gluonts/dataset/arrow/file.py b/src/gluonts/dataset/arrow/file.py
index 40f1a06638..7bdb6cf898 100644
--- a/src/gluonts/dataset/arrow/file.py
+++ b/src/gluonts/dataset/arrow/file.py
@@ -51,16 +51,13 @@ def infer(
return ArrowStreamFile(path)
@abc.abstractmethod
- def metadata(self) -> Dict[str, str]:
- ...
+ def metadata(self) -> Dict[str, str]: ...
@abc.abstractmethod
- def __iter__(self):
- ...
+ def __iter__(self): ...
@abc.abstractmethod
- def __len__(self):
- ...
+ def __len__(self): ...
@dataclass
diff --git a/src/gluonts/dataset/repository/_lstnet.py b/src/gluonts/dataset/repository/_lstnet.py
index ba46cf03ae..e933666c77 100644
--- a/src/gluonts/dataset/repository/_lstnet.py
+++ b/src/gluonts/dataset/repository/_lstnet.py
@@ -200,9 +200,9 @@ def generate_lstnet_dataset(
meta = MetaData(
**metadata(
cardinality=ds_info.num_series,
- freq=ds_info.freq
- if ds_info.agg_freq is None
- else ds_info.agg_freq,
+ freq=(
+ ds_info.freq if ds_info.agg_freq is None else ds_info.agg_freq
+ ),
prediction_length=prediction_length or ds_info.prediction_length,
)
)
diff --git a/src/gluonts/dataset/stat.py b/src/gluonts/dataset/stat.py
index 0becdb4bc4..c3b9a39dfb 100644
--- a/src/gluonts/dataset/stat.py
+++ b/src/gluonts/dataset/stat.py
@@ -418,12 +418,12 @@ def calculate_dataset_statistics(ts_dataset: Any) -> DatasetStatistics:
max_target_length=max_target_length,
min_target=min_target,
num_missing_values=num_missing_values,
- feat_static_real=observed_feat_static_real
- if observed_feat_static_real
- else [],
- feat_static_cat=observed_feat_static_cat
- if observed_feat_static_cat
- else [],
+ feat_static_real=(
+ observed_feat_static_real if observed_feat_static_real else []
+ ),
+ feat_static_cat=(
+ observed_feat_static_cat if observed_feat_static_cat else []
+ ),
num_past_feat_dynamic_real=num_past_feat_dynamic_real,
num_feat_dynamic_real=num_feat_dynamic_real,
num_feat_dynamic_cat=num_feat_dynamic_cat,
diff --git a/src/gluonts/evaluation/_base.py b/src/gluonts/evaluation/_base.py
index f913c7d8fd..b623bf3d75 100644
--- a/src/gluonts/evaluation/_base.py
+++ b/src/gluonts/evaluation/_base.py
@@ -378,9 +378,9 @@ def get_base_metrics(
return {
"item_id": forecast.item_id,
"forecast_start": forecast.start_date,
- "MSE": mse(pred_target, mean_fcst)
- if mean_fcst is not None
- else None,
+ "MSE": (
+ mse(pred_target, mean_fcst) if mean_fcst is not None else None
+ ),
"abs_error": abs_error(pred_target, median_fcst),
"abs_target_sum": abs_target_sum(pred_target),
"abs_target_mean": abs_target_mean(pred_target),
diff --git a/src/gluonts/ext/rotbaum/_model.py b/src/gluonts/ext/rotbaum/_model.py
index 29bd1e3cf7..3c0270a49a 100644
--- a/src/gluonts/ext/rotbaum/_model.py
+++ b/src/gluonts/ext/rotbaum/_model.py
@@ -340,11 +340,11 @@ def _get_and_cache_quantile_computation(
The quantile of the associated true value bin.
"""
if feature_vector_in_train not in self.quantile_dicts[quantile]:
- self.quantile_dicts[quantile][
- feature_vector_in_train
- ] = np.percentile(
- self.id_to_bins[self.preds_to_id[feature_vector_in_train]],
- quantile * 100,
+ self.quantile_dicts[quantile][feature_vector_in_train] = (
+ np.percentile(
+ self.id_to_bins[self.preds_to_id[feature_vector_in_train]],
+ quantile * 100,
+ )
)
return self.quantile_dicts[quantile][feature_vector_in_train]
diff --git a/src/gluonts/ext/rotbaum/_predictor.py b/src/gluonts/ext/rotbaum/_predictor.py
index 63daea1a97..6631e8dde0 100644
--- a/src/gluonts/ext/rotbaum/_predictor.py
+++ b/src/gluonts/ext/rotbaum/_predictor.py
@@ -254,13 +254,17 @@ def train(
target_data[:, train_QRX_only_using_timestep],
)
self.model_list = [
- QRX(
- xgboost_params=self.model_params,
- min_bin_size=self.min_bin_size,
- model=self.model_list[train_QRX_only_using_timestep].model,
+ (
+ QRX(
+ xgboost_params=self.model_params,
+ min_bin_size=self.min_bin_size,
+ model=self.model_list[
+ train_QRX_only_using_timestep
+ ].model,
+ )
+ if i != train_QRX_only_using_timestep
+ else self.model_list[i]
)
- if i != train_QRX_only_using_timestep
- else self.model_list[i]
for i in range(n_models)
]
with concurrent.futures.ThreadPoolExecutor(
diff --git a/src/gluonts/itertools.py b/src/gluonts/itertools.py
index e90281a1d8..198559d6f6 100644
--- a/src/gluonts/itertools.py
+++ b/src/gluonts/itertools.py
@@ -42,11 +42,9 @@
@runtime_checkable
class SizedIterable(Protocol):
- def __len__(self):
- ...
+ def __len__(self): ...
- def __iter__(self):
- ...
+ def __iter__(self): ...
T = TypeVar("T")
diff --git a/src/gluonts/model/forecast_generator.py b/src/gluonts/model/forecast_generator.py
index ac33aae158..d66a1d361d 100644
--- a/src/gluonts/model/forecast_generator.py
+++ b/src/gluonts/model/forecast_generator.py
@@ -132,9 +132,11 @@ def __call__(
yield QuantileForecast(
output.T,
start_date=batch[FieldName.FORECAST_START][i],
- item_id=batch[FieldName.ITEM_ID][i]
- if FieldName.ITEM_ID in batch
- else None,
+ item_id=(
+ batch[FieldName.ITEM_ID][i]
+ if FieldName.ITEM_ID in batch
+ else None
+ ),
info=batch["info"][i] if "info" in batch else None,
forecast_keys=self.quantiles,
)
@@ -181,9 +183,11 @@ def __call__(
yield SampleForecast(
output,
start_date=batch[FieldName.FORECAST_START][i],
- item_id=batch[FieldName.ITEM_ID][i]
- if FieldName.ITEM_ID in batch
- else None,
+ item_id=(
+ batch[FieldName.ITEM_ID][i]
+ if FieldName.ITEM_ID in batch
+ else None
+ ),
info=batch["info"][i] if "info" in batch else None,
)
assert i + 1 == len(batch[FieldName.FORECAST_START])
@@ -221,9 +225,11 @@ def __call__(
yield make_distribution_forecast(
distr,
start_date=batch[FieldName.FORECAST_START][i],
- item_id=batch[FieldName.ITEM_ID][i]
- if FieldName.ITEM_ID in batch
- else None,
+ item_id=(
+ batch[FieldName.ITEM_ID][i]
+ if FieldName.ITEM_ID in batch
+ else None
+ ),
info=batch["info"][i] if "info" in batch else None,
)
assert i + 1 == len(batch[FieldName.FORECAST_START])
diff --git a/src/gluonts/mx/batchify.py b/src/gluonts/mx/batchify.py
index ea1de2adb0..b534b7f523 100644
--- a/src/gluonts/mx/batchify.py
+++ b/src/gluonts/mx/batchify.py
@@ -117,13 +117,16 @@ def as_in_context(batch: dict, ctx: mx.Context = None) -> DataBatch:
Move data into new context, should only be in main process.
"""
batch = {
- k: v.as_in_context(ctx) if isinstance(v, mx.nd.NDArray)
- # Workaround due to MXNet not being able to handle NDArrays with 0 in
- # shape properly:
- else (
- stack(v, ctx=ctx, dtype=v.dtype, variable_length=False)
- if isinstance(v[0], np.ndarray) and 0 in v[0].shape
- else v
+ k: (
+ v.as_in_context(ctx)
+ if isinstance(v, mx.nd.NDArray)
+ # Workaround due to MXNet not being able to handle NDArrays with 0 in
+ # shape properly:
+ else (
+ stack(v, ctx=ctx, dtype=v.dtype, variable_length=False)
+ if isinstance(v[0], np.ndarray) and 0 in v[0].shape
+ else v
+ )
)
for k, v in batch.items()
}
diff --git a/src/gluonts/mx/block/dropout.py b/src/gluonts/mx/block/dropout.py
index e1de5e22e0..2522820a2c 100644
--- a/src/gluonts/mx/block/dropout.py
+++ b/src/gluonts/mx/block/dropout.py
@@ -231,9 +231,11 @@ def mask(p, like):
# mask as output, instead of simply copy output to the first element
# in case that the base cell is ResidualCell
new_states = [
- F.where(output_mask, next_states[0], states[0])
- if p_outputs != 0.0
- else next_states[0]
+ (
+ F.where(output_mask, next_states[0], states[0])
+ if p_outputs != 0.0
+ else next_states[0]
+ )
]
new_states.extend(
[
diff --git a/src/gluonts/mx/distribution/box_cox_transform.py b/src/gluonts/mx/distribution/box_cox_transform.py
index 46d9afe87a..99125a032a 100644
--- a/src/gluonts/mx/distribution/box_cox_transform.py
+++ b/src/gluonts/mx/distribution/box_cox_transform.py
@@ -116,6 +116,7 @@ class BoxCoxTransform(Bijection):
`tol_lambda_1`
F
"""
+
arg_names = ["box_cox.lambda_1", "box_cox.lambda_2"]
@validated()
diff --git a/src/gluonts/mx/distribution/inflated_beta.py b/src/gluonts/mx/distribution/inflated_beta.py
index 345126a291..93c0781893 100644
--- a/src/gluonts/mx/distribution/inflated_beta.py
+++ b/src/gluonts/mx/distribution/inflated_beta.py
@@ -113,6 +113,7 @@ class ZeroInflatedBeta(ZeroAndOneInflatedBeta):
`(*batch_shape, *event_shape)`.
F
"""
+
is_reparameterizable = False
@validated()
@@ -145,6 +146,7 @@ class OneInflatedBeta(ZeroAndOneInflatedBeta):
`(*batch_shape, *event_shape)`.
F
"""
+
is_reparameterizable = False
@validated()
diff --git a/src/gluonts/mx/distribution/lds.py b/src/gluonts/mx/distribution/lds.py
index 6167a00d1e..31d3ae225a 100644
--- a/src/gluonts/mx/distribution/lds.py
+++ b/src/gluonts/mx/distribution/lds.py
@@ -409,9 +409,11 @@ def sample(
# (num_samples, batch_size, latent_dim, latent_dim)
# innovation_coeff_t: (num_samples, batch_size, 1, latent_dim)
emission_coeff_t, transition_coeff_t, innovation_coeff_t = (
- _broadcast_param(coeff, axes=[0], sizes=[num_samples])
- if num_samples is not None
- else coeff
+ (
+ _broadcast_param(coeff, axes=[0], sizes=[num_samples])
+ if num_samples is not None
+ else coeff
+ )
for coeff in [
self.emission_coeff[t],
self.transition_coeff[t],
@@ -458,9 +460,11 @@ def sample(
if scale is None
else F.broadcast_mul(
samples,
- scale.expand_dims(axis=1).expand_dims(axis=0)
- if num_samples is not None
- else scale.expand_dims(axis=1),
+ (
+ scale.expand_dims(axis=1).expand_dims(axis=0)
+ if num_samples is not None
+ else scale.expand_dims(axis=1)
+ ),
)
)
diff --git a/src/gluonts/mx/distribution/lowrank_gp.py b/src/gluonts/mx/distribution/lowrank_gp.py
index 443cc40acb..2e6de964a8 100644
--- a/src/gluonts/mx/distribution/lowrank_gp.py
+++ b/src/gluonts/mx/distribution/lowrank_gp.py
@@ -101,9 +101,7 @@ def hybrid_forward(self, F, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
D_vector = self.proj[1](x_plus_w).squeeze(axis=-1)
d_bias = (
- 0.0
- if self.sigma_init == 0.0
- else inv_softplus(self.sigma_init**2)
+ 0.0 if self.sigma_init == 0.0 else inv_softplus(self.sigma_init**2)
)
D_positive = (
diff --git a/src/gluonts/mx/distribution/lowrank_multivariate_gaussian.py b/src/gluonts/mx/distribution/lowrank_multivariate_gaussian.py
index b8131178a5..f446799367 100644
--- a/src/gluonts/mx/distribution/lowrank_multivariate_gaussian.py
+++ b/src/gluonts/mx/distribution/lowrank_multivariate_gaussian.py
@@ -408,9 +408,7 @@ def domain_map(self, F, mu_vector, D_vector, W_vector=None):
"""
d_bias = (
- inv_softplus(self.sigma_init**2)
- if self.sigma_init > 0.0
- else 0.0
+ inv_softplus(self.sigma_init**2) if self.sigma_init > 0.0 else 0.0
)
# sigma_minimum helps avoiding cholesky problems, we could also jitter
diff --git a/src/gluonts/mx/model/deepar/_network.py b/src/gluonts/mx/model/deepar/_network.py
index f59bc517f9..2b2049afd2 100644
--- a/src/gluonts/mx/model/deepar/_network.py
+++ b/src/gluonts/mx/model/deepar/_network.py
@@ -572,9 +572,11 @@ def unroll_encoder_imputation(
static_feat = F.concat(
embedded_cat,
feat_static_real,
- F.log(scale)
- if len(self.target_shape) == 0
- else F.log(scale.squeeze(axis=1)),
+ (
+ F.log(scale)
+ if len(self.target_shape) == 0
+ else F.log(scale.squeeze(axis=1))
+ ),
dim=1,
)
@@ -603,9 +605,9 @@ def unroll_encoder_imputation(
begin_state = self.rnn.begin_state(
func=F.zeros,
dtype=self.dtype,
- batch_size=inputs.shape[0]
- if isinstance(inputs, mx.nd.NDArray)
- else 0,
+ batch_size=(
+ inputs.shape[0] if isinstance(inputs, mx.nd.NDArray) else 0
+ ),
)
unroll_results = self.imputation_rnn_unroll(
@@ -726,9 +728,11 @@ def unroll_encoder_default(
static_feat = F.concat(
embedded_cat,
feat_static_real,
- F.log(scale)
- if len(self.target_shape) == 0
- else F.log(scale.squeeze(axis=1)),
+ (
+ F.log(scale)
+ if len(self.target_shape) == 0
+ else F.log(scale.squeeze(axis=1))
+ ),
dim=1,
)
@@ -757,9 +761,9 @@ def unroll_encoder_default(
begin_state = self.rnn.begin_state(
func=F.zeros,
dtype=self.dtype,
- batch_size=inputs.shape[0]
- if isinstance(inputs, mx.nd.NDArray)
- else 0,
+ batch_size=(
+ inputs.shape[0] if isinstance(inputs, mx.nd.NDArray) else 0
+ ),
)
state = begin_state
# This is a dummy computation to avoid deferred initialization error
diff --git a/src/gluonts/mx/model/deepvar/_estimator.py b/src/gluonts/mx/model/deepvar/_estimator.py
index 0a1828369d..9881aa5df9 100644
--- a/src/gluonts/mx/model/deepvar/_estimator.py
+++ b/src/gluonts/mx/model/deepvar/_estimator.py
@@ -304,9 +304,9 @@ def __init__(
self.scaling = scaling
if self.use_marginal_transformation:
- self.output_transform: Optional[
- Callable
- ] = cdf_to_gaussian_forward_transform
+ self.output_transform: Optional[Callable] = (
+ cdf_to_gaussian_forward_transform
+ )
else:
self.output_transform = None
diff --git a/src/gluonts/mx/model/estimator.py b/src/gluonts/mx/model/estimator.py
index 1cf992a796..122e34fad3 100644
--- a/src/gluonts/mx/model/estimator.py
+++ b/src/gluonts/mx/model/estimator.py
@@ -177,9 +177,9 @@ def train_model(
transformation = self.create_transformation()
with env._let(max_idle_transforms=max(len(training_data), 100)):
- transformed_training_data: Union[
- TransformedDataset, Cached
- ] = transformation.apply(training_data)
+ transformed_training_data: Union[TransformedDataset, Cached] = (
+ transformation.apply(training_data)
+ )
if cache_data:
transformed_training_data = Cached(transformed_training_data)
diff --git a/src/gluonts/mx/model/gpvar/_estimator.py b/src/gluonts/mx/model/gpvar/_estimator.py
index 58edcf3982..29aea6ac31 100644
--- a/src/gluonts/mx/model/gpvar/_estimator.py
+++ b/src/gluonts/mx/model/gpvar/_estimator.py
@@ -63,7 +63,6 @@
class GPVAREstimator(GluonEstimator):
-
"""
Constructs a GPVAR estimator.
diff --git a/src/gluonts/mx/model/gpvar/_network.py b/src/gluonts/mx/model/gpvar/_network.py
index 03691aeeec..1a8a60a8b8 100644
--- a/src/gluonts/mx/model/gpvar/_network.py
+++ b/src/gluonts/mx/model/gpvar/_network.py
@@ -116,9 +116,9 @@ def unroll(
length=unroll_length,
layout="NTC",
merge_outputs=True,
- begin_state=begin_state[i]
- if begin_state is not None
- else None,
+ begin_state=(
+ begin_state[i] if begin_state is not None else None
+ ),
)
outputs.append(outputs_single_dim)
states.append(state)
diff --git a/src/gluonts/mx/model/n_beats/_ensemble.py b/src/gluonts/mx/model/n_beats/_ensemble.py
index f1f8f8eb81..4351163251 100644
--- a/src/gluonts/mx/model/n_beats/_ensemble.py
+++ b/src/gluonts/mx/model/n_beats/_ensemble.py
@@ -201,9 +201,11 @@ def predict(
yield SampleForecast(
output,
start_date=start_date,
- item_id=item[FieldName.ITEM_ID]
- if FieldName.ITEM_ID in item
- else None,
+ item_id=(
+ item[FieldName.ITEM_ID]
+ if FieldName.ITEM_ID in item
+ else None
+ ),
info=item["info"] if "info" in item else None,
)
diff --git a/src/gluonts/mx/model/renewal/_network.py b/src/gluonts/mx/model/renewal/_network.py
index aa83273506..d0d7bc0edd 100644
--- a/src/gluonts/mx/model/renewal/_network.py
+++ b/src/gluonts/mx/model/renewal/_network.py
@@ -89,9 +89,11 @@ def distribution(
cond_interval, cond_size = F.split(cond_mean, num_outputs=2, axis=-1)
alpha_biases = [
- F.broadcast_mul(F.ones_like(cond_interval), bias)
- if bias is not None
- else None
+ (
+ F.broadcast_mul(F.ones_like(cond_interval), bias)
+ if bias is not None
+ else None
+ )
for bias in [interval_alpha_bias, size_alpha_bias]
]
diff --git a/src/gluonts/mx/model/renewal/_transform.py b/src/gluonts/mx/model/renewal/_transform.py
index 479c6a7f83..884ec31051 100644
--- a/src/gluonts/mx/model/renewal/_transform.py
+++ b/src/gluonts/mx/model/renewal/_transform.py
@@ -53,9 +53,11 @@ def transform(self, data: DataEntry) -> DataEntry:
target = data[self.target_field]
data[self.output_field] = np.array(
[
- len(target)
- if isinstance(target, list)
- else target.shape[self.axis]
+ (
+ len(target)
+ if isinstance(target, list)
+ else target.shape[self.axis]
+ )
]
)
return data
diff --git a/src/gluonts/mx/model/seq2seq/_transform.py b/src/gluonts/mx/model/seq2seq/_transform.py
index 4134e8b01b..7c443e82d7 100644
--- a/src/gluonts/mx/model/seq2seq/_transform.py
+++ b/src/gluonts/mx/model/seq2seq/_transform.py
@@ -180,21 +180,21 @@ def flatmap_transform(
# (Fortran) ordering with strides =
# (dtype, dtype*n_rows)
stride = decoder_fields.strides
- out[self._future(ts_field)][
- pad_length_dec:
- ] = as_strided(
- decoder_fields,
- shape=(
- self.num_forking - pad_length_dec,
- self.dec_len,
- ts_len,
- ),
- # strides for 2D array expanded to 3D array of
- # shape (dim1, dim2, dim3) =(1, n_rows, n_cols).
- # For transposed data, strides = (dtype, dtype *
- # dim1, dtype*dim1*dim2) = (dtype, dtype,
- # dtype*n_rows).
- strides=stride[0:1] + stride,
+ out[self._future(ts_field)][pad_length_dec:] = (
+ as_strided(
+ decoder_fields,
+ shape=(
+ self.num_forking - pad_length_dec,
+ self.dec_len,
+ ts_len,
+ ),
+ # strides for 2D array expanded to 3D array of
+ # shape (dim1, dim2, dim3) =(1, n_rows, n_cols).
+ # For transposed data, strides = (dtype, dtype *
+ # dim1, dtype*dim1*dim2) = (dtype, dtype,
+ # dtype*n_rows).
+ strides=stride[0:1] + stride,
+ )
)
# edge case for prediction_length = 1
diff --git a/src/gluonts/mx/model/tft/_estimator.py b/src/gluonts/mx/model/tft/_estimator.py
index db19544603..a4d8335719 100644
--- a/src/gluonts/mx/model/tft/_estimator.py
+++ b/src/gluonts/mx/model/tft/_estimator.py
@@ -172,13 +172,13 @@ def __init__(
self.past_dynamic_feature_dims = {}
for name in self.past_dynamic_features:
if name in self.dynamic_cardinalities:
- self.past_dynamic_cardinalities[
- name
- ] = self.dynamic_cardinalities.pop(name)
+ self.past_dynamic_cardinalities[name] = (
+ self.dynamic_cardinalities.pop(name)
+ )
elif name in self.dynamic_feature_dims:
- self.past_dynamic_feature_dims[
- name
- ] = self.dynamic_feature_dims.pop(name)
+ self.past_dynamic_feature_dims[name] = (
+ self.dynamic_feature_dims.pop(name)
+ )
else:
raise ValueError(
f"Feature name {name} is not provided in feature dicts"
diff --git a/src/gluonts/mx/model/tpp/distribution/base.py b/src/gluonts/mx/model/tpp/distribution/base.py
index a4d93c88aa..990d57d880 100644
--- a/src/gluonts/mx/model/tpp/distribution/base.py
+++ b/src/gluonts/mx/model/tpp/distribution/base.py
@@ -187,6 +187,7 @@ class TPPDistributionOutput(DistributionOutput):
1. Location param cannot be specified (all distributions must start at 0).
2. The return type is either TPPDistribution or TPPTransformedDistribution.
"""
+
distr_cls: type
def distribution(
diff --git a/src/gluonts/mx/model/tpp/distribution/weibull.py b/src/gluonts/mx/model/tpp/distribution/weibull.py
index 3055d47c68..09b73b51f6 100644
--- a/src/gluonts/mx/model/tpp/distribution/weibull.py
+++ b/src/gluonts/mx/model/tpp/distribution/weibull.py
@@ -34,6 +34,7 @@ class Weibull(TPPDistribution):
parameter :math:`\lambda > 0` and the shape parameter :math:`k > 0`, and
:math:`\lambda = b^{-1/k}`.
"""
+
is_reparametrizable = True
@validated()
diff --git a/src/gluonts/mx/model/tpp/predictor.py b/src/gluonts/mx/model/tpp/predictor.py
index cb706931e1..159427651e 100644
--- a/src/gluonts/mx/model/tpp/predictor.py
+++ b/src/gluonts/mx/model/tpp/predictor.py
@@ -86,9 +86,9 @@ def __call__( # type: ignore
start_date=batch["forecast_start"][i],
freq=freq,
prediction_interval_length=prediction_net.prediction_interval_length, # noqa: E501
- item_id=batch["item_id"][i]
- if "item_id" in batch
- else None,
+ item_id=(
+ batch["item_id"][i] if "item_id" in batch else None
+ ),
info=batch["info"][i] if "info" in batch else None,
)
diff --git a/src/gluonts/mx/model/transformer/_network.py b/src/gluonts/mx/model/transformer/_network.py
index 5e3082e968..245acd0c57 100644
--- a/src/gluonts/mx/model/transformer/_network.py
+++ b/src/gluonts/mx/model/transformer/_network.py
@@ -194,9 +194,11 @@ def create_network_input(
# prediction too(batch_size, num_features + prod(target_shape))
static_feat = F.concat(
embedded_cat,
- F.log(scale)
- if len(self.target_shape) == 0
- else F.log(scale.squeeze(axis=1)),
+ (
+ F.log(scale)
+ if len(self.target_shape) == 0
+ else F.log(scale.squeeze(axis=1))
+ ),
dim=1,
)
diff --git a/src/gluonts/mx/model/wavenet/_estimator.py b/src/gluonts/mx/model/wavenet/_estimator.py
index 28fca9d2cc..293676af6b 100644
--- a/src/gluonts/mx/model/wavenet/_estimator.py
+++ b/src/gluonts/mx/model/wavenet/_estimator.py
@@ -309,9 +309,11 @@ def _create_instance_splitter(self, mode: str):
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=instance_sampler,
past_length=self.context_length,
- future_length=self.prediction_length
- if mode == "test"
- else self.train_window_length,
+ future_length=(
+ self.prediction_length
+ if mode == "test"
+ else self.train_window_length
+ ),
output_NTC=False,
time_series_fields=[
FieldName.FEAT_TIME,
diff --git a/src/gluonts/nursery/SCott/dataset_tools/synthetic.py b/src/gluonts/nursery/SCott/dataset_tools/synthetic.py
index a16034b83c..8e2dc383b6 100644
--- a/src/gluonts/nursery/SCott/dataset_tools/synthetic.py
+++ b/src/gluonts/nursery/SCott/dataset_tools/synthetic.py
@@ -57,12 +57,12 @@ def get_mixed_pattern(unit_length=16, num_duplicates=1000):
for j in range(num_duplicates):
context = torch.arange(context_length, dtype=torch.float)
for i in range(1, pattern_number):
- context[
- unit_length * (i - 1) : unit_length * i
- ] = _get_mixed_pattern(
- context[unit_length * (i - 1) : unit_length * i]
- - unit_length * (i - 1),
- pattern[(gid + i) % pattern_number],
+ context[unit_length * (i - 1) : unit_length * i] = (
+ _get_mixed_pattern(
+ context[unit_length * (i - 1) : unit_length * i]
+ - unit_length * (i - 1),
+ pattern[(gid + i) % pattern_number],
+ )
)
ts_sample = torch.cat(
[
diff --git a/src/gluonts/nursery/SCott/preprocess_data.py b/src/gluonts/nursery/SCott/preprocess_data.py
index 4f247eb229..40b181820f 100644
--- a/src/gluonts/nursery/SCott/preprocess_data.py
+++ b/src/gluonts/nursery/SCott/preprocess_data.py
@@ -57,12 +57,12 @@ def get_mixed_pattern(unit_length=16, num_duplicates=1000):
for j in range(num_duplicates):
context = torch.arange(context_length, dtype=torch.float)
for i in range(1, pattern_number):
- context[
- unit_length * (i - 1) : unit_length * i
- ] = _get_mixed_pattern(
- context[unit_length * (i - 1) : unit_length * i]
- - unit_length * (i - 1),
- pattern[(gid + i) % pattern_number],
+ context[unit_length * (i - 1) : unit_length * i] = (
+ _get_mixed_pattern(
+ context[unit_length * (i - 1) : unit_length * i]
+ - unit_length * (i - 1),
+ pattern[(gid + i) % pattern_number],
+ )
)
ts_sample = torch.cat(
[
diff --git a/src/gluonts/nursery/daf/network/kernel.py b/src/gluonts/nursery/daf/network/kernel.py
index 3a26e78595..9dccdd3e3b 100644
--- a/src/gluonts/nursery/daf/network/kernel.py
+++ b/src/gluonts/nursery/daf/network/kernel.py
@@ -105,17 +105,21 @@ def __init__(
_query_weight = nn.Parameter(Tensor(d_hidden, d_hidden))
self._query_weights.append(_query_weight)
self._key_weights.append(
- _query_weight
- if self.symmetric
- else nn.Parameter(Tensor(d_hidden, d_hidden)),
+ (
+ _query_weight
+ if self.symmetric
+ else nn.Parameter(Tensor(d_hidden, d_hidden))
+ ),
)
if self.bias:
_query_bias = nn.Parameter(Tensor(d_hidden))
self._query_biases.append(_query_bias)
self._key_biases.append(
- _query_bias
- if self.symmetric
- else nn.Parameter(Tensor(d_hidden)),
+ (
+ _query_bias
+ if self.symmetric
+ else nn.Parameter(Tensor(d_hidden))
+ ),
)
else:
self._query_biases.append(None)
diff --git a/src/gluonts/nursery/daf/tslib/dataset/timeseries.py b/src/gluonts/nursery/daf/tslib/dataset/timeseries.py
index 0036ef6acf..4bd63964b8 100644
--- a/src/gluonts/nursery/daf/tslib/dataset/timeseries.py
+++ b/src/gluonts/nursery/daf/tslib/dataset/timeseries.py
@@ -335,12 +335,10 @@ def __len__(self):
return len(self.target)
@overload
- def index_by_timestamp(self, index: pd.Timestamp) -> int:
- ...
+ def index_by_timestamp(self, index: pd.Timestamp) -> int: ...
@overload
- def index_by_timestamp(self, index: List[pd.Timestamp]) -> List[int]:
- ...
+ def index_by_timestamp(self, index: List[pd.Timestamp]) -> List[int]: ...
def index_by_timestamp(self, index):
return pd.Series(
@@ -348,24 +346,19 @@ def index_by_timestamp(self, index):
).loc[index]
@overload
- def __getitem__(self, index: int) -> TimeSeriesInstant:
- ...
+ def __getitem__(self, index: int) -> TimeSeriesInstant: ...
@overload
- def __getitem__(self, index: pd.Timestamp) -> TimeSeriesInstant:
- ...
+ def __getitem__(self, index: pd.Timestamp) -> TimeSeriesInstant: ...
@overload
- def __getitem__(self, index: slice) -> TimeSeries:
- ...
+ def __getitem__(self, index: slice) -> TimeSeries: ...
@overload
- def __getitem__(self, index: List[int]) -> TimeSeries:
- ...
+ def __getitem__(self, index: List[int]) -> TimeSeries: ...
@overload
- def __getitem__(self, index: List[pd.Timestamp]) -> TimeSeries:
- ...
+ def __getitem__(self, index: List[pd.Timestamp]) -> TimeSeries: ...
def __getitem__(self, index):
if isinstance(index, pd.Timestamp) or (
diff --git a/src/gluonts/nursery/daf/tslib/engine/evaluator.py b/src/gluonts/nursery/daf/tslib/engine/evaluator.py
index 636f6e6b0d..f61c5a16fe 100644
--- a/src/gluonts/nursery/daf/tslib/engine/evaluator.py
+++ b/src/gluonts/nursery/daf/tslib/engine/evaluator.py
@@ -63,9 +63,11 @@ def load(self, tag: str):
path = self.log_dir.joinpath(f"{tag}.pt.tar")
state = pt.load(
path,
- map_location=f"cuda:{self.cuda_device}"
- if self.cuda_device >= 0
- else "cpu",
+ map_location=(
+ f"cuda:{self.cuda_device}"
+ if self.cuda_device >= 0
+ else "cpu"
+ ),
)
print(f"Load checkpoint from {path}")
except FileNotFoundError:
diff --git a/src/gluonts/nursery/daf/tslib/engine/trainer.py b/src/gluonts/nursery/daf/tslib/engine/trainer.py
index 07d7f9f9c4..63ad4484ec 100644
--- a/src/gluonts/nursery/daf/tslib/engine/trainer.py
+++ b/src/gluonts/nursery/daf/tslib/engine/trainer.py
@@ -143,9 +143,11 @@ def load(
path = self.log_dir.joinpath(f"{tag}.pt.tar")
state = pt.load(
path,
- map_location=f"cuda:{self.cuda_device}"
- if self.cuda_device >= 0
- else "cpu",
+ map_location=(
+ f"cuda:{self.cuda_device}"
+ if self.cuda_device >= 0
+ else "cpu"
+ ),
)
print(f"Load checkpoint from {path}")
except FileNotFoundError:
diff --git a/src/gluonts/nursery/few_shot_prediction/src/meta/data/batch.py b/src/gluonts/nursery/few_shot_prediction/src/meta/data/batch.py
index 9c9ff7f227..6512d7f5df 100644
--- a/src/gluonts/nursery/few_shot_prediction/src/meta/data/batch.py
+++ b/src/gluonts/nursery/few_shot_prediction/src/meta/data/batch.py
@@ -29,12 +29,14 @@ class SeriesBatch:
and the splits sizes indicating the corresponding base dataset.
"""
- sequences: torch.Tensor # shape [batch, num_sequences, max_sequence_length]
+ sequences: (
+ torch.Tensor
+ ) # shape [batch, num_sequences, max_sequence_length]
lengths: torch.Tensor # shape [batch]
split_sections: torch.Tensor # shape [batch]
- scales: Optional[
- torch.Tensor
- ] = None # shape[batch, 2] contains mean and std the ts has been scaled with
+ scales: Optional[torch.Tensor] = (
+ None # shape[batch, 2] contains mean and std the ts has been scaled with
+ )
@classmethod
def from_lists(
@@ -61,9 +63,11 @@ def from_lists(
pad_sequence(values, batch_first=True),
lengths=torch.as_tensor([len(s) for s in series]),
split_sections=torch.as_tensor(split_sections),
- scales=torch.stack([s.scale for s in series])
- if series[0].scale is not None
- else None,
+ scales=(
+ torch.stack([s.scale for s in series])
+ if series[0].scale is not None
+ else None
+ ),
)
def pin_memory(self):
diff --git a/src/gluonts/nursery/few_shot_prediction/src/meta/data/sampling.py b/src/gluonts/nursery/few_shot_prediction/src/meta/data/sampling.py
index a86ddd348b..9fc089fafe 100644
--- a/src/gluonts/nursery/few_shot_prediction/src/meta/data/sampling.py
+++ b/src/gluonts/nursery/few_shot_prediction/src/meta/data/sampling.py
@@ -170,12 +170,14 @@ def __iter__(self) -> Iterator[Triplet]:
supps_size=self.support_set_size,
length=self.support_length,
dataset=self.dataset,
- cheat_query=cheat_query[0]
- if np.random.rand() < self.cheat
- else None,
- index_iterator=self.index_iterator
- if self.catch22_nn is None
- else iter(self.catch22_nn[query_idx]),
+ cheat_query=(
+ cheat_query[0] if np.random.rand() < self.cheat else None
+ ),
+ index_iterator=(
+ self.index_iterator
+ if self.catch22_nn is None
+ else iter(self.catch22_nn[query_idx])
+ ),
)
yield Triplet(support_set, query_past, query_future)
@@ -311,9 +313,11 @@ def __getitem__(self, index: int) -> Triplet:
# but this will be slower
# seed=self.seed + index if self.seed else None,
cheat_query=cheat_query if np.random.rand() < self.cheat else None,
- index_iterator=self.index_iterator
- if self.catch22_nn is None
- else iter(self.catch22_nn[query_past[0].item_id]),
+ index_iterator=(
+ self.index_iterator
+ if self.catch22_nn is None
+ else iter(self.catch22_nn[query_past[0].item_id])
+ ),
)
return Triplet(support_set, query_past, query_future)
diff --git a/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/artificial.py b/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/artificial.py
index 19e1475c66..5a2dab200b 100644
--- a/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/artificial.py
+++ b/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/artificial.py
@@ -593,9 +593,9 @@ def generate_artificial_tuplets(
np.arange(0, context_length - signal_length)
)
si = np.random.choice(support_set_size)
- support_set[si][
- marker_start : marker_start + signal_length
- ] = query[-signal_length:]
+ support_set[si][marker_start : marker_start + signal_length] = (
+ query[-signal_length:]
+ )
else:
signal = np.concatenate(
(np.ones((4,)), query[-prediction_length:])
@@ -647,9 +647,9 @@ def generate_artificial_tuplets(
np.arange(0, context_length - signal_length)
)
si = np.random.choice(support_set_size)
- support_set[si][
- marker_start : marker_start + signal_length
- ] = query[-signal_length:]
+ support_set[si][marker_start : marker_start + signal_length] = (
+ query[-signal_length:]
+ )
# else:
# signal = query[-prediction_length:]
# signal_length = prediction_length
diff --git a/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/m4.py b/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/m4.py
index 240afc9b2f..a510676208 100644
--- a/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/m4.py
+++ b/src/gluonts/nursery/few_shot_prediction/src/meta/datasets/m4.py
@@ -127,9 +127,11 @@ def generate_m4_dataset(
start_dates = list(meta_df.StartingDate)
start_dates = [
- sd
- if pd.Timestamp(sd) <= pd.Timestamp("2022")
- else str(pd.Timestamp("1900"))
+ (
+ sd
+ if pd.Timestamp(sd) <= pd.Timestamp("2022")
+ else str(pd.Timestamp("1900"))
+ )
for sd in start_dates
]
diff --git a/src/gluonts/nursery/few_shot_prediction/src/meta/models/module.py b/src/gluonts/nursery/few_shot_prediction/src/meta/models/module.py
index 3a4e24080c..c5f7b3ad7f 100644
--- a/src/gluonts/nursery/few_shot_prediction/src/meta/models/module.py
+++ b/src/gluonts/nursery/few_shot_prediction/src/meta/models/module.py
@@ -109,7 +109,7 @@ def configure_optimizers(self) -> Dict:
verbose=True,
),
"monitor": self.lr_scheduler_monitor,
- "frequency": 1
+ "frequency": 1,
# If "monitor" references validation metrics, then "frequency" should be set to a
# multiple of "trainer.check_val_every_n_epoch".
},
diff --git a/src/gluonts/nursery/gmm_tpp/gmm_base.py b/src/gluonts/nursery/gmm_tpp/gmm_base.py
index a2ad97691e..902c05fa31 100644
--- a/src/gluonts/nursery/gmm_tpp/gmm_base.py
+++ b/src/gluonts/nursery/gmm_tpp/gmm_base.py
@@ -54,9 +54,11 @@ def __init__(
"log_prior_",
shape=(num_clusters,),
lr_mult=lr_mult,
- init=mx.init.Constant(np.log(1 / self.num_clusters))
- if log_prior_ is None
- else mx.init.Constant(log_prior_),
+ init=(
+ mx.init.Constant(np.log(1 / self.num_clusters))
+ if log_prior_ is None
+ else mx.init.Constant(log_prior_)
+ ),
)
self.mu_ = self.params.get(
diff --git a/src/gluonts/nursery/robust-mts-attack/attack_and_save.py b/src/gluonts/nursery/robust-mts-attack/attack_and_save.py
index 0f1918bf6f..a3366e3bfc 100644
--- a/src/gluonts/nursery/robust-mts-attack/attack_and_save.py
+++ b/src/gluonts/nursery/robust-mts-attack/attack_and_save.py
@@ -133,9 +133,11 @@ def main():
)
best_perturbation = attack.attack_batch(
batch,
- true_future_target=future_target
- if device == "cpu"
- else torch.from_numpy(future_target).float().to(device),
+ true_future_target=(
+ future_target
+ if device == "cpu"
+ else torch.from_numpy(future_target).float().to(device)
+ ),
)
batch_res = AttackResults(
diff --git a/src/gluonts/nursery/robust-mts-attack/attack_sparse_layer.py b/src/gluonts/nursery/robust-mts-attack/attack_sparse_layer.py
index a1309388cd..739888ee89 100644
--- a/src/gluonts/nursery/robust-mts-attack/attack_sparse_layer.py
+++ b/src/gluonts/nursery/robust-mts-attack/attack_sparse_layer.py
@@ -127,9 +127,11 @@ def main():
)
best_perturbation = attack.attack_batch(
batch,
- true_future_target=future_target
- if device == "cpu"
- else torch.from_numpy(future_target).float().to(device),
+ true_future_target=(
+ future_target
+ if device == "cpu"
+ else torch.from_numpy(future_target).float().to(device)
+ ),
)
batch_res = AttackResults(
diff --git a/src/gluonts/nursery/robust-mts-attack/multivariate/datasets/dataset.py b/src/gluonts/nursery/robust-mts-attack/multivariate/datasets/dataset.py
index a56828f49e..768fe06070 100644
--- a/src/gluonts/nursery/robust-mts-attack/multivariate/datasets/dataset.py
+++ b/src/gluonts/nursery/robust-mts-attack/multivariate/datasets/dataset.py
@@ -121,9 +121,11 @@ def make_multivariate_dataset(
align_data=False, num_test_dates=num_test_dates, max_target_dim=dim
)
return MultivariateDatasetInfo(
- dataset_name
- if dataset_benchmark_name is None
- else dataset_benchmark_name,
+ (
+ dataset_name
+ if dataset_benchmark_name is None
+ else dataset_benchmark_name
+ ),
grouper_train(train_ds),
grouper_test(test_ds),
prediction_length,
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/causal_deepar/causal_deepar_network.py b/src/gluonts/nursery/robust-mts-attack/pts/model/causal_deepar/causal_deepar_network.py
index c162cb97f6..12fa525867 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/causal_deepar/causal_deepar_network.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/causal_deepar/causal_deepar_network.py
@@ -216,12 +216,16 @@ def unroll_encoder(
(
embedded_cat,
feat_static_real,
- scale.log()
- if len(self.target_shape) == 0
- else scale.squeeze(1).log(),
- control_scale.log()
- if len(self.target_shape) == 0
- else control_scale.squeeze(1).log(),
+ (
+ scale.log()
+ if len(self.target_shape) == 0
+ else scale.squeeze(1).log()
+ ),
+ (
+ control_scale.log()
+ if len(self.target_shape) == 0
+ else control_scale.squeeze(1).log()
+ ),
),
dim=1,
)
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/deepar/deepar_network.py b/src/gluonts/nursery/robust-mts-attack/pts/model/deepar/deepar_network.py
index b756bc17e9..8d66c91ec0 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/deepar/deepar_network.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/deepar/deepar_network.py
@@ -190,9 +190,11 @@ def unroll_encoder(
(
embedded_cat,
feat_static_real,
- scale.log()
- if len(self.target_shape) == 0
- else scale.squeeze(1).log(),
+ (
+ scale.log()
+ if len(self.target_shape) == 0
+ else scale.squeeze(1).log()
+ ),
),
dim=1,
)
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_estimator.py b/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_estimator.py
index fec7d5a2ee..4bea3ddf23 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_estimator.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_estimator.py
@@ -139,9 +139,9 @@ def __init__(
self.scaling = scaling
if self.use_marginal_transformation:
- self.output_transform: Optional[
- Callable
- ] = cdf_to_gaussian_forward_transform
+ self.output_transform: Optional[Callable] = (
+ cdf_to_gaussian_forward_transform
+ )
else:
self.output_transform = None
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_network.py b/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_network.py
index 5a4e0e0534..64353b4adf 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_network.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/deepvar/deepvar_network.py
@@ -188,9 +188,11 @@ def unroll(
(
embedded_cat,
feat_static_real,
- scale.log()
- if len(self.target_shape) == 0
- else scale.squeeze(1).log(),
+ (
+ scale.log()
+ if len(self.target_shape) == 0
+ else scale.squeeze(1).log()
+ ),
),
dim=1,
)
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/n_beats/n_beats_ensemble.py b/src/gluonts/nursery/robust-mts-attack/pts/model/n_beats/n_beats_ensemble.py
index eb8c3ef7ae..ff9234273d 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/n_beats/n_beats_ensemble.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/n_beats/n_beats_ensemble.py
@@ -102,9 +102,11 @@ def predict(
output,
start_date=start_date,
freq=start_date.freqstr,
- item_id=item[FieldName.ITEM_ID]
- if FieldName.ITEM_ID in item
- else None,
+ item_id=(
+ item[FieldName.ITEM_ID]
+ if FieldName.ITEM_ID in item
+ else None
+ ),
info=item["info"] if "info" in item else None,
)
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/tft/tft_estimator.py b/src/gluonts/nursery/robust-mts-attack/pts/model/tft/tft_estimator.py
index af2805c0b3..fdb47f7d87 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/tft/tft_estimator.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/tft/tft_estimator.py
@@ -106,13 +106,13 @@ def __init__(
self.past_dynamic_feature_dims = {}
for name in self.past_dynamic_features:
if name in self.dynamic_cardinalities:
- self.past_dynamic_cardinalities[
- name
- ] = self.dynamic_cardinalities.pop(name)
+ self.past_dynamic_cardinalities[name] = (
+ self.dynamic_cardinalities.pop(name)
+ )
elif name in self.dynamic_feature_dims:
- self.past_dynamic_feature_dims[
- name
- ] = self.dynamic_feature_dims.pop(name)
+ self.past_dynamic_feature_dims[name] = (
+ self.dynamic_feature_dims.pop(name)
+ )
else:
raise ValueError(
f"Feature name {name} is not provided in feature dicts"
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/transformer/transformer_network.py b/src/gluonts/nursery/robust-mts-attack/pts/model/transformer/transformer_network.py
index 37ae2b2bf8..3ac17fef6e 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/transformer/transformer_network.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/transformer/transformer_network.py
@@ -206,9 +206,11 @@ def create_network_input(
(
embedded_cat,
feat_static_real,
- torch.log(scale)
- if len(self.target_shape) == 0
- else torch.log(scale.squeeze(1)),
+ (
+ torch.log(scale)
+ if len(self.target_shape) == 0
+ else torch.log(scale.squeeze(1))
+ ),
),
dim=1,
)
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/model/transformer_tempflow/transformer_tempflow_network.py b/src/gluonts/nursery/robust-mts-attack/pts/model/transformer_tempflow/transformer_tempflow_network.py
index 56b7b4d457..55d60d1537 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/model/transformer_tempflow/transformer_tempflow_network.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/model/transformer_tempflow/transformer_tempflow_network.py
@@ -165,7 +165,11 @@ def create_network_input(
future_time_feat: Optional[torch.Tensor],
future_target_cdf: Optional[torch.Tensor],
target_dimension_indicator: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor,]:
+ ) -> Tuple[
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ ]:
"""
Unrolls the RNN encoder over past and, if present, future data.
Returns outputs and state of the encoder, plus the scale of
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/modules/gaussian_diffusion.py b/src/gluonts/nursery/robust-mts-attack/pts/modules/gaussian_diffusion.py
index 2819be62a4..73195e7957 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/modules/gaussian_diffusion.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/modules/gaussian_diffusion.py
@@ -80,9 +80,7 @@ def __init__(
if beta_schedule == "linear":
betas = np.linspace(1e-4, beta_end, diff_steps)
elif beta_schedule == "quad":
- betas = (
- np.linspace(1e-4**0.5, beta_end**0.5, diff_steps) ** 2
- )
+ betas = np.linspace(1e-4**0.5, beta_end**0.5, diff_steps) ** 2
elif beta_schedule == "const":
betas = beta_end * np.ones(diff_steps)
elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1
diff --git a/src/gluonts/nursery/robust-mts-attack/utils.py b/src/gluonts/nursery/robust-mts-attack/utils.py
index 3bafea8d4d..75a36ec169 100644
--- a/src/gluonts/nursery/robust-mts-attack/utils.py
+++ b/src/gluonts/nursery/robust-mts-attack/utils.py
@@ -263,13 +263,13 @@ def calc_loss(
if (
true_future_target[:, attack_idx][..., target_items] != 0
).prod() == 0:
- mape[attack_type][
- testset_idx : testset_idx + batch_size
- ] = np.abs(
- forecasts[attack_type][i][:, :, attack_idx][
- ..., target_items
- ].mean(1)
- - true_future_target[:, attack_idx][..., target_items]
+ mape[attack_type][testset_idx : testset_idx + batch_size] = (
+ np.abs(
+ forecasts[attack_type][i][:, :, attack_idx][
+ ..., target_items
+ ].mean(1)
+ - true_future_target[:, attack_idx][..., target_items]
+ )
)
mse[attack_type][testset_idx : testset_idx + batch_size] = (
forecasts[attack_type][i][:, :, attack_idx][
@@ -290,14 +290,14 @@ def calc_loss(
j, testset_idx : testset_idx + batch_size
] = quantile_loss(true, pred, quantile)
else:
- mape[attack_type][
- testset_idx : testset_idx + batch_size
- ] = np.abs(
- forecasts[attack_type][i][:, :, attack_idx][
- ..., target_items
- ].mean(1)
- / true_future_target[:, attack_idx][..., target_items]
- - 1
+ mape[attack_type][testset_idx : testset_idx + batch_size] = (
+ np.abs(
+ forecasts[attack_type][i][:, :, attack_idx][
+ ..., target_items
+ ].mean(1)
+ / true_future_target[:, attack_idx][..., target_items]
+ - 1
+ )
)
mse[attack_type][testset_idx : testset_idx + batch_size] = (
mape[attack_type][testset_idx : testset_idx + batch_size]
diff --git a/src/gluonts/nursery/san/_network.py b/src/gluonts/nursery/san/_network.py
index b3d08665f2..126dbdd0c2 100644
--- a/src/gluonts/nursery/san/_network.py
+++ b/src/gluonts/nursery/san/_network.py
@@ -213,9 +213,11 @@ def _assemble_covariates(
covariates.append(
feat_static_real.expand_dims(axis=1).repeat(
axis=1,
- repeats=self.context_length
- if is_past
- else self.prediction_length,
+ repeats=(
+ self.context_length
+ if is_past
+ else self.prediction_length
+ ),
)
)
if len(covariates) > 0:
@@ -231,9 +233,11 @@ def _assemble_covariates(
categories.append(
feat_static_cat.expand_dims(axis=1).repeat(
axis=1,
- repeats=self.context_length
- if is_past
- else self.prediction_length,
+ repeats=(
+ self.context_length
+ if is_past
+ else self.prediction_length
+ ),
)
)
if len(categories) > 0:
diff --git a/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/_estimator.py b/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/_estimator.py
index 03bb055f00..8c8b297fa6 100644
--- a/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/_estimator.py
+++ b/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/_estimator.py
@@ -238,10 +238,11 @@ def __init__(
# adapt window_length if RollingMeanValueImputation is used
if isinstance(imputation_method, RollingMeanValueImputation):
- base_estimator_hps_agg[
- "imputation_method"
- ] = RollingMeanValueImputation(
- window_size=imputation_method.window_size // agg_multiple
+ base_estimator_hps_agg["imputation_method"] = (
+ RollingMeanValueImputation(
+ window_size=imputation_method.window_size
+ // agg_multiple
+ )
)
# Hack to enforce correct serialization of lags_seq and history length
diff --git a/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/gluonts_fixes.py b/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/gluonts_fixes.py
index a8ac13bc14..b66c8f6887 100644
--- a/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/gluonts_fixes.py
+++ b/src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/gluonts_fixes.py
@@ -52,15 +52,17 @@ def batchify_with_dict(
is_right_pad: bool = True,
) -> DataBatch:
return {
- key: stack(
- data=[item[key] for item in data],
- ctx=ctx,
- dtype=dtype,
- variable_length=variable_length,
- is_right_pad=is_right_pad,
+ key: (
+ stack(
+ data=[item[key] for item in data],
+ ctx=ctx,
+ dtype=dtype,
+ variable_length=variable_length,
+ is_right_pad=is_right_pad,
+ )
+ if not isinstance(data[0][key], dict)
+ else batchify_with_dict(data=[item[key] for item in data])
)
- if not isinstance(data[0][key], dict)
- else batchify_with_dict(data=[item[key] for item in data])
for key in data[0].keys()
}
diff --git a/src/gluonts/nursery/tsbench/src/cli/analysis/scripts/ensemble.py b/src/gluonts/nursery/tsbench/src/cli/analysis/scripts/ensemble.py
index e1d03db304..8d1a17668c 100644
--- a/src/gluonts/nursery/tsbench/src/cli/analysis/scripts/ensemble.py
+++ b/src/gluonts/nursery/tsbench/src/cli/analysis/scripts/ensemble.py
@@ -66,9 +66,9 @@ def main(
tracker,
ensemble_size=size,
ensemble_weighting=weighting,
- config_class=MODEL_REGISTRY[model_class]
- if model_class is not None
- else None,
+ config_class=(
+ MODEL_REGISTRY[model_class] if model_class is not None else None
+ ),
)
df, configs = evaluator.run()
diff --git a/src/gluonts/nursery/tsbench/src/cli/evaluations/schedule.py b/src/gluonts/nursery/tsbench/src/cli/evaluations/schedule.py
index a49864cc59..80cb563da4 100644
--- a/src/gluonts/nursery/tsbench/src/cli/evaluations/schedule.py
+++ b/src/gluonts/nursery/tsbench/src/cli/evaluations/schedule.py
@@ -177,12 +177,14 @@ def job_factory() -> str:
tags=[
{"Key": "Experiment", "Value": experiment},
],
- instance_type="local"
- if local
- else (
- configuration["__instance_type__"]
- if "__instance_type__" in configuration
- else instance_type
+ instance_type=(
+ "local"
+ if local
+ else (
+ configuration["__instance_type__"]
+ if "__instance_type__" in configuration
+ else instance_type
+ )
),
instance_count=1,
volume_size=30,
diff --git a/src/gluonts/nursery/tsbench/src/cli/utils/config.py b/src/gluonts/nursery/tsbench/src/cli/utils/config.py
index 0ec10b3863..41425e22e7 100644
--- a/src/gluonts/nursery/tsbench/src/cli/utils/config.py
+++ b/src/gluonts/nursery/tsbench/src/cli/utils/config.py
@@ -73,14 +73,16 @@ def explode_key_values(
into independent configurations.
"""
all_combinations = {
- primary: itertools.product(
- *[
- [(option["key"], value) for value in option["values"]]
- for option in choices
- ]
+ primary: (
+ itertools.product(
+ *[
+ [(option["key"], value) for value in option["values"]]
+ for option in choices
+ ]
+ )
+ if choices
+ else []
)
- if choices
- else []
for primary, choices in mapping.items()
}
diff --git a/src/gluonts/nursery/tsbench/src/tsbench/recommender/_base.py b/src/gluonts/nursery/tsbench/src/tsbench/recommender/_base.py
index 7ff3c335c6..078692d442 100644
--- a/src/gluonts/nursery/tsbench/src/tsbench/recommender/_base.py
+++ b/src/gluonts/nursery/tsbench/src/tsbench/recommender/_base.py
@@ -116,9 +116,11 @@ def recommend(
# Then, we perform a nondominated sort
argsort = argsort_nondominated(
df.to_numpy(), # type: ignore
- dim=df.columns.tolist().index(self.focus)
- if self.focus is not None
- else None,
+ dim=(
+ df.columns.tolist().index(self.focus)
+ if self.focus is not None
+ else None
+ ),
max_items=max_count,
)
diff --git a/src/gluonts/nursery/tsbench/src/tsbench/recommender/_factory.py b/src/gluonts/nursery/tsbench/src/tsbench/recommender/_factory.py
index 96cbb43b37..a84ba975ff 100644
--- a/src/gluonts/nursery/tsbench/src/tsbench/recommender/_factory.py
+++ b/src/gluonts/nursery/tsbench/src/tsbench/recommender/_factory.py
@@ -16,9 +16,9 @@
from ._base import Recommender
RECOMMENDER_REGISTRY: Dict[str, Type[Recommender[ModelConfig]]] = {}
-ENSEMBLE_RECOMMENDER_REGISTRY: Dict[
- str, Type[Recommender[EnsembleConfig]]
-] = {}
+ENSEMBLE_RECOMMENDER_REGISTRY: Dict[str, Type[Recommender[EnsembleConfig]]] = (
+ {}
+)
R = TypeVar("R", bound=Type[Recommender[ModelConfig]])
E = TypeVar("E", bound=Type[Recommender[EnsembleConfig]])
diff --git a/src/gluonts/time_feature/lag.py b/src/gluonts/time_feature/lag.py
index 9abadb3a7f..d93fee8547 100644
--- a/src/gluonts/time_feature/lag.py
+++ b/src/gluonts/time_feature/lag.py
@@ -146,7 +146,9 @@ def _make_lags_for_month(multiple, num_cycles=3):
+ _make_lags_for_hour(offset.n / (60 * 60))
)
else:
- raise ValueError(f"invalid frequency | `freq_str={freq_str}` -> `offset_name={offset_name}`")
+ raise ValueError(
+ f"invalid frequency | `freq_str={freq_str}` -> `offset_name={offset_name}`"
+ )
# flatten lags list and filter
lags = [
diff --git a/src/gluonts/torch/distributions/binned_uniforms.py b/src/gluonts/torch/distributions/binned_uniforms.py
index d8672e8028..46a8a5e586 100644
--- a/src/gluonts/torch/distributions/binned_uniforms.py
+++ b/src/gluonts/torch/distributions/binned_uniforms.py
@@ -35,6 +35,7 @@ class BinnedUniforms(Distribution):
These are softmaxed. The tensor is of shape (*batch_shape,)
validate_args (bool) from the pytorch Distribution class
"""
+
arg_constraints = {"logits": constraints.real}
support = constraints.real
has_rsample = False
diff --git a/src/gluonts/torch/distributions/generalized_pareto.py b/src/gluonts/torch/distributions/generalized_pareto.py
index a35a2ba1dc..9255474ad2 100644
--- a/src/gluonts/torch/distributions/generalized_pareto.py
+++ b/src/gluonts/torch/distributions/generalized_pareto.py
@@ -37,6 +37,7 @@ class GeneralizedPareto(Distribution):
Tensor containing the beta scale parameters. The tensor is of
shape (*batch_shape, 1)
"""
+
arg_constraints = {
"xi": constraints.positive,
"beta": constraints.positive,
diff --git a/src/gluonts/torch/distributions/spliced_binned_pareto.py b/src/gluonts/torch/distributions/spliced_binned_pareto.py
index 93eef5fa3c..94e36a56cf 100644
--- a/src/gluonts/torch/distributions/spliced_binned_pareto.py
+++ b/src/gluonts/torch/distributions/spliced_binned_pareto.py
@@ -36,6 +36,7 @@ class SplicedBinnedPareto(BinnedUniforms):
each tail. Default value is 0.05. NB: This symmetric percentile
can still represent asymmetric upper and lower tails.
"""
+
arg_constraints = {
"logits": constraints.real,
"lower_gp_xi": constraints.positive,
diff --git a/src/gluonts/torch/model/deep_npts/_estimator.py b/src/gluonts/torch/model/deep_npts/_estimator.py
index 8d396d7c96..f3cca87da1 100755
--- a/src/gluonts/torch/model/deep_npts/_estimator.py
+++ b/src/gluonts/torch/model/deep_npts/_estimator.py
@@ -343,9 +343,11 @@ def train_model(
)
data_loader = self.training_data_loader(
- transformed_dataset
- if not cache_data
- else Cached(transformed_dataset),
+ (
+ transformed_dataset
+ if not cache_data
+ else Cached(transformed_dataset)
+ ),
batch_size=self.batch_size,
num_batches_per_epoch=self.num_batches_per_epoch,
)
diff --git a/src/gluonts/torch/model/deepar/module.py b/src/gluonts/torch/model/deepar/module.py
index 406468079c..ebe4ef2479 100644
--- a/src/gluonts/torch/model/deepar/module.py
+++ b/src/gluonts/torch/model/deepar/module.py
@@ -221,7 +221,11 @@ def prepare_rnn_input(
past_observed_values: torch.Tensor,
future_time_feat: torch.Tensor,
future_target: Optional[torch.Tensor] = None,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor,]:
+ ) -> Tuple[
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ ]:
context = past_target[..., -self.context_length :]
observed_context = past_observed_values[..., -self.context_length :]
diff --git a/src/gluonts/torch/model/tft/module.py b/src/gluonts/torch/model/tft/module.py
index f0dd7c00a1..f113160cb7 100644
--- a/src/gluonts/torch/model/tft/module.py
+++ b/src/gluonts/torch/model/tft/module.py
@@ -102,65 +102,66 @@ def __init__(
self.target_proj = nn.Linear(in_features=1, out_features=self.d_var)
# Past-only dynamic features
if self.d_past_feat_dynamic_real:
- self.past_feat_dynamic_proj: Optional[
- FeatureProjector
- ] = FeatureProjector(
- feature_dims=self.d_past_feat_dynamic_real,
- embedding_dims=[self.d_var]
- * len(self.d_past_feat_dynamic_real),
+ self.past_feat_dynamic_proj: Optional[FeatureProjector] = (
+ FeatureProjector(
+ feature_dims=self.d_past_feat_dynamic_real,
+ embedding_dims=[self.d_var]
+ * len(self.d_past_feat_dynamic_real),
+ )
)
else:
self.past_feat_dynamic_proj = None
if self.c_past_feat_dynamic_cat:
- self.past_feat_dynamic_embed: Optional[
- FeatureEmbedder
- ] = FeatureEmbedder(
- cardinalities=self.c_past_feat_dynamic_cat,
- embedding_dims=[self.d_var]
- * len(self.c_past_feat_dynamic_cat),
+ self.past_feat_dynamic_embed: Optional[FeatureEmbedder] = (
+ FeatureEmbedder(
+ cardinalities=self.c_past_feat_dynamic_cat,
+ embedding_dims=[self.d_var]
+ * len(self.c_past_feat_dynamic_cat),
+ )
)
else:
self.past_feat_dynamic_embed = None
# Known dynamic features
if self.d_feat_dynamic_real:
- self.feat_dynamic_proj: Optional[
- FeatureProjector
- ] = FeatureProjector(
- feature_dims=self.d_feat_dynamic_real,
- embedding_dims=[self.d_var] * len(self.d_feat_dynamic_real),
+ self.feat_dynamic_proj: Optional[FeatureProjector] = (
+ FeatureProjector(
+ feature_dims=self.d_feat_dynamic_real,
+ embedding_dims=[self.d_var]
+ * len(self.d_feat_dynamic_real),
+ )
)
else:
self.feat_dynamic_proj = None
if self.c_feat_dynamic_cat:
- self.feat_dynamic_embed: Optional[
- FeatureEmbedder
- ] = FeatureEmbedder(
- cardinalities=self.c_feat_dynamic_cat,
- embedding_dims=[self.d_var] * len(self.c_feat_dynamic_cat),
+ self.feat_dynamic_embed: Optional[FeatureEmbedder] = (
+ FeatureEmbedder(
+ cardinalities=self.c_feat_dynamic_cat,
+ embedding_dims=[self.d_var] * len(self.c_feat_dynamic_cat),
+ )
)
else:
self.feat_dynamic_embed = None
# Static features
if self.d_feat_static_real:
- self.feat_static_proj: Optional[
- FeatureProjector
- ] = FeatureProjector(
- feature_dims=self.d_feat_static_real,
- embedding_dims=[self.d_var] * len(self.d_feat_static_real),
+ self.feat_static_proj: Optional[FeatureProjector] = (
+ FeatureProjector(
+ feature_dims=self.d_feat_static_real,
+ embedding_dims=[self.d_var] * len(self.d_feat_static_real),
+ )
)
else:
self.feat_static_proj = None
if self.c_feat_static_cat:
- self.feat_static_embed: Optional[
- FeatureEmbedder
- ] = FeatureEmbedder(
- cardinalities=self.c_feat_static_cat,
- embedding_dims=[self.d_var] * len(self.c_feat_static_cat),
+ self.feat_static_embed: Optional[FeatureEmbedder] = (
+ FeatureEmbedder(
+ cardinalities=self.c_feat_static_cat,
+ embedding_dims=[self.d_var] * len(self.c_feat_static_cat),
+ )
)
else:
self.feat_static_embed = None
diff --git a/src/gluonts/torch/model/wavenet/estimator.py b/src/gluonts/torch/model/wavenet/estimator.py
index 2da9e87d91..234ecff237 100644
--- a/src/gluonts/torch/model/wavenet/estimator.py
+++ b/src/gluonts/torch/model/wavenet/estimator.py
@@ -267,12 +267,18 @@ def create_transformation(self) -> Transformation:
return Chain(
[
RemoveFields(field_names=remove_field_names),
- SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0])
- if self.num_feat_static_cat == 0
- else Identity(),
- SetField(output_field=FieldName.FEAT_STATIC_REAL, value=[0.0])
- if self.num_feat_static_real == 0
- else Identity(),
+ (
+ SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0])
+ if self.num_feat_static_cat == 0
+ else Identity()
+ ),
+ (
+ SetField(
+ output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]
+ )
+ if self.num_feat_static_real == 0
+ else Identity()
+ ),
AsNumpyArray(
field=FieldName.FEAT_STATIC_CAT, expected_ndim=1, dtype=int
),
diff --git a/src/gluonts/transform/feature.py b/src/gluonts/transform/feature.py
index ff9f7bdd5e..f5f519d5a7 100644
--- a/src/gluonts/transform/feature.py
+++ b/src/gluonts/transform/feature.py
@@ -529,11 +529,11 @@ def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
lags = np.vstack(
[
agg_vals[
- -(l * self.ratio - self.half_window + len(t)) : -(
- l * self.ratio - self.half_window
+ -(l * self.ratio - self.half_window + len(t)) : (
+ -(l * self.ratio - self.half_window)
+ if -(l * self.ratio - self.half_window) != 0
+ else None
)
- if -(l * self.ratio - self.half_window) != 0
- else None
]
for l in self.valid_lags
]
diff --git a/src/gluonts/zebras/_period.py b/src/gluonts/zebras/_period.py
index 66f32e311b..4cda3178a9 100644
--- a/src/gluonts/zebras/_period.py
+++ b/src/gluonts/zebras/_period.py
@@ -330,12 +330,10 @@ def __len__(self):
return len(self.data)
@overload
- def __getitem__(self, idx: int) -> Period:
- ...
+ def __getitem__(self, idx: int) -> Period: ...
@overload
- def __getitem__(self, idx: slice) -> Periods:
- ...
+ def __getitem__(self, idx: slice) -> Periods: ...
def __getitem__(self, idx):
if _is_number(idx):
diff --git a/test/mx/model/seq2seq/test_forking_sequence_splitter.py b/test/mx/model/seq2seq/test_forking_sequence_splitter.py
index 6f9076d5d5..00d1df560f 100644
--- a/test/mx/model/seq2seq/test_forking_sequence_splitter.py
+++ b/test/mx/model/seq2seq/test_forking_sequence_splitter.py
@@ -125,9 +125,11 @@ def make_dataset(N, train_length):
pred_length=10,
),
ForkingSequenceSplitter(
- instance_sampler=ValidationSplitSampler(min_future=dec_len)
- if is_train
- else TSplitSampler(),
+ instance_sampler=(
+ ValidationSplitSampler(min_future=dec_len)
+ if is_train
+ else TSplitSampler()
+ ),
enc_len=enc_len,
dec_len=dec_len,
num_forking=num_forking,