def __init__(
self,
freq: str,
prediction_length: int,
trainer: Trainer = Trainer(),
context_length: Optional[int] = None,
num_layers: int = 2,
num_cells: int = 40,
cell_type: str = 'lstm',
num_eval_samples: int = 100,
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
cardinality: Optional[List[int]] = None,
embedding_dimension: int = 20,
distr_output: DistributionOutput = StudentTOutput(),
scaling: bool = True,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
) -> None:
super().__init__(trainer=trainer)
assert (prediction_length >
0), "The value of `prediction_length` should be > 0"
assert (context_length is None or context_length > 0
), "The value of `context_length` should be > 0"
assert num_layers > 0, "The value of `num_layers` should be > 0"
assert num_cells > 0, "The value of `num_cells` should be > 0"
assert (num_eval_samples >
0), "The value of `num_eval_samples` should be > 0"
assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0"
assert (cardinality is not None or not use_feat_static_cat
), "You must set `cardinality` if `use_feat_static_cat=True`"
assert cardinality is None or [
c > 0 for c in cardinality
], "Elements of `cardinality` should be > 0"
assert (embedding_dimension >
0), "The value of `embedding_dimension` should be > 0"
self.freq = freq
self.context_length = (context_length if context_length is not None
else prediction_length)
self.prediction_length = prediction_length
self.distr_output = distr_output
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.num_sample_paths = num_eval_samples
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_static_cat = use_feat_static_cat
self.cardinality = cardinality if use_feat_static_cat else [1]
self.embedding_dimension = embedding_dimension
self.scaling = scaling
self.lags_seq = (lags_seq if lags_seq is not None else
get_lags_for_frequency(freq_str=freq))
self.time_features = (time_features if time_features is not None else
time_features_from_frequency_str(self.freq))
self.history_length = self.context_length + max(self.lags_seq)
def test_lags():
freq_strs = [
"min",
"1min",
"15min",
"30min",
"59min",
"60min",
"61min",
"H",
"1H",
"6H",
"12H",
"23H",
"24H",
"25H",
"D",
"1D",
"2D",
"6D",
"7D",
"8D",
"W",
"1W",
"3W",
"4W",
"5W",
"M",
"6M",
"12M",
]
for freq_str in freq_strs:
lags = date_feature_set.get_lags_for_frequency(freq_str)
assert (
lags == expected_lags[freq_str]
), "lags do not match for the frequency '{}':\nexpected: {},\nprovided: {}".format(
freq_str, expected_lags[freq_str], lags)
def __init__(
self,
freq: str,
prediction_length: int,
context_length: Optional[int] = None,
trainer: Trainer = Trainer(),
dropout_rate: float = 0.1,
cardinality: Optional[List[int]] = None,
embedding_dimension: int = 20,
distr_output: DistributionOutput = StudentTOutput(),
model_dim: int = 32,
inner_ff_dim_scale: int = 4,
pre_seq: str = "dn",
post_seq: str = "drn",
act_type: str = "softrelu",
num_heads: int = 8,
scaling: bool = True,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
num_parallel_samples: int = 100,
) -> None:
super().__init__(trainer=trainer)
assert (
prediction_length > 0
), "The value of `prediction_length` should be > 0"
assert (
context_length is None or context_length > 0
), "The value of `context_length` should be > 0"
assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0"
assert (
cardinality is not None or not use_feat_static_cat
), "You must set `cardinality` if `use_feat_static_cat=True`"
assert cardinality is None or [
c > 0 for c in cardinality
], "Elements of `cardinality` should be > 0"
assert (
embedding_dimension > 0
), "The value of `embedding_dimension` should be > 0"
assert (
num_parallel_samples > 0
), "The value of `num_parallel_samples` should be > 0"
self.freq = freq
self.prediction_length = prediction_length
self.context_length = (
context_length if context_length is not None else prediction_length
)
self.distr_output = distr_output
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_static_cat = use_feat_static_cat
self.cardinality = cardinality if use_feat_static_cat else [1]
self.embedding_dimension = embedding_dimension
self.num_parallel_samples = num_parallel_samples
self.lags_seq = (
lags_seq
if lags_seq is not None
else get_lags_for_frequency(freq_str=freq)
)
self.time_features = (
time_features
if time_features is not None
else time_features_from_frequency_str(self.freq)
)
self.history_length = self.context_length + max(self.lags_seq)
self.scaling = scaling
self.config = {
"model_dim": model_dim,
"pre_seq": pre_seq,
"post_seq": post_seq,
"dropout_rate": dropout_rate,
"inner_ff_dim_scale": inner_ff_dim_scale,
"act_type": act_type,
"num_heads": num_heads,
}
self.encoder = TransformerEncoder(
self.context_length, self.config, prefix="enc_"
)
self.decoder = TransformerDecoder(
self.prediction_length, self.config, prefix="dec_"
)
