def __init__(
self,
freq: str,
prediction_length: int,
sampling: bool = True,
trainer: Trainer = Trainer(),
num_hidden_dimensions: Optional[List[int]] = None,
context_length: Optional[int] = None,
distr_output: DistributionOutput = StudentTOutput(),
imputation_method: Optional[MissingValueImputation] = None,
batch_normalization: bool = False,
mean_scaling: bool = True,
num_parallel_samples: int = 100,
train_sampler: Optional[InstanceSampler] = None,
validation_sampler: Optional[InstanceSampler] = None,
batch_size: int = 32,
) -> None:
"""
Defines an estimator. All parameters should be serializable.
"""
super().__init__(trainer=trainer, batch_size=batch_size)
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_hidden_dimensions is None or ([
d > 0 for d in num_hidden_dimensions
]), "Elements of `num_hidden_dimensions` should be > 0"
assert (num_parallel_samples >
0), "The value of `num_parallel_samples` should be > 0"
self.num_hidden_dimensions = (num_hidden_dimensions
if num_hidden_dimensions is not None else
list([40, 40]))
self.prediction_length = prediction_length
self.context_length = (context_length if context_length is not None
else prediction_length)
self.freq = freq
self.distr_output = distr_output
self.batch_normalization = batch_normalization
self.mean_scaling = mean_scaling
self.num_parallel_samples = num_parallel_samples
self.sampling = sampling
self.imputation_method = (imputation_method if imputation_method
is not None else DummyValueImputation(
self.distr_output.value_in_support))
self.train_sampler = (train_sampler if train_sampler is not None else
ExpectedNumInstanceSampler(
num_instances=1.0,
min_future=prediction_length))
self.validation_sampler = (validation_sampler if validation_sampler
is not None else ValidationSplitSampler(
min_future=prediction_length))
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",
dropoutcell_type: str = "ZoneoutCell",
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
use_feat_static_real: bool = False,
cardinality: Optional[List[int]] = None,
embedding_dimension: Optional[List[int]] = None,
distr_output: DistributionOutput = StudentTOutput(),
scaling: bool = True,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
num_parallel_samples: int = 100,
imputation_method: Optional[MissingValueImputation] = None,
train_sampler: Optional[InstanceSampler] = None,
validation_sampler: Optional[InstanceSampler] = None,
dtype: DType = np.float32,
alpha: float = 0.0,
beta: float = 0.0,
batch_size: int = 32,
default_scale: Optional[float] = None,
minimum_scale: float = 1e-10,
impute_missing_values: bool = False,
num_imputation_samples: int = 1,
) -> None:
super().__init__(trainer=trainer, batch_size=batch_size, dtype=dtype)
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"
supported_dropoutcell_types = [
"ZoneoutCell",
"RNNZoneoutCell",
"VariationalDropoutCell",
"VariationalZoneoutCell",
]
assert (
dropoutcell_type in supported_dropoutcell_types
), f"`dropoutcell_type` should be one of {supported_dropoutcell_types}"
assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0"
assert (cardinality and use_feat_static_cat) or (
not (cardinality or use_feat_static_cat)
), "You should set `cardinality` if and only if `use_feat_static_cat=True`"
assert cardinality is None or all(
[c > 0
for c in cardinality]), "Elements of `cardinality` should be > 0"
assert embedding_dimension is None or all([
e > 0 for e in embedding_dimension
]), "Elements of `embedding_dimension` should be > 0"
assert (num_parallel_samples >
0), "The value of `num_parallel_samples` should be > 0"
assert alpha >= 0, "The value of `alpha` should be >= 0"
assert beta >= 0, "The value of `beta` 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.distr_output.dtype = dtype
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.dropoutcell_type = dropoutcell_type
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_static_cat = use_feat_static_cat
self.use_feat_static_real = use_feat_static_real
self.cardinality = (cardinality
if cardinality and use_feat_static_cat else [1])
self.embedding_dimension = (
embedding_dimension if embedding_dimension is not None else
[min(50, (cat + 1) // 2) for cat in self.cardinality])
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)
self.num_parallel_samples = num_parallel_samples
self.imputation_method = (imputation_method if imputation_method
is not None else DummyValueImputation(
self.distr_output.value_in_support))
self.train_sampler = (train_sampler if train_sampler is not None else
ExpectedNumInstanceSampler(
num_instances=1.0,
min_future=prediction_length))
self.validation_sampler = (validation_sampler if validation_sampler
is not None else ValidationSplitSampler(
min_future=prediction_length))
self.alpha = alpha
self.beta = beta
self.num_imputation_samples = num_imputation_samples
self.default_scale = default_scale
self.minimum_scale = minimum_scale
self.impute_missing_values = impute_missing_values
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",
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
use_feat_static_real: bool = False,
cardinality: Optional[List[int]] = None,
embedding_dimension: Optional[List[int]] = None,
distr_output: DistributionOutput = StudentTOutput(),
scaling: bool = True,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
num_parallel_samples: int = 100,
imputation_method: Optional[MissingValueImputation] = None,
dtype: DType = np.float32,
) -> None:
super().__init__(trainer=trainer, dtype=dtype)
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 dropout_rate >= 0, "The value of `dropout_rate` should be >= 0"
assert (cardinality and use_feat_static_cat) or (
not (cardinality or use_feat_static_cat)
), "You should set `cardinality` if and only if `use_feat_static_cat=True`"
assert cardinality is None or all(
[c > 0 for c in cardinality]
), "Elements of `cardinality` should be > 0"
assert embedding_dimension is None or all(
[e > 0 for e in embedding_dimension]
), "Elements of `embedding_dimension` should be > 0"
assert (
num_parallel_samples > 0
), "The value of `num_parallel_samples` 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.distr_output.dtype = dtype
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_static_cat = use_feat_static_cat
self.use_feat_static_real = use_feat_static_real
self.cardinality = (
cardinality if cardinality and use_feat_static_cat else [1]
)
self.embedding_dimension = (
embedding_dimension
if embedding_dimension is not None
else [min(50, (cat + 1) // 2) for cat in self.cardinality]
)
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)
self.num_parallel_samples = num_parallel_samples
self.imputation_method = (
imputation_method
if imputation_method is not None
else DummyValueImputation(self.distr_output.value_in_support)
)