initial deepar

pts/feature/__init__.py

@@ -1,4 +1,5 @@
 from pts.feature.time_feature import (
+    TimeFeature,
     MinuteOfHour,
     HourOfDay,
     DayOfWeek,

pts/model/deepar/deepar_estimator.py

@@ -1,4 +1,10 @@
+from typing import List, Optional
+
+import numpy as np
+
 from pts.model import PTSEstimator
+from pts.feature import TimeFeature, get_lags_for_frequency, time_features_from_frequency_str
+from pts import Trainer
 
 class DeepAREstimator(PTSEstimator):
     def __init__(self,
@@ -20,8 +26,45 @@ class DeepAREstimator(PTSEstimator):
         lags_seq: Optional[List[int]] = None,
         time_features: Optional[List[TimeFeature]] = None,
         num_parallel_samples: int = 100,
+        dtype : np.dtype = np.float32,
     ) -> None:
         super().__init__(trainer=trainer)
-        
-        
+    
 
+        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

pts/model/estimator.py

@@ -12,6 +12,7 @@ import torch.nn as nn
 from .predictor import Predictor
 from .utils import get_module_forward_input_names
 
+
 class Estimator(ABC):
     prediction_length: int
     freq: str
@@ -47,11 +48,9 @@ class TrainOutput(NamedTuple):
 
 
 class PTSEstimator(Estimator):
-    def __init__(self, trainer: Trainer,
-                 dtype: np.dtype = np.float32) -> None:
+    def __init__(self, trainer: Trainer, dtype: np.dtype = np.float32) -> None:
         self.trainer = trainer
         self.dtype = dtype
-        
 
     @abstractmethod
     def create_transformation(self) -> Transformation:

pts/model/forecast.py

@@ -113,14 +113,15 @@ class SampleForecast(Forecast):
             info: Optional[Dict] = None,
     ):
         assert isinstance(
-            samples, (np.ndarray, torch.Tensor
-                      )), "samples should be either a numpy array or an torch tensor"
+            samples,
+            (np.ndarray, torch.Tensor
+             )), "samples should be either a numpy array or an torch tensor"
         assert (
             len(np.shape(samples)) == 2 or len(np.shape(samples)) == 3
         ), "samples should be a 2-dimensional or 3-dimensional array. Dimensions found: {}".format(
             len(np.shape(samples)))
-        self.samples = (samples if (isinstance(samples, np.ndarray)) else
-                        samples.numpy())
+        self.samples = (samples if
+                        (isinstance(samples, np.ndarray)) else samples.numpy())
         self._sorted_samples_value = None
         self._mean = None
         self._dim = None

pts/model/quantile.py

@@ -1,6 +1,7 @@
 from typing import NamedTuple, Union
 import re
 
+
 class Quantile(NamedTuple):
     value: float
     name: str

pts/model/utils.py

@@ -2,6 +2,7 @@ import inspect
 
 import torch.nn as nn
 
+
 def get_module_forward_input_names(module: nn.Module):
     params = inspect.signature(module.forward).parameters
     return list(params)