forecast and predictor

pts/dataset/__init__.py

@@ -1,6 +1,6 @@
 from .common import DataEntry, FieldName, Dataset
 from .list_dataset import ListDataset
-from .loader import TrainDataLoader
+from .loader import TrainDataLoader, InferenceDataLoader
 from .sampler import (
     InstanceSampler,
     BucketInstanceSampler,

pts/model/__init__.py

@@ -1,5 +1,5 @@
 from .estimator import Estimator, PTSEstimator
-from .forecast import Forecast
+from .forecast import Forecast, SampleForecast, QuantileForecast
 from .predictor import Predictor
 from .quantile import Quantile
 from .utils import get_module_forward_input_names, copy_parameters

pts/model/forecast.py

@@ -322,84 +322,83 @@ class QuantileForecast(Forecast):
         )
 
 
-# class DistributionForecast(Forecast):
-#     """
-#     A `Forecast` object that uses a distribution directly.
-#     This can for instance be used to represent marginal probability
-#     distributions for each time point -- although joint distributions are
-#     also possible, e.g. when using MultiVariateGaussian).
+class DistributionForecast(Forecast):
+    """
+    A `Forecast` object that uses a distribution directly.
+    This can for instance be used to represent marginal probability
+    distributions for each time point -- although joint distributions are
+    also possible, e.g. when using MultiVariateGaussian).
 
-#     Parameters
-#     ----------
-#     distribution
-#         Distribution object. This should represent the entire prediction
-#         length, i.e., if we draw `num_samples` samples from the distribution,
-#         the sample shape should be
+    Parameters
+    ----------
+    distribution
+        Distribution object. This should represent the entire prediction
+        length, i.e., if we draw `num_samples` samples from the distribution,
+        the sample shape should be
 
-#            samples = trans_dist.sample(num_samples)
-#            samples.shape -> (num_samples, prediction_length)
+           samples = trans_dist.sample(num_samples)
+           samples.shape -> (num_samples, prediction_length)
 
-#     start_date
-#         start of the forecast
-#     freq
-#         forecast frequency
-#     info
-#         additional information that the forecaster may provide e.g. estimated
-#         parameters, number of iterations ran etc.
-#     """
-#     @validated()
-#     def __init__(
-#             self,
-#             distribution: Distribution,
-#             start_date,
-#             freq,
-#             item_id: Optional[str] = None,
-#             info: Optional[Dict] = None,
-#     ):
-#         self.distribution = distribution
-#         self.shape = (self.distribution.batch_shape +
-#                       self.distribution.event_shape)
-#         self.prediction_length = self.shape[0]
-#         self.item_id = item_id
-#         self.info = info
+    start_date
+        start of the forecast
+    freq
+        forecast frequency
+    info
+        additional information that the forecaster may provide e.g. estimated
+        parameters, number of iterations ran etc.
+    """
+    def __init__(
+            self,
+            distribution: Distribution,
+            start_date,
+            freq,
+            item_id: Optional[str] = None,
+            info: Optional[Dict] = None,
+    ):
+        self.distribution = distribution
+        self.shape = (self.distribution.batch_shape +
+                      self.distribution.event_shape)
+        self.prediction_length = self.shape[0]
+        self.item_id = item_id
+        self.info = info
 
-#         assert isinstance(
-#             start_date,
-#             pd.Timestamp), "start_date should be a pandas Timestamp object"
-#         self.start_date = start_date
+        assert isinstance(
+            start_date,
+            pd.Timestamp), "start_date should be a pandas Timestamp object"
+        self.start_date = start_date
 
-#         assert isinstance(freq, str), "freq should be a string"
-#         self.freq = freq
-#         self._mean = None
+        assert isinstance(freq, str), "freq should be a string"
+        self.freq = freq
+        self._mean = None
 
-#     @property
-#     def mean(self):
-#         """
-#         Forecast mean.
-#         """
-#         if self._mean is not None:
-#             return self._mean
-#         else:
-#             self._mean = self.distribution.mean.asnumpy()
-#             return self._mean
+    @property
+    def mean(self):
+        """
+        Forecast mean.
+        """
+        if self._mean is not None:
+            return self._mean
+        else:
+            self._mean = self.distribution.mean.asnumpy()
+            return self._mean
 
-#     @property
-#     def mean_ts(self):
-#         """
-#         Forecast mean, as a pandas.Series object.
-#         """
-#         return pd.Series(self.index, self.mean)
+    @property
+    def mean_ts(self):
+        """
+        Forecast mean, as a pandas.Series object.
+        """
+        return pd.Series(self.index, self.mean)
 
-#     def quantile(self, level):
-#         level = Quantile.parse(level).value
-#         q = self.distribution.quantile(mx.nd.array([level])).asnumpy()[0]
-#         return q
+    def quantile(self, level):
+        level = Quantile.parse(level).value
+        q = self.distribution.quantile(mx.nd.array([level])).asnumpy()[0]
+        return q
 
-#     def to_sample_forecast(self, num_samples: int = 200) -> SampleForecast:
-#         return SampleForecast(
-#             samples=self.distribution.sample(num_samples),
-#             start_date=self.start_date,
-#             freq=self.freq,
-#             item_id=self.item_id,
-#             info=self.info,
-#         )
+    def to_sample_forecast(self, num_samples: int = 200) -> SampleForecast:
+        return SampleForecast(
+            samples=self.distribution.sample(num_samples),
+            start_date=self.start_date,
+            freq=self.freq,
+            item_id=self.item_id,
+            info=self.info,
+        )

pts/model/forecast_generator.py

@@ -0,0 +1,43 @@
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Iterator, List, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from pts.dataset import InferenceDataLoader, DataEntry
+from pts.model import Forecast, DistributionForecast
+from pts.modules import DistributionOutput
+
+OutputTransform = Callable[[DataEntry, np.ndarray], np.ndarray]
+
+
+class ForecastGenerator(ABC):
+    """
+    Classes used to bring the output of a network into a class.
+    """
+    @abstractmethod
+    def __call__(self, 
+                 inference_data_loader: InferenceDataLoader,
+                 prediction_net: nn.Module,
+                 input_names: List[str],
+                 freq: str,
+                 output_transform: Optional[OutputTransform],
+                 num_samples: Optional[int], 
+                 **kwargs) -> Iterator[Forecast]:
+        pass
+
+
+class DistributionForecastGenerator(ForecastGenerator):
+    def __init__(self, distr_output: DistributionOutput) -> None:
+        self.distr_output = distr_output
+
+    def __call__(self,
+                 inference_data_loader: InferenceDataLoader,
+                 prediction_net: nn.Module,
+                 input_names: List[str],
+                 freq: str,
+                 output_transform: Optional[OutputTransform],
+                 num_samples: Optional[int], 
+                 **kwargs) -> Iterator[DistributionForecast]:
+        

pts/model/predictor.py

@@ -20,3 +20,5 @@ class Predictor(ABC):
 
 class PTSPredictor(Predictor):
     BlockType = nn.Module
+
+