Remove freq attribute from NPTS

src/gluonts/model/npts/_predictor.py

@@ -38,7 +38,7 @@ class NPTSPredictor(RepresentablePredictor):
 
  Forecasts of NPTS for time step :math:`T` are one of the previous values
  of the time series (these could be known values or predictions), sampled
- according to the (un-normalized) distribution :math:`q_T(t) > 0`, where
+ according to the (un-normalized) distribution :math:`q_T(t) > 0` where
  :math:`0 <= t < T`.
 
  The distribution :math:`q_T` is expressed in terms of a feature map
@@ -95,9 +95,9 @@ class NPTSPredictor(RepresentablePredictor):
  Forecaster (i.e., with the `exponential` kernel) and no features for the
  Climatological Forecaster (i.e., the `uniform` kernel).
 
- For seasonal NPTS and seasonal Climatological, time features determined
- based on the frequency of the time series are added to the default
- feature map.
+ For seasonal NPTS and seasonal Climatological, time features are
+ determined based on the frequency of the time series are added to the
+ default feature map.
 
  The default time features for various frequencies are
 
@@ -122,8 +122,6 @@ class NPTSPredictor(RepresentablePredictor):
  Parameters
  ----------
 
- freq
- time frequency string
  prediction_length
  number of time steps to predict
  context_length
@@ -153,7 +151,6 @@ class NPTSPredictor(RepresentablePredictor):
  @validated()
  def __init__(
  self,
- freq: str,
  prediction_length: int,
  context_length: Optional[int] = None,
  kernel_type: KernelType = KernelType.exponential,
@@ -174,7 +171,6 @@ def __init__(
  self.use_seasonal_model = use_seasonal_model
  self.use_default_time_features = use_default_time_features
  self.feature_scale = feature_scale
- self.freq = freq
 
  if not self._is_exp_kernel():
  self.kernel = NPTS.uniform_kernel()
@@ -198,7 +194,7 @@ def predict(
  start = data["start"]
  target = np.asarray(data["target"], np.float32)
  index = pd.period_range(
- start=start, freq=self.freq, periods=len(target)
+ start=start, freq=start.freq, periods=len(target)
  )
  item_id = data.get("item_id", None)
 
@@ -248,6 +244,7 @@ def predict_time_series(
  number of samples to draw
  item_id
  item_id to identify the time series
+
  Returns
  -------
  Forecast

test/model/npts/test_npts.py

@@ -17,15 +17,17 @@
 import pandas as pd
 import pytest
 
-from gluonts.dataset.common import DataEntry, Dataset, ListDataset
+from gluonts.dataset.common import DataEntry, Dataset
 from gluonts.exceptions import GluonTSDataError
 from gluonts.model.npts import KernelType, NPTSPredictor
 from gluonts.model.npts._weighted_sampler import WeightedSampler
 
 
-def get_test_data(history_length: int, freq: str) -> pd.Series:
+def get_test_data(
+ history_length: int, freq: str, dtype=np.float32
+) -> pd.Series:
  index = pd.date_range("1/1/2011", periods=history_length, freq=freq)
- return pd.Series(np.arange(len(index)), index=index)
+ return pd.Series(np.arange(len(index), dtype=dtype), index=index)
 
 
 @pytest.mark.parametrize(
@@ -96,15 +98,16 @@ def test_climatological_forecaster(
  predictor = NPTSPredictor(
  prediction_length=pred_length,
  context_length=context_length,
- freq=freq,
  use_seasonal_model=use_seasonal_model,
  kernel_type=KernelType.uniform,
  )
 
- dataset = ListDataset(
- [{"start": str(train_ts.index[0]), "target": train_ts.values}],
- freq=freq,
- )
+ dataset = [
+ {
+ "start": pd.Period(train_ts.index[0], freq=freq),
+ "target": train_ts.values,
+ }
+ ]
 
  # validate that the predictor works with targets with NaNs
  _test_nans_in_target(predictor, dataset)
@@ -259,16 +262,17 @@ def test_npts_forecaster(
  predictor = NPTSPredictor(
  prediction_length=pred_length,
  context_length=context_length,
- freq=freq,
  kernel_type=KernelType.exponential,
  feature_scale=feature_scale,
  use_seasonal_model=use_seasonal_model,
  )
 
- dataset = ListDataset(
- [{"start": str(train_ts.index[0]), "target": train_ts.values}],
- freq=freq,
- )
+ dataset = [
+ {
+ "start": pd.Period(train_ts.index[0], freq=freq),
+ "target": train_ts.values,
+ }
+ ]
 
  # validate that the predictor works with targets with NaNs
  _test_nans_in_target(predictor, dataset)
@@ -422,24 +426,20 @@ def test_npts_custom_features(
 
  predictor = NPTSPredictor(
  prediction_length=pred_length,
- freq=freq,
  context_length=context_length,
  kernel_type=KernelType.exponential,
  feature_scale=feature_scale,
  use_seasonal_model=use_seasonal_model,
  use_default_time_features=False, # disable default time features
  )
 
- dataset = ListDataset(
- [
- {
- "start": str(train_ts.index[0]),
- "target": train_ts.values,
- "feat_dynamic_real": feat_dynamic_real,
- }
- ],
- freq=freq,
- )
+ dataset = [
+ {
+ "start": pd.Period(train_ts.index[0], freq=freq),
+ "target": train_ts.values,
+ "feat_dynamic_real": np.array(feat_dynamic_real),
+ }
+ ]
 
  # validate that the predictor works with targets with NaNs
  _test_nans_in_target(predictor, dataset)
@@ -532,20 +532,14 @@ def _test_nans_in_target(predictor: NPTSPredictor, dataset: Dataset) -> None:
  """
 
  # a copy of dataset with 90% of the target entries NaNs
- ds_090pct_nans = ListDataset(
- data_iter=[
- _inject_nans_in_target(data_entry, p=0.9) for data_entry in dataset
- ],
- freq=predictor.freq,
- )
+ ds_090pct_nans = [
+ _inject_nans_in_target(data_entry, p=0.9) for data_entry in dataset
+ ]
 
  # a copy of dataset with 100% of the target entries NaNs
- ds_100pct_nans = ListDataset(
- data_iter=[
- _inject_nans_in_target(data_entry, p=1.0) for data_entry in dataset
- ],
- freq=predictor.freq,
- )
+ ds_100pct_nans = [
+ _inject_nans_in_target(data_entry, p=1.0) for data_entry in dataset
+ ]
 
  # assert that we can tolerate a high percentage of NaNs
  for forecast in predictor.predict(ds_090pct_nans):