message: Rename "_periods" private property in ARModel with "_periodicities" to make it more accurate.
diff: @@ -264,10 +264,10 @@ class ARModel(model.TimeSeriesModel):
  elif (not isinstance(periodicities, list) and
  not isinstance(periodicities, tuple)):
  periodicities = [periodicities]
  - self._periods = [int(p) for p in periodicities]
  - for p in self._periods:
  + self._periodicities = [int(p) for p in periodicities]
  + for p in self._periodicities:
  assert p > 0
  - assert len(self._periods) or self.input_window_size
  + assert len(self._periodicities) or self.input_window_size
  assert output_window_size > 0
  def initialize_graph(self, input_statistics=None):
  @@ -364,9 +364,9 @@ class ARModel(model.TimeSeriesModel):
  input_feature_size = 0
  output_window_features = []
  output_feature_size = 0
  - if self._periods:
  + if self._periodicities:
  _, time_features = self._compute_time_features(times)
  - num_time_features = self._buckets * len(self._periods)
  + num_time_features = self._buckets * len(self._periodicities)
  time_features = array_ops.reshape(
  time_features,
  [batch_size,
  @@ -849,12 +849,12 @@ class ARModel(model.TimeSeriesModel):
  def _compute_time_features(self, time):
  """Compute some features on the time value."""
  batch_size = array_ops.shape(time)[0]
  - num_periods = len(self._periods)
  + num_periods = len(self._periodicities)
  # Reshape to 3D.
  periods = constant_op.constant(
  - self._periods, shape=[1, 1, num_periods, 1], dtype=time.dtype)
  + self._periodicities, shape=[1, 1, num_periods, 1], dtype=time.dtype)
  time = array_ops.reshape(time, [batch_size, -1, 1, 1])
  - window_offset = time / self._periods
  + window_offset = time / self._periodicities
  # Cast to appropriate type and scale to [0, 1) range
  mod = (math_ops.cast(time % periods, self.dtype) * self._buckets /
  math_ops.cast(periods, self.dtype))
  