20240226 fsaad float time

src/bayesnf/__init__.py

@@ -16,7 +16,7 @@
 
 # A new PyPI release will be pushed every time `__version__` is increased.
 # When changing this, also update the CHANGELOG.md
-__version__ = '0.1.2'
+__version__ = '0.1.3'
 
 from .spatiotemporal import BayesianNeuralFieldMAP
 from .spatiotemporal import BayesianNeuralFieldMLE

src/bayesnf/spatiotemporal.py

@@ -60,8 +60,9 @@ def seasonality_to_float(seasonality: str, freq: str) -> float:
 
 
 def seasonalities_to_array(
-    seasonalities: Sequence[float | str], freq: str
-) -> np.ndarray:
+      seasonalities: Sequence[float | str],
+      freq: str
+      ) -> np.ndarray:
   """Convert a list of floats or strings to durations relative to a frequency.
 
   Args:
@@ -99,6 +100,10 @@ def _convert_datetime_col(table, time_column, timetype, freq, time_min=None):
     first_date = pd.to_datetime('2020-01-01').to_period(freq)
     table[time_column] = table[time_column].dt.to_period(freq)
     table[time_column] = (table[time_column] - first_date).apply(lambda x: x.n)
+  elif timetype == 'float':
+    table[time_column] = table[time_column].apply(float)
+  else:
+    raise ValueError(f'Unknown timetype: {timetype}')
   if time_min is None:
     time_min = table[time_column].min()
   table[time_column] = table[time_column] - time_min
@@ -217,7 +222,7 @@ def __init__(
       num_seasonal_harmonics: Sequence[int] | None = None,
       fourier_degrees: Sequence[float] | None = None,
       interactions: Sequence[tuple[int, int]] | None = None,
-      freq: str,
+      freq: str | None = None,
       timetype: str = 'index',
       depth: int = 2,
       width: int = 512,
@@ -237,16 +242,18 @@ def __init__(
 
       seasonality_periods:
         A list of numbers representing the seasonal frequencies of the data
-        in the time domain. It is also possible to specify a string such as
-        'W', 'D', etc. corresponding to a valid Pandas frequency: see the
-        Pandas [Offset Aliases](
-        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)
-        for valid values.
+        in the time domain. If timetype == 'index', then it is possible
+        to specify numeric frequencies by using string short hands such as
+        'W', 'D', etc., which correspond to a valid Pandas frequency.
+        See Pandas [Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)
+        for valid string values.
 
       num_seasonal_harmonics:
         A list of seasonal harmonics, one for each entry in
         `seasonality_periods`. The number of seasonal harmonics (h) for a
-        given seasonal period `p` must satisfy `h < p//2`.
+        given seasonal period `p` must satisfy `h < p//2`. It is an error
+        fir `len(num_seasonal_harmonics) != len(seasonality_periods)`.
+        Should be used only if `timetype == 'index'`.
 
       fourier_degrees:
         A list of integer degrees for the Fourier features of the inputs.
@@ -263,13 +270,13 @@ def __init__(
       freq:
         A frequency string for the sampling rate at which the data is
         collected. See the Pandas
-        [Offset Aliases](
-        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)
-        for valid values.
+        [Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)
+        for valid values. Should be used if and only if `timetype == 'index'`.
 
       timetype:
-        Must be specified as `index`. The general versions will be
-        integrated pending https://github.com/google/bayesnf/issues/16.
+        Either `index` or `float`. If `index`, then the time column must
+        be a `datetime` type and `freq` must be given.
+        Otherwise, if `float`, then the time column must be `float`.
 
       depth:
         The number of hidden layers in the BayesNF architecture.
@@ -337,18 +344,47 @@ def _get_interactions(self) -> np.ndarray:
             f' passed shape was {interactions.shape})')
     return interactions
 
+  def _get_seasonality_periods(self):
+    """Return array of seasonal periods."""
+    if (
+        (self.timetype == 'index' and self.freq is None) or
+        (self.timetype == 'float' and self.freq is not None)):
+      raise ValueError(f'Invalid {self.freq=} with {self.timetype=}.')
+    if self.seasonality_periods is None:
+      return np.zeros(0)
+    if self.timetype == 'index':
+        return seasonalities_to_array(self.seasonality_periods, self.freq)
+    if self.timetype == 'float':
+      return np.asarray(self.seasonality_periods, dtype=float)
+    assert False, f'Impossible {self.timetype=}.'
+
+  def _get_num_seasonal_harmonics(self):
+    """Return array of seasonal harmonics per seasonal period."""
+    # Discrete time.
+    if self.timetype == 'index':
+        return (
+          np.array(self.num_seasonal_harmonics)
+          if self.num_seasonal_harmonics is not None else
+          np.zeros(0))
+    # Continuous time.
+    if self.timetype == 'float':
+      if self.num_seasonal_harmonics is not None:
+        raise ValueError(
+          f'Cannot use num_seasonal_harmonics with {self.timetype=}.')
+      # HACK: models.make_seasonal_frequencies assumes the data is discrete
+      # time where each harmonic h is between 1, ..., p/2 and the harmonic
+      # factors are np.arange(1, h + 1). Since our goal with continuous
+      # time data is exactly 1 harmonic per seasonal factor, any h between
+      # 0 and min(0.5, p/2) will work, as np.arange(1, 1+h) = [1]
+      return np.fmin(.5, self._get_seasonality_periods() / 2)
+    assert False, f'Impossible {timetype=}.'
+
   def _model_args(self, batch_shape):
     return {
         'depth': self.depth,
         'input_scales': self.data_handler.get_input_scales(),
-        'num_seasonal_harmonics':
-            np.array(self.num_seasonal_harmonics)
-            if self.num_seasonal_harmonics is not None
-            else np.zeros(0),
-        'seasonality_periods':
-            seasonalities_to_array(self.seasonality_periods, self.freq)
-            if self.seasonality_periods is not None
-            else np.zeros(0),
+        'num_seasonal_harmonics': self._get_num_seasonal_harmonics(),
+        'seasonality_periods': self._get_seasonality_periods(),
         'width': self.width,
         'init_x': batch_shape,
         'fourier_degrees': self._get_fourier_degrees(batch_shape),