def _reshape_out(
series: TimeSeries,
vals: np.ndarray,
component_mask: Optional[np.ndarray] = None,
) -> np.ndarray:
"""Reshapes the 2-D matrix coming out of a transformer into a 3-D matrix suitable to build a TimeSeries.
The output is a 3-D matrix, built by taking each column of the 2-D matrix (the flattened components)
and reshaping them to (len(series), n_samples), then stacking them on 2nd axis.
Parameters
----------
series
input TimeSeries that was fed into transformer.
vals:
transformer output
component_mask
Optionally, np.ndarray boolean mask of shape (n_components, 1) specifying which components were extracted
from `series`. If given, insert `vals` back into the columns of the original array.
"""
raise_if_not(
component_mask is None or isinstance(component_mask, np.ndarray)
and component_mask.dtype == bool,
"If `component_mask` is given, must be a boolean np.ndarray`",
logger,
)
series_width = series.width if component_mask is None else component_mask.sum(
)
reshaped = np.stack(
[
vals[:, i].reshape(-1, series.n_samples)
for i in range(series_width)
],
axis=1,
)
if component_mask is None:
return reshaped
raise_if_not(
series.width == len(component_mask),
"mismatch between number of components in `series` and length of `component_mask`",
logger,
)
series_vals = series.all_values(copy=True)
series_vals[:, component_mask, :] = reshaped
return series_vals
def _get_values(
series: TimeSeries, stochastic_quantile: Optional[float] = 0.5
) -> np.ndarray:
"""
Returns the numpy values of a time series.
For stochastic series, return either all sample values with (stochastic_quantile=None) or the quantile sample value
with (stochastic_quantile {>=0,<=1})
"""
if series.is_deterministic:
series_values = series.univariate_values()
else: # stochastic
if stochastic_quantile is None:
series_values = series.all_values(copy=False)
else:
series_values = series.quantile_timeseries(
quantile=stochastic_quantile
).univariate_values()
return series_values
def _reshape_in(series: TimeSeries,
component_mask: Optional[np.ndarray] = None) -> np.ndarray:
"""Reshapes the series' values to be fed in input to a transformer.
The output is a 2-D matrix where each column corresponds to a component (dimension)
of the series, and the columns' values are the flattened values over all samples
Parameters
----------
series
input TimeSeries to be fed into transformer.
component_mask
Optionally, np.ndarray boolean mask of shape (n_components, 1) specifying which components to
extract from `series`.
"""
if component_mask is None:
component_mask = np.ones(series.n_components, dtype=bool)
raise_if_not(
isinstance(component_mask, np.ndarray)
and component_mask.dtype == bool,
"`component_mask` must be a boolean np.ndarray`",
logger,
)
raise_if_not(
series.width == len(component_mask),
"mismatch between number of components in `series` and length of `component_mask`",
logger,
)
vals = series.all_values(copy=False)[:, component_mask, :]
return np.stack(
[vals[:, i, :].reshape(-1) for i in range(component_mask.sum())],
axis=1)