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Make google docstrings #79

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merged 3 commits into from
Oct 16, 2023
Merged

Make google docstrings #79

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22aa8d2
Work commences!
Oct 13, 2023
7390847
Additional docstrings fixed
Oct 16, 2023
7b36be9
Merge branch 'main' into make-google-docstrings
kallewesterling Oct 16, 2023
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362 changes: 156 additions & 206 deletions deepsensor/active_learning/acquisition_fns.py
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165 changes: 88 additions & 77 deletions deepsensor/active_learning/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,8 +33,12 @@ class GreedyAlgorithm:
def __init__(
self,
model: DeepSensorModel,
X_s: Union[xr.Dataset, xr.DataArray, pd.DataFrame, pd.Series, pd.Index],
X_t: Union[xr.Dataset, xr.DataArray, pd.DataFrame, pd.Series, pd.Index],
X_s: Union[
xr.Dataset, xr.DataArray, pd.DataFrame, pd.Series, pd.Index
],
X_t: Union[
xr.Dataset, xr.DataArray, pd.DataFrame, pd.Series, pd.Index
],
X_s_mask: Optional[Union[xr.Dataset, xr.DataArray]] = None,
X_t_mask: Optional[Union[xr.Dataset, xr.DataArray]] = None,
N_new_context: int = 1,
Expand All @@ -53,47 +57,45 @@ def __init__(
"""
...

Parameters
----------
model : :class:`~.model.model.DeepSensorModel`
Trained model to use for proposing new context points.
X_s : :class:`xarray.Dataset` | :class:`xarray.DataArray` | :class:`pandas.DataFrame` | :class:`pandas.Series` | :class:`pandas.Index`
Search coordinates.
X_t : :class:`xarray.Dataset` | :class:`xarray.DataArray`
Target coordinates.
X_s_mask : :class:`xarray.Dataset` | :class:`xarray.DataArray`, optional
Mask for search coordinates. If provided, only points where mask
is True will be considered. Defaults to None.
X_t_mask : :class:`xarray.Dataset` | :class:`xarray.DataArray`, optional
..., by default None.
N_new_context : int, optional
..., by default 1.
X_normalised : bool, optional
..., by default False.
model_infill_method : str, optional
..., by default "mean".
query_infill : :class:`xarray.DataArray`, optional
..., by default None.
proposed_infill : :class:`xarray.DataArray`, optional
..., by default None.
context_set_idx : int, optional
..., by default 0.
target_set_idx : int, optional
..., by default 0.
progress_bar : bool, optional
..., by default False.
min_or_max : str, optional
..., by default "min".
task_loader : :class:`~.data.loader.TaskLoader`, optional
..., by default None.
verbose : bool, optional
..., by default False.

Raises
------
ValueError
If the ``model`` passed does not inherit from
:class:`~.model.model.DeepSensorModel`.
Args:
model (:class:`~.model.model.DeepSensorModel`):
Trained model to use for proposing new context points.
X_s (:class:`xarray.Dataset` | :class:`xarray.DataArray` | :class:`pandas.DataFrame` | :class:`pandas.Series` | :class:`pandas.Index`):
Search coordinates.
X_t (:class:`xarray.Dataset` | :class:`xarray.DataArray`):
Target coordinates.
X_s_mask (:class:`xarray.Dataset` | :class:`xarray.DataArray`, optional):
Mask for search coordinates. If provided, only points where mask
is True will be considered. Defaults to None.
X_t_mask (:class:`xarray.Dataset` | :class:`xarray.DataArray`, optional):
[Description of the X_t_mask parameter.], defaults to None.
N_new_context (int, optional):
[Description of the N_new_context parameter.], defaults to 1.
X_normalised (bool, optional):
[Description of the X_normalised parameter.], defaults to False.
model_infill_method (str, optional):
[Description of the model_infill_method parameter.], defaults to "mean".
query_infill (:class:`xarray.DataArray`, optional):
[Description of the query_infill parameter.], defaults to None.
proposed_infill (:class:`xarray.DataArray`, optional):
[Description of the proposed_infill parameter.], defaults to None.
context_set_idx (int, optional):
[Description of the context_set_idx parameter.], defaults to 0.
target_set_idx (int, optional):
[Description of the target_set_idx parameter.], defaults to 0.
progress_bar (bool, optional):
[Description of the progress_bar parameter.], defaults to False.
min_or_max (str, optional):
[Description of the min_or_max parameter.], defaults to "min".
task_loader (:class:`~.data.loader.TaskLoader`, optional):
[Description of the task_loader parameter.], defaults to None.
verbose (bool, optional):
[Description of the verbose parameter.], defaults to False.

Raises:
ValueError:
If the ``model`` passed does not inherit from
:class:`~.model.model.DeepSensorModel`.
"""
if not isinstance(model, DeepSensorModel):
raise ValueError(
Expand Down Expand Up @@ -136,11 +138,15 @@ def __init__(
self.X_t_mask = process_X_mask_for_X(self.X_t_mask, self.X_t)

# Interpolate overridden infill datasets at search points if necessary
if query_infill is not None and not da1_da2_same_grid(query_infill, X_s):
if query_infill is not None and not da1_da2_same_grid(
query_infill, X_s
):
if verbose:
print("query_infill not on search grid, interpolating.")
query_infill = interp_da1_to_da2(query_infill, self.X_s)
if proposed_infill is not None and not da1_da2_same_grid(proposed_infill, X_s):
if proposed_infill is not None and not da1_da2_same_grid(
proposed_infill, X_s
):
if verbose:
print("proposed_infill not on search grid, interpolating.")
proposed_infill = interp_da1_to_da2(proposed_infill, self.X_s)
Expand All @@ -153,7 +159,9 @@ def __init__(
self.X_t_arr = xarray_to_coord_array_normalised(X_t)
if self.X_t_mask is not None:
# Remove points that lie outside the mask
self.X_t_arr = mask_coord_array_normalised(self.X_t_arr, self.X_t_mask)
self.X_t_arr = mask_coord_array_normalised(
self.X_t_arr, self.X_t_mask
)
elif isinstance(X_t, (pd.DataFrame, pd.Series, pd.Index)):
# Targets off-grid
self.X_t_arr = X_t.reset_index()[["x1", "x2"]].values.T
Expand Down Expand Up @@ -200,7 +208,9 @@ def _get_times_from_tasks(self):

def _model_infill_at_search_points(
self,
X_s: Union[xr.Dataset, xr.DataArray, pd.DataFrame, pd.Series, pd.Index],
X_s: Union[
xr.Dataset, xr.DataArray, pd.DataFrame, pd.Series, pd.Index
],
):
"""
Computes and sets the model infill y-values over whole search grid
Expand Down Expand Up @@ -345,7 +355,9 @@ def _search(self, acquisition_fn: AcquisitionFunction):
and self.task_loader.aux_at_contexts
):
# Add auxiliary variable sampled at context set as a new context variable
X_c = task_with_new["X_c"][self.task_loader.aux_at_contexts[0]]
X_c = task_with_new["X_c"][
self.task_loader.aux_at_contexts[0]
]
Y_c_aux = self.task_loader.sample_offgrid_aux(
X_c, self.task_loader.aux_at_contexts[1]
)
Expand Down Expand Up @@ -426,32 +438,25 @@ def __call__(
"""
Iteratively... docstring TODO

Returns a tensor of proposed new sensor locations (in greedy
iteration/priority order) and their corresponding list of indexes in
the search space.

Parameters
----------
acquisition_fn: :class:`~.active_learning.acquisition_fns.AcquisitionFunction`
...
tasks: List[:class:`~.data.task.Task`] | :class:`~.data.task.Task`
...

Returns
-------
X_new_df, acquisition_fn_ds: Tuple[:class:`pandas.DataFrame`, :class:`xarray.Dataset`]
...

Raises
------
ValueError
If ``acquisition_fn`` is an
:class:`~.active_learning.acquisition_fns.AcquisitionFunctionOracle`
and ``task_loader`` is None.
ValueError
If ``min_or_max`` is not ``"min"`` or ``"max"``.
ValueError
If ``Y_t_aux`` is in ``tasks`` but ``task_loader`` is None.
Args:
acquisition_fn (:class:`~.active_learning.acquisition_fns.AcquisitionFunction`):
[Description of the acquisition_fn parameter.]
tasks (List[:class:`~.data.task.Task`] | :class:`~.data.task.Task`):
[Description of the tasks parameter.]

Returns:
Tuple[:class:`pandas.DataFrame`, :class:`xarray.Dataset`]:
X_new_df, acquisition_fn_ds - [Description of the return values.]

Raises:
ValueError:
If ``acquisition_fn`` is an
:class:`~.active_learning.acquisition_fns.AcquisitionFunctionOracle`
and ``task_loader`` is None.
ValueError:
If ``min_or_max`` is not ``"min"`` or ``"max"``.
ValueError:
If ``Y_t_aux`` is in ``tasks`` but ``task_loader`` is None.
"""
if (
isinstance(acquisition_fn, AcquisitionFunctionOracle)
Expand All @@ -465,7 +470,8 @@ def __call__(
self.min_or_max = acquisition_fn.min_or_max
if self.min_or_max not in ["min", "max"]:
raise ValueError(
f"min_or_max must be either 'min' or 'max', got " f"{self.min_or_max}."
f"min_or_max must be either 'min' or 'max', got "
f"{self.min_or_max}."
)

if diff and isinstance(acquisition_fn, AcquisitionFunctionParallel):
Expand Down Expand Up @@ -496,7 +502,10 @@ def __call__(
"Model expects Y_t_aux data but a TaskLoader isn't "
"provided to GreedyAlgorithm."
)
if self.task_loader is not None and self.task_loader.aux_at_target_dims > 0:
if (
self.task_loader is not None
and self.task_loader.aux_at_target_dims > 0
):
tasks[i]["Y_t_aux"] = self.task_loader.sample_offgrid_aux(
self.X_t_arr, self.task_loader.aux_at_targets
)
Expand Down Expand Up @@ -532,7 +541,9 @@ def __call__(
if self.model_infill_method == "sample":
total_iterations *= self.n_samples

with tqdm(total=total_iterations, disable=not self.progress_bar) as self.pbar:
with tqdm(
total=total_iterations, disable=not self.progress_bar
) as self.pbar:
for iteration in range(self.N_new_context):
self.iteration = iteration
x_new = self._single_greedy_iteration(acquisition_fn)
Expand Down
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