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WIP: [python-package] ensure predict() always returns an array #6348

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WIP: [python-package] ensure predict() always returns an array #6348

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d3782da
[python-package] ensure predict() always returns an array
jameslamb Mar 1, 2024
7605296
update tests
jameslamb Mar 1, 2024
5a45947
remove special case from dask test
jameslamb Mar 1, 2024
92d3171
add scikit-learn type hints
jameslamb Mar 1, 2024
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[python-package] ensure predict() always returns an array
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@jameslamb
jameslamb committed Mar 1, 2024
commit d3782da962051b9c4340ff615430e64ce16ec3fa
33 changes: 17 additions & 16 deletions python-package/lightgbm/basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1083,7 +1083,7 @@ def predict(
pred_contrib: bool = False,
data_has_header: bool = False,
validate_features: bool = False,
) -> Union[np.ndarray, scipy.sparse.spmatrix, List[scipy.sparse.spmatrix]]:
) -> Union[np.ndarray, scipy.sparse.spmatrix]:
"""Predict logic.

Parameters
Expand Down Expand Up @@ -1112,9 +1112,9 @@ def predict(

Returns
-------
result : numpy array, scipy.sparse or list of scipy.sparse
result : numpy array or scipy.sparse
Prediction result.
Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).
If ``data`` is a sparse matrix, result will be a sparse matrix.
"""
if isinstance(data, Dataset):
raise TypeError("Cannot use Dataset instance for prediction, please use raw data instead")
Expand Down Expand Up @@ -1354,7 +1354,7 @@ def __create_sparse_native(
indptr_type: int,
data_type: int,
is_csr: bool,
) -> Union[List[scipy.sparse.csc_matrix], List[scipy.sparse.csr_matrix]]:
) -> Union[scipy.sparse.csc_matrix, scipy.sparse.csr_matrix]:
# create numpy array from output arrays
data_indices_len = out_shape[0]
indptr_len = out_shape[1]
Expand Down Expand Up @@ -1402,9 +1402,10 @@ def __create_sparse_native(
ctypes.c_int(data_type),
)
)
if len(cs_output_matrices) == 1:
return cs_output_matrices[0]
return cs_output_matrices
if is_csr:
return scipy.sparse.hstack(cs_output_matrices, format="csr")
else:
return scipy.sparse.hstack(cs_output_matrices, format="csc")

def __inner_predict_csr(
self,
Expand Down Expand Up @@ -1462,7 +1463,7 @@ def __inner_predict_csr_sparse(
start_iteration: int,
num_iteration: int,
predict_type: int,
) -> Tuple[Union[List[scipy.sparse.csc_matrix], List[scipy.sparse.csr_matrix]], int]:
) -> Tuple[Union[scipy.sparse.csc_matrix, scipy.sparse.csr_matrix], int]:
ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr)
ptr_data, type_ptr_data, _ = _c_float_array(csr.data)
csr_indices = csr.indices.astype(np.int32, copy=False)
Expand Down Expand Up @@ -1501,7 +1502,7 @@ def __inner_predict_csr_sparse(
ctypes.byref(out_ptr_data),
)
)
matrices = self.__create_sparse_native(
out_mat = self.__create_sparse_native(
cs=csr,
out_shape=out_shape,
out_ptr_indptr=out_ptr_indptr,
Expand All @@ -1512,7 +1513,7 @@ def __inner_predict_csr_sparse(
is_csr=True,
)
nrow = len(csr.indptr) - 1
return matrices, nrow
return out_mat, nrow

def __pred_for_csr(
self,
Expand Down Expand Up @@ -1563,7 +1564,7 @@ def __inner_predict_sparse_csc(
start_iteration: int,
num_iteration: int,
predict_type: int,
):
) -> Tuple[scipy.sparse.csc_matrix, int]:
ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr)
ptr_data, type_ptr_data, _ = _c_float_array(csc.data)
csc_indices = csc.indices.astype(np.int32, copy=False)
Expand Down Expand Up @@ -1602,7 +1603,7 @@ def __inner_predict_sparse_csc(
ctypes.byref(out_ptr_data),
)
)
matrices = self.__create_sparse_native(
out_mat = self.__create_sparse_native(
cs=csc,
out_shape=out_shape,
out_ptr_indptr=out_ptr_indptr,
Expand All @@ -1613,7 +1614,7 @@ def __inner_predict_sparse_csc(
is_csr=False,
)
nrow = csc.shape[0]
return matrices, nrow
return out_mat, nrow

def __pred_for_csc(
self,
Expand Down Expand Up @@ -4677,7 +4678,7 @@ def predict(
data_has_header: bool = False,
validate_features: bool = False,
**kwargs: Any,
) -> Union[np.ndarray, scipy.sparse.spmatrix, List[scipy.sparse.spmatrix]]:
) -> Union[np.ndarray, scipy.sparse.spmatrix]:
"""Make a prediction.

Parameters
Expand Down Expand Up @@ -4719,9 +4720,9 @@ def predict(

Returns
-------
result : numpy array, scipy.sparse or list of scipy.sparse
result : numpy array or scipy.sparse
Prediction result.
Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).
If ``data`` is a sparse matrix, result will be a sparse matrix.
"""
predictor = _InnerPredictor.from_booster(
booster=self,
Expand Down
4 changes: 0 additions & 4 deletions python-package/lightgbm/compat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,8 +157,6 @@ class _LGBMRegressorBase: # type: ignore
try:
from dask import delayed
from dask.array import Array as dask_Array
from dask.array import from_delayed as dask_array_from_delayed
from dask.bag import from_delayed as dask_bag_from_delayed
from dask.dataframe import DataFrame as dask_DataFrame
from dask.dataframe import Series as dask_Series
from dask.distributed import Client, Future, default_client, wait
Expand All @@ -167,8 +165,6 @@ class _LGBMRegressorBase: # type: ignore
except ImportError:
DASK_INSTALLED = False

dask_array_from_delayed = None # type: ignore[assignment]
dask_bag_from_delayed = None # type: ignore[assignment]
delayed = None
default_client = None # type: ignore[assignment]
wait = None # type: ignore[assignment]
Expand Down
74 changes: 3 additions & 71 deletions python-package/lightgbm/dask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,8 +28,6 @@
LGBMNotFittedError,
concat,
dask_Array,
dask_array_from_delayed,
dask_bag_from_delayed,
dask_DataFrame,
dask_Series,
default_client,
Expand Down Expand Up @@ -906,7 +904,7 @@ def _predict(
The predicted values.
X_leaves : Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]
If ``pred_leaf=True``, the predicted leaf of every tree for each sample.
X_SHAP_values : Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]
X_SHAP_values : Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]
If ``pred_contrib=True``, the feature contributions for each sample.
"""
if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):
Expand All @@ -922,72 +920,6 @@ def _predict(
**kwargs,
).values
elif isinstance(data, dask_Array):
# for multi-class classification with sparse matrices, pred_contrib predictions
# are returned as a list of sparse matrices (one per class)
num_classes = model._n_classes

if num_classes > 2 and pred_contrib and isinstance(data._meta, ss.spmatrix):
predict_function = partial(
_predict_part,
model=model,
raw_score=False,
pred_proba=pred_proba,
pred_leaf=False,
pred_contrib=True,
**kwargs,
)

delayed_chunks = data.to_delayed()
bag = dask_bag_from_delayed(delayed_chunks[:, 0])

@delayed
def _extract(items: List[Any], i: int) -> Any:
return items[i]

preds = bag.map_partitions(predict_function)

# pred_contrib output will have one column per feature,
# plus one more for the base value
num_cols = model.n_features_ + 1

nrows_per_chunk = data.chunks[0]
out: List[List[dask_Array]] = [[] for _ in range(num_classes)]

# need to tell Dask the expected type and shape of individual preds
pred_meta = data._meta

for j, partition in enumerate(preds.to_delayed()):
for i in range(num_classes):
part = dask_array_from_delayed(
value=_extract(partition, i),
shape=(nrows_per_chunk[j], num_cols),
meta=pred_meta,
)
out[i].append(part)

# by default, dask.array.concatenate() concatenates sparse arrays into a COO matrix
# the code below is used instead to ensure that the sparse type is preserved during concatentation
if isinstance(pred_meta, ss.csr_matrix):
concat_fn = partial(ss.vstack, format="csr")
elif isinstance(pred_meta, ss.csc_matrix):
concat_fn = partial(ss.vstack, format="csc")
else:
concat_fn = ss.vstack

# At this point, `out` is a list of lists of delayeds (each of which points to a matrix).
# Concatenate them to return a list of Dask Arrays.
out_arrays: List[dask_Array] = []
for i in range(num_classes):
out_arrays.append(
dask_array_from_delayed(
value=delayed(concat_fn)(out[i]),
shape=(data.shape[0], num_cols),
meta=pred_meta,
)
)

return out_arrays

data_row = client.compute(data[[0]]).result()
predict_fn = partial(
_predict_part,
Expand Down Expand Up @@ -1263,7 +1195,7 @@ def predict(
output_name="predicted_result",
predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]",
X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]",
)

def predict_proba(
Expand Down Expand Up @@ -1298,7 +1230,7 @@ def predict_proba(
output_name="predicted_probability",
predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]",
X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]",
)

def to_local(self) -> LGBMClassifier:
Expand Down