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Add predict_fn_accept_dense_only to explain_instance when input is sparse but model takes dense #705

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Add predict_fn_accept_dense_only to explain_instance when input is sparse but model takes dense #705

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12 changes: 11 additions & 1 deletion lime/lime_tabular.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,7 +305,8 @@ def explain_instance(self,
num_samples=5000,
distance_metric='euclidean',
model_regressor=None,
sampling_method='gaussian'):
sampling_method='gaussian',
predict_fn_accept_dense_only=False):
"""Generates explanations for a prediction.

First, we generate neighborhood data by randomly perturbing features
Expand Down Expand Up @@ -358,8 +359,17 @@ def explain_instance(self,
metric=distance_metric
).ravel()

is_convert_to_dense = False
if sp.sparse.isspmatrix_csr(inverse) and predict_fn_accept_dense_only:
inverse = inverse.toarray()
is_convert_to_dense = True

yss = predict_fn(inverse)

if is_convert_to_dense:
inverse = sp.sparse.csr_matrix(inverse)
is_convert_to_dense = False

# for classification, the model needs to provide a list of tuples - classes
# along with prediction probabilities
if self.mode == "classification":
Expand Down