Externalize common test code to methods.

Support for sparse matrix.

Signed-off-by: abigailt <abigailt@il.ibm.com>

apt/utils/datasets/datasets.py

@@ -15,6 +15,7 @@ import pandas as pd
 import logging
 import torch
 from torch import Tensor
+from scipy.sparse import csr_matrix
 
 logger = logging.getLogger(__name__)
 
@@ -37,6 +38,8 @@ def array2numpy(arr: INPUT_DATA_ARRAY_TYPE) -> OUTPUT_DATA_ARRAY_TYPE:
         return np.array(arr)
     if type(arr) == Tensor:
         return arr.detach().cpu().numpy()
+    if type(arr) == csr_matrix:
+        return arr.toarray()
 
     raise ValueError("Non supported type: ", type(arr).__name__)
 
@@ -53,6 +56,8 @@ def array2torch_tensor(arr: INPUT_DATA_ARRAY_TYPE) -> Tensor:
         return torch.tensor(arr)
     if type(arr) == Tensor:
         return arr
+    if type(arr) == csr_matrix:
+        return torch.from_numpy(arr.toarray())
 
     raise ValueError("Non supported type: ", type(arr).__name__)
 

apt/utils/models/sklearn_model.py

@@ -46,7 +46,7 @@ class SklearnClassifier(SklearnModel):
     def __init__(self, model: BaseEstimator, output_type: ModelOutputType, black_box_access: Optional[bool] = True,
                  unlimited_queries: Optional[bool] = True, **kwargs):
         super().__init__(model, output_type, black_box_access, unlimited_queries, **kwargs)
-        self._art_model = ArtSklearnClassifier(model)
+        self._art_model = ArtSklearnClassifier(model, preprocessing=None)
 
     def fit(self, train_data: Dataset, **kwargs) -> None:
         """