Improvements on CheckpointWrapper

Added the optional argument hash_fn in the CheckpointWrapper class. Once this is set, sample.key generates a hash code and this hash code is used to compose the final path where sample will be checkpointed.

This is optional and generic enough for our purposes. This hash function can be shipped in the database interface.

Closes #25 (closed)

bob/pipelines/tests/test_wrappers.py

@@ -11,8 +11,9 @@ from sklearn.preprocessing import FunctionTransformer
 from sklearn.utils.estimator_checks import check_estimator
 from sklearn.utils.validation import check_array
 from sklearn.utils.validation import check_is_fitted
-
+from bob.pipelines.utils import hash_string
 import bob.pipelines as mario
+import tempfile
 
 
 def _offset_add_func(X, offset=1):
@@ -175,6 +176,22 @@ def test_checkpoint_function_sample_transfomer():
     features = transformer.transform(samples)
     _assert_all_close_numpy_array(oracle, [s.data for s in features])
 
+    # test when both model_path and features_dir is None
+    with tempfile.TemporaryDirectory() as dir_name:
+        transformer = mario.wrap(
+            [FunctionTransformer, "sample", "checkpoint"],
+            func=_offset_add_func,
+            kw_args=dict(offset=offset),
+            validate=True,
+            features_dir=dir_name,
+            hash_fn=hash_string,
+        )
+
+        features = transformer.transform(samples)
+        # Checking if we have 8 chars in the second level
+        assert len(features[0].load.args[0].split("/")[-2]) == 8
+        _assert_all_close_numpy_array(oracle, [s.data for s in features])
+
 
 def test_checkpoint_fittable_sample_transformer():
     X = np.ones(shape=(10, 2), dtype=int)