wip

fashion_mnist_label_drift.py

@@ -172,37 +172,4 @@ class FashionMNISTLabelDrift(datasets.GeneratorBasedBuilder):
                     "prediction_ts":pred_ts[idx],
                     "label":labels[idx],
                     "image":images[idx]
-                }
-#        with open(filepath) as csv_file:
-#            csv_reader = csv.reader(csv_file, delimiter='\t')
-#            for id_, row in enumerate(csv_reader):
-#                prediction_ts,language,text,ner_tags = row
-#                ner_tags_list = list(ner_tags.strip('[]').split(' '))
-#                tokens = text.split(":-:")
-#                if id_==0:
-#                    continue
-#                yield id_, {
-#                    "prediction_ts":prediction_ts,
-#                    "language":language,
-#                    "split_text": tokens,
-#                    "ner_tags":ner_tags_list,
-#                }
-#
-#    def _generate_examples(self, filepath, split):
-#        """This function returns the examples in the raw form."""
-#        # Images
-#        with open(filepath[0], "rb") as f:
-#            # First 16 bytes contain some metadata
-#            _ = f.read(4)
-#            size = struct.unpack(">I", f.read(4))[0]
-#            _ = f.read(8)
-#            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
-#
-#        # Labels
-#        with open(filepath[1], "rb") as f:
-#            # First 8 bytes contain some metadata
-#            _ = f.read(8)
-#            labels = np.frombuffer(f.read(), dtype=np.uint8)
-#
-#        for idx in range(size):
-#            yield idx, {"image": images[idx], "label": int(labels[idx])}
+                }