feat: change labels are handled

deepstarr/test.fna

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:40fbc405ea0041cabd82946fb6486f6b2835e3f75246b8a9c9225a3fffb84d73
-size 16498412

deepstarr/train.fna

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:afdd362a17bae77f1d7e4ba87a8b8fe2bb7a533b2bce14a67c153ac991d8e80c
-size 160497156

nucleotide_transformer_downstream_tasks_multilabel.py

@@ -45,24 +45,26 @@ _LICENSE = "https://github.com/instadeepai/nucleotide-transformer/LICENSE.md"
 # with 5 samples in both the train and test fasta files. It is notably used in order to
 # test the scripts.
 _TASKS_NUM_LABELS_DTYPE = [
-    ("deepstarr", 6, "float32"),
     ("toy_classification", 2, "int32"),
     ("toy_regression", 2, "float32"),
 ]
 
-_SPLIT_SIZES = {
-    "deepstarr": {"train": 402034, "test": 41184},
-    "toy_classification": {"train": 35, "test": 35},
-    "toy_regression": {"train": 25, "test": 15},
+_TASK_NAMES = [
+    "toy_classification",
+    "toy_regression",
+]
+
+
+_TASK_INFO = {
+    "toy_classification": {"type": "binary"},
+    "toy_regression": {"type": "regression"},
 }
 
 
 class NucleotideTransformerDownstreamTasksConfig(datasets.BuilderConfig):
     """BuilderConfig for The Nucleotide Transformer downstream taks dataset."""
 
-    def __init__(
-        self, *args, task: str, num_labels=int, dtype: str = "int32", **kwargs
-    ):
+    def __init__(self, *args, task: str, **kwargs):
         """BuilderConfig downstream tasks dataset.
         Args:
             task (:obj:`str`): Task name.
@@ -74,33 +76,29 @@ class NucleotideTransformerDownstreamTasksConfig(datasets.BuilderConfig):
             **kwargs,
         )
         self.task = task
-        self.num_labels = num_labels
-        self.dtype = dtype
-        self.dataset_size = _SPLIT_SIZES[task]
+        self.task_type = _TASK_INFO[self.task]["type"]
 
 
 class NucleotideTransformerDownstreamTasks(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
     BUILDER_CONFIG_CLASS = NucleotideTransformerDownstreamTasksConfig
     BUILDER_CONFIGS = [
-        NucleotideTransformerDownstreamTasksConfig(
-            task=task, num_labels=num_labels, dtype=dtype
-        )
-        for (task, num_labels, dtype) in _TASKS_NUM_LABELS_DTYPE
+        NucleotideTransformerDownstreamTasksConfig(task=task) for task in _TASK_NAMES
     ]
-    DEFAULT_CONFIG_NAME = "deepstarr"
+    DEFAULT_CONFIG_NAME = "toy_classification"
 
     def _info(self):
-        features_dict = {
+        feature_dit = {
             "sequence": datasets.Value("string"),
             "name": datasets.Value("string"),
         }
-        labels_dict = {
-            f"label_{i}": datasets.Value(self.config.dtype)
-            for i in range(self.config.num_labels)
-        }
-        features_dict.update(labels_dict)
-        features = datasets.Features(features_dict)
+
+        if self.config.task_type == "regression":
+            feature_dit["labels"] = [datasets.Value("float32")]
+        elif self.config.task_type == "binary":
+            feature_dit["labels"] = [datasets.Value("int8")]
+
+        features = datasets.Features(feature_dit)
 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
@@ -113,8 +111,6 @@ class NucleotideTransformerDownstreamTasks(datasets.GeneratorBasedBuilder):
             license=_LICENSE,
             # Citation for the dataset
             citation=_CITATION,
-            # Number of sequences
-            dataset_size=self.config.dataset_size,
         )
 
     def _split_generators(
@@ -134,25 +130,15 @@ class NucleotideTransformerDownstreamTasks(datasets.GeneratorBasedBuilder):
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, file):
-        key = 0
-        with open(file, "rt") as f:
-            fasta_sequences = SeqIO.parse(f, "fasta")
-
-            for record in fasta_sequences:
-
-                # parse descriptions in the fasta file
-                sequence, name = str(record.seq), str(record.name)
-                labels = [float(label) for label in name.split("|")[1:]]
-
-                sequence_name_dict = {
-                    "sequence": sequence,
-                    "name": name,
-                }
-
-                labels_dict = {
-                    f"label_{i}": labels[i] for i in range(self.config.num_labels)
-                }
-                sequence_name_dict.update(labels_dict)
-                # yield example
-                yield key, sequence_name_dict
+        with open(file, "r") as f:
+            key = 0
+            for record in SeqIO.parse(f, "fasta"):
+                # Yields examples as (key, example) tuples
+
+                split_name = record.name.split("|")
+                name = split_name[0]
+                labels = split_name[1:]
+
+                yield key, {"sequence": str(record.seq), "name": name, "labels": labels}
+
                 key += 1