added custom config options

SPIDER.py

@@ -18,7 +18,7 @@
 import csv
 import json
 import os
-from typing import Dict, List, Optional, Set, Tuple
+from typing import Dict, List, Mapping, Optional, Set, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -62,14 +62,27 @@ _LICENSE = """Creative Commons Attribution 4.0 International License \
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
-    "first_domain": {
-        "images":"https://zenodo.org/records/10159290/files/images.zip",
-        "masks":"https://zenodo.org/records/10159290/files/masks.zip",
-        "overview":"https://zenodo.org/records/10159290/files/overview.csv",
-        "gradings":"https://zenodo.org/records/10159290/files/radiological_gradings.csv",
-    }
+    "images":"https://zenodo.org/records/10159290/files/images.zip",
+    "masks":"https://zenodo.org/records/10159290/files/masks.zip",
+    "overview":"https://zenodo.org/records/10159290/files/overview.csv",
+    "gradings":"https://zenodo.org/records/10159290/files/radiological_gradings.csv",
 }
 
+class CustomBuilderConfig(datasets.BuilderConfig):
+    
+    def __init__(
+        self,
+        name: str = 'default',
+        version: str = '0.0.0',
+        data_dir: Optional[str] = None,
+        data_files: Optional[Union[str, Sequence, Mapping]] = None,
+        description: Optional[str] = None,
+        scan_types: List[str] = ['t1', 't2', 't2_SPACE'],
+    ):
+        super().__init__(name, version, data_dir, data_files, description)
+        self.scan_types = scan_types
+
+
 class SPIDER(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
@@ -81,38 +94,54 @@ class SPIDER(datasets.GeneratorBasedBuilder):
 
     # If you need to make complex sub-parts in the datasets with configurable options
     # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+    BUILDER_CONFIG_CLASS = CustomBuilderConfig
 
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+        CustomBuilderConfig(
+            name="all_scan_types",
+            version=VERSION,
+            description="Use images of all scan types (t1, t2, t2 SPACE)",
+            scan_types=['t1', 't2', 't2_SPACE'],
+        ),
+        CustomBuilderConfig(
+            name="t1_scan_types",
+            version=VERSION,
+            description="Use images of t1 scan types only",
+            scan_types=['t1'],
+        ),
+        CustomBuilderConfig(
+            name="t2_scan_types",
+            version=VERSION,
+            description="Use images of t2 scan types only",
+            scan_types=['t2'],
+        ),
+        CustomBuilderConfig(
+            name="t2_SPACE_scan_types",
+            version=VERSION,
+            description="Use images of t2 SPACE scan types only",
+            scan_types=['t2_SPACE'],
+        ),
     ]
 
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "all_scan_types"
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
+        """
+        This method specifies the datasets.DatasetInfo object which contains 
+        informations and typings for the dataset.
+        """
+        features = datasets.Features(
+            {
+                "sentence": datasets.Value("string"),
+                "option1": datasets.Value("string"),
+                "answer": datasets.Value("string")
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -130,15 +159,16 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        # TODO: This method is tasked with downloading/extracting the data 
+        # and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), 
+        # the configuration selected by the user is in self.config.name
 
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        paths_dict = dl_manager.download_and_extract(urls)
-
+        paths_dict = dl_manager.download_and_extract(_URLS)
+        scan_types = self.config.scan_types
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -146,6 +176,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "paths_dict": paths_dict,
                     "split": "train",
+                    "scan_types": scan_types,
                 },
             ),
             datasets.SplitGenerator(
@@ -153,7 +184,8 @@ class SPIDER(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "paths_dict": paths_dict,
-                    "split": "dev",
+                    "split": "validate",
+                    "scan_types": scan_types,
                 },
             ),
             datasets.SplitGenerator(
@@ -161,7 +193,8 @@ class SPIDER(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "paths_dict": paths_dict,
-                    "split": "test"
+                    "split": "test",
+                    "scan_types": scan_types,
                 },
             ),
         ]
@@ -170,8 +203,8 @@ class SPIDER(datasets.GeneratorBasedBuilder):
     def _generate_examples(
         self,
         paths_dict: Dict[str, str],
+        split: str = 'train',
         scan_types: List[str] = ['t1', 't2', 't2_SPACE'],
-        split: str = 'train', 
         validate_share: float = 0.3,
         test_share: float = 0.2,
         raw_image: bool = True,
@@ -186,15 +219,24 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         (tfds) and is not important in itself, but must be unique for each example.
         
         Args
-            paths_dict
-            scan_types:
-            split:
-            validate_share
-            test_share
-            raw_image
-            numeric_array
-            metadata
-            rad_gradings
+            paths_dict: mapping of data element name to temporary file location
+            split: specify training, validation, or testing set;
+                options = 'train', 'validate', OR 'test'
+            scan_types: list of sagittal scan types to use in examples; 
+                options = ['t1', 't2', 't2_SPACE']
+            validate_share: float indicating share of data to use for validation;
+                must be in range (0.0, 1.0); note that training share is 
+                calculated as (1 - validate_share - test_share)
+            test_share: float indicating share of data to use for testing;
+                must be in range (0.0, 1.0); note that training share is 
+                calculated as (1 - validate_share - test_share)
+            raw_image: indicates whether to include .mha image file in example
+            numeric_array: indicates whether to include numpy numeric array of
+                image in example
+            metadata: indicates whether to include patient and scanner metadata
+                with image example
+            rad_gradings: indicates whether to include patient's radiological
+                gradings with image example
         
         Yields
             Tuple (unique patient-scan ID, dict of