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SPIDER.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+from typing import Dict, List, Optional, Set
+
+import numpy as np
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+_HOMEPAGE = "https://zenodo.org/records/10159290"
+
+_LICENSE = """Creative Commons Attribution 4.0 International License \
+(https://creativecommons.org/licenses/by/4.0/legalcode)"""
+
+# TODO: Add link to the official dataset URLs here
+# 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",
+    }
+}
+
+class SPIDER(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # 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
+
+    # 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"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    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 ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    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
+
+        # 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)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "paths_dict": paths_dict,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "paths_dict": paths_dict,
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "paths_dict": paths_dict,
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, paths_dict, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        
+        # Generate train/validate/test partitions of patient IDs
+        np.random.seed(9999)
+        N_PATIENTS = 257 #TODO: make hardcoded values dynamic
+        VALIDATE_SHARE = 0.3
+        TEST_SHARE = 0.2
+        TRAIN_SHARE = (1.0 - VALIDATE_SHARE - TEST_SHARE)
+        partition = np.random.choice(
+            ['train', 'dev', 'test'],
+            p=[TRAIN_SHARE, VALIDATE_SHARE, TEST_SHARE],
+            size=N_PATIENTS,
+        )
+        patient_ids = (np.arange(N_PATIENTS) + 1)
+        train_ids = set(patient_ids[partition == 'train'])
+        validate_ids = set(patient_ids[partition == 'dev'])
+        test_ids = set(patient_ids[partition == 'test'])
+        assert len(train_ids.union(validate_ids, test_ids)) == N_PATIENTS
+        
+        
+        
+        # Import patient/scanner data and radiological gradings data
+        overview_data = import_csv_data(paths_dict['overview'])
+        grades_data = import_csv_data(paths_dict['gradings'])
+        
+        # Import image and mask data
+        image_files = [
+            file for file in os.listdir(os.path.join(paths_dict['images'], 'images'))
+            if file.endswith('.mha')
+        ]
+        assert len(image_files) > 0, "No image files found--check directory path."
+        
+        mask_files = [
+            file for file in os.listdir(os.path.join(paths_dict['masks'], 'masks'))
+            if file.endswith('.mha')
+        ]
+        assert len(mask_files) > 0, "No mask files found--check directory path."
+        
+        images = []
+        masks = []
+        if split == 'train':
+            for patient_id in train_ids:
+                
+            
+        elif split == 'validate':
+            
+        elif split == 'test':
+        
+            
+    def import_csv_data(filepath: str) -> List[Dict[str, str]]:
+        """Import all rows of CSV file."""
+        results = []
+        with open(filepath, encoding='utf-8') as f:
+            reader = csv.DictReader(f)
+            for line in reader:
+                results.append(line)
+        return results
+                
+        
+        
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "first_domain":
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "option1": data["option1"],
+                        "answer": "" if split == "test" else data["answer"],
+                    }
+                else:
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "option2": data["option2"],
+                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
+                    }