Create EEG-semantic-text-relevance.py

EEG-semantic-text-relevance.py

@@ -0,0 +1,157 @@
+import datasets
+import numpy as np
+import pandas as pd
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{Submitted to ICLR 2025,
+title = {An EEG dataset of word-level brain responses for
+semantic text relevance},
+author={},
+year={2024}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+A dataset containing 23,270 time-locked (0.7s) word-level EEG 
+recordings acquired from participants who read both text that was 
+semantically relevant and irrelevant to self-selected topics.
+"""
+
+_HOMEPAGE = "https://anonymous.4open.science/r/EEG-semantic-text-relevance-651D"
+
+_LICENSE = "apache-2.0"
+
+_URLS = {
+    "data": {
+        "eeg": "./data/cleanedEEG.npy",
+        "metadata": "./data/metadataForCleanedEEG.pkl"
+    }
+}
+
+
+class EEGSemanticTextRelevance(datasets.GeneratorBasedBuilder):
+    """
+    A dataset containing 23,270 time-locked (0.7s) word-level EEG
+    recordings acquired from 15 participants who read both text that was
+    semantically relevant and irrelevant to self-selected topics."""
+
+    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="data", version=VERSION,
+                               description="Load the preprocessed (data) EEG data"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "data"  # 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 == "data":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "event": datasets.Value("int64"),
+                    "word": datasets.Value("string"),
+                    "topic": datasets.Value("string"),
+                    "selected_topic": datasets.Value("string"),
+                    "semantic_relevance": datasets.Value("int64"),
+                    "interestingness": datasets.Value("int64"),
+                    "pre-knowledge": datasets.Value("int64"),
+                    "sentence_number": datasets.Value("int64"),
+                    "participant": datasets.Value("string"),
+                    "eeg": datasets.Array2D(shape=(32, 2001), dtype="float64"),
+                    # 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"
+            raise ValueError("Not implemented.")
+
+        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]
+        # data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath_eeg": urls["eeg"],
+                    "filepath_metadata": urls["metadata"],
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #         "split": "dev",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath_eeg, filepath_metadata):
+        # 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.
+        eeg_data = np.load(filepath_eeg)
+        metadata = pd.read_pickle(filepath_metadata)
+        for key, row in metadata.iterrows():
+            if self.config.name == "data":
+                # Yields examples as (key, example) tuples
+                yield key, {
+                    "event": row["event"],
+                    "word": row["word"],
+                    "topic": row["topic"],
+                    "selected_topic": row["selected_topic"],
+                    "semantic_relevance": row["semantic_relevance"],
+                    "interestingness": row["interestingness"],
+                    "pre-knowledge": row["pre-knowledge"],
+                    "sentence_number": row["sentence_number"],
+                    "participant": row["participant"],
+                    "eeg": eeg_data[key],
+                }
+            else:
+                raise ValueError("Not implemented.")