Convert dataset to Parquet (#4)

- Convert dataset to Parquet (d77a99e6499f42dd4e031b0ea0845ccaa69aaa8d)
- Delete data file (7589957123fc6aa686c3ebaee671da717bdf5efc)
- Delete loading script (08a19786ffd04846adf149f6105ec78778156155)
- Delete data file (18754307222ceffa4b92630398e6400c030e5d36)

EEG-semantic-text-relevance.py

@@ -1,176 +0,0 @@
-# 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 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 NeurIPS 2024,
-title = {An EEG dataset of word-level brain responses for
-semantic text relevance},
-author={Vadym Gryshchuk, Michiel Spapé, Maria Maistro, Christina Lioma,
-Tuukka Ruotsalo},
-year={2024}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-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.
-"""
-
-_HOMEPAGE = "https://vadymv.github.io/EEG-dataset-for-semantic-text-relevance/"
-
-_LICENSE = "Apache Licence 2.0"
-
-_URLS = {
-    "cleaned": {
-        "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="cleaned", version=VERSION,
-                               description="Load the preprocessed (cleaned) EEG data"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "cleaned"  # 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 == "cleaned":  # 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 == "cleaned":
-                # 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.")

README.md

@@ -1,3 +1,45 @@
+---
+dataset_info:
+  config_name: cleaned
+  features:
+  - name: event
+    dtype: int64
+  - name: word
+    dtype: string
+  - name: topic
+    dtype: string
+  - name: selected_topic
+    dtype: string
+  - name: semantic_relevance
+    dtype: int64
+  - name: interestingness
+    dtype: int64
+  - name: pre-knowledge
+    dtype: int64
+  - name: sentence_number
+    dtype: int64
+  - name: participant
+    dtype: string
+  - name: eeg
+    dtype:
+      array2_d:
+        shape:
+        - 32
+        - 2001
+        dtype: float64
+  splits:
+  - name: train
+    num_bytes: 11925180913
+    num_examples: 23270
+  download_size: 11927979870
+  dataset_size: 11925180913
+configs:
+- config_name: cleaned
+  data_files:
+  - split: train
+    path: cleaned/train-*
+  default: true
+---
 
 
 We release 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.

data/metadataForCleanedEEG.pkl → cleaned/train-00000-of-00024.parquet

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data/cleanedEEG.npy → cleaned/train-00001-of-00024.parquet

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