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climate_data.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93fc0e5aacc47d0206cb2dac3dcf627494c4817171c63db8e300ee8cc1a3388c
+size 16723047

comment_data.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4a15f498476cf271def33ae786f72ea7926e3bd1634aff6f7d166c9bfa94b0f
+size 17846138

csvtransformerjson.py

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+import csv
+import pandas as pd
+
+class CSVtoJSONTransformer:
+    def __init__(self, csv_reader):
+        self.csv_reader = csv_reader
+    
+    def transform_to_json(self):
+        df = pd.DataFrame(self.csv_reader)  # Convert csv_reader to DataFrame
+        df["Author"] = df["Author"].replace({pd.NA: None})
+        df["Comment Body"] = df["Comment Body"].replace({pd.NA: None})
+
+        # Define an empty list to store the transformed data
+        transformed_data = []
+
+        # Group the DataFrame by the 'Subreddit' column
+        grouped_df = df.groupby('Subreddit')
+
+        # Iterate through each subreddit group
+        for subreddit, group in grouped_df:
+            subreddit_data = {
+                "Subreddit": subreddit,
+                "Posts": []
+            }
+            
+            # Iterate through each row in the subreddit group
+            for index, row in group.iterrows():
+                post_title = row['Post Title']
+                comment_body = row['Comment Body']
+                timestamp = row['Timestamp']
+                upvotes = row['Upvotes']
+                ID = row['ID']
+                author = row['Author']
+                replies = row['Number of Replies']
+                
+                # Check if the post title already exists under the subreddit
+                post_exists = False
+                for post in subreddit_data["Posts"]:
+                    if post['PostTitle'] == post_title:
+                        post_exists = True
+                        post['Comments'].append({
+                            "CommentID": ID,
+                            "Author": author,
+                            "CommentBody": comment_body,
+                            "Timestamp": timestamp,
+                            "Upvotes": upvotes,
+                            "NumberofReplies": replies
+                        })
+                        break
+                
+                # If the post title does not exist, create a new entry
+                if not post_exists:
+                    subreddit_data["Posts"].append({
+                        "PostID": len(subreddit_data["Posts"]) + 1,
+                        "PostTitle": post_title,
+                        "Comments": [{
+                            "CommentID": ID,
+                            "Author": author,
+                            "CommentBody": comment_body,
+                            "Timestamp": timestamp,
+                            "Upvotes": upvotes,
+                            "NumberofReplies": replies
+                        }]
+                    })
+            
+            # Append the subreddit data to the transformed data list
+            transformed_data.append(subreddit_data)
+
+        return transformed_data

reddit_climate_data.py

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+
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+import logging
+import datasets
+from csvtransformerjson import CSVtoJSONTransformer
+
+
+# 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={2024}
+}
+"""
+
+# 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.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# 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 = {
+    "reddit_climate": "cathw/comment_data"
+}
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(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="reddit_climate", version=VERSION, description="This part of my dataset covers a first domain")
+    ]
+
+    DEFAULT_CONFIG_NAME = "reddit_climate"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        
+        features = datasets.Features({
+            "Subreddit": datasets.Value("string"),
+            "Posts": datasets.Sequence({
+                "PostID": datasets.Value("int32"),
+                "PostTitle": datasets.Value("string"),
+                "Comments": datasets.Sequence({
+                    "CommentID": datasets.Value("string"),
+                    "Author": datasets.Value("string"),
+                    "CommentBody": datasets.Value("string"),
+                    "Timestamp": datasets.Value("string"),
+                    "Upvotes": datasets.Value("int32"),
+                    "NumberofReplies": datasets.Value("int32"),
+                }),
+            }),
+        })
+        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
+        config_name = getattr(self.config, 'name', self.DEFAULT_CONFIG_NAME)
+        urls = _URLS.get(config_name, {})  # Get the URLs for the configuration name, if not found, return an empty dictionary
+        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": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # 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.
+        with open(filepath, encoding="utf-8") as f:
+            csv_reader = csv.reader(f)
+            data = CSVtoJSONTransformer(csv_reader)
+            for idx, row in enumerate(data):
+                subreddit = row["Subreddit"]
+                posts = []
+                # Check if the "Posts" key is present in the current row
+                if "Posts" in row:
+                    for post in row["Posts"]:
+                        post_id = post["PostID"]
+                        post_title = post["PostTitle"]
+                        comments = []
+                        for comment in post["Comments"]:
+                            comment_id = comment["CommentID"]
+                            author = comment["Author"]
+                            comment_body = comment["CommentBody"]
+                            timestamp = comment["Timestamp"]
+                            upvotes = comment["Upvotes"]
+                            number_of_replies = comment["NumberofReplies"]
+                            comments.append({
+                                "CommentID": comment_id,
+                                "Author": author,
+                                "CommentBody": comment_body,
+                                "Timestamp": timestamp,
+                                "Upvotes": upvotes,
+                                "NumberofReplies": number_of_replies
+                            })
+                        posts.append({
+                            "PostID": post_id,
+                            "PostTitle": post_title,
+                            "Comments": comments
+                        })
+                else:
+                    # Handle cases where the "Posts" key is missing
+                    posts = None
+                
+                yield idx, {
+                    "Subreddit": subreddit,
+                    "Posts": posts
+                }