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README.md

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+---
+dataset_info:
+  config_name: first_domain
+  features:
+  - name: f_0
+    dtype: int64
+  - name: f_1
+    dtype: int8
+  - name: f_2
+    dtype: int32
+  - name: f_3
+    dtype: int32
+  - name: f_4
+    dtype: int32
+  - name: f_5
+    dtype: int32
+  - name: f_6
+    dtype: int32
+  - name: f_7
+    dtype: int32
+  - name: f_8
+    dtype: int32
+  - name: f_9
+    dtype: int32
+  - name: f_10
+    dtype: int32
+  - name: f_11
+    dtype: int32
+  - name: f_12
+    dtype: int32
+  - name: f_13
+    dtype: int32
+  - name: f_14
+    dtype: int32
+  - name: f_15
+    dtype: int32
+  - name: f_16
+    dtype: int32
+  - name: f_17
+    dtype: int32
+  - name: f_18
+    dtype: int32
+  - name: f_19
+    dtype: int32
+  - name: f_20
+    dtype: int32
+  - name: f_21
+    dtype: int32
+  - name: f_22
+    dtype: int32
+  - name: f_23
+    dtype: int32
+  - name: f_24
+    dtype: int32
+  - name: f_25
+    dtype: int32
+  - name: f_26
+    dtype: int32
+  - name: f_27
+    dtype: int32
+  - name: f_28
+    dtype: int32
+  - name: f_29
+    dtype: int32
+  - name: f_30
+    dtype: int32
+  - name: f_31
+    dtype: int32
+  - name: f_32
+    dtype: int32
+  - name: f_33
+    dtype: int8
+  - name: f_34
+    dtype: int8
+  - name: f_35
+    dtype: int8
+  - name: f_36
+    dtype: int8
+  - name: f_37
+    dtype: int8
+  - name: f_38
+    dtype: int8
+  - name: f_39
+    dtype: int8
+  - name: f_40
+    dtype: int8
+  - name: f_41
+    dtype: int8
+  - name: f_42
+    dtype: float32
+  - name: f_43
+    dtype: float32
+  - name: f_44
+    dtype: float32
+  - name: f_45
+    dtype: float32
+  - name: f_46
+    dtype: float32
+  - name: f_47
+    dtype: float32
+  - name: f_48
+    dtype: float32
+  - name: f_49
+    dtype: float32
+  - name: f_50
+    dtype: float32
+  - name: f_51
+    dtype: float32
+  - name: f_52
+    dtype: float32
+  - name: f_53
+    dtype: float32
+  - name: f_54
+    dtype: float32
+  - name: f_55
+    dtype: float32
+  - name: f_56
+    dtype: float32
+  - name: f_57
+    dtype: float32
+  - name: f_58
+    dtype: float32
+  - name: f_59
+    dtype: float32
+  - name: f_60
+    dtype: float32
+  - name: f_61
+    dtype: float32
+  - name: f_62
+    dtype: float32
+  - name: f_63
+    dtype: float32
+  - name: f_64
+    dtype: float32
+  - name: f_65
+    dtype: float32
+  - name: f_66
+    dtype: float32
+  - name: f_67
+    dtype: float32
+  - name: f_68
+    dtype: float32
+  - name: f_69
+    dtype: float32
+  - name: f_70
+    dtype: float32
+  - name: f_71
+    dtype: float32
+  - name: f_72
+    dtype: float32
+  - name: f_73
+    dtype: float32
+  - name: f_74
+    dtype: float32
+  - name: f_75
+    dtype: float32
+  - name: f_76
+    dtype: float32
+  - name: f_77
+    dtype: float32
+  - name: f_78
+    dtype: float32
+  - name: f_79
+    dtype: float32
+  - name: is_clicked
+    dtype: int8
+  - name: is_installed
+    dtype: int8
+  splits:
+  - name: train
+    num_bytes: 964563810
+    num_examples: 3240869
+  - name: validation
+    num_bytes: 43754365
+    num_examples: 147011
+  - name: test
+    num_bytes: 29159092
+    num_examples: 97972
+  download_size: 488593303
+  dataset_size: 1037477267
+---

sharechat.py

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+"""The dataset corresponds to roughly 10M random users who visited the ShareChat + Moj app over three months. 
+We have sampled each user's activity to generate 10 impressions corresponding to each user. 
+Our target variable is whether there was an install for an app by the user or not.
+"""
+
+
+import csv
+import json
+import os
+import glob
+import polars as pl
+
+import datasets
+
+
+_CITATION = """\
+@incollection{agrawal2023recsys,
+  title={RecSys Challenge 2023 Dataset: Ads Recommendations in Online Advertising},
+  author={Agrawal, Rahul and Brahme, Sarang and Maitra, Sourav and Srivastava, Abhishek and Irissappane, Athirai and Liu, Yong and Kalloori, Saikishore},
+  booktitle={Proceedings of the Recommender Systems Challenge 2023},
+  pages={1--3},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset corresponds to roughly 10M random users who visited the ShareChat + Moj app over three months. 
+We have sampled each user's activity to generate 10 impressions corresponding to each user. 
+Our target variable is whether there was an install for an app by the user or not.
+"""
+
+_HOMEPAGE = "https://www.recsyschallenge.com/2023/"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+
+_URLS = {
+    "first_domain": "https://cdn.sharechat.com/2a161f8e_1679936280892_sc.zip",
+}
+
+
+class Sharechat(datasets.ArrowBasedBuilder):
+    """The dataset for RecSys Challenge 2024."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # 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_CONFIGS = [
+        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first 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):
+        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            # data format: f0,f1,...,f79, is_clicked, is_installed
+            # all_cat_columns = time_columns + cat_columns + binary_columns
+            id_columns = [('f_0', datasets.Value("int64"))]
+            time_columns = [('f_1', datasets.Value("int8"))]
+            cat_columns = [(f'f_{i}', datasets.Value("int32")) for i in range(2, 33)]
+            binary_columns = [(f'f_{i}', datasets.Value("int8")) for i in range(33, 42)]
+            dense_columns = [(f'f_{i}', datasets.Value("float")) for i in range(42, 80)]
+            other_columns = [('is_clicked', datasets.Value("int8"))]
+            label_columns = [('is_installed', datasets.Value("int8"))]
+            all_columns = id_columns + time_columns + cat_columns + binary_columns + dense_columns + other_columns + label_columns
+            
+            features = datasets.Features(dict(all_columns))
+        else:  # This is an example to show how to have different features for other domains
+            raise NotImplementedError("This configuration is not implemented yet")
+        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):
+        # 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)
+
+        id_columns = [('f_0', pl.Int64)]
+        time_columns = [('f_1', pl.Int8)]
+        cat_columns = [(f'f_{i}', pl.Int32) for i in range(2, 33)]
+        binary_columns = [(f'f_{i}', pl.Int8) for i in range(33, 42)]
+        dense_columns = [(f'f_{i}', pl.Float32) for i in range(42, 80)]
+        other_columns = [('is_clicked', pl.Int8)]
+        label_columns = [('is_installed', pl.Int8)]
+        all_columns = id_columns + time_columns + cat_columns + binary_columns + dense_columns + other_columns + label_columns
+        self.dtypes_dict = dict(all_columns)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": sorted(glob.glob(os.path.join(data_dir, "sharechat_recsys2023_data", "train", "*.csv"))),
+                    "date_start": 45,
+                    "date_end": 64,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": sorted(glob.glob(os.path.join(data_dir, "sharechat_recsys2023_data", "train", "*.csv"))),
+                    "date_start": 65,
+                    "date_end": 65,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": sorted(glob.glob(os.path.join(data_dir, "sharechat_recsys2023_data", "train", "*.csv"))),
+                    "date_start": 66,
+                    "date_end": 66,
+                },
+            ),
+        ]
+
+    def _generate_tables(self, filepaths, date_start, date_end):
+        # This method handles input defined in _split_generators to yield (key, table) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        for id, filepath in enumerate(filepaths):
+            if self.config.name == "first_domain":
+                pa_table = (
+                    pl.scan_csv(filepath, separator='\t', dtypes=self.dtypes_dict)
+                    .filter(pl.col("f_1").is_between(date_start, date_end))
+                    .sort("f_1", descending=False)
+                    .with_columns(pl.col("f_1").mod(7).alias("f_1"))
+                    .collect().to_arrow()
+                )
+                yield id, pa_table
+            else:
+                raise NotImplementedError
+
+
+
+# test the script
+# datasets-cli test sharechat.py --save_info --all_configs
+# share the dataset
+# huggingface-cli login
+# huggingface-cli repo create sharechat-dataset --type dataset
+# huggingface-cli upload sharechat-dataset . . --repo-type dataset