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"""The dataset corresponds to roughly 10M random users who visited the ShareChat + Moj app over three months. |
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We have sampled each user's activity to generate 10 impressions corresponding to each user. |
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Our target variable is whether there was an install for an app by the user or not. |
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""" |
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import csv |
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import json |
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import os |
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import glob |
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import polars as pl |
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import datasets |
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_CITATION = """\ |
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@incollection{agrawal2023recsys, |
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title={RecSys Challenge 2023 Dataset: Ads Recommendations in Online Advertising}, |
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author={Agrawal, Rahul and Brahme, Sarang and Maitra, Sourav and Srivastava, Abhishek and Irissappane, Athirai and Liu, Yong and Kalloori, Saikishore}, |
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booktitle={Proceedings of the Recommender Systems Challenge 2023}, |
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pages={1--3}, |
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year={2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The dataset corresponds to roughly 10M random users who visited the ShareChat + Moj app over three months. |
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We have sampled each user's activity to generate 10 impressions corresponding to each user. |
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Our target variable is whether there was an install for an app by the user or not. |
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""" |
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_HOMEPAGE = "https://www.recsyschallenge.com/2023/" |
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_LICENSE = "" |
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_URLS = { |
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"first_domain": "https://cdn.sharechat.com/2a161f8e_1679936280892_sc.zip", |
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} |
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class Sharechat(datasets.ArrowBasedBuilder): |
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"""The dataset for RecSys Challenge 2024.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), |
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] |
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DEFAULT_CONFIG_NAME = "first_domain" |
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def _info(self): |
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if self.config.name == "first_domain": |
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id_columns = [('f_0', datasets.Value("int64"))] |
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time_columns = [('f_1', datasets.Value("int8"))] |
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cat_columns = [(f'f_{i}', datasets.Value("int32")) for i in range(2, 33)] |
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binary_columns = [(f'f_{i}', datasets.Value("int8")) for i in range(33, 42)] |
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dense_columns = [(f'f_{i}', datasets.Value("float")) for i in range(42, 80)] |
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other_columns = [('is_clicked', datasets.Value("int8"))] |
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label_columns = [('is_installed', datasets.Value("int8"))] |
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all_columns = id_columns + time_columns + cat_columns + binary_columns + dense_columns + other_columns + label_columns |
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features = datasets.Features(dict(all_columns)) |
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else: |
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raise NotImplementedError("This configuration is not implemented yet") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls = _URLS[self.config.name] |
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data_dir = dl_manager.download_and_extract(urls) |
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id_columns = [('f_0', pl.Int64)] |
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time_columns = [('f_1', pl.Int8)] |
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cat_columns = [(f'f_{i}', pl.Int32) for i in range(2, 33)] |
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binary_columns = [(f'f_{i}', pl.Int8) for i in range(33, 42)] |
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dense_columns = [(f'f_{i}', pl.Float32) for i in range(42, 80)] |
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other_columns = [('is_clicked', pl.Int8)] |
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label_columns = [('is_installed', pl.Int8)] |
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all_columns = id_columns + time_columns + cat_columns + binary_columns + dense_columns + other_columns + label_columns |
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self.dtypes_dict = dict(all_columns) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepaths": sorted(glob.glob(os.path.join(data_dir, "sharechat_recsys2023_data", "train", "*.csv"))), |
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"date_start": 45, |
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"date_end": 64, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepaths": sorted(glob.glob(os.path.join(data_dir, "sharechat_recsys2023_data", "train", "*.csv"))), |
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"date_start": 65, |
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"date_end": 65, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepaths": sorted(glob.glob(os.path.join(data_dir, "sharechat_recsys2023_data", "train", "*.csv"))), |
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"date_start": 66, |
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"date_end": 66, |
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}, |
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), |
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] |
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def _generate_tables(self, filepaths, date_start, date_end): |
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for id, filepath in enumerate(filepaths): |
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if self.config.name == "first_domain": |
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pa_table = ( |
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pl.scan_csv(filepath, separator='\t', dtypes=self.dtypes_dict) |
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.filter(pl.col("f_1").is_between(date_start, date_end)) |
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.sort("f_1", descending=False) |
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.with_columns(pl.col("f_1").mod(7).alias("f_1")) |
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.collect().to_arrow() |
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) |
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yield id, pa_table |
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else: |
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raise NotImplementedError |
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