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import pandas as pd |
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import datasets |
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from sklearn.model_selection import train_test_split |
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import numpy as np |
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_CITATION = "N/A" |
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_DESCRIPTION = "N/A" |
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_HOMEPAGE = "N/A" |
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_LICENSE = "apache-2.0" |
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class JesterEmbedding(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("0.0.1") |
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def _info(self): |
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features = datasets.Features({ |
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"user_id": datasets.Value("int64"), |
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"item_id": datasets.Value("int64"), |
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"rating": datasets.Value("float64"), |
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}) |
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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 = "./jester_rating.parquet" |
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data_dir = dl_manager.download_and_extract(urls) |
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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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"filepath": data_dir, |
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"split": "train", |
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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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"filepath": data_dir, |
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"split": "dev", |
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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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"filepath": data_dir, |
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"split": "test" |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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df = pd.read_parquet(filepath) |
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rng = np.random.RandomState(42) |
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train_df, test_df, val_df = [], [], [] |
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for user_id in df.user_id.unique(): |
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if len(df[df.user_id == user_id]) < 3: |
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continue |
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_train_df, _test_df = train_test_split(df[df.user_id == user_id], test_size=0.2, random_state=rng) |
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_train_df, _val_df = train_test_split(_train_df, test_size=0.2, random_state=rng) |
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train_df.append(_train_df) |
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val_df.append(_val_df) |
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test_df.append(_test_df) |
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train_df = pd.concat(train_df) |
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test_df = pd.concat(test_df) |
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val_df = pd.concat(val_df) |
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if split == "train": |
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for _id, row in train_df.iterrows(): |
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user_id, item_id, rating = row |
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user_id, item_id = int(user_id), int(item_id) |
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yield _id, {"user_id": user_id, "item_id": item_id, "rating": rating} |
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elif split == "test": |
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for _id, row in test_df.iterrows(): |
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user_id, item_id, rating = row |
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user_id, item_id = int(user_id), int(item_id) |
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yield _id, {"user_id": user_id, "item_id": item_id, "rating": rating} |
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else: |
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for _id, row in val_df.iterrows(): |
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user_id, item_id, rating = row |
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user_id, item_id = int(user_id), int(item_id) |
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yield _id, {"user_id": user_id, "item_id": item_id, "rating": rating} |
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