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from enum import Enum
from typing import List
import datasets
import pandas as pd
from datasets import Features, Value, Array2D, Sequence, SplitGenerator, Split
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {philipphager/baidu-ultr_baidu-mlm-ctr},
author={Philipp Hager, Romain Deffayet},
year={2023}
}
"""
_DESCRIPTION = """\
Query-document vectors and clicks for a subset of the Baidu Unbiased Learning to Rank
dataset: https://arxiv.org/abs/2207.03051
This dataset uses the BERT cross-encoder with 12 layers from Baidu released
in the official starter-kit to compute query-document vectors (768 dims):
https://github.com/ChuXiaokai/baidu_ultr_dataset/
We link the model checkpoint also under `model/`.
"""
_HOMEPAGE = "https://huggingface.co/datasets/philipphager/baidu-ultr_baidu-mlm-ctr/"
_LICENSE = "cc-by-nc-4.0"
_VERSION = "0.1.0"
class Config(str, Enum):
ANNOTATIONS = "annotations"
CLICKS = "clicks"
class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=Config.CLICKS,
version=VERSION,
description="Load train/val/test clicks from the Baidu ULTR dataset",
),
datasets.BuilderConfig(
name=Config.ANNOTATIONS,
version=VERSION,
description="Load expert annotations from the Baidu ULTR dataset",
),
]
CLICK_FEATURES = Features(
{
### Query features
"query_id": Value("string"),
"query_md5": Value("string"),
"query": Sequence(Value("int32")),
"query_length": Value("int32"),
"n": Value("int32"),
### Doc features
"url_md5": Sequence(Value("string")),
"text_md5": Sequence(Value("string")),
"title": Sequence(Sequence(Value("int32"))),
"abstract": Sequence(Sequence(Value("int32"))),
"query_document_embedding": Array2D((None, 768), "float16"),
"click": Sequence(Value("int32")),
### SERP features
"position": Sequence(Value("int32")),
"media_type": Sequence(Value("int32")),
"displayed_time": Sequence(Value("float32")),
"serp_height": Sequence(Value("int32")),
"slipoff_count_after_click": Sequence(Value("int32")),
### LTR features
"bm25": Sequence(Value("float32")),
"bm25_title": Sequence(Value("float32")),
"bm25_abstract": Sequence(Value("float32")),
"tf_idf": Sequence(Value("float32")),
"tf": Sequence(Value("float32")),
"idf": Sequence(Value("float32")),
"ql_jelinek_mercer_short": Sequence(Value("float32")),
"ql_jelinek_mercer_long": Sequence(Value("float32")),
"ql_dirichlet": Sequence(Value("float32")),
"document_length": Sequence(Value("int32")),
"title_length": Sequence(Value("int32")),
"abstract_length": Sequence(Value("int32")),
}
)
ANNOTATION_FEATURES = Features(
{
### Query features
"query_id": Value("string"),
"query_md5": Value("string"),
"query": Sequence(Value("int32")),
"query_length": Value("int32"),
"frequency_bucket": Value("int32"),
"n": Value("int32"),
### Doc features
"text_md5": Sequence(Value("string")),
"title": Sequence(Sequence(Value("int32"))),
"abstract": Sequence(Sequence(Value("int32"))),
"query_document_embedding": Array2D((None, 768), "float16"),
"label": Sequence(Value("int32")),
### LTR features
"bm25": Sequence(Value("float32")),
"bm25_title": Sequence(Value("float32")),
"bm25_abstract": Sequence(Value("float32")),
"tf_idf": Sequence(Value("float32")),
"tf": Sequence(Value("float32")),
"idf": Sequence(Value("float32")),
"ql_jelinek_mercer_short": Sequence(Value("float32")),
"ql_jelinek_mercer_long": Sequence(Value("float32")),
"ql_dirichlet": Sequence(Value("float32")),
"document_length": Sequence(Value("int32")),
"title_length": Sequence(Value("int32")),
"abstract_length": Sequence(Value("int32")),
}
)
DEFAULT_CONFIG_NAME = Config.CLICKS
def _info(self):
if self.config.name == Config.CLICKS:
features = self.CLICK_FEATURES
elif self.config.name == Config.ANNOTATIONS:
features = self.ANNOTATION_FEATURES
else:
raise ValueError(
f"Config {self.config.name} must be in ['clicks', 'annotations']"
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.name == Config.CLICKS:
train_files = self.download_clicks(dl_manager, parts=[1, 2, 3])
test_files = self.download_clicks(dl_manager, parts=[0])
query_columns = [
"query_id",
"query_md5",
"query",
"query_length",
]
agg_columns = [
"url_md5",
"text_md5",
"title",
"abstract",
"query_document_embedding",
"click",
"position",
"media_type",
"displayed_time",
"serp_height",
"slipoff_count_after_click",
"bm25",
"bm25_title",
"bm25_abstract",
"tf_idf",
"tf",
"idf",
"ql_jelinek_mercer_short",
"ql_jelinek_mercer_long",
"ql_dirichlet",
"document_length",
"title_length",
"abstract_length",
]
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"files": train_files,
"query_columns": query_columns,
"agg_columns": agg_columns,
},
),
SplitGenerator(
name=Split.TEST,
gen_kwargs={
"files": test_files,
"query_columns": query_columns,
"agg_columns": agg_columns,
},
),
]
elif self.config.name == Config.ANNOTATIONS:
test_files = dl_manager.download(["parts/validation.feather"])
query_columns = [
"query_id",
"query_md5",
"query",
"query_length",
"frequency_bucket",
]
agg_columns = [
"text_md5",
"title",
"abstract",
"query_document_embedding",
"label",
"bm25",
"bm25_title",
"bm25_abstract",
"tf_idf",
"tf",
"idf",
"ql_jelinek_mercer_short",
"ql_jelinek_mercer_long",
"ql_dirichlet",
"document_length",
"title_length",
"abstract_length",
]
return [
SplitGenerator(
name=Split.TEST,
gen_kwargs={
"files": test_files,
"query_columns": query_columns,
"agg_columns": agg_columns,
},
)
]
else:
raise ValueError("Config name must be in ['clicks', 'annotations']")
def download_clicks(self, dl_manager, parts: List[int], splits_per_part: int = 10):
urls = [
f"parts/part-{p}_split-{s}.feather"
for p in parts
for s in range(splits_per_part)
]
return dl_manager.download(urls)
def _generate_examples(
self,
files: List[str],
query_columns: List[str],
agg_columns: List[str],
):
"""
Reads dataset partitions and aggregates document features per query.
:param files: List of .feather files to load from disk.
:param query_columns: Columns with one value per query. E.g., query_id,
frequency bucket, etc.
:param agg_columns: Columns with one value per document that should be
aggregated per query. E.g., click, position, query_document_embeddings, etc.
:return:
"""
for file in files:
df = pd.read_feather(file)
current_query = None
sample_key = None
sample = None
for i in range(len(df)):
row = df.iloc[i]
if current_query != row["query_no"]:
if current_query is not None:
yield sample_key, sample
current_query = row["query_no"]
# Use the query number in sample_key as query_ids are not unique.
sample_key = f"{file}-{current_query}"
sample = {"n": 0}
for column in query_columns:
sample[column] = row[column]
for column in agg_columns:
sample[column] = []
for column in agg_columns:
sample[column].append(row[column])
sample["n"] += 1
yield sample_key, sample
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