Create baidu-ultr_uva-mlm-ctr.py

baidu-ultr_uva-mlm-ctr.py

@@ -0,0 +1,217 @@
+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 a BERT cross-encoder with 12 layers trained on a Masked Language Modeling (MLM) and click-through-rate (CTR) prediction task to compute query-document vectors (768 dims).
+
+The model is available 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_id": Value("string"),
+            "query_md5": Value("string"),
+            "url_md5": Sequence(Value("string")),
+            "text_md5": Sequence(Value("string")),
+            "query_document_embedding": Array2D((None, 768), "float16"),
+            "click": Sequence(Value("int32")),
+            "n": Value("int32"),
+            "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")),
+        }
+    )
+
+    ANNOTATION_FEATURES = Features(
+        {
+            "query_id": Value("string"),
+            "query_md5": Value("string"),
+            "text_md5": Value("string"),
+            "query_document_embedding": Array2D((None, 768), "float16"),
+            "label": Sequence(Value("int32")),
+            "n": Value("int32"),
+            "frequency_bucket": 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",
+            ]
+
+            agg_columns = [
+                "query_md5",
+                "url_md5",
+                "text_md5",
+                "position",
+                "click",
+                "query_document_embedding",
+                "media_type",
+                "displayed_time",
+                "serp_height",
+                "slipoff_count_after_click",
+            ]
+
+            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",
+                "frequency_bucket",
+            ]
+            agg_columns = [
+                "text_md5",
+                "label",
+                "query_document_embedding",
+            ]
+
+            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_id = None
+            sample_key = None
+            sample = None
+
+            for i in range(len(df)):
+                row = df.iloc[i]
+
+                if current_query_id != row["query_id"]:
+                    if current_query_id is not None:
+                        yield sample_key, sample
+
+                    current_query_id = row["query_id"]
+                    sample_key = f"{file}-{current_query_id}"
+                    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