custom data loading

README.md

@@ -1,5 +1,26 @@
 ---
 configs:
+- config_name: input
+  data_files:
+  - split: valid
+    path:
+    - "interaction_splits/valid_inter_input.tsv"
+  - split: test
+    path: 
+    - "interaction_splits/test_inter_input.tsv"
+  default: true
+- config_name: target 
+  data_files:
+  - split: valid
+    path:
+    - "interaction_splits/valid_inter_target.csv"
+- config_name: mapping 
+  data_files: "cwid_to_id.tsv"
+license: mit
+---
+
+
+<!-- configs:
 - config_name: ordered_id_splits
   data_files:
   - split: train
@@ -18,10 +39,7 @@ configs:
   - split: test
     path: "interaction_splits/test_input.tsv"
 - config_name: clueweb_id_mapping
-  data_files: "cwid_to_id.tsv"
-license: mit
----
-
+  data_files: "cwid_to_id.tsv" -->
 
 
 # ClueWeb-Reco: 

clueweb_reco.py

@@ -0,0 +1,219 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+
+
+
+import csv
+import json
+import os
+from decimal import Decimal
+
+import datasets
+
+# TODO: citation 
+# # Find for instance the citation on arxiv or on the dataset repo/website
+# _CITATION = """\
+# @InProceedings{huggingface:dataset,
+# title = {A great new dataset},
+# author={huggingface, Inc.
+# },
+# year={2020}
+# }
+# """
+_CITATION = ""
+
+# 
+_DESCRIPTION = """\
+ClueWeb-Reco is a novel zero-shot test set derived from real, \
+    consented user browsing sequences, 
+    aligning with modern recommendation scenarios while ensuring privacy.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco"
+
+_LICENSE = "mit"
+
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "input": "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco/tree/main/interaction_splits",
+    "target": "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco/tree/main/interaction_splits",
+    "mapping": "https://huggingface.co/datasets/cx-cmu/ClueWeb-Reco/tree/main",
+}
+
+
+class ClueWebRecoDataset(datasets.GeneratorBasedBuilder):
+    """Process the ClueWeb-Reco zero-shot dataset"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # 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
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="input", version=VERSION, description="This is the input parts of the dataset"),
+        datasets.BuilderConfig(name="target", version=VERSION, description="This is the target parts of the dataset"),
+        datasets.BuilderConfig(name="mapping", version=VERSION, description="This is the mapping between official ClueWeb ids and our internal ClueWeb ids"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "input"  
+
+    def _info(self):
+        if self.config.name == "input":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "session_id": datasets.Value("string"),
+                    "cw_internal_id": datasets.Value("int32"),
+                    "timestamp": datasets.Value("string")
+                }
+            )
+        elif self.config.name == "target":
+            features = datasets.Features(
+                {
+                    "session_id": datasets.Value("string"),
+                    "target_cw_internal_id": datasets.Value("int32"),
+                    "timestamp": datasets.Value("string")
+                }
+            )
+        elif self.config.name == "mapping": 
+            features = datasets.Features(
+                {
+                    "cwid": datasets.Value("string"),
+                    "cw_internal_id": datasets.Value("int32"),
+                }
+            )
+        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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # 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)
+        data_dir = self.config.data_dir 
+
+        if self.config.name == "input": 
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "input_path": os.path.join(data_dir, "interaction_splits/valid_inter_input.tsv"),
+                    }
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "input_path": os.path.join(data_dir, "interaction_splits/test_inter_input.tsv"),
+                    },
+                ),
+            ]
+        elif self.config.name == "target": 
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "target_path": os.path.join(data_dir, "interaction_splits/valid_inter_target.tsv"),
+                    },
+                ),
+            ]
+
+        elif self.config.name == "mapping": 
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "mapping_path": os.path.join(data_dir, "cwid_to_id.tsv"),
+                    },
+                ),
+            ]
+    
+
+    def _generate_examples(self, input_path=None, target_path=None, mapping_path=None):
+        """
+        Generates examples based on the input and (optionally) target files.
+        If the configuration is `input`, `target`, or `mapping`, this handles each separately.
+        """
+
+        if self.config.name == "input":
+            if input_path is None:
+                raise ValueError("Input configuration requires an input_path.")
+            
+            # Process the `input` configuration
+            with open(input_path, encoding="utf-8") as f:
+                input_lines = f.readlines()[1:]
+            for idx, line in enumerate(input_lines):
+                session_id, cw_internal_id, timestamp = line.strip().split("\t")
+                yield idx, {
+                    "session_id": session_id.strip(),
+                    "cw_internal_id": int(cw_internal_id.strip()),
+                    "timestamp": str(Decimal(timestamp)),
+                }
+        
+        elif self.config.name == "target":
+            # if target_path is None:
+            #     # Test target is hidden; yield nothing
+            #     return
+            
+            if target_path is None:
+                raise ValueError("Target configuration requires an target_path.")
+            
+            with open(target_path, encoding="utf-8") as f:
+                target_lines = f.readlines()[1:]
+                for idx, line in enumerate(target_lines):
+                    session_id, target_cw_internal_id, timestamp = line.strip().split("\t")
+                    yield idx, {
+                        "session_id": session_id.strip(),
+                        "target_cw_internal_id": int(target_cw_internal_id.strip()),
+                        "timestamp": str(Decimal(timestamp)),
+                    }
+
+        elif self.config.name == "mapping":
+
+            if mapping_path is None:
+                raise ValueError("Mapping configuration requires an mapping_path.")
+    
+            # Process the `mapping` configuration
+            with open(mapping_path, encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    cwid, cw_internal_id = line.strip().split("\t")
+                    yield idx, {
+                        "cwid": cwid.strip(),
+                        "cw_internal_id": int(cw_internal_id.strip()),
+                    }