feat: converting CRAG sampler to a package for simpler install and use

.gitignore

@@ -0,0 +1,5 @@
+.DS_Store
+.env
+.venv
+*.egg-info*
+*__pycache__*

crag_sampler/__init__.py

@@ -0,0 +1,3 @@
+from .sampler import CragSampler
+
+__all__ = ["CragSampler"]

crag_sampler/sampler.py

@@ -0,0 +1,118 @@
+import bz2
+from typing import Iterator, Dict, Any, List, Optional
+import pandas as pd
+import os
+import hashlib
+import json
+from sklearn.model_selection import StratifiedKFold
+import numpy as np
+from multiprocessing import Pool, cpu_count
+from functools import partial
+import subprocess
+
+from .utils import (
+    read_jsonl_fields_fast,
+    process_answer_types,
+    create_stratified_subsamples,
+    subsample_jsonl_file,
+)
+
+
+class CragSampler:
+    """Main class for handling CRAG dataset sampling operations."""
+
+    def __init__(
+        self,
+        input_file: str,
+        required_fields: Optional[List[str]] = None,
+        use_cache: bool = True,
+    ):
+        """Initialize CragSampler.
+
+        Args:
+            input_file: Path to input JSONL file (can be bz2 compressed)
+            required_fields: List of field names to extract. If None, uses default fields
+            use_cache: Whether to use/create cache file
+        """
+        self.input_file = input_file
+        self.required_fields = required_fields or [
+            "domain",
+            "answer",
+            "question_type",
+            "static_or_dynamic",
+        ]
+        self.use_cache = use_cache
+        self.df = self._load_data()
+
+    def _load_data(self) -> pd.DataFrame:
+        """Load and process data from JSONL file."""
+        df = read_jsonl_fields_fast(
+            self.input_file, self.required_fields, self.use_cache
+        )
+        return process_answer_types(df)
+
+    def create_subsamples(
+        self,
+        n_subsamples: int = 5,
+        stratify_columns: Optional[List[str]] = None,
+        output_path: Optional[str] = None,
+        force_compute: bool = False,
+    ) -> Dict:
+        """Create stratified subsamples of the dataset.
+
+        Args:
+            n_subsamples: Number of subsamples to create
+            stratify_columns: Columns to use for stratification. If None, uses defaults
+            output_path: Path to save/load the JSON output
+            force_compute: If True, always compute subsamples even if file exists
+
+        Returns:
+            Dictionary containing the subsamples information
+        """
+        if stratify_columns is None:
+            stratify_columns = [
+                "domain",
+                "answer_type",
+                "question_type",
+                "static_or_dynamic",
+            ]
+
+        if output_path is None:
+            output_path = os.path.join(
+                os.path.dirname(self.input_file),
+                f"{os.path.splitext(os.path.basename(self.input_file))[0]}_subsamples.json",
+            )
+
+        return create_stratified_subsamples(
+            self.df,
+            n_subsamples=n_subsamples,
+            stratify_columns=stratify_columns,
+            output_path=output_path,
+            force_compute=force_compute,
+        )
+
+    def write_subsamples(
+        self,
+        subsamples_file: str,
+        output_dir: Optional[str] = None,
+        compress: bool = True,
+        n_processes: Optional[int] = None,
+        overwrite: bool = False,
+    ) -> None:
+        """Write subsamples to separate files.
+
+        Args:
+            subsamples_file: Path to JSON file containing subsample indices
+            output_dir: Directory to save subsample files
+            compress: Whether to compress output files with bz2
+            n_processes: Number of processes to use
+            overwrite: If False, skip existing output files
+        """
+        subsample_jsonl_file(
+            self.input_file,
+            subsamples_file,
+            output_dir=output_dir,
+            compress=compress,
+            n_processes=n_processes,
+            overwrite=overwrite,
+        )

crag_to_subsamples.py → crag_sampler/utils.py

@@ -1,17 +1,16 @@
-import bz2
-from typing import Iterator, Dict, Any
-import pandas as pd
 import os
 import hashlib
+import bz2
 import json
-from sklearn.model_selection import StratifiedKFold
+import subprocess
+from typing import Dict, List, Optional, Any
+import pandas as pd
 import numpy as np
 from multiprocessing import Pool, cpu_count
-from functools import partial
-import subprocess
+from sklearn.model_selection import StratifiedKFold
 
 
-def get_cache_path(file_path: str, required_fields: list[str]) -> str:
+def get_cache_path(file_path: str, required_fields: List[str]) -> str:
     """
     Generate a unique cache file path based on input file and fields.
 
@@ -36,7 +35,7 @@ def get_cache_path(file_path: str, required_fields: list[str]) -> str:
 
 
 def read_jsonl_fields_fast(
-    file_path: str, required_fields: list[str], use_cache: bool = True
+    file_path: str, required_fields: List[str], use_cache: bool = True
 ) -> pd.DataFrame:
     """
     Quickly extract specific fields from a compressed JSONL file using string operations.
@@ -171,7 +170,7 @@ def process_answer_types(df: pd.DataFrame) -> pd.DataFrame:
 def create_stratified_subsamples(
     df: pd.DataFrame,
     n_subsamples: int,
-    stratify_columns: list[str] = [
+    stratify_columns: List[str] = [
         "domain",
         "answer_type",
         "question_type",
@@ -179,7 +178,7 @@ def create_stratified_subsamples(
     ],
     output_path: str = "subsamples.json",
     force_compute: bool = False,
-) -> dict:
+) -> Dict[str, Any]:
     """
     Create stratified subsamples of the dataset and save them to a JSON file.
     Each subsample gets a unique ID based on its indices.
@@ -270,7 +269,7 @@ def create_stratified_subsamples(
 
 
 def write_subsample(
-    input_file: str, indices: list[int], output_file: str, compress: bool = True
+    input_file: str, indices: List[int], output_file: str, compress: bool = True
 ) -> None:
     """
     Write a single subsample to a file using awk.
@@ -334,9 +333,9 @@ def write_subsample(
 def subsample_jsonl_file(
     input_file: str,
     subsamples_file: str,
-    output_dir: str = None,
+    output_dir: Optional[str] = None,
     compress: bool = True,
-    n_processes: int = None,
+    n_processes: Optional[int] = None,
     overwrite: bool = False,
 ) -> None:
     """
@@ -395,52 +394,3 @@ def subsample_jsonl_file(
             pool.starmap(write_subsample, write_args)
     else:
         print("No files to process - all files exist and overwrite=False")
-
-
-def run_crag_task_1_and_2(
-    file_path: str,
-    fields_to_extract: list[str],
-    n_subsamples: int = 5,
-    output_dir: str = None,
-    compress: bool = True,
-    n_processes: int = None,
-    overwrite: bool = False,
-):
-    # Load and process data
-    df = read_jsonl_fields_fast(file_path, fields_to_extract)
-    df = process_answer_types(df)
-    print(df.head())
-
-    output_path = os.path.join(
-        os.path.dirname(file_path),
-        os.path.basename(file_path).split(".")[0] + "_subsamples.json",
-    )
-
-    # This will load from file if it exists and parameters match
-    subsamples_data = create_stratified_subsamples(
-        df, n_subsamples=5, output_path=output_path
-    )
-
-    # Example of how to read and use the subsamples
-    with open(output_path, "r") as f:
-        subsamples_data = json.load(f)
-
-    # Print some information about the subsamples
-    print(f"Created {subsamples_data['metadata']['n_subsamples']} subsamples")
-    print("\nGlobal statistics:")
-    print(json.dumps(subsamples_data["metadata"]["global_statistics"], indent=2))
-
-    # Print statistics for first subsample
-    print("\nFirst subsample statistics:")
-    print(json.dumps(subsamples_data["subsamples"][0]["statistics"], indent=2))
-
-    # This will use all available CPU cores
-    subsample_jsonl_file(file_path, output_path, compress=True)
-
-
-# Example usage
-if __name__ == "__main__":
-    file_path = "./local_data/crag_task_1_and_2_dev_v4.jsonl.bz2"
-    fields_to_extract = ["domain", "answer", "question_type", "static_or_dynamic"]
-
-    run_crag_task_1_and_2(file_path, fields_to_extract)

examples/basic_sampling.py

@@ -0,0 +1,65 @@
+# Example usage
+from crag_sampler import CragSampler
+import json
+import os
+
+
+def run_crag_task_1_and_2(
+    file_path: str,
+    fields_to_extract: list[str] = None,
+    n_subsamples: int = 5,
+    output_dir: str = None,
+    compress: bool = True,
+    n_processes: int = None,
+    overwrite: bool = False,
+):
+    """Run the CRAG sampling pipeline for tasks 1 and 2.
+
+    Args:
+        file_path: Path to input JSONL file
+        fields_to_extract: List of fields to extract from JSONL
+        n_subsamples: Number of subsamples to create
+        output_dir: Directory for output files
+        compress: Whether to compress output files
+        n_processes: Number of processes for parallel processing
+        overwrite: Whether to overwrite existing files
+    """
+    # Initialize sampler
+    sampler = CragSampler(
+        input_file=file_path, required_fields=fields_to_extract, use_cache=True
+    )
+
+    # Create output path for subsamples
+    output_path = os.path.join(
+        os.path.dirname(file_path),
+        f"{os.path.splitext(os.path.basename(file_path))[0]}_subsamples.json",
+    )
+
+    # Create subsamples
+    subsamples_data = sampler.create_subsamples(
+        n_subsamples=n_subsamples, output_path=output_path
+    )
+
+    # Print statistics
+    print(f"Created {subsamples_data['metadata']['n_subsamples']} subsamples")
+    print("\nGlobal statistics:")
+    print(json.dumps(subsamples_data["metadata"]["global_statistics"], indent=2))
+    print("\nFirst subsample statistics:")
+    print(json.dumps(subsamples_data["subsamples"][0]["statistics"], indent=2))
+
+    # Write subsamples to files
+    sampler.write_subsamples(
+        subsamples_file=output_path,
+        output_dir=output_dir,
+        compress=compress,
+        n_processes=n_processes,
+        overwrite=overwrite,
+    )
+
+
+# Example usage
+if __name__ == "__main__":
+    file_path = "./local_data/crag_task_1_and_2_dev_v4.jsonl.bz2"
+    fields_to_extract = ["domain", "answer", "question_type", "static_or_dynamic"]
+
+    run_crag_task_1_and_2(file_path, fields_to_extract)

pyproject.toml

@@ -1,29 +0,0 @@
-[project]
-name = "lejuge"
-version = "0.1.0"
-description = "Add your description here"
-authors = [
-    { name = "Jacopo Chevallard", email = "[email protected]" }
-]
-dependencies = [
-    "ipykernel>=6.29.5",
-    "pandas>=2.2.3",
-    "fastparquet>=2024.11.0",
-    "scikit-learn>=1.6.1",
-]
-readme = "README.md"
-requires-python = ">= 3.11"
-
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[tool.rye]
-managed = true
-dev-dependencies = []
-
-[tool.hatch.metadata]
-allow-direct-references = true
-
-[tool.hatch.build.targets.wheel]
-packages = ["src/lejuge"]

requirements-dev.lock

@@ -1,109 +0,0 @@
-# generated by rye
-# use `rye lock` or `rye sync` to update this lockfile
-#
-# last locked with the following flags:
-#   pre: false
-#   features: []
-#   all-features: false
-#   with-sources: false
-#   generate-hashes: false
-#   universal: false
-
--e file:.
-appnope==0.1.4
-    # via ipykernel
-asttokens==3.0.0
-    # via stack-data
-comm==0.2.2
-    # via ipykernel
-cramjam==2.9.1
-    # via fastparquet
-debugpy==1.8.12
-    # via ipykernel
-decorator==5.1.1
-    # via ipython
-executing==2.2.0
-    # via stack-data
-fastparquet==2024.11.0
-    # via lejuge
-fsspec==2024.12.0
-    # via fastparquet
-ipykernel==6.29.5
-    # via lejuge
-ipython==8.31.0
-    # via ipykernel
-jedi==0.19.2
-    # via ipython
-joblib==1.4.2
-    # via scikit-learn
-jupyter-client==8.6.3
-    # via ipykernel
-jupyter-core==5.7.2
-    # via ipykernel
-    # via jupyter-client
-matplotlib-inline==0.1.7
-    # via ipykernel
-    # via ipython
-nest-asyncio==1.6.0
-    # via ipykernel
-numpy==2.2.2
-    # via fastparquet
-    # via pandas
-    # via scikit-learn
-    # via scipy
-packaging==24.2
-    # via fastparquet
-    # via ipykernel
-pandas==2.2.3
-    # via fastparquet
-    # via lejuge
-parso==0.8.4
-    # via jedi
-pexpect==4.9.0
-    # via ipython
-platformdirs==4.3.6
-    # via jupyter-core
-prompt-toolkit==3.0.50
-    # via ipython
-psutil==6.1.1
-    # via ipykernel
-ptyprocess==0.7.0
-    # via pexpect
-pure-eval==0.2.3
-    # via stack-data
-pygments==2.19.1
-    # via ipython
-python-dateutil==2.9.0.post0
-    # via jupyter-client
-    # via pandas
-pytz==2024.2
-    # via pandas
-pyzmq==26.2.0
-    # via ipykernel
-    # via jupyter-client
-scikit-learn==1.6.1
-    # via lejuge
-scipy==1.15.1
-    # via scikit-learn
-six==1.17.0
-    # via python-dateutil
-stack-data==0.6.3
-    # via ipython
-threadpoolctl==3.5.0
-    # via scikit-learn
-tornado==6.4.2
-    # via ipykernel
-    # via jupyter-client
-traitlets==5.14.3
-    # via comm
-    # via ipykernel
-    # via ipython
-    # via jupyter-client
-    # via jupyter-core
-    # via matplotlib-inline
-typing-extensions==4.12.2
-    # via ipython
-tzdata==2025.1
-    # via pandas
-wcwidth==0.2.13
-    # via prompt-toolkit

requirements.lock

@@ -1,109 +0,0 @@
-# generated by rye
-# use `rye lock` or `rye sync` to update this lockfile
-#
-# last locked with the following flags:
-#   pre: false
-#   features: []
-#   all-features: false
-#   with-sources: false
-#   generate-hashes: false
-#   universal: false
-
--e file:.
-appnope==0.1.4
-    # via ipykernel
-asttokens==3.0.0
-    # via stack-data
-comm==0.2.2
-    # via ipykernel
-cramjam==2.9.1
-    # via fastparquet
-debugpy==1.8.12
-    # via ipykernel
-decorator==5.1.1
-    # via ipython
-executing==2.2.0
-    # via stack-data
-fastparquet==2024.11.0
-    # via lejuge
-fsspec==2024.12.0
-    # via fastparquet
-ipykernel==6.29.5
-    # via lejuge
-ipython==8.31.0
-    # via ipykernel
-jedi==0.19.2
-    # via ipython
-joblib==1.4.2
-    # via scikit-learn
-jupyter-client==8.6.3
-    # via ipykernel
-jupyter-core==5.7.2
-    # via ipykernel
-    # via jupyter-client
-matplotlib-inline==0.1.7
-    # via ipykernel
-    # via ipython
-nest-asyncio==1.6.0
-    # via ipykernel
-numpy==2.2.2
-    # via fastparquet
-    # via pandas
-    # via scikit-learn
-    # via scipy
-packaging==24.2
-    # via fastparquet
-    # via ipykernel
-pandas==2.2.3
-    # via fastparquet
-    # via lejuge
-parso==0.8.4
-    # via jedi
-pexpect==4.9.0
-    # via ipython
-platformdirs==4.3.6
-    # via jupyter-core
-prompt-toolkit==3.0.50
-    # via ipython
-psutil==6.1.1
-    # via ipykernel
-ptyprocess==0.7.0
-    # via pexpect
-pure-eval==0.2.3
-    # via stack-data
-pygments==2.19.1
-    # via ipython
-python-dateutil==2.9.0.post0
-    # via jupyter-client
-    # via pandas
-pytz==2024.2
-    # via pandas
-pyzmq==26.2.0
-    # via ipykernel
-    # via jupyter-client
-scikit-learn==1.6.1
-    # via lejuge
-scipy==1.15.1
-    # via scikit-learn
-six==1.17.0
-    # via python-dateutil
-stack-data==0.6.3
-    # via ipython
-threadpoolctl==3.5.0
-    # via scikit-learn
-tornado==6.4.2
-    # via ipykernel
-    # via jupyter-client
-traitlets==5.14.3
-    # via comm
-    # via ipykernel
-    # via ipython
-    # via jupyter-client
-    # via jupyter-core
-    # via matplotlib-inline
-typing-extensions==4.12.2
-    # via ipython
-tzdata==2025.1
-    # via pandas
-wcwidth==0.2.13
-    # via prompt-toolkit

requirements.txt

@@ -0,0 +1,3 @@
+pandas>=1.0.0
+scikit-learn>=0.24.0
+fastparquet>=2024.11.0

setup.py

@@ -0,0 +1,20 @@
+from setuptools import setup, find_packages
+
+setup(
+    name="crag-sampler",
+    version="0.1.0",
+    packages=find_packages(),
+    install_requires=["pandas>=1.0.0", "scikit-learn>=0.24.0"],
+    author="Jacopo Chevallard",
+    author_email="[email protected]",
+    description="A tool for sampling CRAG datasets",
+    long_description=open("README.md").read(),
+    long_description_content_type="text/markdown",
+    url="https://huggingface.co/Quivr/CRAG",
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.7",
+)