Rename raft-ann-bench module to raft_ann_bench

docs/source/ann_benchmarks_dataset.md

@@ -52,12 +52,12 @@ If you have a dataset, but no corresponding ground truth file, then you can gene
 
 ```bash
 # With existing query file
-python -m raft-ann-bench.generate_groundtruth --dataset /dataset/base.fbin --output=groundtruth_dir --queries=/dataset/query.public.10K.fbin
+python -m raft_ann_bench.generate_groundtruth --dataset /dataset/base.fbin --output=groundtruth_dir --queries=/dataset/query.public.10K.fbin
 
 # With randomly generated queries
-python -m raft-ann-bench.generate_groundtruth --dataset /dataset/base.fbin --output=groundtruth_dir --queries=random --n_queries=10000
+python -m raft_ann_bench.generate_groundtruth --dataset /dataset/base.fbin --output=groundtruth_dir --queries=random --n_queries=10000
 
 # Using only a subset of the dataset. Define queries by randomly
 # selecting vectors from the (subset of the) dataset.
-python -m raft-ann-bench.generate_groundtruth --dataset /dataset/base.fbin --nrows=2000000 --output=groundtruth_dir --queries=random-choice --n_queries=10000
+python -m raft_ann_bench.generate_groundtruth --dataset /dataset/base.fbin --nrows=2000000 --output=groundtruth_dir --queries=random-choice --n_queries=10000
 ```

docs/source/ann_benchmarks_low_level.md

@@ -8,7 +8,7 @@ cd raft
 
 # (1) prepare a dataset
 export PYTHONPATH=python/raft-ann-bench/src:$PYTHONPATH
-python -m raft-ann-bench.get_dataset --dataset glove-100-angular --normalize
+python -m raft_ann_bench.get_dataset --dataset glove-100-angular --normalize
 
 # option --normalize is used here to normalize vectors so cosine distance is converted
 # to inner product; don't use -n for l2 distance

docs/source/raft_ann_benchmarks.md

@@ -265,16 +265,16 @@ The steps below demonstrate how to download, install, and run benchmarks on a su
 ```bash
 
 # (1) prepare dataset.
-python -m raft-ann-bench.get_dataset --dataset deep-image-96-angular --normalize
+python -m raft_ann_bench.get_dataset --dataset deep-image-96-angular --normalize
 
 # (2) build and search index
-python -m raft-ann-bench.run --dataset deep-image-96-inner --algorithms raft_cagra --batch-size 10 -k 10
+python -m raft_ann_bench.run --dataset deep-image-96-inner --algorithms raft_cagra --batch-size 10 -k 10
 
 # (3) export data
-python -m raft-ann-bench.data_export --dataset deep-image-96-inner
+python -m raft_ann_bench.data_export --dataset deep-image-96-inner
 
 # (4) plot results
-python -m raft-ann-bench.plot --dataset deep-image-96-inner
+python -m raft_ann_bench.plot --dataset deep-image-96-inner
 ```
 
 Configuration files already exist for the following list of the million-scale datasets. Please refer to [ann-benchmarks datasets](https://github.com/erikbern/ann-benchmarks/#data-sets) for more information, including actual train and sizes. These all work out-of-the-box with the `--dataset` argument. Other million-scale datasets from `ann-benchmarks.com` will work, but will require a json configuration file to be created in `$CONDA_PREFIX/lib/python3.xx/site-packages/raft-ann-bench/run/conf`, or you can specify the `--configuration` option to use a specific file.
@@ -308,20 +308,20 @@ mkdir -p datasets/deep-1B
 # (1) prepare dataset
 # download manually "Ground Truth" file of "Yandex DEEP"
 # suppose the file name is deep_new_groundtruth.public.10K.bin
-python -m raft-ann-bench.split_groundtruth --groundtruth datasets/deep-1B/deep_new_groundtruth.public.10K.bin
+python -m raft_ann_bench.split_groundtruth --groundtruth datasets/deep-1B/deep_new_groundtruth.public.10K.bin
 # two files 'groundtruth.neighbors.ibin' and 'groundtruth.distances.fbin' should be produced
 
 # (2) build and search index
-python -m raft-ann-bench.run --dataset deep-1B --algorithms raft_cagra --batch-size 10 -k 10
+python -m raft_ann_bench.run --dataset deep-1B --algorithms raft_cagra --batch-size 10 -k 10
 
 # (3) export data
-python -m raft-ann-bench.data_export --dataset deep-1B
+python -m raft_ann_bench.data_export --dataset deep-1B
 
 # (4) plot results
-python -m raft-ann-bench.plot --dataset deep-1B
+python -m raft_ann_bench.plot --dataset deep-1B
 ```
 
-The usage of `python -m raft-ann-bench.split_groundtruth` is:
+The usage of `python -m raft_ann_bench.split_groundtruth` is:
 ```bash
 usage: split_groundtruth.py [-h] --groundtruth GROUNDTRUTH
 
@@ -395,7 +395,7 @@ docker run --gpus all --rm -it -u $(id -u)          \
 This will drop you into a command line in the container, with the `raft-ann-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:
 
 ```
-(base) root@00b068fbb862:/data/benchmarks# python -m raft-ann-bench.get_dataset --dataset deep-image-96-angular --normalize
+(base) root@00b068fbb862:/data/benchmarks# python -m raft_ann_bench.get_dataset --dataset deep-image-96-angular --normalize
 ```
 
 Additionally, the containers can be run in detached mode without any issue.

python/raft-ann-bench/pyproject.toml

@@ -26,6 +26,7 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
 ]
 
 [project.urls]
@@ -59,4 +60,4 @@ skip = [
 ]
 
 [tool.setuptools.dynamic]
-version = { attr = "raft-ann-bench.__version__" }
+version = { file = "raft_ann_bench/VERSION" }

python/raft-ann-bench/src/raft-ann-bench/generate_groundtruth/__main__.py → python/raft-ann-bench/src/raft_ann_bench/generate_groundtruth/__main__.py

@@ -96,16 +96,16 @@ def main():
         "The input and output files are in big-ann-benchmark's binary format.",
         epilog="""Example usage
     # With existing query file
-    python -m raft-ann-bench.generate_groundtruth --dataset /dataset/base.\
+    python -m raft_ann_bench.generate_groundtruth --dataset /dataset/base.\
 fbin --output=groundtruth_dir --queries=/dataset/query.public.10K.fbin
 
     # With randomly generated queries
-    python -m raft-ann-bench.generate_groundtruth --dataset /dataset/base.\
+    python -m raft_ann_bench.generate_groundtruth --dataset /dataset/base.\
 fbin --output=groundtruth_dir --queries=random --n_queries=10000
 
     # Using only a subset of the dataset. Define queries by randomly
     # selecting vectors from the (subset of the) dataset.
-    python -m raft-ann-bench.generate_groundtruth --dataset /dataset/base.\
+    python -m raft_ann_bench.generate_groundtruth --dataset /dataset/base.\
 fbin --nrows=2000000 --cols=128 --output=groundtruth_dir \
 --queries=random-choice --n_queries=10000
     """,