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Misc updates for run
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‎README.md

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@@ -124,9 +124,9 @@ The only prerequisite is Python (tested with 3.10.6) and Docker.
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Running
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=======
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1. Run `python run.py` (this can take an extremely long time, potentially days)
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2. Run `python plot.py` or `python create_website.py` to plot results.
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3. Run `python data_export.py --out res.csv` to export all results into a csv file for additional post-processing.
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1. Run `python run.py` (this can take an extremely long time, potentially days).
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2. Run `python plot.py --x-scale logit --y-scale log` to plot results.
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3. Run `python create_website.py` to create a website with lots of plots.
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You can customize the algorithms and datasets as follows:
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‎ann_benchmarks/algorithms/base/Dockerfile

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FROM ubuntu:22.04
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RUN apt-get update
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RUN apt-get install -y python3-numpy python3-scipy python3-pip build-essential git
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RUN pip3 install -U pip
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RUN python3 --version | grep 'Python 3.10'
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RUN apt-get install -y python3-pip python-is-python3 build-essential git
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RUN python --version | grep 'Python 3.10'
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WORKDIR /home/app
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COPY requirements.txt run_algorithm.py ./
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RUN pip3 install -r requirements.txt
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RUN pip install -r requirements.txt
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ENTRYPOINT ["python3", "-u", "run_algorithm.py"]
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ENTRYPOINT ["python", "-u", "run_algorithm.py"]

‎ann_benchmarks/algorithms/hnswlib/Dockerfile

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FROM ann-benchmarks
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RUN apt-get install -y python-setuptools python-pip
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RUN pip3 install pybind11 numpy setuptools
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RUN git clone https://github.com/nmslib/hnsw.git;cd hnsw; git checkout denorm
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RUN pip install pybind11 numpy setuptools
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RUN git clone https://github.com/nmslib/hnsw.git; cd hnsw; git checkout denorm
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RUN cd hnsw/python_bindings; python3 setup.py install
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RUN python3 -c 'import hnswlib'
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RUN python -c 'import hnswlib'
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‎ann_benchmarks/algorithms/kgraph/Dockerfile

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FROM ann-benchmarks
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RUN apt-get update && apt-get install -y libboost-timer-dev libboost-chrono-dev libboost-program-options-dev libboost-system-dev libboost-python-dev
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RUN apt-get update && apt-get install -y libboost-timer-dev libboost-chrono-dev libboost-program-options-dev libboost-system-dev libboost-python-dev libopenblas-dev
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RUN git clone https://github.com/aaalgo/kgraph
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RUN cd kgraph && python3 setup.py build && python3 setup.py install
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RUN python3 -c 'import pykgraph'
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RUN cd kgraph && pip install -e .
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RUN python -c 'import kgraph'

‎ann_benchmarks/algorithms/kgraph/module.py

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import os
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import numpy
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import pykgraph
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import kgraph
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from ...constants import INDEX_DIR
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from ..base.module import BaseANN
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def fit(self, X):
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if X.dtype != numpy.float32:
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X = X.astype(numpy.float32)
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self._kgraph = pykgraph.KGraph(X, self._metric)
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self._kgraph = kgraph.KGraph(X, self._metric)
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path = os.path.join(INDEX_DIR, "kgraph-index-%s" % self._metric)
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if os.path.exists(path):
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self._kgraph.load(path)

‎ann_benchmarks/algorithms/nearpy/Dockerfile

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FROM ann-benchmarks
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RUN apt-get install -y libhdf5-openmpi-dev cython
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RUN pip3 install nearpy bitarray redis sklearn
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RUN python3 -c 'import nearpy'
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RUN apt-get install -y libhdf5-openmpi-dev cython3
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RUN pip install nearpy bitarray redis
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RUN python -c 'import nearpy'

‎ann_benchmarks/algorithms/puffinn/Dockerfile

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FROM ann-benchmarks
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RUN pip3 install pypandoc==1.4
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RUN pip install pypandoc==1.4 pybind11
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RUN git clone https://github.com/puffinn/puffinn
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RUN cd puffinn && python3 setup.py install
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RUN python3 -c 'import puffinn'
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RUN cd puffinn && python setup.py install
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RUN python -c 'import puffinn'

‎ann_benchmarks/algorithms/vearch/Dockerfile

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FROM ann-benchmarks
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RUN pip3 install vearch==3.2.8.3
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RUN python3 -c 'import vearch'
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RUN pip install 'numpy<2'
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RUN pip install vearch==3.2.8.3
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RUN python -c 'import vearch'

‎ann_benchmarks/algorithms/voyager/Dockerfile

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FROM ann-benchmarks
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RUN apt-get install -y python-setuptools python-pip
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RUN pip3 install voyager
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RUN apt-get install -y python-setuptools
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RUN pip install voyager
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RUN python3 -c 'import voyager'
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RUN python -c 'import voyager'
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‎plot.py

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@@ -88,7 +88,7 @@ def inv_fun(x):
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# Workaround for bug https://github.com/matplotlib/matplotlib/issues/6789
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ax.spines["bottom"]._adjust_location()
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plt.savefig(fn_out, bbox_inches="tight")
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plt.savefig(fn_out, bbox_inches="tight", dpi=144)
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plt.close()
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"-y", "--y-axis", help="Which metric to use on the Y-axis", choices=metrics.keys(), default="qps"
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)
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parser.add_argument(
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"-X", "--x-scale", help="Scale to use when drawing the X-axis. Typically linear, logit or a2", default="linear"
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"-X", "--x-scale", help="Scale to use when drawing the X-axis. Typically linear, logit or a2", default="linear",
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)
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parser.add_argument(
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"-Y",

‎requirements.txt

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ansicolors==1.1.8
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docker==7.1.0
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h5py==3.8.0
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matplotlib==3.6.3
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numpy==1.24.2
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pyyaml==6.0
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psutil==5.9.4
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scikit-learn==1.2.1
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jinja2==3.1.2
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pytest==7.2.2
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datasets==2.12.0
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requests==2.31.0
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h5py==3.13.0
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matplotlib==3.10.1
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numpy==2.2.4
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pyyaml==6.0.2
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psutil==7.0.0
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scikit-learn==1.6.1
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jinja2==3.1.6
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pytest==8.3.5
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datasets==3.4.0
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requests==2.32.3

‎results/fashion-mnist-784-euclidean.png

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‎results/gist-960-euclidean.png

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‎results/glove-100-angular.png

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‎results/glove-25-angular.png

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‎results/nytimes-256-angular.png

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‎results/sift-128-euclidean.png

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