metric-py 0.0.6

Metric python3 module

METRIC-PY

A python wrapper for METRIC library (https://panda.technology/de/metric)

Installation

You need Python 3.6+

Linux & OS X

python -m pip install metric-py -i https://test.pypi.org/simple/

Windows (x64 only)

You will need to install any BLAS implementation. The easiest way is by using Miniconda:

conda config --add channels conda-forge
conda update -n base conda -y
conda install -c conda-forge libopenblas openblas -y

Then you can use pip to install

python -m pip install metric-py -i https://test.pypi.org/simple/

Build from the source

git clone --recurse-submodules https://github.com/panda-official/metric

Download and extract Boost (1.67+). For Windows there are pre-build binaries available.

Install Prerequisites

Ubuntu

sudo apt-get install cmake
sudo apt-get install libboost-all-dev
sudo apt-get install libopenblas-dev

Windows

Install Miniconda. In Conda CLI initialize your virtual environment with desired Python version:

conda create --name my_env -y python=3.8
conda activate my_env

Install OpenBLAS from alternative repo

conda config --add channels conda-forge
conda update -n base conda -y
conda install -c conda-forge libopenblas openblas -y

Build package

At least 2GB of RAM is required

python setup.py bdist_wheel

to limit memory usage during build add MAKE="make -j1":

MAKE="make -j1" python3 setup.py bdist_wheel

Install module

python -m pip install dist/*

Examples

import numpy
from metric.correlation import Entropy
from metric.distance import Euclidean, P_norm, Manhatten

aent = numpy.float_([
    [5.0, 5.0],
    [2.0, 2.0],
    [3.0, 3.0],
    [5.0, 1.0],
])

print("Entropies:")
for metric in (Euclidean, P_norm, Manhatten):
    res = Entropy(metric=metric(), p=3, k=2)(aent)
    print(f'using {metric}: {res:.5f}')

res = Entropy(p=3, k=2)(aent)
print(f'using Default: {res:.5f}')

for more examples please check examples/ folder