Fix pip install lshash for python3

AUTHORS.rst

@@ -0,0 +1,5 @@
+==========
+Developers
+==========
+
+* Kay Zhu (a.k.a He Zhu) <[email protected]>

lshash/__init__.py

@@ -1,12 +1,6 @@
-# lshash/__init__.py
-# Copyright 2012 Kay Zhu (a.k.a He Zhu) and contributors (see CONTRIBUTORS.txt)
-#
-# This module is part of lshash and is released under
-# the MIT License: http://www.opensource.org/licenses/mit-license.php
+import pkg_resources
 
-__title__ = 'lshash'
-__author__ = 'Kay Zhu ([email protected])'
-__license__ = 'MIT'
-__version__ = '0.0.4dev'
-
-from lshash import LSHash
+try:
+    __version__ = pkg_resources.get_distribution(__name__).version
+except:
+    __version__ = '0.0.4dev'

requirements.txt

@@ -0,0 +1,3 @@
+# bitarray
+numpy
+# redis

setup.cfg

@@ -0,0 +1,84 @@
+[metadata]
+
+name = lshash
+summary = A fast Python implementation of locality sensitive hashing with persistance (Redis) support.
+author = Kay Zhu
+author-email = [email protected]
+license = MIT
+home-page = http://...
+description-file = README.rst
+# Add here all kinds of additional classifiers as defined under
+# https://pypi.python.org/pypi?%3Aaction=list_classifiers
+classifier =
+    Development Status :: 4 - Beta
+    Programming Language :: Python
+    Programming Language :: Python :: 2
+    Intended Audience :: Developers
+    License :: OSI Approved :: MIT License
+    Operating System :: OS Independent
+    Topic :: Software Development :: Libraries
+
+[entry_points]
+# Add here console scripts like:
+# console_scripts =
+#     script_name = lshash.module:function
+# For example:
+# console_scripts =
+#     fibonacci = lshash.skeleton:run
+# as well as other entry_points.
+
+
+[files]
+# Add here 'data_files', 'packages' or 'namespace_packages'.
+# Additional data files are defined as key value pairs of source and target:
+packages =
+    lshash
+# data_files =
+#    share/lshash_docs = docs/*
+
+[extras]
+# Add here additional requirements for extra features, like:
+Redis =
+    redis>=2.10.0
+Hamming = 
+    bitarray
+
+[test]
+# py.test options when running `python setup.py test`
+addopts = tests
+
+[pytest]
+# Options for py.test:
+# Specify command line options as you would do when invoking py.test directly.
+# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml
+# in order to write a coverage file that can be read by Jenkins.
+addopts =
+    --cov lshash --cov-report term-missing
+    --verbose
+
+[aliases]
+docs = build_sphinx
+
+[bdist_wheel]
+# Use this option if your package is pure-python
+universal = 1
+
+[build_sphinx]
+source_dir = docs
+build_dir = docs/_build
+
+[pbr]
+# Let pbr run sphinx-apidoc
+autodoc_tree_index_modules = True
+# autodoc_tree_excludes = ...
+# Let pbr itself generate the apidoc
+# autodoc_index_modules = True
+# autodoc_exclude_modules = ...
+# Convert warnings to errors
+# warnerrors = True
+
+[devpi:upload]
+# Options for the devpi: PyPI server and packaging tool
+# VCS export must be deactivated since we are using setuptools-scm
+no-vcs = 1
+formats = bdist_wheel

setup.py

@@ -1,42 +1,19 @@
+#!/usr/bin/env python
 # -*- coding: utf-8 -*-
+""" Setup file for lshash.
+    This file was generated with PyScaffold 2.5.6
+"""
 
-import lshash
+import sys
+from setuptools import setup
 
-try:
-    from setuptools import setup
-except ImportError:
-    from distutils.core import setup
 
+def setup_package():
+    needs_sphinx = {'build_sphinx', 'upload_docs'}.intersection(sys.argv)
+    sphinx = ['sphinx'] if needs_sphinx else []
+    setup(setup_requires=['six', 'pyscaffold>=2.5a0,<2.6a0'] + sphinx,
+          use_pyscaffold=True)
 
-with open('README.rst') as f:
-    readme = f.read()
 
-with open('LICENSE') as f:
-    license = f.read()
-
-with open('CHANGES.rst') as f:
-    changes = f.read()
-
-required = ['numpy']
-
-setup(
-    name='lshash',
-    version=lshash.__version__,
-    packages=['lshash'],
-    author='Kay Zhu',
-    author_email='[email protected]',
-    maintainer='Kay Zhu',
-    maintainer_email='[email protected]',
-    description='A fast Python implementation of locality sensitive hashing with persistance support.',
-    long_description=readme + '\n\n' + changes,
-    license=license,
-    requires=required,
-    classifiers=[
-        'Intended Audience :: Developers',
-        'License :: OSI Approved :: MIT License',
-        'Operating System :: OS Independent',
-        'Programming Language :: Python',
-        'Programming Language :: Python :: 2',
-        'Topic :: Software Development :: Libraries',
-        ],
-)
+if __name__ == "__main__":
+    setup_package()

test-requirements.txt

@@ -0,0 +1,5 @@
+# Add requirements only needed for your unittests and during development here.
+# They will be installed automatically when running `python setup.py test`.
+# ATTENTION: Don't remove pytest-cov and pytest as they are needed.
+pytest-cov
+pytest

tests/conftest.py

@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+    Dummy conftest.py for lshash.
+
+    If you don't know what this is for, just leave it empty.
+    Read more about conftest.py under:
+    https://pytest.org/latest/plugins.html
+"""
+from __future__ import print_function, absolute_import, division
+
+import pytest

tests/test_spheres.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import pytest
+import numpy as np
+
+from lshash import LSHash
+
+__author__ = "Hobson Lane"
+__copyright__ = "Kay Zhu (a.k.a He Zhu)"
+__license__ = "MIT"
+
+
+def test_sphere():
+    X = np.random.normal(size=(1000, 3))
+    lsh = LSHash(10, 3, num_hashtables=5)
+    for x in X:
+        x /= np.linalg.norm(x)
+        lsh.index(x)
+    closest = lsh.query(X[0] + np.array([-0.001, 0.001, -0.001]), distance_func="cosine")
+    assert len(closest) >= 10
+    assert 0.05 >= closest[9][-1] > 0.0003
+
+
+def test_hyperspheres():
+    tenthclosest = []
+    for D in range(2, 11):
+        X = np.random.normal(size=(200000, D))
+        lsh = LSHash(int(64 / D) + D, D, num_hashtables=D)
+
+        # query vector
+        q = np.random.normal(size=(D,))
+        q /= np.linalg.norm(q)
+
+        distances = []
+        for x in X:
+            lsh.index(x)
+            x /= np.linalg.norm(x)
+            distances += [1 - np.sum(x * q)]
+        distances = sorted(distances)
+        closest = lsh.query(q, distance_func='cosine')
+        N = len(closest)
+        rank = min(10, N)
+        tenthclosest += [[D, N - 1, closest[rank - 1][-1] if N else None, distances[rank - 1]]]
+        print(tenthclosest[-1])
+    for i, tc in enumerate(tenthclosest):
+        assert 1e-9 < tc[-2] or 1e-6 < 0.2
+    return tenthclosest