Merge pull request #7 from erikbern/erikbern/tox

setting up travis

.travis.yml

@@ -0,0 +1,9 @@
+language: python
+python:
+  - "2.7_with_system_site_packages"
+  - "3.4"
+
+install:
+  - sudo bash install.sh
+
+script: nosetests

README.rst

@@ -28,7 +28,9 @@ Doing fast searching of nearest neighbors in high dimensional spaces is an incre
 Install
 -------
 
-Clone the repo and run ``bash install.sh``. This will install all libraries as well as downloading and preprocessing all data sets. It could take a while. It has been tested in Ubuntu 14.04. 
+Clone the repo and run ``bash install.sh``. This will install all libraries. It could take a while. It has been tested in Ubuntu 14.04.
+
+To download and preprocess the data sets, run ``bash install/glove.sh`` and ``bash install/sift.sh``.
 
 There is also a Docker image available under `erikbern/ann <https://registry.hub.docker.com/u/erikbern/ann/>`__ containing all libraries and data sets.
 

install.sh

@@ -1,6 +1,15 @@
-sudo apt-get install -y python-numpy python-scipy python-sklearn
+apt-get update
+apt-get install -y python-numpy python-scipy python-pip python-nose
+pip install scikit-learn
+
+# Install GCC 4.8
+add-apt-repository ppa:ubuntu-toolchain-r/test -y
+apt-get update -qq
+apt-get install -y libboost1.48-all-dev g++-4.8
+export CXX="g++-4.8" CC="gcc-4.8"
+
 cd install
-for fn in annoy.sh panns.sh nearpy.sh sklearn.sh flann.sh kgraph.sh nmslib.sh glove.sh sift.sh
+for fn in annoy.sh panns.sh nearpy.sh sklearn.sh flann.sh kgraph.sh nmslib.sh
 do
     source $fn
 done

install/annoy.sh

@@ -1,5 +1,5 @@
-sudo apt-get install -y python-dev python-setuptools
+apt-get install -y python-dev python-setuptools
 git clone https://github.com/spotify/annoy
 cd annoy
-sudo python setup.py install
+python setup.py install
 cd ..

install/glove.sh

@@ -1,3 +1,4 @@
+cd "$(dirname "$0")"
 wget "http://www-nlp.stanford.edu/data/glove.twitter.27B.100d.txt.gz"
 gunzip -d glove.twitter.27B.100d.txt.gz
 cut -d " " -f 2- glove.twitter.27B.100d.txt > glove.txt # strip first column

install/kgraph.sh

@@ -1,5 +1,6 @@
 git clone https://github.com/aaalgo/kgraph
 pushd kgraph
+apt-get install -y libboost-timer-dev libbooost-chrono-dev
 sudo make deps-ubuntu
 make
 make release

install/nearpy.sh

@@ -1,2 +1,3 @@
-sudo apt-get install -y python-pip
-sudo pip install nearpy bitarray redis
+apt-get install -y python-pip libhdf5-dev
+pip install cython
+pip install nearpy bitarray redis h5py

install/nmslib.sh

@@ -4,13 +4,12 @@ rm -rf NonMetricSpaceLib
 # Note that we use the develop branch here:
 git clone https://github.com/searchivarius/NonMetricSpaceLib.git
 cd NonMetricSpaceLib/similarity_search
-git checkout ann-benchmark  
-sudo apt-get install -y cmake libeigen3-dev libgsl0-dev libboost-all-dev g++-4.8 
-# Actually let's make g++ an alias
-alias g++=g++-4.8
+git checkout ann-benchmark
+apt-get install -y cmake libeigen3-dev libgsl0-dev libboost-all-dev
+echo "CC: $CC, CXX: $CXX"
 cmake .
 make -j 4
 cd ../python_binding
 make
-sudo make install
+make install
 cd ../..

install/sift.sh

@@ -1,3 +1,4 @@
+cd "$(dirname "$0")"
 wget "ftp://ftp.irisa.fr/local/texmex/corpus/sift.tar.gz"
 tar -xzf sift.tar.gz
 rm -rf sift.tar.gz

setup.py

@@ -0,0 +1,3 @@
+from setuptools import setup
+
+setup(packages=['ann_benchmarks'])

test/test.py

@@ -0,0 +1,27 @@
+import random
+import inspect
+import ann_benchmarks
+from sklearn.datasets.samples_generator import make_blobs
+
+# Generate dataset
+X, labels_true = make_blobs(n_samples=10000, n_features=10,
+                            centers=10, cluster_std=5,
+                            random_state=0)
+
+def check_algo(algo_name, algo):
+    algo.fit(X)
+    result = algo.query(X[42], 10)
+    if len(result) != 10:
+        raise AssertionError('Expected results to have length 10: Result: %s' % result)
+    if len(set(result)) != 10:
+        raise AssertionError('Expected results to be unique: Result: %s' % result)
+    #if result[0] != 42:
+    #    raise AssertionError('Expected first item to be 42: Result: %s' % result)
+
+
+def test_all_algos():
+    for metric in ['angular', 'euclidean']:
+        algos = ann_benchmarks.get_algos(metric)
+        for algo_key in algos.keys():
+            algo = random.choice(algos[algo_key]) # Just pick one of each
+            yield check_algo, algo.name, algo # pass name just so unittest can capture it