Merge pull request #6 from maumueller/small-bitstring-dataset

Random bitstring dataset

.travis.yml

@@ -8,30 +8,30 @@ services:
   - docker
 
 env:
-  - LIBRARY=annoy
-  - LIBRARY=dolphinn
-  - LIBRARY=faiss
-  - LIBRARY=flann
-  - LIBRARY=hdidx
-  - LIBRARY=kgraph
-  - LIBRARY=mrpt
-  - LIBRARY=nearpy
-  - LIBRARY=ngt
-  - LIBRARY=nmslib
-  - LIBRARY=hnswlib
-  - LIBRARY=panns
-  - LIBRARY=pynndescent
-  - LIBRARY=rpforest
-  - LIBRARY=sklearn
-  - LIBRARY=mih
+  - LIBRARY=annoy DATASET=random-xs-20-angular
+  - LIBRARY=dolphinn DATASET=random-xs-20-angular
+  - LIBRARY=faiss  DATASET=random-xs-20-angular
+  - LIBRARY=flann DATASET=random-xs-20-angular
+  - LIBRARY=hdidx DATASET=random-xs-20-angular
+  - LIBRARY=kgraph DATASET=random-xs-20-angular
+  - LIBRARY=mrpt DATASET=random-xs-20-angular
+  - LIBRARY=nearpy DATASET=random-xs-20-angular
+  - LIBRARY=ngt DATASET=random-xs-20-angular
+  - LIBRARY=nmslib DATASET=random-xs-20-angular
+  - LIBRARY=hnswlib DATASET=random-xs-20-angular
+  - LIBRARY=panns DATASET=random-xs-20-angular
+  - LIBRARY=pynndescent DATASET=random-xs-20-angular
+  - LIBRARY=rpforest DATASET=random-xs-20-angular
+  - LIBRARY=sklearn DATASET=random-xs-20-angular
+  - LIBRARY=mih DATASET=random-xs-16-hamming
 
 before_install:
   - pip install -r requirements.txt
   - python install.py
 
 script:
-  - python run.py --docker-tag ann-benchmarks-${LIBRARY} --max-n-algorithms 5 --dataset random-xs-20-angular --run-disabled
-  - python run.py --docker-tag ann-benchmarks-${LIBRARY} --max-n-algorithms 5 --dataset random-xs-20-angular --run-disabled --batch
+  - python run.py --docker-tag ann-benchmarks-${LIBRARY} --max-n-algorithms 5 --dataset $DATASET --run-disabled
+  - python run.py --docker-tag ann-benchmarks-${LIBRARY} --max-n-algorithms 5 --dataset $DATASET --run-disabled --batch
   - python plot.py --dataset random-xs-20-angular --output plot.png
   - python plot.py --dataset random-xs-20-angular --output plot-batch.png --batch
   - python -m unittest test/test-metrics.py

algos.yaml

@@ -468,7 +468,7 @@ bit:
           # takes places. Otherwise, n / r points from the dataset are used for a
           # re-ording process. MIH requires either r = 0 or r >= 2.
           args: { "d" : "@dimension", "r" : [0, 2, 3],
-            "chunk-factor" : [0, 0.5, 0.8] }
+            "chunk-factor" : [0.2, 0.5, 0.8] }
     kgraph:
       docker-tag: ann-benchmarks-kgraph
       module: ann_benchmarks.algorithms.kgraph

ann_benchmarks/datasets.py

@@ -214,6 +214,17 @@ def random(out_fn, n_dims, n_samples, centers, distance):
     X_train, X_test = train_test_split(X, test_size=0.1)
     write_output(X_train, X_test, out_fn, distance)
 
+def random_bitstring(out_fn, n_dims, n_samples, n_queries):
+    import sklearn.datasets
+
+    Y, _ = sklearn.datasets.make_blobs(n_samples=n_samples, n_features=n_dims, centers=n_queries, random_state=1)
+    X = numpy.zeros((n_samples, n_dims), dtype=numpy.bool)
+    for i, vec in enumerate(Y):
+        X[i] = numpy.array([v > 0 for v in vec], dtype=numpy.bool)
+
+    X_train, X_test = train_test_split(X, test_size=n_queries)
+    write_output(X_train, X_test, out_fn, 'hamming', 'bit')
+
 
 def word2bits(out_fn, path, fn):
     import tarfile
@@ -297,6 +308,9 @@ def lastfm(out_fn, n_dimensions, test_size=50000):
     'random-s-100-euclidean': lambda out_fn: random(out_fn, 100, 100000, 1000, 'euclidean'),
     'random-xs-20-angular': lambda out_fn: random(out_fn, 20, 10000, 100, 'angular'),
     'random-s-100-angular': lambda out_fn: random(out_fn, 100, 100000, 1000, 'angular'),
+    'random-xs-16-hamming': lambda out_fn: random_bitstring(out_fn, 16, 10000, 100),
+    'random-s-128-hamming': lambda out_fn: random_bitstring(out_fn, 128, 50000, 1000),
+    'random-l-256-hamming': lambda out_fn: random_bitstring(out_fn, 256, 100000, 1000),
     'sift-128-euclidean': sift,
     'nytimes-256-angular': lambda out_fn: nytimes(out_fn, 256),
     'nytimes-16-angular': lambda out_fn: nytimes(out_fn, 16),