add some test

pyengine/engine/ingestion/build_index.py

@@ -35,7 +35,7 @@ class DefaultIndex(Index):
         # maybe need to specif parameters
         pass
 
-    def build(d, vectors, DEVICE=INDEX_DEVICES.CPU):
+    def build(self, d, vectors, DEVICE=INDEX_DEVICES.CPU):
         index = faiss.IndexFlatL2(d) # trained
         index.add(vectors)
         return index

pyengine/engine/ingestion/tests/test_build.py

+from ..build_index import *
+
+import faiss
+import numpy as np
+import unittest
+
+
+class TestBuildIndex(unittest.TestCase):
+    def test_factory_method(self):
+        pass
+
+    def test_default_index(self):
+        d = 64
+        nb = 10000
+        nq = 100
+        _, xb, xq = get_dataset(d, nb, 500, nq)
+
+        # Expected result
+        index = faiss.IndexFlatL2(d)
+        index.add(xb)
+        Dref, Iref = index.search(xq, 5)
+
+        builder = DefaultIndex()
+        index2 = builder.build(d, xb)
+        Dnew, Inew = index2.search(xq, 5)
+
+        assert np.all(Dnew == Dref) and np.all(Inew == Iref)
+
+    def test_increase(self):
+        d = 64
+        nb = 10000
+        nq = 100
+        _, xb, xq = get_dataset(d, nb, 500, nq)
+
+        index = faiss.IndexFlatL2(d)
+        index.add(xb)
+
+        pass
+
+    def test_serialize(self):
+        pass
+
+
+def get_dataset(d, nb, nt, nq):
+    """A dataset that is not completely random but still challenging to
+    index
+    """
+    d1 = 10     # intrinsic dimension (more or less)
+    n = nb + nt + nq
+    rs = np.random.RandomState(1338)
+    x = rs.normal(size=(n, d1))
+    x = np.dot(x, rs.rand(d1, d))
+    # now we have a d1-dim ellipsoid in d-dimensional space
+    # higher factor (>4) -> higher frequency -> less linear
+    x = x * (rs.rand(d) * 4 + 0.1)
+    x = np.sin(x)
+    x = x.astype('float32')
+    return x[:nt], x[nt:-nq], x[-nq:]
+
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file

pyengine/engine/retrieval/tests/basic_test.py

@@ -77,8 +77,7 @@ faiss.write_index(index, writer)
 ar_data = faiss.vector_to_array(writer.data)
 import pickle
 pickle.dump(ar_data, open("/tmp/faiss/ser_1", "wb"))
-
-#index_3 = pickle.load("/tmp/faiss/ser_1")
+index_3 = pickle.load("/tmp/faiss/ser_1")
 
 
 # index_2 = faiss.IndexFlatL2(d)   # build the index