def testSerializeString(self):
vocab = {
"hear": 2, "nothing": 4, "but": 1, "a": 1, "rushing": 4,
"sound": 3}
document = [
"hear", "any", "sound", "sound", "louder", "but", "walls"]
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.encodeOrphans = True
params.vocabulary = vocab
enc1 = SimHashDocumentEncoder(params)
serialized = enc1.writeToString()
output1 = enc1.encode(document)
params.size = 40
params.sparsity = 0.1
enc2 = SimHashDocumentEncoder(params)
assert(enc1.size != enc2.size)
assert(enc1.parameters.size != enc2.parameters.size)
assert(enc1.parameters.activeBits != enc2.parameters.activeBits)
enc2.loadFromString(serialized)
output2 = enc1.encode(document)
assert(enc1.size == enc2.size)
assert(enc1.parameters.size == enc2.parameters.size)
assert(enc1.parameters.activeBits == enc2.parameters.activeBits)
assert(output1 == output2)
def testSerializeToFile(self):
vocab = {
"hear": 2, "nothing": 4, "but": 1, "a": 1, "rushing": 4,
"sound": 3}
document = [
"hear", "any", "sound", "sound", "louder", "but", "walls"]
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.encodeOrphans = True
params.vocabulary = vocab
enc1 = SimHashDocumentEncoder(params)
# The SimHashDocumentEncoder now has some data in it, try serialization.
file = "SimHashDocumentEncoder_test_save2.json"
enc1.saveToFile(file, "JSON")
output1 = enc1.encode(document)
# change the parameters so we know the params were replaced from contents in file.
# Note: we should have a constructor without parameters for this situation.
params.size = 10
params.sparsity = 0.5
enc2 = SimHashDocumentEncoder(params)
enc2.loadFromFile(file, "JSON")
os.remove(file)
output2 = enc2.encode(document)
assert(enc1.size == enc2.size)
assert(enc1.parameters.size == enc2.parameters.size)
assert(enc1.parameters.activeBits == enc2.parameters.activeBits)
def testConstructor(self):
params1 = SimHashDocumentEncoderParameters()
params1.size = 400
params1.activeBits = 20
encoder1 = SimHashDocumentEncoder(params1)
assert(encoder1)
assert(encoder1.dimensions == [params1.size])
assert(encoder1.size == params1.size)
assert(encoder1.parameters.size == params1.size)
assert(encoder1.parameters.activeBits == params1.activeBits)
assert(not encoder1.parameters.tokenSimilarity)
# test bad encoder params - both activeBits and sparsity
params2 = SimHashDocumentEncoderParameters()
params2.size = 400
params2.activeBits = 20
params2.sparsity = 0.666
encoder2 = None
assert(not encoder2)
with self.assertRaises(RuntimeError):
encoder2 = SimHashDocumentEncoder(params2)
# test bad encoder params - neither activeBits or sparsity
params3 = SimHashDocumentEncoderParameters()
params3.size = 400
encoder3 = None
assert(not encoder3)
with self.assertRaises(RuntimeError):
encoder3 = SimHashDocumentEncoder(params3)
# test good encoder param - using 'sparsity' instead of 'activeBits'
params4 = SimHashDocumentEncoderParameters()
params4.size = 400
params4.sparsity = 0.05
encoder4 = SimHashDocumentEncoder(params4)
assert(encoder4)
assert(encoder4.dimensions == [params4.size])
assert(encoder4.size == params4.size)
assert(encoder4.parameters.size == params4.size)
assert(encoder4.parameters.activeBits == 20)
assert(not encoder4.parameters.tokenSimilarity)
# test bad encoder params - frequency should be ceiling > floor
params5 = SimHashDocumentEncoderParameters()
params5.size = 400
params5.sparsity = 0.05
params5.frequencyCeiling = 3
params5.frequencyFloor = 6
encoder5 = None
with self.assertRaises(RuntimeError):
encoder5 = SimHashDocumentEncoder(params5)
assert(not encoder5)
def testUnicode(self):
testDocUni1 = [
"\u0395\u0396\u0397\u0398\u0399",
"\u0400\u0401\u0402\u0403\u0404",
"\u0405\u0406\u0407\u0408\u0409"]
testDocUni2 = [
"\u0395\u0396\u0397\u0398\u0399\u0410",
"\u0400\u0401\u0402\u0403\u0404\u0410",
"\u0405\u0406\u0407\u0408\u0409\u0410"]
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
# unicode 'tokenSimilarity' ON
params.tokenSimilarity = True
encoder1 = SimHashDocumentEncoder(params)
output1 = SDR(params.size)
output2 = SDR(params.size)
encoder1.encode(testDocUni1, output1)
encoder1.encode(testDocUni2, output2)
assert(output1.getOverlap(output2) > 65)
# unicode 'tokenSimilarity' OFF
params.tokenSimilarity = False
encoder2 = SimHashDocumentEncoder(params)
output1.zero()
output2.zero()
encoder2.encode(testDocUni1, output1)
encoder2.encode(testDocUni2, output2)
assert(output1.getOverlap(output2) < 65)
def testTokenVocabulary(self):
vocabulary = {
"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6,
"g": 1, "h": 2, "i": 3, "j": 4, "k": 5, "l": 6}
input1 = "a b c d e f"
input2 = "a b c d e f t u w x y z"
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.vocabulary = vocabulary
# vocabulary +encodeOrphans
params.encodeOrphans = True
encoder1 = SimHashDocumentEncoder(params)
output1a = encoder1.encode(input1)
output1b = encoder1.encode(input2)
assert(output1a != output1b)
# vocabulary -encodeOrphans
params.encodeOrphans = False
encoder2 = SimHashDocumentEncoder(params)
output2a = encoder2.encode(input1)
output2b = encoder2.encode(input2)
assert(output2a == output2b)
def testTokenSimilarity(self):
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.caseSensitivity = True
# tokenSimilarity ON
params.tokenSimilarity = True
encoder1 = SimHashDocumentEncoder(params)
output1 = SDR(params.size)
output2 = SDR(params.size)
output3 = SDR(params.size)
output4 = SDR(params.size)
encoder1.encode(testDoc1, output1)
encoder1.encode(testDoc2, output2)
encoder1.encode(testDoc3, output3)
encoder1.encode(testDoc4, output4)
assert(output3.getOverlap(output4) > output2.getOverlap(output3))
assert(output2.getOverlap(output3) > output1.getOverlap(output3))
assert(output1.getOverlap(output3) > output1.getOverlap(output4))
# tokenSimilarity OFF
params.tokenSimilarity = False
encoder2 = SimHashDocumentEncoder(params)
output1.zero()
output2.zero()
output3.zero()
output4.zero()
encoder2.encode(testDoc1, output1)
encoder2.encode(testDoc2, output2)
encoder2.encode(testDoc3, output3)
encoder2.encode(testDoc4, output4)
assert(output1.getOverlap(output2) > output2.getOverlap(output3))
assert(output2.getOverlap(output3) > output3.getOverlap(output4))
assert(output3.getOverlap(output4) > output1.getOverlap(output3))
def testTokenCaseSensitivity(self):
# Case-sensitivite strings
testDocCase1 = [
"alpha", "bravo", "delta", "echo", "foxtrot", "hotel"]
testDocCase2 = [
"ALPHA", "BRAVO", "DELTA", "ECHO", "FOXTROT", "HOTEL"]
part = ["eCHo", "foXTROt", "hOtEl"]
discard = ["AlPHa", "BRaVo", "dELTa"]
vocab = {"EcHo": 1, "FOxtRoT": 1, "HoTeL": 1}
# caseSensitivity ON
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.caseSensitivity = True
encoder1 = SimHashDocumentEncoder(params)
output1 = encoder1.encode(testDocCase1)
output2 = encoder1.encode(testDocCase2)
assert(output1 != output2)
# caseSensitivity OFF
params.caseSensitivity = False
encoder2 = SimHashDocumentEncoder(params)
output1 = encoder2.encode(testDocCase1)
output2 = encoder2.encode(testDocCase2)
assert(output1 == output2)
# caseSensitivity=OFF +excludes
params.excludes = discard
encoder3 = SimHashDocumentEncoder(params)
output3a = encoder3.encode(testDocCase1)
output3b = encoder3.encode(part)
assert(output3a == output3b)
# caseSensitivity=OFF +vocabulary
params4 = SimHashDocumentEncoderParameters()
params4.size = 400
params4.sparsity = 0.33
params4.caseSensitivity = False
params4.encodeOrphans = False
params4.vocabulary = vocab
encoder4 = SimHashDocumentEncoder(params4)
output4a = encoder4.encode(testDocCase1)
output4b = encoder4.encode(part)
assert(output4a == output4b)
def testDeterminism(self):
GOLD = SDR(1000)
GOLD.sparse = [
2, 34, 37, 38, 69, 79, 114, 170, 200, 234, 254, 258, 279, 289, 291,
292, 295, 307, 321, 336, 345, 350, 361, 373, 378, 400, 450, 461,
462, 487, 520, 532, 539, 548, 576, 583, 616, 623, 626, 627, 663,
681, 695, 716, 794, 799, 830, 835, 837, 841]
params = SimHashDocumentEncoderParameters()
params.size = GOLD.size
params.sparsity = 0.05
encoder = SimHashDocumentEncoder(params)
current = encoder.encode("I came to the fork in the road")
assert(current == GOLD)
def testFrequency(self):
tokens = "a a a b b c d d d d e e f" # min 1 max 4
charTokens = "abbbbbbcccdefg aaaaaabccchijk aaabcccccclmno"
# Test token frequency floor/ceiling
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
encoder1 = SimHashDocumentEncoder(params)
output1 = encoder1.encode(tokens)
params.frequencyFloor = 1
encoder2 = SimHashDocumentEncoder(params)
output2 = encoder2.encode(tokens)
params.frequencyFloor = 0
params.frequencyCeiling = 4
encoder3 = SimHashDocumentEncoder(params)
output3 = encoder3.encode(tokens)
assert(output1 != output2)
assert(output1 != output3)
assert(output2 != output3)
# Test character frequency ceiling (only)
params4 = SimHashDocumentEncoderParameters()
params4.size = 400
params4.sparsity = 0.33
params4.tokenSimilarity = True
encoder4 = SimHashDocumentEncoder(params4)
output4 = encoder4.encode(charTokens)
params4.frequencyCeiling = 3
encoder5 = SimHashDocumentEncoder(params4)
output5 = encoder5.encode(charTokens)
assert(output4 != output5)
def testBasicExampleUseCase(self):
testDocEasy1 = "The sky is beautiful today"
testDocEasy2 = "The sun is beautiful today" # similar up, differ down
testDocEasy3 = "Who did my homework today"
# setup params
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
# init encoder
encoder = SimHashDocumentEncoder(params)
# encode!
output1 = encoder.encode(testDocEasy1)
output2 = encoder.encode(testDocEasy2)
output3 = encoder.encode(testDocEasy3)
# encodings for Docs 1 and 2 should be more similar than the encodings
# for Docs 2 and 3 (which should be more disparate).
assert(output1.getOverlap(output2) > output2.getOverlap(output3))
def testTokenWeightMap(self):
weights = {
"aaa": 4, "bbb": 2, "ccc": 2, "ddd": 4, "eee": 2, "fff": 2, "sss": 1}
doc1 = ["aaa", "bbb", "ccc", "ddd", "sss"]
doc2 = ["eee", "bbb", "ccc", "fff", "sss"]
doc3 = ["aaa", "eee", "fff", "ddd"]
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.tokenSimilarity = False
params.encodeOrphans = False
params.vocabulary = weights
encoder = SimHashDocumentEncoder(params)
output1 = encoder.encode(doc1)
output2 = encoder.encode(doc2)
output3 = encoder.encode(doc3)
assert(output1.getOverlap(output3) > output1.getOverlap(output2))
assert(output1.getOverlap(output2) > output2.getOverlap(output3))
def testExcludes(self):
keepList = ["but", "it", "all", "stays", "the", "same"]
nopeList = ["seasons", "change", "mad", "things", "rearrange"]
fullList = keepList + nopeList
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
encoder1 = SimHashDocumentEncoder(params)
output1 = encoder1.encode(fullList)
encoder2 = SimHashDocumentEncoder(params)
output2 = encoder2.encode(keepList)
params.excludes = nopeList
encoder3 = SimHashDocumentEncoder(params)
output3 = encoder3.encode(fullList)
assert(output1 != output2) # full != part
assert(output1 != output3) # full != (full - nope)
assert(output2 == output3) # part == (full - nope)
def testSerializePickle(self):
vocab = {
"hear": 2, "nothing": 4, "but": 1, "a": 1, "rushing": 4,
"sound": 3}
document = [
"hear", "any", "sound", "sound", "louder", "but", "walls"]
params = SimHashDocumentEncoderParameters()
params.size = 400
params.sparsity = 0.33
params.encodeOrphans = True
params.vocabulary = vocab
enc1 = SimHashDocumentEncoder(params)
pickled = pickle.dumps(enc1)
output1 = enc1.encode(document)
enc2 = pickle.loads(pickled)
output2 = enc2.encode(document)
assert(enc1.size == enc2.size)
assert(enc1.parameters.size == enc2.parameters.size)
assert(enc1.parameters.activeBits == enc2.parameters.activeBits)
assert(output1 == output2)
def testStatistics(self):
# 100 random simple English words run mass encoding stats against
testCorpus = [
"find", "any", "new", "work", "part", "take", "get", "place",
"made", "live", "where", "after", "back", "little", "only",
"round", "man", "year", "came", "show", "every", "good", "me",
"give", "our", "under", "name", "very", "through", "just", "form",
"sentence", "great", "think", "say", "help", "low", "line",
"differ", "turn", "cause", "much", "mean", "before", "move",
"right", "boy", "old", "too", "same", "tell", "does", "set",
"three", "want", "air", "well", "also", "play", "small", "end",
"put", "home", "read", "hand", "port", "large", "spell", "add",
"even", "land", "here", "must", "big", "high", "such", "follow",
"act", "why", "ask", "men", "change", "went", "light", "kind",
"off", "need", "house", "picture", "try", "us", "again", "animal",
"point", "mother", "world", "near", "build", "self", "earth"]
num_samples = 1000 # number of documents to run
num_tokens = 10 # tokens per document
# Case 1 = tokenSimilarity OFF
params1 = SimHashDocumentEncoderParameters()
params1.size = 400
params1.sparsity = 0.33
params1.tokenSimilarity = False
encoder1 = SimHashDocumentEncoder(params1)
# Case 2 = tokenSimilarity ON
params2 = params1
params2.tokenSimilarity = True
encoder2 = SimHashDocumentEncoder(params2)
sdrs1 = []
sdrs2 = []
for _ in range(num_samples):
document = []
for _ in range(num_tokens - 1):
token = testCorpus[random.randint(0, len(testCorpus) - 1)]
document.append(token)
sdrs1.append(encoder1.encode(document))
sdrs2.append(encoder2.encode(document))
report1 = Metrics([encoder1.size], len(sdrs1) + 1)
report2 = Metrics([encoder2.size], len(sdrs2) + 1)
for sdr in sdrs1:
report1.addData(sdr)
for sdr in sdrs2:
report2.addData(sdr)
# Assertions for Case 1 = tokenSimilarity OFF
assert(report1.activationFrequency.entropy() > 0.87)
assert(report1.activationFrequency.min() > 0.01)
assert(report1.activationFrequency.max() < 0.99)
assert(report1.activationFrequency.mean() > params1.sparsity - 0.005)
assert(report1.activationFrequency.mean() < params1.sparsity + 0.005)
assert(report1.overlap.min() > 0.21)
assert(report1.overlap.max() > 0.53)
assert(report1.overlap.mean() > 0.38)
assert(report1.sparsity.min() > params1.sparsity - 0.01)
assert(report1.sparsity.max() < params1.sparsity + 0.01)
assert(report1.sparsity.mean() > params1.sparsity - 0.005)
assert(report1.sparsity.mean() < params1.sparsity + 0.005)
# Assertions for Case 2 = tokenSimilarity ON
assert(report2.activationFrequency.entropy() > 0.59)
assert(report2.activationFrequency.min() >= 0)
assert(report2.activationFrequency.max() <= 1)
assert(report2.activationFrequency.mean() > params2.sparsity - 0.005)
assert(report2.activationFrequency.mean() < params2.sparsity + 0.005)
assert(report2.overlap.min() > 0.38)
assert(report2.overlap.max() > 0.78)
assert(report2.overlap.mean() > 0.61)
assert(report2.sparsity.min() > params2.sparsity - 0.01)
assert(report2.sparsity.max() < params2.sparsity + 0.01)
assert(report2.sparsity.mean() > params2.sparsity - 0.005)
assert(report2.sparsity.mean() < params2.sparsity + 0.005)
