def testOverlapDistanceMethodBadSparsity(self):
"""Sparsity (input dimensionality) less than input array"""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
# Learn with incorrect dimensionality, less than some bits (23, 29)
with self.assertRaises(RuntimeError):
classifier.learn(a, 0, isSparse=20)
def simulateCategories(numSamples=100, numDimensions=500):
"""Simulate running KNN classifier on many disjoint categories"""
failures = ""
LOGGER.info("Testing the sparse KNN Classifier on many disjoint categories")
knn = KNNClassifier(k=1, distanceNorm=1.0, useSparseMemory=True)
for i in range(0, numSamples):
# select category randomly and generate vector
c = 2*numpy.random.randint(0, 50) + 50
v = createPattern(c, numDimensions)
knn.learn(v, c)
# Go through each category and ensure we have at least one from each!
for i in range(0, 50):
c = 2*i+50
v = createPattern(c, numDimensions)
knn.learn(v, c)
errors = 0
for i in range(0, numSamples):
# select category randomly and generate vector
c = 2*numpy.random.randint(0, 50) + 50
v = createPattern(c, numDimensions)
inferCat, _kir, _kd, _kcd = knn.infer(v)
if inferCat != c:
LOGGER.info("Mistake with %s %s %s %s %s", v[v.nonzero()], \
"mapped to category", inferCat, "instead of category", c)
LOGGER.info(" %s", v.nonzero())
errors += 1
if errors != 0:
failures += "Failure in handling non-consecutive category indices\n"
# Test closest methods
errors = 0
for i in range(0, 10):
# select category randomly and generate vector
c = 2*numpy.random.randint(0, 50) + 50
v = createPattern(c, numDimensions)
p = knn.closestTrainingPattern(v, c)
if not (c in p.nonzero()[0]):
LOGGER.info("Mistake %s %s", p.nonzero(), v.nonzero())
LOGGER.info("%s %s", p[p.nonzero()], v[v.nonzero()])
errors += 1
if errors != 0:
failures += "Failure in closestTrainingPattern method\n"
return failures, knn
def __init__(self, verbosity=1):
super(ClassificationModelRandomSDR, self).__init__(verbosity)
# Init kNN classifier:
# specify 'distanceMethod'='rawOverlap' for overlap; Euclidean is std.
# verbosity=1 for debugging
# standard k is 1
self.classifier = KNNClassifier(exact=True, verbosity=verbosity - 1)
# SDR dimensions:
self.n = 100
self.w = 20
def testMinSparsity(self):
"""Tests overlap distance with min sparsity"""
# Require sparsity >= 20%
params = {"distanceMethod": "rawOverlap", "minSparsity": 0.2}
classifier = KNNClassifier(**params)
dimensionality = 30
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
b = np.array([2, 4, 8, 12, 14, 18, 20, 21, 28], dtype=np.int32)
# This has 20% sparsity and should be inserted
c = np.array([2, 3, 8, 11, 14, 18], dtype=np.int32)
# This has 17% sparsity and should NOT be inserted
d = np.array([2, 3, 8, 11, 18], dtype=np.int32)
numPatterns = classifier.learn(a, 0, isSparse=dimensionality)
self.assertEquals(numPatterns, 1)
numPatterns = classifier.learn(b, 1, isSparse=dimensionality)
self.assertEquals(numPatterns, 2)
numPatterns = classifier.learn(c, 1, isSparse=dimensionality)
self.assertEquals(numPatterns, 3)
numPatterns = classifier.learn(d, 1, isSparse=dimensionality)
self.assertEquals(numPatterns, 3)
# Test that inference ignores low sparsity vectors but not others
e = np.array([2, 4, 5, 6, 8, 12, 14, 18, 20], dtype=np.int32)
dense = np.zeros(dimensionality)
dense[e] = 1.0
cat, inference, _, _ = classifier.infer(dense)
self.assertIsNotNone(cat)
self.assertGreater(inference.sum(), 0.0)
# This has 20% sparsity and should be used for inference
f = np.array([2, 5, 8, 11, 14, 18], dtype=np.int32)
dense = np.zeros(dimensionality)
dense[f] = 1.0
cat, inference, _, _ = classifier.infer(dense)
self.assertIsNotNone(cat)
self.assertGreater(inference.sum(), 0.0)
# This has 17% sparsity and should return null inference results
g = np.array([2, 3, 8, 11, 19], dtype=np.int32)
dense = np.zeros(dimensionality)
dense[g] = 1.0
cat, inference, _, _ = classifier.infer(dense)
self.assertIsNone(cat)
self.assertEqual(inference.sum(), 0.0)
def testOverlapDistanceMethodStandardUnsorted(self):
"""If sparse representation indices are unsorted expect error."""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([29, 3, 7, 11, 13, 17, 19, 23, 1], dtype=np.int32)
b = np.array([2, 4, 20, 12, 14, 18, 8, 28, 30], dtype=np.int32)
with self.assertRaises(RuntimeError):
classifier.learn(a, 0, isSparse=dimensionality)
with self.assertRaises(RuntimeError):
classifier.learn(b, 1, isSparse=dimensionality)
def testExtractVectorsFromKNN(self):
vectors = numpy.random.rand(10, 25) < 0.1
# Populate KNN
knn = KNNClassifier()
for i in xrange(vectors.shape[0]):
knn.learn(vectors[i], 0)
# Extract vectors from KNN
sparseDataMatrix = HierarchicalClustering._extractVectorsFromKNN(knn)
self.assertEqual(
sorted(sparseDataMatrix.todense().tolist()),
sorted(vectors.tolist())
)
def testOverlapDistanceMethodEmptyArray(self):
"""Tests case where pattern has no ON bits"""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([], dtype=np.int32)
numPatterns = classifier.learn(a, 0, isSparse=dimensionality)
self.assertEquals(numPatterns, 1)
denseA = np.zeros(dimensionality)
denseA[a] = 1.0
cat, _, _, _ = classifier.infer(denseA)
self.assertEquals(cat, 0)
def __init__(self,
n=100,
w=20,
verbosity=1,
classifierMetric="rawOverlap",
k=1,
**kwargs):
super(ClassificationModelKeywords, self).__init__(**kwargs)
self.classifier = KNNClassifier(exact=True,
distanceMethod=classifierMetric,
k=k,
verbosity=verbosity - 1)
self.n = n
self.w = w
def __init__(self,
n=100,
w=20,
verbosity=1,
numLabels=3,
modelDir="ClassificationModelKeywords"):
super(ClassificationModelKeywords, self).__init__(n,
w,
verbosity=verbosity,
numLabels=numLabels,
modelDir=modelDir)
self.classifier = KNNClassifier(exact=True,
distanceMethod="rawOverlap",
k=numLabels,
verbosity=verbosity - 1)
def testOverlapDistanceMethodInconsistentDimensionality(self):
"""Inconsistent sparsity (input dimensionality)"""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
# Learn with incorrect dimensionality, greater than largest ON bit, but
# inconsistent when inferring
numPatterns = classifier.learn(a, 0, isSparse=31)
self.assertEquals(numPatterns, 1)
denseA = np.zeros(dimensionality)
denseA[a] = 1.0
cat, _, _, _ = classifier.infer(denseA)
self.assertEquals(cat, 0)
def testOverlapDistanceMethod_ClassifySparse(self):
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
b = np.array([2, 4, 8, 12, 14, 18, 20, 28, 30], dtype=np.int32)
classifier.learn(a, 0, isSparse=dimensionality)
classifier.learn(b, 1, isSparse=dimensionality)
# TODO Test case where infer is passed a sparse representation after
# infer() has been extended to handle sparse and dense
cat, _, _, _ = classifier.infer(a)
self.assertEquals(cat, 0)
cat, _, _, _ = classifier.infer(b)
self.assertEquals(cat, 1)
def simulateKMoreThanOne():
"""A small test with k=3"""
failures = ""
LOGGER.info("Testing the sparse KNN Classifier with k=3")
knn = KNNClassifier(k=3)
v = numpy.zeros((6, 2))
v[0] = [1.0, 0.0]
v[1] = [1.0, 0.2]
v[2] = [1.0, 0.2]
v[3] = [1.0, 2.0]
v[4] = [1.0, 4.0]
v[5] = [1.0, 4.5]
knn.learn(v[0], 0)
knn.learn(v[1], 0)
knn.learn(v[2], 0)
knn.learn(v[3], 1)
knn.learn(v[4], 1)
knn.learn(v[5], 1)
winner, _inferenceResult, _dist, _categoryDist = knn.infer(v[0])
if winner != 0:
failures += "Inference failed with k=3\n"
winner, _inferenceResult, _dist, _categoryDist = knn.infer(v[2])
if winner != 0:
failures += "Inference failed with k=3\n"
winner, _inferenceResult, _dist, _categoryDist = knn.infer(v[3])
if winner != 0:
failures += "Inference failed with k=3\n"
winner, _inferenceResult, _dist, _categoryDist = knn.infer(v[5])
if winner != 1:
failures += "Inference failed with k=3\n"
if len(failures) == 0:
LOGGER.info("Tests passed.")
return failures
def testPartitionIdExcluded(self):
"""
Tests that paritionId properly excludes training data points during
inference
"""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
b = np.array([2, 4, 8, 12, 14, 18, 20, 28, 30], dtype=np.int32)
denseA = np.zeros(dimensionality)
denseA[a] = 1.0
denseB = np.zeros(dimensionality)
denseB[b] = 1.0
classifier.learn(a, 0, isSparse=dimensionality, partitionId=0)
classifier.learn(b, 1, isSparse=dimensionality, partitionId=1)
cat, _, _, _ = classifier.infer(denseA, partitionId=1)
self.assertEquals(cat, 0)
cat, _, _, _ = classifier.infer(denseA, partitionId=0)
self.assertEquals(cat, 1)
cat, _, _, _ = classifier.infer(denseB, partitionId=0)
self.assertEquals(cat, 1)
cat, _, _, _ = classifier.infer(denseB, partitionId=1)
self.assertEquals(cat, 0)
# Ensure it works even if you invoke learning again. To make it a bit more
# complex this time we insert A again but now with Id=2
classifier.learn(a, 0, isSparse=dimensionality, partitionId=2)
# Even though first A should be ignored, the second instance of A should
# not be ignored.
cat, _, _, _ = classifier.infer(denseA, partitionId=0)
self.assertEquals(cat, 0)
def __init__(self,
verbosity=1,
numLabels=3,
fingerprintType=EncoderTypes.document):
super(ClassificationModelFingerprint,
self).__init__(verbosity, numLabels)
# Init kNN classifier and Cortical.io encoder; need valid API key (see
# CioEncoder init for details).
self.classifier = KNNClassifier(k=numLabels,
distanceMethod='rawOverlap',
exact=False,
verbosity=verbosity - 1)
if fingerprintType is (not EncoderTypes.document
or not EncoderTypes.word):
raise ValueError("Invaid type of fingerprint encoding; see the "
"EncoderTypes class for eligble types.")
self.encoder = CioEncoder(cacheDir="./fluent/experiments/cioCache",
fingerprintType=fingerprintType)
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity / 100) * self.n)
def __init__(self, tmOverrides=None, upOverrides=None,
classifierOverrides=None, seed=42, consoleVerbosity=0):
print "Initializing Temporal Memory..."
params = dict(self.DEFAULT_TEMPORAL_MEMORY_PARAMS)
params.update(tmOverrides or {})
params["seed"] = seed
self.tm = MonitoredFastExtendedTemporalMemory(mmName="TM", **params)
print "Initializing Union Temporal Pooler..."
start = time.time()
params = dict(self.DEFAULT_UNION_POOLER_PARAMS)
params.update(upOverrides or {})
params["inputDimensions"] = [self.tm.numberOfCells()]
params["potentialRadius"] = self.tm.numberOfCells()
params["seed"] = seed
self.up = MonitoredUnionTemporalPooler(mmName="UP", **params)
elapsed = int(time.time() - start)
print "Total time: {0:2} seconds.".format(elapsed)
print "Initializing KNN Classifier..."
params = dict(self.DEFAULT_CLASSIFIER_PARAMS)
params.update(classifierOverrides or {})
self.classifier = KNNClassifier(**params)
def __init__(self,
verbosity=1,
numLabels=3,
modelDir="ClassificationModelFingerprint",
fingerprintType=EncoderTypes.word,
unionSparsity=0.20,
retinaScaling=1.0,
retina="en_associative",
apiKey=None,
classifierMetric="rawOverlap",
cacheRoot=None):
super(ClassificationModelFingerprint,
self).__init__(verbosity=verbosity,
numLabels=numLabels,
modelDir=modelDir)
# Init kNN classifier and Cortical.io encoder; need valid API key (see
# CioEncoder init for details).
self.classifier = KNNClassifier(k=numLabels,
distanceMethod=classifierMetric,
exact=False,
verbosity=verbosity - 1)
if fingerprintType is (not EncoderTypes.document
or not EncoderTypes.word):
raise ValueError("Invaid type of fingerprint encoding; see the "
"EncoderTypes class for eligble types.")
cacheRoot = cacheRoot or os.path.dirname(os.path.realpath(__file__))
self.encoder = CioEncoder(retinaScaling=retinaScaling,
cacheDir=os.path.join(cacheRoot, "CioCache"),
fingerprintType=fingerprintType,
unionSparsity=unionSparsity,
retina=retina,
apiKey=apiKey)
def testOverlapDistanceMethodStandard(self):
"""Tests standard learning case for raw overlap"""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
b = np.array([2, 4, 8, 12, 14, 18, 20, 28, 30], dtype=np.int32)
numPatterns = classifier.learn(a, 0, isSparse=dimensionality)
self.assertEquals(numPatterns, 1)
numPatterns = classifier.learn(b, 1, isSparse=dimensionality)
self.assertEquals(numPatterns, 2)
denseA = np.zeros(dimensionality)
denseA[a] = 1.0
cat, _, _, _ = classifier.infer(denseA)
self.assertEquals(cat, 0)
denseB = np.zeros(dimensionality)
denseB[b] = 1.0
cat, _, _, _ = classifier.infer(denseB)
self.assertEquals(cat, 1)
def __init__(self,
tmOverrides=None,
upOverrides=None,
classifierOverrides=None,
seed=42,
consoleVerbosity=0):
print "Initializing Temporal Memory..."
params = dict(self.DEFAULT_TEMPORAL_MEMORY_PARAMS)
params.update(tmOverrides or {})
params["seed"] = seed
self.tm = MonitoredFastGeneralTemporalMemory(mmName="TM", **params)
print "Initializing Union Pooler..."
params = dict(self.DEFAULT_UNION_POOLER_PARAMS)
params.update(upOverrides or {})
params["inputDimensions"] = [self.tm.numberOfCells()]
params["potentialRadius"] = self.tm.numberOfCells()
params["seed"] = seed
self.up = MonitoredUnionPooler(mmName="UP", **params)
print "Initializing KNN Classifier..."
params = dict(self.DEFAULT_CLASSIFIER_PARAMS)
params.update(classifierOverrides or {})
self.classifier = KNNClassifier(**params)
def runTestKNNClassifier(self, short=0):
""" Test the KNN classifier in this module. short can be:
0 (short), 1 (medium), or 2 (long)
"""
failures = ""
if short != 2:
numpy.random.seed(42)
else:
seed_value = int(time.time())
# seed_value = 1276437656
#seed_value = 1277136651
numpy.random.seed(seed_value)
LOGGER.info('Seed used: %d', seed_value)
f = open('seedval', 'a')
f.write(str(seed_value))
f.write('\n')
f.close()
failures += simulateKMoreThanOne()
LOGGER.info("\nTesting KNN Classifier on dense patterns")
numPatterns, numClasses = getNumTestPatterns(short)
patterns = numpy.random.rand(numPatterns, 100)
patternDict = dict()
# Assume there are no repeated patterns -- if there are, then
# numpy.random would be completely broken.
for i in xrange(numPatterns):
randCategory = numpy.random.randint(0, numClasses - 1)
patternDict[i] = dict()
patternDict[i]['pattern'] = patterns[i]
patternDict[i]['category'] = randCategory
LOGGER.info("\nTesting KNN Classifier with L2 norm")
knn = KNNClassifier(k=1)
failures += simulateClassifier(knn, patternDict, \
"KNN Classifier with L2 norm test")
LOGGER.info("\nTesting KNN Classifier with L1 norm")
knnL1 = KNNClassifier(k=1, distanceNorm=1.0)
failures += simulateClassifier(knnL1, patternDict, \
"KNN Classifier with L1 norm test")
numPatterns, numClasses = getNumTestPatterns(short)
patterns = (numpy.random.rand(numPatterns, 25) >
0.7).astype(RealNumpyDType)
patternDict = dict()
for i in patterns:
iString = str(i.tolist())
if not patternDict.has_key(iString):
randCategory = numpy.random.randint(0, numClasses - 1)
patternDict[iString] = dict()
patternDict[iString]['pattern'] = i
patternDict[iString]['category'] = randCategory
LOGGER.info("\nTesting KNN on sparse patterns")
knnDense = KNNClassifier(k=1)
failures += simulateClassifier(knnDense, patternDict, \
"KNN Classifier on sparse pattern test")
self.assertEqual(len(failures), 0, "Tests failed: \n" + failures)
if short == 2:
f = open('seedval', 'a')
f.write('Pass\n')
f.close()
def testGetPartitionId(self):
"""
Test a sequence of calls to KNN to ensure we can retrieve partition Id:
- We first learn on some patterns (including one pattern with no
partitionId in the middle) and test that we can retrieve Ids.
- We then invoke inference and then check partitionId again.
- We check incorrect indices to ensure we get an exception.
- We check the case where the partitionId to be ignored is not in
the list.
- We learn on one more pattern and check partitionIds again
- We remove rows and ensure partitionIds still work
"""
params = {"distanceMethod": "rawOverlap"}
classifier = KNNClassifier(**params)
dimensionality = 40
a = np.array([1, 3, 7, 11, 13, 17, 19, 23, 29], dtype=np.int32)
b = np.array([2, 4, 8, 12, 14, 18, 20, 28, 30], dtype=np.int32)
c = np.array([1, 2, 3, 14, 16, 19, 22, 24, 33], dtype=np.int32)
d = np.array([2, 4, 8, 12, 14, 19, 22, 24, 33], dtype=np.int32)
e = np.array([1, 3, 7, 12, 14, 19, 22, 24, 33], dtype=np.int32)
denseA = np.zeros(dimensionality)
denseA[a] = 1.0
classifier.learn(a, 0, isSparse=dimensionality, partitionId=433)
classifier.learn(b, 1, isSparse=dimensionality, partitionId=213)
classifier.learn(c, 1, isSparse=dimensionality, partitionId=None)
classifier.learn(d, 1, isSparse=dimensionality, partitionId=433)
self.assertEquals(classifier.getPartitionId(0), 433)
self.assertEquals(classifier.getPartitionId(1), 213)
self.assertEquals(classifier.getPartitionId(2), None)
self.assertEquals(classifier.getPartitionId(3), 433)
cat, _, _, _ = classifier.infer(denseA, partitionId=213)
self.assertEquals(cat, 0)
# Test with patternId not in classifier
cat, _, _, _ = classifier.infer(denseA, partitionId=666)
self.assertEquals(cat, 0)
# Partition Ids should be maintained after inference
self.assertEquals(classifier.getPartitionId(0), 433)
self.assertEquals(classifier.getPartitionId(1), 213)
self.assertEquals(classifier.getPartitionId(2), None)
self.assertEquals(classifier.getPartitionId(3), 433)
# Should return exceptions if we go out of bounds
with self.assertRaises(RuntimeError):
classifier.getPartitionId(4)
with self.assertRaises(RuntimeError):
classifier.getPartitionId(-1)
# Learn again
classifier.learn(e, 4, isSparse=dimensionality, partitionId=413)
self.assertEquals(classifier.getPartitionId(4), 413)
# Test getPatternIndicesWithPartitionId
self.assertItemsEqual(classifier.getPatternIndicesWithPartitionId(433),
[0, 3])
self.assertItemsEqual(classifier.getPatternIndicesWithPartitionId(666),
[])
self.assertItemsEqual(classifier.getPatternIndicesWithPartitionId(413),
[4])
self.assertEquals(classifier.getNumPartitionIds(), 3)
# Check that the full set of partition ids is what we expect
self.assertItemsEqual(classifier.getPartitionIdPerPattern(),
[433, 213, np.inf, 433, 413])
self.assertItemsEqual(classifier.getPartitionIdList(),[433, 413, 213])
# Remove two rows - all indices shift down
self.assertEquals(classifier._removeRows([0,2]), 2)
self.assertItemsEqual(classifier.getPatternIndicesWithPartitionId(433),
[1])
self.assertItemsEqual(classifier.getPatternIndicesWithPartitionId(413),
[2])
# Remove another row and check number of partitions have decreased
classifier._removeRows([0])
self.assertEquals(classifier.getNumPartitionIds(), 2)
# Check that the full set of partition ids is what we expect
self.assertItemsEqual(classifier.getPartitionIdPerPattern(), [433, 413])
self.assertItemsEqual(classifier.getPartitionIdList(),[433, 413])
def runTestPCAKNN(self, short = 0):
LOGGER.info('\nTesting PCA/k-NN classifier')
LOGGER.info('Mode=%s', short)
numDims = 10
numClasses = 10
k = 10
numPatternsPerClass = 100
numPatterns = int(.9 * numClasses * numPatternsPerClass)
numTests = numClasses * numPatternsPerClass - numPatterns
numSVDSamples = int(.1 * numPatterns)
keep = 1
train_data, train_class, test_data, test_class = \
pca_knn_data.generate(numDims, numClasses, k, numPatternsPerClass,
numPatterns, numTests, numSVDSamples, keep)
pca_knn = KNNClassifier(k=k,numSVDSamples=numSVDSamples,
numSVDDims=keep)
knn = KNNClassifier(k=k)
LOGGER.info('Training PCA k-NN')
for i in range(numPatterns):
knn.learn(train_data[i], train_class[i])
pca_knn.learn(train_data[i], train_class[i])
LOGGER.info('Testing PCA k-NN')
numWinnerFailures = 0
numInferenceFailures = 0
numDistFailures = 0
numAbsErrors = 0
for i in range(numTests):
winner, inference, dist, categoryDist = knn.infer(test_data[i])
pca_winner, pca_inference, pca_dist, pca_categoryDist \
= pca_knn.infer(test_data[i])
if winner != test_class[i]:
numAbsErrors += 1
if pca_winner != winner:
numWinnerFailures += 1
if (numpy.abs(pca_inference - inference) > 1e-4).any():
numInferenceFailures += 1
if (numpy.abs(pca_dist - dist) > 1e-4).any():
numDistFailures += 1
s0 = 100*float(numTests - numAbsErrors) / float(numTests)
s1 = 100*float(numTests - numWinnerFailures) / float(numTests)
s2 = 100*float(numTests - numInferenceFailures) / float(numTests)
s3 = 100*float(numTests - numDistFailures) / float(numTests)
LOGGER.info('PCA/k-NN success rate=%s%s', s0, '%')
LOGGER.info('Winner success=%s%s', s1, '%')
LOGGER.info('Inference success=%s%s', s2, '%')
LOGGER.info('Distance success=%s%s', s3, '%')
self.assertEqual(s1, 100.0,
"PCA/k-NN test failed")
def testDistanceMetrics(self):
classifier = KNNClassifier(distanceMethod="norm", distanceNorm=2.0)
dimensionality = 40
protoA = np.array([0, 1, 3, 7, 11], dtype=np.int32)
protoB = np.array([20, 28, 30], dtype=np.int32)
classifier.learn(protoA, 0, isSparse=dimensionality)
classifier.learn(protoB, 0, isSparse=dimensionality)
# input is an arbitrary point, close to protoA, orthogonal to protoB
input = np.zeros(dimensionality)
input[:4] = 1.0
# input0 is used to test that the distance from a point to itself is 0
input0 = np.zeros(dimensionality)
input0[protoA] = 1.0
# Test l2 norm metric
_, _, dist, _ = classifier.infer(input)
l2Distances = [0.65465367, 1.0]
for actual, predicted in zip(l2Distances, dist):
self.assertAlmostEqual(
actual, predicted, places=5,
msg="l2 distance norm is not calculated as expected.")
_, _, dist0, _ = classifier.infer(input0)
self.assertEqual(
0.0, dist0[0], msg="l2 norm did not calculate 0 distance as expected.")
# Test l1 norm metric
classifier.distanceNorm = 1.0
_, _, dist, _ = classifier.infer(input)
l1Distances = [0.42857143, 1.0]
for actual, predicted in zip(l1Distances, dist):
self.assertAlmostEqual(
actual, predicted, places=5,
msg="l1 distance norm is not calculated as expected.")
_, _, dist0, _ = classifier.infer(input0)
self.assertEqual(
0.0, dist0[0], msg="l1 norm did not calculate 0 distance as expected.")
# Test raw overlap metric
classifier.distanceMethod = "rawOverlap"
_, _, dist, _ = classifier.infer(input)
rawOverlaps = [1, 4]
for actual, predicted in zip(rawOverlaps, dist):
self.assertEqual(
actual, predicted, msg="Raw overlap is not calculated as expected.")
_, _, dist0, _ = classifier.infer(input0)
self.assertEqual(
0.0, dist0[0],
msg="Raw overlap did not calculate 0 distance as expected.")
# Test pctOverlapOfInput metric
classifier.distanceMethod = "pctOverlapOfInput"
_, _, dist, _ = classifier.infer(input)
pctOverlaps = [0.25, 1.0]
for actual, predicted in zip(pctOverlaps, dist):
self.assertAlmostEqual(
actual, predicted, places=5,
msg="pctOverlapOfInput is not calculated as expected.")
_, _, dist0, _ = classifier.infer(input0)
self.assertEqual(
0.0, dist0[0],
msg="pctOverlapOfInput did not calculate 0 distance as expected.")
# Test pctOverlapOfProto metric
classifier.distanceMethod = "pctOverlapOfProto"
_, _, dist, _ = classifier.infer(input)
pctOverlaps = [0.40, 1.0]
for actual, predicted in zip(pctOverlaps, dist):
self.assertAlmostEqual(
actual, predicted, places=5,
msg="pctOverlapOfProto is not calculated as expected.")
_, _, dist0, _ = classifier.infer(input0)
self.assertEqual(
0.0, dist0[0],
msg="pctOverlapOfProto did not calculate 0 distance as expected.")
# Test pctOverlapOfLarger metric
classifier.distanceMethod = "pctOverlapOfLarger"
_, _, dist, _ = classifier.infer(input)
pctOverlaps = [0.40, 1.0]
for actual, predicted in zip(pctOverlaps, dist):
self.assertAlmostEqual(
actual, predicted, places=5,
msg="pctOverlapOfLarger is not calculated as expected.")
_, _, dist0, _ = classifier.infer(input0)
self.assertEqual(
0.0, dist0[0],
msg="pctOverlapOfLarger did not calculate 0 distance as expected.")
def runTestKNNClassifier(self, short = 0):
""" Test the KNN classifier in this module. short can be:
0 (short), 1 (medium), or 2 (long)
"""
failures = ""
if short != 2:
numpy.random.seed(42)
else:
seed_value = int(time.time())
numpy.random.seed(seed_value)
LOGGER.info('Seed used: %d', seed_value)
f = open('seedval', 'a')
f.write(str(seed_value))
f.write('\n')
f.close()
failures += simulateKMoreThanOne()
LOGGER.info("\nTesting KNN Classifier on dense patterns")
numPatterns, numClasses = getNumTestPatterns(short)
patternSize = 100
patterns = numpy.random.rand(numPatterns, patternSize)
patternDict = dict()
testDict = dict()
# Assume there are no repeated patterns -- if there are, then
# numpy.random would be completely broken.
# Patterns in testDict are identical to those in patternDict but for the
# first 2% of items.
for i in xrange(numPatterns):
patternDict[i] = dict()
patternDict[i]['pattern'] = patterns[i]
patternDict[i]['category'] = numpy.random.randint(0, numClasses-1)
testDict[i] = copy.deepcopy(patternDict[i])
testDict[i]['pattern'][:0.02*patternSize] = numpy.random.rand()
testDict[i]['category'] = None
LOGGER.info("\nTesting KNN Classifier with L2 norm")
knn = KNNClassifier(k=1)
failures += simulateClassifier(knn, patternDict, \
"KNN Classifier with L2 norm test")
LOGGER.info("\nTesting KNN Classifier with L1 norm")
knnL1 = KNNClassifier(k=1, distanceNorm=1.0)
failures += simulateClassifier(knnL1, patternDict, \
"KNN Classifier with L1 norm test")
# Test with exact matching classifications.
LOGGER.info("\nTesting KNN Classifier with exact matching. For testing we "
"slightly alter the training data and expect None to be returned for the "
"classifications.")
knnExact = KNNClassifier(k=1, exact=True)
failures += simulateClassifier(knnExact,
patternDict,
"KNN Classifier with exact matching test",
testDict=testDict)
numPatterns, numClasses = getNumTestPatterns(short)
patterns = (numpy.random.rand(numPatterns, 25) > 0.7).astype(RealNumpyDType)
patternDict = dict()
for i in patterns:
iString = str(i.tolist())
if not patternDict.has_key(iString):
randCategory = numpy.random.randint(0, numClasses-1)
patternDict[iString] = dict()
patternDict[iString]['pattern'] = i
patternDict[iString]['category'] = randCategory
LOGGER.info("\nTesting KNN on sparse patterns")
knnDense = KNNClassifier(k=1)
failures += simulateClassifier(knnDense, patternDict, \
"KNN Classifier on sparse pattern test")
self.assertEqual(len(failures), 0,
"Tests failed: \n" + failures)
if short == 2:
f = open('seedval', 'a')
f.write('Pass\n')
f.close()