def testReadWrite(self):
le = LogEncoder(w=5,
resolution=0.1,
minval=1,
maxval=10000,
name="amount",
forced=True)
originalValue = le.encode(1.0)
proto1 = LogEncoderProto.new_message()
le.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = LogEncoderProto.read(f)
encoder = LogEncoder.read(proto2)
self.assertIsInstance(encoder, LogEncoder)
self.assertEqual(encoder.minScaledValue, le.minScaledValue)
self.assertEqual(encoder.maxScaledValue, le.maxScaledValue)
self.assertEqual(encoder.minval, le.minval)
self.assertEqual(encoder.maxval, le.maxval)
self.assertEqual(encoder.name, le.name)
self.assertEqual(encoder.verbosity, le.verbosity)
self.assertEqual(encoder.clipInput, le.clipInput)
self.assertEqual(encoder.width, le.width)
self.assertEqual(encoder.description, le.description)
self.assertIsInstance(encoder.encoder, ScalarEncoder)
self.assertTrue(numpy.array_equal(encoder.encode(1), originalValue))
self.assertEqual(le.decode(encoder.encode(1)),
encoder.decode(le.encode(1)))
# Feed in a new value and ensure the encodings match
result1 = le.encode(10)
result2 = encoder.encode(10)
self.assertTrue(numpy.array_equal(result1, result2))
def testReadWrite(self):
le = LogEncoder(w=5,
resolution=0.1,
minval=1,
maxval=10000,
name="amount",
forced=True)
originalValue = le.encode(1.0)
proto1 = LogEncoderProto.new_message()
le.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = LogEncoderProto.read(f)
encoder = LogEncoder.read(proto2)
self.assertIsInstance(encoder, LogEncoder)
self.assertEqual(encoder.minScaledValue, le.minScaledValue)
self.assertEqual(encoder.maxScaledValue, le.maxScaledValue)
self.assertEqual(encoder.minval, le.minval)
self.assertEqual(encoder.maxval, le.maxval)
self.assertEqual(encoder.name, le.name)
self.assertEqual(encoder.verbosity, le.verbosity)
self.assertEqual(encoder.clipInput, le.clipInput)
self.assertEqual(encoder.width, le.width)
self.assertEqual(encoder.description, le.description)
self.assertIsInstance(encoder.encoder, ScalarEncoder)
self.assertTrue(numpy.array_equal(encoder.encode(1), originalValue))
self.assertEqual(le.decode(encoder.encode(1)),
encoder.decode(le.encode(1)))
# Feed in a new value and ensure the encodings match
result1 = le.encode(10)
result2 = encoder.encode(10)
self.assertTrue(numpy.array_equal(result1, result2))
def testLogEncoder(self):
# Create the encoder
# use of forced=True is not recommended, but is used in the example for
# readibility, see scalar.py
le = LogEncoder(w=5,
resolution=0.1,
minval=1,
maxval=10000,
name="amount",
forced=True)
# Verify we're setting the description properly
self.assertEqual(le.getDescription(), [("amount", 0)])
# Verify we're getting the correct field types
types = le.getDecoderOutputFieldTypes()
self.assertEqual(types[0], FieldMetaType.float)
# Verify the encoder ends up with the correct width
#
# 10^0 -> 10^4 => 0 -> 4; With a resolution of 0.1
# 41 possible values plus padding = 4 = width 45
self.assertEqual(le.getWidth(), 45)
# Verify we have the correct number of possible values
self.assertEqual(len(le.getBucketValues()), 41)
# Verify closeness calculations
testTuples = [([1], [10000], 0.0),
([1], [1000], 0.25),
([1], [1], 1.0),
([1], [-200], 1.0)]
for tm in testTuples:
expected = tm[0]
actual = tm[1]
expectedResult = tm[2]
self.assertEqual(le.closenessScores(expected, actual),
expectedResult,
"exp: %s act: %s expR: %s" % (str(expected),
str(actual),
str(expectedResult)))
# Verify a value of 1.0 is encoded as expected
value = 1.0
output = le.encode(value)
# Our expected encoded representation of the value 1 is the first
# w bits on in an array of len width.
expected = [1, 1, 1, 1, 1] + 40 * [0]
# Convert to numpy array
expected = numpy.array(expected, dtype="uint8")
self.assertTrue(numpy.array_equal(output, expected))
# Test reverse lookup
decoded = le.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [1, 1]))
# Verify an input representing a missing value is handled properly
mvOutput = le.encode(SENTINEL_VALUE_FOR_MISSING_DATA)
self.assertEqual(sum(mvOutput), 0)
# Test top-down for all values
value = le.minval
while value <= le.maxval:
output = le.encode(value)
topDown = le.topDownCompute(output)
# Do the scaling by hand here.
scaledVal = math.log10(value)
# Find the range of values that would also produce this top down
# output.
minTopDown = math.pow(10, (scaledVal - le.encoder.resolution))
maxTopDown = math.pow(10, (scaledVal + le.encoder.resolution))
# Verify the range surrounds this scaled val
self.assertGreaterEqual(topDown.value, minTopDown)
self.assertLessEqual(topDown.value, maxTopDown)
# Test bucket support
bucketIndices = le.getBucketIndices(value)
topDown = le.getBucketInfo(bucketIndices)[0]
# Verify our reconstructed value is in the valid range
self.assertGreaterEqual(topDown.value, minTopDown)
self.assertLessEqual(topDown.value, maxTopDown)
# Same for the scalar value
self.assertGreaterEqual(topDown.scalar, minTopDown)
self.assertLessEqual(topDown.scalar, maxTopDown)
# That the encoding portion of our EncoderResult matched the result of
# encode()
self.assertTrue(numpy.array_equal(topDown.encoding, output))
# Verify our reconstructed value is the same as the bucket value
bucketValues = le.getBucketValues()
self.assertEqual(topDown.value,
bucketValues[bucketIndices[0]])
# Next value
scaledVal += le.encoder.resolution / 4.0
value = math.pow(10, scaledVal)
# Verify next power of 10 encoding
output = le.encode(100)
# increase of 2 decades = 20 decibels
# bit 0, 1 are padding; bit 3 is 1, ..., bit 22 is 20 (23rd bit)
expected = 20 * [0] + [1, 1, 1, 1, 1] + 20 * [0]
expected = numpy.array(expected, dtype="uint8")
self.assertTrue(numpy.array_equal(output, expected))
# Test reverse lookup
decoded = le.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [100, 100]))
# Verify next power of 10 encoding
output = le.encode(10000)
expected = 40 * [0] + [1, 1, 1, 1, 1]
expected = numpy.array(expected, dtype="uint8")
self.assertTrue(numpy.array_equal(output, expected))
# Test reverse lookup
decoded = le.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [10000, 10000]))
def testLogEncoder(self):
# Create the encoder
# use of forced=True is not recommended, but is used in the example for
# readibility, see scalar.py
le = LogEncoder(w=5,
resolution=0.1,
minval=1,
maxval=10000,
name="amount",
forced=True)
# Verify we're setting the description properly
self.assertEqual(le.getDescription(), [("amount", 0)])
# Verify we're getting the correct field types
types = le.getDecoderOutputFieldTypes()
self.assertEqual(types[0], FieldMetaType.float)
# Verify the encoder ends up with the correct width
#
# 10^0 -> 10^4 => 0 -> 4; With a resolution of 0.1
# 41 possible values plus padding = 4 = width 45
self.assertEqual(le.getWidth(), 45)
# Verify we have the correct number of possible values
self.assertEqual(len(le.getBucketValues()), 41)
# Verify closeness calculations
testTuples = [([1], [10000], 0.0), ([1], [1000], 0.25),
([1], [1], 1.0), ([1], [-200], 1.0)]
for tm in testTuples:
expected = tm[0]
actual = tm[1]
expectedResult = tm[2]
self.assertEqual(
le.closenessScores(expected, actual), expectedResult,
"exp: %s act: %s expR: %s" %
(str(expected), str(actual), str(expectedResult)))
# Verify a value of 1.0 is encoded as expected
value = 1.0
output = le.encode(value)
# Our expected encoded representation of the value 1 is the first
# w bits on in an array of len width.
expected = [1, 1, 1, 1, 1] + 40 * [0]
# Convert to numpy array
expected = numpy.array(expected, dtype="uint8")
self.assertTrue(numpy.array_equal(output, expected))
# Test reverse lookup
decoded = le.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [1, 1]))
# Verify an input representing a missing value is handled properly
mvOutput = le.encode(SENTINEL_VALUE_FOR_MISSING_DATA)
self.assertEqual(sum(mvOutput), 0)
# Test top-down for all values
value = le.minval
while value <= le.maxval:
output = le.encode(value)
topDown = le.topDownCompute(output)
# Do the scaling by hand here.
scaledVal = math.log10(value)
# Find the range of values that would also produce this top down
# output.
minTopDown = math.pow(10, (scaledVal - le.encoder.resolution))
maxTopDown = math.pow(10, (scaledVal + le.encoder.resolution))
# Verify the range surrounds this scaled val
self.assertGreaterEqual(topDown.value, minTopDown)
self.assertLessEqual(topDown.value, maxTopDown)
# Test bucket support
bucketIndices = le.getBucketIndices(value)
topDown = le.getBucketInfo(bucketIndices)[0]
# Verify our reconstructed value is in the valid range
self.assertGreaterEqual(topDown.value, minTopDown)
self.assertLessEqual(topDown.value, maxTopDown)
# Same for the scalar value
self.assertGreaterEqual(topDown.scalar, minTopDown)
self.assertLessEqual(topDown.scalar, maxTopDown)
# That the encoding portion of our EncoderResult matched the result of
# encode()
self.assertTrue(numpy.array_equal(topDown.encoding, output))
# Verify our reconstructed value is the same as the bucket value
bucketValues = le.getBucketValues()
self.assertEqual(topDown.value,
bucketValues[int(bucketIndices[0])])
# Next value
scaledVal += le.encoder.resolution / 4.0
value = math.pow(10, scaledVal)
# Verify next power of 10 encoding
output = le.encode(100)
# increase of 2 decades = 20 decibels
# bit 0, 1 are padding; bit 3 is 1, ..., bit 22 is 20 (23rd bit)
expected = 20 * [0] + [1, 1, 1, 1, 1] + 20 * [0]
expected = numpy.array(expected, dtype="uint8")
self.assertTrue(numpy.array_equal(output, expected))
# Test reverse lookup
decoded = le.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [100, 100]))
# Verify next power of 10 encoding
output = le.encode(10000)
expected = 40 * [0] + [1, 1, 1, 1, 1]
expected = numpy.array(expected, dtype="uint8")
self.assertTrue(numpy.array_equal(output, expected))
# Test reverse lookup
decoded = le.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [10000, 10000]))