def __init__(self, numFolds, data, labels):
self._numFolds = numFolds
self._curFold = 0
self._trainData = array_split(data, self._numFolds)
self._testData = None
self._trainLabels = array_split(labels, self._numFolds)
self._testLabels = None
def test_integer_split_2D_rows(self):
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3, axis=0)
tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]), np.zeros((0, 10))]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
# Same thing for manual splits:
res = array_split(a, [0, 1, 2], axis=0)
tgt = [np.zeros((0, 10)), np.array([np.arange(10)]), np.array([np.arange(10)])]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
def crossValidation(numFolds, data, labels, algorithm, accuracyList, learningCurveList, numLearningCurveIterations, learningCurveIndexMod):
dataFolds = array_split(data, numFolds)
labelFolds = array_split(labels, numFolds)
for testIndex in range(numFolds):
print testIndex,
testData = dataFolds.pop(testIndex)
testLabels = labelFolds.pop(testIndex)
trainData = vstack(dataFolds)
trainLabels = hstack(labelFolds)
accuracyList.append(algorithm(trainData, trainLabels, testData, testLabels))
learningCurve(algorithm, learningCurveList, trainData, trainLabels, testData, testLabels, numLearningCurveIterations, learningCurveIndexMod)
dataFolds.insert(testIndex, testData)
labelFolds.insert(testIndex, testLabels)
print ''
def test_integer_split_2D_rows(self):
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3, axis=0)
tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
np.zeros((0, 10))]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
# Same thing for manual splits:
res = array_split(a, [0, 1, 2], axis=0)
tgt = [np.zeros((0, 10)), np.array([np.arange(10)]),
np.array([np.arange(10)])]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
def test_integer_split_2D_rows_greater_max_int32(self):
a = np.broadcast_to([0], (1 << 32, 2))
res = array_split(a, 4)
chunk = np.broadcast_to([0], (1 << 30, 2))
tgt = [chunk] * 4
for i in range(len(tgt)):
assert_equal(res[i].shape, tgt[i].shape)
def test_two_dimensional_two_integer_remainder_split(self):
matrix = np.reshape(np.arange(16), (4, 4))
res = array_split(matrix, [3, 3], 0, True)
desired = [[[0, 1], [4, 5]], [[2], [6]], [[3], [7]], [[8, 9]], [[10]],
[[11]], [[12, 13]], [[14]], [[15]]]
compare_results(res, desired)
def test_integer_split_2D_cols(self):
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3, axis=-1)
desired = [np.array([np.arange(4), np.arange(4)]),
np.array([np.arange(4, 7), np.arange(4, 7)]),
np.array([np.arange(7, 10), np.arange(7, 10)])]
compare_results(res, desired)
def test_index_split_simple(self):
a = np.arange(10)
indices = [1, 5, 7]
res = array_split(a, indices, axis=-1)
desired = [np.arange(0, 1), np.arange(1, 5), np.arange(5, 7),
np.arange(7, 10)]
compare_results(res, desired)
def test_index_split_high_bound(self):
a = np.arange(10)
indices = [0, 5, 7, 10, 12]
res = array_split(a, indices, axis=-1)
desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
np.arange(7, 10), np.array([]), np.array([])]
compare_results(res, desired)
def test_integer_split_2D_rows_greater_max_int32(self):
a = np.broadcast_to([0], (1 << 32, 2))
res = array_split(a, 4)
chunk = np.broadcast_to([0], (1 << 30, 2))
tgt = [chunk] * 4
for i in range(len(tgt)):
assert_equal(res[i].shape, tgt[i].shape)
def test_index_split_simple(self):
a = np.arange(10)
indices = [1, 5, 7]
res = array_split(a, indices, axis=-1)
desired = [np.arange(0, 1), np.arange(1, 5), np.arange(5, 7),
np.arange(7, 10)]
compare_results(res, desired)
def test_integer_split_2D_cols(self):
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3, axis=-1)
desired = [np.array([np.arange(4), np.arange(4)]),
np.array([np.arange(4, 7), np.arange(4, 7)]),
np.array([np.arange(7, 10), np.arange(7, 10)])]
compare_results(res, desired)
def test_index_split_high_bound(self):
a = np.arange(10)
indices = [0, 5, 7, 10, 12]
res = array_split(a, indices, axis=-1)
desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
np.arange(7, 10), np.array([]), np.array([])]
compare_results(res, desired)
def test_integer_split_2D_default(self):
""" This will fail if we change default axis
"""
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3)
tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]), np.zeros((0, 10))]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
def test_integer_split_2D_default(self):
""" This will fail if we change default axis
"""
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3)
tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
np.zeros((0, 10))]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
datasets = getDataSets(dataDir, ['ionosphere', 'iris', 'wine'])
#datasets = getDataSets(dataDir, ['by_hand'])
#datasets = getDataSets(dataDir, ['ionosphere'])
tp = ThreadPool(4)
for name, (data, labels) in datasets.iteritems():
datasetOutDir = getDatasetOutDir(outDir, name)
print "Computing on", name
#do a split into overall test and overall train
overallTestTrainRatio = 1.0 / 3.0
overallTestTrainSplitIndex = array([int(overallTestTrainRatio * len(data))])
overallTestData, overallTrainData = array_split(data, array([overallTestTrainSplitIndex]))
overallTestLabels, overallTrainLabels = array_split(labels, overallTestTrainSplitIndex)
#test a whole bunch of generic kernels on the overall split data
fileIdentifier = 'overall'
#print "train:", overallTrainData
#print "test:", overallTestData
compareAlgorithmsOnSameKernels(tp, overallTrainData, overallTrainLabels, overallTestData, overallTestLabels, name, fileIdentifier)
#now, try to find an optimal kernel for either svm or kfd
#do it for each kernel type
numOptimizationFolds = 3
fileIdentifier = 'optimized'
compareAlgorithmsOnOptimizedKernel(tp, overallTrainData, overallTrainLabels, overallTestData, overallTestLabels, numOptimizationFolds, datasetOutDir, name, fileIdentifier)
#remove ignored columns and class column, in reverse sorted order
#do it in reverse sorted order so the indexes stay correct
for removeCol in sorted(ignoreColList + [classCol], reverse=True):
if removeCol == classCol:
label = features.pop(removeCol)
else:
features.pop(removeCol)
datasetDict[label].append(features)
origDataFile.close()
#make it into a 2 class problem by lumping classes together
#don't have rhyme or reason - don't want to favor one class or another, or make our data artificially clean
numOrigClasses = len(datasetDict.keys())
#split into 2, possibly unequal, groups of class labels
newClassMap = array_split(datasetDict.keys(), 2)
#reorganize the data
dataWithNewLabelMap = defaultdict(list)
for newClassLabel, oldClassLabelList in enumerate(newClassMap):
for oldClassLabel in oldClassLabelList:
for featureRow in datasetDict[oldClassLabel]:
dataWithNewLabelMap[newClassLabel].append(featureRow)
#make the two datasets the same size
dataWithNewLabelTupleList = []
minClassSize = min([len(x) for x in dataWithNewLabelMap.values()])
for newClassLabel, featureRowList in dataWithNewLabelMap.iteritems():
for featureRow in featureRowList[:minClassSize]:
dataWithNewLabelTupleList.append((featureRow, newClassLabel))
def test_integer_split(self):
a = np.arange(10)
res = array_split(a, 1)
desired = [np.arange(10)]
compare_results(res, desired)
res = array_split(a, 2)
desired = [np.arange(5), np.arange(5, 10)]
compare_results(res, desired)
res = array_split(a, 3)
desired = [np.arange(4), np.arange(4, 7), np.arange(7, 10)]
compare_results(res, desired)
res = array_split(a, 4)
desired = [np.arange(3), np.arange(3, 6), np.arange(6, 8),
np.arange(8, 10)]
compare_results(res, desired)
res = array_split(a, 5)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 8), np.arange(8, 10)]
compare_results(res, desired)
res = array_split(a, 6)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 8), np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 7)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 8)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 5),
np.arange(5, 6), np.arange(6, 7), np.arange(7, 8),
np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 9)
desired = [np.arange(2), np.arange(2, 3), np.arange(3, 4),
np.arange(4, 5), np.arange(5, 6), np.arange(6, 7),
np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 10)
desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 11)
desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10), np.array([])]
compare_results(res, desired)
def test_integer_split(self):
a = np.arange(10)
res = array_split(a, 1)
desired = [np.arange(10)]
compare_results(res, desired)
res = array_split(a, 2)
desired = [np.arange(5), np.arange(5, 10)]
compare_results(res, desired)
res = array_split(a, 3)
desired = [np.arange(4), np.arange(4, 7), np.arange(7, 10)]
compare_results(res, desired)
res = array_split(a, 4)
desired = [np.arange(3), np.arange(3, 6), np.arange(6, 8),
np.arange(8, 10)]
compare_results(res, desired)
res = array_split(a, 5)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 8), np.arange(8, 10)]
compare_results(res, desired)
res = array_split(a, 6)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 8), np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 7)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 8)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 5),
np.arange(5, 6), np.arange(6, 7), np.arange(7, 8),
np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 9)
desired = [np.arange(2), np.arange(2, 3), np.arange(3, 4),
np.arange(4, 5), np.arange(5, 6), np.arange(6, 7),
np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 10)
desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 11)
desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10), np.array([])]
compare_results(res, desired)
