def test():
testing = Testing()
# -dd auto -sw 1 -sb 1 -sz 1 -pd 10 -l 2 -mp 5 -v --evaluationFrequency 1 -plf L2
args = ell.ProtoNNTrainerParameters()
args.projectedDimension = 10
args.numPrototypesPerLabel = 5
args.numLabels = 2
args.sparsityW = 1
args.sparsityB = 1
args.sparsityZ = 1
args.gamma = -1
args.lossFunction = ell.ProtoNNLossFunction.L2
args.numInnerIterations = 1
args.numFeatures = 0
args.verbose = True
trainer = ell.ProtoNNTrainer(args)
dataset = ell.AutoSupervisedDataset()
testFile = os.path.join(find_ell.get_ell_root(),
"examples/data/protonnTestData.txt")
print("Loading: " + testFile)
dataset.Load(testFile)
total = dataset.NumExamples()
features = dataset.NumFeatures()
testing.ProcessTest("Proton dataset loaded",
testing.IsEqual(int(total), 200))
trainer.SetDataset(dataset)
numIterations = 20
print("Training...")
for i in range(numIterations):
trainer.Update()
predictor = trainer.GetPredictor()
accuracy = get_accuracy(predictor, dataset, features)
print("Accuracy %f" % (accuracy))
testing.ProcessTest("Proton accuracy test",
testing.IsEqual(int(accuracy), 1))
map = predictor.GetMap()
map.Save("protonnTestData.ell")
testing.ProcessTest(
"Saving protonnTestData.ell",
testing.IsEqual(os.path.isfile("protonnTestData.ell"), True))
if testing.DidTestFail():
raise Exception("protonn_trainer_test failed")
return 0
def test():
testing = Testing()
dataset = ell.data.AutoSupervisedDataset()
dataset.Load(
os.path.join(find_ell.get_ell_root(), "examples/data/testData.txt"))
num = dataset.NumExamples()
print("Number of Examples:", num)
testing.ProcessTest("Dataset NumExamples test",
testing.IsEqual(int(num), 200))
features = dataset.NumFeatures()
print("Number of Features:", features)
testing.ProcessTest("Dataset NumFeatures test",
testing.IsEqual(int(features), 21))
for i in range(num):
exampleTest(dataset.GetExample(i))
testing.ProcessTest("Dataset eumeration test", True)
return 0
def test_double():
testing = Testing()
# empty vector
e = ell.math.DoubleVector()
np.testing.assert_equal(e.size(), 0)
# vector from list
l = [
1.1,
2.2,
3.3,
4.4,
]
e = ell.math.DoubleVector(l)
np.testing.assert_equal(list(e), l)
# vector from numpy array
a = np.array(range(10), dtype=float)
e = ell.math.DoubleVector(a)
np.testing.assert_equal(np.asarray(e), a)
# conver to numpy using array
b = np.array(e).ravel()
np.testing.assert_equal(a, b)
# copy_from numpy array
e = ell.math.DoubleVector()
e.copy_from(a)
np.testing.assert_equal(np.asarray(e), a)
# convert data types
a = a.astype(np.float32)
e = ell.math.DoubleVector(a)
np.testing.assert_equal(np.asarray(e), a)
# enumerating array
for i in range(a.shape[0]):
x = a[i]
y = e[i]
np.testing.assert_equal(x, y)
# auto-ravel numpy arrays
a = np.ones((10, 10), dtype=float)
a *= range(10)
e = ell.math.DoubleVector(a)
np.testing.assert_equal(np.asarray(e), a.ravel())
testing.ProcessTest("DoubleVector test", True)
def test_float():
testing = Testing()
# empty vector
e = ELL.FloatVector()
np.testing.assert_equal(e.size(), 0)
# vector from list of floats
l = [1.1, 2.2, 3.3, 4.4]
e = ELL.FloatVector(l)
assert_compare_floats(e, l)
# vector from numpy array
a = np.array(range(10), dtype=np.float32)
e = ELL.FloatVector(a)
np.testing.assert_equal(np.asarray(e), a)
# convert to numpy using array
b = np.array(e).ravel()
np.testing.assert_equal(a, b)
# copy_from numpy array
e = ELL.FloatVector()
e.copy_from(a)
np.testing.assert_equal(np.asarray(e), a)
# convert data types
a = a.astype(np.float)
e = ELL.FloatVector(a)
np.testing.assert_equal(np.asarray(e), a)
# enumerating array
for i in range(a.shape[0]):
x = a[i]
y = e[i]
np.testing.assert_equal(x, y)
# auto-ravel numpy arrays
a = np.ones((10, 10), dtype=np.float32)
a *= range(10)
e = ELL.FloatVector(a)
np.testing.assert_equal(np.asarray(e), a.ravel())
testing.ProcessTest("FloatVector test", True)
def test():
testing = Testing()
# -dd auto -sw 1 -sb 1 -sz 1 -pd 10 -l 2 -mp 5 -v --evaluationFrequency 1 -plf L2
args = ell.trainers.ProtoNNTrainerParameters()
args.projectedDimension = 10
args.numPrototypesPerLabel = 5
args.numLabels = 2
args.sparsityW = 1
args.sparsityB = 1
args.sparsityZ = 1
args.gamma = -1
args.lossFunction = ell.trainers.ProtoNNLossFunction.L2
args.numInnerIterations = 1
args.numFeatures = 0
args.verbose = True
trainer = ell.trainers.ProtoNNTrainer(args)
dataset = ell.data.AutoSupervisedDataset()
testFile = os.path.join(find_ell.get_ell_root(),
"examples/data/protonnTestData.txt")
print("Loading: " + testFile)
dataset.Load(testFile)
total = dataset.NumExamples()
features = dataset.NumFeatures()
testing.ProcessTest("ProtoNN dataset loaded",
testing.IsEqual(int(total), 200))
trainer.SetDataset(dataset)
numIterations = 20
print("Training...")
for i in range(numIterations):
trainer.Update()
predictor = trainer.GetPredictor()
accuracy = get_accuracy(predictor, dataset, features, predictor.Predict)
print("Accuracy %f" % (accuracy))
testing.ProcessTest("ProtoNN accuracy test",
testing.IsEqual(int(accuracy), 1))
map = predictor.GetMap()
map.Save("protonnTestData.ell")
testing.ProcessTest(
"Saving protonnTestData.ell",
testing.IsEqual(os.path.isfile("protonnTestData.ell"), True))
# make sure we can compile this map.
try:
compilerSettings = ell.model.MapCompilerOptions()
compilerSettings.useBlas = False
optimizerSettings = ell.model.ModelOptimizerOptions()
compiledMap = map.CompileDouble("host", "protonn", "predict",
compilerSettings, optimizerSettings)
compiled = True
except Exception as e:
print("Compile ProtoNN model failed: {}", e)
compiled = False
testing.ProcessTest("Compiling protonnTestData.ell",
testing.IsEqual(compiled, True))
if compiled:
accuracy = get_accuracy(predictor, dataset, features,
compiledMap.ComputeDouble)
print("Compiled Accuracy %f" % (accuracy))
testing.ProcessTest("ProtoNN compiled accuracy test",
testing.IsEqual(int(accuracy), 1))
if testing.DidTestFail():
raise Exception("protonn_trainer_test failed")
return 0
