def setUp(self):
self.slowParser = SlowParser()
self.fastParser = FastParser()
def setUp(self):
self.slowParser = SlowParser()
self.fastParser = FastParser()
class TestParse(unittest.TestCase):
maxDiff = None
def setUp(self):
self.slowParser = SlowParser()
self.fastParser = FastParser()
def test_parseSlowEqualsFast1(self):
program = """
CSVFile trainf("train.dat")
RealFeatures feats_train(trainf)
CSVFile testf("test.dat")
"""
slowParsedProgram = self.slowParser.parse(program)
fastParsedProgram = self.fastParser.parse(program)
self.assertEqual(slowParsedProgram, fastParsedProgram)
def test_parseSlowEqualsFast2(self):
program = """
# Importing generated data
CSVFile X("../data/gaussian_process_X.csv")
CSVFile X_test("../data/gaussian_process_X_test.csv")
CSVFile Y("../data/gaussian_process_Y.csv")
CSVFile Y_test("../data/gaussian_process_Y_test.csv")
RegressionLabels labels(Y)
RegressionLabels labels_test(Y_test)
RealFeatures feats_train(X)
RealFeatures feats_test(X_test)
# GP specification
GaussianKernel kernel(10, 2)
ZeroMean zmean()
GaussianLikelihood lik()
lik.set_sigma(0.5)
ExactInferenceMethod inf(kernel, feats_train, zmean, labels, lik)
# train GP
GaussianProcessRegression gp(inf)
gp.train()
# some things we can do
RealVector alpha = inf.get_alpha()
RealVector diagonal = inf.get_diagonal_vector()
RealMatrix cholesky = inf.get_cholesky()
# get mean and variance vectors
RealVector mean = gp.get_mean_vector(feats_test)
RealVector variance = gp.get_variance_vector(feats_test)
# plotting in python
#from numpy import sqrt
#from pylab import plot, show, legend, fill_between
#plot(feats_train[0],labels,'x') # training observations
#plot(feats_test[0],labels_test,'-') # ground truth of test
#plot(feats_test[0],mean, '-') # mean predictions of test
#fill_between(feats_test[0],mean-1.96*sqrt(variance),mean+1.96*sqrt(variance),color='grey') # 95% confidence interval
#legend(["training", "ground truth", "mean predictions"])
#show()
print "Alpha:"
print alpha
print "Diagonal:"
print diagonal
print "Variance:"
print variance
print "Mean:"
print mean
print "Cholesky:"
print cholesky
"""
slowParsedProgram = self.slowParser.parse(program)
fastParsedProgram = self.fastParser.parse(program)
self.assertEqual(slowParsedProgram, fastParsedProgram)
def test_parseSlowEqualsFast3(self):
program = """
CSVFile train_data("../data/fm_train_real.dat")
CSVFile test_data("../data/fm_test_real.dat")
CSVFile label_data("../data/label_train_twoclass.dat")
RealFeatures feats_train(train_data)
RealFeatures feats_test(test_data)
BinaryLabels labels(label_data)
LibLinear svm(0.9, feats_train, labels)
svm.set_liblinear_solver_type(enum LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC_DUAL)
svm.set_epsilon(0.001)
svm.set_bias_enabled(True)
svm.train()
BinaryLabels predictions = svm.apply_binary(feats_test)
print predictions.get_labels()
"""
slowParsedProgram = self.slowParser.parse(program)
fastParsedProgram = self.fastParser.parse(program)
self.assertEqual(slowParsedProgram, fastParsedProgram)
class TestParse(unittest.TestCase):
maxDiff = None
def setUp(self):
self.slowParser = SlowParser()
self.fastParser = FastParser()
def test_parseSlowEqualsFast1(self):
program = """
CSVFile trainf("train.dat")
RealFeatures feats_train(trainf)
CSVFile testf("test.dat")
"""
slowParsedProgram = self.slowParser.parse(program)
fastParsedProgram = self.fastParser.parse(program)
self.assertEqual(slowParsedProgram, fastParsedProgram)
def test_parseSlowEqualsFast2(self):
program = """
# Importing generated data
CSVFile X("../data/gaussian_process_X.csv")
CSVFile X_test("../data/gaussian_process_X_test.csv")
CSVFile Y("../data/gaussian_process_Y.csv")
CSVFile Y_test("../data/gaussian_process_Y_test.csv")
RegressionLabels labels(Y)
RegressionLabels labels_test(Y_test)
RealFeatures feats_train(X)
RealFeatures feats_test(X_test)
# GP specification
GaussianKernel kernel(10, 2)
ZeroMean zmean()
GaussianLikelihood lik()
lik.set_sigma(0.5)
ExactInferenceMethod inf(kernel, feats_train, zmean, labels, lik)
# train GP
GaussianProcessRegression gp(inf)
gp.train()
# some things we can do
RealVector alpha = inf.get_alpha()
RealVector diagonal = inf.get_diagonal_vector()
RealMatrix cholesky = inf.get_cholesky()
# get mean and variance vectors
RealVector mean = gp.get_mean_vector(feats_test)
RealVector variance = gp.get_variance_vector(feats_test)
# plotting in python
#from numpy import sqrt
#from pylab import plot, show, legend, fill_between
#plot(feats_train[0],labels,'x') # training observations
#plot(feats_test[0],labels_test,'-') # ground truth of test
#plot(feats_test[0],mean, '-') # mean predictions of test
#fill_between(feats_test[0],mean-1.96*sqrt(variance),mean+1.96*sqrt(variance),color='grey') # 95% confidence interval
#legend(["training", "ground truth", "mean predictions"])
#show()
print "Alpha:"
print alpha
print "Diagonal:"
print diagonal
print "Variance:"
print variance
print "Mean:"
print mean
print "Cholesky:"
print cholesky
"""
slowParsedProgram = self.slowParser.parse(program)
fastParsedProgram = self.fastParser.parse(program)
self.assertEqual(slowParsedProgram, fastParsedProgram)
def test_parseSlowEqualsFast3(self):
program = """
CSVFile train_data("../data/fm_train_real.dat")
CSVFile test_data("../data/fm_test_real.dat")
CSVFile label_data("../data/label_train_twoclass.dat")
RealFeatures feats_train(train_data)
RealFeatures feats_test(test_data)
BinaryLabels labels(label_data)
LibLinear svm(0.9, feats_train, labels)
svm.set_liblinear_solver_type(enum LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC_DUAL)
svm.set_epsilon(0.001)
svm.set_bias_enabled(True)
svm.train()
BinaryLabels predictions = svm.apply_binary(feats_test)
print predictions.get_labels()
"""
slowParsedProgram = self.slowParser.parse(program)
fastParsedProgram = self.fastParser.parse(program)
self.assertEqual(slowParsedProgram, fastParsedProgram)
