def feature_function():
from modshogun import RealFeatures
from modshogun import CSVFile
import numpy as np
#3x3 random matrix
feat_arr = np.random.rand(3, 3)
#initialize RealFeatures from numpy array
features = RealFeatures(feat_arr)
#get matrix value function
print features.get_feature_matrix(features)
#get selected column of matrix
print features.get_feature_vector(1)
#get number of columns
print features.get_num_features()
#get number of rows
print features.get_num_vectors()
feats_from_csv = RealFeatures(CSVFile("csv/feature.csv"))
print "csv is ", feats_from_csv.get_feature_matrix()
def classifier_featureblock_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup
try:
from modshogun import FeatureBlockLogisticRegression
except ImportError:
print("FeatureBlockLogisticRegression not available")
exit(0)
features = RealFeatures(hstack((traindat,traindat)))
labels = BinaryLabels(hstack((label_train,label_train)))
n_features = features.get_num_features()
block_one = IndexBlock(0,n_features//2)
block_two = IndexBlock(n_features//2,n_features)
block_group = IndexBlockGroup()
block_group.add_block(block_one)
block_group.add_block(block_two)
mtlr = FeatureBlockLogisticRegression(0.1,features,labels,block_group)
mtlr.set_regularization(1) # use regularization ratio
mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlr.train()
out = mtlr.apply().get_labels()
return out
def classifier_featureblock_logistic_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat):
from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup
try:
from modshogun import FeatureBlockLogisticRegression
except ImportError:
print("FeatureBlockLogisticRegression not available")
exit(0)
features = RealFeatures(hstack((traindat, traindat)))
labels = BinaryLabels(hstack((label_train, label_train)))
n_features = features.get_num_features()
block_one = IndexBlock(0, n_features // 2)
block_two = IndexBlock(n_features // 2, n_features)
block_group = IndexBlockGroup()
block_group.add_block(block_one)
block_group.add_block(block_two)
mtlr = FeatureBlockLogisticRegression(0.1, features, labels, block_group)
mtlr.set_regularization(1) # use regularization ratio
mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlr.train()
out = mtlr.apply().get_labels()
return out
def neuralnets_simple_modular (train_fname, test_fname, label_fname, C, epsilon): from modshogun import NeuralLayers, NeuralNetwork, RealFeatures, BinaryLabels from modshogun import Math_init_random, CSVFile Math_init_random(17) feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=BinaryLabels(CSVFile(label_fname)) layers = NeuralLayers() network = NeuralNetwork(layers.input(feats_train.get_num_features()).linear(50).softmax(2).done()) network.quick_connect() network.initialize_neural_network() network.set_labels(labels) network.train(feats_train) return network, network.apply_multiclass(feats_test)
def load_data(num_train_samples=7291, m_data_dict=data_dict):
from modshogun import RealFeatures, MulticlassLabels
import numpy
train_vec = m_data_dict['yTr'][0][:num_train_samples].astype(numpy.float64)
train_labels = MulticlassLabels(train_vec)
test_vec = m_data_dict['yTe'][0].astype(numpy.float64)
test_labels = MulticlassLabels(test_vec)
print "#train_labels = " + str(train_labels.get_num_labels())
print "#test_labels = " + str(test_labels.get_num_labels())
train_mat = m_data_dict['xTr'][:, :num_train_samples].astype(numpy.float64)
train_features = RealFeatures(train_mat)
test_mat = m_data_dict['xTe'].astype(numpy.float64)
test_features = RealFeatures(test_mat)
print "#train_vectors = " + str(train_features.get_num_vectors())
print "#test_vectors = " + str(test_features.get_num_vectors())
print "data dimension = " + str(test_features.get_num_features())
return train_features, train_labels, test_features, test_labels
def load_data(num_train_samples=7291, m_data_dict=data_dict): from modshogun import RealFeatures, MulticlassLabels import numpy train_vec = m_data_dict['yTr'][0][:num_train_samples].astype(numpy.float64) train_labels = MulticlassLabels(train_vec) test_vec = m_data_dict['yTe'][0].astype(numpy.float64) test_labels = MulticlassLabels(test_vec) print "#train_labels = " + str(train_labels.get_num_labels()) print "#test_labels = " + str(test_labels.get_num_labels()) train_mat = m_data_dict['xTr'][:,:num_train_samples].astype(numpy.float64) train_features = RealFeatures(train_mat) test_mat = m_data_dict['xTe'].astype(numpy.float64) test_features = RealFeatures(test_mat) print "#train_vectors = " + str(train_features.get_num_vectors()) print "#test_vectors = " + str(test_features.get_num_vectors()) print "data dimension = " + str(test_features.get_num_features()) return train_features, train_labels, test_features, test_labels
def neuralnets_simple_modular(train_fname, test_fname, label_fname, C,
epsilon):
from modshogun import NeuralLayers, NeuralNetwork, RealFeatures, BinaryLabels
from modshogun import Math_init_random, CSVFile
Math_init_random(17)
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
labels = BinaryLabels(CSVFile(label_fname))
layers = NeuralLayers()
network = NeuralNetwork(
layers.input(
feats_train.get_num_features()).linear(50).softmax(2).done())
network.quick_connect()
network.initialize_neural_network()
network.set_labels(labels)
network.train(feats_train)
return network, network.apply_multiclass(feats_test)
xi = x[y==val]
axis.scatter(xi[:,0], xi[:,1], s=50, facecolors='none', edgecolors=COLS[idx])
def plot_neighborhood_graph(x, nn, axis):
for i in xrange(x.shape[0]):
xs = [x[i,0], x[nn[1,i], 0]]
ys = [x[i,1], x[nn[1,i], 1]]
axis.plot(xs, ys, COLS[int(y[i])])
figure, axarr = pyplot.subplots(3, 1)
x, y = sandwich_data()
features = RealFeatures(x.T)
labels = MulticlassLabels(y)
print('%d vectors with %d features' % (features.get_num_vectors(), features.get_num_features()))
assert(features.get_num_vectors() == labels.get_num_labels())
distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)
plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)
lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()
def plot_neighborhood_graph(x, nn, axis):
for i in xrange(x.shape[0]):
xs = [x[i, 0], x[nn[1, i], 0]]
ys = [x[i, 1], x[nn[1, i], 1]]
axis.plot(xs, ys, COLS[int(y[i])])
figure, axarr = pyplot.subplots(3, 1)
x, y = sandwich_data()
features = RealFeatures(x.T)
labels = MulticlassLabels(y)
print('%d vectors with %d features' %
(features.get_num_vectors(), features.get_num_features()))
assert (features.get_num_vectors() == labels.get_num_labels())
distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)
plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)
lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()
#!/usr/bin/python
from modshogun import CSVFile, RealFeatures, RescaleFeatures
from scipy.linalg import solve_triangular, cholesky, sqrtm, inv
import matplotlib.pyplot as pyplot
import numpy
# load wine features
features = RealFeatures(CSVFile('../data/fm_wine.dat'))
print('%d vectors with %d features.' % (features.get_num_vectors(), features.get_num_features()))
print('original features mean = ' + str(numpy.mean(features, axis=1)))
# rescale the features to [0,1]
feature_rescaling = RescaleFeatures()
feature_rescaling.init(features)
features.add_preprocessor(feature_rescaling)
features.apply_preprocessor()
print('mean after rescaling = ' + str(numpy.mean(features, axis=1)))
# remove mean from data
data = features.get_feature_matrix()
data = data.T
data-= numpy.mean(data, axis=0)
print numpy.mean(data, axis=0)
fig, axarr = pyplot.subplots(1,2)
axarr[0].matshow(numpy.cov(data.T))
#### whiten data
#!/usr/bin/python
from scipy import io
data_dict = io.loadmat('../data/NBData20_train_preprocessed.mat')
xt = data_dict['xt']
yt = data_dict['yt']
import numpy
from modshogun import RealFeatures, MulticlassLabels, LMNN, MSG_DEBUG
features = RealFeatures(xt.T)
labels = MulticlassLabels(numpy.squeeze(yt))
k = 6
lmnn = LMNN(features, labels, k)
lmnn.io.set_loglevel(MSG_DEBUG)
lmnn.set_diagonal(True)
lmnn.set_maxiter(10000)
lmnn.train(numpy.eye(features.get_num_features()))
#!/usr/bin/python
from scipy import io
data_dict = io.loadmat('../data/NBData20_train_preprocessed.mat')
xt = data_dict['xt']
yt = data_dict['yt']
import numpy
from modshogun import RealFeatures,MulticlassLabels,LMNN,MSG_DEBUG
features = RealFeatures(xt.T)
labels = MulticlassLabels(numpy.squeeze(yt))
k = 6
lmnn = LMNN(features,labels,k)
lmnn.io.set_loglevel(MSG_DEBUG)
lmnn.set_diagonal(True)
lmnn.set_maxiter(10000)
lmnn.train(numpy.eye(features.get_num_features()))
#!/usr/bin/python
from modshogun import CSVFile, RealFeatures, RescaleFeatures
from scipy.linalg import solve_triangular, cholesky, sqrtm, inv
import matplotlib.pyplot as pyplot
import numpy
# load wine features
features = RealFeatures(CSVFile('../data/fm_wine.dat'))
print('%d vectors with %d features.' %
(features.get_num_vectors(), features.get_num_features()))
print('original features mean = ' + str(numpy.mean(features, axis=1)))
# rescale the features to [0,1]
feature_rescaling = RescaleFeatures()
feature_rescaling.init(features)
features.add_preprocessor(feature_rescaling)
features.apply_preprocessor()
print('mean after rescaling = ' + str(numpy.mean(features, axis=1)))
# remove mean from data
data = features.get_feature_matrix()
data = data.T
data -= numpy.mean(data, axis=0)
print numpy.mean(data, axis=0)
fig, axarr = pyplot.subplots(1, 2)
axarr[0].matshow(numpy.cov(data.T))
acc = evaluator.evaluate(predicted_labels, test_labels)
err = 1-acc
return err
features_file = '../data/fm_ape_gut.txt'
labels_file = '../data/label_ape_gut.txt'
features = RealFeatures(CSVFile(features_file))
labels = MulticlassLabels(CSVFile(labels_file))
# reduce the number of features to use so that the training is faster but still
# the results of feature selection are significant
fm = features.get_feature_matrix()
features = RealFeatures(fm[:500, :])
assert(features.get_num_vectors() == labels.get_num_labels())
print('Number of examples = %d, number of features = %d.' % (features.get_num_vectors(), features.get_num_features()))
visualize_tdsne(features, labels)
lmnn = diagonal_lmnn(features, labels, max_iter=1200)
diagonal_transform = lmnn.get_linear_transform()
diagonal = numpy.diag(diagonal_transform)
print('%d out of %d elements are non-zero' % (numpy.sum(diagonal != 0), diagonal.shape[0]))
statistics = lmnn.get_statistics()
pyplot.plot(statistics.obj.get())
pyplot.show()
