def main():
# Get training file name from the command line
traindatafile = sys.argv[1]
# The training file is in libSVM format
tr_data = load_svmlight_file(traindatafile)
print("loaded data")
init_transform = np.eye(tr_data[0].toarray().shape[1])
print(init_transform)
Xtr = tr_data[0][:6000].toarray()
# Converts sparse matrices to dense
Ytr = tr_data[1][:6000]
# The trainig labels
# Cast data to Shogun format to work with LMNN
features = RealFeatures(Xtr.T)
labels = MulticlassLabels(Ytr.astype(np.float64))
### Do magic stuff here to learn the best metric you can ###
# Number of target neighbours per example - tune this using validation
k = 21
# Initialize the LMNN package
print("starting lmnn train....")
lmnn = LMNN(features, labels, k)
# Choose an appropriate timeout
lmnn.set_maxiter(3000)
lmnn.train(init_transform)
# Let LMNN do its magic and return a linear transformation
# corresponding to the Mahalanobis metric it has learnt
L = lmnn.get_linear_transform()
M = np.matrix(np.dot(L.T, L))
print(M)
# Save the model for use in testing phase
# Warning: do not change this file name
statistics = lmnn.get_statistics()
pyplot.plot(statistics.obj.get())
pyplot.grid(True)
pyplot.xlabel('Number of iterations')
pyplot.ylabel('LMNN objective')
pyplot.show()
np.save("model.npy", M)
def metric_lmnn_statistics(
k=3,
fname_features="../../data/fm_train_multiclass_digits.dat.gz",
fname_labels="../../data/label_train_multiclass_digits.dat",
):
try:
from modshogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
import matplotlib.pyplot as pyplot
except ImportError:
print "Error importing modshogun or other required modules. Please, verify their installation."
return
features = RealFeatures(load_compressed_features(fname_features).T)
labels = MulticlassLabels(CSVFile(fname_labels))
# print 'number of examples = %d' % features.get_num_vectors()
# print 'number of features = %d' % features.get_num_features()
assert features.get_num_vectors() == labels.get_num_labels()
# train LMNN
lmnn = LMNN(features, labels, k)
lmnn.set_correction(100)
# lmnn.io.set_loglevel(MSG_DEBUG)
print "Training LMNN, this will take about two minutes..."
lmnn.train()
print "Training done!"
# plot objective obtained during training
statistics = lmnn.get_statistics()
pyplot.plot(statistics.obj.get())
pyplot.grid(True)
pyplot.xlabel("Iterations")
pyplot.ylabel("LMNN objective")
pyplot.title("LMNN objective during training for the multiclass digits data set")
pyplot.show()
def metric_lmnn_statistics(
k=3,
fname_features='../../data/fm_train_multiclass_digits.dat.gz',
fname_labels='../../data/label_train_multiclass_digits.dat'):
try:
from modshogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
import matplotlib.pyplot as pyplot
except ImportError:
print 'Error importing modshogun or other required modules. Please, verify their installation.'
return
features = RealFeatures(load_compressed_features(fname_features).T)
labels = MulticlassLabels(CSVFile(fname_labels))
# print 'number of examples = %d' % features.get_num_vectors()
# print 'number of features = %d' % features.get_num_features()
assert (features.get_num_vectors() == labels.get_num_labels())
# train LMNN
lmnn = LMNN(features, labels, k)
lmnn.set_correction(100)
# lmnn.io.set_loglevel(MSG_DEBUG)
print 'Training LMNN, this will take about two minutes...'
lmnn.train()
print 'Training done!'
# plot objective obtained during training
statistics = lmnn.get_statistics()
pyplot.plot(statistics.obj.get())
pyplot.grid(True)
pyplot.xlabel('Iterations')
pyplot.ylabel('LMNN objective')
pyplot.title(
'LMNN objective during training for the multiclass digits data set')
pyplot.show()
