def metric_lmnn_modular(train_fname=traindat,
test_fname=testdat,
label_train_fname=label_traindat,
k=3):
try:
from modshogun import RealFeatures, MulticlassLabels, LMNN, KNN, CSVFile
except ImportError:
return
# wrap features and labels into Shogun objects
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
labels = MulticlassLabels(CSVFile(label_train_fname))
# LMNN
lmnn = LMNN(feats_train, labels, k)
lmnn.train()
lmnn_distance = lmnn.get_distance()
# perform classification with KNN
knn = KNN(k, lmnn_distance, labels)
knn.train()
output = knn.apply(feats_test).get_labels()
return lmnn, output
def run_knn(Xtrain,Ytrain,Xtest,Ytest):
prod_features = RealFeatures(Xtrain)
prod_labels = MulticlassLabels(Ytrain)
test_features = RealFeatures(Xtest)
test_labels = MulticlassLabels(Ytest)
if os.path.exists(".lmnn_model30000_5_reg05_cor20"):
print "Using LMNN distance"
lmnn = LMNN()
sf = SerializableAsciiFile(".lmnn_model30000_5_reg05_cor20", 'r')
lmnn.load_serializable(sf)
diagonal = np.diag(lmnn.get_linear_transform())
#print('%d out of %d elements are non-zero.' % (np.sum(diagonal != 0), diagonal.size))
#diagonal = lmnn.get_linear_transform()
np.set_printoptions(precision=1,threshold=1e10,linewidth=500)
#lmnn.set_diagonal(True)
dist = lmnn.get_distance()
else:
dist = EuclideanDistance()
# classifier
knn = KNN(K, dist, prod_labels)
#knn.set_use_covertree(True)
parallel = knn.get_global_parallel()
parallel.set_num_threads(4)
knn.set_global_parallel(parallel)
knn.train(prod_features)
print "Classifying test set..."
pred = knn.apply_multiclass(test_features)
print "Accuracy = %2.2f%%" % (100*np.mean(pred == Ytest))
cm = build_confusion_matrix(Ytest, pred, NCLASSES)
#save_confusion_matrix(cm)
#cm = load_confusion_matrix()
print "Confusion matrix: "
print cm
#plot_confusion_matrix(cm)
#results = predict_class_prob(pred, cm)
#nn = build_neighbours_matrix(knn, prod_labels)
#results = predict_class_from_neighbours(nn)
#print "Log loss: " + str(calculate_log_loss(results, Ytest))
#print_prediction_output(results)
return cm
def lmnn_diagonal(train_features, train_labels, test_features, test_labels, k=1): from modshogun import LMNN, KNN, MSG_DEBUG, MulticlassAccuracy import numpy lmnn = LMNN(train_features, train_labels, k) lmnn.set_diagonal(True) lmnn.train() distance = lmnn.get_distance() knn = KNN(k, distance, train_labels) knn.train() train_output = knn.apply() test_output = knn.apply(test_features) evaluator = MulticlassAccuracy() print 'LMNN-diagonal training error is %.4f' % ((1-evaluator.evaluate(train_output, train_labels))*100) print 'LMNN-diagonal test error is %.4f' % ((1-evaluator.evaluate(test_output, test_labels))*100)
def lmnn(train_features, train_labels, test_features, test_labels, k=1): from modshogun import LMNN, KNN, MSG_DEBUG, MulticlassAccuracy import numpy # dummy = LMNN() # dummy.io.set_loglevel(MSG_DEBUG) lmnn = LMNN(train_features, train_labels, k) lmnn.train() distance = lmnn.get_distance() knn = KNN(k, distance, train_labels) knn.train() train_output = knn.apply() test_output = knn.apply(test_features) evaluator = MulticlassAccuracy() print 'LMNN training error is %.4f' % ((1-evaluator.evaluate(train_output, train_labels))*100) print 'LMNN test error is %.4f' % ((1-evaluator.evaluate(test_output, test_labels))*100)
def lmnn(train_features, train_labels, test_features, test_labels, k=1):
from modshogun import LMNN, KNN, MSG_DEBUG, MulticlassAccuracy
import numpy
# dummy = LMNN()
# dummy.io.set_loglevel(MSG_DEBUG)
lmnn = LMNN(train_features, train_labels, k)
lmnn.train()
distance = lmnn.get_distance()
knn = KNN(k, distance, train_labels)
knn.train()
train_output = knn.apply()
test_output = knn.apply(test_features)
evaluator = MulticlassAccuracy()
print 'LMNN training error is %.4f' % (
(1 - evaluator.evaluate(train_output, train_labels)) * 100)
print 'LMNN test error is %.4f' % (
(1 - evaluator.evaluate(test_output, test_labels)) * 100)
def metric_lmnn_modular(train_fname=traindat,test_fname=testdat,label_train_fname=label_traindat,k=3): try: from modshogun import RealFeatures,MulticlassLabels,LMNN,KNN,CSVFile except ImportError: return # wrap features and labels into Shogun objects feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=MulticlassLabels(CSVFile(label_train_fname)) # LMNN lmnn=LMNN(feats_train,labels,k) lmnn.train() lmnn_distance=lmnn.get_distance() # perform classification with KNN knn=KNN(k,lmnn_distance,labels) knn.train() output=knn.apply(feats_test).get_labels() return lmnn,output
def lmnn_classify(traindat, testdat, k=3):
from modshogun import LMNN, KNN, MulticlassAccuracy, MSG_DEBUG
train_features, train_labels = traindat.features, traindat.labels
lmnn = LMNN(train_features, train_labels, k)
lmnn.set_maxiter(1200)
lmnn.io.set_loglevel(MSG_DEBUG)
lmnn.train()
distance = lmnn.get_distance()
knn = KNN(k, distance, train_labels)
knn.train()
test_features, test_labels = testdat.features, testdat.labels
predicted_labels = knn.apply(test_features)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(predicted_labels, test_labels)
err = 1 - acc
return err
def lmnn_classify(traindat, testdat, k=3): from modshogun import LMNN, KNN, MulticlassAccuracy, MSG_DEBUG train_features, train_labels = traindat.features, traindat.labels lmnn = LMNN(train_features, train_labels, k) lmnn.set_maxiter(1200) lmnn.io.set_loglevel(MSG_DEBUG) lmnn.train() distance = lmnn.get_distance() knn = KNN(k, distance, train_labels) knn.train() test_features, test_labels = testdat.features, testdat.labels predicted_labels = knn.apply(test_features) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(predicted_labels, test_labels) err = 1-acc return err
def lmnn_diagonal(train_features,
train_labels,
test_features,
test_labels,
k=1):
from modshogun import LMNN, KNN, MSG_DEBUG, MulticlassAccuracy
import numpy
lmnn = LMNN(train_features, train_labels, k)
lmnn.set_diagonal(True)
lmnn.train()
distance = lmnn.get_distance()
knn = KNN(k, distance, train_labels)
knn.train()
train_output = knn.apply()
test_output = knn.apply(test_features)
evaluator = MulticlassAccuracy()
print 'LMNN-diagonal training error is %.4f' % (
(1 - evaluator.evaluate(train_output, train_labels)) * 100)
print 'LMNN-diagonal test error is %.4f' % (
(1 - evaluator.evaluate(test_output, test_labels)) * 100)
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()
L = lmnn.get_linear_transform()
knn.set_distance(lmnn.get_distance())
plot_data(x, y, axarr[1])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[1])
axarr[1].set_aspect('equal')
axarr[1].set_xlim(-6, 4)
axarr[1].set_ylim(-3, 2)
xL = numpy.dot(x, L.T) ## to see the data after the linear transformation
features = RealFeatures(xL.T)
distance = EuclideanDistance(features, features)
knn.set_distance(distance)
plot_data(xL, y, axarr[2])
plot_neighborhood_graph(xL, knn.nearest_neighbors(), axarr[2])
axarr[2].set_aspect('equal')
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()
L = lmnn.get_linear_transform()
knn.set_distance(lmnn.get_distance())
plot_data(x, y, axarr[1])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[1])
axarr[1].set_aspect('equal')
axarr[1].set_xlim(-6, 4)
axarr[1].set_ylim(-3, 2)
xL = numpy.dot(x, L.T) ## to see the data after the linear transformation
features = RealFeatures(xL.T)
distance = EuclideanDistance(features, features)
knn.set_distance(distance)
plot_data(xL, y, axarr[2])
plot_neighborhood_graph(xL, knn.nearest_neighbors(), axarr[2])
axarr[2].set_aspect('equal')
prod_features = RealFeatures(Xtrain.T)
prod_labels = MulticlassLabels(Ytrain.T)
test_features = RealFeatures(Xtest.T)
k = 5
# load LMNN
if os.path.exists(".lmnn_model30000_5_reg05_cor20"):
sf = SerializableAsciiFile(".lmnn_model30000_5_reg05_cor20", 'r')
lmnn = LMNN()
lmnn.load_serializable(sf)
diagonal = np.diag(lmnn.get_linear_transform())
print('%d out of %d elements are non-zero.' % (np.sum(diagonal != 0), diagonal.size))
#print diagonal
dist = lmnn.get_distance()
else:
dist = EuclideanDistance()
cm = load_confusion_matrix()
print cm
# classifier
knn = KNN(k, dist, prod_labels)
parallel = knn.get_global_parallel()
parallel.set_num_threads(4)
knn.set_global_parallel(parallel)
knn.train(prod_features)
print "Classifying test set..."
pred = knn.apply_multiclass(test_features)
