def RunAllKnnShogun(q):
totalTimer = Timer()
# Load input dataset.
# If the dataset contains two files then the second file is the query
# file.
try:
Log.Info("Loading dataset", self.verbose)
if len(self.dataset) == 2:
referenceData = np.genfromtxt(self.dataset[0],
delimiter=',')
queryData = np.genfromtxt(self.dataset[1], delimiter=',')
queryFeat = RealFeatures(queryFeat.T)
else:
referenceData = np.genfromtxt(self.dataset, delimiter=',')
# Labels are the last row of the dataset.
labels = MulticlassLabels(
referenceData[:, (referenceData.shape[1] - 1)])
referenceData = referenceData[:, :-1]
with totalTimer:
# Get all the parameters.
k = re.search("-k (\d+)", options)
if not k:
Log.Fatal(
"Required option: Number of furthest neighbors to find."
)
q.put(-1)
return -1
else:
k = int(k.group(1))
if (k < 1 or k > referenceData.shape[0]):
Log.Fatal("Invalid k: " + k.group(1) +
"; must be greater than 0" +
" and less or equal than " +
str(referenceData.shape[0]))
q.put(-1)
return -1
referenceFeat = RealFeatures(referenceData.T)
distance = EuclideanDistance(referenceFeat, referenceFeat)
# Perform All K-Nearest-Neighbors.
model = SKNN(k, distance, labels)
model.train()
if len(self.dataset) == 2:
out = model.apply(queryFeat).get_labels()
else:
out = model.apply(referenceFeat).get_labels()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def knn(train_features, train_labels, test_features, test_labels, k=1): from modshogun import KNN, MulticlassAccuracy, EuclideanDistance distance = EuclideanDistance(train_features, train_features) knn = KNN(k, distance, train_labels) knn.train() train_output = knn.apply() test_output = knn.apply(test_features) evaluator = MulticlassAccuracy() print 'KNN training error is %.4f' % ((1-evaluator.evaluate(train_output, train_labels))*100) print 'KNN test error is %.4f' % ((1-evaluator.evaluate(test_output, test_labels))*100)
def RunAllKnnShogun(q):
totalTimer = Timer()
# Load input dataset.
# If the dataset contains two files then the second file is the query
# file.
try:
Log.Info("Loading dataset", self.verbose)
if len(self.dataset) == 2:
referenceData = np.genfromtxt(self.dataset[0], delimiter=',')
queryData = np.genfromtxt(self.dataset[1], delimiter=',')
queryFeat = RealFeatures(queryFeat.T)
else:
referenceData = np.genfromtxt(self.dataset, delimiter=',')
# Labels are the last row of the dataset.
labels = MulticlassLabels(referenceData[:, (referenceData.shape[1] - 1)])
referenceData = referenceData[:,:-1]
with totalTimer:
# Get all the parameters.
k = re.search("-k (\d+)", options)
if not k:
Log.Fatal("Required option: Number of furthest neighbors to find.")
q.put(-1)
return -1
else:
k = int(k.group(1))
if (k < 1 or k > referenceData.shape[0]):
Log.Fatal("Invalid k: " + k.group(1) + "; must be greater than 0"
+ " and less or equal than " + str(referenceData.shape[0]))
q.put(-1)
return -1
referenceFeat = RealFeatures(referenceData.T)
distance = EuclideanDistance(referenceFeat, referenceFeat)
# Perform All K-Nearest-Neighbors.
model = SKNN(k, distance, labels)
model.train()
if len(self.dataset) == 2:
out = model.apply(queryFeat).get_labels()
else:
out = model.apply(referenceFeat).get_labels()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def knn(train_features, train_labels, test_features, test_labels, k=1):
from modshogun import KNN, MulticlassAccuracy, EuclideanDistance
distance = EuclideanDistance(train_features, train_features)
knn = KNN(k, distance, train_labels)
knn.train()
train_output = knn.apply()
test_output = knn.apply(test_features)
evaluator = MulticlassAccuracy()
print 'KNN training error is %.4f' % (
(1 - evaluator.evaluate(train_output, train_labels)) * 100)
print 'KNN 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_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 assign_labels(data, centroids, ncenters): from modshogun import EuclideanDistance from modshogun import RealFeatures, MulticlassLabels from modshogun import KNN from numpy import arange labels = MulticlassLabels(arange(0.,ncenters)) fea = RealFeatures(data) fea_centroids = RealFeatures(centroids) distance = EuclideanDistance(fea_centroids, fea_centroids) knn = KNN(1, distance, labels) knn.train() return knn.apply(fea)
def assign_labels(data, centroids, ncenters):
from modshogun import EuclideanDistance
from modshogun import RealFeatures, MulticlassLabels
from modshogun import KNN
from numpy import arange
labels = MulticlassLabels(arange(0., ncenters))
fea = RealFeatures(data)
fea_centroids = RealFeatures(centroids)
distance = EuclideanDistance(fea_centroids, fea_centroids)
knn = KNN(1, distance, labels)
knn.train()
return knn.apply(fea)
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 knn_classify(traindat, testdat, k=3): from modshogun import KNN, MulticlassAccuracy, EuclideanDistance train_features, train_labels = traindat.features, traindat.labels distance = EuclideanDistance(train_features, train_features) 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 knn_classify(traindat, testdat, k=3):
from modshogun import KNN, MulticlassAccuracy, EuclideanDistance
train_features, train_labels = traindat.features, traindat.labels
distance = EuclideanDistance(train_features, train_features)
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 classifier_knn_modular(train_fname=traindat,
test_fname=testdat,
label_train_fname=label_traindat,
k=3):
from modshogun import RealFeatures, MulticlassLabels, KNN, EuclideanDistance, CSVFile
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
distance = EuclideanDistance(feats_train, feats_train)
labels = MulticlassLabels(CSVFile(label_train_fname))
knn = KNN(k, distance, labels)
knn_train = knn.train()
output = knn.apply(feats_test).get_labels()
multiple_k = knn.classify_for_multiple_k()
return knn, knn_train, output, multiple_k
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
from modshogun import EuclideanDistance, KNN, MulticlassLabels, CSVFile, RealFeatures
#![begin]
#![load_data]
trainf = CSVFile("../data/fm_train_real.dat")
feats_train = RealFeatures(trainf)
testf = CSVFile("../data/fm_test_real.dat")
feats_test = RealFeatures(testf)
train_labels = CSVFile("../data/label_train_multiclass.dat")
labels = MulticlassLabels(train_labels)
#![load_data]
#![choose_distance]
distance = EuclideanDistance(feats_train, feats_test)
#![choose_distance]
#![create_instance]
knn = KNN(3, distance, labels)
#![create_instance]
#![train_and_apply]
knn.train()
test_labels = knn.apply(feats_test)
output = test_labels.get_values()
print output
#![train_and_apply]
#![end]
