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 shogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
import matplotlib.pyplot as pyplot
except ImportError:
print 'Error importing shogun 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 transfer_multitask_logistic_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat):
from shogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
from shogun import MultitaskLogisticRegression
except ImportError:
print("MultitaskLogisticRegression not available")
exit()
features = RealFeatures(hstack((traindat, traindat)))
labels = BinaryLabels(hstack((label_train, label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0, n_vectors // 2)
task_two = Task(n_vectors // 2, n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlr = MultitaskLogisticRegression(0.1, features, labels, task_group)
mtlr.set_regularization(1) # use regularization ratio
mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlr.train()
mtlr.set_current_task(0)
out = mtlr.apply().get_labels()
return out
def transfer_multitask_leastsquares_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from shogun import RegressionLabels, RealFeatures, Task, TaskGroup
try:
from shogun import MultitaskLeastSquaresRegression
except ImportError:
print("MultitaskLeastSquaresRegression not available")
exit(0)
features = RealFeatures(traindat)
labels = RegressionLabels(label_train)
n_vectors = features.get_num_vectors()
task_one = Task(0,n_vectors//2)
task_two = Task(n_vectors//2,n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlsr = MultitaskLeastSquaresRegression(0.1,features,labels,task_group)
mtlsr.set_regularization(1) # use regularization ratio
mtlsr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlsr.train()
mtlsr.set_current_task(0)
out = mtlsr.apply_regression().get_labels()
return out
def transfer_multitask_clustered_logistic_regression(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat
):
from shogun import BinaryLabels, RealFeatures, Task, TaskGroup, MSG_DEBUG
try:
from shogun import MultitaskClusteredLogisticRegression
except ImportError:
print("MultitaskClusteredLogisticRegression not available")
exit()
features = RealFeatures(hstack((traindat, sin(traindat), cos(traindat))))
labels = BinaryLabels(hstack((label_train, label_train, label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0, n_vectors // 3)
task_two = Task(n_vectors // 3, 2 * n_vectors // 3)
task_three = Task(2 * n_vectors // 3, n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
task_group.append_task(task_three)
mtlr = MultitaskClusteredLogisticRegression(1.0, 100.0, features, labels,
task_group, 2)
#mtlr.io.set_loglevel(MSG_DEBUG)
mtlr.set_tolerance(1e-3) # use 1e-2 tolerance
mtlr.set_max_iter(100)
mtlr.train()
mtlr.set_current_task(0)
#print mtlr.get_w()
out = mtlr.apply_regression().get_labels()
return out
def transfer_multitask_trace_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from shogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
from shogun import MultitaskTraceLogisticRegression
except ImportError:
print("MultitaskTraceLogisticRegression not available")
exit(0)
features = RealFeatures(hstack((traindat,traindat)))
labels = BinaryLabels(hstack((label_train,label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0,n_vectors//2)
task_two = Task(n_vectors//2,n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
mtlr = MultitaskTraceLogisticRegression(0.1,features,labels,task_group)
mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
mtlr.set_max_iter(10)
mtlr.train()
mtlr.set_current_task(0)
out = mtlr.apply_regression().get_labels()
return out
def transfer_multitask_clustered_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
from shogun import BinaryLabels, RealFeatures, Task, TaskGroup, MSG_DEBUG
try:
from shogun import MultitaskClusteredLogisticRegression
except ImportError:
print("MultitaskClusteredLogisticRegression not available")
exit()
features = RealFeatures(hstack((traindat,sin(traindat),cos(traindat))))
labels = BinaryLabels(hstack((label_train,label_train,label_train)))
n_vectors = features.get_num_vectors()
task_one = Task(0,n_vectors//3)
task_two = Task(n_vectors//3,2*n_vectors//3)
task_three = Task(2*n_vectors//3,n_vectors)
task_group = TaskGroup()
task_group.append_task(task_one)
task_group.append_task(task_two)
task_group.append_task(task_three)
mtlr = MultitaskClusteredLogisticRegression(1.0,100.0,features,labels,task_group,2)
#mtlr.io.set_loglevel(MSG_DEBUG)
mtlr.set_tolerance(1e-3) # use 1e-2 tolerance
mtlr.set_max_iter(100)
mtlr.train()
mtlr.set_current_task(0)
#print mtlr.get_w()
out = mtlr.apply_regression().get_labels()
return out
def multiclass_c45classifiertree(train=traindat,
test=testdat,
labels=label_traindat,
ft=feattypes):
try:
from shogun import RealFeatures, MulticlassLabels, CSVFile, C45ClassifierTree
from numpy import random, int32
except ImportError:
print("Could not import Shogun and/or numpy modules")
return
# wrap features and labels into Shogun objects
feats_train = RealFeatures(CSVFile(train))
feats_test = RealFeatures(CSVFile(test))
train_labels = MulticlassLabels(CSVFile(labels))
# divide train dataset into training and validation subsets in the ratio 2/3 to 1/3
subset = int32(random.permutation(feats_train.get_num_vectors()))
vsubset = subset[1:int(subset.size / 3)]
trsubset = subset[1 + int(subset.size / 3):subset.size]
# C4.5 Tree formation using training subset
train_labels.add_subset(trsubset)
feats_train.add_subset(trsubset)
c = C45ClassifierTree()
c.set_labels(train_labels)
c.set_feature_types(ft)
c.train(feats_train)
train_labels.remove_subset()
feats_train.remove_subset()
# prune tree using validation subset
train_labels.add_subset(vsubset)
feats_train.add_subset(vsubset)
c.prune_tree(feats_train, train_labels)
train_labels.remove_subset()
feats_train.remove_subset()
# Classify test data
output = c.apply_multiclass(feats_test).get_labels()
output_certainty = c.get_certainty_vector()
return c, output, output_certainty
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 shogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
import matplotlib.pyplot as pyplot
except ImportError:
print 'Error importing shogun 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 train(self, images, labels):
"""
Train eigenfaces
"""
print "Train...",
#copy labels
self._labels = labels
#transform the numpe vector to shogun structure
features = RealFeatures(images)
#PCA
self.pca = PCA()
#set dimension
self.pca.set_target_dim(self._num_components)
#compute PCA
self.pca.init(features)
for sampleIdx in range(features.get_num_vectors()):
v = features.get_feature_vector(sampleIdx)
p = self.pca.apply_to_feature_vector(v)
self._projections.insert(sampleIdx, p)
print "ok!"
def hsic_graphical():
# parameters, change to get different results
m=250
difference=3
# setting the angle lower makes a harder test
angle=pi/30
# number of samples taken from null and alternative distribution
num_null_samples=500
# use data generator class to produce example data
data=DataGenerator.generate_sym_mix_gauss(m,difference,angle)
# create shogun feature representation
features_x=RealFeatures(array([data[0]]))
features_y=RealFeatures(array([data[1]]))
# compute median data distance in order to use for Gaussian kernel width
# 0.5*median_distance normally (factor two in Gaussian kernel)
# However, shoguns kernel width is different to usual parametrization
# Therefore 0.5*2*median_distance^2
# Use a subset of data for that, only 200 elements. Median is stable
subset=int32(array([x for x in range(features_x.get_num_vectors())])) # numpy
subset=random.permutation(subset) # numpy permutation
subset=subset[0:200]
features_x.add_subset(subset)
dist=EuclideanDistance(features_x, features_x)
distances=dist.get_distance_matrix()
features_x.remove_subset()
median_distance=np.median(distances)
sigma_x=median_distance**2
features_y.add_subset(subset)
dist=EuclideanDistance(features_y, features_y)
distances=dist.get_distance_matrix()
features_y.remove_subset()
median_distance=np.median(distances)
sigma_y=median_distance**2
print "median distance for Gaussian kernel on x:", sigma_x
print "median distance for Gaussian kernel on y:", sigma_y
kernel_x=GaussianKernel(10,sigma_x)
kernel_y=GaussianKernel(10,sigma_y)
# create hsic instance. Note that this is a convienience constructor which copies
# feature data. features_x and features_y are not these used in hsic.
# This is only for user-friendlyness. Usually, its ok to do this.
# Below, the alternative distribution is sampled, which means
# that new feature objects have to be created in each iteration (slow)
# However, normally, the alternative distribution is not sampled
hsic=HSIC(kernel_x,kernel_y,features_x,features_y)
# sample alternative distribution
alt_samples=zeros(num_null_samples)
for i in range(len(alt_samples)):
data=DataGenerator.generate_sym_mix_gauss(m,difference,angle)
features_x.set_feature_matrix(array([data[0]]))
features_y.set_feature_matrix(array([data[1]]))
# re-create hsic instance everytime since feature objects are copied due to
# useage of convienience constructor
hsic=HSIC(kernel_x,kernel_y,features_x,features_y)
alt_samples[i]=hsic.compute_statistic()
# sample from null distribution
# permutation, biased statistic
hsic.set_null_approximation_method(PERMUTATION)
hsic.set_num_null_samples(num_null_samples)
null_samples_boot=hsic.sample_null()
# fit gamma distribution, biased statistic
hsic.set_null_approximation_method(HSIC_GAMMA)
gamma_params=hsic.fit_null_gamma()
# sample gamma with parameters
null_samples_gamma=array([gamma(gamma_params[0], gamma_params[1]) for _ in range(num_null_samples)])
# plot
figure()
# plot data x and y
subplot(2,2,1)
gca().xaxis.set_major_locator( MaxNLocator(nbins = 4) ) # reduce number of x-ticks
gca().yaxis.set_major_locator( MaxNLocator(nbins = 4) ) # reduce number of x-ticks
grid(True)
plot(data[0], data[1], 'o')
title('Data, rotation=$\pi$/'+str(1/angle*pi)+'\nm='+str(m))
xlabel('$x$')
ylabel('$y$')
# compute threshold for test level
alpha=0.05
null_samples_boot.sort()
null_samples_gamma.sort()
thresh_boot=null_samples_boot[floor(len(null_samples_boot)*(1-alpha))];
thresh_gamma=null_samples_gamma[floor(len(null_samples_gamma)*(1-alpha))];
type_one_error_boot=sum(null_samples_boot<thresh_boot)/float(num_null_samples)
type_one_error_gamma=sum(null_samples_gamma<thresh_boot)/float(num_null_samples)
# plot alternative distribution with threshold
subplot(2,2,2)
gca().xaxis.set_major_locator( MaxNLocator(nbins = 3) ) # reduce number of x-ticks
gca().yaxis.set_major_locator( MaxNLocator(nbins = 3) ) # reduce number of x-ticks
grid(True)
hist(alt_samples, 20, normed=True);
axvline(thresh_boot, 0, 1, linewidth=2, color='red')
type_two_error=sum(alt_samples<thresh_boot)/float(num_null_samples)
title('Alternative Dist.\n' + 'Type II error is ' + str(type_two_error))
# compute range for all null distribution histograms
hist_range=[min([min(null_samples_boot), min(null_samples_gamma)]), max([max(null_samples_boot), max(null_samples_gamma)])]
# plot null distribution with threshold
subplot(2,2,3)
gca().xaxis.set_major_locator( MaxNLocator(nbins = 3) ) # reduce number of x-ticks
gca().yaxis.set_major_locator( MaxNLocator(nbins = 3) ) # reduce number of x-ticks
grid(True)
hist(null_samples_boot, 20, range=hist_range, normed=True);
axvline(thresh_boot, 0, 1, linewidth=2, color='red')
title('Sampled Null Dist.\n' + 'Type I error is ' + str(type_one_error_boot))
# plot null distribution gamma
subplot(2,2,4)
gca().xaxis.set_major_locator( MaxNLocator(nbins = 3) ) # reduce number of x-ticks
gca().yaxis.set_major_locator( MaxNLocator(nbins = 3) ) # reduce number of x-ticks
grid(True)
hist(null_samples_gamma, 20, range=hist_range, normed=True);
axvline(thresh_gamma, 0, 1, linewidth=2, color='red')
title('Null Dist. Gamma\nType I error is ' + str(type_one_error_gamma))
grid(True)
# pull plots a bit apart
subplots_adjust(hspace=0.5)
subplots_adjust(wspace=0.5)
