def sandwich_demo():
x, y = sandwich_data()
knn = nearest_neighbors(x, k=2)
ax = pyplot.subplot(3, 1, 1) # take the whole top row
plot_sandwich_data(x, y, ax)
plot_neighborhood_graph(x, knn, y, ax)
ax.set_title('input space')
ax.set_aspect('equal')
ax.set_xticks([])
ax.set_yticks([])
num_constraints = 60
mls = [
(LMNN(), (x, y)),
(ITML(), (x, ITML.prepare_constraints(y, len(x), num_constraints))),
(SDML(), (x, SDML.prepare_constraints(y, len(x), num_constraints))),
(LSML(), (x, LSML.prepare_constraints(y, num_constraints)))
]
for ax_num, (ml,args) in zip(xrange(3,7), mls):
ml.fit(*args)
tx = ml.transform()
ml_knn = nearest_neighbors(tx, k=2)
ax = pyplot.subplot(3,2,ax_num)
plot_sandwich_data(tx, y, ax)
plot_neighborhood_graph(tx, ml_knn, y, ax)
ax.set_title('%s space' % ml.__class__.__name__)
ax.set_xticks([])
ax.set_yticks([])
pyplot.show()
def sandwich_demo():
x, y = sandwich_data()
knn = nearest_neighbors(x, k=2)
ax = pyplot.subplot(3, 1, 1) # take the whole top row
plot_sandwich_data(x, y, ax)
plot_neighborhood_graph(x, knn, y, ax)
ax.set_title('input space')
ax.set_aspect('equal')
ax.set_xticks([])
ax.set_yticks([])
num_constraints = 60
mls = [(LMNN(), (x, y)),
(ITML(), (x, ITML.prepare_constraints(y, len(x), num_constraints))),
(SDML(), (x, SDML.prepare_constraints(y, len(x), num_constraints))),
(LSML(), (x, LSML.prepare_constraints(y, num_constraints)))]
for ax_num, (ml, args) in zip(xrange(3, 7), mls):
ml.fit(*args)
tx = ml.transform()
ml_knn = nearest_neighbors(tx, k=2)
ax = pyplot.subplot(3, 2, ax_num)
plot_sandwich_data(tx, y, ax)
plot_neighborhood_graph(tx, ml_knn, y, ax)
ax.set_title('%s space' % ml.__class__.__name__)
ax.set_xticks([])
ax.set_yticks([])
pyplot.show()
def test_iris(self):
num_constraints = 200
n = self.iris_points.shape[0]
C = ITML.prepare_constraints(self.iris_labels, n, num_constraints)
itml = ITML().fit(self.iris_points, C, verbose=False)
csep = class_separation(itml.transform(), self.iris_labels)
self.assertLess(csep, 0.4) # it's not great
def test_iris(self):
num_constraints = 200
n = self.iris_points.shape[0]
C = ITML.prepare_constraints(self.iris_labels, n, num_constraints)
itml = ITML().fit(self.iris_points, C, verbose=False)
csep = class_separation(itml.transform(), self.iris_labels)
self.assertLess(csep, 0.4) # it's not great
def constructSimilartyMatrixITML(self, k=5):
print 'Now doing itml'
num_constraints = 100
itml = ITML()
C = ITML.prepare_constraints(self.y_train,
self.trainVectorsPCA.shape[0],
num_constraints)
itml.fit(self.trainVectorsPCA, C, verbose=True)
self.L_itml = itml.transformer()
name = 'itml/ITML transformer matrix with dataset shape ' + str(
self.trainVectorsPCA.shape)
print 'L itml shape is ', self.L_itml.shape
np.save(name, self.L_itml)
# Input data transformed to the metric space by X*L.T
self.transformedTrainITML = copy(itml.transform(self.trainVectorsPCA))
self.transformedTestITML = copy(itml.transform(self.testVectorsPCA))
self.transformedAllITML = copy(itml.transform(self.allDataPCA))
# now we can simply calculate the eucledian distances on the above transformed dataset
#Visualizing the dataset by TSNE
projectedDigits = TSNE(random_state=randomState).fit_transform(
self.transformedTrainITML)
plt.scatter(projectedDigits[:, 0],
projectedDigits[:, 1],
c=self.y_train)
plt.title(
'ITML Transformed Train set projected to 2 Dimensions by TSNE with k='
+ str(k) + ' and num_constraints = ' + str(num_constraints))
plt.savefig(pp, format='pdf')
self.pwdis = copy(
pairwise_distances(self.transformedAllITML, metric='euclidean'))
self.D = np.zeros(self.pwdis.shape)
for i in range(0, self.pwdis.shape[0]):
l1 = self.pwdis[i].tolist()
#print 'l1 is ',l1,'\n\n'
allnearestNeighbours = sorted(range(len(l1)), key=lambda i: l1[i])
#now set the all the weights except for k+1 to 0
self.pwdis[i, allnearestNeighbours[k:]] = 0
self.D[i, i] = sum(self.pwdis[i])
print 'accuracy for ITML\n'
self.labelPropogation()
def constructSimilartyMatrixITML(self,k=5):
print 'Now doing itml'
num_constraints=100
itml=ITML()
C = ITML.prepare_constraints(self.y_train, self.trainVectorsPCA.shape[0], num_constraints)
itml.fit(self.trainVectorsPCA, C, verbose=True)
self.L_itml=itml.transformer()
name='itml/ITML transformer matrix with dataset shape '+str(self.trainVectorsPCA.shape)
print 'L itml shape is ',self.L_itml.shape
np.save(name,self.L_itml)
# Input data transformed to the metric space by X*L.T
self.transformedTrainITML=copy(itml.transform(self.trainVectorsPCA))
self.transformedTestITML=copy(itml.transform(self.testVectorsPCA))
self.transformedAllITML=copy(itml.transform(self.allDataPCA))
# now we can simply calculate the eucledian distances on the above transformed dataset
#Visualizing the dataset by TSNE
projectedDigits = TSNE(random_state=randomState).fit_transform(self.transformedTrainITML)
plt.scatter(projectedDigits[:,0],projectedDigits[:,1],c=self.y_train)
plt.title('ITML Transformed Train set projected to 2 Dimensions by TSNE with k='+str(k)+' and num_constraints = '+str(num_constraints))
plt.savefig(pp,format='pdf')
self.pwdis=copy(pairwise_distances(self.transformedAllITML,metric='euclidean'))
self.D=np.zeros(self.pwdis.shape)
for i in range(0,self.pwdis.shape[0]):
l1=self.pwdis[i].tolist()
#print 'l1 is ',l1,'\n\n'
allnearestNeighbours=sorted(range(len(l1)),key=lambda i : l1[i])
#now set the all the weights except for k+1 to 0
self.pwdis[i,allnearestNeighbours[k:]]=0
self.D[i,i]=sum(self.pwdis[i])
print 'accuracy for ITML\n'
self.labelPropogation()
def constructSimilartyMatrixITML(self):
print 'Now doing itml'
counter = 1
ks = [
3, 5, 7, 10, 12, 15, 20, 22, 25, 27, 30, 33, 35, 37, 40, 43, 45,
47, 50, 53, 55, 57, 60, 65
]
constraints = [80, 100, 120, 150, 180, 200]
constraints = [100]
for k in ks:
for num_constraints in constraints:
itml = ITML()
self.y_train = self.y_train.reshape(-1, )
C = ITML.prepare_constraints(self.y_train,
self.trainVectorsPCA.shape[0],
num_constraints)
itml.fit(self.trainVectorsPCA, C, verbose=True)
self.L_itml = copy(itml.transformer())
name = 'itml/ITML transformer matrix with dataset shape ' + str(
self.trainVectorsPCA.shape) + ' and k=' + str(
k) + ' and num_constraints=' + str(num_constraints)
#print 'L itml shape is ',self.L_itml.shape
np.save(name, self.L_itml)
# Input data transformed to the metric space by X*L.T
self.transformedTrainITML = copy(
itml.transform(self.trainVectorsPCA))
self.transformedTestITML = copy(
itml.transform(self.testVectorsPCA))
self.transformedAllITML = copy(itml.transform(self.allDataPCA))
# now we can simply calculate the eucledian distances on the above transformed dataset
#Visualizing the dataset by TSNE
projectedDigits = TSNE(random_state=randomState).fit_transform(
self.transformedAllITML)
print 'projectedDigits is ', projectedDigits.shape
plt.figure()
plt.scatter(projectedDigits[:, 0],
projectedDigits[:, 1],
c=self.labels)
plt.title(
'ITML Transformed ALL set projected to 2 Dimensions by TSNE with'
+ str(k) + ' and num_constraints=' + str(num_constraints))
plt.savefig(pp, format='pdf')
#plt.show()
plt.close()
self.pwdis = copy(
pairwise_distances(self.transformedAllITML,
metric='euclidean'))
#sigmas=[1,2.5,2,2.5,3,3.5,4,4.5,5]
#for sigma in sigmas:
self.D = np.zeros(self.pwdis.shape)
for i in range(0, self.pwdis.shape[0]):
l1 = self.pwdis[i].tolist()
#print 'l1 is ',l1,'\n\n'
allnearestNeighbours = sorted(range(len(l1)),
key=lambda i: l1[i])
#now set the all the weights except for k+1 to 0
#since we exponentiated the distances with the minus sign we need to set the lowest weights to 0, so everything except for the last k go to 0
self.pwdis[i, allnearestNeighbours[k:]] = 0
self.D[i, i] = sum(self.pwdis[i])
print 'accuracy for ITML for k= ', k, ' and num_constraints=' + str(
num_constraints), '\n'
self.labelPropogation()
import numpy as np from metric_learn import ITML from sklearn.datasets import load_iris iris_data = load_iris() X = iris_data['data'] Y = iris_data['target'] print 'Y is ',Y.shape print 'X.shape is ',X.shape itml = ITML() num_constraints = 200 C = ITML.prepare_constraints(Y, X.shape[0], num_constraints) itml.fit(X, C, verbose=False) x2=itml.transform(X) print 'x2 is ',x2 l=itml.transformer() print '\n\n\nafter transforming is ',np.dot(X,l.T)
import numpy as np
from metric_learn import ITML
from sklearn.datasets import load_iris
from scipy.sparse import rand
x = rand(10, 10)
print 'S is ', x.todense()
x = x.todense()
mat1 = np.zeros(x.shape)
for i in range(0, mat1.shape[0]):
mat1[i, i] = 112.0
for j in range(0, mat1.shape[1]):
if i == j:
continue
mat1[i, j] = x[i, j]
print 'mat1 is ', mat1
y = np.ones((10, ))
y[5:] = 0
itml = ITML()
print 'X is ', mat1.shape, ' y is ', y.shape
num_constraints = 5
C = ITML.prepare_constraints(y, mat1.shape[0], num_constraints)
itml.fit(mat1, C, verbose=False)
xl = itml.transform(mat1)
print 'xl is ', xl
def fit(self, X, y):
num_constraints = NUM_CONSTRAINTS
constraints = ITML.prepare_constraints(y, len(X), num_constraints)
return super(ITML_sk, self).fit(X, constraints)
import numpy as np from metric_learn import ITML from sklearn.datasets import load_iris iris_data = load_iris() X = iris_data['data'] Y = iris_data['target'] print 'Y is ', Y.shape print 'X.shape is ', X.shape itml = ITML() num_constraints = 200 C = ITML.prepare_constraints(Y, X.shape[0], num_constraints) itml.fit(X, C, verbose=False) x2 = itml.transform(X) print 'x2 is ', x2 l = itml.transformer() print '\n\n\nafter transforming is ', np.dot(X, l.T)
