def constructSimilartyMatrixLMNN(self,ks):
print 'now doing LMNN for k= ',ks
self.y_train=self.y_train.reshape(-1,)
lmnn=LMNN(k=ks, learn_rate=1e-7,max_iter=3000)
lmnn.fit(self.trainVectorsPCA, self.y_train, verbose=False)
self.L_lmnn = lmnn.transformer()
name='lmnn/LMNN transformer matrix with dataset shape '+str(self.trainVectorsPCA.shape)
np.save(name,self.L_lmnn)
print 'L.shape is ',self.L_lmnn.shape,'\n\n'
# Input data transformed to the metric space by X*L.T
self.transformedTrainLMNN=copy(lmnn.transform(self.trainVectorsPCA))
self.transformedTestLMNN=copy(lmnn.transform(self.testVectorsPCA))
self.transformedAllLMNN=copy(lmnn.transform(self.allDataPCA)) #we compute the pairwise distance on this now
projectedDigits = TSNE(random_state=randomState).fit_transform(self.transformedAllLMNN)
plt.scatter(projectedDigits[:,0],projectedDigits[:,1],c=self.labels)
plt.title('LMNN Transformed ALL set projected to 2 Dimensions by TSNE with k='+str(ks))
plt.savefig(pp,format='pdf')
self.pwdis=copy(pairwise_distances(self.transformedAllLMNN,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[ks:]]=0
self.D[i,i]=sum(self.pwdis[i])
print 'accuracy for LMNN for k= ',ks,'\n'
self.labelPropogation()
def constructSimilartyMatrixLMNN(self, ks):
print('now doing LMNN for k= ', ks)
self.y_train = self.y_train.reshape(-1, )
lmnn = LMNN(k=ks, learn_rate=1e-7, max_iter=1000)
lmnn.fit(self.trainVectorsPCA, self.y_train)
self.L_lmnn = lmnn.transformer()
name = 'lmnn/LMNN transformer matrix with dataset shape ' + str(
self.trainVectorsPCA.shape)
np.save(name, self.L_lmnn)
print('L.shape is ', self.L_lmnn.shape, '\n\n')
# Input data transformed to the metric space by X*L.T
self.transformedTrainLMNN = copy(lmnn.transform(self.trainVectorsPCA))
self.transformedTestLMNN = copy(lmnn.transform(self.testVectorsPCA))
self.transformedAllLMNN = copy(lmnn.transform(
self.allDataPCA)) #we compute the pairwise distance on this now
projectedDigits = TSNE(random_state=randomState).fit_transform(
self.transformedAllLMNN)
self.pwdis = copy(
pairwise_distances(self.transformedAllLMNN, 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[ks:]] = 0
self.D[i, i] = sum(self.pwdis[i])
print('accuracy for LMNN for k= ', ks, '\n')
self.labelPropogation()
def process_lmnn(self, **option):
'''Metric Learning algorithm: LMNN'''
GeneExp = self.GeneExp_train
Label = self.Label_train
lmnn = LMNN(**option)
lmnn.fit(GeneExp, Label)
self.Trans['LMNN'] = lmnn.transformer()
vectors = vectorizer.transform(data)
print 'vectorizer is ' ,vectors[0].todense()
itml=ITML()
arr2=copy(vectors.todense())
arr=np.zeros((vectors.shape[0],vectors.shape[1]))
for i in range(0,vectors.shape[0]):
for j in range(0,vectors.shape[1]):
arr[i,j]=arr2[i,j]
print 'arr .shape is ',arr.shape
target=newsgroups_train.target
lab=[]
for i in target:
lab.append(i)
lab=np.asarray(lab)
print 'lab is ',(lab)
print 'target is ',type(arr)
#C=itml.prepare_constraints(target,vectors.shape[0],200)
#itml.fit(arr,C,verbose=False)
lmnn = LMNN(k=20, learn_rate=1e-3,use_pca=True)
lmnn.fit(arr,target,verbose=False)
print 'Now doing LMNN'
l=lmnn.transformer()
np.save('LMNN transformer',l)
def test_lmnn(self): lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False) lmnn.fit(self.X, self.y) L = lmnn.transformer() assert_array_almost_equal(L.T.dot(L), lmnn.metric())
def test_lmnn(self): lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False) lmnn.fit(self.X, self.y) L = lmnn.transformer() assert_array_almost_equal(L.T.dot(L), lmnn.metric())
