def extendAS(self,ext_vocab=[]):
ext_vocab=[x.lower() for x in ext_vocab]
#this is just a restart so labels are still valid
labels={key: value for key, value in enumerate(self.activeSearch.labels.tolist()) if value > -1}
self.extendedVocabulary.update(set(ext_vocab))
#attach only
ngram_range=(500,0)
if len(ext_vocab)==0:
return
for x in ext_vocab:
l=len(x.split())
ngram_range=(min((l,ngram_range[0])),max((l,ngram_range[1])))
tempvectorizer=CountVectorizer(analyzer='word',vocabulary=ext_vocab,binary=True,ngram_range=ngram_range,decode_error=u'ignore')
addX=tempvectorizer.fit_transform(self.text)
#scale by mean distance and some factor
#some_factor=2
#addX.multiply(self.scalefactor*float(some_factor))
#add column
self.Xsparse = sparse.hstack((self.Xsparse,addX))
if self.dimred:
print self.Xsparse.shape
svd=TruncatedSVD(n_components=self.n_components)
X=svd.fit_transform(self.Xsparse)
print("dimensionalty reduction leads to explained variance ratio sum of "+str(svd.explained_variance_ratio_.sum()))
self.sparse=False
else:
X=self.Xsparse
params=asI.Parameters(pi=self.prevalence,verbose=False,sparse=self.sparse,eta=self.eta)
self.activeSearch = asI.kernelAS(params=params) ##fast
self.activeSearch.initialize(X.transpose(),init_labels = labels)
def test_nan ():
import cPickle as pickle
import os, os.path as osp
with open(osp.join(os.getenv('HOME'), 'Research/Data/ActiveSearch/ben/forumthreadsSparseMatrix.pkl'),'r') as fl:
X = pickle.load(fl)
X = X.T
# import IPython
# IPython.embed()
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
print X.shape
r,n = X.shape
nt = int(0.05*n)
num_eval = 50
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
nr.shuffle(Y)
pi = sum(Y)/len(Y)
init_pt = 537
# import IPython
# IPython.embed()
# A = np.array((X.T.dot(X)).todense())
t1 = time.time()
verbose = True
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
init_lbls = {init_pt:1}
kAS.firstMessage(init_pt)
# fs2 = [kAS.f]
import IPython
IPython.embed()
for i in range(num_eval):
idx1 = kAS.getNextMessage()
kAS.setLabelCurrent(Y[idx1])
init_lbls[idx1] = Y[idx1]
import IPython
IPython.embed()
def test_CC ():
nac = np.allclose
n = 1000
r = 100
nt = 200
rcross = 0
X,Y = createFakeData3(n, r, nt, rcross)
num_eval = 50
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = X.T.dot(X)
t1 = time.time()
verbose = True
prms = ASI.Parameters(pi=pi,sparse=False, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
sAS = ASI.shariAS(prms)
sAS.initialize(A)
#sAS2 = ASI.naiveShariAS(prms)
kAS.firstMessage(init_pt)
sAS.firstMessage(init_pt)
# fs2 = [kAS.f]
for i in range(num_eval):
idx1 = kAS.getNextMessage()
kAS.setLabelCurrent(Y[idx1])
# init_lbls[idx1] = Y[idx1]
idx2 = sAS.getNextMessage()
sAS.setLabelCurrent(Y[idx2])
print('NEXT')
print idx1==idx2
print nac(kAS.f, sAS.f)
# fs2.append(kAS.f)
# fs3.append(sAS.f)
import IPython
IPython.embed()
def test_interface ():
verbose = False
#ts_data = ef.load_timestamps (tsfile)
Xfull = load_sparse_csr('Xfull1.npz')
r,n = Xfull.shape
nt = int(0.05*n)
num_eval = 1000
# num_eval = nt*2
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
pi = sum(Y)/len(Y)
init_pt = 100
t1 = time.time()
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(Xfull)
kAS.firstMessage(init_pt)
fs2 = [kAS.f]
for i in range(num_eval):
idx = kAS.getNextMessage()
kAS.setLabelCurrent(Y[idx])
fs2.append(kAS.f)
t2 = time.time()
f1,h1,s1,fs1,dtinv1 = AS.kernel_AS (Xfull, Y, pi=pi, num_eval=num_eval, init_pt=init_pt, verbose=verbose,all_fs=True,tinv=True,sparse=True)
t3 = time.time()
checks = [np.allclose(fs1[i],fs2[i]) for i in range(len(fs1))]
import IPython
IPython.embed()
dataConn = mysql_conn.flatfileDataConnect()
message_count = dataConn.connect(args.JSON_path)
else:
dataConn = mysql_conn.mysqlDataConnect()
message_count = dataConn.connect(args.database, args.database_hostname, args.database_user, args.database_pass)
activeSearch = None
# when firstMessage is called we reinitialize the kernel algorithm. However calling
# initialize again requires us to invert C so we could be smarter and save that
# For now the invert time is a couple of seconds so we can do that as future work
restart_save = None
first_run = True
if (args.method == "kernel"):
print "Using kernelAS"
activeSearch = asI.kernelAS()
wMat = dataConn.getFinalFeatureMatrix(args.wordlimit,args.skip_stemmer, args.num_cpus, message_count, args.out_to_database, args.in_from_database, 0,0)
restart_save = wMat.copy()
activeSearch.initialize(wMat)
elif (args.method == "shari"):
print "Using shariAS"
activeSearch = asI.shariAS()
A = dataConn.getAffinityMatrix(args.wordlimit,args.skip_stemmer,args.num_cpus, message_count, args.out_to_database, args.in_from_database, 0,0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
elif (args.method == "naiveshari"):
print "Using naieveShariAS"
activeSearch = asI.naiveShariAS()
A = dataConn.getAffinityMatrix(args.wordlimit,args.skip_stemmer,args.num_cpus, message_count, args.out_to_database, args.in_from_database, 0,0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
def test_warm_start ():
verbose = True
nac = np.allclose
#ts_data = ef.load_timestamps (tsfile)
Xfull = load_sparse_csr('Xfull1.npz')
# print Xfull.shape
# Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
# Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# # r,n = Xfull.shape
# print Xfull.shape
# Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
# Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# getting rid of features which are zero for all these elements
n = 300
r = 600
X = Xfull[:,:n]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
X = X[:r,:]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
print X.shape
#X = np.load('X11.npy')
r,n = X.shape
nt = int(0.05*n)
num_eval = 50
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
nr.shuffle(Y)
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = np.array((X.T.dot(X)).todense())
t1 = time.time()
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
kAS2 = ASI.kernelAS(prms)
sAS = ASI.shariAS(prms)
sAS2 = ASI.naiveShariAS(prms)
# import IPython
# IPython.embed()
init_lbls = {init_pt:1}
kAS.firstMessage(init_pt)
fs2 = [kAS.f]
for i in range(num_eval):
idx1 = kAS.getNextMessage()
kAS.setLabelCurrent(Y[idx1])
init_lbls[idx1] = Y[idx1]
# sAS.setLabelCurrent(Y[idx2])
# fs2.append(kAS.f)
# fs3.append(sAS.f)
print("Batch initializing:")
print("Kernel AS:")
kAS2.initialize(X, init_lbls)
print("Shari AS:")
sAS.initialize(A, init_lbls)
print("Naive Shari AS:")
sAS2.initialize(A, init_lbls)
import IPython
IPython.embed()
def test_interface3 ():
verbose = True
nac = np.allclose
#ts_data = ef.load_timestamps (tsfile)
Xfull = load_sparse_csr('Xfull1.npz')
print Xfull.shape
Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# r,n = Xfull.shape
print Xfull.shape
Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# getting rid of features which are zero for all these elements
# n = 300
# r = 600
X = Xfull#[:,:n]
# X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
# X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
# X = X[:r,:]
# X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
# X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
# print X.shape
# #X = np.load('X11.npy')
r,n = X.shape
nt = int(0.05*n)
num_eval = 50
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = np.array((X.T.dot(X)).todense())
t1 = time.time()
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
sAS = ASI.shariAS(prms)
sAS.initialize(A)
# ofk = kAS.f
# ofs = sAS.f
# import IPython
# IPython.embed()
kAS.firstMessage(init_pt)
fs2 = [kAS.f]
sAS.firstMessage(init_pt)
fs3 = [sAS.f]
# #
# lbl = 1
# idx = 5
# B = np.ones(n)/(1+prms.w0)
# D = A.sum(axis=1)
# BDinv = np.diag(np.squeeze(B*1./D))
# IA = np.eye(n) - BDinv.dot(A)
# IAi = np.matrix(nlg.inv(IA))
# IAk = nlg.inv(np.eye(n) + kAS.BDinv.dot(X.T.dot(nlg.inv(np.eye(r) - X.dot(kAS.BDinv.dot(X.T))))).dot(X.todense()))
# IAki = nlg.inv(IAk)
# t = (1+prms.w0)*(1-prms.eta)
# e = np.zeros((n,1))
# e[idx] = 1
# IA2 = IA + (1-t)*e.dot(e.T).dot(BDinv.dot(A))
# ai = (1./D)[idx]/(1+ prms.w0)*A[idx,:]
# Ad = (1-t)*IAi[:,idx].dot(ai.dot(IAi))/(1 + (1-t)*ai.dot(IAi[:,idx]))
# IA2i = IAi - Ad
#
# import IPython
# IPython.embed()
for i in range(num_eval):
idx1 = kAS.getNextMessage()
idx2 = sAS.getNextMessage()
print('NEXT')
print idx1==idx2
print nac(kAS.f, sAS.f)
# import IPython
# IPython.embed()
kAS.setLabelCurrent(Y[idx1])
sAS.setLabelCurrent(Y[idx2])
fs2.append(kAS.f)
fs3.append(sAS.f)
t2 = time.time()
# f1,h1,s1,fs1,dtinv1 = AS.kernel_AS (Xfull, Y, pi=pi, num_eval=num_eval, init_pt=init_pt, verbose=verbose,all_fs=True,tinv=True,sparse=True)
t3 = time.time()
# checks = [np.allclose(fs1[i],fs2[i]) for i in range(len(fs1))]
import IPython
IPython.embed()
def test_interface2 ():
verbose = True
nac = np.allclose
#ts_data = ef.load_timestamps (tsfile)
Xfull = load_sparse_csr('Xfull1.npz')
print Xfull.shape
# Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
# Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# r,n = Xfull.shape
# print Xfull.shape
# Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
# Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# getting rid of features which are zero for all these elements
n = 300
r = 600
X = Xfull[:,:n]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
X = X[:r,:]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
print X.shape
#X = np.load('X11.npy')
r,n = X.shape
nt = int(0.05*n)
num_eval = 50
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = np.array((X.T.dot(X)).todense())
t1 = time.time()
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
sAS = ASI.naiveShariAS(prms)
sAS.initialize(A)
import IPython
IPython.embed()
kAS.firstMessage(init_pt)
fs2 = [kAS.f]
sAS.firstMessage(init_pt)
fs3 = [sAS.f]
import IPython
IPython.embed()
for i in range(num_eval):
idx1 = kAS.getNextMessage()
idx2 = sAS.getNextMessage()
print('NEXT')
print idx1==idx2
print nac(kAS.f, sAS.f)
# import IPython
# IPython.embed()
kAS.setLabelCurrent(Y[idx1])
sAS.setLabelCurrent(Y[idx2])
fs2.append(kAS.f)
fs3.append(sAS.f)
t2 = time.time()
# f1,h1,s1,fs1,dtinv1 = AS.kernel_AS (Xfull, Y, pi=pi, num_eval=num_eval, init_pt=init_pt, verbose=verbose,all_fs=True,tinv=True,sparse=True)
t3 = time.time()
# checks = [np.allclose(fs1[i],fs2[i]) for i in range(len(fs1))]
import IPython
IPython.embed()
message_count = dataConn.connect(args.JSON_path)
else:
dataConn = mysql_conn.mysqlDataConnect()
message_count = dataConn.connect(args.database, args.database_hostname,
args.database_user, args.database_pass)
activeSearch = None
# when firstMessage is called we reinitialize the kernel algorithm. However calling
# initialize again requires us to invert C so we could be smarter and save that
# For now the invert time is a couple of seconds so we can do that as future work
restart_save = None
first_run = True
if (args.method == "kernel"):
print "Using kernelAS"
activeSearch = asI.kernelAS()
wMat = dataConn.getFinalFeatureMatrix(args.wordlimit, args.skip_stemmer,
args.num_cpus, message_count,
args.out_to_database,
args.in_from_database, 0, 0)
restart_save = wMat.copy()
activeSearch.initialize(wMat)
elif (args.method == "shari"):
print "Using shariAS"
activeSearch = asI.shariAS()
A = dataConn.getAffinityMatrix(args.wordlimit, args.skip_stemmer,
args.num_cpus, message_count,
args.out_to_database, args.in_from_database,
0, 0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
def startAS(self,corpus,labeled_corpus=[],labels=[],starting_points=[]):
"""
corpus --> list of touples (id,text) where id is external id
"""
num_labels = len(labels)
if num_labels != len(labeled_corpus):
raise Exception ("Number of lables and number of previously labeled objects does not match")
if num_labels > 0:
self.prev_corpus.extend(labeled_corpus)
self.prev_labels.extend(labels)
#initialise with previous information
self.start_idx=len(self.prev_labels)
#get map from external id to internal index for the new corpus
self.id_to_idx={}#maps external id (e.g. AdId) to internal index
for i,el in enumerate(corpus):
self.id_to_idx[el[0]]=i+self.start_idx #do not include indices pointing to already labeled objects from previous AS
self.curr_corpus=corpus
self.num_messages=len(corpus)
self.unlabeled_idxs=set(xrange(self.start_idx,self.num_messages))
self.hashlookup={}
if self.dedupe:
#calculate all minhash values
self.hashed=[self.hashing(tup[1].lower()) for i,tup in enumerate(corpus)]#minhash
#for now, save collisions in a dictionary. Replace with locality sensitive hashing later
for i,h in enumerate(self.hashed):
if h in self.hashlookup:
self.hashlookup[h].append(i)
else:
self.hashlookup[h]=[i]
text = [x[1] for x in self.prev_corpus] + [y[1] for y in corpus]
#save text so that restart is possible
self.text=text
#featurize
ngram_range=(500,0)
if len(self.extendedVocabulary)==0:
ngram_range=(1,1)
for x in self.extendedVocabulary:
l=len(x.split())
ngram_range=(min((l,ngram_range[0])),max((l,ngram_range[1])))
if self.vocab == None:
vocabulary = self.getVocabulary(text,extendedVoc=list(self.extendedVocabulary))
else:
vocabulary = self.vocab+list(self.extendedVocabulary)
if self.tfidf:
self.setTfidf(vocab=vocabulary,ngram_range=ngram_range)
else:
self.setCountVectorizer(vocab=vocabulary,ngram_range=ngram_range)
self.Xsparse=self.vectorizer.fit_transform(text)
#add column with ones for empty rows
a = self.Xsparse.dot(np.ones(self.Xsparse.shape[1]))#this works because features are non-negative
anonz=a.nonzero()[0]
if anonz.shape[0] != self.Xsparse.shape[0]:#matrix contains empty rows
b=np.ones(self.Xsparse.shape[0])
b[anonz]=0
self.Xsparse=sparse.hstack((self.Xsparse,sparse.csr_matrix(b).T))
if self.dimred:
print self.Xsparse.shape
svd=TruncatedSVD(n_components=self.n_components)
X=svd.fit_transform(self.Xsparse)
print("dimensionalty reduction leads to explained variance ratio sum of "+str(svd.explained_variance_ratio_.sum()))
self.sparse=False
#b=np.array([len(x) for x in text,ndmin=2).transpose()
#X=np.hstack((X,b))
else:
#b=np.array([len(x) for x in text,ndmin=2).transpose()
#self.Xsparse=sparse.hstack((X,b))
X=self.Xsparse
#get scale
#extimate pairwise distances through random sampling
#pairwise_dists = squareform(pdist(X[np.random.choice(X.shape[0], 1000, replace=False),:], 'euclidean'))
#self.scalefactor = np.mean(pairwise_dists)
params=asI.Parameters(pi=self.prevalence,verbose=False,sparse=self.sparse,eta=self.eta)
self.activeSearch = asI.kernelAS(params=params) ##fast
if len(starting_points)==0:
if len(self.prev_labels)==0:
raise Exception ("No start point and no labels provided")
init_labels = {key: value for key, value in enumerate(self.prev_labels)}
for x in starting_points:
idx=self.id_to_idx[x]
self.unlabeled_idxs.remove(idx)
init_labels[idx]=1
self.activeSearch.initialize(X.transpose(),init_labels = init_labels)