def pseudoinverse(Mat, precision):
"""
Pseudoinverse computation.
Objective:
----------
To compute pseudoinverse using Singular Value Depcomposition
Reason:
-------
SVD using Scipy is slow and consumes a lot of memory, similarly
pysparse matrix consumes a lot of memory. This is a better
alternative to a direct computation of inverse.
Process:
--------
The function uses sparsesvd to compute the SVD of a sparse matrix,
there is a precision attached in the function, this controls the
cutting (or the k) of the SVD. Precision is actually a percentage
and uses this to get the k.
k = (Precision/100) * rows of the matrix.
The function takes a sparse matrix and a precision score as the input.
"""
matrix = Mat.tocsc()
if matrix.shape[0] <= matrix.shape[1]:
k = int((precision * matrix.shape[0]) / 100)
ut, s, vt = sparsesvd(matrix.tocsc(), k)
UT = ss.csr_matrix(ut)
SI = ss.csr_matrix(np.diag(1 / s))
VT = ss.csr_matrix(vt)
temp_matrix = spmatrixmul(VT.transpose(), SI)
pinv_matrix = spmatrixmul(temp_matrix, UT)
del ut, s, vt, UT, SI, VT, temp_matrix
else:
k = int((precision * matrix.transpose().shape[0]) / 100)
ut, s, vt = sparsesvd(matrix.transpose().tocsc(), k)
UT = ss.csr_matrix(ut)
SI = ss.csr_matrix(np.diag(1 / s))
VT = ss.csr_matrix(vt)
temp_matrix = spmatrixmul(UT.transpose(), SI)
pinv_matrix = spmatrixmul(temp_matrix, VT)
del ut, s, vt, UT, SI, VT, temp_matrix
return pinv_matrix.tocsr()
def pseudoinverse(Mat, precision):
"""
Pseudoinverse computation.
Objective:
----------
To compute pseudoinverse using Singular Value Depcomposition
Reason:
-------
SVD using Scipy is slow and consumes a lot of memory, similarly
pysparse matrix consumes a lot of memory. This is a better
alternative to a direct computation of inverse.
Process:
--------
The function uses sparsesvd to compute the SVD of a sparse matrix,
there is a precision attached in the function, this controls the
cutting (or the k) of the SVD. Precision is actually a percentage
and uses this to get the k.
k = (Precision/100) * rows of the matrix.
The function takes a sparse matrix and a precision score as the input.
"""
matrix = Mat.tocsc()
if matrix.shape[0] <= matrix.shape[1]:
k = int((precision * matrix.shape[0]) / 100)
ut, s, vt = sparsesvd(matrix.tocsc(), k)
UT = ss.csr_matrix(ut)
SI = ss.csr_matrix(np.diag(1 / s))
VT = ss.csr_matrix(vt)
temp_matrix = spmatrixmul(VT.transpose(), SI)
pinv_matrix = spmatrixmul(temp_matrix, UT)
del ut, s, vt, UT, SI, VT, temp_matrix
else:
k = int((precision * matrix.transpose().shape[0]) / 100)
ut, s, vt = sparsesvd(matrix.transpose().tocsc(), k)
UT = ss.csr_matrix(ut)
SI = ss.csr_matrix(np.diag(1 / s))
VT = ss.csr_matrix(vt)
temp_matrix = spmatrixmul(UT.transpose(), SI)
pinv_matrix = spmatrixmul(temp_matrix, VT)
del ut, s, vt, UT, SI, VT, temp_matrix
return pinv_matrix.tocsr()
def tune(my_corpus, dictionary, min_topics=2,max_topics=50,step=2):
def sym_kl(p,q):
return np.sum([scipy.stats.entropy(p,q),scipy.stats.entropy(q,p)])
kl = []
Hbar = []
perplexity = []
n_topics = []
l = np.array([sum(cnt for _, cnt in doc) for doc in my_corpus])
corpus = Index.get_corpus('train features')
for i in range(min_topics,max_topics,step):
n_topics.append(i)
lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary,num_topics=i, alpha = 'auto')
m1 = scipy.sparse.csc_matrix(lda.expElogbeta)
U,cm1,V = sparsesvd(m1, m1.shape[0])
#Document-topic matrix
lda_topics = lda[my_corpus]
m2 = gensim.matutils.corpus2dense(lda_topics, lda.num_topics).transpose()
cm2 = l.dot(m2)
cm2 = cm2 + 0.0001
cm2norm = np.linalg.norm(l)
cm2 = cm2/cm2norm
kl.append(sym_kl(cm1,cm2))
entropy_list = [scipy.stats.entropy([x[1] for x in lda[v]] ) for v in my_corpus]
Hbar.append(np.mean(entropy_list))
perplexity.append( lda.log_perplexity(my_corpus) )
print("NumTopics: %s | Unscaled Entropy: %s | Per-word-bound: %s | Per-word-perplexity: %s | Arun measure %s" % \
(i, Hbar[-1], perplexity[-1], np.exp2(-perplexity[-1]), kl[-1]))
return n_topics, Hbar, perplexity, kl
def LSA(M, k): ##will return top k sentences
SM = scipy.sparse.csc_matrix(M) # convert to sparse CSC format
u, s, vt = sparsesvd(SM, k + 10) #
##SVD calculated at this stage, concept matrix vt, from now we can apply various approaches
##to filter out top k sentences.
##We are using OzSoy's approach
##Using Cross Method
m, n = M.shape
Avg = numpy.average(M, 1)
for i in range(0, m):
for j in range(0, n):
if M[i][j] < Avg[i]:
M[i][j] = 0
Length = numpy.dot(s, vt)
L = []
##returning top k sentences
for i in range(0, n):
L.append(tuple([Length[i], i]))
if k >= len(L):
return L
#building min heap
count = int(k / 2 - 1)
while (count >= 0):
L = heapify(L, count, k)
count -= 1
for i in range(k, len(L)):
if L[0][0] < L[i][0]:
L[0] = L[i]
L = heapify(L, 0, k)
return L[:k]
def generate_model(in_path, title_limit, user_limit, features, out_path):
# connect to db
db = pg.connect(in_path)
# load scores
scores = load_scores(db)
db.close()
print "Loaded scores"
# filter insignificant titles/users, second filtering to remove empty cols/rows
(mat, old_ids_1) = filter_too_small(scores, title_limit, user_limit)
(mat, old_ids_2) = filter_too_small(mat.tocsc(), 1, 1)
print "Filtered insignificant titles and users"
# matrix is in csr format, calc row nnz averages and convert to csc
averages = map(lambda x: row_nnz_average(mat,x), range(0, mat.shape[0]))
mat = mat.tocsc()
# build compact titleid translation tables
old_ids = join_old_id_dicts(old_ids_1, old_ids_2)
(title_to_document, document_to_tile) = build_title_mapping(old_ids, mat.shape[0])
# run svd
print "Built additional data"
(ut, s, vt) = sparsesvd(mat.tocsc(), features)
print "Factorization finished"
s_sqrt = numpy.diag(numpy.sqrt(s))
s_inv = numpy.diag(numpy.power(s,-1))
terms = ut.transpose().dot(s_sqrt)
documents = s_sqrt.dot(s_inv).dot(ut)
# dump results
savemat(out_path, {"Terms": terms, "Documents": documents, "Averages": averages, "TitleMapping": title_to_document, "DocumentMapping" : document_to_tile}, oned_as='row')
print "Saved generated results"
def append_lines_update_svd(m_old, m_new):
if m_old.shape[1] != m_new.shape[1]:
print(
'\nAppend_Lines:New matrix muss has the same colums as old matrix!'
)
return
m, n = m_old.shape
factor = min(m, n)
# factor = 5
c = m_new.shape[0] - m
# print("c {}, factor {}".format(c, factor))
temp = sps.csc_matrix(m_old)
u, s, v = sparsesvd.sparsesvd(temp, factor)
u = u.T
# print("u shape {}".format(u.shape))
B = m_new[m:, :].T
A = np.concatenate((np.zeros((m, c)), np.eye(c)), axis=0)
S = np.diag(s)
U = np.concatenate((u, np.zeros((c, u.shape[1]))), axis=0)
V = v.T
# print("u shape {}, A shape {}, U shape {}, V shape {}".format(u.shape, A.shape, U.shape, V.shape))
u_new, s_new, v_new = increment_svd(U, S, V, A, B)
e = np.linalg.norm(m_new - np.dot(u_new, np.dot(s_new, v_new.T)), 2)
print('Error is', e)
def computeSVD(urm, K):
U, s, Vt = sparsesvd(urm, K)
dim = (len(s), len(s))
S = np.zeros(dim, dtype=np.float32)
for i in range(0, len(s)):
S[i, i] = math.sqrt(s[i])
return np.transpose(U), S.dot(Vt)
def build(self):
# Strictly speaking, enumeration order of Python dict's is not defined.
# In other words, iterating over same dict twice may yield different results. So, we have to keep mapping
# between user/item, and row/column.
self.user_row_index = dict() # type: Dict[str, int]
self.item_col_index = dict() # type: Dict[str, int]
self.rating_matrix_demeaned = dok_matrix((len(self.user_histories), len(self.item_histories)))
for col_index, (item_id, histories) in enumerate(self.item_histories.items()):
self.item_col_index[item_id] = col_index
avg_rating = np.mean([x.rating for x in histories])
self.item_average_rating[item_id] = avg_rating
for record in histories:
row_index = self.user_row_index.get(record.user_id, len(self.user_row_index))
self.user_row_index[record.user_id] = row_index
self.rating_matrix_demeaned[row_index, col_index] = record.rating - avg_rating
self.global_average = np.mean(list(self.item_average_rating.values()))
u, s, v = sparsesvd(csc_matrix(self.rating_matrix_demeaned), self.components)
row_vectors = u.T
self.col_vectors = np.dot(np.diag(s), v).T
self.col_vectors_inv = np.linalg.pinv(self.col_vectors.transpose())
for u, row in self.user_row_index.items():
self.user_vectors[u] = row_vectors[row]
for i, col in self.item_col_index.items():
self.item_vectors[i] = self.col_vectors[col]
def main():
args = docopt("""
Usage:
pmi2svd.py [options] <repres> <pmi_path> <output_path>
Options:
--dim NUM Dimensionality of eigenvectors [default: 500]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
--k NUM [default: 1]
""")
repres = args['<repres>']
pmi_path = args['<pmi_path>']
output_path = args['<output_path>']
dim = int(args['--dim'])
neg = int(args['--neg'])
k = int(args['--k'])
if (repres == "BPMI"):
explicit = BinExplicit(pmi_path, normalize=False)
elif (repres == "PMI"):
explicit = NoExplicit(pmi_path, normalize=False, k=k)
elif (repres == "NPMI"):
explicit = NegExplicit(pmi_path, normalize=False)
else:
explicit = PositiveExplicit(pmi_path, normalize=False, neg=neg)
ut, s, vt = sparsesvd(explicit.m.tocsc(), dim)
np.save(output_path + '.ut.npy', ut)
np.save(output_path + '.s.npy', s)
np.save(output_path + '.vt.npy', vt)
save_vocabulary(output_path + '.words.vocab', explicit.iw)
save_vocabulary(output_path + '.contexts.vocab', explicit.ic)
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--ppmi_file", type=str, required=True,
help="Path to the counts (matrix) file.")
parser.add_argument("--svd_file", type=str, required=True,
help="Path to the SVD file.")
parser.add_argument("--input_vocab_file", type=str, required=True,
help="Path to the input vocabulary file.")
parser.add_argument("--output_vocab_file", type=str, required=True,
help="Path to the output vocabulary file.")
parser.add_argument("--size", type=int, default=100,
help="Vector size.")
parser.add_argument("--normalize", action="store_true",
help="If set, we factorize normalized PPMI matrix")
args = parser.parse_args()
print("Ppmi2svd")
input_vocab, _ = load_vocabulary(args.input_vocab_file)
output_vocab, _ = load_vocabulary(args.output_vocab_file)
ppmi, _, _ = load_sparse(args.ppmi_file)
if args.normalize:
ppmi = normalize(ppmi, sparse=True)
ut, s, vt = sparsesvd(ppmi.tocsc(), args.size)
np.save(args.svd_file + ".ut.npy", ut)
np.save(args.svd_file + ".s.npy", s)
np.save(args.svd_file + ".vt.npy", vt)
save_dense(args.svd_file + ".input", ut.T, input_vocab)
save_dense(args.svd_file + ".output", vt.T, output_vocab)
print("Ppmi2svd finished")
def __init__(self, m, k, docs=None, use_svdlibc=False, power_iters=P2_EXTRA_ITERS, extra_dims=P2_EXTRA_DIMS):
"""
Construct the (U, S) projection from a corpus `docs`. The projection can
be later updated by merging it with another Projection via `self.merge()`.
This is the class taking care of the 'core math'; interfacing with corpora,
splitting large corpora into chunks and merging them etc. is done through
the higher-level `LsiModel` class.
"""
self.m, self.k = m, k
self.power_iters = power_iters
self.extra_dims = extra_dims
if docs is not None:
# base case decomposition: given a job `docs`, compute its decomposition,
# *in-core*.
if not use_svdlibc:
u, s = stochastic_svd(docs, k, chunksize=sys.maxsize,
num_terms=m, power_iters=self.power_iters,
extra_dims=self.extra_dims)
else:
try:
import sparsesvd
except ImportError:
raise ImportError("`sparsesvd` module requested but not found; run `easy_install sparsesvd`")
logger.info("computing sparse SVD of %s matrix" % str(docs.shape))
if not scipy.sparse.issparse(docs):
docs = matutils.corpus2csc(docs)
ut, s, vt = sparsesvd.sparsesvd(docs, k + 30) # ask for extra factors, because for some reason SVDLIBC sometimes returns fewer factors than requested
u = ut.T
del ut, vt
k = clip_spectrum(s**2, self.k)
self.u = u[:, :k].copy()
self.s = s[:k].copy()
else:
self.u, self.s = None, None
def learnProjection(sourceDomain, targetDomain):
"""
Learn the projection matrix and store it to a file.
"""
h = 50 # no. of latent dimensions.
print "Loading the bipartite matrix...",
coocData = sio.loadmat("../work/%s-%s/DSxDI.mat" %
(sourceDomain, targetDomain))
M = sp.lil_matrix(coocData['DSxDI'])
(nDS, nDI) = M.shape
print "Done."
print "Computing the Laplacian...",
D1 = sp.lil_matrix((nDS, nDS), dtype=np.float64)
D2 = sp.lil_matrix((nDI, nDI), dtype=np.float64)
for i in range(0, nDS):
D1[i, i] = 1.0 / np.sqrt(np.sum(M[i, :].data[0]))
for i in range(0, nDI):
D2[i, i] = 1.0 / np.sqrt(np.sum(M[:, i].T.data[0]))
B = (D1.tocsr().dot(M.tocsr())).dot(D2.tocsr())
print "Done."
print "Computing SVD...",
ut, s, vt = sparsesvd(B.tocsc(), h)
sio.savemat("../work/%s-%s/proj.mat" % (sourceDomain, targetDomain),
{'proj': ut.T})
print "Done."
pass
def matrixsvd(self):
svd_matrix = self.projection_matrix.tocsc()
if self.svd is 'scipy':
Utemp, Stemp, VTtemp = ssl.svds(
svd_matrix.tocsc(),
k=(int(self.projection_matrix.tocsr().shape[0] *
self.precision) / 100))
UT = np.nan_to_num(Utemp.transpose())
S = np.nan_to_num(Stemp)
VT = np.nan_to_num(VTtemp)
elif self.svd is 'sparsesvd':
(UT, S,
VT) = sparsesvd(svd_matrix,
(int(svd_matrix.shape[0] * self.precision) / 100))
elif self.svd is 'fast':
Utemp, Stemp, VTtemp = fast_svd(svd_matrix, (
int(self.projection_matrix.tocsr().shape[0] * self.precision) /
100))
UT = np.nan_to_num(Utemp.transpose())
S = np.nan_to_num(Stemp)
VT = np.nan_to_num(VTtemp)
else:
Utemp, Stemp, VTtemp = np.linalg.svd(svd_matrix.todense())
UT = np.nan_to_num(Utemp.transpose())
S = np.nan_to_num(Stemp)
VT = np.nan_to_num(VTtemp)
return UT, S, VT
def decompose(self, corpus):
skip_counts = bounter(size_mb=1024)
word_counts = bounter(size_mb=1024)
for l in corpus:
wds = l.split()
skips = list(skipgrams(wds, 2, 5))
skips = ["#".join(t) for t in skips]
if len(wds) > 0 and len(skips) > 0:
skip_counts.update(skips)
word_counts.update(wds)
vocabulary = list(word_counts)
shift = 1 # shift 1 does nothing since log(1) == 0.0
M = count_skipgrams(skip_counts, word_counts, vocabulary, shift)
# TODO: eigen something trick
# singular value decomposition
# U, _, V = svds(M, k=256) # U, S, V
U, _, V = sparsesvd(M, 300)
# add context to U
word_vecs = U.T + V.T
del U
del V
# normalize rows
word_vecs_norm = word_vecs / np.sqrt(
np.sum(word_vecs * word_vecs, axis=0, keepdims=True))
del word_vecs
return vocabulary, word_vecs_norm
def cnt2svd(count_file, vocab_file, PPMI):
with open(count_file, "r", encoding="UTF-8-sig") as src_file:
text = src_file.readlines()
word2index = read_vocab(vocab_file)
print("length of word_dict: " + str(len(word2index)))
counts = csc_matrix((len(word2index), len(word2index)), dtype="float32")
tmp_counts = dok_matrix((len(word2index), len(word2index)),
dtype="float32")
times = 0
for i in range(len(text)):
word, context, count = text[i].strip().split()
tmp_counts[word2index[word], word2index[context]] = int(count)
times += 1
if times == st.UPDATE_THRESHOLD:
counts = counts + tmp_counts.tocsc()
tmp_counts = dok_matrix((len(word2index), len(word2index)),
dtype="float32")
times = 0
counts = counts + tmp_counts.tocsc()
#calculate e^pmi
sum_r = np.array(counts.sum(axis=1))[:, 0]
sum_c = np.array(counts.sum(axis=0))[0, :]
sum_total = sum_c.sum()
sum_r = np.reciprocal(sum_r)
sum_c = np.reciprocal(sum_c)
pmi = csc_matrix(counts)
normalizer = dok_matrix((len(sum_r), len(sum_r)))
normalizer.setdiag(sum_r)
pmi = normalizer.tocsc().dot(pmi)
normalizer = dok_matrix((len(sum_c), len(sum_c)))
normalizer.setdiag(sum_c)
pmi = pmi.dot(normalizer.tocsc())
pmi = pmi * sum_total
pmi.data = np.log(pmi.data)
if PPMI:
pmi[pmi < 0] = 0
I = eye(pmi.shape[0], format="csc")
print("start svd")
start = time.time()
ut, s = sparsesvd(pmi, I, st.VECTOR_LENGTH)[:2]
if PPMI:
for i in range(len(s)):
ut[i, :] *= np.sqrt(s[i])
else:
for i in range(len(s)):
ut[i, :] *= s[i]
print(time.time() - start)
return ut.T, word2index
def LSA(M,k): ##will return top k sentences
SM = scipy.sparse.csc_matrix(M) # convert to sparse CSC format
u, s, vt = sparsesvd(SM,k+10) #
##SVD calculated at this stage, concept matrix vt, from now we can apply various approaches
##to filter out top k sentences.
##We are using OzSoy's approach
##Using Cross Method
m,n=M.shape
Avg=numpy.average(M,1)
for i in range(0,m):
for j in range(0,n):
if M[i][j]<Avg[i]:
M[i][j]=0
Length=numpy.dot(s,vt)
L=[]
##returning top k sentences
for i in range(0,n):
L.append(tuple([Length[i],i]))
if k>=len(L):
return L
#building min heap
count= int(k/2-1)
while(count>=0):
L=heapify(L,count,k)
count-=1
for i in range(k,len(L)):
if L[0][0]<L[i][0]:
L[0]=L[i]
L=heapify(L,0,k)
return L[:k]
def __init__(self, m, k, docs = None):
"""
Store (U, S) projection itself. This is the class taking care of 'core math';
interfacing with corpora, training etc is done through class LsiModel.
`docs` is either a spare matrix or a corpus which, when converted to a
sparse matrix, must fit comfortably into main memory.
"""
self.m, self.k = m, k
if docs is not None:
# base case decomposition: given a job `docs`, compute its decomposition
# in core, algorithm 1
if utils.isCorpus(docs):
docs = matutils.corpus2csc(m, docs)
if m * k < 10000:
# SVDLIBC gives spurious results for small matrices.. run full
# LAPACK svd on them instead
docs = docs.todense()
logger.info("computing dense SVD of %s matrix" % str(docs.shape))
u, s, vt = numpy.linalg.svd(docs, full_matrices = False)
else:
try:
import sparsesvd
except ImportError:
raise ImportError("for LSA, the `sparsesvd` module is needed but not found; run `easy_install sparsesvd`")
logger.info("computing sparse SVD of %s matrix" % str(docs.shape))
ut, s, vt = sparsesvd.sparsesvd(docs, k + 30) # ask for extra factors, because for some reason SVDLIBC sometimes returns fewer factors than requested
u = ut.T
del ut
del vt
k = clipSpectrum(s, self.k)
self.u, self.s = u[:, :k], s[:k]
else:
self.u, self.s = None, None
def __init__(self, m, k, docs=None, use_svdlibc=False, power_iters=P2_EXTRA_ITERS, extra_dims=P2_EXTRA_DIMS):
"""
Construct the (U, S) projection from a corpus `docs`. The projection can
be later updated by merging it with another Projection via `self.merge()`.
This is the class taking care of the 'core math'; interfacing with corpora,
splitting large corpora into chunks and merging them etc. is done through
the higher-level `LsiModel` class.
"""
self.m, self.k = m, k
self.power_iters = power_iters
self.extra_dims = extra_dims
if docs is not None:
# base case decomposition: given a job `docs`, compute its decomposition,
# *in-core*.
if not use_svdlibc:
u, s = stochastic_svd(docs, k, chunksize=sys.maxsize,
num_terms=m, power_iters=self.power_iters,
extra_dims=self.extra_dims)
else:
try:
import sparsesvd
except ImportError:
raise ImportError("`sparsesvd` module requested but not found; run `easy_install sparsesvd`")
logger.info("computing sparse SVD of %s matrix" % str(docs.shape))
if not scipy.sparse.issparse(docs):
docs = matutils.corpus2csc(docs)
ut, s, vt = sparsesvd.sparsesvd(docs, k + 30) # ask for extra factors, because for some reason SVDLIBC sometimes returns fewer factors than requested
u = ut.T
del ut, vt
k = clip_spectrum(s**2, self.k)
self.u = u[:, :k].copy()
self.s = s[:k].copy()
else:
self.u, self.s = None, None
def applySvd(self):
len_row = max(self.array_row) + 1
len_col = max(self.array_col) + 1
print 'Applying SVD with ROW: ' + str(len_row) + ' and COL: ' + str(
len_col)
sparse_matrix = scipy.sparse.csc_matrix(
(self.array_data, (self.array_row, self.array_col)),
shape=(len_row, len_col))
print 'sparsed matrix'
Ut, Sigma, Vt = sparsesvd(sparse_matrix, self.svd_dimension)
print 'U Sigma Vt done!'
sparse_matrix = array(0)
print 'Mounting Matrix SVD'
self.svd_matrix = numpy.dot(Ut.T, numpy.dot(numpy.diag(Sigma), Vt))
print 'Done!'
print Ut.T
print '\n'
print Sigma
print '\n'
print Vt
print '\n'
print self.svd_matrix.T
print '\n'
Ut = None
Sigma = None
Vt = None
def calc_svd(matrix, dim, impl, impl_args):
"""
apply truncated SVD with several implementations
truncated SVD:
sparsesvd: https://pypi.org/project/sparsesvd/
scipy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.svd.html
randomized truncated SVD:
gensim: https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/lsimodel.py
scikit: https://scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_svd.html
Check out the comparision: https://github.com/jfilter/sparse-svd-benchmark
"""
if impl == "sparsesvd":
# originally used SVD implementation
ut, s, _ = sparsesvd(matrix.m.tocsc(), dim)
# returns in a different format
ut = ut.T
if impl == "scipy":
ut, s, _ = linalg.svds(matrix.m, dim)
# randomized (but fast) truncated SVD
if impl == "gensim":
# better default arguments
args = {"power_iters": 5, "extra_dims": 10, **impl_args}
ut, s = stochastic_svd(matrix.m, dim, matrix.m.shape[0], **args)
if impl == "scikit":
ut, s, _ = randomized_svd(matrix.m, dim, **impl_args)
return ut, s
def test_svd_matrix(W, WT, D, DT):
Winv = ss.csr_matrix(np.linalg.pinv(W.todense()))
WTinv = ss.csr_matrix(np.linalg.pinv(W.transpose().todense()))
# A = np.dot(np.dot(Winv, D), WTinv)
A = ((Winv * D) * WTinv)
A = A.tocsc()
res_dict = {}
old_z = 0
for k in range(270, 280):
(ut, s, vt) = sparsesvd(A, k)
U = ss.csr_matrix(ut.T)
S = ss.csr_matrix(np.diag(s))
V = ss.csr_matrix(vt)
L = (W * U) * (S * V * WT.transpose())
z = U.shape[1]
if z == old_z:
break
else:
Res = fnorm(L, DT)
res_dict[z] = Res
Result = OrderedDict(sorted(res_dict.items(),
key=lambda t: np.float64(t[1])))
old_z = z
return Result
def load(self, dirname, svd_k = 0):
"""
Load the embedding and optionally perform SVD
on load. If svd_k is set to 0, no SVD is performed.
"""
self.dirname = dirname
try:
self.emb.x, self.emb.y = load_svmlight_file(dirname + EMBEDDING_FILENAME)
except (ValueError, IOError):
return None
if svd_k != 0:
try:
import sparsesvd
import scipy.sparse
X = self.emb.x.T
X = scipy.sparse.csc_matrix(X)
Ut, S, Vt = sparsesvd.sparsesvd(X, svd_k)
self.emb.x = scipy.sparse.csr_matrix(Vt.T)
except ImportError:
print('Warning: Cannot perform SVD without sparsesvd module')
self._loadFeatureTable()
self._loadTOC()
return self.emb
def sparse_svd():
svd_input = np.empty((len(request.get_json()), len(request.get_json()[0])))
for i in range(0, len(request.get_json())):
svd_input[i] = request.get_json()[i]
smat = scipy.sparse.csc_matrix(svd_input)
u, s, vh = sparsesvd(smat, min(smat.shape))
return json.dumps(u.T.tolist())
def computeSVDpackage(urm, K):
U, s, Vt = sparsesvd(urm, K)
# print(U.shape)
# print(U)
# print(len(s))
# print(s.shape)
print(s)
# # print(Vt)
print(Vt.shape)
print(Vt)
# # print(Vt.transpose())
# print(Ux - U)
dim = (len(s), len(s))
S = np.zeros(dim, dtype=np.float32)
for i in range(0, len(s)):
# S[i, i] = mt.sqrt(s[i])
S[i, i] = s[i]
U = csc_matrix(np.transpose(U), dtype=np.float32)
S = csc_matrix(S, dtype=np.float32)
Vt = csc_matrix(Vt, dtype=np.float32)
return U, S, Vt
def main():
args = docopt("""
Usage:
pmi2svd.py [options] <pmi_path> <output_path>
Options:
--dim NUM Dimensionality of eigenvectors [default: 500]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
""")
pmi_path = args['<pmi_path>']
output_path = args['<output_path>']
dim = int(args['--dim'])
neg = int(args['--neg'])
explicit = PositiveExplicit(pmi_path, normalize=False, neg=neg)
start = time.time()
ut, s, vt = sparsesvd(explicit.m.tocsc(), dim)
print("Time elapsed for SVD: %f" % (time.time() - start))
np.save(output_path + '.ut.npy', ut)
np.save(output_path + '.s.npy', s)
np.save(output_path + '.vt.npy', vt)
save_vocabulary(output_path + '.words.vocab', explicit.iw)
save_vocabulary(output_path + '.contexts.vocab', explicit.ic)
def main():
en_vector=ENVector()
en_vector.read_freq("results/freq_en_fixed_pmi.txt")
#print "Reading Pair Co-occurence"
#en_vector.read_and_duplicate("results/pair_en_test.txt")
en_vector.read_pair_pmi("results/pair_en_fixed_pmi.txt")
en_vector.sort_by_freq()
#print "Generating Label"
en_vector.generate_label()
# print "Generating Matrix Label"
en_vector.generate_matrix_label()
#print "Calculating Vector Size"
en_vector.calculate_size()
matrix=sp.lil_matrix((limit,limit))
for i in range(min(limit,len(en_vector.matrix_label))):
for j in range((len(en_vector.matrix_label[i]))):
if en_vector.matrix_label[i][j]>=limit:
continue
word1=en_vector.word_list[i]
word2=en_vector.word_list[en_vector.matrix_label[i][j]]
matrix[i,en_vector.matrix_label[i][j]]=en_vector.pair_count[(word1,word2)]
smat=sp.csc_matrix(matrix)
ut,s,vt=sparsesvd(smat,10)
for i in range(limit):
for j in range(10):
print (ut[j][i]*s[j]),
print
def main():
args = docopt("""
Usage:
counts2svd.py [options] <counts_path>
Options:
--dim NUM Dimensionality of eigenvectors [default: 500]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
--pos NUM Number of positive samples; add its log to PMI [default: 1]
--cds NUM Context distribution smoothing [default: 0.75]
--randomized Use randomized SVD
--normalized Use normalized embedder
--oversample NUM Number of oversamples in randomized SVD [default: 10]
--power_iter NUM Number of iterations of power method in randomized SVD [default: 2]
""")
start = time.time()
counts_path = args['<counts_path>']
dim = int(args['--dim'])
neg = int(args['--neg'])
pos = int(args['--pos'])
cds = float(args['--cds'])
randomized = args['--randomized']
normalized = args['--normalized']
oversample = int(args['--oversample'])
power_iter = int(args['--power_iter'])
output_path = counts_path + "_svd_dim=%d_neg=%d_pos=%d_cds=%.2f" % (dim, neg, pos, cds)
if randomized:
output_path += "_rand_oversample=%d_power_iter=%d" % (oversample, power_iter)
if normalized:
output_path += "_normalized_power_iter=%d" % power_iter
logging.basicConfig(filename=output_path + ".log", filemode="w", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler())
_, iw = load_vocabulary(counts_path + '.words.vocab')
adjacency_matrix = load_adjacency_matrix(counts_path)
ppmi = build_ppmi_matrix(adjacency_matrix, cds, neg, pos)
start_learning = time.time()
logging.info("Starting SVD")
if randomized:
# ppmi = normalize(ppmi, norm='l2', axis=1)
s, ut = randomized_eigh(ppmi, dim, oversample, power_iter, row_normalized=normalized)
elif normalized:
ut = normalized_embedder(ppmi, dim, power_iter)
s = np.zeros(dim)
else:
# ppmi = normalize(ppmi, norm='l2', axis=1)
ut, s, _ = sparsesvd(ppmi.tocsc(), dim)
ut = ut.T
logging.info("Time elapsed on learning: %f" % (time.time() - start_learning))
np.save(output_path + '.vecs.npy', ut)
np.save(output_path + '.vals.npy', s)
save_vocabulary(output_path + '.words.vocab', iw)
logging.info("Time elapsed: %f" % (time.time() - start))
def lsa( ):
from sparsesvd import sparsesvd
from numpy import array
import scipy.sparse as sp
# calculate svd and perform lsa
print "######## READING TERM DOC MATRIX #########"
termDocEntries = pickle.load(open(outfile +"/tdm.p" ,"rb"))
id2title = pickle.load(open(outfile + "/id_file.p","rb"))
word2id = pickle.load(open(outfile + "/word_id.p","rb"))
fileCount = len(id2title)
#fileCount = 60000
vocab_size = len(word2id)
print "######## READING COMPLETE #########"
I = array([ i for ((i,j),v) in termDocEntries] )
J = array([ j for ((i,j),v) in termDocEntries] )
V = array([ v for ((i,j),v) in termDocEntries] )
shape = (fileCount, vocab_size)
print "Dimension of TDM is : ", shape
print "######## STARTING LSA #########"
termDocMatrix = sp.csc_matrix( (V,(I,J)), shape= (fileCount, vocab_size ), dtype=np.float32)
UT , S, V = sparsesvd(termDocMatrix, 300)
(m1,m2) = UT.T.shape
S1 = np.zeros((m2,m2), dtype=np.float32)
for i in range(m2):
S1[i][i] = S[i]
US = np.dot(UT.T, S1)
print m1, m2
(n1,n2) = V.shape
pickle.dump( US , open( outfile + "/u_sigma.p", "wb" ) )
pickle.dump( V.T , open( outfile + "/v.p", "wb" ) )
print "######## LSA COMPLETE #########"
def applySvd(self):
len_row = max(self.array_row) + 1
len_col = max(self.array_col) + 1
print "Applying SVD with ROW: " + str(len_row) + " and COL: " + str(len_col)
sparse_matrix = scipy.sparse.csc_matrix(
(self.array_data, (self.array_row, self.array_col)), shape=(len_row, len_col)
)
print "sparsed matrix"
Ut, Sigma, Vt = sparsesvd(sparse_matrix, self.svd_dimension)
print "U Sigma Vt done!"
sparse_matrix = array(0)
print "Mounting Matrix SVD"
self.svd_matrix = numpy.dot(Ut.T, numpy.dot(numpy.diag(Sigma), Vt))
print "Done!"
print Ut.T
print "\n"
print Sigma
print "\n"
print Vt
print "\n"
print self.svd_matrix.T
print "\n"
Ut = None
Sigma = None
Vt = None
def arun(corpus, dictionary, min_topics=10, max_topics=21, step=5):
print "Arun runing"
output = []
for i in range(min_topics, max_topics, step):
lda = LDA(dictionary, corpus, i, "lda20/lda_training_" + str(i))
print "Модель построена/загружена"
m1 = lda.expElogbeta
# U, cm1, V = np.linalg.svd(m1)
smat = scipy.sparse.csc_matrix(m1) # convert to sparse CSC format
U, cm1, V = sparsesvd(smat, i + 30) # do SVD, asking for 100 factors
print "sparsesvd сделано"
#Document-topic matrix
lda_topics = lda[my_corpus]
m2 = matutils.corpus2dense(lda_topics, lda.num_topics).transpose()
cm2 = l.dot(m2)
cm2 = cm2 + 0.0001
print "cm2norm begin"
cm2norm = np.linalg.norm(l)
print "cm2norm end"
cm2 = cm2/cm2norm
print len(cm1), len(cm2)
kl = sym_kl(cm1, cm2)
output.append((i, kl))
print i, kl
print output
return output
def matrixsvd(self):
svd_matrix = self.projection_matrix.tocsc()
if self.svd is 'scipy':
Utemp, Stemp, VTtemp = ssl.svds(svd_matrix.tocsc(),
k=(int(self.projection_matrix.tocsr().shape[0] *
self.precision) / 100))
UT = np.nan_to_num(Utemp.transpose())
S = np.nan_to_num(Stemp)
VT = np.nan_to_num(VTtemp)
elif self.svd is 'sparsesvd':
(UT, S, VT) = sparsesvd(svd_matrix, (int(svd_matrix.shape[0] * self.precision) / 100))
elif self.svd is 'fast':
Utemp, Stemp, VTtemp = fast_svd(svd_matrix,
(int(self.projection_matrix.tocsr().shape[0] *
self.precision) / 100))
UT = np.nan_to_num(Utemp.transpose())
S = np.nan_to_num(Stemp)
VT = np.nan_to_num(VTtemp)
else:
Utemp, Stemp, VTtemp = np.linalg.svd(svd_matrix.todense())
UT = np.nan_to_num(Utemp.transpose())
S = np.nan_to_num(Stemp)
VT = np.nan_to_num(VTtemp)
return UT, S, VT
def learn(mat):
print "Starting learning process..."
start_time = time.time()
user_mat, axis_weights, movie_mat = sparsesvd(mat, NUM_COMPONENTS)
print "Matrix decomposition complete (elapsed time: %f s)." % (time.time() - start_time)
print "Learning process complete."
return (user_mat, axis_weights, movie_mat)
def __init__(self, m, k, docs = None):
"""
Store (U, S) projection itself. This is the class taking care of 'core math';
interfacing with corpora, training etc is done through class LsiModel.
`docs` is either a spare matrix or a corpus which, when converted to a
sparse matrix, must fit comfortably into main memory.
"""
self.m, self.k = m, k
if docs is not None:
# base case decomposition: given a job `docs`, compute its decomposition
# in core, algorithm 1
if utils.isCorpus(docs):
docs = matutils.corpus2csc(m, docs)
if m * k < 10000:
# SVDLIBC gives spurious results for small matrices.. run full
# LAPACK svd on them instead
docs = docs.todense()
logger.info("computing dense SVD of %s matrix" % str(docs.shape))
u, s, vt = numpy.linalg.svd(docs, full_matrices = False)
else:
try:
import sparsesvd
except ImportError:
raise ImportError("for LSA, the `sparsesvd` module is needed but not found; run `easy_install sparsesvd`")
logger.info("computing sparse SVD of %s matrix" % str(docs.shape))
ut, s, vt = sparsesvd.sparsesvd(docs, k + 30) # ask for extra factors, because for some reason SVDLIBC sometimes returns fewer factors than requested
u = ut.T
del ut
del vt
k = clipSpectrum(s, self.k)
self.u, self.s = u[:, :k], s[:k]
else:
self.u, self.s = None, None
def main():
#===========================================================================
# mat = numpy.random.rand(300, 300)
# smat = scipy.sparse.csc_matrix(mat)
# ut, s, vt = sparsesvd(smat,100)
# tmp=numpy.diag(s)
# test=numpy.dot(ut.T, numpy.dot(numpy.diag(s), vt))#vt=(300,300), ut=(300,300), s=(300,1)
# u2, s2, v2=svds(mat, k=100)
#
# print ""
#===========================================================================
#ut, s, vt = sparsesvd(smat,100) # do SVD, asking for 100 factors
# ut - Unitary matrices.
#s -The singular values for every matrix, sorted in descending order.
#vt - Unitary matrices
#assert numpy.allclose(mat, numpy.dot(ut.T, numpy.dot(numpy.diag(s), vt))) #test if mat is close to numpy.dot(ut.T, numpy.dot(numpy.diag(s), vt))
################################################################################################################
mat1 = ss.load_npz(
'/home/ira/Dropbox/IraTechnion/Patterns_Research/sp_sg/mat_ppmi_round_allpats.npz'
)
(nrows, ncols) = mat1.get_shape()
#u1, s1, v1 = svds(mat1, k=500)
u1, s1, v1 = sparsesvd(csc_matrix(mat1),
500) #v1(500,746K), u1(500,746K) s1[500,1]
reduced_mat = numpy.dot(u1.T, numpy.diag(s1))
ss.save_npz('svd_reduced_mat_500_allpats', csr_matrix(reduced_mat))
print "I'm here"
def generate_archetypes(singer_resumes, archetype_count_k=20, cache_file=CACHE):
""" Generate and write to disk an archetype matrix given a population """
# Generate a unique, ordered, list of characters
characters = set() # Could optimized by using single comprehension
for singer_resume in singer_resumes:
characters.update(singer_resume)
characters = list(characters)
# Create a dict to lookup character index by id
character_positions = dict()
for i, character in enumerate(characters):
character_positions[character] = i
# Construct an empty matrix to populate
dimensions = len(singer_resumes), len(characters)
singer_matrix = scipy.sparse.lil_matrix(dimensions)
# Populate the matrix
for j, singer_resume in enumerate(singer_resumes):
for character in singer_resume:
position = character_positions[character]
singer_matrix[j, position] = True
# Convert matrix to a sparse matrix
sparse_singer_matrix = scipy.sparse.csc_matrix(singer_matrix)
# Do magic with maths
U, s, V = sparsesvd(sparse_singer_matrix, archetype_count_k)
archetypes = V
# Cache the data for later use
arrays = {CHARACTERS: character_positions, ARCHETYPES: archetypes}
np.savez(cache_file, **arrays)
def single_line_update_svd(m_old, m_new):
if m_old.shape != m_new.shape:
print("\nSingel_line: Matrix shape don't match")
shape = m_old.shape
diff = m_new - m_old
r, c = np.nonzero(diff)
# print(r,c)
x = diff[diff != 0]
# print(x, x.shape)
if x.shape[0] == shape[0]:
# update colum
col = c[0]
a = x.reshape((shape[0], 1))
b = np.zeros((shape[1], 1))
b[c, 0] = 1
else:
# update row
row = r[0]
a = np.zeros((shape[0], 1))
a[row, 0] = 1
b = x.reshape((shape[1], 1))
# print(np.allclose(m_new, m_old + [email protected]))
temp = sps.csc_matrix(m_old)
u, s, v = sparsesvd.sparsesvd(temp, min(shape))
u = u.T
v = v.T
s = np.diag(s)
u_new, s_new, v_new = increment_svd(u, s, v, a, b)
e = np.linalg.norm(m_new - (u_new @ s_new @ v_new.T), 2)
print("Error is ", e)
def learn(mat):
print "Starting learning process..."
start_time = time.time()
user_mat, axis_weights, movie_mat = sparsesvd(mat, NUM_COMPONENTS)
print "Matrix decomposition complete (elapsed time: %f s)." % (
time.time() - start_time)
print "Learning process complete."
return (user_mat, axis_weights, movie_mat)
def test():
mat = sp.rand(200, 100, density=0.01) # create a random matrix
smat = csc_matrix(mat) # convert to sparse CSC format
ut, s, vt = sparsesvd(smat, 1) # do SVD, asking for 100 factors
mat_prime = np.dot(ut.T, np.dot(np.diag(s), vt))
print (len(np.transpose(mat.nonzero())))
print (len(np.transpose(mat_prime.nonzero())))
def decompose(self):
sigular_vals = 100
print "decomposing, with %d singular-value requested." % sigular_vals
ut, s, vt = sparsesvd(csc_matrix(self.matrix), sigular_vals)
print "s*vt:", os.linesep, numpy.dot(s, vt)
print "ut:", os.linesep, ut
print "s:", os.linesep, s
print "vt", os.linesep, vt
def svd_reduction(dataArray, k, get="feature-latent"):
sparseDataArray = csc_matrix(dataArray)
ut, s, vt = sparsesvd(sparseDataArray, k)
if get=="feature-latent":
return np.matmul(dataArray.transpose(), ut.transpose())
else:
return np.matmul(dataArray, vt.transpose())
def projections(self):
"""Get the set of vectors for all URIs"""
if self._ut is None:
self._ut, self._s, self._vt = sparsesvd(self._adjacency,
self._rank)
(self._ut_shape, self._s_shape,
self._vt_shape) = (self._ut.shape, self._s.shape, self._vt.shape)
return self._ut.T
def _compute_svd(self, normalize_data = True):
self.logger.info('Computing the Singular Value Decomposition of the relation matrix')
if normalize_data:
self.data_normalization()
self.relationship_matrix_csc = self.relationship_matrix.tocsc()
self.svd_u, self.svd_s, self.svd_v = sparsesvd(self.relationship_matrix_csc, self.dimensionality)
def learnProjection(dataset, pivotsMethod, n):
"""
Learn the projection matrix and store it to a file.
"""
h = 50 # no. of SVD dimensions.
#n = 500 # no. of pivots.
# Parameters to reduce the number of features in the tail
# domainTh = {'books':5, 'dvd':5, 'kitchen':5, 'electronics':5}
# Load pivots.
pivotsFile = "../work/%s/obj/%s" % (dataset, pivotsMethod)
features = pi.load_stored_obj(pivotsFile)
pivots = dict(features[:n]).keys()
print "selecting top-%d features in %s as pivots" % (len(pivots), pivotsMethod)
# Load features and get domain specific features
fname = "../work/%s/obj/freq" % (dataset)
if "un_" in pivotsMethod:
fname = "../work/%s/obj/un_freq" % (dataset)
features = pi.load_stored_obj(fname)
feats = dict(features)
# print feats.keys()
# DSwords = [item for item in feats if item not in pivots]
feats = feats.keys()
# Load train vectors.
print "Loading Training vectors...",
startTime = time.time()
vects = []
vects.extend(loadFeatureVecors("../data/%s/train-sentences" % dataset, feats))
endTime = time.time()
print "%ss" % str(round(endTime-startTime, 2))
print "Total no. of documents =", len(vects)
print "Total no. of features =", len(feats)
# Learn pivot predictors.
print "Learning Pivot Predictors.."
startTime = time.time()
M = sp.lil_matrix((len(feats), len(pivots)), dtype=np.float)
for (j, w) in enumerate(pivots):
print "%d of %d %s" % (j, len(pivots), w)
for (feat, val) in getWeightVector(w, vects):
i = feats.index(feat)
M[i,j] = val
endTime = time.time()
print "Took %ss" % str(round(endTime-startTime, 2))
# Perform SVD on M
print "Perform SVD on the weight matrix...",
startTime = time.time()
ut, s, vt = sparsesvd(M.tocsc(), h)
endTime = time.time()
print "%ss" % str(round(endTime-startTime, 2))
sio.savemat("../work/%s/proj_scl.mat" % (dataset), {'proj':ut.T})
pass
def testBeyondAccurracyMetrics(train_filename, eval_item_filename, user_means_filename):
logging.info('testing beyond-accuracy topNLists with data files {0}; {1}; {2}...'.format(train_filename, eval_item_filename, user_means_filename))
train_data = trainData.TrainData(train_filename, user_means_filename)
_, _, Q = sparsesvd(train_data.rating_matrix.tocsc(), config.FACTOR_MODEL_SIZE)
with open(eval_item_filename,'rb') as eval_file:
for line in eval_file:
data = line.split('\t')
user_id = data[0]
user_index = train_data.getUserIndex(user_id)
if len(train_data.getUserProfileByIndex(user_index)) < 1:
continue
ground_truth_items = data[1].split(',')
random_unrated_items = data[2].rstrip('\n').split(',')
evaluation_item_ids = ground_truth_items + random_unrated_items
rec_list_szie = config.RECOMMENDATION_LIST_SIZE * config.DIVERSIFICATION_CANDIDATES_FACTOR
# predictions = train_data.getFactorBasedRecommendations(user_id, Q, evaluation_item_ids)
# top_recs = topNLists.getTopNList(predictions, rec_list_szie)
# predictions_ib = train_data.getItemBasedRecommendations(user_id, evaluation_item_ids, 'non_normalized')
# top_recs_ib = topNLists.getTopNList(predictions_ib, rec_list_szie)
# predictions = library_recommender.recommend_items(mrec_train_data.X, int(user_id)-config.MREC_INDEX_OFFSET, max_items=10000, return_scores=True)
# top_recs = topNLists.getTopNList(predictions, rec_list_szie, evaluation_item_ids)
predictions_ub = train_data.getUserBasedRecommendations(user_id, evaluation_item_ids, 'non_normalized')
top_recs_ub = topNLists.getTopNList(predictions_ub, rec_list_szie)
# print 'user',user_id
# print top_recs_ib, top_recs_ub
# rare = train_data.getPopularityInfo()[:10]
# pop = train_data.getPopularityInfo()[-10:]
top_recs = top_recs_ub
print 'diversity_ratings',diversity.getListDiversity(train_data, top_recs, 'div_r')
print 'diversity_content',diversity.getListDiversity(train_data, top_recs, 'div_c')
print 'content',serendipity.getListSerendipity(train_data, user_index, top_recs, 'sur_c')
# print 'rare cooccurrence',serendipity.getListSerendipity(train_data, user_index, rare, 'sur_r')
# print 'rare cooccurrence normalized',serendipity.getListSerendipity(train_data, user_index, rare, 'sur_r_n')
#
# print 'pop cooccurrence',serendipity.getListSerendipity(train_data, user_index, pop, 'sur_r')
# print 'pop cooccurrence normalized',serendipity.getListSerendipity(train_data, user_index, pop, 'sur_r_n')
#
# print 'rare novelty',novelty.getListNovelty(train_data, rare)
#
# print 'pop novelty',novelty.getListNovelty(train_data, pop)
print '------------------------------'
def main(size, thr, ns, sppmi, f_in, f_out):
size = int(size)
thr = int(thr)
ns = float(ns)
sppmi = int(sppmi) #1: sppmi 0: raw
print "Input text file: ", f_in
print "Building dict..."
vocab_count, text_num = build_dict(f_in, thr)
alltokens = vocab_count.keys()
vocab_id = dict((t, i) for i, t in enumerate(alltokens))
vocab_size = len(vocab_id)
print "the number of texts: ", text_num
print "vocabulary size: ", vocab_size
train_num = sum(vocab_count.values())
print "The number of tokens: ", train_num
i = 0
row = []
col = []
data = []
for l in open(f_in).readlines():
tokens = l.split()
indexes = list(np.zeros(vocab_size))
for t in tokens:
try:
if sppmi == 1:
indexes[vocab_id[t]] += train_num / ns / (len(tokens) *
vocab_count[t])
else:
indexes[vocab_id[t]] += 1
except KeyError:
pass
if sppmi == 1:
for j in range(len(indexes)):
if indexes[j] > 1: # only positive values are retained
row.append(i)
col.append(j)
data.append(np.log(indexes[j]))
else:
for j in range(len(indexes)):
if indexes[j] > 0:
row.append(i)
col.append(j)
data.append(indexes[j])
i += 1
print "the size of the co-occurrence matrix of term-document type doc_num, vocab_size:", text_num, vocab_size
s_co_mat = scipy.sparse.csc_matrix(
(np.array(data), (np.array(row), np.array(col))),
shape=(text_num, vocab_size))
ut, s, vt = sparsesvd(s_co_mat, size)
ut = ut.transpose()
f = open(f_out, "w")
for i in range(ut.shape[0]):
f.write(str(i) + " ")
for j in range(ut.shape[1]):
f.write(str(ut[i, j]) + " ")
f.write("\n")
def __factorize_rating_matrix(self):
mat = sparse.lil_matrix((self.m, self.n))
for user in self.user_ratings.iterkeys():
for item in self.user_ratings[user]:
mat[self.user_positions[user], self.item_positions[item]] = self.user_ratings[user][item]
u, s, q = sparsesvd(sparse.csc_matrix(mat), self.num_facs)
return u.T, numpy.diag(s), q.T
def sparseSVD(D):
import scipy.sparse
try:
import sparsesvd
except:
print 'bummer ... better get sparsesvd'
exit(0)
Ds = scipy.sparse.csc_matrix(D)
a = sparsesvd.sparsesvd(Ds,Ds.shape[0])
return a
def run(self):
denseMat = loadMatFile(self.rawMatFile)
sparseMat = scipy.sparse.csc_matrix(denseMat)
if int(len(denseMat)) >= self.cutOffSVD :
ut, s, vt = sparsesvd.sparsesvd(sparseMat, self.cutOffSVD)
dump2File(vt, self.VtFile)
reducedMatrix = computeReduction(vt, denseMat, self.cutOffSVD)
dump2File(reducedMatrix, self.redMatFile)
else :
print('--> not enough contexts')
def transform_data(X, X_test):
X_all = np.vstack((X,X_test))
tfidf = feature_extraction.text.TfidfTransformer()
X_all = tfidf.fit_transform(X_all).toarray()
X_all_sparse = scipy.sparse.csc_matrix(X_all)
U, s, V = sparsesvd(X_all_sparse, 60)
print U.shape, s.shape, V.shape
S = np.diag(s)
X_all = np.dot(np.transpose(U), np.dot(S, V))
return X_all[0:X.shape[0],:], X_all[X.shape[0]:,:]
def __init__(self, corpus):
term_doc = TermDoc()
for document in corpus:
# Tokenize/stem each document
tokens = process_document(document)
# Add it to the sparse term-document matrix
term_doc.add_document(document, tokens)
print len(term_doc._words), len(term_doc._documents)
# Calculate the SVD
self.T, self.s, self.D = sparsesvd(term_doc._matrix.as_csc(), 300)
def testLearnModel2(self):
X = scipy.sparse.rand(10, 10, 0.2)
X = X.tocsc()
lmbdas = numpy.array([10.0, 0.0])
eps = 0.01
k = 9
#Check out singular values
U, s, V = sparsesvd(X.tocsc(), k)
softImpute = SoftImpute(lmbdas, eps, k)
ZList = softImpute.learnModel2(X)
#Test that when lambda=0 get approx original matrix back
X2 = ZList[1].todense()
nptst.assert_almost_equal(X.todense(), X2)
#When lambda is greater or equal to largest singular value, get 0
U, s, V = sparsesvd(X.tocsc(), k)
lmbdas = numpy.array([numpy.max(s)])
softImpute = SoftImpute(lmbdas, eps, k)
Z = softImpute.learnModel2(X)
self.assertEquals(numpy.linalg.norm(Z.todense()), 0)
#Check solution for medium values of lambda
eps = 0.1
lmbdas = numpy.array([0.1, 0.2, 0.5, 1.0])
softImpute = SoftImpute(lmbdas, eps, k)
ZList = softImpute.learnModel2(X)
for j, Z in enumerate(ZList):
Z = Z.todense()
Zomega = numpy.zeros(X.shape)
rowInds, colInds = X.nonzero()
for i in range(X.nonzero()[0].shape[0]):
Zomega[rowInds[i], colInds[i]] = Z[rowInds[i], colInds[i]]
U, s, V = ExpSU.SparseUtils.svdSoft(numpy.array(X-Zomega+Z), lmbdas[j])
tol = 0.1
self.assertTrue(numpy.linalg.norm(Z -(U*s).dot(V.T))**2 < tol)
def process_SVD1(inputFileName, outputFileName, n, p):
"""
Peform SVD1.
"""
mat, rowids = loadMatrix(inputFileName)
X = mat.tocsc()
ut, s, vt = sparsesvd(X, n)
A = np.dot(ut.T, np.diag(s ** p))
saveMatrix(A, rowids, outputFileName)
mmwrite("%s.ut" % inputFileName, ut)
np.savetxt("%s.s" % inputFileName, s)
mmwrite("%s.vt" % inputFileName, vt)
pass
def psp_pseudoinverse(Mat, precision):
list_nz = (Mat.sum(axis=1) == 1)
list_mat = []
for i in range(list_nz):
if list_nz[i]:
list_mat.append(i)
temp_Mat = Mat[list_mat, :]
matrix = spmatrix.ll_mat(temp_Mat.shape[0], temp_Mat.shape[1])
matrix.update_add_at(temp_Mat.tocoo().data, temp_Mat.tocoo().row,
temp_Mat.tocoo().col)
if matrix.shape[0] <= matrix.shape[1]:
k = int((precision * matrix.shape[0]) / 100)
ut, s, vt = sparsesvd(matrix.tocsc(), k)
UT = ss.csr_matrix(ut)
SI = ss.csr_matrix(np.diag(1 / s))
VT = ss.csr_matrix(vt)
temp_matrix = spmatrixmul(VT.transpose(), SI)
pinv_matrix = spmatrixmul(temp_matrix, UT)
del ut, s, vt, UT, SI, VT, temp_matrix
else:
k = int((precision * matrix.transpose().shape[0]) / 100)
ut, s, vt = sparsesvd(matrix.transpose().tocsc(), k)
UT = ss.csr_matrix(ut)
SI = ss.csr_matrix(np.diag(1 / s))
VT = ss.csr_matrix(vt)
temp_matrix = spmatrixmul(UT.transpose(), SI)
pinv_matrix = spmatrixmul(temp_matrix, VT)
del ut, s, vt, UT, SI, VT, temp_matrix
return pinv_matrix.tocsr()
def debug():
"""
Test the various functions implemented in this module.
"""
mat, rowids = loadMatrix("../work/testMatrix")
#convertPPMI(mat)
#saveMatrix(mat, rowids, "../work/pmiMatrix")
X = mat.tocsc()
ut, s, vt = sparsesvd(X, 50)
#print allclose(X, np.dot(ut.T, np.dot(np.diag(s), vt)))
A = np.dot(ut.T, np.diag(s))
saveMatrix(A, rowids, "../work/featMatrix")
pass
def __init__(self, m, k, docs=None, use_svdlibc=False, power_iters=P2_EXTRA_ITERS,
extra_dims=P2_EXTRA_DIMS, dtype=np.float64):
"""Construct the (U, S) projection from a corpus.
Parameters
----------
m : int
Number of features (terms) in the corpus.
k : int
Desired rank of the decomposed matrix.
docs : {iterable of list of (int, float), scipy.sparse.csc}
Corpus in BoW format or as sparse matrix.
use_svdlibc : bool, optional
If True - will use `sparsesvd library <https://pypi.python.org/pypi/sparsesvd/>`_,
otherwise - our own version will be used.
power_iters: int, optional
Number of power iteration steps to be used. Tune to improve accuracy.
extra_dims : int, optional
Extra samples to be used besides the rank `k`. Tune to improve accuracy.
dtype : numpy.dtype, optional
Enforces a type for elements of the decomposed matrix.
"""
self.m, self.k = m, k
self.power_iters = power_iters
self.extra_dims = extra_dims
if docs is not None:
# base case decomposition: given a job `docs`, compute its decomposition,
# *in-core*.
if not use_svdlibc:
u, s = stochastic_svd(
docs, k, chunksize=sys.maxsize,
num_terms=m, power_iters=self.power_iters,
extra_dims=self.extra_dims, dtype=dtype)
else:
try:
import sparsesvd
except ImportError:
raise ImportError("`sparsesvd` module requested but not found; run `easy_install sparsesvd`")
logger.info("computing sparse SVD of %s matrix", str(docs.shape))
if not scipy.sparse.issparse(docs):
docs = matutils.corpus2csc(docs)
# ask for extra factors, because for some reason SVDLIBC sometimes returns fewer factors than requested
ut, s, vt = sparsesvd.sparsesvd(docs, k + 30)
u = ut.T
del ut, vt
k = clip_spectrum(s ** 2, self.k)
self.u = u[:, :k].copy()
self.s = s[:k].copy()
else:
self.u, self.s = None, None
def setupencodersandimages(listoffiles, scalew, scaleh, numgabor):
combined=[]
for file_ in listoffiles:
combined.append(converttoarray(file_, scalew, scaleh))
imgs=numpy.array([img/numpy.linalg.norm(img) for img in combined]).T
csc=csc_matrix(imgs)
ut, S, vt=sparsesvd(csc, len(listoffiles))
M=numpy.diag([numpy.linalg.norm(ut[i:]) for i in range(ut.shape[0])])
#W is M inverse
UW=numpy.dot(ut.T, numpy.linalg.inv(M))
MSvt=numpy.dot(M, numpy.dot(numpy.diag(S), vt))
gaborenc=[makerandomgabor(scalew*scaleh) for i in range(numgabor)]
gaborenc=[(1/numpy.linalg.norm(i).flatten())*i for i in gaborenc]
return imgs, UW, MSvt, gaborenc
def convertor2matrix(self, rank):
self.loadDataFileMovieBased(moiveRatingTrainsetFilename)
self.loadDataFile(moiveRatingTrainsetFilename)
self.matrix = [[0 for x in range(0, len(self.allMovieRatingRecord))] for y in range(0, len(self.allUserRatingRecord))]
self.users = list(self.allUserRatingRecord.keys())
self.movies = list(self.allMovieRatingRecord)
matrix = self.matrix
for u in range(len(self.users)):
user = self.users[u]
userEntry = self.allUserRatingRecord[user]
for movie in userEntry:
matrix[u][self.movies.index(movie)] = int(userEntry[movie][0])
avg = 0.0
userAvg = []
for i in range(len(matrix)):
c = 0.0
s = 0.0
for j in range(len(matrix[i])):
if matrix[i][j] != 0:
c += 1
s += matrix[i][j]
userAvg.append(s/c)
movieAvg=[]
for i in range(len(matrix[0])):
c = 0.0
s = 0.0
for j in range(len(matrix)):
if matrix[j][i] != 0:
c += 1
s += matrix[j][i]
if c == 0.0:
print self.movies[i]
movieAvg.append(s/c)
for i in range(len(matrix)):
for j in range(len(matrix[i])):
if matrix[i][j] != 0:
matrix[i][j] -= userAvg[i]
smat = scipy.sparse.csc_matrix(matrix)
u, s, v = sparsesvd(smat,rank)
u = u.transpose()
s = diag(s)
res = dot(dot(u,s),v)
for i in range(len(res)):
for j in range(len(res[i])):
res[i][j] += userAvg[i]
self.result = res
def run(self):
self.context = loadContextFile(self.contextFile)
self.lexicon = buildLexicon(self.context, self.stopList, self.lexiconCutoff)
self.lexiconSorted = getSortedLexicon(self.lexicon)
self.lexiconPosition = buildLexiconPositionTable(self.lexiconSorted)
writeLexicon(self.lexiconFile, self.lexicon, self.lexiconSorted)
self.denseMat = computeMatrix(int(self.winLength) , self.lexicon, self.lexiconPosition, self.context)
if int(len(self.denseMat)) >= self.cutOffSVD :
self.sparseMat = scipy.sparse.csc_matrix(self.denseMat)
ut, s, self.vt = sparsesvd.sparsesvd(self.sparseMat, self.cutOffSVD)
dump2File(self.vt, self.VtFile)
self.reducedMatrix = computeReduction(self.vt, self.denseMat, self.cutOffSVD)
dump2File(self.reducedMatrix, self.redMatFile)
else :
print('--> not enough contexts')