def create_sptensors(self):
"""
Create a sparse tensor
:param :
:return:
"""
tuples = []
# TODO: add edges between timeframes
for i, (t, graph) in enumerate(graphs.iteritems()):
for u, v in graph.edges_iter():
tuples.append([self.node_pos[u], self.node_pos[v], i])
tuples.append([self.node_pos[v], self.node_pos[u], i])
triplets = np.array(list(set([(u, v, t) for u, v, t in tuples])))
a = sptensor(tuple(triplets.T),
vals=np.ones(len(triplets)),
shape=(len(self.node_ids), len(self.node_ids),
len(graphs)))
o_values = []
# FIXME: Tensor O should be columnt normalized
sum_rows = np.zeros((a.shape[0], a.shape[2]))
for t in range(a.shape[2]):
for i in range(a.shape[0]):
for j in range(a.shape[1]):
# TODO : just add another for loop instead of : to access .sum()
# TODO : check sparse tensor performance and library
sum_rows[i, t] += a[i, j, t]
for i in range(a.shape[0]):
if sum_rows[i, t] != 0:
for j in range(i):
if a[i, j, t] != 0:
o_values.append(a[j, i, t] / sum_rows[j, t])
if i != j:
o_values.append(a[i, j, t] / sum_rows[i, t])
o = sptensor(tuple(triplets.T),
vals=o_values,
shape=(len(self.node_ids), len(self.node_ids),
len(graphs)))
r_values = []
sum_time = np.zeros((a.shape[0], a.shape[1]))
for i in range(a.shape[0]):
# OPTIMIZE: sum is a dense matrix/array. Should be sparse for memory
for j in range(a.shape[1]):
for t in range(a.shape[2]):
# TODO : just add another for loop instead of : to access .sum()
# TODO : check sparse tensor performance and library
if a[i, j, t] != 0:
sum_time[i, j] += a[i, j, t]
for t in range(a.shape[2]):
for i in range(a.shape[0]):
for j in range(i):
if a[j, i, t] != 0:
r_values.append(a[j, i, t] / sum_time[j, i])
r_values.append(a[i, j, t] / sum_time[i, j])
r = sptensor(tuple(triplets.T),
vals=r_values,
shape=(len(self.node_ids), len(self.node_ids),
len(graphs)))
return a, o, r, sum_rows, sum_time
def build_sparse_B_from_A(A):
"""
Create the sptensor adjacency tensor of a networkX graph.
Parameters
----------
A : list
List of MultiDiGraph NetworkX objects.
Returns
-------
data : sptensor
Graph adjacency tensor.
data_T : sptensor
Graph adjacency tensor (transpose).
v_T : ndarray
Array with values of entries A[j, i] given non-zero entry (i, j).
rw : list
List whose elements are reciprocity (considering the weights of the edges) values, one per each layer.
"""
N = A[0].number_of_nodes()
L = len(A)
rw = []
d1 = np.array((), dtype='int64')
d2, d2_T = np.array((), dtype='int64'), np.array((), dtype='int64')
d3, d3_T = np.array((), dtype='int64'), np.array((), dtype='int64')
v, vT, v_T = np.array(()), np.array(()), np.array(())
for l in range(L):
b = nx.to_scipy_sparse_matrix(A[l])
b_T = nx.to_scipy_sparse_matrix(A[l]).transpose()
rw.append(np.sum(b.multiply(b_T))/np.sum(b))
nz = b.nonzero()
nz_T = b_T.nonzero()
d1 = np.hstack((d1, np.array([l] * len(nz[0]))))
d2 = np.hstack((d2, nz[0]))
d2_T = np.hstack((d2_T, nz_T[0]))
d3 = np.hstack((d3, nz[1]))
d3_T = np.hstack((d3_T, nz_T[1]))
v = np.hstack((v, np.array([b[i, j] for i, j in zip(*nz)])))
vT = np.hstack((vT, np.array([b_T[i, j] for i, j in zip(*nz_T)])))
v_T = np.hstack((v_T, np.array([b[j, i] for i, j in zip(*nz)])))
subs_ = (d1, d2, d3)
subs_T_ = (d1, d2_T, d3_T)
data = skt.sptensor(subs_, v, shape=(L, N, N), dtype=v.dtype)
data_T = skt.sptensor(subs_T_, vT, shape=(L, N, N), dtype=vT.dtype)
return data, data_T, v_T, rw
def update_expec(self, data, avgs):
if isinstance(data, skt.dtensor):
ind = data.nonzero()
non_zero_ent = data[ind]
elif isinstance(data, skt.sptensor):
ind = data.subs
non_zero_ent = data.vals
size_ind = ind[0].size
log_a = np.ones((size_ind, self.comp))
for m in xrange(self.modes):
log_a *= avgs[m][ind[m], :]
log_a = np.log(log_a.sum(axis=1))
q = np.empty((size_ind, self.n_trunc), dtype=np.float64)
for i in range(1, self.n_trunc + 1):
q[:, i - 1] = (-i * self.lam) + (non_zero_ent -
i) * np.log(i) + i * log_a + i - 1
norm = logsumexp(q, axis=1)
q = np.exp(q - norm[:, np.newaxis])
self.expec = np.zeros((size_ind, ))
for i in range(1, self.n_trunc + 1):
self.expec += i * q[:, i - 1]
self.expec = skt.sptensor(ind,
self.expec,
shape=self.dim,
dtype=np.float64)
def build_sparse_B_from_A(A):
"""
Create the sptensor adjacency tensor of a networkX graph.
Parameters
----------
A : list
List of MultiDiGraph NetworkX objects.
Returns
-------
data : sptensor
Graph adjacency tensor.
"""
N = A[0].number_of_nodes()
L = len(A)
d1 = np.array((), dtype='int64')
d2 = np.array((), dtype='int64')
d3 = np.array((), dtype='int64')
v = np.array(())
for l in range(L):
b = nx.to_scipy_sparse_matrix(A[l])
nz = b.nonzero()
d1 = np.hstack((d1, np.array([l] * len(nz[0]))))
d2 = np.hstack((d2, nz[0]))
d3 = np.hstack((d3, nz[1]))
v = np.hstack((v, np.array([b[i, j] for i, j in zip(*nz)])))
subs_ = (d1, d2, d3)
data = skt.sptensor(subs_, v, shape=(L, N, N), dtype=v.dtype)
return data
def __init__(self, subs, vals, shape=None, dtype=int, accumfun=sum.__call__):
if len(vals) <= 0:
ValueError("the input tensor is ZERO!")
subs = np.asarray(subs)
ns, ndims = subs.shape
self._dimsmin_ = np.min(subs, 0)
self._dimsmap_ = list()
for d in range(ndims):
undim = np.unique(subs[:, d])
self._dimsmap_.append(dict(zip(undim, range(len(undim)))))
nwsubs = list()
for k in range(ns):
term = list()
for d in range(ndims):
term.append(self._dimsmap_[d][subs[k, d]])
nwsubs.append(np.asarray(term))
tensor = sptensor(tuple(np.asarray(nwsubs).T), np.asarray(vals),
shape, dtype, accumfun=accumfun)
self.data = dict(zip(map(tuple, np.asarray(tensor.subs).T), tensor.vals))
self.shape = tensor.shape
self.ndim = tensor.ndim
self.nnz = tensor.nnz()
self.vals = np.sum(self.data.values())
def test_spttv(subs, vals, shape):
S = sptensor(subs, vals, shape=shape)
K = ktensor([
np.random.randn(shape[0], 2),
np.random.randn(shape[1], 2),
np.random.randn(shape[2], 2)
])
K.innerprod(S)
def test_spttv():
subs = (
array([0, 1, 0, 5, 7, 8]),
array([2, 0, 4, 5, 3, 9]),
array([0, 1, 2, 2, 1, 0])
)
vals = array([1, 1, 1, 1, 1, 1])
S = sptensor(subs, vals, shape=[10, 10, 3])
K = ktensor([randn(10, 2), randn(10, 2), randn(3, 2)])
K.innerprod(S)
def test_spttv():
# subs = (
# array([0, 1, 0, 5, 7, 8]),
# array([2, 0, 4, 5, 3, 9]),
# array([0, 1, 2, 2, 1, 0])
# )
# vals = array([1, 1, 1, 1, 1, 1])
S = sptensor(subs, vals, shape=shape)
K = ktensor([randn(shape[0], 2), randn(shape[1], 2), randn(shape[2], 2)])
K.innerprod(S)
def test_spttv():
#subs = (
# array([0, 1, 0, 5, 7, 8]),
# array([2, 0, 4, 5, 3, 9]),
# array([0, 1, 2, 2, 1, 0])
#)
#vals = array([1, 1, 1, 1, 1, 1])
S = sptensor(subs, vals, shape=shape)
K = ktensor([randn(shape[0], 2), randn(shape[1], 2), randn(shape[2], 2)])
K.innerprod(S)
def _update_theta_gamma(self, m):
subs_I_M = np.where(self.y_E_DIMS > 1e-4)
y_spt_DIMS = skt.sptensor(subs_I_M,
self.y_E_DIMS[subs_I_M],
shape=self.y_E_DIMS.shape,
dtype=float)
tmp_DIMS = y_spt_DIMS.vals / self._reconstruct_nz(y_spt_DIMS.subs)
uttkrp_nonzero_DK = sp_uttkrp(tmp_DIMS, y_spt_DIMS.subs, m,
self.theta_G_DK_M)
self.theta_shp_DK_M[
m][:, :] = self.alpha + self.theta_G_DK_M[m] * uttkrp_nonzero_DK
def assignBlock(self, i, s, X,Y, Z1, Z2, S, tensor_dim_ceiling, subs_idx, num_workers, tensor_dim_size):
_dict = {}
num_ways = len(tensor_dim_ceiling)
strata_index = [int(math.floor(i + sum([float(s) / num_workers**way_index for way_index in range(way_index+1)]))) % num_workers for way_index in range(num_ways)]
strata_range = [range(int(math.ceil(strata_index[way_index] * tensor_dim_ceiling[way_index])), int(math.ceil((strata_index[way_index]+1) * tensor_dim_ceiling[way_index]))) for way_index in range(num_ways)]
strata_range = [[o for o in each_range if o < tensor_dim_size[index]] for index, each_range in enumerate(strata_range)]
strata_range = [range(each_range[0], (each_range[-1] + 1)) for each_range in strata_range]
total_nb_points = len(subs_idx.value)
subs = [idx for idx in subs_idx.value if all([idx[way_index] in strata_range[way_index] for way_index in range(num_ways)])]
subs_x = [tuple(idx) for idx in subs_idx.value if all([idx[way_index] in strata_range[way_index] for way_index in range(num_ways)])]
X_vals = []
Y_vals = []
ZX_vals = []
ZY_vals = []
S_vals = []
if len(subs_x) > 0:
for i in range(len(subs_x)):
tensor_index = tuple(np.array(subs_x[i]).T)
X_vals.append(X[tensor_index][0])
Y_vals.append(Y[tensor_index][0])
ZX_vals.append(Z1[tensor_index][0])
ZY_vals.append(Z2[tensor_index][0])
S_vals.append(S[tensor_index][0])
X_subs = sptensor(tuple(np.array(subs_x).T), X_vals,shape=tensor_dim_size, dtype=np.float)
Y_subs = sptensor(tuple(np.array(subs_x).T), Y_vals,shape=tensor_dim_size, dtype=np.float)
ZX_subs = sptensor(tuple(np.array(subs_x).T), ZX_vals,shape=tensor_dim_size, dtype=np.float)
ZY_subs = sptensor(tuple(np.array(subs_x).T), ZY_vals,shape=tensor_dim_size, dtype=np.float)
S_subs = sptensor(tuple(np.array(subs_x).T), S_vals,shape=tensor_dim_size, dtype=np.float)
_dict['ratio'] = len(subs_x) / float(total_nb_points)
_dict['X_subs'] = X_subs
_dict['Y_subs'] = Y_subs
_dict['ZX_subs'] = ZX_subs
_dict['ZY_subs'] = ZY_subs
_dict['S_subs'] = S_subs
_dict['subs'] = subs
return _dict
else:
return None
def load_sptensor(fp, start_index=1, dtype=None):
nmodes = int(fp.readline())
ndims = tuple(map(int, fp.readline().split()))
assert nmodes == len(ndims)
subs = tuple(([] for m in ndims))
vals = []
while True:
line = fp.readline()
if not line:
break
linesep = line.split()
for m, x in enumerate(linesep[:-1]):
subs[m].append(int(x) - start_index)
vals.append((dtype or float)(linesep[-1]))
return sktensor.sptensor(subs, vals, shape=ndims, dtype=dtype)
def create_sptensor(self, graphs):
"""
Create a sparse tensor
:param graphs:
:return:
"""
tuples = []
# triplets = np.array([(u, v, t) for t in range(1, len(graphs)+1) for u, v in graphs[i].edges_iter()] +
# [(v, u, t) for t in range(1, len(graphs)+1) for u, v in graphs[i].edges_iter()])
for i, graph in graphs.iteritems():
for u, v in graph.edges_iter():
tuples.append([self.node_pos[u], self.node_pos[v], i-1])
tuples.append([self.node_pos[v], self.node_pos[u], i-1])
triplets = np.array([(u, v, t) for u, v, t in tuples])
T = sptensor(tuple(triplets.T), vals=np.ones(len(triplets)), shape=(len(self.node_ids), len(self.node_ids),
len(graphs)))
return T
def get_tensor(middle_end='sktensor', cutoff=10):
logging.info('Reweighting: log')
verb_tensor_path = os.path.join(
projdir, '{}/tensor_{}.pkl'.format(middle_end, cutoff))
if os.path.exists(verb_tensor_path):
logging.info('Loading tensor from {}'.format(verb_tensor_path))
tensor, indices = pickle.load(open(verb_tensor_path, mode='rb'))
logging.debug(tensor.shape)
return tensor, indices
occurrence, marginals = mazsola_reader()
def get_index(freq_dict):
items = sorted(filter(lambda item: item[1] >= cutoff,
freq_dict.items()),
key=operator.itemgetter(1),
reverse=True)
logging.debug(items[-3:])
return dict([(w, i) for i, (w, f) in enumerate(items)])
coords, data = ([], [], []), []
indices = [get_index(fd) for fd in marginals]
logging.info('Building tensor...')
logging.info(' Pupulating lists...')
for i, ((svo), freq) in enumerate(occurrence.items()):
if not i % 2000000:
logging.debug(' {:,}'.format(i)) #'{} {}'.format(svo[1], freq))
for i, word in enumerate(svo):
if svo[i] not in indices[i]:
break
else:
for i, word in enumerate(svo):
coords[i].append(indices[i][svo[i]])
data.append(np.log(freq))
logging.info(' Creating array')
shape = tuple(map(len, indices))
logging.info(shape)
if middle_end == 'tensorly':
tensor = sparse.COO(coords, data,
shape=shape) #, has_duplicates=False)
elif middle_end == 'sktensor':
tensor = sktensor.sptensor(coords, data, shape=shape)
else:
raise NotImplementedError
pickle.dump((tensor, indices), open(verb_tensor_path, mode='wb'))
logging.info(tensor)
return tensor, indices
def sptensor_from_dense_array(X):
"""
Create an sptensor from a ndarray or dtensor.
Parameters
----------
X : ndarray
Input data.
Returns
-------
sptensor from a ndarray or dtensor.
"""
subs = X.nonzero()
vals = X[subs]
return skt.sptensor(subs, vals, shape=X.shape, dtype=X.dtype)
def mach(X, ranks, p):
"""
Implementation of MACH prposed in
C. E. Tsourakakis. Mach: Fast randomized tensor decompositions. In ICDM, pages 689–700, 2010.
"""
prod_ns = np.prod(X.shape)
indn = np.random.choice(prod_ns, int(prod_ns * p), replace=False)
multinds = np.unravel_index(indn, X.shape)
X_sp = st.sptensor(multinds, (1 / p) * X[multinds], shape=X.shape)
### for sparse eigen decomposition (scipy linalg problem)
_ranks = np.array(ranks)
_shape = np.array(X.shape)
_ind = _ranks >= _shape
_ranks[_ind] = _shape[_ind] - 1
return st.tucker_hooi(X_sp, _ranks.tolist(), init='nvecs')
def _init_data(self, data, mask=None):
if isinstance(data, np.ndarray):
data = skt.sptensor(data.nonzero(), data[data.nonzero()],
data.shape)
assert isinstance(data, skt.sptensor)
assert data.ndim == 4
assert data.shape[0] == data.shape[1]
V, A, T = data.shape[1:]
self.n_actors = V
self.n_actions = A
self.n_timesteps = T
if mask is not None:
assert isinstance(mask, np.ndarray)
assert (mask.ndim == 2) or (mask.ndim == 3)
assert mask.shape[-2:] == (V, V)
assert np.issubdtype(mask.dtype, np.integer)
return data
def _init_data(self, data, mask=None):
if isinstance(data, np.ndarray):
data = skt.sptensor(data.nonzero(),
data[data.nonzero()],
data.shape)
assert isinstance(data, skt.sptensor)
assert data.ndim == 4
assert data.shape[0] == data.shape[1]
V, A, T = data.shape[1:]
self.n_actors = V
self.n_actions = A
self.n_timesteps = T
if mask is not None:
assert isinstance(mask, np.ndarray)
assert (mask.ndim == 2) or (mask.ndim == 3)
assert mask.shape[-2:] == (V, V)
assert np.issubdtype(mask.dtype, np.integer)
return data
points_c = []
vals = []
for k in range(len(predicates)):
tups = dictTrp1[k]
for tup in tups:
points_a.append(tup[0])
points_b.append(tup[1])
points_c.append(k)
vals.append(tup[2])
L = []
L.append(points_a)
L.append(points_b)
L.append(points_c)
X1 = sptensor(tuple(L), vals, shape=(I, J, len(predicates)), dtype=float)
points_a = []
points_b = []
points_c = []
vals = []
for k in range(len(predicates)):
tups = dictTrp2[k]
for tup in tups:
points_a.append(tup[0])
points_b.append(tup[1])
points_c.append(k)
vals.append(tup[2])
L = []
def get_tensor(self, log=True, divide_by_marginal=False):
def get_index(freq_dict):
items = sorted(filter(lambda item: item[1] >= self.cutoff,
freq_dict.items()),
key=operator.itemgetter(1),
reverse=True)
logging.debug(items[-3:])
return dict([(w, i) for i, (w, f) in enumerate(items)])
verb_tensor_path = os.path.join(self.projdir, '{}_{}.pkl').format(
'pmi' if divide_by_marginal else 'logfreq', # TODO
self.cutoff)
if False: #os.path.exists(verb_tensor_path):
logging.info('Loading tensor from {}'.format(verb_tensor_path))
tensor, indices = pickle.load(open(verb_tensor_path, mode='rb'))
logging.debug(tensor.shape)
return tensor, indices
occurrence, marginals = self.mazsola_reader()
coords, data = tuple([] for _ in range(self.ndim)), []
indices = [get_index(fd) for fd in marginals]
logging.info('Building tensor...')
logging.info(' Pupulating lists...')
total = 0
for i, ((svo), freq) in enumerate(occurrence.items()):
if not i % 2000000:
logging.debug(' {:,}'.format(i))
for j, word in enumerate(svo):
if svo[j] not in indices[j]:
break
else:
for i, word in enumerate(svo):
coords[i].append(indices[i][svo[i]])
to_debug = (coords[0][-1] == 1 and coords[1][-1] == 1
and coords[2][-1] == 0)
#if not pmi:
# TODO freq += 1
if to_debug:
logging.debug(freq)
total += freq
if to_debug:
logging.debug(freq)
if divide_by_marginal:
# TODO PPMI
for i in range(self.ndim):
freq /= marginals[i][svo[i]]
if to_debug:
logging.debug((marginals[i][svo[i]], freq))
if log:
freq = np.log(freq)
if to_debug:
logging.debug(freq)
data.append(freq)
logging.info(' Total: {}'.format(total))
logging.info(' Creating array')
shape = tuple(map(len, indices))
logging.info(shape)
data = np.array(data)
if divide_by_marginal:
if log:
data += 2 * np.log(total)
else:
data *= total**2
tensor = sktensor.sptensor(coords, data, shape=shape)
#pickle.dump((tensor, indices), open(verb_tensor_path, mode='wb'))
logging.info(tensor)
return tensor, indices
import numpy.random as rn
import cPickle as pickle
import numpy as np
import sktensor as skt
data = rn.poisson(0.2, size=(25, 25, 100)) # 3-mode SPARSE count tensor of size 10 x 8 x 3
subs = data.nonzero() # subscripts where the ndarray has non-zero entries
vals = data[data.nonzero()] # corresponding values of non-zero entries
sp_data = skt.sptensor(subs, # create an sktensor.sptensor
vals,
shape=data.shape,
dtype=data.dtype)
with open('data.dat', 'w+') as f: # can be stored as a .dat using pickle
pickle.dump(sp_data, f)
with open('data.dat', 'r') as f: # can be loaded back in using pickle.load
tmp = pickle.load(f)
assert np.allclose(tmp.vals, sp_data.vals)
def tosptensor(self):
return sptensor(tuple(np.asarray(self.data.keys()).T), np.asarray(self.data.values()), self.shape)
def HOALS(data,dims,ranks,model='tucker',lambda_=0.8,alpha=0.1,num_iters=5,implicit=False):
"""
Parameters
data : DataFrame
[0] : userId
[1] : itemId
[2] : actionId
[3] : rating
dims : list
[0] : number of users
[1] : number of items
[2] : number of actions
"""
data.columns = np.arange(data.shape[1])
C_train = sktensor.sptensor((data[2],data[0],data[1]),
data[3],shape=(dims[2],dims[0],dims[1]))
#==============================================================================
# recuparation of the (user,item,rate) of the unfold matrix
#==============================================================================
# train set
C1 = sktensor.csr_matrix(C_train.unfold(1))
y1 = list(C1.indices)
indptr1 = C1.indptr
r1 = list(C1.data)
tmp1 = indptr1[1:len(indptr1)]-indptr1[0:(len(indptr1)-1)]
x1 = []
for i in np.arange(len(tmp1)):
x1.extend(np.repeat(i,tmp1[i]))
C2 = sktensor.csr_matrix(C_train.unfold(2))
y2 = list(C2.indices)
indptr2 = C2.indptr
r2 = list(C2.data)
tmp2 = indptr2[1:len(indptr2)]-indptr2[0:(len(indptr2)-1)]
x2 = []
for i in np.arange(len(tmp2)):
x2.extend(np.repeat(i,tmp2[i]))
C3 = sktensor.csr_matrix(C_train.unfold(0))
y3 = list(C3.indices)
indptr3 = C3.indptr
r3 = list(C3.data)
tmp3 = indptr3[1:len(indptr3)]-indptr3[0:(len(indptr3)-1)]
x3 = []
for i in np.arange(len(tmp3)):
x3.extend(np.repeat(i,tmp3[i]))
dataTrain = {}
dataTrain[0] = pd.DataFrame([x1,y1,r1]).T
dataTrain[1] = pd.DataFrame([x2,y2,r2]).T
dataTrain[2] = pd.DataFrame([x3,y3,r3]).T
dataTrain[0] = dataTrain[0][dataTrain[0][2]!=0] # where the rating is not null
dataTrain[1] = dataTrain[1][dataTrain[1][2]!=0]
dataTrain[2] = dataTrain[2][dataTrain[2][2]!=0]
#==============================================================================
# Factorization
#==============================================================================
ratings = {}
res = {}
features = {}
times = []
for i in range(3):
if i==0:
mode = 'User'
elif i==1:
mode = 'Item'
elif i==2:
mode = 'Action'
print("Start "+mode+" Learning")
dataTrain[i] = sqlContext.createDataFrame(dataTrain[i]).rdd
ratings[i] = dataTrain[i].map(lambda l: Rating(float(l[0]), float(l[1]), float(l[2])))
#ratings[i] = dataTrain[i].map(lambda l: array([float(l[0]), float(l[1]), float(l[2])]))
# Build the recommendation model using Alternating Least Squares
t0 = time.time()
if implicit:
res[i] = ALS.trainImplicit(ratings=ratings[i], rank=ranks[i], iterations=num_iters, seed=0, lambda_=lambda_, alpha=alpha)
else:
res[i] = ALS.train(ratings=ratings[i], rank=ranks[i], iterations=num_iters, seed=0, lambda_=lambda_)
t1 = time.time()
delta = t1-t0
print('time :',delta)
times.append(delta)
#features[i] = res[i].userFeatures()
print('longest mode time :',np.max(times))
subs_1 = np.append(data[:,:2], np.zeros((n, 1)), 1)
subs_2 = np.append(data[:,:2], np.ones((n, 1)), 1)
subs = np.vstack([subs_1, subs_2])
subs = subs.astype(int)
vals = np.hstack([data[:,2], data[:, 3]])
vals = vals.flatten()
# convert subs tuple of arrays (rows, cols, tubes)
subs = (subs[:,0], subs[:,1], subs[:,2])
# load into sparse tensor
T = sptensor(subs, vals)
logging.debug("Starting Tucker decomposition")
#T = loadmat('../datasets/alyawarra/alyawarradata.mat')['Rs']
#X = [lil_matrix(T[:, :, k]) for k in range(T.shape[2])]
#X = [lil_matrix(T[:, :, k]) for k in range(T.shape[2])]
# Decompose tensor using RESCAL-ALS
P = tucker_hooi(T, [10, 10, 2], init='random')
logging.debug("Finished tucker decomposition")
def main(n_top_words, alpha, beta, rank, priv, n_iters=200):
# output_data_shape = (n_docs, n_words)
# theta_DK = np.random.gamma(alpha, beta, (n_docs, rank))
# phi_KV = np.random.gamma(alpha, beta, (rank, n_words))
# poisson_priors_DV = parafac((theta_DK, phi_KV.T))
# data_DV = np.random.poisson(poisson_priors_DV, output_data_shape)
with np.load('sotu_years.npz') as dat_file:
data_DV = dat_file['Y_DV']
vocab = dat_file['types_V']
n_docs, n_words = data_DV.shape
bpptf_model = BPPTF(n_modes=2, n_components=rank, verbose=True, max_iter=1)
bptf_model = BPTF(n_modes=2, n_components=rank, verbose=True, max_iter=1)
# initialize both models
modes = (0, 1)
data_usable = preprocess(data_DV)
if isinstance(data_usable, skt.dtensor):
bpptf_model.data_DIMS = data_usable.copy()
else:
bpptf_model.data_DIMS = skt.sptensor(
tuple((np.copy(ds) for ds in data_usable.subs)),
data_usable.vals.copy())
bpptf_model._init_all_components(data_usable.shape)
bptf_model._init_all_components(data_usable.shape)
bpptf_model.y_E_DIMS = data_usable
if isinstance(data_usable, skt.sptensor):
bpptf_model.y_E_DIMS = bpptf_model.y_E_DIMS.toarray()
for i in range(n_iters):
print i
for m in modes:
# check_equal(bpptf_model, bptf_model, m)
bpptf_model._update_theta_gamma(m)
bptf_model._update_gamma(m, data_usable)
# check_equal(bpptf_model, bptf_model, m)
bpptf_model._update_theta_delta(m, None)
bptf_model._update_delta(m, None)
# check_equal(bpptf_model, bptf_model, m)
bpptf_model._update_cache(m)
bptf_model._update_cache(m)
# check_equal(bpptf_model, bptf_model, m)
bpptf_model._update_beta(m) # must come after cache update!
bptf_model._update_beta(m)
# check_equal(bpptf_model, bptf_model, m)
bpptf_model._check_component(m)
bptf_model._check_component(m)
# check_equal(bpptf_model, bptf_model, m)
print "Old topics"
new_phi = bptf_model.E_DK_M[1].T
top_words = np.argpartition(new_phi, n_words - n_top_words)[:,
-n_top_words:]
for topic in xrange(rank):
top_word_vals = zip(-new_phi[topic, top_words[topic]],
vocab[top_words[topic]])
print topic, ' '.join(
['{}'.format(wd) for (_, wd) in sorted(top_word_vals)])
print "\nNew topics"
new_phi = bpptf_model.theta_E_DK_M[1].T
top_words = np.argpartition(new_phi, n_words - n_top_words)[:,
-n_top_words:]
for topic in xrange(rank):
top_word_vals = zip(-new_phi[topic, top_words[topic]],
vocab[top_words[topic]])
print topic, ' '.join(
['{}'.format(wd) for (_, wd) in sorted(top_word_vals)])
def sptensor_from_dense_array(X):
"""Creates an sptensor from an ndarray or dtensor."""
subs = X.nonzero()
vals = X[subs]
return skt.sptensor(subs, vals, shape=X.shape, dtype=X.dtype)
def fromarray(A):
"""Create a sptensor from a dense numpy array"""
subs = np.nonzero(A)
vals = A[subs]
return sptensor(subs, vals, shape=A.shape, dtype=A.dtype)
# Generate a synthetic toy data set
true_A_IK = prg(alpha, lambd,
size=(n_genes, n_feats)) # synthetic genes x feats matrix
true_P_JK = prg(alpha, lambd,
size=(n_cells, n_feats)) # synthetic cells x feats matrix
true_M_IJ = true_A_IK.dot(true_P_JK.T) # synthetic mean of observed counts
true_Y_IJ = np.zeros_like(true_M_IJ, dtype=int) # synthetic observed counts
true_Y_IJ[true_M_IJ > 0] = rn.poisson(true_M_IJ[true_M_IJ > 0])
subs = true_Y_IJ.nonzero() # subscripts where the ndarray has non-zero entries
vals = true_Y_IJ[
true_Y_IJ.nonzero()] # corresponding values of non-zero entries
sp_data = skt.sptensor(
subs, # create an sktensor.sptensor
vals,
shape=true_Y_IJ.shape,
dtype=true_Y_IJ.dtype)
sns.heatmap(true_Y_IJ, cmap='Blues')
plt.show()
model = PRGPMF(n_genes=n_genes,
n_cells=n_cells,
n_feats=n_feats,
alpha=alpha,
lambd=lambd,
seed=seed,
n_threads=n_threads)
n_samples = 100 # how many posterior samples to collect
def RDFParsing(self,path,isSparse):
#parse RDF
g=rdflib.Graph()
g.parse(file=open(path, "r"),
format="application/rdf+xml")
entities = []
predicates = []
for s in g.subjects(None, None):
ss=s.encode('utf-8').__str__()
if ss not in entities:
entities.extend([ss])
for o in g.objects(None, None):
oo=o.encode('utf-8').__str__()
if type(o).__name__ != "Literal" :
if oo not in entities:
entities.extend([oo])
for p in g.predicates(None, None):
pp=p.encode('utf-8').__str__()
if pp not in predicates:
predicates.extend([pp])
entities = np.array(entities)
#print entities
logging.warning( "*************************" )
predicates = np.array(predicates)
#print predicates
#prepare tensor frontal slices as np matrices
lenentities = len(entities)
lenpredicates = len(predicates)
logging.warning("Tensor: "+str(lenentities)+" X "+str(lenentities)+" X "+str(lenpredicates))
T = np.zeros((lenentities, lenentities, lenpredicates),dtype=np.int)
print "entities".join(map(str, entities))
for s,p,o in g:
try:
logging.warning("try to build the tensor")
ss=s.__str__().encode('utf-8')
oo=o.__str__().encode('utf-8')
pp=p.__str__().encode('utf-8')
i,j,k=entities.tolist().index(ss),entities.tolist().index(oo),predicates.tolist().index(pp)
#logging.warning("ijk".join(map(str, [i,j,k])))
#print "ijk".join(map(str, [i,j,k]))
value=1.0
if "__" in p:
#print p.split("__")[len(p.split("__"))-1]
value=float(p.split("__")[len(p.split("__"))-1])
#print "value"+value
T[i, j, k] = value
except:
#printException()
continue
if isSparse:
xyz, c = self.sparsed(T)
logging.warning("xyz: ")
logging.warning("-".join(map(str, xyz)))
#logging.warning("c: ".join(c))
Tensor = sptensor(xyz, c, shape=(lenentities, lenentities, lenpredicates), dtype=np.int)
else:
Tensor = dtensor(T)
#print "*************************"
#print Tensor[:,:,slice]
return Tensor,g,entities,predicates
def sptensor_from_dense_array(X):
"""Creates an sptensor from an ndarray or dtensor."""
subs = X.nonzero()
vals = X[subs]
return skt.sptensor(subs, vals, shape=X.shape, dtype=X.dtype)
def run_case_study():
iter_cnt = 10
nb_trial = 3
alg_names = [PAIRFAC]
# alg_names = [SDCDT]
iters = [iter_cnt] * len(alg_names)
if alg_names[0].__name__ in ["SDCDT"]:
distance = int(sys.argv[1])
alpha = float(sys.argv[2])
beta = float(sys.argv[3])
alpha_pars = [alpha]
beta_pars = [beta]
gamma_pars = [1e+0]
delta_pars = [1e+0]
else:
distance = 3
alpha = float(sys.argv[1])
beta = float(sys.argv[2])
gamma = float(sys.argv[3])
alpha_pars = [alpha]
beta_pars = [beta]
gamma_pars = [gamma]
delta_pars = [1e-8]
case_study = "ha_{}mp4d".format(str(sys.argv[4]))
case_study = "wpi_{}mp4d".format(str(sys.argv[4]))
sub_dir = "classification"
num_workers = 2
nb_points = 40000 # nyc
train_proportions = [10]
layers = [0]
_type = "function"
if case_study.startswith("wpi_"):
nb_points = 4000
dims = [10, 59, 2]
dims = [266, 59, 4, 10]
if case_study.endswith("4d"):
dims = [376, 58, 7, 10]
if "5" in case_study:
dims = [266, 59, 4, 10]
if "6" in case_study:
dims = [408, 68, 4, 10]
domain = "math"
source1 = "good"
source2 = "bad"
R_set = {0: 6}
R_check = 6
if case_study.startswith("ha_"):
nb_points = 4000
dims = [5, 34, 6, 54]
domain = "mooc"
source1 = "good"
source2 = "bad"
R_set = {0: 6}
R_check = 6
k = domain + "_" + case_study
bootstrap_seed_list = [0, 1, 2, 3, 4]
for bootstrap_seed in bootstrap_seed_list:
_log.info("distance:{}".format(distance))
_log.info("bootstrap_seed:{}".format(bootstrap_seed))
from datetime import datetime
idx_list, value_list_X, value_list_Y, value_list_ZX, value_list_ZY, value_list_S = read_domain_data(
source1,
source2,
dims,
domain,
_type,
case_study,
k,
nb_points,
bootstrap_seed=bootstrap_seed)
X = sptensor(tuple(np.asarray(idx_list).T),
value_list_X,
shape=dims,
dtype=np.float)
Y = sptensor(tuple(np.asarray(idx_list).T),
value_list_Y,
shape=dims,
dtype=np.float)
conf = SparkConf().setAppName("PairFac...")
sc = SparkContext(conf=conf)
Lambda_all = [
alpha_pars, beta_pars, gamma_pars, delta_pars, train_proportions
]
Lambda_all = list(itertools.product(*Lambda_all))
len_paraset = len(Lambda_all)
cur_para_index = 0
train_cur_proportion = float(10)
test_portion = (10.0 - train_cur_proportion) / 2.0
np.random.seed(2)
Weight = np.random.choice(
[0, 1, 2, 3],
size=(len(value_list_ZX), ),
p=[
test_portion / 10, test_portion / 10,
train_cur_proportion / 10,
(10.0 - train_cur_proportion - test_portion * 2) / 10
])
train_index_1 = train_index_2 = np.where(Weight == 2)
validation_index_1 = validation_index_2 = np.where(Weight == 1)
test_index_1 = test_index_2 = np.where(Weight == 0)
X_train = construct_tensor(value_list_X, train_index_1, idx_list, dims)
Y_train = construct_tensor(value_list_Y, train_index_1, idx_list, dims)
ZX_train = construct_tensor(value_list_ZX, train_index_1, idx_list,
dims)
ZY_train = construct_tensor(value_list_ZY, train_index_1, idx_list,
dims)
S_train = construct_tensor(value_list_S, train_index_1, idx_list, dims)
non_zero_idxs = np.asarray(idx_list)[train_index_1[0]]
D_matrix = np.zeros((X_train.shape[0], X_train.shape[0]))
W_matrix = np.zeros((X_train.shape[0], X_train.shape[0]))
for alg_name, iter_cnt in zip(alg_names, iters):
for each_lambda in Lambda_all:
progress = cur_para_index * 1.0 / len_paraset
cur_para_index += 1
_log.info('[{}] Running {}...'.format(alg_names[0].__name__,
progress))
alg = alg_name()
Lambda = list(each_lambda)
cur_paras = '_'.join([str(x) for x in each_lambda])
fname = 'weight_s_t_2_{}_layer_0_distance_{}_seed_{}_R_{}'.format(
alg.__class__.__name__, distance, bootstrap_seed, R_check)
directory_ = PROJECT_DIR + "/output/output_" + k + "_" + sub_dir + ""
if not os.path.exists(directory_):
os.makedirs(directory_)
layer_fileName = directory_ + "/weights/" + str(
cur_paras) + "/" + str(fname)
embeddings_dir = directory_ + "/embeddings/" + str(
alg_names[0].__name__) + "/" + str(
bootstrap_seed) + "/" + cur_paras
if not os.path.exists(embeddings_dir):
os.makedirs(embeddings_dir)
weights_dir = directory_ + "/weights/" + cur_paras
if not os.path.exists(weights_dir):
os.makedirs(weights_dir)
if os.path.exists(layer_fileName):
_log.info("{} exists".format(layer_fileName))
continue
alg.run_multi_trials( sc, X_train, Y_train, ZX_train, ZY_train, S_train, k,k, Lambda, D_matrix, W_matrix, \
num_trial=nb_trial, max_iter=iter_cnt, verbose=2,
noise=0.01,nb_points=nb_points,
non_zero_idxs=non_zero_idxs,
num_workers=num_workers,distance=distance, R_set = R_set,layers = layers,bootstrap_seed = bootstrap_seed)
gc.collect()
sc.stop()
def NTF_sampling_24H(sampling,
df_classified,
flags,
start,
stop,
path,
vectorizer_new=0,
vectorizer_s=0,
n_topics=10,
n_features=1000,
matlab=False,
monuments=False):
# Feed the vectorizer with all the words in the dataset. Counts is the tweet/term matrix.
# fit_transform: fit first (build the features list with the relevant words)
# then transform: build the tweet/term matrix with the relevant tokens.
if not vectorizer_new:
print 'No vectorizer defined. Returning None'
return None
if matlab:
name_matlab = path + 'matlab/TorInst{}Matr'.format(n_features)
Coord_CRS_global = []
Data_CRS_global = []
Ncells = []
snapshots = df_classified.columns.tolist()[0:-1]
if start >= stop:
print 'incorrect start and/or stop dates. performing NTF on whole passed dataset'
stop = min(len(flags), len(snapshots))
#For every snapshot taken
ct = 0
for month in snapshots:
ct += 1
print flags[snapshots.index(month)]
This_Month = df_classified[month].tolist()
#print len(list_reviews_rest), 'tagged cells for ', year_month
# Learn the vocabulary dictionary and return term-document matrix.
print len(This_Month)
counts = vectorizer_new.transform(This_Month)
#Transform a count matrix to a normalized tf-idf representation.
#(i.e terms with frequencies too hi or lo are removed)
# Weights are indexed by (postID, term): weight
tfidf = TfidfTransformer().fit_transform(counts)
if matlab:
savemat(name_matlab + str(ct), {'tfidf': tfidf})
#print 'tfidf done:'
#print tfidf
C, D = IL.read_CRS_totensor(
tfidf, n_features,
snapshots.index(month) - flags.index(start))
#print 'C,D'#, C,D
Coord_CRS_global.append(C)
Data_CRS_global.append(D)
triples = []
triples_data = []
#For every month in the timeline
for i in range(0, len(Coord_CRS_global)):
c = Coord_CRS_global[i]
# For every post in this month
for e in c:
#Add the non-zero elements coordinates
triples.append(e)
for d in Data_CRS_global:
for e in d:
triples_data.append(e)
triples = [list(i) for i in triples]
try:
# maxNcells=max([e[0] for e in triples])
maxNcells = len(df_classified[start])
except ValueError:
print 'no non-zero element. returning None'
print triples
# Build a sktensor, which is ncp friendly. The dimensions have to be
# N_bins x n_features x N_months.
# N_months = len(Nposts) e.g, or len(Coord_CRS_global)
# N_posts_total=sum(Nposts)
X = sktensor.sptensor(tuple(list(np.asarray(triples).T)),
triples_data,
shape=(maxNcells, n_features,
df_classified.shape[1]))
X_approx_ks = ncp.nonnegative_tensor_factorization(X,
n_topics,
method='anls_bpp')
A = X_approx_ks.U[0]
B = X_approx_ks.U[1]
C = X_approx_ks.U[2]
lambdas = X_approx_ks.lmbda
voc_vector = {k: v for v, k in vectorizer_s.vocabulary_.iteritems()}
voc_serie = pd.Series(voc_vector)
TermVectors = []
TermVectorsIndex = []
for row in B.T:
row = list(row)
row = [(r, row.index(r)) for r in sorted(row)[::-1]]
TermVectors.append(set([voc_vector[e[1]] for e in row]))
TermVectorsIndex.append([(voc_vector[e[1]], e[0]) for e in row])
for i in range(0, len(TermVectorsIndex)):
TermVectorsIndex[i].sort(key=lambda tup: tup[1])
TermVectorsIndex[i] = TermVectorsIndex[i][::-1]
return A, B, C, TermVectorsIndex, TermVectors, lambdas
def construct_tensor(value_list, value_index, idx_list, dims):
return sptensor(tuple(np.asarray(idx_list)[value_index[0]].T),
list(np.asarray(value_list)[value_index[0]]),
shape=dims,
dtype=np.float)