def compute_log_likelihoods(target, source, env):
args = source[-1].read()
old = numpy.seterr(divide='ignore', invalid='ignore')
mat, labels, features = unpack_numpy(source[0].rstr(), dense=False)
mat = numpy.asarray(mat.todense())
totals = mat.sum(1)
logging.info("created totals matrix of shape %s", totals.shape)
newmat = numpy.empty(shape=mat.shape)
for i in range(mat.shape[1]):
newmat[:, i] = ct_log_likelihood(mat[:, i], totals)
pack_numpy(target[0].rstr(), data=newmat, labels=labels, features=features)
numpy.seterr(divide="warn", invalid="warn")
return None
def log_likelihood(target, source, env):
args = source[-1].read()
mat, labels, features = unpack_numpy(source[0].rstr())
newmat = numpy.empty(mat.shape)
obs_totals = mat.sum(1)
feat_totals = mat.sum(0)
total = feat_totals.sum()
for col, feature in enumerate(features):
for row, label in enumerate(labels):
ll = ct_log_likelihood([
(mat[row][col], obs_totals[row]),
(feat_totals[col] - mat[row][col], total - obs_totals[row])
])
newmat[row][col] = ll
#pickle.dump((newmat, labels, features), meta_open(target[0].rstr(), "w"))
return None
def pairwise_log_likelihood(target, source, env):
args = source[-1].read()
mat, labels, features = unpack_numpy(source[0].rstr())
combos = list(set([frozenset([x, y]) for x in labels for y in labels if x != y]))
newmat = numpy.empty((len(combos), len(features)))
obs_totals = mat.sum(1)
for row, combo in enumerate(combos):
for col, feature in enumerate(features):
rowA = labels.index(list(combo)[0])
rowB = labels.index(list(combo)[1])
if mat[rowA][col] == 0 and mat[rowB][col] == 0:
ll = 0.0
else:
ll = ct_log_likelihood([
(mat[rowA][col], obs_totals[rowA]),
(mat[rowB][col], obs_totals[rowB])
])
newmat[row][col] = ll
#pickle.dump((newmat, combos, [x for x in features]), meta_open(target[0].rstr(), "w"))
return None
