def apply(self, matrix_, column_marginal=None):
"""
Performs epmi weighting.
Args:
matrix_ (Matrix): Input matrix
column_marginal (np.ndarray): column marginals of the
core matrix if the matrix is a peripheral matrix
Returns:
Matrix: the matrix after applying epmi.
"""
matrix_.assert_positive()
row_sum = matrix_.sum(axis = 1)
if not column_marginal is None:
col_sum = column_marginal
else:
col_sum = matrix_.sum(axis = 0)
total = col_sum.sum()
row_sum = nonzero_invert(row_sum)
col_sum = nonzero_invert(col_sum)
col_sum = col_sum * total
matrix_ = matrix_.scale_rows(row_sum)
matrix_ = matrix_.scale_columns(col_sum)
return matrix_
def apply(self, matrix_, column_marginal=None):
"""
Performs epmi weighting.
Args:
matrix_ (Matrix): Input matrix
column_marginal (np.ndarray): column marginals of the
core matrix if the matrix is a peripheral matrix
Returns:
Matrix: the matrix after applying epmi.
"""
matrix_.assert_positive()
row_sum = matrix_.sum(axis=1)
if not column_marginal is None:
col_sum = column_marginal
else:
col_sum = matrix_.sum(axis=0)
total = col_sum.sum()
row_sum = nonzero_invert(row_sum)
col_sum = nonzero_invert(col_sum)
col_sum = col_sum * total
matrix_ = matrix_.scale_rows(row_sum)
matrix_ = matrix_.scale_columns(col_sum)
return matrix_
def _sims_to_matrix(self, vector, matrix_):
sims = DotProdSimilarity()._sims_to_matrix(vector, matrix_)
vector_norm = vector.norm()
row_norms = vector_norm * matrix_.norm(1)
row_norms = nonzero_invert(row_norms)
return sims.scale_rows(row_norms)
def apply(self, matrix_):
if self.criterion == "length":
row_norms = matrix_.norm(axis=1)
else:
row_norms = matrix_.sum(axis=1)
inv_row_norm = nonzero_invert(row_norms)
matrix_ = matrix_.scale_rows(inv_row_norm)
return matrix_
def apply(self, matrix_):
if self.criterion == "length":
row_norms = matrix_.norm(axis=1)
else:
row_norms = matrix_.sum(axis=1)
inv_row_norm = nonzero_invert(row_norms)
matrix_ = matrix_.scale_rows(inv_row_norm)
return matrix_
def main():
"""
Compute the smoothed and shifted (P)PMI matrix from a co-occurrence matrix. Smoothing is performed as described in
Omer Levy, Yoav Goldberg, and Ido Dagan. 2015. Improving distributional similarity with lessons learned from word embeddings. Trans. ACL, 3.
"""
# Get the arguments
args = docopt(
'''Compute the smoothed and shifted (P)PMI matrix from a co-occurrence matrix and save it in pickle format.
Usage:
ppmi.py [-l] <dsm_prefix> <k> <alpha> <outPath>
<dsm_prefix> = the prefix for the input files (.sm for the matrix, .rows and .cols) and output files (.ppmi)
<k> = shifting parameter
<alpha> = smoothing parameter
<outPath> = output path for space
Options:
-l, --len normalize final vectors to unit length
''')
is_len = args['--len']
dsm_prefix = args['<dsm_prefix>']
k = int(args['<k>'])
alpha = float(args['<alpha>'])
outPath = args['<outPath>']
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
logging.info(__file__.upper())
start_time = time.time()
# Get space with sparse matrix
dsm = load_pkl_files(dsm_prefix)
id2row = dsm.get_id2row()
id2column = dsm.get_id2column()
# Get probabilities
matrix_ = dsm.cooccurrence_matrix
matrix_.assert_positive()
row_sum = matrix_.sum(axis=1)
col_sum = matrix_.sum(axis=0)
# Compute smoothed P_alpha(c)
smooth_col_sum = np.power(col_sum, alpha)
col_sum = smooth_col_sum / smooth_col_sum.sum()
# Compute P(w)
row_sum = nonzero_invert(row_sum)
col_sum = nonzero_invert(col_sum)
# Apply epmi weighting (without log)
matrix_ = matrix_.scale_rows(row_sum)
matrix_ = matrix_.scale_columns(col_sum)
# Apply log weighting
matrix_.mat.data = np.log(matrix_.mat.data)
# Shift values
matrix_.mat.data -= np.log(k)
# Eliminate negative counts
matrix_.mat.data[matrix_.mat.data <= 0] = 0.0
# Eliminate zero counts
matrix_.mat.eliminate_zeros()
matrix_ = matrix_.get_mat()
if is_len:
# L2-normalize vectors
l2norm1 = linalg.norm(matrix_, axis=1, ord=2)
l2norm1[l2norm1 == 0.0] = 1.0 # Convert 0 values to 1
matrix_ /= l2norm1.reshape(len(l2norm1), 1)
dsm = Space(SparseMatrix(matrix_), id2row, id2column)
# Save the Space object in pickle format
save_pkl_files(dsm, outPath + ".ppmi.sm", save_in_one_file=False)
logging.info("--- %s seconds ---" % (time.time() - start_time))
