def visualize_col():
"""
"""
adj_matrix = col_matrix(col_k, nnc_mps, agreement_matrix, nnc_arbit, mps)
classes = get_equiv_classes(nnc_mps, agreement_matrix, nnc_arbit, mps)
classes_set = set(classes)
classes_set.remove(-1)
num_classes = len(classes_set)
classes_ord = list(classes_set)
classes_ord.sort()
class_indices = {classes_ord[i]: i for i in range(num_classes)}
class_sizes = [classes.count(cls) for cls in classes_ord]
groups = [None for i in range(len(classes))]
for i in range(len(classes_ord)):
class_index = i
rel_p = [mp[1][0] for mp in mps if classes[mps.index(mp)] == classes_ord[class_index]]
counts = [rel_p.count(rel_p[j]) for j in range(len(rel_p))]
dominant = rel_p[counts.index(max(counts))]
groups[i] = dominant
names = [str(i) for i in range(len(adj_matrix))]
#groups = [str(i) for i in range(len(adj_matrix))]
write_json_graph_general(adj_matrix,
names,
groups,
filename = "viz/col/" + agr_method + '_' + str(start_session) +
'_' + str(end_session) + 'minsess' +
str(min_sessions) + '_' + 'k=' + str(col_k) +
'_' + str(nnc_arbit) + ".json")
def visualize_col_equiv():
"""
"""
adj_matrix = general_equiv(col_matrix(col_k, nnc_mps, agreement_matrix, nnc_arbit, mps), nnc_arbit)
names = [str(i) for i in range(len(adj_matrix))]
groups = [str(i) for i in range(len(adj_matrix))]
write_json_graph_general(adj_matrix,
names,
groups,
filename = "viz/coleq/" + agr_method + '_' + str(start_session) +
'_' + str(end_session) + 'minsess' +
str(min_sessions) + '_' + 'k=' + str(col_k) +
'_' + str(nnc_arbit) + ".json")
def visualize_sen_avg():
"""
"""
adj_matrix = col_matrix(col_k, sen_nnc_mps, sen_agreement_matrix, nnc_arbit, sen_mps)
classes = get_equiv_classes(sen_nnc_mps, sen_agreement_matrix, nnc_arbit, sen_mps)
classes_set = set(classes)
classes_set = [thing for thing in classes_set if thing != -1]
num_classes = len(classes_set)
classes_ord = list(classes_set)
classes_ord.sort()
class_indices = {classes_ord[i]: i for i in range(num_classes)}
class_sizes = [classes.count(cls) for cls in classes_ord]
groups = [None for i in range(len(classes))]
for i in range(len(classes_ord)):
class_index = i
rel_p = [mp[1][0] for mp in sen_mps if classes[sen_mps.index(mp)] == classes_ord[class_index]]
counts = [rel_p.count(rel_p[j]) for j in range(len(rel_p))]
dominant = rel_p[counts.index(max(counts))]
groups[i] = dominant
names = [str(classes.count(classes_ord[i])) for i in range(len(adj_matrix))]
#groups = [str(i) for i in range(len(adj_matrix))]
lis = []
for row in adj_matrix:
lis += row
lis = [thing for thing in lis if thing != 0]
average = float(sum(lis))/len(lis)
for i in range(len(adj_matrix)):
for j in range(len(adj_matrix)):
if adj_matrix[i][j] < average:
adj_matrix[i][j] = 0
else:
adj_matrix[i][j] = 1
write_json_graph_general(adj_matrix,
names,
groups,
filename = "viz/col/sen" + str(sen_n) + agr_method + '_' + str(sen_start_session) +
'_' + str(sen_end_session) + 'minsess' +
str(sen_min_sessions) + '_' + 'k=' + str(col_k) +
'_' + str(nnc_arbit) + "avg.json")
work[i].remove(j)
work[j].remove(i)
left -= 2
comp, C = compon(work)
qual = quality(adj, elen, comp, C, total)
print qual
if qual > top_qual:
top_qual = qual
top_comp = comp[:]
return top_comp
if __name__ == '__main__':
import cPickle as pickle
from global_parameters import (mps, filename_agr,
agr_method, min_sessions,
start_session, end_session,
nnc_k, nnc_mps, nnc_internal,
col_k, nnc_arbit, none_value)
f = open('data/full_voting_data.pkl', 'rb')
mps = pickle.load(f)
f.close()
f = open(filename_agr, 'rb')
agreement_matrix = pickle.load(f)
common_session_matrix = pickle.load(f)
f.close()
from nnc import col_matrix
print cluster(col_matrix(col_k, nnc_mps, agreement_matrix,
nnc_arbit, mps))