def slice_and_return_siblings(self, query):
sliced = self.records
result_contexts = {}
for attr, val in query.items():
hier = self.hiers[attr]
val_ori_id = hier.nid[val]
sibling_ids = hier.idd[hier.ipd[val_ori_id][0]]
# print attr
# sib_names = [hier.ind[x] for x in sibling_ids]
# print sib_names
query_copy = query.copy()
print val_ori_id
for sid in sibling_ids:
s_name = hier.ind[sid]
sliced = self.records
if sid == val_ori_id:
continue
cell_name = ''
for attr2, val2 in query_copy.items():
if attr2 == attr:
val_id = sid
value = s_name
else:
val_id = self.hiers[attr2].nid[val2]
value = val2
cell_name += attr2 + "|" + value + ";"
legal_vals = get_all_legal_vals(self.hiers[attr2], val_id)
sliced = sliced.loc[sliced[attr2].isin(legal_vals)]
if len(sliced['DocID']) > 0:
result_contexts[cell_name] = sliced['DocID']
return result_contexts#[x['DocID'] for x in result_contexts]
def slice_and_return_siblings(self, query):
sliced = self.records
result_contexts = {}
for attr, val in query.items():
hier = self.hiers[attr]
val_ori_id = hier.nid[val]
sibling_ids = hier.idd[hier.ipd[val_ori_id][0]]
# print attr
# sib_names = [hier.ind[x] for x in sibling_ids]
# print sib_names
query_copy = query.copy()
print val_ori_id
for sid in sibling_ids:
s_name = hier.ind[sid]
sliced = self.records
if sid == val_ori_id:
continue
cell_name = ''
for attr2, val2 in query_copy.items():
if attr2 == attr:
val_id = sid
value = s_name
else:
val_id = self.hiers[attr2].nid[val2]
value = val2
cell_name += attr2 + "|" + value + ";"
legal_vals = get_all_legal_vals(self.hiers[attr2], val_id)
sliced = sliced.loc[sliced[attr2].isin(legal_vals)]
if len(sliced['DocID']) > 0:
result_contexts[cell_name] = sliced['DocID']
return result_contexts #[x['DocID'] for x in result_contexts]
def slice(self, args):
sliced = self.records
for attr, val in args.items():
if attr == "Date":
print "do not support date for now"
else:
val_id = self.hiers[attr].nid[val]
legal_vals = get_all_legal_vals(self.hiers[attr], val_id)
sliced = sliced.loc[sliced[attr].isin(legal_vals)]
return sliced.copy()
def slice(self, args): sliced = self.records for attr, val in args.items(): if attr == "Date": print "do not support date for now" else: val_id = self.hiers[attr].nid[val] legal_vals = get_all_legal_vals(self.hiers[attr], val_id) sliced = sliced.loc[sliced[attr].isin(legal_vals)] return sliced.copy()
def slice_and_return_parents(self, query):
sliced = self.records
result_contexts = {}
if len(query.items()) == 1:
return {'all': [-1]}
#return [[-1]]
for attr_2, val_2 in query.items():
sliced = self.records
query_copy = query.copy()
cell_name = ''
for attr, val in query_copy.items():
if attr_2 != attr:
cell_name += attr + "_" + str(val) + "_"
val_id = self.hiers[attr].nid[val]
legal_vals = get_all_legal_vals(self.hiers[attr], val_id)
sliced = sliced.loc[sliced[attr].isin(legal_vals)]
result_contexts[cell_name] = sliced['DocID']
return result_contexts #[x['DocID'] for x in result_contexts]
def slice_and_return_parents(self, query):
sliced = self.records
result_contexts = {}
if len(query.items()) == 1:
return {'all':[-1]}
#return [[-1]]
for attr_2, val_2 in query.items():
sliced = self.records
query_copy = query.copy()
cell_name = ''
for attr, val in query_copy.items():
if attr_2 != attr:
cell_name += attr + "_" + str(val) + "_"
val_id = self.hiers[attr].nid[val]
legal_vals = get_all_legal_vals(self.hiers[attr], val_id)
sliced = sliced.loc[sliced[attr].isin(legal_vals)]
result_contexts[cell_name] = sliced['DocID']
return result_contexts#[x['DocID'] for x in result_contexts]
def count_ne_cells(dt, freq_data):
def get_ne_siblings_cnt(cell):
tokens = cell.split('_')
index = 0
count = 0
sib_list = []
for hier_name in hier_names:
tokens_copy = list(tokens)
hier_obj = hiers[hier_name]
val_ori_id = int(tokens[index])
# print hier_name
# print val_ori_id
# import ipdb
# ipdb.set_trace()
if val_ori_id == 0:
sibling_ids = []
else:
sibling_ids = hier_obj.idd[hier_obj.ipd[val_ori_id][0]]
for sid in sibling_ids:
if sid == val_ori_id:
pass
tokens_copy[index] = sid
tmp_str = '_'.join(str(x) for x in tokens_copy)
if tmp_str in non_empty_cells:
count += 1
sib_list.append(tmp_str)
index += 1
return count, sib_list
non_empty_cells = {} # with siblings, format: {cell_str:(phrase_set, doc_num, sibling_num)}
count = dt.records.shape[0]
hiers = dt.hiers
hier_names = hiers.keys()
hier_values = {}
total_uni_phrase = 0
for hier_name, hier_obj in hiers.items():
allnodes = get_all_legal_vals(hier_obj, 0)
node_dict = {} # the ancestor dict
for node in allnodes:
node_list = get_all_ancestors(hier_obj, node)
node_dict[node] = node_list
hier_values[hier_name] = node_dict
children_dict = {}
children_dict_by_dim = {}
for i in range(count):
if i % 1000 == 0:
print i
total_uni_phrase += len(freq_data[i])
record = dt.records.loc[i]
tmp_valid_lists = [] # all ancesters organized by dimensions
is_valid_doc = True
for hier_name in hier_names:
hier_value = record[hier_name]
if hier_value not in hier_values[hier_name]:
print record
is_valid_doc = False
break
else:
tmp_valid_lists.append(hier_values[hier_name][hier_value])
#print tmp_valid_lists
if is_valid_doc:
for element in itertools.product(*tmp_valid_lists):
# print element
tmp_str = '_'.join(str(x) for x in element)
if tmp_str not in non_empty_cells:
non_empty_cells[tmp_str] = {'phrases': Set(), 'docN': 0}
non_empty_cells[tmp_str]['docN'] += 1
phrases = freq_data[i].keys()
non_empty_cells[tmp_str]['phrases'].update(phrases)
for cell_str in non_empty_cells:
tokens = cell_str.split('_')
if cell_str not in children_dict:
children_dict[cell_str] = Set()
children_dict_by_dim[cell_str] = {}
for idx, hier_name in enumerate(hier_names):
hier_obj = hiers[hier_name]
parent = get_direct_parent(hier_obj, int(tokens[idx]))
if parent != None:
tokens_copy = list(tokens)
tokens_copy[idx] = parent
tmp_str = '_'.join(str(x) for x in tokens_copy)
if tmp_str not in children_dict:
children_dict[tmp_str] = Set()
children_dict_by_dim[tmp_str] = {}
children_dict[tmp_str].add(cell_str)
if hier_name not in children_dict_by_dim[tmp_str]:
children_dict_by_dim[tmp_str][hier_name] = []
children_dict_by_dim[tmp_str][hier_name].append(cell_str)
# if tmp_str == '0_0':
# print children_dict_by_dim[tmp_str]
print 'computing average sibling number'
# print non_empty_cells
for cell in non_empty_cells:
num_sib, sib_list = get_ne_siblings_cnt(cell)
non_empty_cells[cell]['sibN'] = num_sib
non_empty_cells[cell]['siblings'] = sib_list
non_empty_cells[cell]['phraseN'] = len(non_empty_cells[cell]['phrases'])
# how to generate the cuboid graph, actually it's just on the sibling space
ne_cell_strs = Set(non_empty_cells.keys())
cuboids = []
while len(ne_cell_strs) > 0:
cell_str = ne_cell_strs.pop()
cuboid_set = Set()
queue = Set([cell_str])
while True:
if len(queue) == 0:
break
cell_str = queue.pop()
cuboid_set.add(cell_str)
siblings = non_empty_cells[cell_str]['siblings']
for sib in siblings:
if sib not in cuboid_set:
queue.add(sib)
for cell_str in cuboid_set:
if cell_str in ne_cell_strs:
ne_cell_strs.remove(cell_str)
cuboids.append((cuboid_set, Set()))
for cuboid_pair in cuboids:
cuboid = cuboid_pair[0]
child_cells = cuboid_pair[1]
for cell_str in cuboid:
child_cells.update(children_dict[cell_str])
sorted_cuboids = []
while len(cuboids) > 0:
cuboids = sorted(cuboids, key=lambda x: len(x[1]))
if len(cuboids[0][1]) > 0:
import ipdb
ipdb.set_trace()
print 'error'
target_cuboid = cuboids[0][0]
sorted_cuboids.append(target_cuboid)
cuboids.pop(0)
for cuboid_pair in cuboids:
child_cells = cuboid_pair[1]
for cell_str in target_cuboid:
if cell_str in child_cells:
child_cells.remove(cell_str)
#print non_empty_cells
print 'Total cell number: %s' % len(non_empty_cells)
print 'Average Unique Phrase Count in Each Doc %s' % (total_uni_phrase / float(count))
# l = [len(non_empty_cells[x][0]) for x in non_empty_cells]
l = [non_empty_cells[x]['phraseN'] for x in non_empty_cells]
print 'Average Phrase Count: %.4f' % (reduce(lambda x, y: x + y, l) / float(len(l)))
l = [non_empty_cells[x]['sibN'] for x in non_empty_cells]
print 'Average Sibling Count: %.4f' % (reduce(lambda x, y: x + y, l) / float(len(l)))
raw_cost = {}
merge_cost = {}
lmda = 38.11
for cell_str in non_empty_cells:
docN = non_empty_cells[cell_str]['docN']
raw_cost[cell_str] = lmda * docN * math.log(docN, 2)
merge_cost[cell_str] = {}
for hier_name in children_dict_by_dim[cell_str]:
m_cost = 0
for subcell in children_dict_by_dim[cell_str][hier_name]:
m_cost += non_empty_cells[subcell]['phraseN']
merge_cost[cell_str][hier_name] = m_cost
# import ipdb
# ipdb.set_trace()
textcube = TextCube(dt, non_empty_cells, sorted_cuboids, children_dict,
children_dict_by_dim, hier_names, raw_cost, merge_cost)
pickle.dump(textcube, open('1d_cube.dump', 'wb'))
