def keyword_neighbors(keyword):
keywords = iotools.load_keywords_dict()
keyword_weight = weighting_function(keyword)
related_datasets = set([x[0] for x in keywords['all'][keyword]])
ret = {}
for dataset in related_datasets:
dataset_keywords = iotools.load_dataset_keywords_dict(dataset)['all']
for keyword2 in dataset_keywords:
ret[keyword2] = ret.get(keyword2, 0) + 1
for keyword2, val in ret.items():
weight = 1.0 * weighting_function(keyword2) * mylog(val) / keyword_weight / mylog(len(related_datasets))
ret[keyword2] = weight
return sorted(ret.items(), key=lambda x: x[1], reverse=True)
def generate_network():
g = nx.Graph()
matrix = all_keywords_neighbors()
for keyword1, kwlist in matrix:
g.add_node(keyword1, weight=weighting_function(keyword1))
for keyword2, weight in kwlist:
if weight > 0.30:
g.add_edge(keyword1, keyword2, weight=weight)
print len(g.edges())
nx.write_gexf(g, "output/keyword-graph.gexf")
def tag_cloud_text_new_keywords_weighted():
ret = []
for keyword, klist in iotools.load_keywords_dict()['all'].items():
val = int(weighting_function(keyword)*1000)
ret += [normalized(keyword)] * val
return ret
def df_new_keywords_list_weighted(dataset):
name = dataset['name']
keywords = iotools.load_dataset_keywords_dict(name)['all'].keys()
weights = [weighting_function(keyword) for keyword in keywords]
return dict(zip(keywords, weights))
