def evaluate_tc_icc_retrieval():
graph_metrics = graph_representation.get_metrics(True, exclude_flow=True)
print '> Reading cases..'
corpus = 'air/problem_descriptions'
context = 'window'
solutions_path = '../data/air/solutions_preprocessed'
path = '../data/air/problem_descriptions_preprocessed'
description_texts, labels = data.read_files(path)
rep = {}
icc = {}
print '> Calculating ICCs..'
for metric in graph_metrics:
print ' ', metric
rep[metric] = []
centralities = retrieve_centralities(corpus, context, metric)
if centralities:
icc[metric] = graph_representation.calculate_icc_dict(centralities)
else:
icc[metric] = None
print '> Creating solution representations..'
solutions_texts, labels = data.read_files(solutions_path)
solutions_rep = freq_representation.text_to_vector(
solutions_texts, freq_representation.FrequencyMetrics.TF_IDF)
print '> Creating problem description representations..'
for i, text in enumerate(description_texts):
if i % 1 == 0:
print ' document', str(i) + '/' + str(len(description_texts))
g = graph_representation.construct_cooccurrence_network(
text, already_preprocessed=True, context='window')
for metric in graph_metrics:
if not icc[metric]: continue
#~ print ' ',metric
d = graph_representation.graph_to_dict(g, metric, icc[metric])
rep[metric].append(d)
g = None # just to make sure..
print '> Creating vector representations..'
for metric in graph_metrics:
if not icc[metric]: continue
rep[metric] = graph_representation.dicts_to_vectors(rep[metric])
print '> Evaluating..'
results = {}
for metric in graph_metrics:
if not icc[metric]:
results[metric] = None
continue
vectors = rep[metric]
score = evaluation.evaluate_retrieval(vectors, solutions_rep)
print ' ', metric, score
results[metric] = score
pp.pprint(results)
data.pickle_to_file(
results, 'output/tc_icc/cooccurrence/' + corpus + '/retrieval.res')
return results
def evaluate_tc_icc_retrieval():
graph_metrics = graph_representation.get_metrics(True, exclude_flow=True)
print '> Reading cases..'
corpus = 'air/problem_descriptions'
context = 'window'
solutions_path = '../data/air/solutions_preprocessed'
path = '../data/air/problem_descriptions_preprocessed'
description_texts, labels = data.read_files(path)
rep = {}
icc = {}
print '> Calculating ICCs..'
for metric in graph_metrics:
print ' ', metric
rep[metric] = []
centralities = retrieve_centralities(corpus, context, metric)
if centralities:
icc[metric] = graph_representation.calculate_icc_dict(centralities)
else:
icc[metric] = None
print '> Creating solution representations..'
solutions_texts, labels = data.read_files(solutions_path)
solutions_rep = freq_representation.text_to_vector(solutions_texts, freq_representation.FrequencyMetrics.TF_IDF)
print '> Creating problem description representations..'
for i, text in enumerate(description_texts):
if i%1==0: print ' document',str(i)+'/'+str(len(description_texts))
g = graph_representation.construct_cooccurrence_network(text, already_preprocessed=True, context='window')
for metric in graph_metrics:
if not icc[metric]: continue
#~ print ' ',metric
d = graph_representation.graph_to_dict(g, metric, icc[metric])
rep[metric].append(d)
g = None # just to make sure..
print '> Creating vector representations..'
for metric in graph_metrics:
if not icc[metric]: continue
rep[metric] = graph_representation.dicts_to_vectors(rep[metric])
print '> Evaluating..'
results = {}
for metric in graph_metrics:
if not icc[metric]:
results[metric] = None
continue
vectors = rep[metric]
score = evaluation.evaluate_retrieval(vectors, solutions_rep)
print ' ', metric, score
results[metric] = score
pp.pprint(results)
data.pickle_to_file(results, 'output/tc_icc/cooccurrence/'+corpus+'/retrieval.res')
return results
def evaluate_tc_icc_classification():
graph_metrics = graph_representation.get_metrics(True, exclude_flow=True)
print '> Reading cases..'
corpus = 'tasa/TASA900'
#~ corpus = 'tasa/TASATest2'
context = 'sentence'
path = '../data/' + corpus + '_text'
texts, labels = data.read_files(path)
rep = {}
icc = {}
print '> Calculating ICCs..'
for metric in graph_metrics:
print ' ', metric
rep[metric] = []
centralities = retrieve_centralities(corpus, context, metric)
if centralities:
icc[metric] = graph_representation.calculate_icc_dict(centralities)
else:
icc[metric] = None
print '> Creating graph representations..'
for i, text in enumerate(texts):
if i % 10 == 0: print ' ', str(i) + '/' + str(len(texts))
g = graph_representation.construct_cooccurrence_network(
text, context=context)
for metric in graph_metrics:
print ' ', metric
if not icc[metric]: continue
d = graph_representation.graph_to_dict(g, metric, icc[metric])
rep[metric].append(d)
g = None # just to make sure..
print '> Creating vector representations..'
for metric in graph_metrics:
if not icc[metric]: continue
rep[metric] = graph_representation.dicts_to_vectors(rep[metric])
print '> Evaluating..'
results = {}
for metric in graph_metrics:
if not icc[metric]:
results[metric] = None
continue
vectors = rep[metric]
score = evaluation.evaluate_classification(vectors, labels)
print ' ', metric, score
results[metric] = score
pp.pprint(results)
data.pickle_to_file(
results,
'output/tc_icc/cooccurrence/' + corpus + '/classification.res')
return results
def evaluate_tc_icc_classification():
graph_metrics = graph_representation.get_metrics(True, exclude_flow=True)
print '> Reading cases..'
corpus = 'tasa/TASA900'
#~ corpus = 'tasa/TASATest2'
context = 'sentence'
path = '../data/'+corpus+'_text'
texts, labels = data.read_files(path)
rep = {}
icc = {}
print '> Calculating ICCs..'
for metric in graph_metrics:
print ' ', metric
rep[metric] = []
centralities = retrieve_centralities(corpus, context, metric)
if centralities:
icc[metric] = graph_representation.calculate_icc_dict(centralities)
else:
icc[metric] = None
print '> Creating graph representations..'
for i, text in enumerate(texts):
if i%10==0: print ' ',str(i)+'/'+str(len(texts))
g = graph_representation.construct_cooccurrence_network(text, context=context)
for metric in graph_metrics:
print ' ', metric
if not icc[metric]: continue
d = graph_representation.graph_to_dict(g, metric, icc[metric])
rep[metric].append(d)
g = None # just to make sure..
print '> Creating vector representations..'
for metric in graph_metrics:
if not icc[metric]: continue
rep[metric] = graph_representation.dicts_to_vectors(rep[metric])
print '> Evaluating..'
results = {}
for metric in graph_metrics:
if not icc[metric]:
results[metric] = None
continue
vectors = rep[metric]
score = evaluation.evaluate_classification(vectors, labels)
print ' ', metric, score
results[metric] = score
pp.pprint(results)
data.pickle_to_file(results, 'output/tc_icc/cooccurrence/'+corpus+'/classification.res')
return results
def _calc_cents(g, metric, gcents=None):
if gcents: icc = graph_representation.calculate_icc_dict(gcents)
else: icc = None
return graph_representation.graph_to_dict(g, metric, icc)
def _calc_cents(g, metric, gcents=None):
if gcents: icc = graph_representation.calculate_icc_dict(gcents)
else: icc = None
return graph_representation.graph_to_dict(g, metric, icc)
