def term_centrality_study(doc='air/reports_text/2005/a05a0059.html', num=20):
def _print_terms(cents, rep, num):
ts = _top_cents(cents, num)
terms = []
for t in ts:
terms.append(t[0])
print rep + ' & ' + ', '.join(terms) + ' \\\\'
def _top_cents(cents, num):
return sorted(cents.iteritems(),
key=operator.itemgetter(1),
reverse=True)[0:num]
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)
import operator
import dependency_experiments
import co_occurrence_experiments
dataset = 'air/reports'
path = '../data/' + doc
doc = data.read_file(path)
metric = graph.GraphMetrics.DEGREE
context = 'window'
g = graph_representation.construct_cooccurrence_network(doc,
context=context)
cents = _calc_cents(g, metric)
_print_terms(cents, 'Co-occurrence TC', num)
gcents = co_occurrence_experiments.retrieve_centralities(
dataset, context, metric)
cents = _calc_cents(g, metric, gcents)
_print_terms(cents, 'Co-occurrence TC-ICC', num)
metric = graph.GraphMetrics.EIGENVECTOR
deps = data._text_to_dependencies(doc)
g = graph_representation.construct_dependency_network(deps)
cents = _calc_cents(g, metric)
_print_terms(cents, 'Dependency TC', num)
gcents = dependency_experiments.retrieve_centralities(dataset, metric)
cents = _calc_cents(g, metric, gcents)
_print_terms(cents, 'Dependency TC-ICC', num)
fdict = freq_representation.text_to_dict(
[doc], freq_representation.FrequencyMetrics.TF_IDF)[0]
_print_terms(fdict, 'TF-IDF', num)
fdict = freq_representation.text_to_dict(
[doc], freq_representation.FrequencyMetrics.TF)[0]
_print_terms(fdict, 'TF', num)
def term_centrality_study(doc='air/reports_text/2005/a05a0059.html', num=20):
def _print_terms(cents, rep, num):
ts = _top_cents(cents, num)
terms = []
for t in ts:
terms.append(t[0])
print rep + ' & ' + ', '.join(terms) + ' \\\\'
def _top_cents(cents,num):
return sorted(cents.iteritems(), key = operator.itemgetter(1), reverse = True)[0:num]
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)
import operator
import dependency_experiments
import co_occurrence_experiments
dataset = 'air/reports'
path = '../data/'+doc
doc = data.read_file(path)
metric = graph.GraphMetrics.DEGREE
context = 'window'
g = graph_representation.construct_cooccurrence_network(doc, context=context)
cents = _calc_cents(g, metric)
_print_terms(cents, 'Co-occurrence TC', num)
gcents = co_occurrence_experiments.retrieve_centralities(dataset, context, metric)
cents = _calc_cents(g, metric, gcents)
_print_terms(cents, 'Co-occurrence TC-ICC', num)
metric = graph.GraphMetrics.EIGENVECTOR
deps = data._text_to_dependencies(doc)
g = graph_representation.construct_dependency_network(deps)
cents = _calc_cents(g, metric)
_print_terms(cents, 'Dependency TC', num)
gcents = dependency_experiments.retrieve_centralities(dataset, metric)
cents = _calc_cents(g, metric, gcents)
_print_terms(cents, 'Dependency TC-ICC', num)
fdict = freq_representation.text_to_dict([doc], freq_representation.FrequencyMetrics.TF_IDF)[0]
_print_terms(fdict, 'TF-IDF', num)
fdict = freq_representation.text_to_dict([doc], freq_representation.FrequencyMetrics.TF)[0]
_print_terms(fdict, 'TF', num)
def classification_comparison_graph(dataset='reuters', graph_type='co-occurrence', icc=None):
"""
Experiment used for comparative evaluation of different network
representations on classification.
graph_type = 'co-occurrence' | 'dependency'
`icc` determines whether to use _inverse corpus centrality_ in the vector representations.
"""
import co_occurrence_experiments
import dependency_experiments
def make_dicts(docs, icc):
rep = []
for i, doc in enumerate(docs):
if i%100==0: print ' graph',str(i)+'/'+str(len(docs))
g = gfuns[graph_type](doc)
d = graph_representation.graph_to_dict(g, metrics[graph_type], icc)
rep.append(d)
return rep
postfix = {'co-occurrence':'_text', 'dependency':'_dependencies'}
gfuns = {'co-occurrence':graph_representation.construct_cooccurrence_network,
'dependency':graph_representation.construct_dependency_network}
metrics = {'co-occurrence':graph.GraphMetrics.WEIGHTED_DEGREE,
'dependency':graph.GraphMetrics.CLOSENESS}
print '--', graph_type
print '> Reading data..', dataset
training_path = '../data/'+dataset+'/training'+postfix[graph_type]
training_docs, training_labels = data.read_files(training_path)
test_path = '../data/'+dataset+'/test'+postfix[graph_type]
test_docs, test_labels = data.read_files(test_path)
icc_training = None
icc_test = None
if icc:
print '> Calculating ICC..'
if graph_type is 'co-occurrence':
icc_training = co_occurrence_experiments.retrieve_centralities(dataset+'/training', 'sentence', metrics[graph_type])
elif graph_type is 'dependency':
icc_training = dependency_experiments.retrieve_centralities(dataset+'/training', metrics[graph_type])
if graph_type is 'co-occurrence':
icc_test = co_occurrence_experiments.retrieve_centralities(dataset+'/test', 'sentence', metrics[graph_type])
elif graph_type is 'dependency':
icc_test = dependency_experiments.retrieve_centralities(dataset+'/test', metrics[graph_type])
print '> Creating representations..'
training_dicts = make_dicts(training_docs, icc_training)
test_dicts = make_dicts(test_docs, icc_test)
print ' dicts -> vectors'
keys = set()
for d in training_dicts + test_dicts:
keys = keys.union(d.keys())
keys = list(keys)
print ' vocabulary size:', len(keys)
training_rep = graph_representation.dicts_to_vectors(training_dicts, keys)
test_rep = graph_representation.dicts_to_vectors(test_dicts, keys)
print '> Evaluating..'
reps = {'training':training_rep, 'test':test_rep}
labels = {'training':training_labels, 'test':test_labels}
results = evaluation.evaluate_classification(reps, labels, mode='split')
print results
s = 'classification comparison '
if icc: s += 'USING TC-ICC'
s += '\nrepresentation: '+graph_type+'\nresult: '+str(results)+'\n\n\n'
data.write_to_file(s, 'output/comparison/classification')
return results
def classification_comparison_graph(dataset='reuters',
graph_type='co-occurrence',
icc=None):
"""
Experiment used for comparative evaluation of different network
representations on classification.
graph_type = 'co-occurrence' | 'dependency'
`icc` determines whether to use _inverse corpus centrality_ in the vector representations.
"""
import co_occurrence_experiments
import dependency_experiments
def make_dicts(docs, icc):
rep = []
for i, doc in enumerate(docs):
if i % 100 == 0: print ' graph', str(i) + '/' + str(len(docs))
g = gfuns[graph_type](doc)
d = graph_representation.graph_to_dict(g, metrics[graph_type], icc)
rep.append(d)
return rep
postfix = {'co-occurrence': '_text', 'dependency': '_dependencies'}
gfuns = {
'co-occurrence': graph_representation.construct_cooccurrence_network,
'dependency': graph_representation.construct_dependency_network
}
metrics = {
'co-occurrence': graph.GraphMetrics.WEIGHTED_DEGREE,
'dependency': graph.GraphMetrics.CLOSENESS
}
print '--', graph_type
print '> Reading data..', dataset
training_path = '../data/' + dataset + '/training' + postfix[graph_type]
training_docs, training_labels = data.read_files(training_path)
test_path = '../data/' + dataset + '/test' + postfix[graph_type]
test_docs, test_labels = data.read_files(test_path)
icc_training = None
icc_test = None
if icc:
print '> Calculating ICC..'
if graph_type is 'co-occurrence':
icc_training = co_occurrence_experiments.retrieve_centralities(
dataset + '/training', 'sentence', metrics[graph_type])
elif graph_type is 'dependency':
icc_training = dependency_experiments.retrieve_centralities(
dataset + '/training', metrics[graph_type])
if graph_type is 'co-occurrence':
icc_test = co_occurrence_experiments.retrieve_centralities(
dataset + '/test', 'sentence', metrics[graph_type])
elif graph_type is 'dependency':
icc_test = dependency_experiments.retrieve_centralities(
dataset + '/test', metrics[graph_type])
print '> Creating representations..'
training_dicts = make_dicts(training_docs, icc_training)
test_dicts = make_dicts(test_docs, icc_test)
print ' dicts -> vectors'
keys = set()
for d in training_dicts + test_dicts:
keys = keys.union(d.keys())
keys = list(keys)
print ' vocabulary size:', len(keys)
training_rep = graph_representation.dicts_to_vectors(training_dicts, keys)
test_rep = graph_representation.dicts_to_vectors(test_dicts, keys)
print '> Evaluating..'
reps = {'training': training_rep, 'test': test_rep}
labels = {'training': training_labels, 'test': test_labels}
results = evaluation.evaluate_classification(reps, labels, mode='split')
print results
s = 'classification comparison '
if icc: s += 'USING TC-ICC'
s += '\nrepresentation: ' + graph_type + '\nresult: ' + str(
results) + '\n\n\n'
data.write_to_file(s, 'output/comparison/classification')
return results
