def classification_comparison_freq(dataset='reuters'):
print '> Reading data..', dataset
training_path = '../data/'+dataset+'/training_preprocessed'
training_docs, training_labels = data.read_files(training_path)
test_path = '../data/'+dataset+'/test_preprocessed'
test_docs, test_labels = data.read_files(test_path)
results = {}
for metric in freq_representation.get_metrics():
print ' ', metric,
training_dicts = freq_representation.text_to_dict(training_docs, metric)
test_dicts = freq_representation.text_to_dict(test_docs, metric)
print ' dicst -> vectors'
keys = set()
for d in training_dicts + test_dicts:
keys = keys.union(d.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)
reps = {'training':training_rep, 'test':test_rep}
labels = {'training':training_labels, 'test':test_labels}
score = evaluation.evaluate_classification(reps, labels, mode='split')
results[metric] = score
print score
pp.pprint(results)
s = 'classification comparison \nrepresentation: frequency\nresult:\n'+str(results)+'\n\n\n'
data.write_to_file(s, 'output/comparison/classification')
return results
def classification_comparison_freq(dataset='reuters'):
print '> Reading data..', dataset
training_path = '../data/' + dataset + '/training_preprocessed'
training_docs, training_labels = data.read_files(training_path)
test_path = '../data/' + dataset + '/test_preprocessed'
test_docs, test_labels = data.read_files(test_path)
results = {}
for metric in freq_representation.get_metrics():
print ' ', metric,
training_dicts = freq_representation.text_to_dict(
training_docs, metric)
test_dicts = freq_representation.text_to_dict(test_docs, metric)
print ' dicst -> vectors'
keys = set()
for d in training_dicts + test_dicts:
keys = keys.union(d.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)
reps = {'training': training_rep, 'test': test_rep}
labels = {'training': training_labels, 'test': test_labels}
score = evaluation.evaluate_classification(reps, labels, mode='split')
results[metric] = score
print score
pp.pprint(results)
s = 'classification comparison \nrepresentation: frequency\nresult:\n' + str(
results) + '\n\n\n'
data.write_to_file(s, 'output/comparison/classification')
return results
def do_retrieval_experiments(
descriptions='air/problem_descriptions',
solutions='air/solutions',
graph_types=['co-occurrence', 'dependency', 'random'],
use_frequency=True):
"""
Experiment used for comparative evaluation of different network
representations on the retrieval task.
Toggle comparison with frequency-based methods using *use_frequency*.
"""
results = {
'_solutions': solutions,
'_descriptions': descriptions,
'_evaluation': 'retrieval'
}
print '> Evaluation type: retrieval'
print '> Reading cases..'
descriptions_path = '../data/' + descriptions
descriptiondata = data.read_data(descriptions_path, graph_types)
solutions_path = '../data/' + solutions + '_preprocessed'
solution_texts, labels = data.read_files(solutions_path)
solution_vectors = freq_representation.text_to_vector(
solution_texts, freq_representation.FrequencyMetrics.TF_IDF)
print '> Evaluating..'
for gtype in graph_types:
print ' ', gtype
docs, labels = descriptiondata[gtype]
graphs = graph_representation.create_graphs(docs, gtype)
results[gtype] = {}
for metric in graph_representation.get_metrics():
print ' -', metric
vectors = graph_representation.graphs_to_vectors(graphs, metric)
results[gtype][metric] = evaluation.evaluate_retrieval(
vectors, solution_vectors)
if use_frequency:
print ' frequency'
results['freq'] = {}
for metric in freq_representation.get_metrics():
print ' -', metric
docs, labels = data.read_files(descriptions_path + '_preprocessed')
vectors = freq_representation.text_to_vector(docs, metric)
results['freq'][metric] = evaluation.evaluate_retrieval(
vectors, solution_vectors)
print
pp.pprint(results)
return results
def freq_classification(dataset='tasa/TASA900'):
results = {'_dataset':dataset,
'_evaluation':'classification'}
corpus_path = '../data/'+dataset
results['results'] = {}
for metric in freq_representation.get_metrics():
print metric
documents, labels = data.read_files(corpus_path+'_preprocessed')
vectors = freq_representation.text_to_vector(documents, metric)
r = evaluation.evaluate_classification(vectors, labels, mode='cross-validation')
results['results'][metric] = r
print ' ', r
print
pp.pprint(results)
return results
def freq_classification(dataset='tasa/TASA900'):
results = {'_dataset': dataset, '_evaluation': 'classification'}
corpus_path = '../data/' + dataset
results['results'] = {}
for metric in freq_representation.get_metrics():
print metric
documents, labels = data.read_files(corpus_path + '_preprocessed')
vectors = freq_representation.text_to_vector(documents, metric)
r = evaluation.evaluate_classification(vectors,
labels,
mode='cross-validation')
results['results'][metric] = r
print ' ', r
print
pp.pprint(results)
return results
def do_retrieval_experiments(descriptions='air/problem_descriptions',
solutions='air/solutions',
graph_types=['co-occurrence','dependency','random'],
use_frequency=True):
"""
Experiment used for comparative evaluation of different network
representations on the retrieval task.
Toggle comparison with frequency-based methods using *use_frequency*.
"""
results = {'_solutions':solutions,
'_descriptions':descriptions,
'_evaluation':'retrieval'}
print '> Evaluation type: retrieval'
print '> Reading cases..'
descriptions_path = '../data/'+descriptions
descriptiondata = data.read_data(descriptions_path, graph_types)
solutions_path = '../data/'+solutions+'_preprocessed'
solution_texts, labels = data.read_files(solutions_path)
solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)
print '> Evaluating..'
for gtype in graph_types:
print ' ',gtype
docs, labels = descriptiondata[gtype]
graphs = graph_representation.create_graphs(docs, gtype)
results[gtype] = {}
for metric in graph_representation.get_metrics():
print ' -', metric
vectors = graph_representation.graphs_to_vectors(graphs, metric)
results[gtype][metric] = evaluation.evaluate_retrieval(vectors, solution_vectors)
if use_frequency:
print ' frequency'
results['freq'] = {}
for metric in freq_representation.get_metrics():
print ' -', metric
docs, labels = data.read_files(descriptions_path+'_preprocessed')
vectors = freq_representation.text_to_vector(docs, metric)
results['freq'][metric] = evaluation.evaluate_retrieval(vectors, solution_vectors)
print
pp.pprint(results)
return results
def do_classification_experiments(
dataset='tasa/TASA900',
graph_types=['co-occurrence', 'dependency', 'random'],
use_frequency=True):
"""
Experiment used for comparative evaluation of different network
representations on classification.
Toggle comparison with frequency-based methods using *use_frequency*.
"""
results = {'_dataset': dataset, '_evaluation': 'classification'}
print '> Evaluation type: classification'
print '> Reading data..', dataset
corpus_path = '../data/' + dataset
docdata = data.read_data(corpus_path, graph_types)
print '> Evaluating..'
for gtype in graph_types:
print ' ', gtype
documents, labels = docdata[gtype]
graphs = graph_representation.create_graphs(documents, gtype)
results[gtype] = {}
for metric in graph_representation.get_metrics():
print ' -', metric
vectors = graph_representation.graphs_to_vectors(graphs, metric)
results[gtype][metric] = evaluation.evaluate_classification(
vectors, labels)
if use_frequency:
print ' frequency'
results['freq'] = {}
for metric in freq_representation.get_metrics():
print ' -', metric
documents, labels = data.read_files(corpus_path + '_preprocessed')
vectors = freq_representation.text_to_vector(documents, metric)
results['freq'][metric] = evaluation.evaluate_classification(
vectors, labels)
print
pp.pprint(results)
return results
def retrieval_comparison_freq(dataset='mir'):
print '> Reading data..', dataset
path = '../data/'+dataset+'/problem_descriptions_preprocessed'
docs, _ = data.read_files(path)
print '> Creating solution representations..'
solutions_path = '../data/'+dataset+'/solutions_preprocessed'
solutions_docs, _ = data.read_files(solutions_path)
solutions_rep = freq_representation.text_to_vector(solutions_docs, freq_representation.FrequencyMetrics.TF_IDF)
print '> Evaluating..'
results = {}
for metric in freq_representation.get_metrics():
print ' ', metric,
descriptions_rep = freq_representation.text_to_vector(docs, metric)
score = evaluation.evaluate_retrieval(descriptions_rep, solutions_rep)
results[metric] = score
print score
pp.pprint(results)
s = 'retrieval comparison \nrepresentation: frequency\ndataset:'+dataset+' \nresult:\n'+str(results)+'\n\n\n'
data.write_to_file(s, 'output/comparison/retrieval')
return results
def do_classification_experiments(dataset='tasa/TASA900',
graph_types = ['co-occurrence','dependency','random'],
use_frequency = True):
"""
Experiment used for comparative evaluation of different network
representations on classification.
Toggle comparison with frequency-based methods using *use_frequency*.
"""
results = {'_dataset':dataset,
'_evaluation':'classification'}
print '> Evaluation type: classification'
print '> Reading data..', dataset
corpus_path = '../data/'+dataset
docdata = data.read_data(corpus_path, graph_types)
print '> Evaluating..'
for gtype in graph_types:
print ' ',gtype
documents, labels = docdata[gtype]
graphs = graph_representation.create_graphs(documents, gtype)
results[gtype] = {}
for metric in graph_representation.get_metrics():
print ' -', metric
vectors = graph_representation.graphs_to_vectors(graphs, metric)
results[gtype][metric] = evaluation.evaluate_classification(vectors, labels)
if use_frequency:
print ' frequency'
results['freq'] = {}
for metric in freq_representation.get_metrics():
print ' -', metric
documents, labels = data.read_files(corpus_path+'_preprocessed')
vectors = freq_representation.text_to_vector(documents, metric)
results['freq'][metric] = evaluation.evaluate_classification(vectors, labels)
print
pp.pprint(results)
return results
def retrieval_comparison_freq(dataset='mir'):
print '> Reading data..', dataset
path = '../data/' + dataset + '/problem_descriptions_preprocessed'
docs, _ = data.read_files(path)
print '> Creating solution representations..'
solutions_path = '../data/' + dataset + '/solutions_preprocessed'
solutions_docs, _ = data.read_files(solutions_path)
solutions_rep = freq_representation.text_to_vector(
solutions_docs, freq_representation.FrequencyMetrics.TF_IDF)
print '> Evaluating..'
results = {}
for metric in freq_representation.get_metrics():
print ' ', metric,
descriptions_rep = freq_representation.text_to_vector(docs, metric)
score = evaluation.evaluate_retrieval(descriptions_rep, solutions_rep)
results[metric] = score
print score
pp.pprint(results)
s = 'retrieval comparison \nrepresentation: frequency\ndataset:' + dataset + ' \nresult:\n' + str(
results) + '\n\n\n'
data.write_to_file(s, 'output/comparison/retrieval')
return results
