"""Function intended to illustrate classification in the experimental framework.

Intended as a basis for new experiments for those not intimately

familiar with the code.

"""

print 'Evaluation type: Classification'

print 'Graph type: Co-occurrence w/2-word window context'

print 'Centrality: Weighted degree'

print

print '> Reading data..'

corpus_path = '../data/tasa/TASA900_preprocessed'

docs, labels = data.read_files(corpus_path)

print '> Creating representations..'

dicts = []

for i, doc in enumerate(docs):

print ' ',str(i)+'/'+str(len(docs))

g = graph_representation.construct_cooccurrence_network(doc)

d = graph_representation.graph_to_dict(g, graph.GraphMetrics.WEIGHTED_DEGREE)

dicts.append(d)

vectors = graph_representation.dicts_to_vectors(dicts)

print '> Evaluating..'

score = evaluation.evaluate_classification(vectors, labels)

print ' score:', score

"""Function intended to illustrate retrieval in the experimental framework.

Intended as a basis for new experiments for those not intimately

familiar with the code.

"""

print 'Evaluation type: Retrieval'

print 'Graph type: Dependency'

print 'Centrality: PageRank'

print

print '> Reading data..'

desc_path = '../data/air/problem_descriptions_dependencies'

sol_path = '../data/air/solutions_preprocessed'

problems, _ = data.read_files(desc_path)

solutions, _ = data.read_files(sol_path)

print '> Creating solution representations..'

metric = freq_representation.FrequencyMetrics.TF_IDF

sol_vectors = freq_representation.text_to_vector(solutions, metric)

print '> Creating problem description representations..'

dicts = []

for i, doc in enumerate(problems):

print ' ',str(i)+'/'+str(len(problems))

g = graph_representation.construct_dependency_network(doc)

d = graph_representation.graph_to_dict(g, graph.GraphMetrics.PAGERANK)

dicts.append(d)

desc_vectors = graph_representation.dicts_to_vectors(dicts)

print '> Evaluating..'

score = evaluation.evaluate_retrieval(desc_vectors, sol_vectors)

print ' score:', score