def print_tarantula_result(project_name, version, ranker_type, filter_type):
failing_to_rankres = evaluator.get_ranker_results(project_name,
version,
ranker_type,
filter_type,
"normal")
assert len(failing_to_rankres) == 1
ranker_res = next(iter(failing_to_rankres.values()))
ranks = ranker_res.ranks
suspiciousness = ranker_res.suspiciousness
line = ranker_res.line
results = pd.DataFrame(data={'rank': ranks, 'susp': suspiciousness})
results.sort_values('rank', inplace=True)
print(results.head(100))
print("line {0}: score {1}".format(line, ranker_res.score))
print("susp: {0}".format(suspiciousness[line]))
print("rank: {0}".format(ranks[line]))
def get_total_scores(project_name, ranker_type, filter_type, provider_type,
versions=None):
version_to_rankres = {}
if versions is None:
versions = projects.get_version_names(project_name)
for version in versions:
failing_to_rankres = evaluator.get_ranker_results(project_name,
version,
ranker_type,
filter_type,
provider_type)
version_to_rankres.update(failing_to_rankres)
version_to_score = {ver: rank_res.score for ver, rank_res in
version_to_rankres.items()}
version_to_score = pd.Series(version_to_score)
version_to_score.sort_values(inplace=True, ascending=False)
return version_to_score
def optimize_classifier(project_name):
initial_scores = {}
for version in projects.get_version_names(project_name):
rank_res_d = evaluator.get_ranker_results(project_name,
version,
"intersection",
"none",
TrivialProvider())
assert len(rank_res_d) == 1
rank_res = next(iter(rank_res_d.values()))
initial_scores[version] = rank_res.score
def to_optimize(classify_vector_with_cutoff):
cutoff = classify_vector_with_cutoff[-1]
classify_vector = classify_vector_with_cutoff[:-1]
score = evaluation_fn(project_name, initial_scores,
classify_vector, cutoff)
# minimize negative
return -1 * score
vecsize = 14 + 1
lower = [-1.0 for i in range(vecsize)]
upper = [1.0 for i in range(vecsize)]
x0 = [0.1 for i in range(vecsize)]
res = scipy.optimize.anneal(to_optimize, x0,
#maxiter=1,
maxeval=10,
dwell=5,
full_output=True)
x0 = res[0]
print("Vec is {0}, cutoff {1}".format(x0[:-1], x0[-1]))
print(res)
