/
dtree.py
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/
dtree.py
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from sklearn import tree, cluster
import pandas as pd
import matplotlib.pyplot as plt
import StringIO
import pydot
# from IPython.display import Image
import sqlite3
import util
import sys
import re
import numpy as np
from mpl_toolkits.mplot3d import Axes3D, axes3d
import itertools
from scipy import sparse
def standardize(A):
return (A - np.mean(A)) / np.std(A)
def allcombinations(featurset):
raise NotImplemented
def groups2csv(data, prefix):
tags = ['function', 'operator', 'class']
combinations = []
for i in range(1, len(tags)+1):
items = itertools.combinations(tags, i)
for it in items:
combinations.append(list(it))
print combinations
comb_names = map(lambda l: ''.join(map(lambda s: s[0],l)), combinations)
tasks = zip(combinations, comb_names)
for c in tasks:
print c
k = data.groupby(c[0])
f = k['isCovered'].agg({'mean_kill': np.mean, 'number of mutants': len, 'number of killed':np.sum})
f.to_csv(prefix + c[1] + '.csv')
def unionDF(df):
# d = sparse.dok_matrix((1, 100000))
# print df
# for e in df:
# print e
# d[0, e] = 1
# return d.tocsc()
k = []
for e in df:
k.append(e)
return str(sorted(k))
def mut_dist(m1, m2):
print 'type:', type(m1)
# return
return max(m1.difference(m2), m2.difference(m1))
def meanDFs(df1, df2):
np.union1d(df1,df2)
def precompute(mat):
k = pd.Series()
# nd = np.ndarray(, 3)
def identity(x):
return x
def clustering(mut_coverage):
killed = mut_coverage[mut_coverage['isCovered'] == 1]
groupby = killed.groupby(['mutantId'])
f = groupby['testId'].agg({'set': unionDF})
# clf = cluster.AgglomerativeClustering(n_clusters=2,
# affinity=mut_dist,
# # memory=Memory(cachedir=None),
# connectivity=None,
# n_components=None,
# compute_full_tree='auto',
# linkage='average',
# pooling_func=unionDF)
# clf.fit(f)
# print f
# groupby = killed.groupby(['class'])
# f = groupby['mutantId'].apply
g = f.groupby(['set'])
print groupby.size()
def learn_dtree(data, csvfile):
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=4)
k = data.groupby(['operator'])
# k = data.groupby(['operator'])
f = k['isCovered'].agg({'mean_kill': np.mean, 'number of mutants': len, 'number of killed':np.sum})
f.to_csv(csvfile)
fig = plt.figure()
# ax = Axes3D(fig)
# ax = fig.add_subplot(111, projection='3d')
plt.scatter(standardize (f['mean']),f['sum'])
plt.ylabel('mutant_size')
plt.xlabel('expected_kill (standatdize)')
# print f[f['len'] > 25000]
# ax.set_xlabel('mean')
# ax.set_ylabel('len')
# ax.set_zlabel('sum')
plt.show()
# plt.show()
# for m in k.groups:
# print m,len(k.groups[m]),
data['op'] = pd.factorize(data['operator'])[0]
data['m'] = pd.factorize(data['method'])[0]
HLdata['c'] = pd.factorize(data['class'])[0]
# plt.show()
plt.close()
x = data[['op', 'c', 'testId']].values
y = data['isCovered'].values
clf.fit(x,y)
dot_data = StringIO.StringIO()
tree.export_graphviz(clf, out_file=dot_data)
return dot_data.getvalue()
# plt.Image(graph.create_png())
#raw_data['norm_pos'] = raw_data.groupby(['cm','operator']).transform(lambda x: np.sum(x[x['line'] > 0]))
def main(file_name):
db_file = file_name
db = util.load(db_file)
tests = db['testcases']['testId']
mutants = db['mutants']
mut_coverage = db['mut_coverage']
# mutants = mut_coverage[mut_coverage['isCovered'] == 1]['mutantid']
# mut_coverage['operator'] = mutants.
print mut_coverage.columns
mut_coverage.columns = [u'testId', u'mutantId', u'isCovered']
print mut_coverage.columns
data = pd.merge(mutants, mut_coverage)
data['ope'] = map(lambda s: s.replace('org.pitest.mutationtest.engine.gregor.mutators.', ''), data['mutator'])
data['operator'] = map(lambda s: re.sub(r'experimental.RemoveSwitchMutator.*', 'experimental.RemoveSwitchMutator', s), data['ope'])
return data
sys.argv = ['', 'data/com_lang.db', 'lang_']
db1 = 'data/com_lang.db'
db_file = sys.argv[1]
prefix = sys.argv[2]
data = main(db_file)
data['function'] = data['class'] + ' ' + data['method']
# groups2csv(data, prefix)
# dot = learn_dtree(data, prefix)
# open(prefix + '.dot', 'w').write(dot)
clustering(data)