def experiemnt_three(dbFileName, meanFlag, outputStrParam, clusterFlag):
from sklearn import cluster
import plotter
clusteringType = None
if clusterFlag:
clusteringType = cluster.KMeans(n_clusters=13)
else:
clusteringType = cluster.AgglomerativeClustering(n_clusters=13)
print "Performing experiemnt # 3: Clustering score into two clusters "
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(versionAndCodeQualityDict, meanFlag)
sanitizedVersions_CQ = sanitizedVersions
# print "Sanitized versions that will be used in study ", len(sanitizedVersions)
# print "Sanitized versions ..." , sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(sanitizedVersions)
# print "zzzz", len(NonZero_sanitizedVersionsWithScore)
### dyumping scores ...
brokenDict = utility.getVScoreList(NonZero_sanitizedVersionsWithScore)
onlyTheNonZeroSanitizedVersionIDs, onlyTheNonZeroSanitizedVScores = brokenDict[0], brokenDict[1]
# print "lalalaa ", onlyTheNonZeroSanitizedVScores
# strOfScoresToDump=""
# for elem in onlyTheNonZeroSanitizedVScores:
# strOfScoresToDump = strOfScoresToDump + str(elem) + "," + "\n"
###
# IO_.writeStrToFile("scores_for_clustering_measure.csv", strOfScoresToDump)
reshapedNonZerSanitizedScores = np.reshape(onlyTheNonZeroSanitizedVScores, (-1, 1))
clusteringType.fit(reshapedNonZerSanitizedScores)
labelsFroVersions = clusteringType.labels_
if clusterFlag:
centroids = clusteringType.cluster_centers_
print "And the centroids are .... ", centroids
NonZer_Santized_versionDictWithLabels = utility.clusterByKmeansLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions
)
##### plotting clusters start
# low_cluster_y, high_cluster_y = utility.plotClusterByLabel( onlyTheNonZeroSanitizedVersionIDs , labelsFroVersions, NonZero_sanitizedVersionsWithScore)
# low_cluster_x = [ 22.35294118 for x in low_cluster_y]
# hig_cluster_x = [ 50.82030058 for x in high_cluster_y]
# plotter.createClusterPlots(low_cluster_x, low_cluster_y, hig_cluster_x, high_cluster_y)
##### plottign clusters end
else:
print "No centroids for Aggolomerative clustering"
NonZer_Santized_versionDictWithLabels = utility.clusterByAggoloLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions
)
print "And the labels are .... "
print len(labelsFroVersions)
cluster_labels = clusteringType.fit_predict(reshapedNonZerSanitizedScores)
silhouette_avg = silhouette_score(reshapedNonZerSanitizedScores, cluster_labels)
print "Silhouette average---> ", silhouette_avg
##############################
themegaFile_All = outputStrParam + "_" + "culsterified_non_zero_all-CQ-HL.csv"
IO_.dumpIntoClusterifiedFile(themegaFile_All, sanitizedVersions_CQ, NonZer_Santized_versionDictWithLabels, False)
def experiemnt_select_classifier(dbFileName, meanFlag, outputStrParam,
clusterFlag, scoreTypeParam):
from sklearn import cluster
from sklearn.metrics import silhouette_samples, silhouette_score, v_measure_score, adjusted_mutual_info_score, completeness_score
scoreListToret = []
print "Performing experiemnt: Select Classifier"
clusters = [x for x in xrange(100) if x > 1]
#clusters=[2]
for clsuter_cnt in clusters:
print "this is iteration #", clsuter_cnt
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(
dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(
versionAndCodeQualityDict, meanFlag)
sanitizedVersions_CQ = sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(
sanitizedVersions)
#print "zzzz", len(NonZero_sanitizedVersionsWithScore)
brokenDict = utility.getVScoreList(NonZero_sanitizedVersionsWithScore)
onlyTheNonZeroSanitizedVersionIDs, onlyTheNonZeroSanitizedVScores = brokenDict[
0], brokenDict[1]
#print "lalalaa ", onlyTheNonZeroSanitizedVScores
reshapedNonZerSanitizedScores = np.reshape(
onlyTheNonZeroSanitizedVScores, (-1, 1))
clusteringType = None
if clusterFlag:
clusteringType = cluster.KMeans(n_clusters=clsuter_cnt)
else:
clusteringType = cluster.AgglomerativeClustering(
n_clusters=clsuter_cnt)
cluster_labels = clusteringType.fit_predict(
reshapedNonZerSanitizedScores)
scores = 0
if scoreTypeParam == 0:
scores = silhouette_score(reshapedNonZerSanitizedScores,
cluster_labels)
score_type = 'Silhouette'
# elif scoreTypeParam==1:
# scores = v_measure_score(reshapedNonZerSanitizedScores, cluster_labels)
# score_type='V-measure'
# elif scoreTypeParam==2:
# scores = adjusted_mutual_info_score(reshapedNonZerSanitizedScores, cluster_labels)
# score_type='adjusted mutual info'
# elif scoreTypeParam==3:
# scores = completeness_score(reshapedNonZerSanitizedScores, cluster_labels)
# score_type='completeness'
score_combo = (clsuter_cnt, scores)
scoreListToret.append(score_combo)
print "::::: score_type={}, For n_clusters ={}, The clustering _score is ={} :::::".format(
score_type, clsuter_cnt, scores)
return scoreListToret
def experiemnt_select_classifier(dbFileName, meanFlag, outputStrParam, clusterFlag, scoreTypeParam):
from sklearn import cluster
from sklearn.metrics import (
silhouette_samples,
silhouette_score,
v_measure_score,
adjusted_mutual_info_score,
completeness_score,
)
scoreListToret = []
print "Performing experiemnt: Select Classifier"
clusters = [x for x in xrange(100) if x > 1]
# clusters=[2]
for clsuter_cnt in clusters:
print "this is iteration #", clsuter_cnt
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(versionAndCodeQualityDict, meanFlag)
sanitizedVersions_CQ = sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(
sanitizedVersions
)
# print "zzzz", len(NonZero_sanitizedVersionsWithScore)
brokenDict = utility.getVScoreList(NonZero_sanitizedVersionsWithScore)
onlyTheNonZeroSanitizedVersionIDs, onlyTheNonZeroSanitizedVScores = brokenDict[0], brokenDict[1]
# print "lalalaa ", onlyTheNonZeroSanitizedVScores
reshapedNonZerSanitizedScores = np.reshape(onlyTheNonZeroSanitizedVScores, (-1, 1))
clusteringType = None
if clusterFlag:
clusteringType = cluster.KMeans(n_clusters=clsuter_cnt)
else:
clusteringType = cluster.AgglomerativeClustering(n_clusters=clsuter_cnt)
cluster_labels = clusteringType.fit_predict(reshapedNonZerSanitizedScores)
scores = 0
if scoreTypeParam == 0:
scores = silhouette_score(reshapedNonZerSanitizedScores, cluster_labels)
score_type = "Silhouette"
# elif scoreTypeParam==1:
# scores = v_measure_score(reshapedNonZerSanitizedScores, cluster_labels)
# score_type='V-measure'
# elif scoreTypeParam==2:
# scores = adjusted_mutual_info_score(reshapedNonZerSanitizedScores, cluster_labels)
# score_type='adjusted mutual info'
# elif scoreTypeParam==3:
# scores = completeness_score(reshapedNonZerSanitizedScores, cluster_labels)
# score_type='completeness'
score_combo = (clsuter_cnt, scores)
scoreListToret.append(score_combo)
print "::::: score_type={}, For n_clusters ={}, The clustering _score is ={} :::::".format(
score_type, clsuter_cnt, scores
)
return scoreListToret
def experiemnt_two(dbFileName, meanFlag, outputStrParam):
print "Performing experiemnt # 2"
#import correlation as corr_
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(
versionAndCodeQualityDict, meanFlag)
print "Sanitized versions that will be used in study ", len(
sanitizedVersions)
#print "Sanitized versions ..." , sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(
sanitizedVersions)
'''
Stats on risk score (non-zero elemnts)-->len=549, median=51.1111111111, mean=49.9387976503, max=53.3333333333, min=15.0
'''
##############################
sanitizedVersions_CQ = sanitizedVersions
riskStatus = sanityCheck.getVulnerbailityScoreStatus(
NonZero_sanitizedVersionsWithScore)
if meanFlag:
threshold = riskStatus[0] ## first returned index is mean
else:
threshold = riskStatus[1]
####### high vScore versions started
high_CQ_dict = utility.getHighVScoreVersions_CQ(
NonZero_sanitizedVersionsWithScore, sanitizedVersions_CQ, threshold)
high_vScore_Dict = utility.getHighVScoreVersions_VScore(
NonZero_sanitizedVersionsWithScore, threshold)
print "non zero high_vscore_versions ", len(high_vScore_Dict)
####### high vScore versions ended
####### low vScore versions started
low_CQ_dict = utility.getLowVScoreVersions_CQ(
NonZero_sanitizedVersionsWithScore, sanitizedVersions_CQ, threshold)
low_vScore_Dict = utility.getLowVScoreVersions_VScore(
NonZero_sanitizedVersionsWithScore, threshold)
print "non zero len_vscore_versions ", len(low_vScore_Dict)
####### low vScore versions ended
##### dumpin time
### three ways: first by dumping all highs then all lows
themegaFile_Seperated = outputStrParam + "_" + "non_zero_all-CQ-HL-Seperated.csv"
IO_.dumpIntoFileByHighAndLow(themegaFile_Seperated, high_CQ_dict,
low_CQ_dict)
### three ways : second by dumping as it si
themegaFile_All = outputStrParam + "_" + "non_zero_all-CQ-HL.csv"
IO_.dumpIntoFile(themegaFile_All, sanitizedVersions_CQ,
NonZero_sanitizedVersionsWithScore, threshold, False)
LGR.performLogiRegression(themegaFile_All)
def experiemnt_two(dbFileName, meanFlag, outputStrParam ): print "Performing experiemnt # 2" #import correlation as corr_ versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName) sanitizedVersions = sanityCheck.getCodeQualityofVersions(versionAndCodeQualityDict, meanFlag) print "Sanitized versions that will be used in study ", len(sanitizedVersions) #print "Sanitized versions ..." , sanitizedVersions NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(sanitizedVersions) ''' Stats on risk score (non-zero elemnts)-->len=549, median=51.1111111111, mean=49.9387976503, max=53.3333333333, min=15.0 ''' ############################## sanitizedVersions_CQ = sanitizedVersions riskStatus = sanityCheck.getVulnerbailityScoreStatus(NonZero_sanitizedVersionsWithScore) if meanFlag: threshold = riskStatus[0] ## first returned index is mean else: threshold = riskStatus[1] ####### high vScore versions started high_CQ_dict = utility.getHighVScoreVersions_CQ( NonZero_sanitizedVersionsWithScore , sanitizedVersions_CQ , threshold) high_vScore_Dict = utility.getHighVScoreVersions_VScore(NonZero_sanitizedVersionsWithScore, threshold) print "non zero high_vscore_versions ", len(high_vScore_Dict) ####### high vScore versions ended ####### low vScore versions started low_CQ_dict = utility.getLowVScoreVersions_CQ( NonZero_sanitizedVersionsWithScore , sanitizedVersions_CQ , threshold) low_vScore_Dict = utility.getLowVScoreVersions_VScore(NonZero_sanitizedVersionsWithScore, threshold) print "non zero len_vscore_versions ", len(low_vScore_Dict) ####### low vScore versions ended ##### dumpin time ### three ways: first by dumping all highs then all lows themegaFile_Seperated = outputStrParam + "_" + "non_zero_all-CQ-HL-Seperated.csv" IO_.dumpIntoFileByHighAndLow( themegaFile_Seperated, high_CQ_dict, low_CQ_dict ) ### three ways : second by dumping as it si themegaFile_All = outputStrParam + "_" + "non_zero_all-CQ-HL.csv" IO_.dumpIntoFile( themegaFile_All,sanitizedVersions_CQ , NonZero_sanitizedVersionsWithScore, threshold, False ) LGR.performLogiRegression(themegaFile_All)
def experiemnt_correlation(dbFileName, meanFlag, outputStrParam, clusterFlag):
import correlation
from sklearn import cluster
clusteringType = None
if clusterFlag:
clusteringType = cluster.KMeans(n_clusters=2)
else:
clusteringType = cluster.AgglomerativeClustering(n_clusters=2)
print "Performing experiemnt # Correlation: Clustering score into two clusters "
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(
versionAndCodeQualityDict, meanFlag)
sanitizedVersions_CQ = sanitizedVersions
#print "Sanitized versions that will be used in study ", len(sanitizedVersions)
#print "Sanitized versions ..." , sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(
sanitizedVersions)
#print "zzzz", len(NonZero_sanitizedVersionsWithScore)
brokenDict = utility.getVScoreList(NonZero_sanitizedVersionsWithScore)
onlyTheNonZeroSanitizedVersionIDs, onlyTheNonZeroSanitizedVScores = brokenDict[
0], brokenDict[1]
#print "lalalaa ", onlyTheNonZeroSanitizedVScores
reshapedNonZerSanitizedScores = np.reshape(onlyTheNonZeroSanitizedVScores,
(-1, 1))
clusteringType.fit(reshapedNonZerSanitizedScores)
labelsFroVersions = clusteringType.labels_
if clusterFlag:
centroids = clusteringType.cluster_centers_
print "And the centroids are .... ", centroids
NonZer_Santized_versionDictWithLabels = utility.clusterByKmeansLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions)
else:
print "No centroids for Aggolomerative clustering"
NonZer_Santized_versionDictWithLabels = utility.clusterByAggoloLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions)
#print "And the labels are .... "
#print labelsFroVersions
#print "versionDictWithLabels"
#print len(versionDictWithLabels)
onlyHighV_Scores_Dict = utility.getH_Scores_ForCorr(
NonZer_Santized_versionDictWithLabels,
NonZero_sanitizedVersionsWithScore)
correlation.performCorrBasedOnIndiMetrics(onlyHighV_Scores_Dict,
sanitizedVersions_CQ)
def experiemnt_correlation(dbFileName, meanFlag, outputStrParam, clusterFlag):
import correlation
from sklearn import cluster
clusteringType = None
if clusterFlag:
clusteringType = cluster.KMeans(n_clusters=2)
else:
clusteringType = cluster.AgglomerativeClustering(n_clusters=2)
print "Performing experiemnt # Correlation: Clustering score into two clusters "
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(versionAndCodeQualityDict, meanFlag)
sanitizedVersions_CQ = sanitizedVersions
# print "Sanitized versions that will be used in study ", len(sanitizedVersions)
# print "Sanitized versions ..." , sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(sanitizedVersions)
# print "zzzz", len(NonZero_sanitizedVersionsWithScore)
brokenDict = utility.getVScoreList(NonZero_sanitizedVersionsWithScore)
onlyTheNonZeroSanitizedVersionIDs, onlyTheNonZeroSanitizedVScores = brokenDict[0], brokenDict[1]
# print "lalalaa ", onlyTheNonZeroSanitizedVScores
reshapedNonZerSanitizedScores = np.reshape(onlyTheNonZeroSanitizedVScores, (-1, 1))
clusteringType.fit(reshapedNonZerSanitizedScores)
labelsFroVersions = clusteringType.labels_
if clusterFlag:
centroids = clusteringType.cluster_centers_
print "And the centroids are .... ", centroids
NonZer_Santized_versionDictWithLabels = utility.clusterByKmeansLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions
)
else:
print "No centroids for Aggolomerative clustering"
NonZer_Santized_versionDictWithLabels = utility.clusterByAggoloLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions
)
# print "And the labels are .... "
# print labelsFroVersions
# print "versionDictWithLabels"
# print len(versionDictWithLabels)
onlyHighV_Scores_Dict = utility.getH_Scores_ForCorr(
NonZer_Santized_versionDictWithLabels, NonZero_sanitizedVersionsWithScore
)
correlation.performCorrBasedOnIndiMetrics(onlyHighV_Scores_Dict, sanitizedVersions_CQ)
def experiemnt_three(dbFileName, meanFlag, outputStrParam, clusterFlag):
from sklearn import cluster
import plotter
clusteringType = None
if clusterFlag:
clusteringType = cluster.KMeans(n_clusters=13)
else:
clusteringType = cluster.AgglomerativeClustering(n_clusters=13)
print "Performing experiemnt # 3: Clustering score into two clusters "
versionAndCodeQualityDict = DEFT.getValuesFrom_CodingStandard(dbFileName)
sanitizedVersions = sanityCheck.getCodeQualityofVersions(
versionAndCodeQualityDict, meanFlag)
sanitizedVersions_CQ = sanitizedVersions
#print "Sanitized versions that will be used in study ", len(sanitizedVersions)
#print "Sanitized versions ..." , sanitizedVersions
NonZero_sanitizedVersionsWithScore = sanityCheck.getNonZeroVulnerbailityScoreOfSelectedVersions(
sanitizedVersions)
#print "zzzz", len(NonZero_sanitizedVersionsWithScore)
### dyumping scores ...
brokenDict = utility.getVScoreList(NonZero_sanitizedVersionsWithScore)
onlyTheNonZeroSanitizedVersionIDs, onlyTheNonZeroSanitizedVScores = brokenDict[
0], brokenDict[1]
#print "lalalaa ", onlyTheNonZeroSanitizedVScores
#strOfScoresToDump=""
#for elem in onlyTheNonZeroSanitizedVScores:
# strOfScoresToDump = strOfScoresToDump + str(elem) + "," + "\n"
###
#IO_.writeStrToFile("scores_for_clustering_measure.csv", strOfScoresToDump)
reshapedNonZerSanitizedScores = np.reshape(onlyTheNonZeroSanitizedVScores,
(-1, 1))
clusteringType.fit(reshapedNonZerSanitizedScores)
labelsFroVersions = clusteringType.labels_
if clusterFlag:
centroids = clusteringType.cluster_centers_
print "And the centroids are .... ", centroids
NonZer_Santized_versionDictWithLabels = utility.clusterByKmeansLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions)
##### plotting clusters start
#low_cluster_y, high_cluster_y = utility.plotClusterByLabel( onlyTheNonZeroSanitizedVersionIDs , labelsFroVersions, NonZero_sanitizedVersionsWithScore)
#low_cluster_x = [ 22.35294118 for x in low_cluster_y]
#hig_cluster_x = [ 50.82030058 for x in high_cluster_y]
#plotter.createClusterPlots(low_cluster_x, low_cluster_y, hig_cluster_x, high_cluster_y)
##### plottign clusters end
else:
print "No centroids for Aggolomerative clustering"
NonZer_Santized_versionDictWithLabels = utility.clusterByAggoloLabel(
onlyTheNonZeroSanitizedVersionIDs, labelsFroVersions)
print "And the labels are .... "
print len(labelsFroVersions)
cluster_labels = clusteringType.fit_predict(reshapedNonZerSanitizedScores)
silhouette_avg = silhouette_score(reshapedNonZerSanitizedScores,
cluster_labels)
print "Silhouette average---> ", silhouette_avg
##############################
themegaFile_All = outputStrParam + "_" + "culsterified_non_zero_all-CQ-HL.csv"
IO_.dumpIntoClusterifiedFile(themegaFile_All, sanitizedVersions_CQ,
NonZer_Santized_versionDictWithLabels, False)
