def userNodeSelectionAct(dataArray):
print dataArray[1]
metadatum = sanitize(raw_input("Input the metadatum that will cluster the set of samples among those written above. [ e.g. " + dataArray[1][0] + " ]\n")).split(";")[0]
isInDatabase([metadatum],dataArray[1])
listnodes = dataArray[3].values()
nodesList = parseListNode(raw_input("Choose the group of nodes you want to consider exclusively. [ Read the taxonomic tree to help you: e.g. " + sanitizeNode(listnodes[-3]) + ";" + sanitizeNode(listnodes[1]) + ";" + sanitizeNode(listnodes[-1]) + " ]\n"))
isInDatabase(nodesList,listnodes)
numberSamples = len(dataArray[0])
numberStartingSamples = sanitize(raw_input("Knowing there is/are " + str(numberSamples) + " sample(s), how many samples do you want to create the training set?\n"))
x = integer.match(numberStartingSamples)
if not x or (x and int(numberStartingSamples) > numberSamples) :
print "\n/!\ ERROR: You should write down an integer inferior or equal to ",numberSamples,"."
raise ValueError
numberStartingSamples = int(numberStartingSamples)
assignedClasses,classes,valueSet = classifyIt(dataArray,metadatum,nodesList,numberStartingSamples)
numberClass = len(classes)
youdenJ = countYouden(assignedClasses,classes,numberStartingSamples)
interpretIt(youdenJ)
answer = raw_input("Do you want to plot the classes obtained as a pie chart? Y/N")
if answer == "Y":
labels = [ metadatum + " = " + str(value) for value in valueSet ]
percentagesAs = [ len(class1) for class1 in assignedClasses ]
percentages = [ len(class1) for class1 in classes ]
plotPieChart(labels,percentagesAs,"Assignments depending on " + str(nodesList) + " to class for metadatum " + metadatum)
plotPieChart(labels,percentages,"Real classes depending on " + str(nodesList) + " for metadatum " + metadatum)
elif not (answer == "N"):
print "\n Answer by Y or N!"
answer = raw_input("Do you want to save the results? Y/N")
if (answer == "Y"):
writeFile("Youden's J statistic for this classification is: " + str(youdenJ) + "\n","Assignments depending on " + listNodes(nodesList) + " to classes for metadatum " + metadatum)
elif not (answer == "N"):
print "\n Answer by Y or N!"
return assignedClasses,youdenJ
def parseInfo(filename):
samplesList = []
file_matrix = open("meta/" + filename + ".csv", "r")
lines = file_matrix.readlines()
file_matrix.close()
#Data need to be sanitized
infoListDirty = lines[0].split(",")
infoList = []
for info in infoListDirty:
infoList.append(sanitize(info.split("\n")[0]))
for line in lines[1:]:
#Construction of the list associated to one sample
thisSampleList = []
lsDirty = line.split(",")
#Checks if lsDirty is not empty
if not lsDirty:
print "\n /!\ ERROR: [BUG] [parsingInfo/parseInfo] Parsing error. (1)"
raise ValueError
ls = []
for data in lsDirty:
if not (data == ""):
ls.append(data)
else:
#unknown values are remplaced by "N"
ls.append("N")
for data in ls:
thisSampleList.append(sanitize(data).split("\n")[0])
#samplesList is the list of every sample's list
if not (len(thisSampleList) == len(infoList)):
print "\n /!\ ERROR: [BUG] [parsingInfo/parseInfo] Parsing error. (2)"
raise ValueError
samplesList.append(thisSampleList)
return samplesList, infoList
def parseInfo(filename):
samplesList = []
file_matrix = open("meta/" + filename + ".csv","r")
lines = file_matrix.readlines()
file_matrix.close()
#Data need to be sanitized
infoListDirty = lines[0].split(",")
infoList = []
for info in infoListDirty:
infoList.append(sanitize(info.split("\n")[0]))
for line in lines[1:]:
#Construction of the list associated to one sample
thisSampleList = []
lsDirty = line.split(",")
#Checks if lsDirty is not empty
if not lsDirty:
print "\n /!\ ERROR: [BUG] [parsingInfo/parseInfo] Parsing error. (1)"
raise ValueError
ls = []
for data in lsDirty:
if not (data == ""):
ls.append(data)
else:
#unknown values are remplaced by "N"
ls.append("N")
for data in ls:
thisSampleList.append(sanitize(data).split("\n")[0])
#samplesList is the list of every sample's list
if not (len(thisSampleList) == len(infoList)):
print "\n /!\ ERROR: [BUG] [parsingInfo/parseInfo] Parsing error. (2)"
raise ValueError
samplesList.append(thisSampleList)
return samplesList,infoList
def parseMatrix(filename):
speciesList = []
samplesList = []
file_matrix = open("meta/" + filename + ".csv", "r")
lines = file_matrix.readlines()
file_matrix.close()
boolean = True
for line in lines:
ls = line.split(",")
# First line gives the name and rank of species in the samples
if boolean:
# ls is then a list of strings of type "rank:name"
# Turns "rank:name" into (name,rank)
for string in ls:
ls1 = string.split(":")
rank = sanitize(ls1[0])
# Deletes the white space after name
# Otherwise equality on strings does not work
name = sanitize(ls1[-1])
speciesList.append((name, rank))
boolean = False
n = len(speciesList)
else:
thisSampleList = []
for number in ls:
number = sanitize(number)
if integer.match(number):
thisSampleList.append(int(number))
else:
thisSampleList.append(number)
if not (len(thisSampleList) == n):
print "\n /!\ ERROR: [BUG] [parsingMatrix/parseMatrix] Parsing error."
raise ValueError
samplesList.append(thisSampleList)
return samplesList, speciesList
def userNodeSelectionAct(dataArray):
print dataArray[1]
metadatum = sanitize(
raw_input(
"Input the metadatum that will cluster the set of samples among those written above. [ e.g. "
+ dataArray[1][0] + " ]\n")).split(";")[0]
isInDatabase([metadatum], dataArray[1])
listnodes = dataArray[3].values()
nodesList = parseListNode(
raw_input(
"Choose the group of nodes you want to consider exclusively. [ Read the taxonomic tree to help you: e.g. "
+ sanitizeNode(listnodes[-3]) + ";" + sanitizeNode(listnodes[1]) +
";" + sanitizeNode(listnodes[-1]) + " ]\n"))
isInDatabase(nodesList, listnodes)
numberSamples = len(dataArray[0])
numberStartingSamples = sanitize(
raw_input(
"Knowing there is/are " + str(numberSamples) +
" sample(s), how many samples do you want to create the training set?\n"
))
x = integer.match(numberStartingSamples)
if not x or (x and int(numberStartingSamples) > numberSamples):
print "\n/!\ ERROR: You should write down an integer inferior or equal to ", numberSamples, "."
raise ValueError
numberStartingSamples = int(numberStartingSamples)
assignedClasses, classes, valueSet = classifyIt(dataArray, metadatum,
nodesList,
numberStartingSamples)
numberClass = len(classes)
youdenJ = countYouden(assignedClasses, classes, numberStartingSamples)
interpretIt(youdenJ)
answer = raw_input(
"Do you want to plot the classes obtained as a pie chart? Y/N")
if answer == "Y":
labels = [metadatum + " = " + str(value) for value in valueSet]
percentagesAs = [len(class1) for class1 in assignedClasses]
percentages = [len(class1) for class1 in classes]
plotPieChart(
labels, percentagesAs, "Assignments depending on " +
str(nodesList) + " to class for metadatum " + metadatum)
plotPieChart(
labels, percentages, "Real classes depending on " +
str(nodesList) + " for metadatum " + metadatum)
elif not (answer == "N"):
print "\n Answer by Y or N!"
answer = raw_input("Do you want to save the results? Y/N")
if (answer == "Y"):
writeFile(
"Youden's J statistic for this classification is: " +
str(youdenJ) + "\n", "Assignments depending on " +
listNodes(nodesList) + " to classes for metadatum " + metadatum)
elif not (answer == "N"):
print "\n Answer by Y or N!"
return assignedClasses, youdenJ
def randomSubSamplingAct(dataArray):
print dataArray[1]
metadatum = sanitize(raw_input("Input the metadatum that will cluster the set of samples among those written above. [ e.g. " + dataArray[1][0] + " ]\n")).split(";")[0]
isInDatabase([metadatum],dataArray[1])
s = raw_input("Input the number s of random samplings.\n")
n = raw_input("Input the number n of nodes to select at each try.\n")
if not integer.match(s) or not integer.match(n):
print "\n/!\ ERROR: s and n must both be integers."
raise ValueError
numberSamples = len(dataArray[0])
numberStartingSamples = sanitize(raw_input("Knowing there is/are " + str(numberSamples) + "sample(s), how many samples do you want to create the training set? \n"))
x = integer.match(numberStartingSamples)
if not x or (x and int(numberStartingSamples) > numberSamples):
print "\n/!\ ERROR: You should write down an integer."
raise ValueError
numberStartingSamples = int(numberStartingSamples)
listnodes = dataArray[3].values()
s,n = int(s),int(n)
#Here the set of classes is a list of two lists containing the samples in C and not C
bestClassification = []
bestClassesList = []
#Worse value for this coefficient
currBestYouden = inf
nodesNumber = len(dataArray[3])
while s:
#Randomly draw n distinct nodes among the nodes in the taxonomic tree
nodesList = randomChoice(listnodes,n)
assignedClasses,classes,valueSet = classifyIt(dataArray,metadatum,nodesList,numberStartingSamples)
numberClass = len(classes)
youdenJ = countYouden(assignedClasses,classes,numberSamples)
res = numberClass - youdenJ
if min(res,currBestYouden) == res:
bestClassification = []
for i in nodesList:
bestClassification.append(i)
currBestYouden = res
bestClassesList = []
for i in assignedClasses:
bestClassesList.append(i)
s -= 1
interpretIt(numberClass - currBestYouden)
if answer == "Y":
percentagesAs = [ len(class1) for class1 in assignedClasses ]
labels = [ metadatum + " = " + str(value) for value in valueSet ]
percentages = [ len(class1) for class1 in classes ]
plotPieChart(labels,percentagesAs,"Assignments depending on " + listNodes(nodesList) + " to class for metadatum " + metadatum)
plotPieChart(labels,percentages,"Real classes depending on " + listNodes(nodesList) + " for metadatum " + metadatum)
answer = raw_input("Do you want to save the results? Y/N")
if (answer == "Y"):
writeFile("Best Youden's J statistic for this classification is: " + str(numberClass - currBestYouden) + "\nand most relevant list of nodes for this metadatum is:" + str(bestClassification),"Assignments to classes for metadatum " + metadatum)
elif not (answer == "N"):
print "\n Answer by Y or N!"
return bestClassification,(numberClass - currBestYouden),bestClassesList
def userNodeSelectionAct(dataArray):
print dataArray[1]
metadata = parseList(raw_input("Input the metadata that will cluster the set of samples among those written above. [ e.g. " + dataArray[1][0] + ";" + dataArray[1][-1] + " ]\n"))
isInDatabase(metadata,dataArray[1])
#@dataArray[2] = idSequences is a dictionary of (key=identifier,value=(name,rank of node))
listofNodes = dataArray[2].values()
nodesList = parseListNode(raw_input("Choose the group of nodes you want to consider exclusively. [ Read the taxonomic tree to help you: e.g. " + sanitizeNode(listofNodes[-3]) + ";" + sanitizeNode(listofNodes[1]) + ";" + sanitizeNode(listofNodes[-1]) + " ]\n"))
isInDatabase(nodesList,listofNodes)
numberofSamples = len(dataArray[0])
numberStartingSamples = sanitize(raw_input("Knowing there is/are " + str(numberofSamples) + "sample(s), how many samples do you want to create the training set? \n"))
x = integer.match(numberStartingSamples)
if not x or (x and (int(numberStartingSamples) > numberofSamples)):
print "\n/!\ ERROR: You should write down an integer less or equal to",numberofSamples,"."
raise ValueError
numberStartingSamples = int(numberStartingSamples)
#@shape for @assignedClasses is the same than the one for @classes
assignedClasses,classes,valueSets = classifyIt(dataArray,metadata,nodesList,numberStartingSamples)
numberClass = classes.lenMDL()
youdenJ = countYouden(assignedClasses,classes,numberofSamples)
interpretIt(youdenJ)
answer = raw_input("Do you want to plot the classes obtained as a pie chart? Y/N\n")
if answer == "Y":
labels = [ "Metadata: " + str(metadata) + ", Values for each metadatum: " + str([ valueSet for valueSet in valueSets]) ]
percentagesAs = assignedClasses.mapMDL(len)
percentages = classes.mapMDL(len)
plotPie(labels,percentagesAs,"Assignments depending on " + str(nodesList) + " to class for metadata " + str(metadata))
plotPie(labels,percentages,"Real classes depending on " + str(nodesList) + " for metadata " + str(metadata))
elif not (answer == "N"):
print "\n Answer by Y or N!"
answer = raw_input("Do you want to save the results? Y/N \n")
if (answer == "Y"):
writeFile("Youden's J statistic for this classification is: " + str(youdenJ) + "\n","Assignments depending on " + listNodes(nodesList) + " to classes for metadata " + str(metadata))
elif not (answer == "N"):
print "\n Answer by Y or N!"
return assignedClasses,youdenJ
def change_name(discord_id, new_name):
cur = db.cursor()
new_name = sanitize(new_name)
uuid = get_uuid(discord_id)
sql = """
UPDATE players
SET name='%s'
WHERE uuid='%s'
"""
cur.execute(sql, new_name, uuid)
def clusteringAct(dataArray):
print dataArray[1]
metadatum = sanitize(
raw_input(
"Select the metadatum among those above to cluster the set of samples. [e.g. "
+ dataArray[1][0] + "]\n")).split(";")[0]
isInDatabase([metadatum], dataArray[1])
valueSet, clusters1 = partitionSampleByMetadatumValue(
metadatum, dataArray[1], dataArray[0])
clusters = [[sample[0] for sample in cluster] for cluster in clusters1]
#that is, k in K-means Algorithm
numberClass = len(valueSet)
print "/!\ Number of classes:", numberClass, "."
startSet = [cluster[0] for cluster in clusters]
#Selects the starting samples of each cluster
kClusters = [[start] for start in startSet]
if not (len(clusters) == numberClass):
print "\n/!\ ERROR: Different lengths: numberClass", numberClass, "clusters:", len(
clusters), "."
raise ValueError
trimmedList = trimList(dataArray[3], startSet)
print "/!\ Clustering with the first distance..."
#@distanceInClusters is a list of lists of (sample,sum of all distances from this sample to others samples in the same cluster)
#@dataArray[8] = distMatchedDict
kClusters, meanSamples, distanceDict, distanceInClusters = kMeans(
trimmedList, numberClass, kClusters, startSet, dataArray[8], dataArray)
print "-- End of first clustering --"
number = 0
for cluster in kClusters:
for _ in cluster:
number += 1
if not (number == len(dataArray[3])):
print "\n/!\ ERROR: A bug occurred during the clustering:", number, "=/=", len(
dataArray[3]), "."
raise ValueError
#Deletes samples in cluster that are too far from the others
kClusters, untaken = cleanClusters(kClusters, distanceInClusters)
startSet = [cluster[0] for cluster in clusters]
#Remove from untaken the starting samples
untaken2 = []
for x in untaken:
if not (x in startSet):
untaken2.append(x)
untaken = untaken2
#Remove the samples in untaken from the total set of samples
sampleSet = []
for cluster in kClusters:
for x in cluster:
if not (x in sampleSet):
sampleSet.append(x)
for x in startSet:
if not (x in sampleSet):
sampleSet.append(x)
trimmedList = trimList(sampleSet, startSet)
print "/!\ Clustering with the second distance..."
#@distanceDict is the distance dictionary (key=(sample1,sample2),value=distance between sample1 and sample2)
#@dataArray[9] = distConsensusDict
kClusters, meanSamples, distanceDict, _ = kMeans(trimmedList, numberClass,
kClusters, startSet,
dataArray[9],
dataArray) #,meanSamples)
print "-- End of second clustering --"
number = 0
for cluster in kClusters:
for _ in cluster:
number += 1
if not (number <= len(dataArray[3])):
print "\n/!\ ERROR: An error occurred during the clustering:", number, ">", len(
dataArray[3]), "."
raise ValueError
print "Printing the", numberClass, "clusters:"
i = 1
#@kClusters contains the list of the k clusters. Each cluster is a list of sample IDs
for cluster in kClusters:
print "\n-- Cluster #", i, "associated to", metadatum, "=", valueSet[
i - 1], ":"
print "Size:", len(cluster)
print sorted(cluster)
i += 1
print "\nScore of the clustering (comprised between 0 and 1):"
print "The more it is close to 1, the more the clustering is relevant."
#The clustering obtained with the K-Means method
kClustersCopy = [cluster for cluster in kClusters]
#The clustering obtained by comparing the values of the metadatum
clustersCopy = [cluster for cluster in clusters]
#Score by using first method of comparison
compareClusterScore = 0
if not (len(kClustersCopy) == numberClass == len(clustersCopy)):
print "\n/!\ ERROR: Length error in clustering:", numberClass, len(
kClustersCopy), len(clustersCopy), "."
raise ValueError
while kClustersCopy and clustersCopy:
cl1 = kClustersCopy.pop()
cl2 = clustersCopy.pop()
#clusters are non-empty
x = compareCluster(cl1, cl2, untaken)
if x:
compareClusterScore += x
else:
compareClusterScore = None
break
if compareClusterScore:
compareClusterScore = compareClusterScore / numberClass
printClusterScore = compareClusterScore
else:
printClusterScore = "None"
#Score by using second method of comparison
#compareCentersScore = compareCenters(meanSamples,distanceDict,numberClass)
print "Compare clusters score is:", printClusterScore, "."
#print "Compare centers score is:",compareCentersScore,"."
answer = raw_input("Do you want to save the results? Y/N\n")
if (answer == "Y"):
answer2 = raw_input(
"Do you want to compute the sets of common nodes for each cluster? [It can be considered relevant when the score of comparing clusters is at least over 0.5] Y/N\n"
)
if (answer2 == "Y"):
commonList = extractCommonNodes(kClusters, dataArray)
elif not (answer2 == "N"):
print "\n/!\ You should answer Y or N, not:", answer2, "."
data = "**** CLUSTERS FOR METADATUM " + metadatum + " WITH VALUES: " + str(
valueSet)
i = 0
for cluster in kClusters:
data += "\n\n-- Cluster #" + str(
i + 1) + " associated to " + metadatum + " = " + str(
valueSet[i])
data += "\nSize: " + str(len(cluster))
if (answer2 == "Y"):
data += "\nSet of common nodes: " + str(commonList[i])
data += "\n" + str(cluster)
i += 1
data += "\n\nCompare clusters score is: " + str(compareClusterScore)
#data += "\n\nCompare centers score is: " + str(compareCentersScore)
data += "\n\nEND OF FILE ****"
print "\n/!\ Saving clusters..."
writeFile(data)
answer2 = raw_input(
"Do you want to compute the graph of the clusters? Y/N\n")
if (answer2 == "Y"):
print "\n/!\ Constructing the graph of the clusters..."
#@dataArray[3] = filenames
graph = convertClustersIntoGraph(kClusters, distanceDict,
len(dataArray[3]))
graphNO(graph)
print "\n/!\ Done. The graph is in DOT format in \"files\" folder."
elif not (answer2 == "N"):
print "\n/!\ You should answer Y or N, not:", answer2, "."
elif not (answer == "N"):
print "/!\ You should answer by Y or N."
def clusteringAct(dataArray):
print dataArray[1]
metadatum = sanitize(raw_input("Select the metadatum among those above to cluster the set of samples. [e.g. " + dataArray[1][0] + "]\n")).split(";")[0]
isInDatabase([metadatum],dataArray[1])
valueSet,clusters1 = partitionSampleByMetadatumValue(metadatum,dataArray[1],dataArray[0])
clusters = [[sample[0] for sample in cluster] for cluster in clusters1]
#that is, k in K-means Algorithm
numberClass = len(valueSet)
print "/!\ Number of classes:",numberClass,"."
startSet = [cluster[0] for cluster in clusters]
#Selects the starting samples of each cluster
kClusters = [[start] for start in startSet]
if not (len(clusters) == numberClass):
print "\n/!\ ERROR: Different lengths: numberClass",numberClass,"clusters:",len(clusters),"."
raise ValueError
trimmedList = trimList(dataArray[3],startSet)
print "/!\ Clustering with the first distance..."
#@distanceInClusters is a list of lists of (sample,sum of all distances from this sample to others samples in the same cluster)
#@dataArray[8] = distMatchedDict
kClusters,meanSamples,distanceDict,distanceInClusters = kMeans(trimmedList,numberClass,kClusters,startSet,dataArray[8],dataArray)
print "-- End of first clustering --"
number = 0
for cluster in kClusters:
for _ in cluster:
number += 1
if not (number == len(dataArray[3])):
print "\n/!\ ERROR: A bug occurred during the clustering:",number,"=/=",len(dataArray[3]),"."
raise ValueError
#Deletes samples in cluster that are too far from the others
kClusters,untaken = cleanClusters(kClusters,distanceInClusters)
startSet = [cluster[0] for cluster in clusters]
#Remove from untaken the starting samples
untaken2 = []
for x in untaken:
if not (x in startSet):
untaken2.append(x)
untaken = untaken2
#Remove the samples in untaken from the total set of samples
sampleSet = []
for cluster in kClusters:
for x in cluster:
if not (x in sampleSet):
sampleSet.append(x)
for x in startSet:
if not (x in sampleSet):
sampleSet.append(x)
trimmedList = trimList(sampleSet,startSet)
print "/!\ Clustering with the second distance..."
#@distanceDict is the distance dictionary (key=(sample1,sample2),value=distance between sample1 and sample2)
#@dataArray[9] = distConsensusDict
kClusters,meanSamples,distanceDict,_ = kMeans(trimmedList,numberClass,kClusters,startSet,dataArray[9],dataArray)#,meanSamples)
print "-- End of second clustering --"
number = 0
for cluster in kClusters:
for _ in cluster:
number += 1
if not (number <= len(dataArray[3])):
print "\n/!\ ERROR: An error occurred during the clustering:",number,">",len(dataArray[3]),"."
raise ValueError
print "Printing the",numberClass,"clusters:"
i = 1
#@kClusters contains the list of the k clusters. Each cluster is a list of sample IDs
for cluster in kClusters:
print "\n-- Cluster #",i,"associated to",metadatum,"=",valueSet[i-1],":"
print "Size:",len(cluster)
print sorted(cluster)
i += 1
print "\nScore of the clustering (comprised between 0 and 1):"
print "The more it is close to 1, the more the clustering is relevant."
#The clustering obtained with the K-Means method
kClustersCopy = [cluster for cluster in kClusters]
#The clustering obtained by comparing the values of the metadatum
clustersCopy = [cluster for cluster in clusters]
#Score by using first method of comparison
compareClusterScore = 0
if not (len(kClustersCopy) == numberClass == len(clustersCopy)):
print "\n/!\ ERROR: Length error in clustering:",numberClass,len(kClustersCopy),len(clustersCopy),"."
raise ValueError
while kClustersCopy and clustersCopy:
cl1 = kClustersCopy.pop()
cl2 = clustersCopy.pop()
#clusters are non-empty
x = compareCluster(cl1,cl2,untaken)
if x:
compareClusterScore += x
else:
compareClusterScore = None
break
if compareClusterScore:
compareClusterScore = compareClusterScore/numberClass
printClusterScore = compareClusterScore
else:
printClusterScore = "None"
#Score by using second method of comparison
#compareCentersScore = compareCenters(meanSamples,distanceDict,numberClass)
print "Compare clusters score is:",printClusterScore,"."
#print "Compare centers score is:",compareCentersScore,"."
answer = raw_input("Do you want to save the results? Y/N\n")
if (answer == "Y"):
answer2 = raw_input("Do you want to compute the sets of common nodes for each cluster? [It can be considered relevant when the score of comparing clusters is at least over 0.5] Y/N\n")
if (answer2 == "Y"):
commonList = extractCommonNodes(kClusters,dataArray)
elif not (answer2 == "N"):
print "\n/!\ You should answer Y or N, not:",answer2,"."
data = "**** CLUSTERS FOR METADATUM " + metadatum + " WITH VALUES: " + str(valueSet)
i = 0
for cluster in kClusters:
data += "\n\n-- Cluster #" + str(i+1) + " associated to " + metadatum + " = " + str(valueSet[i])
data += "\nSize: " + str(len(cluster))
if (answer2 == "Y"):
data += "\nSet of common nodes: " + str(commonList[i])
data += "\n" + str(cluster)
i += 1
data += "\n\nCompare clusters score is: " + str(compareClusterScore)
#data += "\n\nCompare centers score is: " + str(compareCentersScore)
data += "\n\nEND OF FILE ****"
print "\n/!\ Saving clusters..."
writeFile(data)
answer2 = raw_input("Do you want to compute the graph of the clusters? Y/N\n")
if (answer2 == "Y"):
print "\n/!\ Constructing the graph of the clusters..."
#@dataArray[3] = filenames
graph = convertClustersIntoGraph(kClusters,distanceDict,len(dataArray[3]))
graphNO(graph)
print "\n/!\ Done. The graph is in DOT format in \"files\" folder."
elif not (answer2 == "N"):
print "\n/!\ You should answer Y or N, not:",answer2,"."
elif not (answer == "N"):
print "/!\ You should answer by Y or N."
def getNameRankList(string):
return [(sanitize(string.split(":")[-1]).split("\n")[0],sanitize(string.split(":")[0]).split("\n")[0])]
def randomSubSamplingAct(dataArray):
print dataArray[1]
metadatum = sanitize(
raw_input(
"Input the metadatum that will cluster the set of samples among those written above. [ e.g. "
+ dataArray[1][0] + " ]\n")).split(";")[0]
isInDatabase([metadatum], dataArray[1])
s = raw_input("Input the number s of random samplings.\n")
n = raw_input("Input the number n of nodes to select at each try.\n")
if not integer.match(s) or not integer.match(n):
print "\n/!\ ERROR: s and n must both be integers."
raise ValueError
numberSamples = len(dataArray[0])
numberStartingSamples = sanitize(
raw_input(
"Knowing there is/are " + str(numberSamples) +
"sample(s), how many samples do you want to create the training set? \n"
))
x = integer.match(numberStartingSamples)
if not x or (x and int(numberStartingSamples) > numberSamples):
print "\n/!\ ERROR: You should write down an integer."
raise ValueError
numberStartingSamples = int(numberStartingSamples)
listnodes = dataArray[3].values()
s, n = int(s), int(n)
#Here the set of classes is a list of two lists containing the samples in C and not C
bestClassification = []
bestClassesList = []
#Worse value for this coefficient
currBestYouden = inf
nodesNumber = len(dataArray[3])
while s:
#Randomly draw n distinct nodes among the nodes in the taxonomic tree
nodesList = randomChoice(listnodes, n)
assignedClasses, classes, valueSet = classifyIt(
dataArray, metadatum, nodesList, numberStartingSamples)
numberClass = len(classes)
youdenJ = countYouden(assignedClasses, classes, numberSamples)
res = numberClass - youdenJ
if min(res, currBestYouden) == res:
bestClassification = []
for i in nodesList:
bestClassification.append(i)
currBestYouden = res
bestClassesList = []
for i in assignedClasses:
bestClassesList.append(i)
s -= 1
interpretIt(numberClass - currBestYouden)
if answer == "Y":
percentagesAs = [len(class1) for class1 in assignedClasses]
labels = [metadatum + " = " + str(value) for value in valueSet]
percentages = [len(class1) for class1 in classes]
plotPieChart(
labels, percentagesAs, "Assignments depending on " +
listNodes(nodesList) + " to class for metadatum " + metadatum)
plotPieChart(
labels, percentages, "Real classes depending on " +
listNodes(nodesList) + " for metadatum " + metadatum)
answer = raw_input("Do you want to save the results? Y/N")
if (answer == "Y"):
writeFile(
"Best Youden's J statistic for this classification is: " +
str(numberClass - currBestYouden) +
"\nand most relevant list of nodes for this metadatum is:" +
str(bestClassification),
"Assignments to classes for metadatum " + metadatum)
elif not (answer == "N"):
print "\n Answer by Y or N!"
return bestClassification, (numberClass - currBestYouden), bestClassesList
def randomSubSamplingAct(dataArray):
print dataArray[1]
metadata = parseList(raw_input("Input the metadata that will cluster the set of samples among those written above. [ e.g. " + dataArray[1][0] + ";" + dataArray[1][-1] + " ]\n"))
isInDatabase(metadata,dataArray[1])
s = raw_input("Input the number s of random samplings.\n")
n = raw_input("Input the number n of nodes to select at each try.\n")
numberofSamples = len(dataArray[0])
if not integer.match(s) or not integer.match(n):
print "\n/!\ ERROR: s and n must both be integers."
raise ValueError
s,n = int(s),int(n)
numberStartingSamples = sanitize(raw_input("Knowing there is/are " + str(numberofSamples) + " sample(s), how many samples do you want to create the training set?\n"))
x = integer.match(numberStartingSamples)
if not x or (x and (int(numberStartingSamples) > numberofSamples)):
print "\n/!\ ERROR: You should write down an integer less or equal to",numberofSamples,"."
raise ValueError
numberStartingSamples = int(numberStartingSamples)
#Here the set of classes is a list of two lists containing the samples in C and not C
bestClassification = []
bestClassesList = []
bestShape = []
bestValuesList = []
#Worse value for this coefficient
currBestYouden = inf
nodesNumber = len(dataArray[3])
#@dataArray[2] = idSequences, which is a dictionary of (key=identifier,values=(name,rank of node))
listofNodes = dataArray[2].values()
while s:
#Randomly draw n distinct nodes among the nodes in the taxonomic tree
nodesList = randomChoice(listofNodes,n)
assignedClasses,classes,valueSets = classifyIt(dataArray,metadata,nodesList,numberStartingSamples)
numberClass = classes.lenMDL(shape)
#len(dataArray[0])?
youdenJ = countYouden(assignedClasses,classes,numberofSamples)
res = numberClass - youdenJ
if min(res,currBestYouden) == res:
bestValuesList = []
for i in valueSets:
bestValuesList.append(i)
bestClassification = []
for i in nodesList:
bestClassification.append(i)
bestShape = []
for i in shape:
bestShape.append(i)
currBestYouden = res
bestClassesList = []
for i in assignedClasses:
bestClassesList.append(i)
s -= 1
interpretIt(numberClass - currBestYouden)
if answer == "Y":
labels = [ "Metadata: " + str(metadata) + ", Values for each metadatum: " + str([ valueSet for valueSet in valueSets]) ]
percentagesAs = assignedClasses.mapMDL(len)
percentages = classes.mapMDL(len)
plotPie(labels,percentagesAs,"Assignments depending on " + str(nodesList) + " to class for metadata " + str(metadata))
plotPie(labels,percentages,"Real classes depending on " + str(nodesList) + " for metadata " + str(metadata))
answer = raw_input("Do you want to save the results? Y/N \n")
if (answer == "Y"):
writeFile("Best Youden's J statistic for this classification is: " + str(numberClass - currBestYouden) + "\nand most relevant list of nodes for this set of metadata is:" + str(bestClassification),"Assignments to classes for metadata " + str(metadata))
elif not (answer == "N"):
print "\n Answer by Y or N!"
return bestClassification,(numberClass - currBestYouden),bestClassesList
