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task3.py
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#!/usr/bin/python
from pyspark import SparkContext
import random
import datetime
import sys
"""task3"""
#sample, fsca, ssca,ly6c, cd11b, sca1
def extract_measurement(record):
try:
sample, fsca, ssca, cd48, ly6g, cd117, sca1, \
cd11b, cd150, cd11c, b220, ly6c, cd115, cd135, \
cd3cd19nk11, cd16cd32, cd45 = record.strip().split(",")
fsca = float(fsca)
ssca = float(ssca)
ly6c = float(ly6c)
cd11b = float(cd11b)
sca1 = float(sca1)
if 150000 >= fsca >= 1 and 1 <= ssca <= 150000:
return (sample.strip(), (ly6c,cd11b,sca1))
else:
return 0
except:
return 0
def merge1(accumulated,current_rating):
a1,b1,c1 = current_rating
part1,count = accumulated
a,b,c = part1
total = (a1+a,b1+b,c1+c)
count += 1
return (total,count)
def merge2(accumulated_pair_1, accumulated_pair_2):
rating_total_1, rating_count_1 = accumulated_pair_1
rating_total_2, rating_count_2 = accumulated_pair_2
a1,b1,c1 = rating_total_1
a2,b2,c2 = rating_total_2
total = (a1+a2,b1+b2,c1+c2)
return (total, rating_count_1+rating_count_2)
def map_to_centroid(line):
centroid,count = line
return ((centroid[0]/count,centroid[1]/count,centroid[2]/count),count)
def map_count(line):
cluster,centroid_count = line
count = centroid_count[1]
return (cluster,count)
def map_new_centroid(line):
cluster,centroid_count = line
centroid = centroid_count[0]
return (cluster,centroid)
def construct_centroid(row,line):# k is rdd
newlist = line #[(1,(ly6c,cd11b,sca1)),(2,(...))...]
pair = tuple()
ly6c, cd11b, sca1 = row
shortest = cal_distance(newlist[0],ly6c,cd11b,sca1)
pair = (1, (ly6c,cd11b,sca1))
for i in range(len(newlist)):
distance = cal_distance(newlist[i],ly6c,cd11b,sca1)
if distance<=shortest:
shortest = distance
pair = (i+1,(ly6c,cd11b,sca1))#not rdd
return (pair[0],pair[1])#rdd
def construct_centroid_task3(row,record): # record is list
newlist = record #[(1,(ly6c,cd11b,sca1)),(2,(...))...] #centroid
pair = tuple()
ly6c, cd11b, sca1 = row #measurement
shortest = cal_distance(newlist[0],ly6c,cd11b,sca1)
pair = (1, (ly6c,cd11b,sca1))
for i in range(len(newlist)):
distance = cal_distance(newlist[i],ly6c,cd11b,sca1)
if distance<=shortest:
shortest = distance
pair = (i+1,(ly6c,cd11b,sca1))#measurement
return (pair[0],(pair[1],distance))#rdd
def cal_distance(newtuple,ly6c,cd11b,sca1):
c_ly6c,c_cd11b,c_sca1 = newtuple[1][0],newtuple[1][1],newtuple[1][2]
return ((ly6c-c_ly6c)**2+(cd11b-c_cd11b)**2+(sca1-c_sca1)**2)**0.5
def format_centroid(line):
cluster_id, measurement_count, ly6c, cd11b, sca1 = line.strip().split("\t")
ly6c = float(ly6c)
cd11b = float(cd11b)
sca1 = float(sca1)
return(cluster_id,(ly6c, cd11b, sca1))
def final_rdd_centroids_distance(record):
clusterid, measurement_all = record
measurement_data, centroids_data_all = measurement_all
measurement_data_ly6c, measurement_data_cd11b, measurement_data_sca1 = measurement_data
centroids_data, number_measurement = centroids_data_all
distance = cal_distance_final(centroids_data,measurement_data_ly6c,measurement_data_cd11b,measurement_data_sca1)
return(clusterid,((measurement_data_ly6c, measurement_data_cd11b, measurement_data_sca1),distance))
def cal_distance_final(newtuple,ly6c,cd11b,sca1):
c_ly6c,c_cd11b,c_sca1 = newtuple[0],newtuple[1],newtuple[2]
return ((ly6c-c_ly6c)**2+(cd11b-c_cd11b)**2+(sca1-c_sca1)**2)**0.5
def count_list(line):
cluster_id, measurement_count, ly6c, cd11b, sca1 = line.strip().split("\t")
measurement_count = float(measurement_count)*0.9
measurement_filter = int(measurement_count)
return(cluster_id,measurement_filter)
def distance_filter(line):
clusterid, measurement_all = line
measurement_data, number_measurement = measurement_all
measurement_data_ly6c, measurement_data_cd11b, measurement_data_sca1 = measurement_data
return (clusterid, ((measurement_data_ly6c, measurement_data_cd11b, measurement_data_sca1),distance))
def new_dataset(line):
clusterid, measurement_all = line
measurement_data, number_measurement = measurement_all
measurement_data_ly6c, measurement_data_cd11b, measurement_data_sca1 = measurement_data
return (clusterid, (measurement_data_ly6c, measurement_data_cd11b, measurement_data_sca1))
def new_random_centroids(k,ly6c_max,ly6c_min,cd11b_max,cd11b_min,sca1_max,sca1_min):
new_centroids_list = []
new_ly6c_delta = ly6c_max-ly6c_min
new_cd11b_delta = cd11b_max-cd11b_min
new_sca1_delta = sca1_max-sca1_min
for i in range(k):
rand = random.uniform(0.4, 0.6)
ly6c = ly6c_min + rand*new_ly6c_delta
cd11b = cd11b_min + rand*new_cd11b_delta
sca1 = sca1_min + rand*new_sca1_delta
new_centroids_list.append((i+1,(ly6c, cd11b, sca1)))
return new_centroids_list
def format_task2(line):
clusterid, measurement_all = line
measurement_data, number_measurement = measurement_all
data_ly6c, data_cd11b, data_sca1 = measurement_data
return ("{}\t{}\t{}\t{}\t{}".format(clusterid, number_measurement, data_ly6c, data_cd11b, data_sca1))
"""MAIN"""
if __name__ == "__main__":
k = int(sys.argv[1])
input_measurement = str(sys.argv[2])
input_centroid = str(sys.argv[3])
output_task3 = str(sys.argv[4])
sc = SparkContext(appName="Assignment 02 task3")
measurement = sc.textFile(input_measurement)
centroid_input = sc.textFile(input_centroid)
"""task3"""
print("{} -> {}".format(str(datetime.datetime.now()),"Task3 begin!"))
centroids_task2 = centroid_input.map(format_centroid) #centroid from task2 [(cluster,((3d))]
centroids_task2_list = [x for x in centroids_task2.toLocalIterator()]
sample_measurement3 = measurement.map(extract_measurement).filter(lambda line: line != 0).repartition(3)
measurement_centroids_distance = sample_measurement3.values().map(lambda row: construct_centroid_task3(row,centroids_task2_list)) # [(cluster,((3d),distance))]
count_list_all = centroid_input.map(count_list)
count_all = [x for x in count_list_all.toLocalIterator()]
count_clusterid = count_all[0][1]
measurement_centroids_total = measurement_centroids_distance.filter(lambda x: x[0] == 1)
total_distance_list = measurement_centroids_total.takeOrdered(count_clusterid,lambda x: x[1][1])
total_distance_filter = sc.parallelize(total_distance_list)
# total_distance_filter.saveAsTextFile("f1_task3_01_total_distance_filter10")
for i in range(k):
count_clusterid = count_all[i][1]
# print(count_clusterid)
each_measurement_centroids_distance = measurement_centroids_distance.filter(lambda x: x[0] == i+1)#.takeOrdered(count_clusterid)
each_distance_list = each_measurement_centroids_distance.takeOrdered(count_clusterid,lambda x: x[1][1])
if i > 0:
each_distance_filter = sc.parallelize(each_distance_list)
total_distance_filter = total_distance_filter.union(each_distance_filter)
new_measurement_dataset = total_distance_filter.map(new_dataset).repartition(3)
# new_measurement_dataset.saveAsTextFile("f1_task3_02_new_measurement_dataset9")
new_ly6c_rdd = new_measurement_dataset.values().map(lambda v:v[0])
new_cd11b_rdd = new_measurement_dataset.values().map(lambda v:v[1])
new_sca1_rdd = new_measurement_dataset.values().map(lambda v:v[2])
new_ly6c_max,new_ly6c_min = new_ly6c_rdd.max(),new_ly6c_rdd.min()
new_cd11b_max,new_cd11b_min = new_cd11b_rdd.max(),new_cd11b_rdd.min()
new_sca1_max,new_sca1_min = new_sca1_rdd.max(),new_sca1_rdd.min()
new_random_centroids_rdd = new_random_centroids(k,new_ly6c_max,new_ly6c_min,new_cd11b_max,new_cd11b_min,new_sca1_max,new_sca1_min)#list of k tuples
new_c_count = sc.parallelize(new_random_centroids_rdd).map(lambda x:(x[0],x[1])).mapValues(lambda v:(v,0))#initialize each cluster to 0 count, (clusterid,((3dimension),count))
task3_new_rdds = 0
for i in range(10):
print("{} = {}/{}".format("Task3 Iteration Process",i+1,10))
task3_new_rdds =new_measurement_dataset.values().map(lambda row: construct_centroid(row,new_random_centroids_rdd))#rdds (clusterid,(ly6c,cd11b,sca1))
new_c_list_with_count = task3_new_rdds.aggregateByKey(((0.0,0.0,0.0),0),merge1,merge2,1).mapValues(map_to_centroid)#cluster,((centroid),count)
new_c_count = new_c_list_with_count
new_random_centroids_rdd = new_c_list_with_count.map(map_new_centroid)
new_random_centroids_rdd = [x for x in new_random_centroids_rdd.toLocalIterator()]
new_c_count_re = new_c_count.repartition(1)
task3_output = new_c_count_re.sortBy(lambda line: line[0]).map(format_task2)
# final_rdd_centroids.saveAsTextFile("task3_01_final_rdd_centroids")
# measurement_centroids_distance.saveAsTextFile("task3_02_measurement_centroids_distance")
# count_list_all.saveAsTextFile("task3_03_count_list_all")
# measurement_centroids_total.saveAsTextFile("task3_04_measurement_centroids_total")
# total_distance_filter.saveAsTextFile("task3_05_total_distance_filter2")
# new_measurement_dataset.saveAsTextFile("task3_06_new_measurement_dataset")
# new_c_list.saveAsTextFile("task3_07_new_c_list")
# new_c_count.saveAsTextFile("task3_08_new_c_count")
task3_output.saveAsTextFile(output_task3)
print("{} -> {}".format(str(datetime.datetime.now()),"Task3 done!"))