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Author: omidbazgirTTU

config.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Oct  8 17:04:34 2020
+
+@author: obazgir
+"""
+
+from argparse import ArgumentParser
+
+parser=ArgumentParser()
+
+parser.add_argument('--init', type=str, default='Init.pickle')
+parser.add_argument('--mapping', type=str, default='theMapping.pickle')
+parser.add_argument('--evolution', type=str, default='Evolv.csv')
+parser.add_argument('--num', type=int, default=5)
+
+args= parser.parse_args()

mpiHill_UF.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Aug 10 18:04:36 2019
+
+@author: Ruibzhan
+"""
+
+
+from mpi4py import MPI 
+import paraHill
+import pickle
+import numpy as np
+from itertools import product
+import time
+import datetime
+import config
+from config import *
+#%% Comm set
+comm = MPI.COMM_WORLD
+my_rank = comm.Get_rank()
+n_processors = comm.Get_size()
+print("Processors found: ",n_processors)
+current_time = datetime.datetime.now()
+print("Time now at the beginning is: ", current_time)
+
+def scatter_list_to_processors(comm, data_list, n_processors):
+    import math
+    data_amount = len(data_list)
+    heap_size = math.ceil(data_amount/(n_processors-1))
+
+    for pidx in range(1,n_processors):
+        try:
+            heap = data_list[heap_size*(pidx-1):heap_size*pidx]
+        except:
+            heap = data_list[heap_size*(pidx-1):]
+        comm.send(heap,dest = pidx)
+
+    return True
+
+def receive_from_processors_to_dict(comm, n_processors):
+    # receives dicts, combine them and return
+    feedback = dict()
+    for pidx in range(1,n_processors):
+        receved = comm.recv(source=pidx)
+        feedback.update(receved)
+    return feedback
+
+#start = time.time()
+#%% load data
+with open(args.init,'rb') as file:
+    gene_names,dist_matr,init_map = pickle.load(file)
+# with open('./Synthetic/Init_Synth8000.pickle','rb') as file:
+   # gene_names,dist_matr,init_map = pickle.load(file)
+Nn = len(gene_names)
+
+kk = args.num
+
+if init_map.shape[0] != init_map.shape[1]:
+    raise ValueError("For now only square images are considered.")
+nn = init_map.shape[0]
+# Convert from 'F34' to int 34
+init_map = np.char.strip(init_map.astype(str),'F').astype(int)
+map_in_int = init_map   
+#%%
+corr_evol = []
+if my_rank == 0:
+    print("Initial corr: >>>",paraHill.universial_corr(dist_matr,map_in_int))
+    for n_iter in range(kk):
+        # 9 initial coordinates. 
+        init_coords = [x for x in product([0,1,2],repeat = 2)]
+        for init_coord in init_coords:
+            # Update the mapping. 
+            broadcast_msg = map_in_int  
+            comm.bcast(broadcast_msg,root = 0)
+            # generate the centroids
+            xxx = [init_coord[0]+i*3 for i in range(int(nn/3)+1) if (init_coord[0]+i*3)<nn]
+            yyy = [init_coord[1]+i*3 for i in range(int(nn/3)+1) if (init_coord[1]+i*3)<nn]
+            centr_list = [x for x in product(xxx,yyy)]
+            # Master send and recv
+            scatter_list_to_processors(comm,centr_list,n_processors)
+            swap_dict = receive_from_processors_to_dict(comm,n_processors)
+            print(swap_dict)
+            map_in_int = paraHill.execute_dict_swap(swap_dict, map_in_int)
+            
+            print(">",init_coord,"Corr:",paraHill.universial_corr(dist_matr,map_in_int))
+
+        print(">>>",n_iter,"Corr:",paraHill.universial_corr(dist_matr,map_in_int))
+        corr_evol.append(paraHill.universial_corr(dist_matr,map_in_int))
+        
+    coords = np.array([[item[0] for item in np.where(map_in_int == ii)] for ii in range(Nn)])
+    with open(args.mapping,'wb') as file:
+        pickle.dump([gene_names,coords,map_in_int],file)
+    #print("Consumed time:",start - time.time())
+    Endtime = datetime.datetime.now()
+    print("Time now at the end: ", Endtime)
+    import pandas as pd
+    pd.Series(corr_evol).to_csv(args.evolution)    
+else:
+    # other processors
+    for n_iter in range(kk):
+        broadcast_msg = init_map    # just for a size
+
+        # 9 initial Centroids
+        for ii in range(9):
+            #Update the mapping
+            map_in_int = comm.bcast(broadcast_msg,root = 0)
+            
+            centr_list = comm.recv(source = 0)
+            each_swap_dict = paraHill.evaluate_centroids_in_list(centr_list,dist_matr,map_in_int)
+            comm.send(each_swap_dict,dest = 0)
+    #result = dict()
+    #for each in data:
+    #    result.update({each: -each})
+    #comm.send(result,dest = 0)
+
+MPI.Finalize