update

models/MJP/MJPRuntime.py

@@ -1,9 +1,3 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
 import pyabc
 import matplotlib.pyplot as plt
 import os
@@ -12,32 +6,40 @@
 import scipy as sp
 import time
 
+path="/p/project/fitmulticell/felipe/scripts/Batch_pyABC/programs/MJP"
 
-db_path = ("sqlite://")
-logfile_path="/home/felipe/testresults/MJPAcceptanceRates.csv"
 
+import argparse
 
-# In[ ]:
+parser = argparse.ArgumentParser(description='Parse necessary arguments')
+parser.add_argument('-pt', '--port', type=str, default="50004",
+                    help='Which port should be used?')
+parser.add_argument('-ip', '--ip', type=str,
+                    help='Dynamically passed - BW: Login Node 3')
+parser.add_argument('-nd','--nodes', type=int, default=8, help='How many nodes are used')
 
+args = parser.parse_args()
 
-port=6358
+port = args.port
+host = args.ip
+nodes=args.nodes
 
-eps_list=[10, 5, 3, 2, 1, 0.8, 0.66]
-eps=pyabc.ListEpsilon(eps_list)
-#eps=pyabc.MedianEpsilon(500, median_multiplier=0.8)
+# Set constants
 
+eps_list=[10, 5, 3, 2, 1, 0.85, 0.75, 0.66]
+eps=pyabc.ListEpsilon(eps_list)
 max_nr_pop=len(eps_list)
 min_eps=0.66
-pop_sizes = [32, 64, 128, 256, 512, 1024, 2048]
-iters_PPP = 15
-iters_ori = 10
-resultfilepath = "/home/freck/sampling/log/MJPruntimeresults.txt"
+pop_sizes = [64, 256, 1024, 4096]
+iters_PPP = 25
+iters_ori = 25
+resultfilepath = os.path.join(path, "results/MJPruntimeresultsN"+str(nodes))
 resultfile = open(resultfilepath, "w")
 resultfile.write("Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime\n")
 resultfile.close()
 
 
-# In[ ]:
+# Define the model
 
 
 def h(x, pre, c):
@@ -98,10 +100,6 @@ def gillespie(x, c, pre, post, max_t):
 
     return np.array(t_store), np.array(x_store)
 
-
-# In[ ]:
-
-
 MAX_T = 0.1
 
 class Model1:
@@ -137,13 +135,13 @@ def distance(x, y):
 prior = pyabc.Distribution(rate=pyabc.RV("uniform", 0, 100))
 
 
-# In[ ]:
+# Run inference in Look-Ahead mode
 
 
-redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host="131.220.224.226",
+redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host=host,
                                                        port=port,
-                                                       look_ahead = True, 
-						       look_ahead_delay_evaluation = True)
+                                                       look_ahead = True,
+                                                       look_ahead_delay_evaluations=False)
 
 histories=[]
 runtimes=np.zeros(iters_PPP)
@@ -152,7 +150,11 @@ def distance(x, y):
     totalstarttime =time.time()
     for i in range(iters_PPP):
         starttime=time.time()
-
+
+        db_path = "sqlite:///" + os.path.join(path,
+                                              "results/database",
+                                              "MJPLAdatabase"+str(i))
+
         abc = pyabc.ABCSMC(models = Model1(),
                    parameter_priors = prior,
                    distance_function = distance,
@@ -181,9 +183,9 @@ def distance(x, y):
     resultfile.close()
 
 
-# In[ ]:
+# Run in Standard mode
 
-redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host="131.220.224.226",
+redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host=host,
                                                        port=port,
                                                        look_ahead = False)
 
@@ -194,15 +196,19 @@ def distance(x, y):
     totalstarttime =time.time()
     for i in range(iters_ori):
         starttime=time.time()
-
+
+        db_path_ori = "sqlite:///" + os.path.join(path,
+                                              "results/database",
+                                              "MJPORIdatabase"+str(i))
+
         abc = pyabc.ABCSMC(models = Model1(),
                    parameter_priors = prior,
                    distance_function = distance,
                    population_size = pop_size,
                    sampler = redis_sampler,
                    eps = eps)
 
-        abc.new(db_path, observations)
+        abc.new(db_path_ori, observations)
         history = abc.run(minimum_epsilon=min_eps, max_nr_populations=max_nr_pop)
 
         endtime=time.time()

models/MJP/Testresults/MJPruntimeresultsN1.txt

@@ -0,0 +1,9 @@
+Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime
+64, True, 25, 4.700620365142822, 0.13697168159373632, 117.5156397819519
+256, True, 25, 15.175513916015625, 0.6333949294039781, 379.3879358768463
+1024, True, 25, 57.17575264930725, 1.7556208926126167, 1429.3938932418823
+4096, True, 25, 225.16033295631408, 20.54652997972526, 5629.008406877518
+64, False, 25, 5.944116010665893, 0.2227856101115552, 148.60300469398499
+256, False, 25, 17.454330921173096, 0.46063299201262, 436.35836267471313
+1024, False, 25, 61.75151098251343, 1.459239199916629, 1543.7878587245941
+4096, False, 25, 240.39604927062987, 11.982908250319703, 6009.9013130664825

models/MJP/Testresults/MJPruntimeresultsN16.txt

@@ -0,0 +1,9 @@
+Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime
+64, True, 25, 3.6584749889373778, 0.18036533743039637, 91.46195960044861
+256, True, 25, 6.604058866500854, 0.08443480460212013, 165.1015546321869
+1024, True, 25, 15.505275402069092, 0.10716168320144735, 387.631977558136
+4096, True, 25, 51.484131298065186, 0.4895708407641618, 1287.1033625602722
+64, False, 25, 4.0463243675231935, 0.10558147371895357, 101.15820908546448
+256, False, 25, 7.240054292678833, 0.07247017450751954, 181.00145149230957
+1024, False, 25, 16.147134351730347, 0.04671725590648293, 403.6784393787384
+4096, False, 25, 55.55671684265137, 0.12557241191903684, 1388.918003320694

models/MJP/Testresults/MJPruntimeresultsN2.txt

@@ -0,0 +1,9 @@
+Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime
+64, True, 25, 2.3337471866607666, 0.03421874610674276, 58.34378480911255
+256, True, 25, 5.730166301727295, 0.025145039378067304, 143.2542543411255
+1024, True, 25, 19.686495542526245, 0.2325956414434404, 492.1624667644501
+4096, True, 25, 74.84271800994873, 0.7970013313419991, 1871.0680329799652
+64, False, 25, 3.443028507232666, 0.06957789079959312, 86.07583379745483
+256, False, 25, 7.611031665802002, 0.06346520408220876, 190.27588510513306
+1024, False, 25, 23.34336711883545, 0.2188870054553896, 583.5842604637146
+4096, False, 25, 87.57662706375122, 0.4119207400624099, 2189.4157552719116

models/MJP/Testresults/MJPruntimeresultsN4.txt

@@ -0,0 +1,9 @@
+Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime
+64, True, 25, 3.0797416877746584, 0.030239835974786366, 76.99363803863525
+256, True, 25, 7.817548322677612, 0.04627855143839497, 195.43878698349
+1024, True, 25, 26.264585189819336, 0.4129806619917486, 656.6147060394287
+4096, True, 25, 101.04926258087158, 3.6060764224627535, 2526.231638431549
+64, False, 25, 3.6492793560028076, 0.026694120594938794, 91.23208713531494
+256, False, 25, 10.680523872375488, 11.654824619263968, 267.01317858695984
+1024, False, 25, 30.21081015586853, 6.013050001153057, 755.2703273296356
+4096, False, 25, 112.19126895904542, 1.5008914598140322, 2804.781801223755

models/MJP/Testresults/MJPruntimeresultsN8.txt

@@ -0,0 +1,9 @@
+Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime
+64, True, 25, 3.8386583042144777, 0.08122198631975298, 95.9665584564209
+256, True, 25, 7.660666446685791, 0.10431252323847692, 191.51674270629883
+1024, True, 25, 22.234130668640137, 0.42869506850045136, 555.8533442020416
+4096, True, 25, 74.61447005271911, 31.34629550457708, 1865.3618392944336
+64, False, 25, 3.965095729827881, 0.09609020697370997, 99.12750816345215
+256, False, 25, 7.706383352279663, 0.09192487934859454, 192.65967750549316
+1024, False, 25, 20.228655223846435, 1.7993651052167394, 505.71645617485046
+4096, False, 25, 72.90948188781738, 27.49854118781281, 1822.73712515831

models/ODE/Testresults/Juwels/Noisy/Var2/sleeptimeresults0.txt

@@ -1 +1,10 @@
 Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total Walltime
+64, Ori, 8, 91.42149505019188, 292.9593020957442, 734.9711499214172
+256, Ori, 8, 345.26164323091507, 207.21000418344875, 2764.3773238658905
+1024, Ori, 8, 1362.1188804209232, 2964.038945602262, 10900.209585428238
+64, PPP, 8, 93.32252585887909, 327.3567601288613, 749.1319305896759
+256, PPP, 8, 348.73482516407967, 812.6784226509708, 2793.88960647583
+1024, PPP, 8, 1384.8556591272354, 968.7753512668869, 11082.6002471447
+64, Stat, 5, 96.72240819931031, 152.72086997927926, 484.7231469154358
+256, Stat, 5, 356.30815567970274, 508.12290408978004, 1782.915586233139
+1024, Stat, 5, 1376.2829179763794, 1209.382377898872, 6882.824419498444

models/ODE/Testresults/Juwels/Noisy/Var2/sleeptimeresults4.txt

@@ -6,6 +6,3 @@ Pop size, Look_ahead, Repetitions, Runtime Expectation, Runtime Variance, total
 64, PPP, 18, 25.816566122902763, 188.4127050137668, 469.52601766586304
 256, PPP, 18, 44.75276900662316, 355.68264332277374, 810.4895243644714
 1024, PPP, 18, 86.24677246146732, 982.001131821476, 1557.9515907764435
-4096, PPP, 18, 171.21058615048727, 1762.042142831896, 3089.6374735832214
-64, Stat, 3, 100.2330223719279, 4.8570529993189036, 302.1996114253998
-256, Stat, 3, 109.11439990997314, 53.35862398350082, 328.8534586429596

models/Tumor/TumorModel.py

@@ -8,21 +8,38 @@
 
 
 from time import time
-import tumor2d
 
 import matplotlib.pyplot as plt
 from string import capwords
+import os
+import tempfile
 
 import pyabc
+import tumor2d
+
+import argparse
 
+parser = argparse.ArgumentParser(description='Parse necessary arguments')
+parser.add_argument('-pt', '--port', type=str, default="50004",
+                    help='Which port should be used?')
+parser.add_argument('-ip', '--ip', type=str,
+                    help='Dynamically passed - BW: Login Node 3')
+args = parser.parse_args()
 
-pop_size = 200
-min_eps=700
-min_eps_ori=min_eps
-max_nr_pop=100
-resultfile = open("/home/freck/sampling/log/TumorRuntimes.txt", "a")
-port=6358
+port = args.port
+host = args.ip
 
+nnodes = 32
+pop_size = 1000
+min_eps = 700
+min_eps_ori = min_eps
+max_nr_pop = 100
+logfilepath = "/p/home/jusers/reck1/juwels/scripts/programs/results/TumorStats"+str(pop_size)+".csv"
+resultfile = open("/p/home/jusers/reck1/juwels/scripts/programs/results/TumorRuntimes"+str(nnodes)+".txt", "a")
+
+db_path = "sqlite:///" + os.path.join("/p/home/jusers/reck1/juwels/scripts/programs/results", "TumorRes"+str(pop_size)+".db")
+
+db_path_ori = "sqlite:///" + os.path.join("/p/home/jusers/reck1/juwels/scripts/programs/results", "TumorRes_ori"+str(pop_size)+".db")
 
 # In[2]:
 
@@ -91,7 +108,7 @@
 # In[6]:
 
 
-redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host="131.220.224.226", port=port, look_ahead = False)
+redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host=host, port=port, look_ahead = False)
 
 starttime=time()
 abc = pyabc.ABCSMC(models=tumor2d.log_model, 
@@ -100,7 +117,7 @@
                    population_size=pop_size, 
                    sampler=redis_sampler)
 
-abc.new("sqlite:////tmp/test.db", data_mean)
+abc.new(db_path_ori, data_mean)
 history_f = abc.run(max_nr_populations=max_nr_pop, minimum_epsilon=min_eps_ori)
 endtime=time()
 
@@ -111,13 +128,13 @@
 
 df, w = history_f.get_distribution(m=0,t=history_f.max_t)
 pyabc.visualization.plot_kde_matrix(df, w, limits=limits);
-plt.savefig("/home/freck/sampling/log/img/TumorResOri.jpg")
+plt.savefig("/p/home/jusers/reck1/juwels/scripts/programs/img/TumorResOri"+str(pop_size)+".jpg")
 
 
 # In[8]:
 
 
-redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host="131.220.224.226", port=port, look_ahead = True, look_ahead_delay_evaluation=True, log_file="/home/freck/sampling/log/tumorlogs/statistics.csv")
+redis_sampler = pyabc.sampler.RedisEvalParallelSampler(host=host, port=port, look_ahead = True, look_ahead_delay_evaluation=True, log_file=logfilepath)
 
 starttime=time()
 abc = pyabc.ABCSMC(tumor2d.log_model, 
@@ -126,17 +143,17 @@
                    population_size=pop_size, 
                    sampler=redis_sampler)
 
-abc.new("sqlite:////tmp/test.db", data_mean)
+abc.new(db_path, data_mean)
 history = abc.run(max_nr_populations=max_nr_pop, minimum_epsilon=min_eps)
 endtime=time()
 
-resultfile.write("Ori, " + str(endtime-starttime)+", " + str(pop_size) + ", " + str(min_eps))
+resultfile.write("LA, " + str(endtime-starttime)+", " + str(pop_size) + ", " + str(min_eps))
 
 # In[10]:
 
 
 df, w = history.get_distribution(m=0, t=history.max_t)
 pyabc.visualization.plot_kde_matrix(df, w, limits=limits);
-plt.savefig("/home/freck/sampling/log/img/TumorResPPP.jpg")
+plt.savefig("/p/home/jusers/reck1/juwels/scripts/programs/img/TumorResPPP"+str(pop_size)+".jpg")