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simulate.py
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simulate.py
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import numpy as np
from myutils import *
from math import *
from predict import *
from time import *
import sys
def split_one(n):
out = list(map(Decimal, np.random.rand(n)))
s = sum(out)
for i in range(n):
out[i] = out[i]/s
return out
def simple_simulate_square(n):
parents = [0] * n
C = [[0] * n]
C[0][0] = 1
indices = list(range(n))
likelihood = one
for i in range(1, n):
parent = np.random.choice(indices, p=C[i-1])
parents[i] = parent
likelihood = likelihood * C[i-1][parent]
new_row = split_one(i+1) + ([zero] * (n - i - 1))
C.append(new_row)
return C, parents, likelihood
def simple_simulator(params):
[n, p] = params
while True:
C, parents, likelihood = simple_simulate_square(n)
n_time_points = np.random.binomial(n, p)
chosen_time_points = list(np.random.choice(range(n-1), replace=False, size=n_time_points-1))
chosen_time_points.append(n-1)
C_chosen = []
for i in sorted(chosen_time_points):
C_chosen.append(C[i])
C_new, parents_new = remove_zero_clones(C_chosen, parents)
if not parents_new:
continue
T = list_to_tree(parents_new)
F = get_f(C_new, T)
for row in F:
del row[0]
var = list(range(1, len(parents_new)))
F, var_r = rearrange(F, var, True)
if not all([v == 0 for v in parents_new]):
step_structure = get_step_structure(F)
if max(map(len, step_structure)) <= 6:
yield F, remap_parents(var_r, parents_new), var, likelihood
def simple_simulator_fixed(params):
[n, p] = params
while True:
C, parents, likelihood = simple_simulate_square(n)
n_time_points = p
chosen_time_points = list(np.random.choice(range(n-1), replace=False, size=n_time_points-1))
chosen_time_points.append(n-1)
C_chosen = []
for i in sorted(chosen_time_points):
C_chosen.append(C[i])
C_new, parents_new = remove_zero_clones(C_chosen, parents)
if not parents_new:
continue
T = list_to_tree(parents_new)
F = get_f(C_new, T)
for row in F:
del row[0]
var = list(range(1, len(parents_new)))
F, var_r = rearrange(F, var, True)
if not all([v == 0 for v in parents_new]):
step_structure = get_step_structure(F)
if max(map(len, step_structure)) <= 6:
yield F, remap_parents(var_r, parents_new), var, likelihood
def clonal_composition(ts):
C = []
for row in ts:
dist = calc_dist(row)
C.append(dist)
return C
def bottleneck(C, b):
dist = calc_dist(C)
out = list(map(lambda x: Decimal(x.item()), list(np.random.multinomial(b, dist))))
return out
def spawn(C, mu, d, parents, fitnesses, fitness_low, fitness_high):
C_out = C[:]
parents_out = parents[:]
fitnesses_out = fitnesses[:]
dist = calc_dist(C_out)
d_res = d - floor(d)
nspawns = np.random.binomial(floor(d), mu)
newparents = np.random.choice(range(len(dist)), size=nspawns, p=dist)
for parent in newparents:
if C_out[parent] > 1:
C_out[parent] -= 1
C_out.append(one)
parents_out.append(parent)
fitnesses_out.append(Decimal(np.random.uniform(fitness_low, fitness_high)).quantize(Decimal('.1')))
return C_out, d_res, parents_out, fitnesses_out
def simulate(params):
[n, parents, fitnesses, lamb, mu, T_max, b, cc, fitness_low, fitness_high] = params
parents_out = parents[:]
fitnesses_out = fitnesses[:]
T = 1
t = 1
timeseries = [n]
N = [sum(n)]
d_res = 0
while T < T_max:
nclones = len(fitnesses_out)
# print("T"+str(T) + "\t" + str(nclones))
C = []
for i in range(0, nclones):
C.append(n[i] * (2 ** (lamb * fitnesses_out[i] * t)))
timeseries.append(C)
N.append(sum(C))
d = N[-1] - N[-2] + d_res
t += 1
if d > 1:
(C, d_res, parents_out, fitnesses_out) = spawn(C, mu, d, parents_out, fitnesses_out, fitness_low,
fitness_high)
n = C[:]
timeseries[-1] = C
t = 1
if N[-1] > cc:
n = bottleneck(C, b)
timeseries[-1] = n
N[-1] = sum(n)
t = 1
T += 1
return timeseries, parents_out, fitnesses_out
def fill_timeseries(timeseries, nclones):
ts = timeseries[:]
out = []
for t in range(0, len(ts)):
row = []
C = ts[t]
m = len(C)
for i in range(0, nclones):
if i < m:
c = C[i]
else:
c = zero
row.append(c)
out.append(row)
return out
def sample_timeseries(timeseries, cutoff, period):
ts_sample = []
for t in range(0, len(timeseries)):
if t < cutoff or t % period != 0:
continue
else:
ts_sample.append(timeseries[t])
return ts_sample
def write_timeseries(timeseries_file, parents_file, timeseries, parents, fitnesses, cutoff, period):
n = len(timeseries)
m = len(timeseries[0])
timeseries_file.write("Generation,Identity,Population,Fitness\n")
for t in range(0, n):
if t < cutoff or t % period != 0:
continue
for i in range(0, m):
timeseries_file.write(str(t) + ",c" + str(i) + "," + "{0:.4f}".format(timeseries[t][i])
+ "," + "{0:.1f}".format(fitnesses[i]) + "\n")
parents_file.write("Parent,Identity\n")
for i in range(1, m):
parents_file.write("c" + str(parents[i]) + ",c" + str(i) + "\n")
def remove_zero_clones(ts_sample, parents):
ts_sample_in = list(map(list, zip(*ts_sample)))
out = []
mapping = [0] * len(parents)
new_parents = []
removed = []
j = 0
for i in range(0, len(ts_sample_in)):
row = ts_sample_in[i]
if not all([v == zero for v in row]):
out.append(row)
mapping[i] = j
parent = parents[i]
while parent in removed:
if parent == 0:
parent = -1
break
parent = parents[parent]
new_parents.append(mapping[parent])
j += 1
else:
removed.append(i)
out = list(map(list, zip(*out)))
return out, new_parents
def timeseries_to_f(timeseries, parents, params):
[cutoff, period] = params
filled = fill_timeseries(timeseries, len(parents))
sample = sample_timeseries(filled, cutoff, period)
timeseries_new, parents_new = remove_zero_clones(sample, parents)
C = clonal_composition(timeseries_new)
T = list_to_tree(parents_new)
F = get_f(C, T)
likelihood = one
for i in range(1, len(T)):
likelihood = likelihood * C[i - 1][parents_new[i]]
for row in F:
del row[0]
# del parents_new[0]
return F, parents_new, likelihood
def exponential_simulator(params):
while True:
(timeseries, parents, fitnesses) = simulate(params[0:10])
F, parents, likelihood = timeseries_to_f(timeseries, parents, params[10:12])
var = list(range(1, len(parents)))
F, var_r = rearrange(F, var, True)
if not all([v == 0 for v in parents]):
step_structure = get_step_structure(F)
if max(map(len, step_structure)) <= 6 and len(parents) <= 10:
yield F, remap_parents(var_r, parents), var, likelihood
def evaluate_non_square(simulator, params, N, t, predictor_pairs):
fails = [0.0] * len(predictor_pairs)
tps = [0.0] * len(predictor_pairs)
close_calls = [0.0] * len(predictor_pairs)
times = [0.0] * len(predictor_pairs)
sim = simulator(params)
for i in range(N):
print(i)
(F, parents, var) = sim.__next__()
print_dm(F)
print(parents)
for j in range(0, len(predictor_pairs)):
(inner_predictor, outer_predictor) = predictor_pairs[j]
start = time()
var1, parents1, p1 = non_square_predict_fast(F, var, inner_predictor, outer_predictor)
end = time()
parents1 = remap_parents(var1, parents1)
num_matches = sum([v1 == v2 for (v1, v2) in zip(parents1[1:], parents[1:])])
freq_matches = (num_matches + 0.0) / (len(parents) - 1)
print(parents1)
if not parents1:
fails[j] += 1
if freq_matches == 1:
tps[j] += 1
if freq_matches > t:
close_calls[j] += 1
times[j] += (end - start)
for i in range(0, len(times)):
fails[i] = fails[i]/N
tps[i] = tps[i]/N
close_calls[i] = close_calls[i]/N
times[i] = times[i]/N
return fails, tps, close_calls, times
def write_simulations(simulator, params, N, f_out, parents_out, like_out):
f_out_file = open(f_out, "w")
parents_out_file = open(parents_out, "w")
like_out_file = open(like_out, "w")
sim = simulator(params)
for i in range(N):
(F, parents, var, likelihood) = sim.__next__()
f_out_file.write("#\n")
write_dm(F, f_out_file)
parents_out_file.write(" ".join(map(str, parents)) + "\n")
like_out_file.write(str(likelihood) + "\n")
f_out_file.close()
parents_out_file.close()
like_out_file.close()
def read_simulations(f_in, parents_in):
f_in_file = open(f_in)
parents_in_file = open(parents_in)
f_out = []
parents_out = []
lines = f_in_file.readlines()
if lines[0][0] != '#':
sys.exit("Invalid input file!")
curr_f = []
for line in lines:
if len(line) > 0:
if line[0] == '#':
if curr_f:
f_out.append(curr_f)
curr_f = []
else:
curr_f.append(list(map(Decimal, line.split(None))))
if curr_f:
f_out.append(curr_f)
for line in parents_in_file:
parents_out.append(list(map(int, line.split(None))))
f_in_file.close()
parents_in_file.close()
return f_out, parents_out
def evaluate_batch(batch_prefix, algos):
batch, truths = read_simulations(batch_prefix + ".sims", batch_prefix + ".truth")
results = []
for i in algos:
results.append(read_results(batch_prefix + "." + str(i) + ".pred"))
evaluate(truths, results, [], batch_prefix + ".histo")
likess = []
timess = []
f = open(batch_prefix + ".like")
true_likes = list(map(float, f.readlines()))
f.close()
for i in algos:
f = open(batch_prefix + "." + str(i) + ".time")
[times, likes] = list(map(list, zip(*(list(map(lambda x: list(map(float, x.split(None))), f.readlines()))))))
times = list(map(lambda x: x if x != 0 else 0.0001, times))
likes = list(map(lambda x, y: np.log10(abs(x)/y), likes, true_likes))
times = list(map(np.log10, times))
likess.append(likes)
timess.append(times)
f.close()
likess = list(map(list, zip(*likess)))
timess = list(map(list, zip(*timess)))
f = open(batch_prefix + ".llike", "w")
for blah in likess:
f.write("\t".join(list(map(str, blah))) + "\n")
f.close()
f = open(batch_prefix + ".ltime", "w")
for blah in timess:
f.write("\t".join(list(map(str, blah))) + "\n")
f.close()