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run_simulation.py
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run_simulation.py
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import sympy
import argparse
import numpy as np
import equations
import data
# from dsr_utils import run_dsr
from gp_utils import run_gp
from interpolate import num_diff, num_diff_gp
import pickle
import os
import time
# # set up ODE config
# ode_param = None
# x_id = 0
#
# # data generation config
# freq = 10
# n_sample = 100
# noise_sigma = 0.0
#
# # set up algorithm config
# alg = 'gp'
def run(ode_name, ode_param, x_id, freq, n_sample, noise_ratio, alg, seed, n_seed):
np.random.seed(999)
print(freq)
ode = equations.get_ode(ode_name, ode_param)
T = ode.T
init_low = ode.init_low
init_high = ode.init_high
has_coef = ode.has_coef
noise_sigma = ode.std_base * noise_ratio
dg = data.DataGenerator(ode, T, freq, n_sample, noise_sigma, init_low, init_high)
yt = dg.generate_data()
if noise_sigma == 0:
dxdt_hat = (yt[1:, :, :] - yt[:-1, :, :]) / (dg.solver.t[1:] - dg.solver.t[:-1])[:, None, None]
elif alg != 'gp':
dxdt_hat = num_diff(yt, dg, alg)
else:
dxdt_hat, xt_hat = num_diff_gp(yt, dg, ode)
print('Numerical differentiation: Done.')
# if alg != 'gp':
X_train = yt[:-1, :, :]
# else:
# X_train = xt_hat[:-1, :, :]
X_train = X_train.reshape(X_train.shape[0] * X_train.shape[1], X_train.shape[2])
y_train = dxdt_hat[:, :, x_id].flatten()
assert X_train.shape[0] == y_train.shape[0]
if alg == 'tv':
path_base = 'results/{}/noise-{}/sample-{}/freq-{}/'.format(ode_name, noise_ratio, n_sample, freq)
elif alg == 'gp':
path_base = 'results_gp/{}/noise-{}/sample-{}/freq-{}/'.format(ode_name, noise_ratio, n_sample, freq)
else:
path_base = 'results_spline/{}/noise-{}/sample-{}/freq-{}/'.format(ode_name, noise_ratio, n_sample, freq)
if not os.path.isdir(path_base):
os.makedirs(path_base)
for s in range(seed, seed+n_seed):
print(' ')
print('Running with seed {}'.format(s))
if x_id == 0:
path = path_base + 'grad_seed_{}.pkl'.format(s)
else:
path = path_base + 'grad_x_{}_seed_{}.pkl'.format(x_id, s)
if os.path.isfile(path):
print('Skipping seed {}'.format(s))
continue
start = time.time()
f_hat, est_gp = run_gp(X_train, y_train, ode, x_id, s)
print(f_hat)
f_true = ode.get_expression()[x_id]
if not isinstance(f_true, tuple):
correct = sympy.simplify(f_hat - f_true) == 0
else:
correct_list = [sympy.simplify(f_hat - f) == 0 for f in f_true]
correct = max(correct_list) == 1
end = time.time()
with open(path, 'wb') as f:
pickle.dump({
'model': est_gp._program,
'X_train': X_train,
'y_train': y_train,
'seed': s,
'correct': correct,
'f_hat': f_hat,
'ode': ode,
'noise_ratio': noise_ratio,
'noise_sigma': noise_sigma,
'dg': dg,
'time': end-start,
}, f)
print(f_hat)
print(correct)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--ode_name", help="name of the ode", type=str)
parser.add_argument("--ode_param", help="parameters of the ode (default: None)", type=str, default=None)
parser.add_argument("--x_id", help="ID of the equation to be learned", type=int, default=0)
parser.add_argument("--freq", help="sampling frequency", type=float, default=10)
parser.add_argument("--n_sample", help="number of trajectories", type=int, default=100)
parser.add_argument("--noise_sigma", help="noise level (default 0)", type=float, default=0.)
parser.add_argument("--alg", help="name of the benchmark", type=str, default='tv', choices=['tv', 'spline', 'gp'])
parser.add_argument("--seed", help="random seed", type=int, default=0)
parser.add_argument("--n_seed", help="random seed", type=int, default=10)
args = parser.parse_args()
print('Running with: ', args)
if args.ode_param is not None:
param = [float(x) for x in args.ode_param.split(',')]
else:
param = None
if args.freq >= 1:
freq = int(args.freq)
else:
freq = args.freq
run(args.ode_name, param, args.x_id, freq, args.n_sample, args.noise_sigma, args.alg, seed=args.seed, n_seed=args.n_seed)