def run_experiment_IRM(args):
if args["seed"] >= 0:
torch.manual_seed(args["seed"])
numpy.random.seed(args["seed"])
torch.set_num_threads(1)
if args["setup_sem"] == "chain":
setup_str = "chain_hidden={}_hetero={}_scramble={}".format(
args["setup_hidden"], args["setup_hetero"], args["setup_scramble"])
elif args["setup_sem"] == "icp":
setup_str = "sem_icp"
else:
raise NotImplementedError
all_methods = {
"ERM": EmpiricalRiskMinimizer,
"ICP": InvariantCausalPrediction,
"IRM": InvariantRiskMinimization
}
if args["methods"] == "all":
methods = all_methods
else:
methods = {m: all_methods[m] for m in args["methods"].split(',')}
all_sems = []
all_solutions = []
all_environments = []
train_size = np.int(args["n_samples"] *
0.8) ## split data into 80:20 train:val
val_size = np.int(args["n_samples"] * 0.2)
for rep_i in range(args["n_reps"]):
if args["setup_sem"] == "chain":
sem = ChainEquationModel(args["dim"],
hidden=args["setup_hidden"],
scramble=args["setup_scramble"],
hetero=args["setup_hetero"])
env_list = args["env_list"]
m = len(env_list)
environments = []
for o in range(2 * m):
if (o < m):
environments.append(sem(train_size, env_list[o % m]))
environments.append(sem(train_size, env_list[o % m]))
else:
environments.append(sem(val_size, env_list[o % m]))
environments.append(sem(val_size, env_list[o % m]))
# environments = [sem(args["n_samples"], e) for e in env_list]
# environments = [sem(args_ns1, 0.2),
# sem(args_ns1, 2.0),
# sem(args_ns2, 0.2),
# sem(args_ns2, 2.0)
# ]
# environments = [sem(train_size, 0.2),
# sem(train_size, 2.0),
# sem(val_size, 0.2),
# sem(val_size, 2.0)
# ]
else:
raise NotImplementedError
all_sems.append(sem)
all_environments.append(environments)
for sem, environments in zip(all_sems, all_environments):
sem_solution, sem_scramble = sem.solution()
solutions = [
"{} SEM {} {:.5f} {:.5f}".format(setup_str, pretty(sem_solution),
0, 0)
]
for method_name, method_constructor in methods.items():
method = method_constructor(environments, args)
msolution = sem_scramble @ method.solution()
err_causal, err_noncausal = errors(sem_solution, msolution)
solutions.append("{} {} {} {:.5f} {:.5f}".format(
setup_str, method_name, pretty(msolution), err_causal,
err_noncausal))
all_solutions += solutions
return all_solutions, all_environments, msolution, sem_solution
def run_experiment(args):
if args["seed"] >= 0:
torch.manual_seed(args["seed"])
numpy.random.seed(args["seed"])
torch.set_num_threads(1)
if args["setup_sem"] == "chain":
setup_str = "chain_hidden={}_hetero={}_scramble={}".format(
args["setup_hidden"], args["setup_hetero"], args["setup_scramble"])
elif args["setup_sem"] == "icp":
setup_str = "sem_icp"
else:
raise NotImplementedError
all_methods = {
"ERM": EmpiricalRiskMinimizer,
"ICP": InvariantCausalPrediction,
"IRM": InvariantRiskMinimization
}
if args["methods"] == "all":
methods = all_methods
else:
methods = {m: all_methods[m] for m in args["methods"].split(',')}
all_sems = []
all_solutions = []
all_environments = []
for rep_i in range(args["n_reps"]):
if args["setup_sem"] == "chain":
sem = ChainEquationModel(args["dim"],
hidden=args["setup_hidden"],
scramble=args["setup_scramble"],
hetero=args["setup_hetero"])
environments = [
sem(args["n_samples"], .2),
sem(args["n_samples"], 2.),
sem(args["n_samples"], 5.)
]
else:
raise NotImplementedError
all_sems.append(sem)
all_environments.append(environments)
for sem, environments in zip(all_sems, all_environments):
solutions = [
"{} SEM {} {:.5f} {:.5f}".format(setup_str, pretty(sem.solution()),
0, 0)
]
for method_name, method_constructor in methods.items():
method = method_constructor(environments, args)
msolution = method.solution()
err_causal, err_noncausal = errors(sem.solution(), msolution)
solutions.append("{} {} {} {:.5f} {:.5f}".format(
setup_str, method_name, pretty(msolution), err_causal,
err_noncausal))
all_solutions += solutions
return all_solutions
def run_experiment(args):
if args["seed"] >= 0:
torch.manual_seed(args["seed"])
numpy.random.seed(args["seed"])
torch.set_num_threads(1)
random.seed(1)
if args["setup_sem"] == "chain":
setup_str = "chain_ones={}_hidden={}_hetero={}_scramble={}".format(
args["setup_ones"], args["setup_hidden"], args["setup_hetero"],
args["setup_scramble"])
elif args["setup_sem"] == "simple":
setup_str = ""
elif args["setup_sem"] == "icp":
setup_str = "sem_icp"
else:
raise NotImplementedError
all_methods = {
"ERM": EmpiricalRiskMinimizer,
"ICP": InvariantCausalPrediction,
"IRM": InvariantRiskMinimization
}
if int(args["env_list"]) > 1:
all_methods["IRM"] = InvariantRiskMinimization
if args["methods"] == "all":
methods = all_methods
else:
methods = {m: all_methods[m] for m in args["methods"].split(',')}
all_sems = []
all_solutions = []
all_environments = []
for rep_i in range(args["n_reps"]):
if args["setup_sem"] == "chain":
sem = ChainEquationModel(args["dim"],
ones=args["setup_ones"],
hidden=args["setup_hidden"],
scramble=args["setup_scramble"],
hetero=args["setup_hetero"])
env_list = [float(e) for e in args["env_list"].split(",")]
environments = [sem(args["n_samples"], e) for e in env_list]
elif args["setup_sem"] == "simple":
sem = SEM_X1YX2X3(args["dim"], args["k"], args["env_shuffle"])
env_list = range(int(args["env_list"]))
ratios = list(map(int, args["env_rat"].split(':')))
n = args["n_samples"]
n_samples = [
math.ceil(ni * 1.0 / sum(ratios) * n) for ni in ratios
]
print("sample in envs ", n_samples)
environments = []
env_orders = []
for e in env_list:
res = sem(n_samples[e], e)
environments += [res[:2]]
env_orders += [res[2]]
else:
raise NotImplementedError
all_sems.append(sem)
all_environments.append(environments)
sol_dict = {}
for sem, environments in zip(all_sems, all_environments):
sem_solution, sem_scramble = sem.solution()
solutions = [
"{} SEM {} {:.5f} {:.5f}".format(setup_str, pretty(sem_solution),
0, 0)
]
#sol_dict["SEM"] = [sem_solution.numpy(), 0, 0]
for method_name, method_constructor in methods.items():
method = method_constructor(environments, args, env_orders)
method_solution = sem_scramble @ method.solution()
err_causal, err_noncausal, ecaus, enoncaus = errors(
sem_solution, method_solution)
solutions.append("{} {} {} {:.5f} {:.5f}".format(
setup_str, method_name, pretty(method_solution), err_causal,
err_noncausal))
sol_dict[method_name] = [method_solution.detach().view(-1).numpy(), \
ecaus.detach().view(-1).numpy(), enoncaus.detach().view(-1).numpy()]
all_solutions += solutions
return all_solutions, sol_dict