"""Start a hyperoptimization from a single node""" import sys import numpy as np import pickle as pkl import hyperopt from hyperopt import hp, fmin, tpe, Trials import eureqa import time import contextlib import numpy as np @contextlib.contextmanager def temp_seed(seed): state = np.random.get_state() np.random.seed(seed) try: yield finally: np.random.set_state(state) #Change the following code to your file ################################################################################ TRIALS_FOLDER = 'trials' NUMBER_TRIALS_PER_RUN = 1 def run_trial(args): """Evaluate the model loss using the hyperparams in args :args: A dictionary containing all hyperparameters :returns: Dict with status and loss from cross-validation """ print("Running on", args) for key in 'niterations npop'.split(' '): args[key] = int(args[key]) total_steps = 10*100*1000 niterations = args['niterations'] npop = args['npop'] if niterations == 0 or npop == 0: print("Bad parameters") return {'status': 'ok', 'loss': np.inf} args['ncyclesperiteration'] = int(total_steps / (niterations * npop)) args['topn'] = 10 args['parsimony'] = 1e-3 args['annealing'] = True if args['npop'] < 20 or args['ncyclesperiteration'] < 3: print("Bad parameters") return {'status': 'ok', 'loss': np.inf} args['weightDoNothing'] = 1.0 maxTime = 30 ntrials = 2 equation_file = f'.hall_of_fame_{np.random.rand():f}.csv' with temp_seed(0): X = np.random.randn(100, 5)*3 eval_str = ["np.sign(X[:, 2])*np.abs(X[:, 2])**2.5 + 5*np.cos(X[:, 3]) - 5", "np.sign(X[:, 2])*np.abs(X[:, 2])**3.5 + 1/(np.abs(X[:, 0])+1)", "np.exp(X[:, 0]/2) + 12.0 + np.log(np.abs(X[:, 0])*10 + 1)", "1.0 + 3*X[:, 0]**2 - 0.5*X[:, 0]**3 + 0.1*X[:, 0]**4", "(np.exp(X[:, 3]) + 3)/(np.abs(X[:, 1]) + np.cos(X[:, 0]) + 1.1)"] print(f"Starting", str(args)) try: trials = [] for i in range(3, 6): print(f"Starting test {i}") for j in range(ntrials): print(f"Starting trial {j}") trial = eureqa.eureqa( test=f"simple{i}", threads=4, binary_operators=["plus", "mult", "pow", "div"], unary_operators=["cos", "exp", "sin", "loga", "abs"], equation_file=equation_file, timeout=maxTime, maxsize=25, verbosity=0, **args) if len(trial) == 0: raise ValueError trials.append( np.min(trial['MSE'])**0.5 / np.std(eval(eval_str[i-1])) ) print(f"Test {i} trial {j} with", str(args), f"got {trials[-1]}") except ValueError: print(f"Broken", str(args)) return { 'status': 'ok', # or 'fail' if nan loss 'loss': np.inf } loss = np.average(trials) print(f"Finished with {loss}", str(args)) return { 'status': 'ok', # or 'fail' if nan loss 'loss': loss } space = { 'niterations': hp.qlognormal('niterations', np.log(10), 1.0, 1), 'npop': hp.qlognormal('npop', np.log(100), 1.0, 1), 'alpha': hp.lognormal('alpha', np.log(10.0), 1.0), 'fractionReplacedHof': hp.lognormal('fractionReplacedHof', np.log(0.1), 1.0), 'fractionReplaced': hp.lognormal('fractionReplaced', np.log(0.1), 1.0), 'perturbationFactor': hp.lognormal('perturbationFactor', np.log(1.0), 1.0), 'weightMutateConstant': hp.lognormal('weightMutateConstant', np.log(4.0), 1.0), 'weightMutateOperator': hp.lognormal('weightMutateOperator', np.log(0.5), 1.0), 'weightAddNode': hp.lognormal('weightAddNode', np.log(0.5), 1.0), 'weightInsertNode': hp.lognormal('weightInsertNode', np.log(0.5), 1.0), 'weightDeleteNode': hp.lognormal('weightDeleteNode', np.log(0.5), 1.0), 'weightSimplify': hp.lognormal('weightSimplify', np.log(0.05), 1.0), 'weightRandomize': hp.lognormal('weightRandomize', np.log(0.25), 1.0), } ################################################################################ def merge_trials(trials1, trials2_slice): """Merge two hyperopt trials objects :trials1: The primary trials object :trials2_slice: A slice of the trials object to be merged, obtained with, e.g., trials2.trials[:10] :returns: The merged trials object """ max_tid = 0 if len(trials1.trials) > 0: max_tid = max([trial['tid'] for trial in trials1.trials]) for trial in trials2_slice: tid = trial['tid'] + max_tid + 1 hyperopt_trial = Trials().new_trial_docs( tids=[None], specs=[None], results=[None], miscs=[None]) hyperopt_trial[0] = trial hyperopt_trial[0]['tid'] = tid hyperopt_trial[0]['misc']['tid'] = tid for key in hyperopt_trial[0]['misc']['idxs'].keys(): hyperopt_trial[0]['misc']['idxs'][key] = [tid] trials1.insert_trial_docs(hyperopt_trial) trials1.refresh() return trials1 loaded_fnames = [] trials = None # Run new hyperparameter trials until killed while True: np.random.seed() # Load up all runs: import glob path = TRIALS_FOLDER + '/*.pkl' for fname in glob.glob(path): if fname in loaded_fnames: continue trials_obj = pkl.load(open(fname, 'rb')) n_trials = trials_obj['n'] trials_obj = trials_obj['trials'] if len(loaded_fnames) == 0: trials = trials_obj else: print("Merging trials") trials = merge_trials(trials, trials_obj.trials[-n_trials:]) loaded_fnames.append(fname) print("Loaded trials", len(loaded_fnames)) if len(loaded_fnames) == 0: trials = Trials() n = NUMBER_TRIALS_PER_RUN try: best = fmin(run_trial, space=space, algo=tpe.suggest, max_evals=n + len(trials.trials), trials=trials, verbose=1, rstate=np.random.RandomState(np.random.randint(1,10**6)) ) except hyperopt.exceptions.AllTrialsFailed: continue print('current best', best) hyperopt_trial = Trials() # Merge with empty trials dataset: save_trials = merge_trials(hyperopt_trial, trials.trials[-n:]) new_fname = TRIALS_FOLDER + '/' + str(np.random.randint(0, sys.maxsize)) + str(time.time()) + '.pkl' pkl.dump({'trials': save_trials, 'n': n}, open(new_fname, 'wb')) loaded_fnames.append(new_fname)