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| """Print the best model parameters and loss""" | |
| import pickle as pkl | |
| from pprint import PrettyPrinter | |
| import hyperopt | |
| import numpy as np | |
| from hyperopt import Trials, fmin, hp, tpe | |
| from space import space | |
| # Change the following code to your file | |
| ################################################################################ | |
| # TODO: Declare a folder to hold all trials objects | |
| TRIALS_FOLDER = "trials2" | |
| ################################################################################ | |
| 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 | |
| np.random.seed() | |
| # Load up all runs: | |
| import glob | |
| path = TRIALS_FOLDER + "/*.pkl" | |
| files = 0 | |
| for fname in glob.glob(path): | |
| trials_obj = pkl.load(open(fname, "rb")) | |
| n_trials = trials_obj["n"] | |
| trials_obj = trials_obj["trials"] | |
| if files == 0: | |
| trials = trials_obj | |
| else: | |
| trials = merge_trials(trials, trials_obj.trials[-n_trials:]) | |
| files += 1 | |
| print(files, "trials merged") | |
| best_loss = np.inf | |
| best_trial = None | |
| try: | |
| trials | |
| except NameError: | |
| raise NameError("No trials loaded. Be sure to set the right folder") | |
| # for trial in trials: | |
| # if trial['result']['status'] == 'ok': | |
| # loss = trial['result']['loss'] | |
| # if loss < best_loss: | |
| # best_loss = loss | |
| # best_trial = trial | |
| # print(best_loss, best_trial['misc']['vals']) | |
| # trials = sorted(trials, key=lambda x: (x['result']['loss'] if trials['result']['status'] == 'ok' else float('inf'))) | |
| clean_trials = [] | |
| for trial in trials: | |
| clean_trials.append((trial["result"]["loss"], trial["misc"]["vals"])) | |
| clean_trials = sorted(clean_trials, key=lambda x: x[0]) | |
| pp = PrettyPrinter(indent=4) | |
| for trial in clean_trials: | |
| loss, params = trial | |
| for k, value in params.items(): | |
| value = value[0] | |
| if isinstance(value, int): | |
| possible_args = space[k].pos_args[1:] | |
| try: | |
| value = possible_args[value].obj | |
| except AttributeError: | |
| value = [arg.obj for arg in possible_args[value].pos_args] | |
| params[k] = value | |
| pp.pprint({"loss": loss, "params": params}) | |