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| """Print the best model parameters and loss""" | |
| import sys | |
| import numpy as np | |
| import pickle as pkl | |
| import hyperopt | |
| from hyperopt import hp, fmin, tpe, Trials | |
| #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]) | |
| for trial in clean_trials: | |
| print(trial) | |