Source code for hpbandster.core.result

import copy
import os
import json

from hpbandster.core.base_iteration import  Datum

[docs]class Run(object): """ Not a proper class, more a 'struct' to bundle important information about a particular run """ def __init__(self, config_id, budget, loss, info, time_stamps, error_logs): self.config_id = config_id self.budget = budget self.error_logs = error_logs self.loss = loss = info self.time_stamps = time_stamps def __repr__(self): return(\ "config_id: %s\t"%(self.config_id,) + \ "budget: %f\t"%self.budget + \ "loss: %s\n"%self.loss + \ "time_stamps: {submitted} (submitted), {started} (started), {finished} (finished)\n".format(**self.time_stamps) + \ "info: %s\n" ) def __getitem__ (self, k): """ in case somebody wants to use it like a dictionary """ return(getattr(self, k))
[docs]def extract_HBS_learning_curves(runs): """ function to get the hyperband learning curves This is an example function showing the interface to use the HB_result.get_learning_curves method. Parameters ---------- runs: list of objects the performed runs for an unspecified config Returns ------- list of learning curves: list of lists of tuples An individual learning curve is a list of (t, x_t) tuples. This function must return a list of these. One could think of cases where one could extract multiple learning curves from these runs, e.g. if each run is an independent training run of a neural network on the data. """ sr = sorted(runs, key=lambda r: r.budget) lc = list(filter(lambda t: not t[1] is None, [(r.budget, r.loss) for r in sr])) return([lc,])
[docs]class json_result_logger(object): def __init__(self, directory, overwrite=False): """ convenience logger for 'semi-live-results' Logger that writes job results into two files (configs.json and results.json). Both files contain propper json objects in each line. This version opens and closes the files for each result. This might be very slow if individual runs are fast and the filesystem is rather slow (e.g. a NFS). Parameters ---------- directory: string the directory where the two files 'configs.json' and 'results.json' are stored overwrite: bool In case the files already exist, this flag controls the behavior: * True: The existing files will be overwritten. Potential risk of deleting previous results * False: A FileExistsError is raised and the files are not modified. """ os.makedirs(directory, exist_ok=True) self.config_fn = os.path.join(directory, 'configs.json') self.results_fn = os.path.join(directory, 'results.json') try: with open(self.config_fn, 'x') as fh: pass except FileExistsError: if overwrite: with open(self.config_fn, 'w') as fh: pass else: raise FileExistsError('The file %s already exists.'%self.config_fn) except: raise try: with open(self.results_fn, 'x') as fh: pass except FileExistsError: if overwrite: with open(self.results_fn, 'w') as fh: pass else: raise FileExistsError('The file %s already exists.'%self.config_fn) except: raise self.config_ids = set()
[docs] def new_config(self, config_id, config, config_info): if not config_id in self.config_ids: self.config_ids.add(config_id) with open(self.config_fn, 'a') as fh: fh.write(json.dumps([config_id, config, config_info])) fh.write('\n')
def __call__(self, job): if not in self.config_ids: #should never happen! TODO: log warning here! self.config_ids.add( with open(self.config_fn, 'a') as fh: fh.write(json.dumps([, job.kwargs['config'], {}])) fh.write('\n') with open(self.results_fn, 'a') as fh: fh.write(json.dumps([, job.kwargs['budget'], job.timestamps, job.result, job.exception])) fh.write("\n")
[docs]def logged_results_to_HBS_result(directory): """ function to import logged 'live-results' and return a HB_result object You can load live run results with this function and the returned HB_result object gives you access to the results the same way a finished run would. Parameters ---------- directory: str the directory containing the results.json and config.json files Returns ------- hpbandster.core.result.Result: :object: TODO """ data = {} time_ref = float('inf') budget_set = set() with open(os.path.join(directory, 'configs.json')) as fh: for line in fh: line = json.loads(line) if len(line) == 3: config_id, config, config_info = line if len(line) == 2: config_id, config, = line config_info = 'N/A' data[tuple(config_id)] = Datum(config=config, config_info=config_info) with open(os.path.join(directory, 'results.json')) as fh: for line in fh: config_id, budget,time_stamps, result, exception = json.loads(line) id = tuple(config_id) data[id].time_stamps[budget] = time_stamps data[id].results[budget] = result data[id].exceptions[budget] = exception budget_set.add(budget) time_ref = min(time_ref, time_stamps['submitted']) # infer the hyperband configuration from the data budget_list = sorted(list(budget_set)) HB_config = { 'eta' : None if len(budget_list) < 2 else budget_list[1]/budget_list[0], 'min_budget' : min(budget_set), 'max_budget' : max(budget_set), 'budgets' : budget_list, 'max_SH_iter': len(budget_set), 'time_ref' : time_ref } return(Result([data], HB_config))
[docs]class Result(object): """ Object returned by the function This class offers a simple API to access the information from a Hyperband run. """ def __init__ (self, HB_iteration_data, HB_config): = HB_iteration_data self.HB_config = HB_config self._merge_results() def __getitem__(self, k): return([k])
[docs] def get_incumbent_id(self): """ Find the config_id of the incumbent. The incumbent here is the configuration with the smallest loss among all runs on the maximum budget! If no run finishes on the maximum budget, None is returned! """ tmp_list = [] for k,v in try: # only things run for the max budget are considered res = v.results[self.HB_config['max_budget']] if not res is None: tmp_list.append((res['loss'], k)) except KeyError as e: pass except: raise if len(tmp_list) > 0: return(min(tmp_list)[1]) return(None)
[docs] def get_incumbent_trajectory(self, all_budgets=True, bigger_is_better=True, non_decreasing_budget=True): """ Returns the best configurations over time Parameters ---------- all_budgets: bool If set to true all runs (even those not with the largest budget) can be the incumbent. Otherwise, only full budget runs are considered bigger_is_better:bool flag whether an evaluation on a larger budget is always considered better. If True, the incumbent might increase for the first evaluations on a bigger budget non_decreasing_budget: bool flag whether the budget of a new incumbent should be at least as big as the one for the current incumbent. Returns ------- dict: dictionary with all the config IDs, the times the runs finished, their respective budgets, and corresponding losses """ all_runs = self.get_all_runs(only_largest_budget = not all_budgets) if not all_budgets: all_runs = list(filter(lambda r: r.budget==res.HB_config['max_budget'], all_runs)) all_runs.sort(key=lambda r: r.time_stamps['finished']) return_dict = { 'config_ids' : [], 'times_finished': [], 'budgets' : [], 'losses' : [], } current_incumbent = float('inf') incumbent_budget = self.HB_config['min_budget'] for r in all_runs: if r.loss is None: continue new_incumbent = False if bigger_is_better and r.budget > incumbent_budget: new_incumbent = True if r.loss < current_incumbent: new_incumbent = True if non_decreasing_budget and r.budget < incumbent_budget: new_incumbent = False if new_incumbent: current_incumbent = r.loss incumbent_budget = r.budget return_dict['config_ids'].append(r.config_id) return_dict['times_finished'].append(r.time_stamps['finished']) return_dict['budgets'].append(r.budget) return_dict['losses'].append(r.loss) if current_incumbent != r.loss: r = all_runs[-1] return_dict['config_ids'].append(return_dict['config_ids'][-1]) return_dict['times_finished'].append(r.time_stamps['finished']) return_dict['budgets'].append(return_dict['budgets'][-1]) return_dict['losses'].append(return_dict['losses'][-1]) return (return_dict)
[docs] def get_runs_by_id(self, config_id): """ returns a list of runs for a given config id The runs are sorted by ascending budget, so '-1' will give the longest run for this config. """ d =[config_id] runs = [] for b in d.results.keys(): try: err_logs = d.exceptions.get(b, None) if d.results[b] is None: r = Run(config_id, b, None, None , d.time_stamps[b], err_logs) else: r = Run(config_id, b, d.results[b]['loss'], d.results[b]['info'] , d.time_stamps[b], err_logs) runs.append(r) except: raise runs.sort(key=lambda r: r.budget) return(runs)
[docs] def get_learning_curves(self, lc_extractor=extract_HBS_learning_curves, config_ids=None): """ extracts all learning curves from all run configurations Parameters ---------- lc_extractor: callable a function to return a list of learning_curves. defaults to hpbanster.HB_result.extract_HP_learning_curves config_ids: list of valid config ids if only a subset of the config ids is wanted Returns ------- dict a dictionary with the config_ids as keys and the learning curves as values """ config_ids = if config_ids is None else config_ids lc_dict = {} for id in config_ids: runs = self.get_runs_by_id(id) lc_dict[id] = lc_extractor(runs) return(lc_dict)
[docs] def get_all_runs(self, only_largest_budget=False): """ returns all runs performed Parameters ---------- only_largest_budget: boolean if True, only the largest budget for each configuration is returned. This makes sense if the runs are continued across budgets and the info field contains the information you care about. If False, all runs of a configuration are returned """ all_runs = [] for k in runs = self.get_runs_by_id(k) if len(runs) > 0: if only_largest_budget: all_runs.append(runs[-1]) else: all_runs.extend(runs) return(all_runs)
[docs] def get_id2config_mapping(self): """ returns a dict where the keys are the config_ids and the values are the actual configurations """ new_dict = {} for k, v in new_dict[k] = {} new_dict[k]['config'] = copy.deepcopy(v.config) try: new_dict[k]['config_info'] = copy.deepcopy(v.config_info) except: pass return(new_dict)
def _merge_results(self): """ hidden function to merge the list of results into one dictionary and 'normalize' the time stamps """ new_dict = {} for it in new_dict.update(it) for k,v in new_dict.items(): for kk, vv in v.time_stamps.items(): for kkk,vvv in vv.items(): new_dict[k].time_stamps[kk][kkk] = vvv - self.HB_config['time_ref'] = new_dict
[docs] def num_iterations(self): return(max([k[0] for k in]) + 1)
[docs] def get_fANOVA_data(self, config_space, budgets=None, loss_fn=lambda r: r.loss, failed_loss=None): import numpy as np import ConfigSpace as CS id2conf = self.get_id2config_mapping() if budgets is None: budgets = self.HB_config['budgets'] if len(budgets)>1: config_space.add_hyperparameter(CS.UniformFloatHyperparameter('budget', min(budgets), max(budgets), log=True)) hp_names = config_space.get_hyperparameter_names() hps = config_space.get_hyperparameters() needs_transform = list(map(lambda h: isinstance(h, CS.CategoricalHyperparameter), hps)) all_runs = self.get_all_runs(only_largest_budget=False) all_runs=list(filter( lambda r: r.budget in budgets, all_runs)) X = [] y = [] for r in all_runs: if r.loss is None: if failed_loss is None: continue else: y.append(failed_loss) else: y.append(loss_fn(r)) config = id2conf[r.config_id]['config'] if len(budgets)>1: config['budget'] = r.budget config = CS.Configuration(config_space, config) x = [] for (name, hp, transform) in zip(hp_names, hps, needs_transform): if transform: x.append(hp._inverse_transform(config[name])) else: x.append(config[name]) X.append(x) return(np.array(X), np.array(y), config_space)
[docs] def get_pandas_dataframe(self, budgets=None, loss_fn=lambda r: r.loss): import numpy as np import pandas as pd id2conf = self.get_id2config_mapping() df_x = pd.DataFrame() df_y = pd.DataFrame() if budgets is None: budgets = self.HB_config['budgets'] all_runs = self.get_all_runs(only_largest_budget=False) all_runs=list(filter( lambda r: r.budget in budgets, all_runs)) all_configs = [] all_losses = [] for r in all_runs: if r.loss is None: continue config = id2conf[r.config_id]['config'] if len(budgets)>1: config['budget'] = r.budget all_configs.append(config) all_losses.append({'loss': r.loss}) #df_x = df_x.append(config, ignore_index=True) #df_y = df_y.append({'loss': r.loss}, ignore_index=True) df_X = pd.DataFrame(all_configs) df_y = pd.DataFrame(all_losses) return(df_X, df_y)