def suggest_multiple_from_name(dbname, host, port, bandit_algo_names, bandit_names,
                     exp_keys, N, bandit_args_list, bandit_kwargs_list,
                     bandit_algo_args_list, bandit_algo_kwargs_list,
                     mql=1, refresh=False):
    port = int(port)
    trials = MongoTrials('mongo://%s:%d/%s/jobs' % (host, port, dbname),
                        refresh=False)
    algos = []
    for bn, ban, ba, bk, baa, bak, ek in zip(bandit_names, bandit_algo_names,
            bandit_args_list, bandit_kwargs_list, bandit_algo_args_list, bandit_algo_kwargs_list,
                                             exp_keys):
        bandit = json_call(bn, ba, bk)
        subtrials = MongoTrials('mongo://%s:%d/%s/jobs' % (host, port, dbname),
                         refresh=False, exp_key=ek)
        if ba or bk:
            subtrials.attachments['bandit_data_' + ek] = cPickle.dumps((bn, ba, bk))
            bak['cmd'] = ('driver_attachment', 'bandit_data_' + ek)
        else:
            bak['cmd'] = ('bandit_json evaluate', bn)
        algo = json_call(ban, (bandit,) + baa, bak)
        algos.append(algo)

    algo = InterleaveAlgo(algos, exp_keys)
    exp = hyperopt.Experiment(trials, algo, poll_interval_secs=.1)
    exp.max_queue_len = mql
    if N is not None:
        exp.run(N, block_until_done=True)
    else:
        return exp
Exemple #2
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def test_mongo_exp_is_picklable():
    algo_jsons = []
    algo_jsons.append('hyperopt.bandit_algos.Random')
    algo_jsons.append('hyperopt.theano_gp.HGP')
    algo_jsons.append('hyperopt.theano_gm.AdaptiveParzenGM')
    for algo_json in algo_jsons:
        with TempMongo() as tm:
            # this triggers if an old stale mongo is running
            assert len(TempMongo.mongo_jobs('foodb')) == 0
            print 'pickling MongoExperiment with', algo_json
            bandit = json_call('hyperopt.bandits.GaussWave2')
            bandit_algo = json_call(algo_json, args=(bandit,))
            exp = MongoExperiment(
                bandit_algo=bandit_algo,
                mongo_handle=tm.mongo_jobs('foodb'),
                workdir=tm.workdir,
                exp_key='exp_key',
                poll_interval_secs=1.0,
                cmd=('asdf', None))
            exp_str = cPickle.dumps(exp)
            cpy = cPickle.loads(exp_str)
Exemple #3
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def test_mongo_exp_with_threads():
    def worker(host_id, n_jobs, timeout):
        mw = MongoWorker(
            mj=TempMongo.mongo_jobs('foodb'),
            )
        t0 = time.time()
        while n_jobs:
            mw.run_one(host_id, timeout)
            print 'worker: ran job'
            n_jobs -= 1

    for bandit_json in ('hyperopt.bandits.GaussWave2',
            'hyperopt.dbn.Dummy_DBN_Base',):
        with TempMongo() as tm:
            assert len(TempMongo.mongo_jobs('foodb')) == 0
            bandit = json_call(bandit_json)
            exp = MongoExperiment(
                bandit_algo=HGP(bandit),
                mongo_handle=tm.mongo_jobs('foodb'),
                workdir=tm.workdir,
                exp_key='exp_key',
                poll_interval_secs=1.0,
                cmd=('bandit_json evaluate', bandit_json))
            exp.ddoc_init()
            assert len(TempMongo.mongo_jobs('foodb')) == 0
            for asdf in exp.mongo_handle:
                print asdf
            assert len(exp.mongo_handle) == 0

            n_trials = 5
            exp.bandit_algo.n_startup_jobs = 3

            wthread = threading.Thread(target=worker,
                    args=(('hostname', 0), n_trials, 660.0))
            wthread.start()
            try:
                print 'running experiment'
                exp.run(n_trials, block_until_done=True)
            finally:
                print 'joining worker thread...'
                wthread.join()

            print exp.trials
            print exp.results
            assert len(exp.trials) == n_trials
            assert len(exp.results) == n_trials

            exp_str = cPickle.dumps(exp)