def optimize_model_pytorch(device, args, train_GWAS, train_y, test_GWAS, test_y, out_folder ="", startupJobs = 40, maxevals = 200, noOut = False):
global numTrials_pytorch
numTrials_pytorch= 0
trials = Trials()
trial_wrapper = partial(trial_pytorch,device = device, args = args , train_GWAS = train_GWAS, train_y = train_y , test_GWAS = test_GWAS , test_y = test_y)
best_pars = fmin(trial_wrapper, parameter_space_pytorch(), algo=partial(tpe.suggest, n_startup_jobs=(startupJobs) ), max_evals=maxevals, trials=trials)
# Print the selected 'best' hyperparameters.
if noOut == False: print('\nBest hyperparameter settings: ',space_eval(parameter_space_pytorch(), best_pars),'\n')
# loops through the 1st entry in the dict that holds all the lookup keys
regression = True
for p in trials.trials[0]['misc']['idxs']: plot_optimization_pytorch(trials, p, regression, out_folder = out_folder)
best_pars = space_eval(parameter_space_pytorch(), best_pars) # this turns the indices into the actual params into the valid aprameter space
# override the epochs with the early start
lowestLossIndex = np.argmin(trials.losses())
trials.trial_attachments(trials.trials[lowestLossIndex])['highestAcc_epoch']
best_pars['earlyStopEpochs'] = trials.trial_attachments(trials.trials[lowestLossIndex])['highestAcc_epoch']
best_pars['earlyStopEpochs'] += 1 # as epochs are 0 based otherwise...
best_pars['epochs'] = best_pars['earlyStopEpochs']
if best_pars['epochs'] <= 0 : best_pars['epochs'] = 1 # we dont want a network without any training, as that will cause a problem for deep dreaming
return(best_pars)
def run_all_dl(csvfile = saving_fp,
space = [hp.quniform('h1', 100, 550, 1),
hp.quniform('h2', 100, 550, 1),
hp.quniform('h3', 100, 550, 1),
#hp.choice('activation', ["RectifierWithDropout", "TanhWithDropout"]),
hp.uniform('hdr1', 0.001, 0.3),
hp.uniform('hdr2', 0.001, 0.3),
hp.uniform('hdr3', 0.001, 0.3),
hp.uniform('rho', 0.9, 0.999),
hp.uniform('epsilon', 1e-10, 1e-4)]):
# maxout works well with dropout (Goodfellow et al 2013), and rectifier has worked well with image recognition (LeCun et al 1998)
start_save(csvfile = csvfile)
trials = Trials()
print "Deep learning..."
best = fmin(objective,
space = space,
algo=tpe.suggest,
max_evals=evals,
trials=trials)
print best
print trials.losses()
with open('output/dlbest.pkl', 'w') as output:
pickle.dump(best, output, -1)
with open('output/dltrials.pkl', 'w') as output:
pickle.dump(trials, output, -1)
def notest_opt_qn_normal(f=hp_normal):
bandit = Bandit(
{'loss': scope.sum([f('v%i' % ii, 0, 1)
for ii in range(25)]) ** 2},
loss_target=0)
algo = TreeParzenEstimator(bandit,
prior_weight=.5,
n_startup_jobs=0,
n_EI_candidates=1,
gamma=0.15)
trials = Trials()
experiment = Experiment(trials, algo, async=False)
experiment.max_queue_len = 1
experiment.run(40)
print 'sorted losses:', list(sorted(trials.losses()))
idxs, vals = miscs_to_idxs_vals(trials.miscs)
if 1:
import hyperopt.plotting
hyperopt.plotting.main_plot_vars(trials, bandit, do_show=1)
else:
import matplotlib.pyplot as plt
begin = [v[:10] for k, v in vals.items()]
end = [v[-10:] for k, v in vals.items()]
plt.subplot(2, 1, 1)
plt.title('before')
plt.hist(np.asarray(begin).flatten())
plt.subplot(2, 1, 2)
plt.title('after')
plt.hist(np.asarray(end).flatten())
plt.show()
def run(self):
start = time.time()
trials = Trials()
best = fmin(self._obj, self.model_param_space._build_space(), tpe.suggest, self.max_evals, trials)
best_params = space_eval(self.model_param_space._build_space(), best)
best_params = self.model_param_space._convert_int_param(best_params)
trial_rmses = np.asarray(trials.losses(), dtype=float)
best_ind = np.argmin(trial_rmses)
best_rmse_mean = trial_rmses[best_ind]
best_rmse_std = trials.trial_attachments(trials.trials[best_ind])["std"]
self.logger.info("-"*50)
self.logger.info("Best RMSE")
self.logger.info(" Mean: %.6f"%best_rmse_mean)
self.logger.info(" std: %.6f"%best_rmse_std)
self.logger.info("Best param")
self.task._print_param_dict(best_params)
end = time.time()
_sec = end - start
_min = int(_sec/60.)
self.logger.info("Time")
if _min > 0:
self.logger.info(" %d mins"%_min)
else:
self.logger.info(" %d secs"%_sec)
self.logger.info("-"*50)
def main():
usage = "%prog text.json labels.csv feature_dir output_dir"
parser = OptionParser(usage=usage)
parser.add_option('-m', dest='max_iter', default=4,
help='Maximum iterations of Bayesian optimization; default=%default')
(options, args) = parser.parse_args()
max_iter = int(options.max_iter)
global data_filename, label_filename, feature_dir, output_dir, log_filename
data_filename = args[0]
label_filename = args[1]
feature_dir = args[2]
output_dir = args[3]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
log_filename = os.path.join(output_dir, 'log.txt')
with open(log_filename, 'w') as logfile:
logfile.write(','.join([data_filename, label_filename, feature_dir, output_dir]))
trials = Trials()
best = fmin(call_experiment,
space=space,
algo=tpe.suggest,
max_evals=max_iter,
trials=trials)
print space_eval(space, best)
print trials.losses()
def work(self):
bandit = self.bandit
random_algo = Random(bandit)
# build an experiment of 10 trials
trials = Trials()
exp = Experiment(trials, random_algo)
#print random_algo.s_specs_idxs_vals
exp.run(10)
ids = trials.tids
assert len(ids) == 10
tpe_algo = TreeParzenEstimator(bandit)
#print pyll.as_apply(tpe_algo.post_idxs)
#print pyll.as_apply(tpe_algo.post_vals)
argmemo = {}
print trials.miscs
idxs, vals = miscs_to_idxs_vals(trials.miscs)
argmemo[tpe_algo.observed['idxs']] = idxs
argmemo[tpe_algo.observed['vals']] = vals
argmemo[tpe_algo.observed_loss['idxs']] = trials.tids
argmemo[tpe_algo.observed_loss['vals']] = trials.losses()
stuff = pyll.rec_eval([tpe_algo.post_below['idxs'],
tpe_algo.post_below['vals']],
memo=argmemo)
print stuff
def main():
usage = "%prog"
parser = OptionParser(usage=usage)
parser.add_option('-o', dest='output_dirname', default='bayes_opt_rnn_chars',
help='Output directory name')
parser.add_option('--reuse', dest='reuse', action="store_true", default=False,
help='Use reusable holdout; default=%default')
(options, args) = parser.parse_args()
global output_dirname, output_filename, reuse, search_alpha, space
reuse = options.reuse
output_dirname = options.output_dirname
if reuse:
output_dirname += '_reuse'
output_filename = fh.make_filename(defines.exp_dir, fh.get_basename_wo_ext(output_dirname), 'log')
with codecs.open(output_filename, 'w') as output_file:
output_file.write(output_dirname + '\n')
#output_file.write('reuse = ' + str(reuse) + '\n')
trials = Trials()
best = fmin(call_experiment,
space=space,
algo=tpe.suggest,
max_evals=100,
trials=trials)
print space_eval(space, best)
print trials.losses()
def optimize(obj_function, inputs, key_file, space, max_eval):
trials = Trials()
f = partial(obj_function, inputs, key_file)
best = fmin(f, space=space, algo=tpe.suggest, max_evals=max_eval,
trials=trials)
LOGGER.info("{}\t{}".format(best, 1 - min(trials.losses())))
def optimize_model_parameter_split(x, y, model_name=None, loss_function="accuracy", parameter=None, max_evals=100, n_folds=5, isWrite=True, times=1, problem_pattern="classification"):
"""
hyperopt model turning
"""
if model_name == None and parameter == None:
print "you must set parameter or model_name"
return None
elif parameter != None:
param = parameter
elif model_name != None:
param = parameter_dictionary[model_name]
else:
return None
x_trains = []
x_tests = []
y_trains = []
y_tests = []
for time in xrange(times):
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
x, y, test_size=0.0125)
x_trains.append(x_train)
x_tests.append(x_test)
y_trains.append(y_train)
y_tests.append(y_test)
trials = Trials()
function = lambda param: optimize_model_function_split(
param, x_trains, x_tests, y_trains, y_tests, loss_function)
print param
print "========================================================================"
best_param = fmin(function, param,
algo=tpe.suggest, max_evals=max_evals, trials=trials)
print "========================================================================"
print "write result to csv files"
# write the csv file
if isWrite:
datas = []
for trial_data in trials.trials:
print trial_data
trial_parameter_dictionary = {}
trial_parameter_dictionary['model'] = model_name
trial_parameter_dictionary['tid'] = trial_data['misc']['tid']
for key, value in trial_data['misc']['vals'].items():
print key, value[0]
trial_parameter_dictionary[key] = value[0]
trial_parameter_dictionary['loss'] = trial_data['result']['loss']
trial_parameter_dictionary[
'status'] = trial_data['result']['status']
datas.append(trial_parameter_dictionary)
filename = str(time.time()) + ".csv"
dictionary_in_list_convert_to_csv(datas, filename)
print trials.statuses()
return best_param
def test_basic(self):
bandit = self._bandit_cls()
#print 'bandit params', bandit.params, bandit
#print 'algo params', algo.vh.params
trials = Trials()
fmin(lambda x: x, bandit.expr,
trials=trials,
algo=suggest,
max_evals=self._n_steps)
assert trials.average_best_error(bandit) - bandit.loss_target < .2
def test_basic(self):
domain = self._domain_cls()
# print 'domain params', domain.params, domain
# print 'algo params', algo.vh.params
trials = Trials()
fmin(lambda x: x, domain.expr,
trials=trials,
algo=suggest,
max_evals=self._n_steps)
assert trials.average_best_error(domain) - domain.loss_target < .2
def main():
set_globals()
trials = Trials()
best = fmin(call_experiment,
space=space,
algo=tpe.suggest,
max_evals=max_iter,
trials=trials)
print space_eval(space, best)
print "losses:", [-l for l in trials.losses()]
print('the best loss: ', max([-l for l in trials.losses()]))
print("number of trials: " + str(len(trials.trials)))
def test_basic(self):
bandit = self._bandit_cls()
algo = Random(bandit)
trials = Trials()
experiment = Experiment(trials, algo, async=False)
experiment.max_queue_len = 50
experiment.run(self._n_steps)
print
print self._bandit_cls
print bandit.loss_target
print trials.average_best_error(bandit)
assert trials.average_best_error(bandit) - bandit.loss_target < .2
print
def test_basic(self):
bandit = self._bandit_cls()
print 'bandit params', bandit.params
algo = Random(bandit)
print 'algo params', algo.vh.params
trials = Trials()
experiment = Experiment(trials, algo, async=False)
experiment.catch_bandit_exceptions = False
experiment.max_queue_len = 50
experiment.run(self._n_steps)
print
print self._bandit_cls
print bandit.loss_target
print trials.average_best_error(bandit)
assert trials.average_best_error(bandit) - bandit.loss_target < .2
print
def TunningParamter(param,data,features,feature,source_name,real_value,int_boolean):
data = data[~pd.isnull(all_data[feature])]
print data.shape
ISOTIMEFORMAT='%Y-%m-%d %X'
start = time.strftime(ISOTIMEFORMAT, time.localtime())
trials = Trials()
objective = lambda p : trainModel(p, data, features, feature,source_name,real_value,int_boolean)
best_parameters = fmin(objective, param, algo =tpe.suggest,max_evals=param['max_evals'],trials= trials)
#now we need to get best_param
trials_loss = np.asanyarray(trials.losses(),dtype=float)
best_loss = min(trials_loss)
ind = np.where(trials_loss==best_loss)[0][0]
best_loss_std = trials.trial_attachments(trials.trials[ind])['std']
end = time.strftime(ISOTIMEFORMAT,time.localtime())
dumpMessage(best_parameters, best_loss, best_loss_std,param['task'],source_name,start,end)
def __init__(self, train_set, holdout_set, command, max_evals=100,
outer_loss_function='logistic',
searcher='tpe', is_regression=False):
self.train_set = train_set
self.holdout_set = holdout_set
self.train_model = './current.model'
self.holdout_pred = './holdout.pred'
self.trials_output = './trials.json'
self.hyperopt_progress_plot = './hyperopt_progress.png'
self.log = './log.log'
self.logger = self._configure_logger()
# hyperopt parameter sample, converted into a string with flags
self.param_suffix = None
self.train_command = None
self.validate_command = None
self.y_true_train = []
self.y_true_holdout = []
self.outer_loss_function = outer_loss_function
self.space = self._get_space(command)
self.max_evals = max_evals
self.searcher = searcher
self.is_regression = is_regression
self.trials = Trials()
self.current_trial = 0
def work(self):
bandit = self.bandit
assert bandit.name is not None
algo = partial(
tree.suggest,
# XXX (begin)
n_trees=10,
logprior_strength=1.0,
# XXX (end)
)
LEN = self.LEN.get(bandit.name, 75)
trials = Trials()
fmin(fn=passthrough,
space=self.bandit.expr,
trials=trials,
algo=algo,
max_evals=LEN)
assert len(trials) == LEN
if 1:
rtrials = Trials()
fmin(fn=passthrough,
space=self.bandit.expr,
trials=rtrials,
algo=rand.suggest,
max_evals=LEN)
print 'RANDOM BEST 6:', list(sorted(rtrials.losses()))[:6]
if 0:
plt.subplot(2, 2, 1)
plt.scatter(range(LEN), trials.losses())
plt.title('TPE losses')
plt.subplot(2, 2, 2)
plt.scatter(range(LEN), ([s['x'] for s in trials.specs]))
plt.title('TPE x')
plt.subplot(2, 2, 3)
plt.title('RND losses')
plt.scatter(range(LEN), rtrials.losses())
plt.subplot(2, 2, 4)
plt.title('RND x')
plt.scatter(range(LEN), ([s['x'] for s in rtrials.specs]))
plt.show()
if 0:
plt.hist(
[t['x'] for t in self.experiment.trials],
bins=20)
#print trials.losses()
print 'OPT BEST 6:', list(sorted(trials.losses()))[:6]
#logx = np.log([s['x'] for s in trials.specs])
#print 'TPE MEAN', np.mean(logx)
#print 'TPE STD ', np.std(logx)
thresh = self.thresholds[bandit.name]
print 'Thresh', thresh
assert min(trials.losses()) < thresh
def minimize(self, restarts=2, epochs=600, tune_space=None):
from hyperopt import fmin, tpe, Trials
if tune_space is None:
initial_values = self.tf_session.run(self.variables)
tune_space = self._make_tune_space(initial_values)
# TODO: This report structure has the downside of not writing
# anything to disk until it's 100% complete.
reports = []
# Make minimize deterministic
R = np.random.RandomState(self.seed)
for restarts in range(restarts):
trials = Trials()
best = fmin(fn=self._evaluate,
space=tune_space,
algo=tpe.suggest,
max_evals=epochs,
trials=trials,
rstate=R)
self._assign_values(best)
reports.extend(trials.trial_attachments(t)['report'] for t in trials.trials)
return self.evaluator.make_agg_report(reports)
def run_all_gbm(csvfile = saving_fp,
space = [hp.quniform('ntrees', 200, 750, 1), hp.quniform('max_depth', 5, 15, 1), hp.uniform('learn_rate', 0.03, 0.35)]):
# Search space is a stochastic argument-sampling program:
start_save(csvfile = csvfile)
trials = Trials()
best = fmin(objective,
space = space,
algo=tpe.suggest,
max_evals=evals,
trials=trials)
print best
# from hyperopt import space_eval
# print space_eval(space, best)
# trials.trials # list of dictionaries representing everything about the search
# trials.results # list of dictionaries returned by 'objective' during the search
print trials.losses() # list of losses (float for each 'ok' trial)
# trials.statuses() # list of status strings
with open('output/gbmbest.pkl', 'w') as output:
pickle.dump(best, output, -1)
with open('output/gbmtrials.pkl', 'w') as output:
pickle.dump(trials, output, -1)
def hyperopt_search(self, parallel=False): # TODO: implement parallel search with MongoTrials
def objective(kwargs):
start = dt.now()
self.get_hyperparam_string(**kwargs)
self.fit_vw()
self.validate_vw()
loss = self.validation_metric_vw()
finish = dt.now()
elapsed = finish - start
self.logger.info("evaluation time for this step: %s" % str(elapsed))
# clean up
subprocess.call(shlex.split('rm %s %s' % (self.train_model, self.holdout_pred)))
to_return = {'status': STATUS_OK,
'loss': loss, # TODO: include also train loss tracking in order to prevent overfitting
'eval_time': elapsed,
'train_command': self.train_command
}
return to_return
trials = Trials()
if self.searcher == 'tpe':
algo = tpe.suggest
elif self.searcher == 'rand':
algo = rand.suggest
logging.debug("starting hypersearch...")
best_params = fmin(objective, space=self.space, trials=trials, algo=algo, max_evals=self.max_evals)
self.logger.debug("the best hyperopt parameters: %s" % str(best_params))
best_configuration = trials.results[np.argmin(trials.losses())]['train_command']
best_loss = trials.results[np.argmin(trials.losses())]['loss']
self.logger.info("\n\nA FULL TRAINING COMMAND WITH THE BEST HYPERPARAMETERS: \n%s" % best_configuration)
self.logger.info("\n\nTHE BEST LOSS VALUE: \n%s" % best_loss)
return best_configuration, best_loss
def hyperopt_search(self, parallel=False): # TODO: implement parallel search with MongoTrials
def objective(kwargs):
start = dt.now()
self.current_trial += 1
self.logger.info('\n\nStarting trial no.%d' % self.current_trial)
self.get_hyperparam_string(**kwargs)
self.fit_vw()
self.validate_vw()
loss = self.validation_metric_vw()
finish = dt.now()
elapsed = finish - start
self.logger.info("evaluation time for this step: %s" % str(elapsed))
# clean up
subprocess.call(shlex.split('rm %s %s' % (self.train_model, self.holdout_pred)))
to_return = {'status': STATUS_OK,
'loss': loss, # TODO: include also train loss tracking in order to prevent overfitting
'eval_time': elapsed.seconds,
'train_command': self.train_command,
'current_trial': self.current_trial
}
return to_return
self.trials = Trials()
if self.searcher == 'tpe':
algo = tpe.suggest
elif self.searcher == 'rand':
algo = rand.suggest
else:
raise KeyError('Invalid searcher')
logging.debug("starting hypersearch...")
best_params = fmin(objective, space=self.space, trials=self.trials, algo=algo, max_evals=self.max_evals)
self.logger.debug("the best hyperopt parameters: %s" % str(best_params))
json.dump(self.trials.results, open(self.trials_output, 'w'))
self.logger.info('All the trials results are saved at %s' % self.trials_output)
best_configuration = self.trials.results[np.argmin(self.trials.losses())]['train_command']
best_loss = self.trials.results[np.argmin(self.trials.losses())]['loss']
self.logger.info("\n\nA full training command with the best hyperparameters: \n%s\n\n" % best_configuration)
self.logger.info("\n\nThe best holdout loss value: \n%s\n\n" % best_loss)
return best_configuration, best_loss
def work(self):
bandit = self.bandit
assert bandit.name is not None
algo = partial(anneal.suggest)
LEN = self.LEN.get(bandit.name, 50)
trials = Trials()
fmin(fn=passthrough, space=self.bandit.expr, trials=trials, algo=algo, max_evals=LEN)
assert len(trials) == LEN
if 1:
rtrials = Trials()
fmin(fn=passthrough, space=self.bandit.expr, trials=rtrials, algo=rand.suggest, max_evals=LEN)
print("RANDOM BEST 6:", list(sorted(rtrials.losses()))[:6])
if 0:
plt.subplot(2, 2, 1)
plt.scatter(list(range(LEN)), trials.losses())
plt.title("TPE losses")
plt.subplot(2, 2, 2)
plt.scatter(list(range(LEN)), ([s["x"] for s in trials.specs]))
plt.title("TPE x")
plt.subplot(2, 2, 3)
plt.title("RND losses")
plt.scatter(list(range(LEN)), rtrials.losses())
plt.subplot(2, 2, 4)
plt.title("RND x")
plt.scatter(list(range(LEN)), ([s["x"] for s in rtrials.specs]))
plt.show()
if 0:
plt.hist([t["x"] for t in self.experiment.trials], bins=20)
# print trials.losses()
print("ANNEAL BEST 6:", list(sorted(trials.losses()))[:6])
# logx = np.log([s['x'] for s in trials.specs])
# print 'TPE MEAN', np.mean(logx)
# print 'TPE STD ', np.std(logx)
thresh = self.thresholds[bandit.name]
print("Thresh", thresh)
assert min(trials.losses()) < thresh
metrics=['accuracy'])
model.fit(X_train,
Y_train,
batch_size={{choice([64, 128])}},
nb_epoch=1,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
X_train, Y_train, X_test, Y_test = data()
best_run, best_model = optim.minimize(model=create_model,
data=data,
algo=tpe.suggest,
max_evals=5,
trials=Trials())
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print(best_run)
best_run, best_model = optim.minimize(model=create_model,
data=data,
algo=tpe.suggest,
max_evals=5,
trials=Trials())
def run(self):
trails = Trials()
if self.clf == 'Boost':
clfdefault_value = {'Boostnestimator': 50, 'BoostLearnrate': 1}
clfparamSpace = {
'Boostnestimator': hp.choice('Boostnestimator',
range(10, 1000)),
'BoostLearnrate': hp.uniform('BoostLearnrate', 0.01, 10)
}
if self.clf == 'RF':
clfdefault_value = {
'n_estimators': 10,
'criterion': 'gini',
'max_features': 'auto',
'RFmin_samples_split': 2
}
clfparamSpace = {
'n_estimators':
hp.choice('n_estimators', range(10, 100)),
'criterion':
hp.choice('criterion', ['gini', 'entropy']),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'RFmin_samples_split':
hp.choice('RFmin_samples_split',
range(2, int(len(self.sy) / 10)))
}
if self.clf == 'BRF':
clfdefault_value = {
'n_estimators': 10,
'criterion': 'gini',
'max_features': 'auto',
'RFmin_samples_split': 2
}
clfparamSpace = {
'n_estimators':
hp.choice('n_estimators', range(10, 100)),
'criterion':
hp.choice('criterion', ['gini', 'entropy']),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'RFmin_samples_split':
hp.choice('RFmin_samples_split',
range(2, int(len(self.sy) / 10)))
}
if self.clf == 'SVM':
clfdefault_value = {
'SVCkernel': {
'kernel': 'poly',
'degree': 3,
'polycoef0': 0.0,
'polygamma': 1
},
'C': 1.0
}
clfparamSpace = {
'SVCkernel':
hp.choice(
'SVCkernel',
[{
'kernel': 'linear'
}, {
'kernel': 'poly',
'degree': hp.choice('degree', range(1, 5)),
'polycoef0': hp.uniform('polycoef0', 0, 10),
'polygamma': hp.choice('polygamma', ["auto", "scale"])
}, {
'kernel': 'sigmoid',
'sigcoef0': hp.uniform('sigcoef0', 0, 10),
'siggamma': hp.choice('siggamma', ["auto", "scale"])
}, {
'kernel': 'rbf',
'rbfgamma': hp.choice('rbfgamma', ["auto", "scale"])
}]),
'C':
hp.uniform('C', 0.001, 1000),
}
if self.clf == 'NN':
clfdefault_value = {
'NNactive': 'relu',
'NNalpha': 0.0001,
'NNmaxiter': 200
}
clfparamSpace = {
'NNactive':
hp.choice('NNactive',
['identity', 'logistic', 'tanh', 'relu']),
'NNalpha':
hp.uniform('NNalpha', 1e-6, 1),
'NNmaxiter':
hp.choice('NNmaxiter', range(100, 1000))
}
if self.clf == 'KNN':
clfdefault_value = {'KNNneighbors': 1}
clfparamSpace = {
'KNNneighbors': hp.choice('KNNneighbors', range(1, 50))
}
if self.clf == 'NB':
clfdefault_value = {'NBType': 'gaussian'}
clfparamSpace = {
'NBType':
hp.choice('NBType', ['gaussian', 'multinomial', 'bernoulli'])
}
if self.clf == 'Ridge':
clfdefault_value = {'Ridgealpha': 1, 'Ridgenormalize': False}
clfparamSpace = {
'Ridgealpha': hp.uniform('Ridgealpha', 0.001, 1000),
'Ridgenormalize': hp.choice('Ridgenormlize', [True, False])
}
if self.clf == 'CART':
clfdefault_value = {
'criterion': 'gini',
'max_features': 'auto',
'CARTsplitter': 'best',
'RFmin_samples_split': 2
}
clfparamSpace = {
'criterion':
hp.choice('criterion', ['gini', 'entropy']),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'CARTsplitter':
hp.choice('CARTsplitter', ['best', 'random']),
'RFmin_samples_split':
hp.choice('RFmin_samples_split',
range(2, int(len(self.sy) / 10)))
}
self.def_value = self.objFunc(clfdefault_value)['result']
best = fmin(self.objFunc,
space=clfparamSpace,
algo=tpe.suggest,
max_evals=self.fe,
trials=self.trails)
his = dict()
his['name'] = list(self.trails.trials[0]['misc']['vals'].keys())
i = 0
for item in self.trails.trials:
results = list(deepflatten(item['misc']['vals'].values()))
results.append(item['result']['result'])
his[i] = results
i += 1
inc_value = self.trails.best_trial['result']['result']
# print(def_value)
return np.asarray([self.def_value, inc_value]), his, best
def run(self):
trails = Trials()
if self.adaptation == 'TCA':
adptdefault_value = {
# 'kernel_type': 'linear',
'dim': 5,
'lamb': 1,
'gamma': 1
}
adptparamSpace = {
# 'kernel_type': hp.choice('kernel_type', ['primal', 'linear', 'rbf', 'sam']),
'dim':
hp.choice('dim',
range(5, max(self.sx.shape[1], self.tx.shape[1]))),
'lamb':
hp.uniform('lamb', 1e-6, 1e2),
'gamma':
hp.uniform('gamma', 1e-5, 1e2)
}
if self.adaptation == 'DBSCANfilter':
adptdefault_value = {'eps': 1, 'min_samples': 10}
adptparamSpace = {
'eps': hp.uniform('eps', 0.1, 1e2),
'min_samples': hp.choice('min_samples', range(1, 100))
}
if self.adaptation == 'Burakfilter':
adptdefault_value = {'n_neighbors': 10}
adptparamSpace = {
'n_neighbors': hp.choice('n_neighbors', range(1, 100))
}
if self.adaptation == 'Peterfilter':
adptdefault_value = {'eachCluster': 10}
adptparamSpace = {
'eachCluster': hp.choice('eachCluster', range(1, 100))
}
if self.adaptation == 'Universal':
adptdefault_value = {'pvalue': 0.05, 'QuantifyType': 'cliff'}
adptparamSpace = {
'pvalue': hp.uniform('pvalue', 0.01, 0.1),
'QuantifyType': hp.choice('QuantifyType', ['cliff', 'cohen'])
}
if self.adaptation == 'HISNN':
adptdefault_value = {'MinHam': 1.0, 'HISNNneighbors': 5}
adptparamSpace = {
'MinHam': hp.uniform('MinHam', 0.5, 100),
'HISNNneighbors': hp.choice('HISNNneighbors', range(1, 100))
}
if self.adaptation == 'DTB':
adptdefault_value = {'DTBneighbors': 10, 'DTBT': 20}
adptparamSpace = {
'DTBneighbors': hp.choice('DTBneighbors', range(1, 50)),
'DTBT': hp.choice('DTBT', range(5, 30))
}
if self.adaptation == 'DS':
adptdefault_value = {'DStopn': 5, 'DSfss': 0.2}
adptparamSpace = {
'DStopn': hp.choice('DStopn', range(1, 15)),
'DSfss': hp.uniform('DSfss', 0.1, 0.5)
}
if self.adaptation == 'DSBF':
adptdefault_value = {'DSBFtopk': 1, 'DSBFneighbors': 10}
adptparamSpace = {
'DSBFtopk': hp.choice('DSBFtopk', range(1, 10)),
'DSBFneighbors': hp.choice('DSBFneighbors', range(1, 100))
}
self.def_value = self.objFunc(adptdefault_value)['result']
best = fmin(self.objFunc,
space=adptparamSpace,
algo=tpe.suggest,
max_evals=self.fe,
trials=self.trails)
his = dict()
his['name'] = list(self.trails.trials[0]['misc']['vals'].keys())
i = 0
for item in self.trails.trials:
results = list(deepflatten(item['misc']['vals'].values()))
results.append(item['result']['result'])
his[i] = results
i += 1
inc_value = self.trails.best_trial['result']['result']
return np.asarray([self.def_value, inc_value]), his, best
def train_model(self, config, reverse_dictionary, train_data, train_labels,
test_data, test_labels, l_tool_tr_samples, class_weights):
"""
Train a model and report accuracy
"""
# convert items to integer
l_batch_size = list(map(int, config["batch_size"].split(",")))
l_embedding_size = list(map(int, config["embedding_size"].split(",")))
l_units = list(map(int, config["units"].split(",")))
# convert items to float
l_learning_rate = list(map(float, config["learning_rate"].split(",")))
l_dropout = list(map(float, config["dropout"].split(",")))
l_spatial_dropout = list(
map(float, config["spatial_dropout"].split(",")))
l_recurrent_dropout = list(
map(float, config["recurrent_dropout"].split(",")))
optimize_n_epochs = int(config["optimize_n_epochs"])
# get dimensions
dimensions = len(reverse_dictionary) + 1
best_model_params = dict()
early_stopping = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
min_delta=1e-1,
restore_best_weights=True)
# specify the search space for finding the best combination of parameters using Bayesian optimisation
params = {
"embedding_size":
hp.quniform("embedding_size", l_embedding_size[0],
l_embedding_size[1], 1),
"units":
hp.quniform("units", l_units[0], l_units[1], 1),
"batch_size":
hp.quniform("batch_size", l_batch_size[0], l_batch_size[1], 1),
"learning_rate":
hp.loguniform("learning_rate", np.log(l_learning_rate[0]),
np.log(l_learning_rate[1])),
"dropout":
hp.uniform("dropout", l_dropout[0], l_dropout[1]),
"spatial_dropout":
hp.uniform("spatial_dropout", l_spatial_dropout[0],
l_spatial_dropout[1]),
"recurrent_dropout":
hp.uniform("recurrent_dropout", l_recurrent_dropout[0],
l_recurrent_dropout[1])
}
def create_model(params):
model = Sequential()
model.add(
Embedding(dimensions,
int(params["embedding_size"]),
mask_zero=True))
model.add(SpatialDropout1D(params["spatial_dropout"]))
model.add(
GRU(int(params["units"]),
dropout=params["dropout"],
recurrent_dropout=params["recurrent_dropout"],
return_sequences=True,
activation="elu"))
model.add(Dropout(params["dropout"]))
model.add(
GRU(int(params["units"]),
dropout=params["dropout"],
recurrent_dropout=params["recurrent_dropout"],
return_sequences=False,
activation="elu"))
model.add(Dropout(params["dropout"]))
model.add(Dense(2 * dimensions, activation="sigmoid"))
optimizer_rms = RMSprop(lr=params["learning_rate"])
batch_size = int(params["batch_size"])
model.compile(loss=utils.weighted_loss(class_weights),
optimizer=optimizer_rms)
print(model.summary())
model_fit = model.fit_generator(
utils.balanced_sample_generator(train_data, train_labels,
batch_size, l_tool_tr_samples),
steps_per_epoch=len(train_data) // batch_size,
epochs=optimize_n_epochs,
callbacks=[early_stopping],
validation_data=(test_data, test_labels),
verbose=2,
shuffle=True)
return {
'loss': model_fit.history["val_loss"][-1],
'status': STATUS_OK,
'model': model
}
# minimize the objective function using the set of parameters above
trials = Trials()
learned_params = fmin(create_model,
params,
trials=trials,
algo=tpe.suggest,
max_evals=int(config["max_evals"]))
best_model = trials.results[np.argmin(
[r['loss'] for r in trials.results])]['model']
# set the best params with respective values
for item in learned_params:
item_val = learned_params[item]
best_model_params[item] = item_val
return best_model_params, best_model
def run(self):
Atrails = Trials()
Ctrails = Trials()
trails = Trials()
if self.imb == 'SMOTE':
imbdefault_value = {'SMOTE_k_neighbors': 5}
imbparamSpace = {
'SMOTE_k_neighbors': hp.choice('SMOTE_k_neighbors',
range(1, 50))
}
if self.imb == 'ENN':
imbdefault_value = {'ENN_n_neighbors': 3}
imbparamSpace = {
'ENN_n_neighbors': hp.choice('ENN_n_neighbors', range(1, 50)),
'kind_sel': hp.choice('kind_sel', ['all', 'mode'])
}
if self.adaptation == 'TCA':
adptdefault_value = {
# 'kernel_type': 'linear',
'dim': 5,
'lamb': 1,
'gamma': 1
}
adptparamSpace = {
# 'kernel_type': hp.choice('kernel_type', ['primal', 'linear', 'rbf', 'sam']),
'dim':
hp.choice('dim',
range(5, max(self.sx.shape[1], self.tx.shape[1]))),
'lamb':
hp.uniform('lamb', 1e-6, 1e2),
'gamma':
hp.uniform('gamma', 1e-5, 1e2)
}
if self.adaptation == 'DBSCANfilter':
adptdefault_value = {'eps': 1, 'min_samples': 10}
adptparamSpace = {
'eps': hp.uniform('eps', 0.1, 1e2),
'min_samples': hp.choice('min_samples', range(1, 100))
}
if self.adaptation == 'Burakfilter':
adptdefault_value = {'n_neighbors': 10}
adptparamSpace = {
'n_neighbors': hp.choice('n_neighbors', range(1, 100))
}
if self.adaptation == 'Peterfilter':
adptdefault_value = {'eachCluster': 10}
adptparamSpace = {
'eachCluster': hp.choice('eachCluster', range(1, 100))
}
if self.adaptation == 'Universal':
adptdefault_value = {'pvalue': 0.05, 'QuantifyType': 'cliff'}
adptparamSpace = {
'pvalue': hp.uniform('pvalue', 0.01, 0.1),
'QuantifyType': hp.choice('QuantifyType', ['cliff', 'cohen'])
}
if self.adaptation == 'DTB':
adptdefault_value = {'DTBneighbors': 10, 'DTBT': 20}
adptparamSpace = {
'DTBneighbors': hp.choice('DTBneighbors', range(1, 50)),
'DTBT': hp.choice('DTBT', range(5, 30))
}
if self.adaptation == 'DS':
adptdefault_value = {'DStopn': 5, 'DSfss': 0.2}
adptparamSpace = {
'DStopn': hp.choice('DStopn', range(1, 15)),
'DSfss': hp.uniform('DSfss', 0.1, 0.5)
}
if self.adaptation == 'DSBF':
adptdefault_value = {'DSBFtopk': 1, 'DSBFneighbors': 10}
adptparamSpace = {
'DSBFtopk': hp.choice('DSBFtopk', range(1, 10)),
'DSBFneighbors': hp.choice('DSBFneighbors', range(1, 100))
}
if self.clf == 'Boost':
clfdefault_value = {'Boostnestimator': 50, 'BoostLearnrate': 1}
clfparamSpace = {
'Boostnestimator': hp.choice('Boostnestimator',
range(10, 1000)),
'BoostLearnrate': hp.uniform('BoostLearnrate', 0.01, 10)
}
if self.clf == 'RF':
clfdefault_value = {
'n_estimators': 10,
'criterion': 'gini',
'max_features': 'auto',
'RFmin_samples_split': 2,
'RFclass_weight': None
}
clfparamSpace = {
'n_estimators':
hp.choice('n_estimators', range(10, 100)),
'criterion':
hp.choice('criterion', ['gini', 'entropy']),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'RFmin_samples_split':
hp.choice('RFmin_samples_split',
range(2, int(len(self.sy) / 10))),
'RFclass_weight':
hp.choice('RFclass_weight',
['balanced', 'balanced_subsample', None])
}
if self.clf == 'BRF':
clfdefault_value = {
'n_estimators': 10,
'criterion': 'gini',
'max_features': 'auto',
'RFmin_samples_split': 2
}
clfparamSpace = {
'n_estimators':
hp.choice('n_estimators', range(10, 100)),
'criterion':
hp.choice('criterion', ['gini', 'entropy']),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'RFmin_samples_split':
hp.choice('RFmin_samples_split',
range(2, int(len(self.sy) / 10)))
}
if self.clf == 'SVM':
clfdefault_value = {
'SVCkernel': {
'kernel': 'poly',
'degree': 3,
'polycoef0': 0.0,
'polygamma': 1
},
'C': 1.0
}
clfparamSpace = {
'SVCkernel':
hp.choice(
'SVCkernel',
[{
'kernel': 'linear'
}, {
'kernel': 'poly',
'degree': hp.choice('degree', range(1, 5)),
'polycoef0': hp.uniform('polycoef0', 0, 10),
'polygamma': hp.choice('polygamma', ["auto", "scale"])
}, {
'kernel': 'sigmoid',
'sigcoef0': hp.uniform('sigcoef0', 0, 10),
'siggamma': hp.choice('siggamma', ["auto", "scale"])
}, {
'kernel': 'rbf',
'rbfgamma': hp.choice('rbfgamma', ["auto", "scale"])
}]),
'C':
hp.uniform('C', 0.001, 1000),
}
if self.clf == 'NN':
clfdefault_value = {
'NNactive': 'relu',
'NNalpha': 0.0001,
'NNmaxiter': 200
}
clfparamSpace = {
'NNactive':
hp.choice('NNactive',
['identity', 'logistic', 'tanh', 'relu']),
'NNalpha':
hp.uniform('NNalpha', 1e-6, 1),
'NNmaxiter':
hp.choice('NNmaxiter', range(100, 1000))
}
if self.clf == 'KNN':
clfdefault_value = {'KNNneighbors': 1}
clfparamSpace = {
'KNNneighbors': hp.choice('KNNneighbors', range(1, 50))
}
if self.clf == 'NB':
clfdefault_value = {'NBType': 'gaussian'}
clfparamSpace = {
'NBType':
hp.choice('NBType', ['gaussian', 'multinomial', 'bernoulli'])
}
if self.clf == 'Ridge':
clfdefault_value = {'Ridgealpha': 1, 'Ridgenormalize': False}
clfparamSpace = {
'Ridgealpha': hp.uniform('Ridgealpha', 0.001, 1000),
'Ridgenormalize': hp.choice('Ridgenormlize', [True, False])
}
if self.clf == 'CART':
clfdefault_value = {
'criterion': 'gini',
'max_features': 'auto',
'CARTsplitter': 'best',
'RFmin_samples_split': 2
}
clfparamSpace = {
'criterion':
hp.choice('criterion', ['gini', 'entropy']),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'CARTsplitter':
hp.choice('CARTsplitter', ['best', 'random']),
'RFmin_samples_split':
hp.choice('RFmin_samples_split',
range(2, int(len(self.sy) / 10)))
}
if self.imb == 'NoImb':
default_value = dict(adptdefault_value, **clfdefault_value)
else:
default_value = dict(adptdefault_value, **clfdefault_value,
**imbdefault_value)
self.def_value = self.objFunc(default_value)['result']
self.Imbbest = fmin(self.objFunc,
space=imbparamSpace,
algo=tpe.suggest,
max_evals=int(self.fe * 0.33),
trials=self.Atrails)
self.Imbbest = space_eval(imbparamSpace, self.Imbbest)
his = dict()
try:
his['name'] = list(self.Atrails.trials[0]['misc']['vals'].keys()) + list(adptdefault_value.keys()) \
+ list(clfdefault_value.keys())
except:
his['name'] = [None]
i = 0
for item in self.Atrails.trials:
if item['state'] == 2:
results = list(deepflatten(item['misc']['vals'].values())) + list(adptdefault_value.values()) \
+ list(clfdefault_value.keys())
results.append(item['result']['result'])
his[i] = results
i += 1
# optimize the parameters of the classifer
self.SEQ = 1
self.Adptbest = fmin(self.objFunc,
space=adptparamSpace,
algo=tpe.suggest,
max_evals=int(self.fe * 0.33),
trials=self.Ctrails)
try:
his['name1'] = list(self.Imbbest.keys()) + list(self.Ctrails.trials[0]['misc']['vals'].keys()) \
+ list(clfdefault_value.keys())
except:
his['name1'] = [None]
for item in self.Ctrails.trials:
if item['state'] == 2:
results = list(self.Imbbest.values()) + list(deepflatten(item['misc']['vals'].values())) \
+ list(clfdefault_value.keys())
results.append(item['result']['result'])
his[i] = results
i += 1
#opt the parameter of sampling method for imbalance learning
self.SEQ = 2
Clfbest = fmin(self.objFunc,
space=clfparamSpace,
algo=tpe.suggest,
max_evals=int(self.fe * 0.33),
trials=self.trails)
try:
his['name2'] = list(self.Imbbest.keys()) + list(
self.Adptbest.keys()) + list(
self.trails.trials[0]['misc']['vals'].keys())
except:
his['name2'] = [None]
for item in self.trails.trials:
if item['state'] == 2:
results = list(self.Imbbest.values()) + list(
self.Adptbest.keys()) + list(
deepflatten(item['misc']['vals'].values()))
results.append(item['result']['result'])
his[i] = results
i += 1
inc_value = self.trails.best_trial['result']['result']
return np.asarray([self.def_value, inc_value]), his, Clfbest
def get_params(self, a=1):
if self.obj == 'binary':
space = {
'bagging_freq':
hp.choice('bagging_freq', np.arange(1, 5, dtype=int)),
'bagging_fraction':
hp.uniform('bagging_fraction', 0.20, 0.90),
'boost':
hp.choice('boost', ['gbdt']),
'feature_fraction':
hp.uniform('feature_fraction', 0.20, 0.90),
'learning_rate':
hp.loguniform('learning_rate', np.log(0.0070), np.log(0.010)),
'min_data_in_leaf':
hp.choice('min_data_in_leaf', np.arange(50, 90, dtype=int)),
'num_leaves':
hp.choice('num_leaves', np.arange(5, 35, dtype=int)),
'min_sum_hessian_in_leaf':
hp.choice('min_sum_hessian_in_leaf', np.arange(5,
35,
dtype=int)),
'max_depth':
hp.choice('max_depth', np.arange(-2, 2, dtype=int)),
'tree_learner':
hp.choice('tree_learner', ['serial']),
'objective':
hp.choice('objective', ['binary']),
'boost_from_average':
hp.choice('boost_from_average', [False]),
'num_threads':
hp.choice('num_threads', np.arange(8, 9, dtype=int)),
'verbosity':
hp.choice('verbosity', np.arange(1, 2, dtype=int))
}
else:
space = {
'num_leaves':
hp.choice('num_leaves', np.arange(5, 35, dtype=int)),
'learning_rate':
hp.loguniform('learning_rate', np.log(0.0070), np.log(0.010)),
'max_depth':
hp.choice('max_depth', np.arange(-2, 2, dtype=int)),
'colsample_bytree':
hp.uniform('colsample_bytree', 0.1, 0.9)
}
new_tpe = tpe.suggest
new_trial = Trials()
global ITERATION
ITERATION = 0
best = fmin(fn=self.objective,
space=space,
algo=new_tpe,
max_evals=self.m_eval,
trials=new_trial,
rstate=np.random.RandomState(50))
bayes_trials_results = sorted(new_trial.results,
key=lambda x: x['loss'])
params = bayes_trials_results[:1]
print('*' * 40)
print('Best Params\n:', bayes_trials_results[:1])
params = params[0]['params']
return params
def run_hyperopt(major_model,
specific_model,
training_params,
sampled_x,
sampled_y,
x_to_predict,
eval_metric=mse,
train_test_inds=None,
hyperopt_rounds=100):
"""
Executes hyperparameter optimization for a given inbuilt model
Parameters
----------
major_model: str
Choice of 'Keras', 'XGB', or 'sklearn-regressor'. This argument
tells MldeModel from which package we will be pulling models.
specific_model: str
This arguments tells MldeModel which regressor to use within the package
defined by major_model.
training_params: dict
These are parameters required for training the models specified by
'major_model' and 'specific_model'. Details on the requirements for each
submodel can be found in the online documentation.
sampled_x: numpy array
Training features
sampled_y: numpy array, 1D
Training labels
x_to_predict: numpy array
Features for which we want to predict labels
eval_metric: func
The function used for evaluating cross-validation error. This metric will
be used to rank model architectures from best to worst. The function must
take the form 'function(real_values, predicted_values)'.
train_test_inds: list of lists
Cross validation indices to use in training.
hyperopt_rounds: int
Number of rounds of hyperparameter optimization to perform
Returns
-------
all_trial_info: pd.DataFrame
The results of "process_trials" post hyperparameter optimization
train_pred_results: tuple
The results of MLDE.Support.RunMlde.TrainAndPredict.train_and_predict()
using the best parameters identified during hyperparameter optimization
"""
# Get the shape of x
x_shape = sampled_x.shape
# Build the search space
space_var_names = list(space_by_model[major_model][specific_model])
search_space = [
search_spaces[major_model][space_var] for space_var in space_var_names
]
# Construct a dictionary for passing in kwargs to Optimize
optimizer_kwargs = {
"space_names": space_var_names,
"x": sampled_x,
"y": sampled_y,
"major_model": major_model,
"specific_model": specific_model,
"training_params": training_params,
"eval_metric": eval_metric,
"train_test_inds": train_test_inds
}
# Construct the optimizer function
complete_optimizer = partial(optimize, **optimizer_kwargs)
# Build the trials object
trials = Trials()
# Run hyperparameter optimization
best_params = fmin(complete_optimizer,
space=search_space,
algo=tpe.suggest,
max_evals=hyperopt_rounds,
trials=trials,
show_progressbar=False)
# Reformat best_params to have the correct datatypes
best_params = process_best(best_params, major_model, specific_model,
x_shape)
# Process the trials
all_trial_info = process_trials(trials, major_model, specific_model)
# Now build the model using the best parameters
best_model = MldeModel(major_model,
specific_model,
model_params=best_params,
training_params=training_params,
eval_metric=eval_metric)
# Train and predict using the best model
train_pred_results = train_and_predict(best_model,
sampled_x=sampled_x,
sampled_y=sampled_y,
x_to_predict=x_to_predict,
train_test_inds=train_test_inds)
# Return all relevant information
return all_trial_info, train_pred_results
def work(self):
bandit = self.bandit
assert bandit.name is not None
algo = partial(tpe.suggest,
gamma=self.gammas.get(bandit.name,
tpe._default_gamma),
prior_weight=self.prior_weights.get(bandit.name,
tpe._default_prior_weight),
n_EI_candidates=self.n_EIs.get(bandit.name,
tpe._default_n_EI_candidates),
)
LEN = self.LEN.get(bandit.name, 50)
trials = Trials()
fmin(passthrough,
space=bandit.expr,
algo=algo,
trials=trials,
max_evals=LEN,
rstate=np.random.RandomState(123),
catch_eval_exceptions=False)
assert len(trials) == LEN
if 1:
rtrials = Trials()
fmin(passthrough,
space=bandit.expr,
algo=rand.suggest,
trials=rtrials,
max_evals=LEN)
print 'RANDOM MINS', list(sorted(rtrials.losses()))[:6]
#logx = np.log([s['x'] for s in rtrials.specs])
#print 'RND MEAN', np.mean(logx)
#print 'RND STD ', np.std(logx)
if 0:
plt.subplot(2, 2, 1)
plt.scatter(range(LEN), trials.losses())
plt.title('TPE losses')
plt.subplot(2, 2, 2)
plt.scatter(range(LEN), ([s['x'] for s in trials.specs]))
plt.title('TPE x')
plt.subplot(2, 2, 3)
plt.title('RND losses')
plt.scatter(range(LEN), rtrials.losses())
plt.subplot(2, 2, 4)
plt.title('RND x')
plt.scatter(range(LEN), ([s['x'] for s in rtrials.specs]))
plt.show()
if 0:
plt.hist(
[t['x'] for t in self.experiment.trials],
bins=20)
#print trials.losses()
print 'TPE MINS', list(sorted(trials.losses()))[:6]
#logx = np.log([s['x'] for s in trials.specs])
#print 'TPE MEAN', np.mean(logx)
#print 'TPE STD ', np.std(logx)
thresh = self.thresholds[bandit.name]
print 'Thresh', thresh
assert min(trials.losses()) < thresh
def main():
n_folds = 5
try:
opts, args = getopt.getopt(sys.argv[1:], '', [
'window_size=', 'wiki=', 'n_feature_maps=', 'epochs=',
'undersample=', 'n_feature_maps=', 'criterion=', 'optimizer=',
'model=', 'genia=', 'tacc=', 'layers=', 'hyperopt=', 'model_name='
])
except getopt.GetoptError as error:
print error
sys.exit(2)
model_type = 'nn'
window_size = 5
wiki = True
n_feature_maps = 100
epochs = 20
undersample = False
binary_cross_entropy = False
criterion = 'categorical_crossentropy'
optimizer = 'adam'
k = 2
use_genia = False
using_tacc = False
layer_sizes = []
hyperopt = False
model_name = 'model'
for opt, arg in opts:
if opt == '--window_size':
window_size = int(arg)
elif opt == '--wiki':
if arg == 0:
wiki = False
elif opt == '--epochs':
epochs = int(arg)
elif opt == '--layers':
layer_sizes = arg.split(',')
elif opt == '--n_feature_maps':
n_feature_maps = int(arg)
elif opt == '--undersample':
option = int(arg)
if option == 1:
undersample = True
elif opt == '--n_feature_maps':
n_feature_maps = int(arg)
elif opt == '--criterion':
criterion = arg
elif opt == '--optimizer':
optimizer = arg
elif opt == '--model':
model_type = arg
elif opt == '--genia':
if int(arg) == 1:
use_genia = True
elif opt == '--tacc':
if int(arg) == 1:
using_tacc = True
elif opt == '--hyperopt':
if int(arg) == 1:
hyperopt = True
elif opt == '--model_name':
model_name = arg
else:
print "Option {} is not valid!".format(opt)
if criterion == 'binary_crossentropy':
binary_cross_entropy = True
k = 1
print('Loading word2vec model...')
if wiki:
print 'Using wiki word2vec...'
word2vec_model = 'wikipedia-pubmed-and-PMC-w2v.bin'
else:
print 'Using non-wiki word2vec...'
word2vec_model = 'PubMed-w2v.bin'
w2v = Word2Vec.load_word2vec_format(word2vec_model, binary=True)
print('Loaded word2vec model')
pmids_dict, pmids, abstracts, lbls, vectorizer, groups_map, one_hot, dicts = \
parse_summerscales.get_tokens_and_lbls(
make_pmids_dict=True, sen=True, use_genia=use_genia, using_tacc=using_tacc)
all_pmids = pmids_dict.keys()
n = len(all_pmids)
kf = KFold(n, random_state=1337, shuffle=True, n_folds=n_folds)
accuracies = []
recalls = []
precisions = []
f1_scores = []
aucs = []
global model
for fold_idx, (train, test) in enumerate(kf):
print("on fold %s" % fold_idx)
train_pmids = [all_pmids[pmid_idx] for pmid_idx in train]
test_pmids = [all_pmids[pmid_idx] for pmid_idx in test]
print train_pmids
print('loading data...')
if model_type == 'cnn':
X_train, y_train = _prep_data(train_pmids,
pmids_dict,
w2v,
window_size,
model_type,
binary_ce=binary_cross_entropy)
X_test, y_test = _prep_data(test_pmids,
pmids_dict,
w2v,
window_size,
model_type,
binary_ce=binary_cross_entropy)
elif model_type == 'nn':
X_train, y_train = _prep_data(train_pmids,
pmids_dict,
w2v,
window_size,
model_type,
binary_ce=binary_cross_entropy)
X_test, y_test = _prep_data(test_pmids,
pmids_dict,
w2v,
window_size,
model_type,
binary_ce=binary_cross_entropy)
elif model_type == 'ladder':
X_train, y_train = _prep_data(train_pmids,
pmids_dict,
w2v,
window_size,
model_type,
binary_ce=binary_cross_entropy)
X_test, y_test = _prep_data(test_pmids,
pmids_dict,
w2v,
window_size,
model_type,
binary_ce=binary_cross_entropy)
if undersample:
# Undersample the non group tags at random....probably a bad idea...
if binary_cross_entropy:
idx_undersample = numpy.where(y_train == 0)[0]
idx_postive = numpy.where(y_train == 1)[0]
else:
idx_undersample = numpy.where(y_train[:, 1] == 0)[0]
idx_postive = numpy.where(y_train[:, 1] == 1)[0]
random_negative_sample = numpy.random.choice(
idx_undersample, idx_postive.shape[0])
if model_type == 'nn':
X_train_postive = X_train[idx_postive, :]
X_train_negative = X_train[random_negative_sample, :]
else:
X_train_postive = X_train[idx_postive, :, :, :]
X_train_negative = X_train[random_negative_sample, :, :, :]
if binary_cross_entropy:
y_train_postive = y_train[idx_postive]
y_train_negative = y_train[random_negative_sample]
else:
y_train_postive = y_train[idx_postive, :]
y_train_negative = y_train[random_negative_sample, :]
X_train = numpy.vstack((X_train_postive, X_train_negative))
if binary_cross_entropy:
y_train = numpy.hstack((y_train_postive, y_train_negative))
else:
y_train = numpy.vstack((y_train_postive, y_train_negative))
print('loaded data...')
if model_type == 'cnn':
model = GroupCNN(window_size=window_size,
n_feature_maps=n_feature_maps,
k_output=k,
name=model_name)
elif model_type == 'nn':
model = GroupNN(window_size=window_size,
k=k,
hyperparameter_search=hyperopt,
name=model_name)
if hyperopt:
best_run, best_model = optim.minimize(model=_model,
data=_data,
algo=tpe.suggest,
max_evals=5,
trials=Trials())
model.model = best_model
else:
model.train(X_train,
y_train,
epochs,
optim_algo=optimizer,
criterion=criterion)
words = []
for pmid in test_pmids:
words.extend(pmids_dict[pmid][0])
predictions = model.predict_classes(X_test)
predicted_words = crf.output2words(predictions, words)
y_test_arg_max = numpy.argmax(y_test, axis=1)
true_words = crf.output2words(y_test_arg_max, words)
accuracy, f1_score, precision, auc, recall = model.test(X_test, y_test)
recall, precision, f1_score = crf.eveluate(predicted_words, true_words)
print "Accuracy: {}".format(accuracy)
print "F1: {}".format(f1_score)
print "Precision: {}".format(precision)
print "AUC: {}".format(auc)
print "Recall: {}".format(recall)
accuracies.append(accuracy)
f1_scores.append(f1_score)
precisions.append(precision)
aucs.append(auc)
recalls.append(recall)
mean_accuracy = numpy.mean(accuracies)
mean_f1_score = numpy.mean(f1_scores)
mean_precision = numpy.mean(precisions)
mean_auc_score = numpy.mean(aucs)
mean_recall = numpy.mean(recalls)
mean_accuracy_string = "Mean Accuracy: {}".format(mean_accuracy)
mean_f1_score_string = "Mean F1: {}".format(mean_f1_score)
mean_precision_string = "Mean Precision: {}".format(mean_precision)
mean_auc_score_string = "Mean AUC: {}".format(mean_auc_score)
mean_recall_string = "Mean Recall: {}".format(mean_recall)
print mean_accuracy_string
print mean_f1_score_string
print mean_precision_string
print mean_auc_score_string
print mean_recall_string
results = open('{}_fold_results'.format(model.model_name), 'w+')
results.write(mean_accuracy_string)
results.write(mean_f1_score_string)
results.write(mean_precision_string)
results.write(mean_auc_score_string)
results.write(mean_recall_string)
log_handler = open(log_file, 'w')
writer = csv.writer(log_handler)
headers = [
'trial_counter', 'log_loss_mean', 'log_loss_std', 'spend_time'
]
for k, v in sorted(param_space.items()):
headers.append(k)
print(headers)
writer.writerow(headers)
log_handler.flush()
print("************************************************************")
print("Search for the best params")
# global trial_counter
trial_counter = 0
trials = Trials()
# lambda在这一步并不会运行，只是定义一个函数而已
objective = lambda p: hyperopt_wrapper(p, feat_folder, feat_name)
# objective放到fmin中，会被调用，且传进三个参数
best_params = fmin(objective,
param_space,
algo=tpe.suggest,
trials=trials,
max_evals=param_space["max_evals"])
for f in int_feat:
if f in best_params:
best_params[f] = int(best_params[f])
print("************************************************************")
print("Best params")
for k, v in best_params.items():
print(" %s: %s" % (k, v))
'objective':
'reg:linear',
'booster':
'gbtree',
'silent':
1,
'seed':
random_state
}
# Use the fmin function from Hyperopt to find the best hyperparameters
best = fmin(score, space, algo=tpe.suggest, trials=trials,
max_evals=25) #more evals
return best
trials = Trials() #store history of search
best = optimize(trials)
print("The best hyperparameters are: ", "\n")
print(best)
sys.stdout.flush()
#add here: train final model
print('training model')
rf1 = MultiOutputRegressor(
XGBRegressor(max_depth=best['max_depth'],
n_estimators=int(best['n_estimators']),
random_state=123,
n_jobs=-1,
silent=False,
colsample_bytree=best['colsample_bytree'],
def run_hyperopt_classification(
model_name,
model_space,
x_train,
y_train,
scoring="f1",
cv=3,
max_evals=20,
verbose=False,
persistIterations=True,
):
print("LABDAPS --- Running Hyperopt Bayesian Optimization")
print("reloaded")
import pandas as pd
import time
import datetime
from hyperopt import fmin, tpe, Trials, space_eval
from sklearn.model_selection import cross_val_score
def objective(space):
### MODEL SELECTION
if model_name == "lr":
# logistic regression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(**space)
elif model_name == "rf":
# print("Setting model as RandomForestClassifier")
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(**space, n_jobs=-1)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "xgb":
# print("Setting model as XGBClassifier")
from xgboost import XGBClassifier
model = XGBClassifier(**space,
objective="binary:logistic",
nthread=-1)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "dt":
# print("Setting model as DecisionTreeClassifier")
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(**space)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "catboost":
# print("Setting model as CatBoost")
from catboost import CatBoostClassifier
model = CatBoostClassifier(**space)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "extratrees":
# print("Setting model as CatBoost")
from sklearn.ensemble import ExtraTreesClassifier
model = ExtraTreesClassifier(**space, n_jobs=-1)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "svc":
from sklearn.svm import SVC
model = SVC(**space)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "ann":
# print("Setting model as ANN")
from sklearn import neural_network
model = neural_network.MLPClassifier(**space)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "lgb":
import lightgbm as lgb
model = lgb.LGBMClassifier(**space, n_jobs=-1, random_state=42)
if verbose:
print("Hyperparameters: ", space)
elif model_name == "knn":
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(**space)
if verbose:
print("Hyperparameters: ", space)
else:
# print("ERRO: Especifique um nome valido para model_name: rf, xgb, dt ou catboost")
raise Exception(
"Invalid model_name - Please specify one of the supported model_name: rf, xgb, ann, dt, svc, lgr, knn or catboost"
)
score = cross_val_score(model,
x_train,
y_train,
cv=3,
scoring=scoring,
verbose=False,
n_jobs=-1).mean()
score = 1 - score ## ajusta para a funcao de minimizacao.
return score
start = time.time()
trials = Trials()
best = fmin(
objective,
space=model_space,
algo=tpe.suggest,
max_evals=max_evals,
trials=trials,
)
if persistIterations:
# Save the hyperparameter at each iteration to a csv file
param_values = [x["misc"]["vals"] for x in trials.trials]
param_values = [{key: value
for key in x for value in x[key]}
for x in param_values]
param_values = [space_eval(model_space, x) for x in param_values]
param_df = pd.DataFrame(param_values)
param_df[scoring] = [1 - x for x in trials.losses()]
param_df.index.name = "Iteration"
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime("%Y%m%d-%H:%M")
param_df.to_csv(f"Hyperopt_{model_name}_{st}.csv")
print(f"Arquivo Hyperopt_{model_name}_{st}.csv gerado com sucesso.")
print(f"Hyperopt search took %.2f seconds for {max_evals} candidates" %
((time.time() - start)))
# print(-best_score, best)
print("** Best Hyperparameters are: **")
print(space_eval(model_space, best))
print('Best validation acc of epoch:', validation_acc)
return {'loss': -validation_acc, 'status': STATUS_OK, 'model': model}
if __name__ == '__main__':
# config TF-----------------------------------------------------------------------------------------------------
CUDA, max_eval = sys.argv[1:]
os.environ['CUDA_VISIBLE_DEVICES'] = CUDA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# x_train, y_train, x_test, y_test = data()
# Conv2DClassifierIn1(x_train, y_train, x_test, y_test)
best_run, best_model = optim.minimize(model=Conv2DClassifierIn1,
data=data,
algo=tpe.suggest,
max_evals=int(max_eval),
keep_temp=False,
trials=Trials())
for trial in Trials():
print(trial)
X_train, Y_train, X_test, Y_test = data()
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print("Best performing model chosen hyper-parameters:")
print(best_run)
coords = get_gene_coords(geneannot, gene)
gene_name = get_gene_name(geneannot, gene)
expr_vec = expr_df[gene]
adj_exp = adjust_for_covariates(list(expr_vec), cov)
cis_gt = get_cis_genotype(gt_df, snpannot, coords)
#build the model
if (type(cis_gt) != int) & (cis_gt.shape[1] > 0):
x = cis_gt.values
y = adj_exp.ravel()
#KNN
trials = Trials() #reset the trials object
best = fmin(fn=objective,
space=knn_space,
algo=algo,
max_evals=max_evals,
trials=trials)
result_table = pd.DataFrame(trials.results)
best_hyperparam = hyperopt.space_eval(knn_space, best)
best_hyperparam.pop("type") #just to remove "type" from the param dict
open(output + pop + "_knn_hyperopt_chr" + chrom + ".txt",
"a").write("\n" + gene + "\t" + gene_name + "\t" + chrom + "\t" +
str(best_hyperparam) + "\t")
for i in range(0, max_evals,
1): #I negate the loss in order to get cvR2
def optimize(X,
y,
lname,
regression=True,
hyperopt_step=100,
arch=False,
epochs=1000,
X_val=False,
y_val=False,
model=False):
'''
Model hyperparameter optimization.
Parameters
---
X: np.array, features
y: np.array, targets
lname: str, name of the target property
regression: boolean, whether it is a regression task
hyperopt_step: int, number of steps for hyperopt
arch: boolean or list, whether to use a fix architecture
epochs: int, maximum epochs during the model training
X_val: boolean or np.array, validation features
y_val: boolean or np.array, validation targets
model: boolean for keras.model, can start with a input model instead of building from scratch.
'''
np.random.seed(1234)
if arch == False:
architectures = [(128, 128), (256, 256), (512, 512), (128, 128, 128),
(256, 256, 256), (512, 512, 512)]
else:
architectures = [arch]
bzs = [16, 32, 64, 128, 256, 512]
ress = [True, False]
bypasses = [True, False]
if not model == False:
space = {
'lr': hp.uniform('lr', 1e-5, 1e-3),
'batch_size': hp.choice('batch_size', bzs),
'beta_1': hp.uniform('beta_1', 0.75, 0.99),
'decay': hp.loguniform('decay', np.log(1e-5), np.log(1e-1)),
'amsgrad': True,
'patience': 50,
}
else:
space = {
'lr': hp.uniform('lr', 1e-5, 1e-3),
'drop_rate': hp.uniform('drop_rate', 0, 0.5),
'reg': hp.loguniform('reg', np.log(1e-5), np.log(5e-1)),
'batch_size': hp.choice('batch_size', bzs),
'hidden_size': hp.choice('hidden_size', architectures),
'beta_1': hp.uniform('beta_1', 0.75, 0.99),
'decay': hp.loguniform('decay', np.log(1e-5), np.log(1e-1)),
'res': hp.choice('res', ress),
'bypass': hp.choice('bypass', bypasses),
'amsgrad': True,
'patience': 50,
}
objective_func = partial(train_model_hyperopt,
X=X,
y=y,
lname=lname,
regression=regression,
epochs=epochs,
X_val=X_val,
y_val=y_val,
input_model=model)
trials = Trials()
best_params = fmin(objective_func,
space,
algo=tpe.suggest,
trials=trials,
max_evals=hyperopt_step,
rstate=np.random.RandomState(0))
if not model == False:
best_params.update({
'batch_size': bzs[best_params['batch_size']],
'amsgrad': True,
'patience': 10,
})
else:
best_params.update({
'hidden_size':
architectures[best_params['hidden_size']],
'batch_size':
bzs[best_params['batch_size']],
'res':
ress[best_params['res']],
'bypass':
bypasses[best_params['bypass']],
'amsgrad':
True,
'patience':
10,
})
# One extra model training on train/validation set to get the number of epoch for the final model training.
returned = train_model_hyperopt(best_params,
X,
y,
lname,
regression=regression,
epochs=epochs,
X_val=X_val,
y_val=y_val,
input_model=model)
best_params.update({'epochs': returned['epochs']})
return best_params
eval_set = [(X_train, y_train), (X_test, y_test)]
clf.fit(X_train,
y_train,
eval_set=eval_set,
eval_metric="rmse",
verbose=False)
y_pred = clf.predict(X_test)
rmse = mean_squared_error(y_test, y_pred)**(0.5)
return {'loss': rmse, 'status': STATUS_OK}
trials_reg = Trials()
best = fmin(fn=objetivo,
space=space,
algo=tpe.suggest,
max_evals=1000,
trials=trials_reg)
print(best)
modelo = xgb.XGBRegressor(n_estimators=int(best['n_estimators']),
x_gamma=best['x_gamma'],
learning_rate=best['learning_rate'],
x_max_depth=best['x_max_depth'],
x_min_child=best['x_min_child'],
x_reg_lambda=best['x_reg_lambda'],
x_subsample=best['x_subsample'],
objective='reg:squarederror')
parser.add_argument("--early_stopping_rounds", default=10, type=int)
parser.add_argument("--seed", default=0, type=int)
parser.add_argument("--nfold", default=10, type=int)
cv_params = vars(parser.parse_args())
parser_2 = argparse.ArgumentParser(
description='Others',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_2.add_argument("--max_evals", default=2, type=int)
parser_2.add_argument("--filename", default='tmp', type=str)
parser_2.add_argument("--n_stack", default=1, type=int)
args = parser_2.parse_args()
# load and combine parameters
params = Params(cv_params=cv_params)
params.lgb_params()
# load dataset
train, train_label = load_dataset(args.n_stack)
bayes_trials_1 = Trials()
obj = HPOpt_cv(train, train_label)
lgb_opt = obj.process(fn_name='lgb_cv',
space=params.get_param(),
trials=bayes_trials_1,
algo=tpe.suggest,
max_evals=args.max_evals)
# save trial
save_obj(bayes_trials_1, args.filename)
# tmp = load_obj('tmp')
def run_train_hopt(self):
logger.info("start training with hopt")
skf = StratifiedKFold(n_splits=self._best_cv,
shuffle=True,
random_state=3655)
sample_weight = self._calc_w(self.y)
def score(params, skf=skf, sample_weight=sample_weight):
prms = {
"C": params["C"],
"gamma": params["gamma"],
"kernel": params['kernel']
}
list_score_acc = []
list_score_logloss = []
for train, val in skf.split(self.X, self.y):
X_train, X_val = self.X[train], self.X[val]
y_train, y_val = self.y[train], self.y[val]
weight_train = sample_weight[train]
weight_val = sample_weight[val]
model = self._set_model()
model.train(prms=prms,
X_tr=X_train,
X_val=X_val,
y_tr=y_train,
y_val=y_val,
w_tr=weight_train,
w_val=weight_val)
list_score_acc.append(model._score_acc)
list_score_logloss.append(model._score_logloss)
"""
##n_estimaters=0 causes error at .fit()
if model.best_iteration_ != -1:
list_best_iter.append(model.best_iteration_)
else:
list_best_iter.append(params['n_estimators'])
break
"""
# logger.info("n_estimators: {}".format(list_best_iter))
# params["n_estimators"] = np.mean(list_best_iter, dtype=int)
score_acc = (np.mean(list_score_acc), np.min(list_score_acc),
np.max(list_score_acc))
# logger.info("score_acc %s" % np.mean(list_score_acc))
# score_logloss = (np.mean(list_score_logloss), np.min(list_score_logloss), np.max(list_score_logloss))
# score_f1 = (np.mean(list_score_f1), np.min(list_score_f1), np.max(list_score_f1))
# score_auc = (np.mean(list_score_auc), np.min(list_score_auc), np.max(list_score_auc))
logloss = np.mean(list_score_logloss)
return {
'loss': logloss,
'status': STATUS_OK,
'localCV_acc': score_acc
}
space = {
'C': hp.uniform('C', 0, 2),
'gamma': hp.loguniform('gamma', -8, 2),
'kernel': hp.choice('kernel', ['rbf', 'poly', 'sigmoid'])
}
trials = Trials()
best_params = fmin(rstate=hopt_random_state,
fn=score,
space=space,
algo=tpe.suggest,
trials=trials,
max_evals=30)
self.localCV_acc = list(
filter(lambda x: x["loss"] == min(trials.losses()),
trials.results))[0]["localCV_acc"][0]
self.localCV_loss = min(trials.losses())
logger.info("localCV_acc %s" % self.localCV_acc)
logger.info("localCV_loss %s" % self.localCV_loss)
self.best_params = {
"C": best_params["C"],
"gamma": best_params["gamma"],
"kernel": best_params["kernel"]
}
logger.info("best params are %s" % self.best_params)
sample_weight = self._calc_w(self.y)
model = self._set_model()
logger.info("start training with best params")
model.train_all(prms=self.best_params,
X_tr=self.X,
y_tr=self.y,
w_tr=sample_weight)
model.save_model()
del model, sample_weight, skf
gc.collect()
logger.info("model with best params is saved")
import hyperopt from hyperopt import Trials, Domain domain = Domain(fn = None) docs = hyperopt.rand.suggest(range(10), domain, Trials(), seed=123)
def main(args):
train_data, val_data, test_data, label_dict, img_mean = load_image(
args.data,
dim=args.dim,
mode="RGB",
zero_center=args.zero_center,
train_size=args.train_size,
crop=args.crop)
# use both val and test as val
val_data = (np.concatenate((val_data[0], test_data[0]), axis=0),
np.concatenate((val_data[1], test_data[1])))
num_channels = train_data[0][0].shape[0]
data_size = (None, num_channels, CROP_DIM, CROP_DIM)
def objective(hyperargs):
net = build_network(args.model, data_size, cudnn=args.dnn)
lr = args.learning_rate
lmbda = args.lmbda
if 'lr' in hyperargs:
lr = hyperargs['lr']
if 'lmbda' in hyperargs:
lmbda = hyperargs['lmbda']
best_val_cost, train_costs, val_costs = net.train(
algorithm,
train_data,
val_data=val_data,
test_data=None,
lr=lr,
lmbda=lmbda,
train_batch_size=TRAIN_BATCH_SIZE,
val_batch_size=10,
epochs=args.epoch,
crop_dim=CROP_DIM,
img_mean=img_mean,
color_jitter=args.color_jitter)
return {'loss': best_val_cost, 'status': STATUS_OK}
space = {}
if not args.learning_rate:
print "Optimizing learning rate"
space['lr'] = hp.uniform('lr', 0, 0.001)
if not args.lmbda:
print "Optimizing regularization rate"
space['lmbda'] = hp.uniform('lambda', 0, 0.1)
trials = Trials()
best = fmin(objective,
space,
algo=tpe.suggest,
max_evals=args.num_trials,
trials=trials)
print best
print hyperopt.space_eval(space, best)
f = open('out.txt', 'w')
cPickle.dump(trials.trials, f)
f.close()
class HyperOptimizer(object):
def __init__(self, train_set, holdout_set, command, max_evals=100,
outer_loss_function='logistic',
searcher='tpe', is_regression=False):
self.train_set = train_set
self.holdout_set = holdout_set
self.train_model = './current.model'
self.holdout_pred = './holdout.pred'
self.trials_output = './trials.json'
self.hyperopt_progress_plot = './hyperopt_progress.png'
self.log = './log.log'
self.logger = self._configure_logger()
# hyperopt parameter sample, converted into a string with flags
self.param_suffix = None
self.train_command = None
self.validate_command = None
self.y_true_train = []
self.y_true_holdout = []
self.outer_loss_function = outer_loss_function
self.space = self._get_space(command)
self.max_evals = max_evals
self.searcher = searcher
self.is_regression = is_regression
self.trials = Trials()
self.current_trial = 0
def _get_space(self, command):
hs = HyperoptSpaceConstructor(command)
hs.string_to_pyll()
return hs.space
def _configure_logger(self):
LOGGER_FORMAT = "%(asctime)s,%(msecs)03d %(levelname)-8s [%(name)s/%(module)s:%(lineno)d]: %(message)s"
LOGGER_DATEFMT = "%Y-%m-%d %H:%M:%S"
LOGFILE = self.log
logging.basicConfig(format=LOGGER_FORMAT,
datefmt=LOGGER_DATEFMT,
level=logging.DEBUG)
formatter = logging.Formatter(LOGGER_FORMAT, datefmt=LOGGER_DATEFMT)
file_handler = logging.FileHandler(LOGFILE)
file_handler.setFormatter(formatter)
logger = logging.getLogger()
logger.addHandler(file_handler)
return logger
def get_hyperparam_string(self, **kwargs):
for arg in ['--passes']: #, '--rank', '--lrq']:
if arg in kwargs:
kwargs[arg] = int(kwargs[arg])
#print 'KWARGS: ', kwargs
flags = [key for key in kwargs if key.startswith('-')]
for flag in flags:
if kwargs[flag] == 'omit':
del kwargs[flag]
self.param_suffix = ' '.join(['%s %s' % (key, kwargs[key]) for key in kwargs if key.startswith('-')])
self.param_suffix += ' %s' % (kwargs['argument'])
def compose_vw_train_command(self):
data_part = ('vw -d %s -f %s --holdout_off -c '
% (self.train_set, self.train_model))
self.train_command = ' '.join([data_part, self.param_suffix])
def compose_vw_validate_command(self):
data_part = 'vw -t -d %s -i %s -p %s --holdout_off -c' \
% (self.holdout_set, self.train_model, self.holdout_pred)
self.validate_command = data_part
def fit_vw(self):
self.compose_vw_train_command()
self.logger.info("executing the following command (training): %s" % self.train_command)
subprocess.call(shlex.split(self.train_command))
def validate_vw(self):
self.compose_vw_validate_command()
self.logger.info("executing the following command (validation): %s" % self.validate_command)
subprocess.call(shlex.split(self.validate_command))
def get_y_true_train(self):
self.logger.info("loading true train class labels...")
yh = open(self.train_set, 'r')
self.y_true_train = []
for line in yh:
self.y_true_train.append(int(line.strip()[0:2]))
if not self.is_regression:
self.y_true_train = [(i + 1.) / 2 for i in self.y_true_train]
self.logger.info("train length: %d" % len(self.y_true_train))
def get_y_true_holdout(self):
self.logger.info("loading true holdout class labels...")
yh = open(self.holdout_set, 'r')
self.y_true_holdout = []
for line in yh:
self.y_true_holdout.append(int(line.strip()[0:2]))
if not self.is_regression:
self.y_true_holdout = [(i + 1.) / 2 for i in self.y_true_holdout]
self.logger.info("holdout length: %d" % len(self.y_true_holdout))
def validation_metric_vw(self):
v = open('%s' % self.holdout_pred, 'r')
y_pred_holdout = []
for line in v:
y_pred_holdout.append(float(line.strip()))
if self.outer_loss_function == 'logistic':
y_pred_holdout_proba = [1. / (1 + exp(-i)) for i in y_pred_holdout]
loss = log_loss(self.y_true_holdout, y_pred_holdout_proba)
elif self.outer_loss_function == 'squared': # TODO: write it
pass
elif self.outer_loss_function == 'hinge': # TODO: write it
pass
elif self.outer_loss_function == 'roc-auc':
y_pred_holdout_proba = [1. / (1 + exp(-i)) for i in y_pred_holdout]
fpr, tpr, _ = roc_curve(self.y_true_holdout, y_pred_holdout_proba)
loss = -auc(fpr, tpr)
self.logger.info('parameter suffix: %s' % self.param_suffix)
self.logger.info('loss value: %.6f' % loss)
return loss
def hyperopt_search(self, parallel=False): # TODO: implement parallel search with MongoTrials
def objective(kwargs):
start = dt.now()
self.current_trial += 1
self.logger.info('\n\nStarting trial no.%d' % self.current_trial)
self.get_hyperparam_string(**kwargs)
self.fit_vw()
self.validate_vw()
loss = self.validation_metric_vw()
finish = dt.now()
elapsed = finish - start
self.logger.info("evaluation time for this step: %s" % str(elapsed))
# clean up
subprocess.call(shlex.split('rm %s %s' % (self.train_model, self.holdout_pred)))
to_return = {'status': STATUS_OK,
'loss': loss, # TODO: include also train loss tracking in order to prevent overfitting
'eval_time': elapsed.seconds,
'train_command': self.train_command,
'current_trial': self.current_trial
}
return to_return
self.trials = Trials()
if self.searcher == 'tpe':
algo = tpe.suggest
elif self.searcher == 'rand':
algo = rand.suggest
logging.debug("starting hypersearch...")
best_params = fmin(objective, space=self.space, trials=self.trials, algo=algo, max_evals=self.max_evals)
self.logger.debug("the best hyperopt parameters: %s" % str(best_params))
json.dump(self.trials.results, open(self.trials_output, 'w'))
self.logger.info('All the trials results are saved at %s' % self.trials_output)
best_configuration = self.trials.results[np.argmin(self.trials.losses())]['train_command']
best_loss = self.trials.results[np.argmin(self.trials.losses())]['loss']
self.logger.info("\n\nA full training command with the best hyperparameters: \n%s\n\n" % best_configuration)
self.logger.info("\n\nThe best holdout loss value: \n%s\n\n" % best_loss)
return best_configuration, best_loss
def plot_progress(self):
try:
sns.set_palette('Set2')
sns.set_style("darkgrid", {"axes.facecolor": ".95"})
except:
pass
self.logger.debug('plotting...')
plt.figure(figsize=(15,10))
plt.subplot(211)
plt.plot(self.trials.losses(), '.', markersize=12)
plt.title('Per-Iteration Outer Loss', fontsize=16)
plt.ylabel('Outer loss function value')
if self.outer_loss_function in ['logloss']:
plt.yscale('log')
xticks = [int(i) for i in np.linspace(plt.xlim()[0], plt.xlim()[1], min(len(self.trials.losses()), 11))]
plt.xticks(xticks, xticks)
plt.subplot(212)
plt.plot(np.minimum.accumulate(self.trials.losses()), '.', markersize=12)
plt.title('Cumulative Minimum Outer Loss', fontsize=16)
plt.xlabel('Iteration number')
plt.ylabel('Outer loss function value')
xticks = [int(i) for i in np.linspace(plt.xlim()[0], plt.xlim()[1], min(len(self.trials.losses()), 11))]
plt.xticks(xticks, xticks)
plt.tight_layout()
plt.savefig(self.hyperopt_progress_plot)
self.logger.info('The diagnostic hyperopt progress plot is saved: %s' % self.hyperopt_progress_plot)
def explore(self, x_train, y_train, x_val, y_val, space, model_specs, train_specs, path, max_evals, epochs,
metric=None, trials=None, net_name='NN', verbose=0, seed=None, val_inference_in_path=None,
callbacks=None, cuda=None):
"""
:param x_train:
:param y_train:
:param x_val:
:param y_val:
:param space:
:param model_specs:
:param train_specs:
:param path:
:param max_evals:
:param epochs:
:param metric:
:param trials:
:param net_name:
:param verbose:
:param seed:
:param val_inference_in_path:
:param callbacks:
:param cuda:
:return:
"""
self.__cuda = cuda
if trials is None:
trials = Trials()
def objective(space):
# Create model
specs = space.copy()
specs.update(model_specs)
model = self.__model_constructor(**specs)
if self.__model_constructor_wrapper:
self.__model_constructor_wrapper(model)
if self.__cuda in specs and BACKEND != 'tensorflow':
model.cuda()
# Print some information
iteration = len(trials.losses())
if verbose > 0:
print('\n')
print('iteration : {}'.format(0 if trials.losses() is None else iteration))
[print('{}: {}'.format(key, value)) for key, value in specs.items()]
print(model.summary(line_length=200))
# Train model
trainer_kargs = train_specs.copy()
trainer_kargs.update({'module': model})
if callbacks:
trainer_kargs.update({'callbacks': callbacks})
trainer = self.__trainer_class(**trainer_kargs)
train_kargs = {}
if not any([val_ is None for val_ in [x_val, y_val]]) and \
all([val_ in list(getfullargspec(trainer.fit))[0] for val_ in ['x_val', 'y_val']]):
train_kargs.update({'x_val': x_train, 'y_val': y_train})
train_kargs.update({'epochs': epochs})
for karg, val in zip(['verbose'], [verbose]):
if karg in list(getfullargspec(trainer.fit))[0]:
train_kargs.update({'verbose': val})
trainer.fit(x_train, y_train, **train_kargs)
# Exploration loss
exp_loss = None # ToDo: compatible with custom metric
if metric in [None, 'categorical_accuracy', 'accuracy']:
def prepare_for_acc(x):
if not isinstance(x, list):
x_ = [x]
else:
x_ = x.copy()
for i in range(len(x_)):
if len(x_[i].shape) == 1:
x_[i] = np.where(x_[i] > 0.5, 1, 0)
else:
x_[i] = np.argmax(x_[i], axis=-1)
return x_
y_pred = prepare_for_acc(trainer.predict(x_val))
y_val_ = prepare_for_acc(y_val)
acc_score = []
for i in range(len(y_pred)):
acc_score.append(accuracy_score(y_pred[i], y_val_[i]))
exp_loss = 1 - np.mean(acc_score)
elif metric == 'i_auc': # ToDo: make this work
y_pred = model.predict(x_val)
if not isinstance(y_pred, list):
y_pred = [y_pred]
exp_loss = []
for i in np.arange(0, self.__total_n_models):
if len(np.bincount(y_val[i][:, -1])) > 1 and not math.isnan(np.sum(y_pred[i])):
fpr, tpr, thresholds = metrics.roc_curve(y_val[i][:, -1], y_pred[i][:, -1])
exp_loss += [(1 - metrics.auc(fpr, tpr))]
exp_loss = np.mean(exp_loss) if len(exp_loss) > 0 else 1
if verbose > 0:
print('\n')
print('Exploration Loss: ', exp_loss)
status = STATUS_OK if not math.isnan(exp_loss) and exp_loss is not None else STATUS_FAIL
# Save trials
with open(path + 'trials.hyperopt', 'wb') as f:
pickle.dump(trials, f)
# Save model if it is the best so far
best_exp_losss_name = path + 'best_' + net_name + '_exp_loss'
trials_losses = [loss_ for loss_ in trials.losses() if loss_]
best_exp_loss = min(trials_losses) if len(trials_losses) > 0 else None
print('best val loss so far: ', best_exp_loss)
print('current val loss: ', exp_loss)
best_exp_loss_cond = best_exp_loss is None or exp_loss < best_exp_loss
print('save: ', status, best_exp_loss_cond)
if status == STATUS_OK and best_exp_loss_cond:
df = pd.DataFrame(data=[exp_loss], columns=['best_exp_loss'])
df.to_pickle(best_exp_losss_name)
self.__save_model(model=model, name=path + 'best_exp_' + net_name + '_json')
with open(path + 'best_exp_' + net_name + '_hparams', 'wb') as f:
pickle.dump(space, f, protocol=pickle.HIGHEST_PROTOCOL)
if val_inference_in_path is not None:
y_val_ = np.concatenate(y_val, axis=1) if isinstance(y_val, list) else y_val
np.savetxt(val_inference_in_path + 'val_target.csv', y_val_, delimiter=',')
y_inf = trainer.predict(x_val)
y_inf = np.concatenate(y_inf, axis=1) if isinstance(y_inf, list) else y_inf
np.savetxt(val_inference_in_path + 'val_target_inference.csv', y_inf, delimiter=',')
clear_session()
del model
return {'loss': exp_loss, 'status': status}
def optimize():
if len(trials.trials) < max_evals:
hyperopt.fmin(
objective,
rstate=None if seed is None else np.random.RandomState(seed),
space=space,
algo=hyperopt.tpe.suggest,
max_evals=max_evals,
trials=trials,
verbose=True,
return_argmin=False)
with open(path + 'best_exp_' + net_name + '_hparams', 'rb') as f:
best_hparams = pickle.load(f)
# Best model
specs = model_specs.copy()
specs.update(best_hparams)
best_model = self.__load_model(name=path + 'best_exp_' + net_name + '_json')
if BACKEND == 'tensorflow':
best_model.compile(optimizer=specs['optimizer'], loss=specs['loss'])
else:
best_model.cuda()
print('best hyperparameters: ' + str(best_hparams))
# Trainer
trainer_kargs = train_specs.copy()
trainer_kargs.update({'module': best_model})
if callbacks:
trainer_kargs.update({'callbacks': callbacks})
trainer = self.__trainer_class(**trainer_kargs)
if hasattr(trainer, 'initialize') and callable(trainer.initialize):
trainer.initialize()
return best_model, trainer
self.__model, self.__trainer = optimize()
for feat_name in feat_names:
param_space = param_spaces[feat_name]
log_file = "%s/%s_hyperopt.log" % (log_path, feat_name)
log_handler = open(log_file, 'wb' )
writer = csv.writer( log_handler )
headers = ['trial_counter', 'kappa_mean', 'kappa_std' ]
for k,v in sorted(param_space.items()):
headers.append(k)
writer.writerow( headers )
log_handler.flush()
print("************************************************************")
print("Search for the best params")
#global trial_counter
trial_counter = 0
trials = Trials()
objective = lambda p: hyperopt_wrapper(p,feat_name)
best_params = fmin(objective, param_space, algo=tpe.suggest,
trials=trials, max_evals=param_space["max_evals"])
for f in int_feat:
if best_params.has_key(f):
best_params[f] = int(best_params[f])
print("************************************************************")
print("Best params")
for k,v in best_params.items():
print " %s: %s" % (k,v)
trial_kappas = -np.asarray(trials.losses(), dtype=float)
best_kappa_mean = max(trial_kappas)
ind = np.where(trial_kappas == best_kappa_mean)[0][0]
best_kappa_std = trials.trial_attachments(trials.trials[ind])['std']
print("Kappa stats")
def __init__(self, *args, **kwargs):
self._optimization_info = {'trials': Trials(), 'best': {}}
self._temporary_opt_file = aux.get_temporary_file()
self.optimize_after_callback_fn = kwargs.get(
'optimize_after_callback_fn')
def optimize_model_parameter_validation(x, y, model_name=None, loss_function="accuracy", parameter=None, max_evals=100, n_folds=5, isWrite=True, problem_pattern="classification"):
"""
hyperopt model turning
"""
if model_name == None and parameter == None:
print "you must set parameter or model_name"
return None
elif parameter != None:
param = parameter
elif model_name != None:
param = parameter_dictionary[model_name]
else:
return None
validation_indexs = []
if problem_pattern == "classification":
for train_index, test_index in cross_validation.StratifiedKFold(y, n_folds=n_folds):
validation_indexs.append((train_index, test_index))
else:
for train_index, test_index in cross_validation.KFold(len(y), n_folds=n_folds):
validation_indexs.append((train_index, test_index))
trials = Trials()
function = lambda param: optimize_model_function(
param, x, y, validation_indexs, loss_function)
print param
print "========================================================================"
best_param = fmin(function, param,
algo=tpe.suggest, max_evals=max_evals, trials=trials)
print "========================================================================"
print "write result to csv files"
# write the csv file
if isWrite:
datas = []
for trial_data in trials.trials:
print trial_data
trial_parameter_dictionary = {}
trial_parameter_dictionary['model'] = model_name
trial_parameter_dictionary['tid'] = trial_data['misc']['tid']
for key, value in trial_data['misc']['vals'].items():
print key, value[0]
trial_parameter_dictionary[key] = value[0]
trial_parameter_dictionary['loss'] = trial_data['result']['loss']
trial_parameter_dictionary[
'status'] = trial_data['result']['status']
datas.append(trial_parameter_dictionary)
filename = str(time.time()) + ".csv"
dictionary_in_list_convert_to_csv(datas, filename)
print trials.statuses()
return best_param
def model_evaluation(clf, x, y, evaluate_function_name, labeled_type, label_convert_type="normal"):
if evaluate_function_name == "accuracy":
y_pred = clf.predict(x)
score = evaluate_function(
y, y_pred, evaluate_function_name)
score = -score
elif evaluate_function_name == "logloss":
y_pred = clf.predict(x)
score = evaluate_function(
y, y_pred, evaluate_function_name)
train_score = -score
elif evaluate_function_name == "mean_squared_error":
y_pred = clf.predict(x)
score = evaluate_function(
y, y_pred, evaluate_function_name)
elif evaluate_function_name == "gini":
y_pred = clf.predict(x)
score = evaluate_function(
y, y_pred, evaluate_function_name)
score = -score
train_score = -train_score
elif evaluate_function_name == "rmsle":
y_pred = clf.predict(x)
score = evaluate_function(
y, y_pred, evaluate_function_name)
elif evaluate_function_name == "auc":
if params['model'] == "XGBREGLOGISTIC":
y_pred = clf.predict_proba(x_test)
else:
y_pred = clf.predict_proba(x_test)[:, 1]
train_score = evaluate_function(
y_train, train_y_pred, evaluate_function_name)
score = evaluate_function(y_test, y_pred, evaluate_function_name)
score = -score
train_score = -train_score
elif evaluate_function_name == "rmspe":
y_pred = clf.predict(x)
score = evaluate_function(
y, y_pred, evaluate_function_name)
score = score
return score
of_connection = open(out_file, 'w') writer = csv.writer(of_connection) writer.writerow(['loss', 'params', 'iteration', 'train_time', 'output']) of_connection.close() from hyperopt import tpe # Create the algorithm tpe_algorithm = tpe.suggest from hyperopt import Trials # Record results bayes_trials = Trials() from hyperopt import fmin ITERATION = 1 best = fmin(fn = objective, space = space, algo = tpe.suggest, trials = bayes_trials, max_evals = MAX_EVALS) print(best) d= best #print(bayes_trials.results) bayes_trials_results = sorted(bayes_trials.results, key = lambda x: x['loss']) #bayes_trials_results[:1]
def main():
usage = "%prog <DRLD|MOLD|MIP|Primary|General|PK-...>"
parser = OptionParser(usage=usage)
parser.add_option('-m', dest='model', default='basic',
help='Model: (basic|GRU|LSTM); default=%default')
parser.add_option('-o', dest='output_dirname', default='bayes_opt_rnn_mod',
help='Output directory name')
parser.add_option('--reuse', dest='reuse', action="store_true", default=False,
help='Use reusable holdout; default=%default')
parser.add_option('--mod', dest='mod', action="store_true", default=False,
help='Use modifications; default=%default')
(options, args) = parser.parse_args()
global output_dirname, output_filename, reuse, search_alpha, space, mod, dataset
reuse = options.reuse
output_dirname = options.output_dirname
model = options.model
mod = options.mod
dataset = args[0]
if model == 'basic':
space['arch']['unit'] = 'basic'
space['arch']['n_hidden'] = hp.quniform('n_hidden', 5, 200, 5)
space['training']['learning_rate'] = hp.loguniform('learning_rate', -4, -1),
elif model == 'GRU':
space['arch']['unit'] = 'GRU'
space['arch']['n_hidden'] = hp.quniform('n_hidden', 5, 150, 5)
space['training']['learning_rate'] = hp.loguniform('learning_rate', -5, -1.5),
elif model == 'LSTM':
space['arch']['unit'] = 'LSTM'
space['arch']['n_hidden'] = hp.quniform('n_hidden', 5, 100, 5)
space['training']['learning_rate'] = hp.loguniform('learning_rate', -5, -1.5),
else:
sys.exit('Model not supported!')
output_dirname += '_' + model
if reuse:
output_dirname += '_reuse'
if mod:
output_dirname += '_mod'
output_dirname += '_' + dataset
output_filename = fh.make_filename(defines.exp_dir, fh.get_basename(output_dirname), 'log')
with codecs.open(output_filename, 'w') as output_file:
output_file.write(output_dirname + '\n')
#output_file.write('reuse = ' + str(reuse) + '\n')
trials = Trials()
best = fmin(call_experiment,
space=space,
algo=tpe.suggest,
max_evals=60,
trials=trials)
print space_eval(space, best)
print trials.losses()
def hyperopt(param_space, X_train, y_train, X_test, y_test, args):
resampling = over_sampling.RandomOverSampler(sampling_strategy='auto',
random_state=42)
start = time.time()
def objective_function(params):
classifier_type = params['type']
del params['type']
if classifier_type == 'rf':
clf = RandomForestClassifier(**params)
elif classifier_type == 'svm':
clf = SVC(**params)
else:
return 0
pl = make_pipeline_imb(resampling, clf)
score = cross_val_score(pl, X_train, y_train, n_jobs=args.cpus,
cv=3).mean()
return {'loss': -score, 'status': STATUS_OK}
rstate = np.random.RandomState(1) # <== Use any number here but fixed
trials = Trials()
best_param = fmin(objective_function,
param_space,
algo=tpe.suggest,
max_evals=args.num_eval,
trials=trials,
rstate=rstate)
loss = [x['result']['loss'] for x in trials.trials]
joblib.dump(
trials,
os.path.join(
args.modeldir, 'hyperopt_trials_niters{}_ssize{}.pkl'.format(
args.num_eval, args.ssize)))
# best_param_values = [ x for x in best_param.values() ]
#
# del best_param_values['classifier_type']
#
# if best_param_values[2] == 0:
# max_features = 'auto'
# else:
# max_features = 'sqrt'
#
# if best_param_values[0] == 0:
# bootstrap = 'True'
# else:
# bootstrap = 'False'
#
# print("Best parameters: ", best_param)
#
# clf_best = RandomForestClassifier(n_estimators=int(best_param_values[5]),
# max_features=max_features,
# max_depth=int(best_param_values[1]),
# min_samples_leaf=int(best_param_values[3]),
# min_samples_split=int(best_param_values[4]),
# bootstrap=bootstrap,
# n_jobs=args.cpus)
#
# pl = make_pipeline_imb(resampling, clf_best)
#
# # clf_best.fit(X_train, y_train)
# estimator_fit = pl.fit(X_train, y_train)
#
print("")
print("##### Results")
print("Score best parameters: ", min(loss) * -1)
print("Best parameters: ", best_param)
# print("Test Score: ", estimator_fit.score(X_test, y_test))
print("Time elapsed: ", round(time.time() - start, 2))
print("Parameter combinations evaluated: ", args.num_eval)
#
# if args.writemodel:
# model_file = os.path.join(args.modeldir, 'model-' + args.classifier + '.h5')
# # -- save the model
# joblib.dump(clf_best, model_file)
# print("Writing the model over path {}".format(model_file))
return trials
def hyper(args):
adata = io.read_dataset(args.input,
transpose=args.transpose,
test_split=False)
hyper_params = {
"data": {
"norm_input_log": hp.choice('d_norm_log', (True, False)),
"norm_input_zeromean": hp.choice('d_norm_zeromean', (True, False)),
"norm_input_sf": hp.choice('d_norm_sf', (True, False)),
},
"model": {
"lr":
hp.loguniform("m_lr", np.log(1e-3), np.log(1e-2)),
"ridge":
hp.loguniform("m_ridge", np.log(1e-7), np.log(1e-1)),
"l1_enc_coef":
hp.loguniform("m_l1_enc_coef", np.log(1e-7), np.log(1e-1)),
"hidden_size":
hp.choice("m_hiddensize",
((64, 32, 64), (32, 16, 32), (64, 64), (32, 32),
(16, 16), (16, ), (32, ), (64, ), (128, ))),
"activation":
hp.choice("m_activation",
('relu', 'selu', 'elu', 'PReLU', 'linear', 'LeakyReLU')),
"aetype":
hp.choice("m_aetype", ('zinb', 'zinb-conddisp')),
"batchnorm":
hp.choice("m_batchnorm", (True, False)),
"dropout":
hp.uniform("m_do", 0, 0.7),
"input_dropout":
hp.uniform("m_input_do", 0, 0.8),
},
"fit": {
"epochs": args.hyperepoch
}
}
def data_fn(norm_input_log, norm_input_zeromean, norm_input_sf):
ad = adata.copy()
ad = io.normalize(ad,
size_factors=norm_input_sf,
logtrans_input=norm_input_log,
normalize_input=norm_input_zeromean)
x_train = {'count': ad.X, 'size_factors': ad.obs.size_factors}
y_train = ad.raw.X
return (x_train, y_train),
def model_fn(train_data, lr, hidden_size, activation, aetype, batchnorm,
dropout, input_dropout, ridge, l1_enc_coef):
net = AE_types[aetype](train_data[1].shape[1],
hidden_size=hidden_size,
l2_coef=0.0,
l1_coef=0.0,
l2_enc_coef=0.0,
l1_enc_coef=l1_enc_coef,
ridge=ridge,
hidden_dropout=dropout,
input_dropout=input_dropout,
batchnorm=batchnorm,
activation=activation,
init='glorot_uniform',
debug=args.debug)
net.build()
net.model.summary()
optimizer = opt.__dict__['RMSprop'](lr=lr, clipvalue=5.0)
net.model.compile(loss=net.loss, optimizer=optimizer)
return net.model
output_dir = os.path.join(args.outputdir, 'hyperopt_results')
objective = CompileFN('autoencoder_hyperpar_db',
'myexp1',
data_fn=data_fn,
model_fn=model_fn,
loss_metric='loss',
loss_metric_mode='min',
valid_split=.2,
save_model=None,
save_results=True,
use_tensorboard=False,
save_dir=output_dir)
test_fn(objective, hyper_params, save_model=None)
trials = Trials()
best = fmin(objective,
hyper_params,
trials=trials,
algo=tpe.suggest,
max_evals=args.hypern,
catch_eval_exceptions=True)
with open(os.path.join(output_dir, 'trials.pickle'), 'wb') as f:
pickle.dump(trials, f)
#TODO: map indices in "best" back to choice-based hyperpars before saving
with open(os.path.join(output_dir, 'best.json'), 'wt') as f:
json.dump(best, f, sort_keys=True, indent=4)
print(best)
def BayesSearch(X, y):
"""Search Hyper parameter"""
global MODEL
if MODEL == "log_reg":
param_space = {
"solver": hp.choice("solver", ["newton-cg", "saga", "lbfgs"]),
"max_iter": scope.int(hp.uniform("max_iter", 100, 1500)),
"C": scope.float(hp.lognormal("C", 0.0001, 3)),
}
elif MODEL == "sgd":
param_space = {
"loss": hp.choice("loss", ["log", "modified_huber"]),
"penalty": hp.choice("penalty", ["l2", "l1", "elasticnet"]),
"alpha": scope.float(hp.uniform("alpha", 0.001, 1)),
"max_iter": scope.int(hp.uniform("max_iter", 100, 1500)),
}
elif MODEL == "rftree":
param_space = {
"max_depth":
scope.int(hp.quniform("max_depth", 6, 15, 1)),
"n_estimators":
scope.int(hp.quniform("n_estimators", 100, 1000, 1)),
"criterion":
hp.choice("criterion", ["gini", "entropy"]),
"max_features":
hp.choice("max_features", ["auto", "log2"]),
"min_samples_leaf":
scope.int(hp.quniform("min_samples_leaf", 6, 100, 1)),
"min_samples_split":
scope.int(hp.quniform("min_samples_split", 6, 100, 1)),
#'bootstrap': hp.choice('bootstrap', [True, False]),
}
elif MODEL == "extree":
param_space = {
"max_depth":
scope.int(hp.quniform("max_depth", 5, 25, 1)),
"n_estimators":
scope.int(hp.quniform("n_estimators", 100, 2000, 1)),
"criterion":
hp.choice("criterion", ["gini", "entropy"]),
"max_features":
hp.choice("max_features", ["auto", "log2"]),
"min_samples_leaf":
scope.int(hp.quniform("min_samples_leaf", 3, 100, 1)),
"min_samples_split":
scope.int(hp.quniform("min_samples_split", 3, 100, 1)),
#'bootstrap': hp.choice('bootstrap', [True, False]),
}
elif MODEL == "gbm":
param_space = {
"learning_rate":
scope.float(hp.uniform("learning_rate", 0.001, 1)),
"n_estimators":
scope.int(hp.quniform("n_estimators", 100, 2000, 1)),
"subsample":
scope.float(hp.uniform("subsample", 0.001, 1)),
"criterion":
hp.choice("criterion", ["friedman_mse", "mse", "mae"]),
"max_features":
hp.choice("max_features", ["auto", "log2"]),
"min_samples_leaf":
scope.int(hp.quniform("min_samples_leaf", 3, 100, 1)),
"min_samples_split":
scope.int(hp.quniform("min_samples_split", 3, 100, 1)),
# 'loss':hp.choice('bootstrap',['deviance', 'exponential']),
}
elif MODEL == "knn":
param_space = {
"n_neighbors": scope.int(hp.quniform("n_neighbors", 5, 100, 1)),
"leaf_size": scope.int(hp.quniform("leaf_size", 30, 200, 1)),
}
elif MODEL == "lgbm":
param_space = {
"learning_rate":
scope.float(hp.uniform("learning_rate", 0.0001, 0.1)),
"n_estimators":
scope.int(hp.quniform("n_estimators", 25, 1000, 1)),
"max_depth":
scope.int(hp.quniform("max_depth", 6, 15, 1)),
"subsample":
scope.float(hp.uniform("subsample", 0.6, 1)),
"colsample_bytree":
scope.float(hp.uniform("colsample_bytree", 0.6, 1)),
# "subsample_freq":scope.int(hp.quniform("subsample_freq", 0, 5, 1)),
"min_child_samples":
scope.int(hp.quniform("min_child_samples", 20, 100, 1)),
"min_split_gain":
scope.float(hp.uniform("min_split_gain", 0.01, 1)),
"reg_alpha":
scope.float(hp.uniform("reg_alpha", 0.0001, 1)),
"reg_lambda":
scope.float(hp.uniform("reg_lambda", 0.0001, 1)),
"num_leaves":
scope.int(hp.quniform("num_leaves", 32, 10000, 100)),
}
elif MODEL == "xgbm":
param_space = {
"learning_rate":
scope.float(hp.uniform("learning_rate", 0.0001, 0.1)),
"n_estimators": scope.int(hp.quniform("n_estimators", 100, 1000,
1)),
"max_depth": scope.int(hp.quniform("max_depth", 6, 10, 1)),
"subsample": scope.float(hp.uniform("subsample", 0.7, 1)),
"colsample_bytree":
scope.float(hp.uniform("colsample_bytree", 0.7, 1)),
"gamma": scope.int(hp.quniform("gamma", 0, 20, 1)),
"reg_alpha": scope.float(hp.uniform("reg_alpha", 0.01, 1)),
"reg_lambda": scope.float(hp.uniform("reg_lambda", 0.01, 1)),
# "scale_pos_weight":scope.float(hp.uniform("scale_pos_weight", 0.001, 1)),
}
# optimize function
trails = Trials()
optimization_function = partial(optimize, X=X, y=y)
result = fmin(
fn=optimization_function,
space=param_space,
algo=tpe.suggest,
max_evals=10,
trials=trails,
verbose=1,
)
print("Best Result is:", "_" * 10, result)
return result, trails
def main():
global objective
if args.mode == "train":
train(args.num)
elif args.mode == "backtest":
backtest(args.num)
elif args.mode == "hyperopt":
if args.train_option == "mongo":
for _ in range(args.rounds):
trials = MongoTrials(f'mongo://localhost:1234/my_db/jobs')
pid = os.fork()
if pid == 0:
# processes = [Process(
# target = os.system("hyperopt-mongo-worker --mongo=localhost:1234/my_db --poll-interval=0.1")
# ) for _ in range(args.workers)]
# for p in processes:
# p.start()
# for p in processes:
# p.join()
continue
else:
main_path, types = "../..", "mongo_workers"
objective = partial(objective,
args=args,
main_path=main_path,
types=types)
best = fmin(fn=objective,
space=HP_SPACE,
algo=tpe.suggest,
max_evals=len(trials._trials) + 8,
trials=trials)
serialize(best)
with open(f"{main_path}/{types}/best_net_config.json",
'w') as of:
json.dump(best, of)
elif args.train_option == "normal":
for _ in range(args.rounds):
p = Path(f"./agents/trials")
p.mkdir(parents=True, exist_ok=True)
if os.path.isfile(f"./agents/trials/trials.p"):
with open(f"./agents/trials/trials.p",
"rb") as file_trials:
trials = pickle.load(file_trials)
print("trials loaded")
else:
trials = Trials()
objective = partial(objective, args=args)
best = fmin(fn=objective,
space=HP_SPACE,
algo=tpe.suggest,
max_evals=len(trials._dynamic_trials) + 10,
trials=trials)
serialize(best)
with open(f"./agents/best_net_config.json", 'w') as of:
json.dump(best, of)
with open(f"./agents/trials/trials.p", "wb") as file_trials:
pickle.dump(trials, file_trials)
else:
raise NameError(
f"train_option should be set to be mongo or normal, but {args.train_option} is supplied!"
)
elif args.mode == "clear_agent":
clear_var_files_for_agent(args.num, root=".")
elif args.mode == "clear":
clear_var_files(root=".")
def work(self, **kwargs):
self.__dict__.update(kwargs)
bandit = opt_q_uniform(self.target)
prior_weight = 2.5
gamma = 0.20
algo = partial(
tpe.suggest,
prior_weight=prior_weight,
n_startup_jobs=2,
n_EI_candidates=128,
gamma=gamma,
)
trials = Trials()
fmin(passthrough,
space=bandit.expr,
algo=algo,
trials=trials,
max_evals=self.LEN)
if self.show_vars:
import hyperopt.plotting
hyperopt.plotting.main_plot_vars(trials, bandit, do_show=1)
idxs, vals = miscs_to_idxs_vals(trials.miscs)
idxs = idxs["x"]
vals = vals["x"]
losses = trials.losses()
from hyperopt.tpe import ap_filter_trials
from hyperopt.tpe import adaptive_parzen_samplers
qu = scope.quniform(1.01, 10, 1)
fn = adaptive_parzen_samplers["quniform"]
fn_kwargs = dict(size=(4, ), rng=np.random)
s_below = pyll.Literal()
s_above = pyll.Literal()
b_args = [s_below, prior_weight] + qu.pos_args
b_post = fn(*b_args, **fn_kwargs)
a_args = [s_above, prior_weight] + qu.pos_args
a_post = fn(*a_args, **fn_kwargs)
# print b_post
# print a_post
fn_lpdf = getattr(scope, a_post.name + "_lpdf")
print(fn_lpdf)
# calculate the llik of b_post under both distributions
a_kwargs = dict([(n, a) for n, a in a_post.named_args
if n not in ("rng", "size")])
b_kwargs = dict([(n, a) for n, a in b_post.named_args
if n not in ("rng", "size")])
below_llik = fn_lpdf(*([b_post] + b_post.pos_args), **b_kwargs)
above_llik = fn_lpdf(*([b_post] + a_post.pos_args), **a_kwargs)
new_node = scope.broadcast_best(b_post, below_llik, above_llik)
print("=" * 80)
do_show = self.show_steps
for ii in range(2, 9):
if ii > len(idxs):
break
print("-" * 80)
print("ROUND", ii)
print("-" * 80)
all_vals = [2, 3, 4, 5, 6, 7, 8, 9, 10]
below, above = ap_filter_trials(idxs[:ii], vals[:ii], idxs[:ii],
losses[:ii], gamma)
below = below.astype("int")
above = above.astype("int")
print("BB0", below)
print("BB1", above)
# print 'BELOW', zip(range(100), np.bincount(below, minlength=11))
# print 'ABOVE', zip(range(100), np.bincount(above, minlength=11))
memo = {b_post: all_vals, s_below: below, s_above: above}
bl, al, nv = pyll.rec_eval([below_llik, above_llik, new_node],
memo=memo)
# print bl - al
print("BB2", dict(list(zip(all_vals, bl - al))))
print("BB3", dict(list(zip(all_vals, bl))))
print("BB4", dict(list(zip(all_vals, al))))
print("ORIG PICKED", vals[ii])
print("PROPER OPT PICKS:", nv)
# assert np.allclose(below, [3, 3, 9])
# assert len(below) + len(above) == len(vals)
if do_show:
plt.subplot(8, 1, ii)
# plt.scatter(all_vals,
# np.bincount(below, minlength=11)[2:], c='b')
# plt.scatter(all_vals,
# np.bincount(above, minlength=11)[2:], c='c')
plt.scatter(all_vals, bl, c="g")
plt.scatter(all_vals, al, c="r")
if do_show:
plt.show()
def main():
usage = "%prog <DRLD|MIP|MOLD|Primary|General|Terrorist|PK-Brown|PK-Roberts|PK-Pelosi|PK-Cheney>"
parser = OptionParser(usage=usage)
parser.add_option("-m", dest="model", default="LR", help="Model: (LR|SVM|MNB|SVMNB); default=%default")
parser.add_option("-t", dest="test_fold", default=0, help="Test fold; default=%default")
parser.add_option("-o", dest="output_dirname", default="bayes_opt", help="Output directory name")
parser.add_option(
"--reuse", dest="reuse", action="store_true", default=False, help="Use reusable holdout; default=%default"
)
parser.add_option(
"--alpha",
dest="alpha",
action="store_true",
default=False,
help="Include alpha in search space (instead of grid search); default=%default",
)
parser.add_option(
"--n_dev_folds",
dest="n_dev_folds",
default=5,
help="Number of dev folds to use when tuning/evaluating; default=%default",
)
# parser.add_option('--codes', dest='n_codes', default=33,
# help='Number of codes (only matters with --alpha); default=%default')
(options, args) = parser.parse_args()
global output_dirname, output_filename, reuse, search_alpha, space, run, group, test_fold, n_dev_folds
run = args[0]
reuse = options.reuse
search_alpha = options.alpha
# n_codes = int(options.n_codes)
output_dirname = options.output_dirname
model = options.model
test_fold = int(options.test_fold)
n_dev_folds = int(options.n_dev_folds)
# allow user to specfiy a particular choice of model
if model == "LR":
space["model"] = {
"model": "LR",
#'regularization': hp.choice('regularization', ['l1', 'l2'])
"regularization": "l1",
}
elif model == "SVM":
space["model"] = {
"model": "SVM",
"kernel": hp.choice(
"ktype",
[{"ktype": "linear"}, {"ktype": "poly", "degree": hp.choice("degree", [2, 3, 4])}, {"ktype": "rbf"}],
),
}
elif model == "MNB":
space["model"] = {"model": "MNB"}
elif model == "SVMNB":
space["model"] = {"model": "SVMNB", "beta": hp.uniform("beta", 0, 1)}
else:
sys.exit("Choice of model not supported!")
if run == "DRLD":
add_drld()
group = ["Democrat-Likes", "Democrat-Dislikes", "Republican-Likes", "Republican-Dislikes"]
n_codes = 33
elif run == "MIP":
add_MIP()
group = ["MIP-Personal-1", "MIP-Personal-2", "MIP-Political-1", "MIP-Political-2"]
n_codes = 74
elif run == "MOLD":
add_MOLD()
group = ["McCain-Likes", "McCain-Dislikes", "Obama-Likes", "Obama-Dislikes"]
n_codes = 34
elif run == "Primary":
add_obama()
add_clinton()
group = ["Obama-Primary", "Clinton-Primary"]
n_codes = 42
elif run == "General":
add_obama()
add_mccain()
group = ["Obama-General", "McCain-General"]
n_codes = 41
elif run == "Terrorists":
group = [run]
n_codes = 28
elif run == "PK-Brown":
group = [run]
n_codes = 14
elif run == "PK-Cheney":
group = [run]
n_codes = 12
elif run == "PK-Pelosi":
group = [run]
n_codes = 15
elif run == "PK-Roberts":
group = [run]
n_codes = 14
else:
sys.exit("Dataset not recognized")
output_dirname += "_" + model
if search_alpha:
space["alphas"] = []
for i in range(n_codes):
space["alphas"].append(hp.loguniform("alpha" + str(i), -1.15, 9.2))
output_dirname += "_alphas"
if reuse:
output_dirname += "_reuse"
else:
output_dirname += "_noreuse"
output_dirname += "_" + run
if n_dev_folds != 5:
output_dirname += "_" + str(n_dev_folds)
output_filename = fh.make_filename(defines.exp_dir, fh.get_basename(output_dirname), "log")
with codecs.open(output_filename, "w") as output_file:
output_file.write(output_dirname + "\n")
output_file.write("reuse = " + str(reuse) + "\n")
output_file.write("search alphas = " + str(search_alpha) + "\n")
trials = Trials()
best = fmin(call_experiment, space=space, algo=tpe.suggest, max_evals=40, trials=trials)
print space_eval(space, best)
print trials.losses()
batch_size=1,
nb_epoch=1,
validation_split=0.3,
shuffle=False)
#optimize_history = optimize_model.fit(train_data, H_t, batch_size=1, nb_epoch=1, validation_split=0.3, shuffle=False)
optimize_model.reset_states()
loss_v = optimize_history.history['loss']
print loss_v
loss_out = loss_v[-1]
return {'loss': loss_out, 'status': STATUS_OK}
trials = Trials()
best = fmin(objective, space, algo=tpe.suggest, trials=trials, max_evals=50)
print best
#Building Stateful Model
lstm_hidden = best
tsteps = 24
out_dim = 24
lstm_model = build_lstm_v1.lstm_model_102(lstm_hidden, train_data.shape[2],
out_dim, tsteps)
for ep in range(20):
lstm_history = lstm_model.fit(train_data,
H_t,
def suggest(self, history, searchspace):
"""
Suggest params to maximize an objective function based on the
function evaluation history using a tree of Parzen estimators (TPE),
as implemented in the hyperopt package.
Use of this function requires that hyperopt be installed.
"""
# This function is very odd, because as far as I can tell there's
# no real documented API for any of the internals of hyperopt. Its
# execution model is that hyperopt calls your objective function
# (instead of merely providing you with suggested points, and then
# you calling the function yourself), and its very tricky (for me)
# to use the internal hyperopt data structures to get these predictions
# out directly.
# so they path we take in this function is to construct a synthetic
# hyperopt.Trials database which from the `history`, and then call
# hyoperopt.fmin with a dummy objective function that logs the value
# used, and then return that value to our client.
# The form of the hyperopt.Trials database isn't really documented in
# the code -- most of this comes from reverse engineering it, by
# running fmin() on a simple function and then inspecting the form of
# the resulting trials object.
if 'hyperopt' not in sys.modules:
raise ImportError('No module named hyperopt')
random = check_random_state(self.seed)
hp_searchspace = searchspace.to_hyperopt()
trials = Trials()
for i, (params, scores, status) in enumerate(history):
if status == 'SUCCEEDED':
# we're doing maximization, hyperopt.fmin() does minimization,
# so we need to swap the sign
result = {'loss': -np.mean(scores), 'status': STATUS_OK}
elif status == 'PENDING':
result = {'status': STATUS_RUNNING}
elif status == 'FAILED':
result = {'status': STATUS_FAIL}
else:
raise RuntimeError('unrecognized status: %s' % status)
# the vals key in the trials dict is basically just the params
# dict, but enum variables (hyperopt hp.choice() nodes) are
# different, because the index of the parameter is specified
# in vals, not the parameter itself.
vals = {}
for var in searchspace:
if isinstance(var, EnumVariable):
# get the index in the choices of the parameter, and use
# that.
matches = [i for i, c in enumerate(var.choices)
if c == params[var.name]]
assert len(matches) == 1
vals[var.name] = matches
else:
# the other big difference is that all of the param values
# are wrapped in length-1 lists.
vals[var.name] = [params[var.name]]
trials.insert_trial_doc({
'misc': {
'cmd': ('domain_attachment', 'FMinIter_Domain'),
'idxs': dict((k, [i]) for k in hp_searchspace.keys()),
'tid': i,
'vals': vals,
'workdir': None},
'result': result,
'tid': i,
# bunch of fixed fields that hyperopt seems to require
'owner': None, 'spec': None, 'state': 2, 'book_time': None,
'exp_key': None, 'refresh_time': None, 'version': 0
})
trials.refresh()
chosen_params_container = []
def mock_fn(x):
# http://stackoverflow.com/a/3190783/1079728
# to get around no nonlocal keywork in python2
chosen_params_container.append(x)
return 0
fmin(fn=mock_fn, algo=tpe.suggest, space=hp_searchspace, trials=trials,
max_evals=len(trials.trials)+1,
**self._hyperopt_fmin_random_kwarg(random))
chosen_params = chosen_params_container[0]
return chosen_params
data_path_label = os.path.join(data_path,'layer_'+str(layer),'Label_cluster')
path = os.path.join(data_path_label,'label_cluster_'+str(num_clust)+'.npz')
data = np.load(path)
label = data['a']
outputpath = os.path.join(main_path,str(num_clust)+'_clustering')
create_saving_folder(outputpath)
print('################### Tests for a ',num_clust,' centers clustering ###################')
for nbc in range(2,num_clust+1):
print('################ Predicting voxels for cluster number ',nbc,' ################')
masker = find_cluster(label,masker,data_path,nbc)
mean = masker.transform(score_img).mean()
maxi = masker.transform(score_img).max()
print('Mean: ',mean, 'Max: ', maxi)
print('')
if optimized == True:
#we look for optimizations
if num_clust == nbc == 2:
best_run, best_model = optim.minimize(model=model_optimization, data=retrieve_data, algo=tpe.suggest, max_evals=5, trials=Trials())
X_train,y_train,X_test,y_test = retrieve_data(loaded_stimuli,fmri_ready)
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, y_test))
print("\nBest performing model chosen hyper-parameters:")
print(best_run)
else:
pass
second_processing(masker,filename_irm,filename_stimuli,outputpath,meanepi,alpha,nbc,num_clust,best_run)
else:
second_processing(masker,filename_irm,filename_stimuli,outputpath,meanepi,alpha,nbc,num_clust)
def work(self, **kwargs):
self.__dict__.update(kwargs)
bandit = opt_q_uniform(self.target)
prior_weight = 2.5
gamma = 0.20
algo = partial(tpe.suggest,
prior_weight=prior_weight,
n_startup_jobs=2,
n_EI_candidates=128,
gamma=gamma)
#print algo.opt_idxs['x']
#print algo.opt_vals['x']
trials = Trials()
fmin(passthrough,
space=bandit.expr,
algo=algo,
trials=trials,
max_evals=self.LEN)
if self.show_vars:
import hyperopt.plotting
hyperopt.plotting.main_plot_vars(trials, bandit, do_show=1)
idxs, vals = miscs_to_idxs_vals(trials.miscs)
idxs = idxs['x']
vals = vals['x']
losses = trials.losses()
from hyperopt.tpe import ap_filter_trials
from hyperopt.tpe import adaptive_parzen_samplers
qu = scope.quniform(1.01, 10, 1)
fn = adaptive_parzen_samplers['quniform']
fn_kwargs = dict(size=(4,), rng=np.random)
s_below = pyll.Literal()
s_above = pyll.Literal()
b_args = [s_below, prior_weight] + qu.pos_args
b_post = fn(*b_args, **fn_kwargs)
a_args = [s_above, prior_weight] + qu.pos_args
a_post = fn(*a_args, **fn_kwargs)
#print b_post
#print a_post
fn_lpdf = getattr(scope, a_post.name + '_lpdf')
print fn_lpdf
# calculate the llik of b_post under both distributions
a_kwargs = dict([(n, a) for n, a in a_post.named_args
if n not in ('rng', 'size')])
b_kwargs = dict([(n, a) for n, a in b_post.named_args
if n not in ('rng', 'size')])
below_llik = fn_lpdf(*([b_post] + b_post.pos_args), **b_kwargs)
above_llik = fn_lpdf(*([b_post] + a_post.pos_args), **a_kwargs)
new_node = scope.broadcast_best(b_post, below_llik, above_llik)
print '=' * 80
do_show = self.show_steps
for ii in range(2, 9):
if ii > len(idxs):
break
print '-' * 80
print 'ROUND', ii
print '-' * 80
all_vals = [2, 3, 4, 5, 6, 7, 8, 9, 10]
below, above = ap_filter_trials(idxs[:ii],
vals[:ii], idxs[:ii], losses[:ii], gamma)
below = below.astype('int')
above = above.astype('int')
print 'BB0', below
print 'BB1', above
#print 'BELOW', zip(range(100), np.bincount(below, minlength=11))
#print 'ABOVE', zip(range(100), np.bincount(above, minlength=11))
memo = {b_post: all_vals, s_below: below, s_above: above}
bl, al, nv = pyll.rec_eval([below_llik, above_llik, new_node],
memo=memo)
#print bl - al
print 'BB2', dict(zip(all_vals, bl - al))
print 'BB3', dict(zip(all_vals, bl))
print 'BB4', dict(zip(all_vals, al))
print 'ORIG PICKED', vals[ii]
print 'PROPER OPT PICKS:', nv
#assert np.allclose(below, [3, 3, 9])
#assert len(below) + len(above) == len(vals)
if do_show:
plt.subplot(8, 1, ii)
#plt.scatter(all_vals,
# np.bincount(below, minlength=11)[2:], c='b')
#plt.scatter(all_vals,
# np.bincount(above, minlength=11)[2:], c='c')
plt.scatter(all_vals, bl, c='g')
plt.scatter(all_vals, al, c='r')
if do_show:
plt.show()
callbacks=[earlyStopping, history],
verbose=0,
nb_epoch=100)
print('MSE:', earlyStopping.best)
return {'loss': earlyStopping.best, 'status': STATUS_OK}
n = 0
#https://github.com/keras-team/keras/issues/3945#issuecomment-281312732
from keras import backend as K
K.set_image_dim_ordering('th')
#for ordering error karas 1->2
trials = Trials()
best = hypfmin(f_nn, space, algo=tpe.suggest, max_evals=50, trials=trials)
print('best: ')
print(best)
with open(model_params_dir + 'hyperparam_test.pkl', 'w') as f:
pickle.dump(trials.trials, f)
opt_params = {}
for p in best:
opt_params[p] = hyperparams[p][best[p]]
opt_params
model = create_model(opt_params)