def worker(i, working_queue, output_q, cv_splits, ds_collection):
while True:
try:
exp_params = working_queue.get_nowait()
exp = Experiment.from_params(exp_params)
exp.cv_splits = cv_splits
exp.set_datasets(ds_collection)
Print.progress("{}: Running Experiment".format(i))
exp.run()
output_q.put(exp.report)
except Empty:
Print.info("Queue Empty")
break
return
def run_experiments(self, fast_datasets=False):
time.sleep(1)
start_run_time = time.time()
ds_collection = DatasetCollection.from_params(self.params,
self.cv_splits,
fast=fast_datasets)
if self.multiprocessing == "exp":
self.run_multi(ds_collection)
else:
for i, exp_params in enumerate(
tqdm(self.exp_params_list, desc="Running Experiments")):
exp = Experiment.from_params(exp_params)
exp.cv_splits = self.cv_splits
exp.index = i
exp.set_datasets(ds_collection)
exp.multiprocessing = (self.multiprocessing == "cv")
exp.run()
if self.best_exp is None or exp.report[
"accuracy"] > self.best_exp.report["accuracy"]:
Print.good("New best: {}".format(
np.round(exp.report["accuracy"], 3)))
self.best_exp = exp
self.exp_reports.append(exp.report)
self.run_time = time.time() - start_run_time
self.generate_report()
if self.save_best:
from sklearn.externals import joblib
fp = Path.classifiers + '/' + "classifier1.pkl"
joblib.dump(self.best_exp.pipeline, fp)
