plasma-python/examples/check_tuning.py at master · PPPLDeepLearning/plasma-python · GitHub

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from plasma.primitives.hyperparameters import (

# CategoricalHyperparam, ContinuousHyperparam, LogContinuousHyperparam,

HyperparamExperiment

)

import sys

import os

import getpass

import numpy as np

import plasma.conf

dir_path = "/{}/{}/hyperparams/".format(

plasma.conf.conf['fs_path'], getpass.getuser())

if len(sys.argv) <= 1:

dir_path = dir_path + os.listdir(dir_path)[0] + '/'

print("using default dir {}".format(dir_path))

else:

dir_path = sys.argv[1]

def get_experiments(path):

experiments = []

num_tot = 0

num_finished = 0

num_success = 0

for name in sorted(os.listdir(path)):

if os.path.isdir(os.path.join(path, name)):

print(os.path.join(path, name))

exp = HyperparamExperiment(os.path.join(path, name))

num_finished += 1 if exp.finished else 0

num_success += 1 if exp.success else 0

num_tot += 1

experiments.append(exp)

print("Read {} experiments, {} finished ({} success)".format(

num_tot, num_finished, num_success))

return experiments

experiments = sorted(get_experiments(dir_path))

print(len(experiments))

best_experiments = np.argsort(

np.array([e.get_maximum(False)[0] for e in experiments]))

for e in experiments:

e.summary()

print("Best experiment so far: \n")

for e in np.array(experiments)[best_experiments][-3:]:

print(e)

plasma-python/examples/check_tuning.py at master · PPPLDeepLearning/plasma-python · GitHub

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