from plasma.primitives.hyperparameters import CategoricalHyperparam,ContinuousHyperparam,LogContinuousHyperparam,HyperparamExperiment

import matplotlib.pylab as plt

from pprint import pprint

import yaml

import datetime

import uuid

import sys,os,getpass

import shutil

import subprocess as sp

import pandas

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,verbose=0):

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)

if verbose:

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

return experiments

experiments = sorted(get_experiments(dir_path))

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

best = []

for e in np.array(experiments)[best_experiments][-5:]:

best.append(e.get_number())

bigdict = {}

for base in best:

f = "/{}/{}/changed_params.out".format(dir_path,base)

data = open(f).readlines()

for line in data:

tuples = line.split(":")

#if len(tuples) == 2:

key, values = tuples[-2:]

key = key.strip()

try:

bigdict[key] += [values]

except KeyError:

bigdict[key] = [values]

def make_comparison_plot(key,tunable,trial):

values,edges = tunable

trial = list(map(lambda x: eval(x),trial))

trial_values,_ = np.histogram(trial,bins=edges)

total = trial_values.sum()

values_percentages =list(map(lambda x: x*100.0/total, trial_values))

plt.bar(edges[:-1], values_percentages, width=np.diff(edges), ec="k", align="edge")

plt.xlabel(key, fontsize=20)

#plt.yscale('log')

plt.ylabel('Fraction of trials [%]/bin', fontsize=20)

plt.bar(edges[:-1], values, width=np.diff(edges), ec="k", align="edge")

plt.savefig(key+".png")

#plt.show()

plt.clf()

#default tunables:

defaults = {}

defaults['lr'] = np.histogram([1e-7,1e-4])

defaults['lr_decay'] = np.histogram([0.97,0.985,1.0])

defaults['positive_example_penalty'] = np.histogram([1.0,4.0,16.0])

#defaults['target'] = np.histogram([50,50,50],bins=['hinge','ttdinv','ttd'])

defaults['batch_size'] = np.histogram([64,256,1024])

defaults['dropout_prob'] = np.histogram([0.1,0.3,0.5])

defaults['rnn_layers'] = np.histogram([1,4])

defaults['rnn_size'] = np.histogram([100,200,300])

defaults['num_conv_filters'] = np.histogram([5,10])

defaults['num_conv_layers'] = np.histogram([2,4])

defaults['T_warning'] = np.histogram([0.256,1.024,10.024])

defaults['cut_shot_ends'] = np.histogram([False,True])

#Histogram it

for key,trial in bigdict.items():

if key == 'target': continue

make_comparison_plot(key,defaults[key],trial)