'''

#########################################################

This file trains a deep learning model to predict

disruptions on time series data from plasma discharges.

Dependencies:

conf.py: configuration of model,training,paths, and data

builder.py: logic to construct the ML architecture

data_processing.py: classes to handle data processing

Author: Julian Kates-Harbeck, [email protected]

This work was supported by the DOE CSGF program.

#########################################################

'''

from __future__ import print_function

import datetime,time,random

import sys,os

import dill

from functools import partial

import matplotlib

matplotlib.use('Agg')

import numpy as np

import multiprocessing as old_mp

from plasma.conf import conf

from pprint import pprint

pprint(conf)

from plasma.primitives.shots import Shot, ShotList

from plasma.preprocessor.normalize import Normalizer

from plasma.preprocessor.preprocess import Preprocessor, guarantee_preprocessed

from plasma.models.loader import Loader

if conf['model']['shallow']:

from plasma.models.shallow_runner import train, make_predictions_and_evaluate_gpu

else:

from plasma.models.runner import train, make_predictions_and_evaluate_gpu

if conf['data']['normalizer'] == 'minmax':

from plasma.preprocessor.normalize import MinMaxNormalizer as Normalizer

elif conf['data']['normalizer'] == 'meanvar':

from plasma.preprocessor.normalize import MeanVarNormalizer as Normalizer

elif conf['data']['normalizer'] == 'var':

from plasma.preprocessor.normalize import VarNormalizer as Normalizer #performs !much better than minmaxnormalizer

elif conf['data']['normalizer'] == 'averagevar':

from plasma.preprocessor.normalize import AveragingVarNormalizer as Normalizer #performs !much better than minmaxnormalizer

else:

print('unkown normalizer. exiting')

exit(1)

shot_list_dir = conf['paths']['shot_list_dir']

shot_files = conf['paths']['shot_files']

shot_files_test = conf['paths']['shot_files_test']

train_frac = conf['training']['train_frac']

stateful = conf['model']['stateful']

# if stateful:

# batch_size = conf['model']['length']

# else:

# batch_size = conf['training']['batch_size_large']

np.random.seed(0)

random.seed(0)

only_predict = len(sys.argv) > 1

custom_path = None

if only_predict:

custom_path = sys.argv[1]

print("predicting using path {}".format(custom_path))

#####################################################

####################PREPROCESSING####################

#####################################################

shot_list_train,shot_list_validate,shot_list_test = guarantee_preprocessed(conf)

#####################################################

####################Normalization####################

#####################################################

print("normalization",end='')

nn = Normalizer(conf)

nn.train()

loader = Loader(conf,nn)

print("...done")

print('Training on {} shots, testing on {} shots'.format(len(shot_list_train),len(shot_list_test)))

#####################################################

######################TRAINING#######################

#####################################################

#train(conf,shot_list_train,loader)

if not only_predict:

p = old_mp.Process(target = train,args=(conf,shot_list_train,shot_list_validate,loader))

p.start()

p.join()

#####################################################

####################PREDICTING#######################

#####################################################

loader.set_inference_mode(True)

#load last model for testing

print('saving results')

y_prime = []

y_prime_test = []

y_prime_train = []

y_gold = []

y_gold_test = []

y_gold_train = []

disruptive= []

disruptive_train= []

disruptive_test= []

# y_prime_train,y_gold_train,disruptive_train = make_predictions(conf,shot_list_train,loader)

# y_prime_test,y_gold_test,disruptive_test = make_predictions(conf,shot_list_test,loader)

y_prime_train,y_gold_train,disruptive_train,roc_train,loss_train = make_predictions_and_evaluate_gpu(conf,shot_list_train,loader,custom_path)

y_prime_test,y_gold_test,disruptive_test,roc_test,loss_test = make_predictions_and_evaluate_gpu(conf,shot_list_test,loader,custom_path)

print('=========Summary========')

print('Train Loss: {:.3e}'.format(loss_train))

print('Train ROC: {:.4f}'.format(roc_train))

print('Test Loss: {:.3e}'.format(loss_test))

print('Test ROC: {:.4f}'.format(roc_test))

disruptive_train = np.array(disruptive_train)

disruptive_test = np.array(disruptive_test)

y_gold = y_gold_train + y_gold_test

y_prime = y_prime_train + y_prime_test

disruptive = np.concatenate((disruptive_train,disruptive_test))

shot_list_validate.make_light()

shot_list_test.make_light()

shot_list_train.make_light()

save_str = 'results_' + datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")

result_base_path = conf['paths']['results_prepath']

if not os.path.exists(result_base_path):

os.makedirs(result_base_path)

np.savez(result_base_path+save_str,

y_gold=y_gold,y_gold_train=y_gold_train,y_gold_test=y_gold_test,

y_prime=y_prime,y_prime_train=y_prime_train,y_prime_test=y_prime_test,

disruptive=disruptive,disruptive_train=disruptive_train,disruptive_test=disruptive_test,

shot_list_validate=shot_list_validate,shot_list_train=shot_list_train,shot_list_test=shot_list_test,

conf = conf)

print('finished.')