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ensemble_evaluate.py
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ensemble_evaluate.py
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import pickle
import keras
import tensorflow as tf
from keras import backend as K
import numpy as np
import sys
import os
sys.path.append(os.path.abspath('../'))
import helpers
from helpers.data_generator import process_data, DataGenerator
from helpers.custom_losses import normed_mse, mean_diff_sum_2, max_diff_sum_2, mean_diff2_sum2, max_diff2_sum2
import time
from time import strftime, localtime
import matplotlib
from matplotlib import pyplot as plt
import copy
from helpers.normalization import normalize, denormalize, renormalize
from tqdm import tqdm
t0 = time.time()
config = tf.ConfigProto(intra_op_parallelism_threads=16,
inter_op_parallelism_threads=16,
allow_soft_placement=True,
device_count={'CPU': 8,
'GPU': 1})
session = tf.Session(config=config)
K.set_session(session)
results_path = os.path.expanduser('~/ensemble_results_08_21.pkl')
base_path = os.path.expanduser('/projects/EKOLEMEN/profile_predictor/')
folders = ['run_results_08-19/']
# profiles = ['temp','dens','rotation']
# actuators = ['target_density', 'pinj', 'tinj', 'curr_target']
# scalars = ['density_estimate', 'li_EFIT01', 'volume_EFIT01', 'triangularity_top_EFIT01', 'triangularity_bot_EFIT01']
scenarios = []
model_paths = []
for folder in folders:
filenames = [foo for foo in os.listdir(os.path.join(base_path,folder)) if foo.endswith('.pkl')]
for filename in filenames:
file_path = os.path.join(base_path, folder, filename)
with open(file_path, 'rb') as f:
scenario = pickle.load(f, encoding='latin1')
if 'bt' in set(scenario['actuator_names']):
# if set(scenario['input_profile_names']) == set(profiles) and \
# set(scenario['target_profile_names']) == set(profiles) and \
# set(scenario['actuator_names']) == set(actuators) and \
# set(scenario['scalar_input_names']) == set(scalars):
scenarios.append(scenario)
model_path = file_path[:-11] + '.h5'
model_paths.append(model_path)
break
scenario = scenarios[0]
models = []
for model_path in model_paths:
model = keras.models.load_model(model_path, compile=False)
models.append(model)
print('loaded models, time={}'.format(time.time()-t0))
#full_data_path = '/scratch/gpfs/jabbate/full_data_with_error/train_data_full.pkl'
test_data_path = '/projects/EKOLEMEN/profile_predictor/DATA/full_data_with_error/test_data.pkl'
traindata, valdata, normalization_dict = helpers.data_generator.process_data(test_data_path,
scenario['sig_names'],
scenario['normalization_method'],
scenario['window_length'],
scenario['window_overlap'],
scenario['lookbacks'],
scenario['lookahead'],
scenario['sample_step'],
scenario['uniform_normalization'],
1, #scenario['train_frac'],
0, #scenario['val_frac'],
scenario['nshots'],
2, #scenario['verbose']
scenario['flattop_only'],
randomize=False,
pruning_functions=scenario['pruning_functions'],
excluded_shots = scenario['excluded_shots'],
delta_sigs = [],
uncertainties=False)
traindata = helpers.normalization.renormalize(helpers.normalization.denormalize(traindata.copy(),normalization_dict),scenario['normalization_dict'])
psi = np.linspace(0,1,scenario['profile_length'])
train_generator = DataGenerator(traindata,
scenario['batch_size'],
scenario['input_profile_names'],
scenario['actuator_names'],
scenario['target_profile_names'],
scenario['scalar_input_names'],
scenario['lookbacks'],
scenario['lookahead'],
scenario['predict_deltas'],
scenario['profile_downsample'],
False,
sample_weights = 'std',
return_uncertainties=False) #scenario['shuffle_generators'])
losses = {'mean_squared_error': keras.losses.mean_squared_error,
'mean_absolute_error': keras.losses.mean_absolute_error,
'normed_mse': normed_mse,
'mean_diff_sum_2': mean_diff_sum_2,
'max_diff_sum_2': max_diff_sum_2,
'mean_diff2_sum2': mean_diff2_sum2,
'max_diff2_sum2': max_diff2_sum2}
ensemble_evaluation_metrics = {}
for profile in model.output_names:
for metric in losses.keys():
ensemble_evaluation_metrics[profile+'_'+metric] = []
results_data = {i:{} for i in range(len(train_generator))}
for index in range(len(train_generator)):
t0 = time.time()
inputs,targets,_ = train_generator[index]
results_data[index]['targets'] = copy.deepcopy(targets)
results_data[index]['inputs'] = copy.deepcopy(inputs)
results_data[index]['shotnum'] = copy.deepcopy(train_generator.cur_shotnum)
results_data[index]['times'] = copy.deepcopy(train_generator.cur_times)
predictions = []
for j, model in enumerate(models):
pred = model.predict_on_batch(inputs)
predictions.append(pred)
uncertainties = np.std(predictions,axis=0)
predictions = np.mean(predictions,axis=0)
predictions = {name:p for name, p in zip(model.output_names,predictions)}
uncertainties = {name:p for name, p in zip(model.output_names,uncertainties)}
results_data[index]['predictions'] = copy.deepcopy(predictions)
results_data[index]['uncertainties'] = copy.deepcopy(uncertainties)
for profile in model.output_names:
for name, metric in losses.items():
ensemble_evaluation_metrics[profile+'_'+name].append(K.eval(metric(targets[profile],predictions[profile])))
results_data['ensemble_metrics'] = ensemble_evaluation_metrics
with open(results_path,'wb+') as f:
pickle.dump(results_data,f)
print('finished {}/{}'.format(index,len(train_generator)))
print('time={}'.format(time.time()-t0))
break
for key,val in ensemble_evaluation_metrics.items():
ensemble_evaluation_metrics[key] = np.mean(val)
for metric in losses:
name = metric if isinstance(metric,str) else str(metric.__name__)
s = 0
for key,val in ensemble_evaluation_metrics.items():
if name in key:
s += val
ensemble_evaluation_metrics[name] = s
for key, val in ensemble_evaluation_metrics.items():
print('{}: {:.3e}'.format(key,val))