#conf.py will parse the yaml and extract parameters based on what is specified

#will do stuff in fs_path / [username] / signal_data | shot_lists | processed shots, etc.

fs_path: '/tigress'

target: 'hinge' #'maxhinge' #'maxhinge' #'binary' #'hinge'

num_gpus: 4

paths:

signal_prepath: '/signal_data/' #/signal_data/jet/

shot_list_dir: '/shot_lists/'

tensorboard_save_path: '/Graph/'

data: jet_data #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data

specific_signals: [] #['q95','li','ip','betan','energy','lm','pradcore','pradedge','pradtot','pin','torquein','tmamp1','tmamp2','tmfreq1','tmfreq2','pechin','energydt','ipdirect','etemp_profile','edens_profile'] #if left empty will use all valid signals defined on a machine. Only use if need a custom set

executable: "mpi_learn.py"

shallow_executable: "learn.py"

data:

bleed_in: 0 #how many shots from the test sit to use in training?

bleed_in_repeat_fac: 1 #how many times to repeat shots in training and validation?

bleed_in_remove_from_test: True

bleed_in_equalize_sets: False

signal_to_augment: None #'plasma current' #or None

augmentation_mode: 'none'

augment_during_training: False

cut_shot_ends: True

T_min_warn: 30

recompute: False

recompute_normalization: False

#specifies which of the signals in the signals_dirs order contains the plasma current info

current_index: 0

plotting: False

#train/validate split

#how many shots to use

use_shots: 200000 #1000 #200000

positive_example_penalty: 1.0 #by what factor to upweight positive examples?

#normalization timescale

dt: 0.001

#maximum TTD considered

T_max: 1000.0

#The shortest works best so far: less overfitting. log TTd prediction also works well. 0.5 better than 0.2

T_warning: 1.024 #1.024 #1.024 #0.512 #0.25 #1.0 #1.0 #warning time in seconds

current_thresh: 750000

current_end_thresh: 10000

#the characteristic decay length of the decaying moving average window

window_decay: 2

#the width of the actual window

window_size: 10

#TODO optimize

normalizer: 'var'

norm_stat_range: 100.0

equalize_classes: False

# shallow_sample_prob: 0.01 #the fraction of samples with which to train the shallow model

floatx: 'float32'

model:

loss_scale_factor: 1.0

use_bidirectional: false

use_batch_norm: false

shallow: False

shallow_model:

num_samples: 1000000 #1000000 #the number of samples to use for training

type: "xgboost" #"xgboost" #"xgboost" #"random_forest" "xgboost"

n_estimators: 100 #for random forest

max_depth: 3 #for random forest and xgboost (def = 3)

C: 1.0 #for svm

kernel: "rbf" #rbf, sigmoid, linear, poly, for svm

learning_rate: 0.1 #xgboost

scale_pos_weight: 10.0 #xgboost

final_hidden_layer_size: 10 #final layers has this many neurons, every layer before twice as many

num_hidden_layers: 3

learning_rate_mlp: 0.0001

mlp_regularization: 0.0001

skip_train: False #should a finished model be loaded if available

#length of LSTM memory

pred_length: 200

pred_batch_size: 128

#TODO optimize

length: 128

skip: 1

#hidden layer size

#TODO optimize

rnn_size: 200

#size 100 slight overfitting, size 20 no overfitting. 200 is not better than 100. Prediction much better with size 100, size 20 cannot capture the data.

rnn_type: 'LSTM'

#TODO optimize

rnn_layers: 2

num_conv_filters: 128

size_conv_filters: 3

num_conv_layers: 3

pool_size: 2

dense_size: 128

extra_dense_input: False

#have not found a difference yet

optimizer: 'adam'

clipnorm: 10.0

regularization: 0.001

dense_regularization: 0.001

#1e-4 is too high, 5e-7 is too low. 5e-5 seems best at 256 batch size, full dataset and ~10 epochs, and lr decay of 0.90. 1e-4 also works well if we decay a lot (i.e ~0.7 or more)

lr: 0.00002 #0.00001 #0.0005 #for adam plots 0.0000001 #0.00005 #0.00005 #0.00005

lr_decay: 0.97 #0.98 #0.9

stateful: True

return_sequences: True

dropout_prob: 0.1

#only relevant if we want to do mpi training. The number of steps with a single replica

warmup_steps: 0

ignore_timesteps: 100 #how many initial timesteps to ignore during evaluation (to let the internal state settle)

backend: 'tensorflow'

training:

as_array_of_shots: True

shuffle_training: True

train_frac: 0.75

validation_frac: 0.33

batch_size: 128 #256

#THIS WAS THE CULPRIT FOR NO TRAINING! Lower than 1000 performs very poorly

max_patch_length: 100000

#How many shots are we loading at once?

num_shots_at_once: 200

num_epochs: 1000

use_mock_data: False

data_parallel: False

hyperparam_tuning: False

batch_generator_warmup_steps: 0

num_batches_minimum: 200 #minimum number of batches per epoch

ranking_difficulty_fac: 1.0 #how much to upweight incorrectly classified shots during training

callbacks:

list: ['earlystop']

metrics: ['val_loss','val_roc','train_loss']

mode: 'max'

monitor: 'val_roc'

patience: 5

write_grads: False

env:

name: 'frnn'

type: 'anaconda'