src.util.strings module

strings module

Module used to control in one place all the strings of the program

class src.util.strings.s

Bases: object

a_net = 'Anchor'

activation_key = 'activation'

adam_strength_key = 'adam'

all_grays = ['grays', 'graysAfterFit', 'graysAfterFit_netReset', 'graysAfterFit_modelCheckpoint', 'graysAfterFit_generateEmbeddings', 'RLTrnFrz_graysAfterFit', 'RLTrnFrz_External_graysAfterFit', 'RLTrnFrz_External_AfterTrivial', 'graysAfterFitCURE_netReset']

appendix_key = 'appendix'

bads_cls = 'bads_classes'

bads_trg = 'bads_targets'

bads_wdw = 'bads_windows'

bads_wdw_norm = 'bads_windows_normalized'

batch_size_key = 'batch_size'

bf_key = 'bf_size'

callbacks_par = 'callBacks_settings'

cb_ModelCheckpoint = 'ModelCheckpoint'

cb_earlyStopping = 'EarlyStopping'

cb_swap = 'swap_cb'

checkpoints_path = 'checkpoint_path'

csv_help = 'Flag to activate the csv output style'

csv_path = 'csv_path'

cv_thr = 'cv_threshold'

data_drop_features = 'drop_features'

data_features = 'features'

data_key = 'data'

data_limit = 'limit'

data_object = 'object'

data_override = 'override'

data_par = 'data_pre_processing'

data_path = 'datasets_path'

data_train_prt = 'train_portion'

data_val_prt = 'val_portion'

data_vmaf_threshold = 'vmaf_threshold'

data_window = 'window_size'

default_conf = 'default_ini'

emb_grays = ['graysAfterFit_generateEmbeddings']

env_par = 'environment_settings'

env_rng = 'rng'

env_tf_rng = 'tf_rng'

epochs_key = 'epochs'

epochs_switch_loss = 'epochs_switch_loss'

es_delta = 'min_delta'

es_patience = 'patience'

es_restore = 'restore_best_weights'

exp_input_dataset = 'input_dataset'

exp_sp_metric = 'sp_metric'

exp_verbose = 'verbose'

exp_vmaf_flag = 'vmaf_flag'

expectation = 'expectation'

experiment_key = 'experiment_settings'

expph_grouping_key = 'group_generator_keys'

expph_triplet_gen = 'triplet_generator_keys'

features = 'features'

file_obj_types = ['file']

fitMethod = 'fitMethod'

folder_obj_types = ['folder', 'direcotry']

goods_cls = 'goods_classes'

goods_trg = 'goods_targets'

goods_wdw = 'goods_windows'

goods_wdw_norm = 'goods_windows_normalized'

grayEvMethod = 'grayEvolutionMethod'

gray_afterFit = 'graysAfterFit'

gray_afterFit_cure_netReset = 'graysAfterFitCURE_netReset'

gray_afterFit_netReset = 'graysAfterFit_netReset'

gray_anchor_only = 'gray_anchor'

gray_checkpoint = 'graysAfterFit_modelCheckpoint'

gray_default = 'grays'

gray_generateEmb = 'graysAfterFit_generateEmbeddings'

gray_in_train_portion = 'gray_in_train_portion'

gray_inf_rep = 'gray_inference_repetitions'

gray_inference = 'gray_inference'

gray_post_train_portion = 'gray_post_train_portion'

grays_cls = 'grays_classes'

grays_trg = 'grays_targets'

grays_wdw = 'grays_windows'

grays_wdw_norm = 'grays_windows_normalized'

group_column = 'group'

group_df_pkl = 'groups_df'

h_origin = 'halfs'

half_file = 'halfs'

history_xlim = 'history_xlim'

history_ylim = 'history_ylim'

index_col = ['exp_id', 'second']

input_data = 'input_data'

io_exists_key = 'exists'

io_name_key = 'name'

io_path_key = 'path'

io_subtype_key = 'subtype'

io_type_key = 'type'

kick_str = 'kick_strength'

last_layer_test = 'tsne-resulting-df-{}'

log_file = 'log_file'

log_format = '%(asctime)s - %(levelname)s - %(message)s'

log_path = 'log_path'

loss_functions = 'loss_functions'

loss_par = 'loss_settings'

loss_variables = 'loss_variables'

main_group_column = 'exp_id'

margin_key = 'margin'

mc_save_freq = 'save_freq'

mc_weights_only = 'save_weights_only'

metrics_functions = 'functions'

metrics_par = 'metrics_settings'

min_delta_key = 'delta'

ml_eagerly = 'run_eagerly'

model_par = 'model_settings'

ms_path = 'model_analysis'

n_net = 'Negative'

n_nodes_key = 'n_nodes'

n_origin = 'negatives'

negative_file = 'negatives'

network_plot_features = 'network_plot_features'

nn_column = 'Network'

no_feature_col = ['vmaf', 'vmafXsecond']

numpy_rng = 'numpy_rng'

origin_column_name = 'From'

p_net = 'Positive'

p_origin = 'positives'

param_Environment_type = 'environment'

param_PreProcessing_type = 'preprocessing'

param_RLActPolicy_type = 'RLActionPolicy'

param_RLEnvExitFunction_type = 'RLEnvExitFunction'

param_RLObsPP_type = 'RLObservationPP'

param_RLPerfEval_type = 'RLPerfEval'

param_action_args = 'args'

param_action_kwargs = 'kwargs'

param_action_type = 'action'

param_args_ast = ['action', 'embedding', 'layer', 'metric', 'loss', 'callback', 'flow', 'fitMethod', 'grayEvolution', 'lossParam', 'rewardFunction', 'RLPerfEval', 'RLObservationPP', 'RLActionPolicy', 'RLEnvExitFunction']

param_callback_type = 'callback'

param_embedding_type = 'embedding'

param_experiment_type = 'experiment'

param_fitmethod_type = 'fitMethod'

param_flow_type = 'flow'

param_generic_type = 'generic'

param_grayEvolution_type = 'grayEvolution'

param_layer_type = 'layer'

param_lossParam_type = 'lossParam'

param_loss_type = 'loss'

param_metric_type = 'metric'

param_model_type = 'model'

param_numpyRng_type = 'numpyRng'

param_reinforce_model_type = 'reinforcementModel'

param_reward_function_type = 'rewardFunction'

param_study_type = 'study'

param_types = ['generic', 'preprocessing', 'environment', 'numpyRng', 'action', 'experiment', 'model', 'embedding', 'layer', 'metric', 'loss', 'callback', 'flow', 'fitMethod', 'study', 'grayEvolution', 'lossParam', 'reinforcementModel', 'rewardFunction', 'RLPerfEval', 'RLObservationPP', 'RLActionPolicy', 'RLEnvExitFunction']

param_value = 'value'

patience_key = 'patience'

pkl_gray = 'gray_normalized'

pkl_path = 'pkl_path'

pkl_test = 'test_normalized'

pkl_training = 'training_normalized'

pkl_validation = 'validation_normalized'

plot_features = 'plot_features'

plot_format = 'plot_format'

plot_key = 'plot_conf'

positive_file = 'positives'

pp_actions = 'actions'

pp_flow_name = 'flow_name'

pp_path = 'pp_analysis'

prefetch_key = 'prefetch'

prp_cons = 'consideration'

prp_par = 'data_proportioning'

prp_pres = 'balancing'

prp_special = 'special_options'

results_path = 'result_path'

rlTrnFrz_external_AfterTrivial = 'RLTrnFrz_External_AfterTrivial'

rlTrnFrz_external_graysAfterFit = 'RLTrnFrz_External_graysAfterFit'

rlTrnFrz_graysAfterFit = 'RLTrnFrz_graysAfterFit'

rl_actions = 'actions'

rl_active = 'active'

rl_actuator = 'actuator'

rl_adam = 'learning_rate'

rl_delta = 'delta'

rl_environment = 'environment'

rl_episodes = 'episodes'

rl_gamma = 'gamma'

rl_infer_episodes = 'inference_episodes'

rl_inputs = 'inputs'

rl_nnodes = 'num_nodes'

rl_par = 'reinforcement_learning_settings'

rl_sigma = 'sigma'

rl_skip = 'skip'

rl_steps = 'steps'

rl_training = ['RLTrnFrz_graysAfterFit', 'RLTrnFrz_External_graysAfterFit', 'RLTrnFrz_External_AfterTrivial']

rng_key = 'rng'

seconds_column = 'second'

shape_key = 'shape'

sigmoid_range = 'sigmoid_range'

sigmoid_value = 'sigmoid_value'

silent_help = 'Flag to deactivate the output to the STDOUT'

slimit = 'slimit'

sub_obj_type = ['input_data']

swap_ep_limit = 'epoch_limit'

swap_function = 'function'

tensorboard_path = 'tensorboard_path'

tf_rng_key = 'tf_rng'

train_dst_key = 'train_portion'

unix_time = 'uxtime_sec'

val_dst_key = 'val_portion'

verbose_help = 'Define the level of verbosity in the logs'

vmafXsecond_column = 'vmafXsecond'

vmaf_column = 'vmaf'

vmaf_threshold = 'vmaf_threshold'

window_size = 'window_size'