def get_configs():
"""
Defines the configurations for hyper parameter search
"""
configurations.DEFINE_string("configs_fname",None,"CSV containing all the configs to run")
configurations.DEFINE_boolean("predict",True,"Run predictions after training")
configurations.DEFINE_integer("num_threads",4,"NUmber of parallel threads (Number of parallel executions)")
configurations.DEFINE_integer("num_gpu",1,"Number of GPU on the machine, Use 0 if there are None")
configurations.DEFINE_integer("sleep_time",1,"Sleep time")
configurations.DEFINE_integer("start_date",None,"First date for prediction on as YYYYMM")
configurations.DEFINE_integer("end_date",None,"Last date for prediction on as YYYYMM")
c = configurations.ConfigValues()
return c
def get_configs():
"""
Defines all configuration params passable to command line.
"""
configs.DEFINE_string("datasource", 'big_datafile',
"The source of the data.")
configs.DEFINE_string("tkrlist", "big_tkrlist.csv",
"The list of filters to use.")
configs.DEFINE_string("datafile", 'big_datafile.dat', "a datafile name.")
configs.DEFINE_string("mse_outfile", None,
"A file to write mse values during predict phase.")
configs.DEFINE_string("default_gpu", '',
"The default GPU to use e.g., /gpu:0")
configs.DEFINE_string("nn_type", 'DeepRnnModel', "Model type")
configs.DEFINE_string("active_field", 'active',
"Key column name header for active indicator")
configs.DEFINE_string("key_field", 'gvkey',
"Key column name header in datafile")
configs.DEFINE_string("target_field", 'oiadpq_ttm',
"Target column name header in datafile")
configs.DEFINE_string("scale_field", 'mrkcap',
"Feature to scale inputs by")
configs.DEFINE_string("feature_fields", '',
"shared input and target field names")
configs.DEFINE_string("aux_input_fields", None,
"non-target, input only fields")
configs.DEFINE_string("data_dir", '', "The data directory")
configs.DEFINE_string("model_dir", '', "Model directory")
configs.DEFINE_string("rnn_cell", 'gru', "lstm or gru")
configs.DEFINE_integer("num_inputs", -1, "")
configs.DEFINE_integer("num_outputs", -1, "")
configs.DEFINE_integer("target_idx", None, "")
configs.DEFINE_integer("min_unrollings", None,
"Min number of unrolling steps")
configs.DEFINE_integer("max_unrollings", None,
"Max number of unrolling steps")
# num_unrollings is being depricated by max_unrollings
configs.DEFINE_integer("num_unrollings", 4, "Number of unrolling steps")
configs.DEFINE_integer("stride", 12,
"How many steps to skip per unrolling")
configs.DEFINE_integer("forecast_n", 12,
"How many steps to forecast into the future")
configs.DEFINE_integer("batch_size", 1, "Size of each batch")
configs.DEFINE_integer("num_layers", 1, "Numer of RNN layers")
configs.DEFINE_integer("num_hidden", 10, "Number of hidden layer units")
configs.DEFINE_float("init_scale", 0.1, "Initial scale for weights")
configs.DEFINE_float("max_grad_norm", 10.0, "Gradient clipping")
configs.DEFINE_integer("start_date", None,
"First date to train on as YYYYMM")
configs.DEFINE_integer("end_date", None, "Last date to train on as YYYYMM")
configs.DEFINE_float("keep_prob", 1.0, "Keep probability for dropout")
configs.DEFINE_boolean("train", True,
"Train model otherwise inference only")
configs.DEFINE_boolean("input_dropout", False, "Do dropout on input layer")
configs.DEFINE_boolean("hidden_dropout", False,
"Do dropout on hidden layers")
configs.DEFINE_boolean("rnn_dropout", False,
"Do dropout on recurrent connections")
configs.DEFINE_boolean(
"skip_connections", False,
"Have direct connections between input and output in MLP")
configs.DEFINE_boolean(
"use_cache", True,
"Load data for logreg from cache (vs processing from batch generator)")
configs.DEFINE_boolean(
"pretty_print_preds", False,
"Print predictions in tabular format with inputs, targets, and keys")
configs.DEFINE_boolean("scale_targets", True, "")
configs.DEFINE_string("data_scaler", None,
'sklearn scaling algorithm or None if no scaling')
configs.DEFINE_string("optimizer", 'GradientDescentOptimizer',
'Any tensorflow optimizer in tf.train')
configs.DEFINE_string("optimizer_params", None,
'Additional optimizer params such as momentum')
configs.DEFINE_float("learning_rate", 0.6,
"The initial starting learning rate")
configs.DEFINE_float("lr_decay", 0.9, "Learning rate decay")
configs.DEFINE_float("validation_size", 0.0,
"Size of validation set as %, ie. .3 = 30% of data")
configs.DEFINE_float("passes", 1.0, "Passes through day per epoch")
configs.DEFINE_float("target_lambda", 0.5,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_float("rnn_lambda", 0.5,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_integer("max_epoch", 0, "Stop after max_epochs")
configs.DEFINE_integer("early_stop", None, "Early stop parameter")
configs.DEFINE_integer("seed", None, "Seed for deterministic training")
configs.DEFINE_integer("cache_id", None,
"A unique experiment key for traking a cahce")
c = configs.ConfigValues()
if c.min_unrollings is None:
c.min_unrollings = c.num_unrollings
if c.max_unrollings is None:
c.max_unrollings = c.num_unrollings
# optimizer_params is a string of the form "param1=value1,param2=value2,..."
# this maps it to dictionary { param1 : value1, param2 : value2, ...}
if c.optimizer_params is None:
c.optimizer_params = dict()
else:
args_list = [p.split('=') for p in c.optimizer_params.split(',')]
params = dict()
for p in args_list:
params[p[0]] = float(p[1])
c.optimizer_params = params
assert ('learning_rate' not in c.optimizer_params)
return c
def get_configs():
configs.DEFINE_string("name", 'trial1', "")
configs.DEFINE_string("datafile", 'Group2-Dataset.csv', "")
configs.DEFINE_string("predict_datafile", None, "")
configs.DEFINE_string("mse_outfile", None, "")
configs.DEFINE_string("scalesfile", None, "")
configs.DEFINE_string("default_gpu", '/gpu:0', "")
configs.DEFINE_string("nn_type", 'DeepRnnModel', "")
configs.DEFINE_string("active_field", 'active', "")
configs.DEFINE_string("date_field", 'date', "")
configs.DEFINE_string("key_field", 'gvkey', "")
configs.DEFINE_string("target_field", 'mkvaltq_ttm', "")
configs.DEFINE_string("scale_field", 'mrkcap', "")
configs.DEFINE_string("financial_fields", 'saleq_ttm-ltq_mrq', "")
configs.DEFINE_string("aux_fields", 'mom3m-mom9m', "")
configs.DEFINE_string("dont_scale", None, "")
configs.DEFINE_string("data_dir", 'datasets', "")
configs.DEFINE_string("model_dir", 'chkpts-wrds-rnn', "")
configs.DEFINE_string("rnn_cell", 'lstm', "")
configs.DEFINE_string("activation_fn", 'relu', "")
configs.DEFINE_integer("num_inputs", -1, "")
configs.DEFINE_integer("num_outputs", -1, "")
configs.DEFINE_integer("target_idx", None, "")
configs.DEFINE_integer("min_unrollings", 5, "")
configs.DEFINE_integer("max_unrollings", 5, "")
configs.DEFINE_integer("min_years", None, "")
configs.DEFINE_integer("max_years", None, "")
configs.DEFINE_integer("pls_years", None, "")
configs.DEFINE_integer("num_unrollings", 5, "")
configs.DEFINE_integer("stride", 12, "")
configs.DEFINE_integer("forecast_n", 3, "")
configs.DEFINE_integer("batch_size", 128, "")
configs.DEFINE_integer("num_layers", 5, "")
configs.DEFINE_integer("num_hidden", 128, "")
configs.DEFINE_float("training_noise", None, "")
configs.DEFINE_float("init_scale", 0.01, "")
configs.DEFINE_float("max_grad_norm", 10.0, "")
configs.DEFINE_integer("start_date", None, "")
configs.DEFINE_integer("end_date", None, "")
configs.DEFINE_integer("split_date", None, "")
configs.DEFINE_float("keep_prob", 0.75, "")
configs.DEFINE_boolean("train", False, "")
configs.DEFINE_boolean("require_targets", False, "")
configs.DEFINE_boolean("input_dropout", False, "")
configs.DEFINE_boolean("hidden_dropout", False, "")
configs.DEFINE_boolean("rnn_dropout", True, "")
configs.DEFINE_boolean("skip_connections", False, "")
configs.DEFINE_boolean("direct_connections", False, "")
configs.DEFINE_boolean("use_cache", True, "")
configs.DEFINE_boolean("pretty_print_preds", True, "")
configs.DEFINE_boolean("scale_targets", True, "")
configs.DEFINE_boolean("backfill", False, "")
configs.DEFINE_boolean("log_squasher", True, "")
configs.DEFINE_boolean("ts_smoother", False, "")
configs.DEFINE_string("data_scaler", 'RobustScaler', '')
configs.DEFINE_string("optimizer", 'AdadeltaOptimizer', '')
configs.DEFINE_string("optimizer_params", None, '')
configs.DEFINE_float("learning_rate", 0.6, "")
configs.DEFINE_float("lr_decay", 0.95, "")
configs.DEFINE_float("validation_size", 0.3, "")
configs.DEFINE_float("train_until", 0.0, "")
configs.DEFINE_float("passes", 0.2, "")
configs.DEFINE_float("target_lambda", 0.8, "")
configs.DEFINE_float("rnn_lambda", 0.2, "")
configs.DEFINE_float("l2_alpha", 0.0, "")
configs.DEFINE_integer("max_epoch", 1000, "")
configs.DEFINE_integer("early_stop", 10, "")
configs.DEFINE_integer("seed", 100, "")
configs.DEFINE_integer("cache_id", 100, "")
configs.DEFINE_string("output_file", "mkvaltq_2016.csv", "")
c = configs.ConfigValues()
if c.min_unrollings is None:
c.min_unrollings = c.num_unrollings
if c.max_unrollings is None:
c.max_unrollings = c.num_unrollings
if c.min_years is not None:
c.min_unrollings = c.min_years * (12 // c.stride)
if c.max_years is not None:
c.max_unrollings = (c.max_years) * (12 // c.stride)
elif c.pls_years is None:
c.max_unrollings = c.min_unrollings
else:
c.max_unrollings = (c.min_years + c.pls_years) * (12 // c.stride)
# optimizer_params is a string of the form "param1=value1,param2=value2,..."
# this maps it to dictionary { param1 : value1, param2 : value2, ...}
if c.optimizer_params is None:
c.optimizer_params = dict()
else:
args_list = [p.split('=') for p in c.optimizer_params.split(',')]
params = dict()
for p in args_list:
params[p[0]] = float(p[1])
c.optimizer_params = params
assert ('learning_rate' not in c.optimizer_params)
return c
def get_configs():
"""
Defines all configuration params passable to command line.
"""
configs.DEFINE_string("name", 'hpo-test', "A name for the config.")
configs.DEFINE_string("datafile", 'source-ml-data-v8-100M.dat',
"a datafile name.")
configs.DEFINE_string(
"predict_datafile", None,
"If predict_datafile is not None, use it instead of datafile for predictions"
)
configs.DEFINE_string("mse_outfile", None,
"A file to write mse values during predict phase.")
configs.DEFINE_string("scalesfile", None,
"Optional file for storing scaling params")
configs.DEFINE_string("default_gpu", '/gpu:0',
"The default GPU to use e.g., /gpu:0")
configs.DEFINE_string("nn_type", 'RNNPointEstimate', "Model type")
configs.DEFINE_string("active_field", 'active',
"Key column name header for active indicator")
configs.DEFINE_string("date_field", 'date', "Name of data column.")
configs.DEFINE_string("key_field", 'gvkey',
"Key column name header in datafile")
configs.DEFINE_string("target_field", 'oiadpq_ttm',
"Target column name header in datafile")
configs.DEFINE_string("scale_field", 'mrkcap',
"Feature to scale inputs by")
configs.DEFINE_string("financial_fields", 'saleq_ttm-ltq_mrq',
"Shared input and target field names")
configs.DEFINE_string("aux_fields", 'rel_mom1m-rel_mom9m',
"non-target, input only fields")
configs.DEFINE_string("dont_scale", None, "Names of fields to not scale")
configs.DEFINE_string("data_dir", 'datasets', "The data directory")
configs.DEFINE_string("model_dir", 'test-model', "Model directory")
configs.DEFINE_string("experiments_dir", './', "Experiments directory")
configs.DEFINE_list_string("rnn_cell", 'lstm', "lstm or gru")
configs.DEFINE_list_string("activation_fn", 'relu',
"MLP activation function in tf.nn.*")
configs.DEFINE_integer("num_inputs", -1, "")
configs.DEFINE_integer("num_outputs", -1, "")
configs.DEFINE_integer("target_idx", None, "")
configs.DEFINE_list_integer("min_unrollings", 5,
"Min number of unrolling steps")
configs.DEFINE_list_integer("max_unrollings", 5,
"Max number of unrolling steps")
configs.DEFINE_list_integer("min_years", None, "Alt to min_unrollings")
configs.DEFINE_list_integer("max_years", None, "Alt to max_unrollings")
configs.DEFINE_integer("pls_years", None,
"Alt to max_years. max_years = min_year+pls_years")
configs.DEFINE_list_integer("stride", 12,
"How many steps to skip per unrolling")
configs.DEFINE_list_integer("batch_size", 256, "Size of each batch")
configs.DEFINE_list_integer("num_layers", 2, "Numer of RNN layers")
configs.DEFINE_integer("forecast_n", 12,
"How many steps to forecast into the future")
configs.DEFINE_list_integer("num_hidden", 64,
"Number of hidden layer units")
configs.DEFINE_list_float("init_scale", 1.0, "Initial scale for weights")
configs.DEFINE_list_float("max_grad_norm", 50.0, "Gradient clipping")
configs.DEFINE_integer("start_date", 197501,
"First date to train on as YYYYMM")
configs.DEFINE_integer("end_date", 199812,
"Last date to train on as YYYYMM")
configs.DEFINE_integer("split_date", None, "Date to split train/test on.")
configs.DEFINE_boolean("train", True,
"Train model otherwise inference only")
configs.DEFINE_list_float("dropout", 0.0, "Dropout rate for hidden layers")
configs.DEFINE_list_float("recurrent_dropout", 0.3,
"Dropout rate for recurrent connections")
configs.DEFINE_boolean(
"log_squasher", True,
"Squash large normalized inputs with natural log function")
configs.DEFINE_list_string(
"data_scaler", 'RobustScaler',
'sklearn scaling algorithm or None if no scaling')
configs.DEFINE_list_string("optimizer", 'Adadelta',
'Any tensorflow optimizer in tf.train')
configs.DEFINE_list_float("learning_rate", 0.6,
"The initial starting learning rate")
configs.DEFINE_list_float("lr_decay", 0.96, "Learning rate decay")
configs.DEFINE_float("validation_size", 0.3,
"Size of validation set as %, ie. 0.3 = 30% of data")
configs.DEFINE_list_float(
"target_lambda", 0.5,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_list_float(
"rnn_lambda", 0.7,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_integer("max_epoch", 35, "Stop after max_epochs")
configs.DEFINE_integer("early_stop", 15, "Early stop parameter")
configs.DEFINE_integer("seed", 521, "Seed for deterministic training")
configs.DEFINE_boolean("UQ", False, "Uncertainty Quantification Mode")
configs.DEFINE_list_float("l2_alpha", 0.0,
"L2 regularization for weight parameters.")
configs.DEFINE_float("recurrent_l2_alpha", 0.0,
"L2 regularization for recurrent weight parameters.")
configs.DEFINE_list_boolean("huber_loss", False,
"Use huber loss instead of mse")
configs.DEFINE_list_float("huber_delta", 1.0, "delta for huber loss")
configs.DEFINE_integer("forecast_steps", 1,
"How many future predictions need to me made")
configs.DEFINE_string('forecast_steps_weights', '1.0',
'weights for the forecast steps')
configs.DEFINE_integer(
"logging_interval", 100,
"Number of batches for logging interval during training")
configs.DEFINE_boolean("write_inp_to_out_file", True,
"Write input sequence to the output files")
configs.DEFINE_string(
"training_type", 'fixed_dates',
'Choose between "fixed_dates" and "iterative" training')
configs.DEFINE_integer("member_id", 1, "Id of member in a population")
configs.DEFINE_boolean('load_saved_weights', False,
'Load weights saved in the checkpoint directory')
configs.DEFINE_integer(
"epoch_logging_interval", 1,
"Number of batches for logging interval during training")
configs.DEFINE_string('preds_fname', 'preds.dat',
'Name of the prediction file')
configs.DEFINE_integer("num_procs", 1,
"Total number of training/prediction processes")
# HPO related params
configs.DEFINE_integer("NPE", 1, "Number of Parallel Executions")
configs.DEFINE_string(
"search_algorithm", "genetic",
"Algorithm for hyper-param optimization. Select from 'genetic', 'grid_search', 'doe' "
)
configs.DEFINE_integer("generations", 5,
"Number of generations for genetic algorithm")
configs.DEFINE_integer("pop_size", 16,
"Population size for genetic algorithm")
configs.DEFINE_integer(
"num_gpu", 1, "Number of GPU on the machine, Use 0 if there are None")
configs.DEFINE_float("mutate_rate", 0.2,
"Mutation rate for genetic algorithm")
configs.DEFINE_string("objective", 'mse', "Select between mse or uq_loss")
configs.DEFINE_string("init_pop", None,
"Initial population to begin hyper param search")
configs.DEFINE_boolean("save_latest_pop", False,
"Save the latest population")
configs.DEFINE_string('doe_file', None, 'Design of experiments csv file')
configs.DEFINE_integer("decay_steps", 100000,
"Number of training steps between decay steps")
configs.DEFINE_string("initializer", 'GlorotUniform',
'variable initializers available in Keras')
configs.DEFINE_boolean(
"use_custom_init", True,
'Use RandomUniform initializer with init_scale values')
configs.DEFINE_boolean(
"aux_masking", False,
'Mask aux features of all time steps except the last one with 0')
configs.DEFINE_integer("max_norm", None, "Max Norm for kernel constraint")
configs.DEFINE_float("sgd_momentum", 0.0, "momentum for SGD optimizer")
configs.DEFINE_float("end_learning_rate", 0.01,
"end lr for polynomial decay")
configs.DEFINE_float(
'decay_power', 0.5,
'power to decay the learning rate with for polynomial decay')
configs.DEFINE_string('piecewise_lr_boundaries', None,
'boundaries for piecewise constant lr')
configs.DEFINE_string('piecewise_lr_values', None,
'values for piecewise constant lr')
configs.DEFINE_string('lr_schedule', 'ExponentialDecay',
'Learning rate scheduler')
c = configs.ConfigValues()
c.data_dir = os.path.join(_data_dir_path, c.data_dir)
c.forecast_steps_weights = [
float(x) for x in c.forecast_steps_weights.split(',')
]
return c
def get_configs():
"""
Defines all configuration params passable to command line.
"""
configs.DEFINE_string("name", 'none', "A name for the config.")
configs.DEFINE_string("datafile", 'open_dataset.dat', "a datafile name.")
configs.DEFINE_string(
"predict_datafile", None,
"If predict_datafile is not None, use it instead of datafile for predictions"
)
configs.DEFINE_string("mse_outfile", None,
"A file to write mse values during predict phase.")
configs.DEFINE_string("scalesfile", None,
"Optional file for storing scaling params")
configs.DEFINE_string(
"mse_var_outfile", None,
"A file to write mse_var values during predict phase.")
configs.DEFINE_string("default_gpu", '',
"The default GPU to use e.g., /gpu:0")
configs.DEFINE_string("nn_type", 'DeepRnnModel', "Model type")
configs.DEFINE_string("active_field", 'active',
"Key column name header for active indicator")
configs.DEFINE_string("date_field", 'date', "Name of data column.")
configs.DEFINE_string("key_field", 'gvkey',
"Key column name header in datafile")
configs.DEFINE_string("target_field", 'oiadpq_ttm',
"Target column name header in datafile")
configs.DEFINE_string("scale_field", 'mrkcap',
"Feature to scale inputs by")
configs.DEFINE_string("financial_fields", '',
"Shared input and target field names")
configs.DEFINE_string("aux_fields", None, "non-target, input only fields")
configs.DEFINE_string("dont_scale", None, "Names of fields to not scale")
configs.DEFINE_string("data_dir", '', "The data directory")
configs.DEFINE_string("model_dir", 'chkpts',
"Model (checkpoint) directory")
configs.DEFINE_string("rnn_cell", 'gru', "lstm or gru")
configs.DEFINE_string("activation_fn", 'relu',
"MLP activation function in tf.nn.*")
configs.DEFINE_integer("num_inputs", -1, "")
configs.DEFINE_integer("num_outputs", -1, "")
configs.DEFINE_integer("target_idx", None, "")
configs.DEFINE_integer("min_unrollings", None,
"Min number of unrolling steps")
configs.DEFINE_integer("max_unrollings", None,
"Max number of unrolling steps")
configs.DEFINE_integer("min_years", None, "Alt to min_unrollings")
configs.DEFINE_integer("max_years", None, "Alt to max_unrollings")
configs.DEFINE_integer("pls_years", None,
"Alt to max_years. max_years = min_year+pls_years")
# num_unrollings is being depricated, replaced with max_unrollings
configs.DEFINE_integer("num_unrollings", 4, "Number of unrolling steps")
configs.DEFINE_integer("stride", 12,
"How many steps to skip per unrolling")
configs.DEFINE_integer("forecast_n", 12,
"How many steps to forecast into the future")
configs.DEFINE_integer("batch_size", 1, "Size of each batch")
configs.DEFINE_integer("num_layers", 1, "Numer of RNN layers")
configs.DEFINE_integer("num_hidden", 10, "Number of hidden layer units")
configs.DEFINE_float("training_noise", None,
"Level of training noise as multiple of 1-stdev")
configs.DEFINE_float("init_scale", 0.1, "Initial scale for weights")
configs.DEFINE_float("max_grad_norm", 10.0, "Gradient clipping")
configs.DEFINE_integer("start_date", None,
"First date to train on as YYYYMM")
configs.DEFINE_integer("end_date", None, "Last date to train on as YYYYMM")
configs.DEFINE_integer("split_date", None, "Date to split train/test on.")
configs.DEFINE_float("keep_prob", 1.0, "Keep probability for dropout")
configs.DEFINE_boolean("train", True,
"Train model otherwise inference only")
configs.DEFINE_boolean("require_targets", False,
"Require target values for test predictions")
configs.DEFINE_boolean("input_dropout", False, "Do dropout on input layer")
configs.DEFINE_boolean("hidden_dropout", False,
"Do dropout on hidden layers")
configs.DEFINE_boolean("rnn_dropout", False,
"Do dropout on recurrent connections")
configs.DEFINE_boolean(
"skip_connections", False,
"Have a linear fully connected weight skip hidden units in MLP")
configs.DEFINE_boolean(
"direct_connections", False,
"Have direct connections between input and output in MLP")
configs.DEFINE_boolean(
"use_cache", True,
"Load data for logreg from cache (vs processing from batch generator)")
configs.DEFINE_boolean(
"pretty_print_preds", False,
"Print predictions in tabular format with inputs, targets, and keys")
configs.DEFINE_boolean(
"print_preds", False,
"Print predictions with just date, gvkey and output values")
configs.DEFINE_string(
"df_dirname", None,
"Saves dataframes for target, output, variance/variance, mse and mse_var in df_dirname"
)
configs.DEFINE_boolean("scale_targets", True, "")
configs.DEFINE_boolean(
"backfill", False,
"Backfill seq history to max_unrollings with data in first time step")
configs.DEFINE_boolean(
"log_squasher", True,
"Squash large normalized inputs with natural log function")
configs.DEFINE_boolean("ts_smoother", False,
"Use smoother on data time series during training")
configs.DEFINE_string("data_scaler", None,
'sklearn scaling algorithm or None if no scaling')
configs.DEFINE_string("optimizer", 'GradientDescentOptimizer',
'Any tensorflow optimizer in tf.train')
configs.DEFINE_string("optimizer_params", None,
'Additional optimizer params such as momentum')
configs.DEFINE_float("learning_rate", 0.6,
"The initial starting learning rate")
configs.DEFINE_float("lr_decay", 0.9, "Learning rate decay")
configs.DEFINE_float("validation_size", 0.0,
"Size of validation set as %, ie. .3 = 30% of data")
configs.DEFINE_float("train_until", 0.0,
"Train until validation MSE is less than this value")
configs.DEFINE_float("passes", 1.0, "Passes through day per epoch")
configs.DEFINE_float("target_lambda", 0.5,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_float("rnn_lambda", 0.5,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_integer("max_epoch", 0, "Stop after max_epochs")
configs.DEFINE_integer("early_stop", None, "Early stop parameter")
configs.DEFINE_integer("seed", None, "Seed for deterministic training")
configs.DEFINE_integer("cache_id", None,
"A unique experiment key for traking a cahce")
configs.DEFINE_float("keep_prob_pred", 1.0,
"Keep Prob for dropout during prediction")
configs.DEFINE_boolean(
"print_normalized_outputs", False,
"Print normalized outputs. Doesn't apply to pretty print")
configs.DEFINE_boolean("UQ", False, "Uncertainty Quantification Mode")
configs.DEFINE_string("UQ_model_type", 'MVE', "Select between MVE or PIE")
configs.DEFINE_float(
"noise_lambda", 1.0,
"Weight decay for noise in the loss function. Refer to DeepBayesUQ Model"
)
configs.DEFINE_float("l2_alpha", 0.0,
"L2 regularization for weight parameters.")
configs.DEFINE_float("picp_lambda", 1.0,
"Contribution of PICP loss term for HQPI UQ model")
configs.DEFINE_float(
"smoothing_pi_check", 100,
"Smoothing parameter for calculation of PI check in HQPI UQ model")
configs.DEFINE_float(
"confidence_alpha", 0.1,
"Alpha used for calculating confidence level (= 1 - alpha)")
configs.DEFINE_boolean("huber_loss", False,
"Use huber loss instead of mse")
configs.DEFINE_float("huber_delta", 1.0, "delta for huber loss")
c = configs.ConfigValues()
if c.min_unrollings is None:
c.min_unrollings = c.num_unrollings
if c.max_unrollings is None:
c.max_unrollings = c.num_unrollings
if c.min_years is not None:
c.min_unrollings = c.min_years * (12 // c.stride)
if c.max_years is not None:
c.max_unrollings = (c.max_years) * (12 // c.stride)
elif c.pls_years is None:
c.max_unrollings = c.min_unrollings
else:
c.max_unrollings = (c.min_years + c.pls_years) * (12 // c.stride)
# optimizer_params is a string of the form "param1=value1,param2=value2,..."
# this maps it to dictionary { param1 : value1, param2 : value2, ...}
if c.optimizer_params is None:
c.optimizer_params = dict()
else:
args_list = [p.split('=') for p in c.optimizer_params.split(',')]
params = dict()
for p in args_list:
params[p[0]] = float(p[1])
c.optimizer_params = params
assert ('learning_rate' not in c.optimizer_params)
return c
def get_configs():
"""
Defines all configuration params passable to command line.
"""
configs.DEFINE_string("name", 'test', "A name for the config.")
configs.DEFINE_string("datafile", None, "a datafile name.")
configs.DEFINE_string("scalesfile", None,
"Optional file for storing scaling params")
configs.DEFINE_string("default_gpu", '/gpu:0',
"The default GPU to use e.g., /gpu:0")
configs.DEFINE_string("nn_type", 'RNNPointEstimate', "Model type")
configs.DEFINE_string("active_field", 'active',
"Key column name header for active indicator")
configs.DEFINE_string("date_field", 'date', "Name of data column.")
configs.DEFINE_string("key_field", 'gvkey',
"Key column name header in datafile")
configs.DEFINE_string("target_field", 'oiadpq_ttm',
"Target column name header in datafile")
configs.DEFINE_string("scale_field", 'mrkcap',
"Feature to scale inputs by")
configs.DEFINE_string("financial_fields", 'saleq_ttm-ltq_mrq',
"Shared input and target field names")
configs.DEFINE_string("aux_fields", 'rel_mom1m-rel_mom9m',
"non-target, input only fields")
configs.DEFINE_string("dont_scale_fields", None,
"Names of fields to not scale")
configs.DEFINE_string("data_dir", 'datasets', "The data directory")
configs.DEFINE_string("model_dir", 'test-model', "Model directory")
configs.DEFINE_string("experiments_dir", './', "Experiments directory")
configs.DEFINE_string("rnn_cell", 'lstm', "lstm or gru")
configs.DEFINE_string("activation_fn", 'relu',
"MLP activation function in tf.nn.*")
configs.DEFINE_integer("num_inputs", -1, "")
configs.DEFINE_integer("num_outputs", -1, "")
configs.DEFINE_integer("target_idx", None, "")
configs.DEFINE_integer("min_unrollings", 5,
"Min number of unrolling steps")
configs.DEFINE_integer("max_unrollings", 5,
"Max number of unrolling steps")
configs.DEFINE_integer("min_years", None, "Alt to min_unrollings")
configs.DEFINE_integer("max_years", None, "Alt to max_unrollings")
configs.DEFINE_integer("pls_years", None,
"Alt to max_years. max_years = min_year+pls_years")
configs.DEFINE_integer("stride", 12,
"How many steps to skip per unrolling")
configs.DEFINE_integer("batch_size", 256, "Size of each batch")
configs.DEFINE_integer("num_layers", 2, "Numer of RNN layers")
configs.DEFINE_integer("forecast_n", 12,
"How many steps to forecast into the future")
configs.DEFINE_integer("num_hidden", 64, "Number of hidden layer units")
configs.DEFINE_float("init_scale", 1.0, "Initial scale for weights")
configs.DEFINE_float("max_grad_norm", 50.0, "Gradient clipping")
configs.DEFINE_integer("start_date", 197501,
"First date to train on as YYYYMM")
configs.DEFINE_integer("end_date", 199912,
"Last date to train on as YYYYMM")
configs.DEFINE_integer("split_date", None, "Date to split train/test on.")
configs.DEFINE_boolean("train", True,
"Train model otherwise inference only")
configs.DEFINE_float("dropout", 0.0, "Dropout rate for hidden layers")
configs.DEFINE_float("recurrent_dropout", 0.0,
"Dropout rate for recurrent connections")
configs.DEFINE_boolean(
"log_squasher", True,
"Squash large normalized inputs with natural log function")
configs.DEFINE_string("data_scaler", 'RobustScaler',
'sklearn scaling algorithm or None if no scaling')
configs.DEFINE_string("optimizer", 'Adadelta',
'Any tensorflow optimizer in tf.train')
configs.DEFINE_float("learning_rate", 0.6,
"The initial starting learning rate")
configs.DEFINE_float("lr_decay", 1.0,
"Learning rate decay for exponential decay")
configs.DEFINE_float("validation_size", 0.3,
"Size of validation set as %, ie. 0.3 = 30% of data")
configs.DEFINE_float("target_lambda", 0.5,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_float("rnn_lambda", 0.7,
"How much to weight last step vs. all steps in loss")
configs.DEFINE_integer("max_epoch", 1, "Stop after max_epochs")
configs.DEFINE_integer("early_stop", 1, "Early stop parameter")
configs.DEFINE_integer("seed", 521, "Seed for deterministic training")
configs.DEFINE_boolean("UQ", False, "Uncertainty Quantification Mode")
configs.DEFINE_float("l2_alpha", 0.0,
"L2 regularization for weight parameters.")
configs.DEFINE_float("recurrent_l2_alpha", 0.0,
"L2 regularization for recurrent weight parameters.")
configs.DEFINE_boolean("huber_loss", False,
"Use huber loss instead of mse")
configs.DEFINE_float("huber_delta", 1.0, "delta for huber loss")
configs.DEFINE_integer("forecast_steps", 1,
"How many future predictions need to me made")
configs.DEFINE_string('forecast_steps_weights', '1.0',
'weights for the forecast steps')
configs.DEFINE_integer(
"logging_interval", 100,
"Number of batches for logging interval during training")
configs.DEFINE_boolean("write_inp_to_out_file", True,
"Write input sequence to the output files")
configs.DEFINE_string(
"training_type", 'fixed_dates',
'Choose between "fixed_dates" and "iterative" training')
configs.DEFINE_integer("NPE", 1, "Number of Parallel Executions")
configs.DEFINE_integer("num_procs", 1,
"Total number of training/prediction processes")
configs.DEFINE_integer("num_gpu", 1, "NUmber of GPUs")
configs.DEFINE_boolean('load_saved_weights', False,
'Load weights saved in the checkpoint directory')
configs.DEFINE_integer(
"epoch_logging_interval", 1,
"Number of batches for logging interval during training")
configs.DEFINE_integer("decay_steps", 1500,
"Number of training steps between decay steps")
configs.DEFINE_string("initializer", 'GlorotUniform',
'variable initializers available in Keras')
configs.DEFINE_boolean(
"use_custom_init", True,
'Use RandomUniform initializer with init_scale values')
configs.DEFINE_boolean(
"aux_masking", False,
'Mask aux features of all time steps except the last one with 0')
configs.DEFINE_integer("max_norm", 3, "Max Norm for kernel constraint")
configs.DEFINE_float("sgd_momentum", 0.0, "momentum for SGD optimizer")
configs.DEFINE_float("end_learning_rate", 0.01,
"end lr for polynomial decay")
configs.DEFINE_float(
'decay_power', 0.5,
'power to decay the learning rate with for polynomial decay')
configs.DEFINE_string('piecewise_lr_boundaries', '4000-5500-5500',
'boundaries for piecewise constant lr')
configs.DEFINE_string('piecewise_lr_values', '0.5-0.1-0.05-0.1',
'values for piecewise constant lr')
configs.DEFINE_string('lr_schedule', 'ExponentialDecay',
'Learning rate scheduler')
configs.DEFINE_string('preds_fname', 'preds.dat',
'Name of the prediction file')
configs.DEFINE_integer("member_id", 1, "Id of member in a population")
configs.DEFINE_boolean("cdrs_inference", False,
'If the execution is for inference on CDRS data')
configs.DEFINE_string('cdrs_src_fname', 'cdrs-src.dat',
'Filename of the CDRS source file')
configs.DEFINE_string('cdrs_ml_fname', 'cdrs-ml-data.dat',
'Filename of the CDRS ML data file')
configs.DEFINE_string('model_ranking_fname', './model-ranking.dat',
'Model Ranking File Name')
configs.DEFINE_string('model_ranking_factor', 'pred_var_entval',
'Model ranking factor')
configs.DEFINE_string("cdrs_inference_date", None,
"CDRS Inference date. Format: '%Y-%m-%d' ")
c = configs.ConfigValues()
if c.min_unrollings is None:
c.min_unrollings = c.num_unrollings
if c.max_unrollings is None:
c.max_unrollings = c.num_unrollings
if c.min_years is not None:
c.min_unrollings = c.min_years * (12 // c.stride)
if c.max_years is not None:
c.max_unrollings = (c.max_years) * (12 // c.stride)
elif c.pls_years is None:
c.max_unrollings = c.min_unrollings
else:
c.max_unrollings = (c.min_years + c.pls_years) * (12 // c.stride)
c.forecast_steps_weights = [
float(x) for x in c.forecast_steps_weights.split('-')
]
c.piecewise_lr_boundaries = [
float(x) for x in c.piecewise_lr_boundaries.split('-')
]
c.piecewise_lr_values = [
float(x) for x in c.piecewise_lr_values.split('-')
]
return c
