import logging import os import tensorflow as tf import seaborn as sns sns.set() ROOT_FOLDER = configs.ROOT_FOLDER MODEL_ROOT = configs.MODEL_ROOT expt_name = "treatment_effects" # EDIT ME ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Specify parameters for model to load - optimal set from paper listed action_inputs_only = configs.load_optimal_parameters( 'treatment_rnn_action_inputs_only', expt_name, add_net_name=True) action_w_trajectory_inputs = configs.load_optimal_parameters('treatment_rnn', expt_name, add_net_name=True) censor_w_action_inputs_only = configs.load_optimal_parameters( 'censor_rnn_action_inputs_only', expt_name, add_net_name=True) censor_w_trajectory_inputs = configs.load_optimal_parameters('censor_rnn', expt_name, add_net_name=True) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if __name__ == "__main__": logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
ROOT_FOLDER = configs.ROOT_FOLDER MODEL_ROOT = configs.MODEL_ROOT RESULTS_FOLDER = configs.RESULTS_FOLDER expt_name = "treatment_effects" logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO) # EDIT ME! ###################################################################################### # Defines specific parameters to train for - otherwise runs full hyperparameter optimisation decoder_specifications = {} # Optimal encoder to load for decoder training # - This allows for states from the encoder to be dumped, and decoder is intialised with them encoder_specifications = { 'rnn_propensity_weighted': configs.load_optimal_parameters('rnn_propensity_weighted', expt_name) } # Specify which networks to train - only use R-MSN by default. Full list in activation map net_names = ['rnn_propensity_weighted'] ################################################################################################## # Data processing Functions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ def process_seq_data( data_map, states, projection_horizon=5, num_features_to_include=1e6): # forecast 10 years into the future def _check_shapes(array1, array2, dims):
import seaborn as sns sns.set() ROOT_FOLDER = configs.ROOT_FOLDER MODEL_ROOT = configs.MODEL_ROOT RESULTS_FOLDER = configs.RESULTS_FOLDER # Default params: expt_name = "treatment_effects" # EDIT ME! ###################################################################################### # Optimal network parameters to load for testing! configs = [ configs.load_optimal_parameters('rnn_propensity_weighted', expt_name, add_net_name=True) ] ################################################################################################## if __name__ == "__main__": logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO) # Setup tensorflow tf_device = 'gpu' if tf_device == "cpu": tf_config = tf.ConfigProto(log_device_placement=False, device_count={'GPU': 0}) else: