ClinicalTrialsDriverScriptSolutionQ6.py

"""
Clinical Trials Driver Script class

Raluca Cobzaru (c) 2018

"""

from collections import namedtuple
import numpy as np
import scipy
import pandas as pd
from ClinicalTrialsModel import ClinicalTrialsModel
from ClinicalTrialsPolicySolutionQ6 import ClinicalTrialsPolicy
import matplotlib.pyplot as plt
import time

if __name__ == "__main__":
	time_total = time.time()
	np.random.seed(2345678173)
	# initializes a policy object and a model object, then runs the policy on the model
	policy_names = ['model_A', 'model_B', 'model_C', 'model_C_extension']
	state_names = ['potential_pop', 'success', 'failure', 'l_response']
	# extracts data from given data set; defines initial state
	file = 'Parameters.xlsx'
	raw_data = pd.ExcelFile(file)
	data = raw_data.parse('Parameters')
	initial_state = {'potential_pop': float(data.iat[0, 0]),
					 'success': data.iat[1, 0],
					  'failure': float(data.iat[2, 0]),
					  'l_response': float(data.iat[3, 0]),
					  'theta_stop_low': data.iat[4, 0],
					  'theta_stop_high': data.iat[5, 0],
					  'alpha': data.iat[6, 0],
					  'K': int(data.iat[7, 0]),
					  'N': int(data.iat[8, 0]),
					  'trial_size': int(data.iat[9, 0]),
					  'patient_cost': data.iat[10, 0],
					  'program_cost': data.iat[11, 0],
					  'success_rev': data.iat[12, 0],
					  'sampling_size': int(data.iat[13, 0]),
					  'enroll_min': int(data.iat[14, 0]),
					  'enroll_max': int(data.iat[15, 0]),
					  'enroll_step': int(data.iat[16, 0]),
					  'H': int(data.iat[17, 0]),
					  'true_l_response': data.iat[18, 0],
					  'true_succ_rate': data.iat[19, 0]}
	model_name = data.iat[20, 0]
	numIterations = int(data.iat[21,0])
	
	decision_names = ['enroll', 'prog_continue', 'drug_success']
	

	M = ClinicalTrialsModel(state_names, decision_names, initial_state, False)
	P = ClinicalTrialsPolicy(M, policy_names)
	t = 0
	stop = False
	policy_info = {'model_A': [-1, stop],
					'model_B': [-1, stop],
					'model_C': [-1, stop],
					'model_C_extension': [-1, stop]}

	policy_value=P.run_policy(policy_info, model_name, t)


	

	

	

	print("Total elapsed time {:.2f} secs".format(time.time()-time_total))
	
	pass