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calc_actionability_score.py
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#!/usr/bin/env python
# coding: utf-8
import argparse
import datetime
import os
os.environ["OMP_NUM_THREADS"] = "8"
import pickle
import random
from math import inf as infinity
import numpy as np
import pandas as pd
import path_search_algorithm as p
import utils
def get_parser():
parser = argparse.ArgumentParser(description='help')
parser.add_argument('--load_dir', default='example',
help='load directory')
parser.add_argument('--mixture_components', default=2, type=int,
help='number of mixture components of surrogate model used for path planning')
parser.add_argument('-m', '--num_movable', default=5, type=int,
help='number of intervention variables')
parser.add_argument('-y', '--y_type', default='pred', choices=['pred', 'glmm'],
help='y used in path planning')
parser.add_argument('-ds', '--destination_state', default='count',
choices=['criteria', 'count'],
help='Destination state selection')
parser.add_argument('-d', '--daystamp', default='',
help='If blank, latest one would be selected')
parser.add_argument('-u', '--update_flag', default=True,
type=lambda x: (str(x).lower() == 'true'),
help='If True, p_class would be updated')
parser.add_argument('-i', '--num_iter', default=10, type=int,
help='number of baseline paths used for the calculation of actionability score')
parser.add_argument('--random_seed', default=0, type=int,
help='baseline path selection seed')
parser.add_argument('--n_bins', default=20, type=int,
help='number of bins for plot')
return parser.parse_args()
def select_load_dir(load_dir, k, num_movable, y_type, destination_state, daystamp):
'''
Args:
load_dir (str):
k (str): num of hb_class
num_movable (int): number of movable values in tree search
y_type (str): choices are [pred, glmm]
destination_state (str): choices are [node, criteria, count]
daystamp (str): daystamp of load_dir (and save_dir)
Returns:
load_dir (str): load_params dir (and save_dir)
'''
model_dir = f'./{load_dir}/'
if daystamp=='':
all_files = os.listdir(model_dir)
folders = sorted([f for f in all_files if f.startswith(f'k{k}m{num_movable}_y_{y_type}_ds_{destination_state}')])
dir_name = folders[-1]
else:
dir_name = f'k{k}m{num_movable}_y_{y_type}_ds_{destination_state}_{daystamp}'
load_dir = model_dir + dir_name + '/'
return load_dir
def main():
args = get_parser()
random.seed(args.random_seed)
load_dir = './' + args.load_dir + '/'
data_dir = load_dir + 'data/'
result_dir = load_dir + 'result/ex/'
ts_dir = select_load_dir(args.load_dir, args.mixture_components, args.num_movable, args.y_type,
args.destination_state, args.daystamp)
print('[INFO] Loading')
xy_for_exp = pickle.load(open(data_dir + 'xy_for_stan.pkl', 'rb'))
dict_emp_bayes = pickle.load(open(result_dir + f'dict_emp_bayes_{args.mixture_components}.pkl', 'rb'))
params = pickle.load(open(ts_dir + 'params.pkl', 'rb'))
model_type = pickle.load(open(data_dir + 'model_type.pkl', 'rb'))
print('[INFO] Done')
df_result = pd.DataFrame(columns=['min_distance'] + [f'rand_distance_{i}' for i in range(args.num_iter)],
index=params['list_pp_i_idx'])
list_detour = []
list_line = []
list_noresult = []
for pp_i in params['list_pp_i_idx']:
pp_dir = ts_dir + f'i_{pp_i}/'
my_check = not os.path.isdir(pp_dir)
if my_check:
print(f'[INFO] pp_i: {pp_i} has no results.')
list_noresult.append(pp_i)
continue
destination_node = pickle.load(open(pp_dir+'destination_node.pkl', 'rb'))
summary = open(pp_dir+'summary.txt').read()
num_step = len([line for line in summary.splitlines() if 'step' in line]) -1
df_result.loc[pp_i, 'min_distance'] = destination_node.tentative_distance
if num_step==abs(np.array(destination_node.r_x)).max():
list_line.append(pp_i)
elif num_step==abs(np.array(destination_node.r_x)).sum():
pass
else:
list_detour.append(pp_i)
destination_abs = abs(np.array(destination_node.r_x))
list_move = []
for j in range(len(destination_abs)):
for _ in range(destination_abs[j]):
np_move = np.zeros(len(destination_abs), dtype=int)
if destination_node.r_x[j] > 0:
np_move[j] = 1
else:
np_move[j] = -1
list_move.append(tuple(np_move))
for k in range(args.num_iter):
random.shuffle(list_move)
initial_x_coords = p.InitialXCoords(xy_for_exp, pp_i)
initial_r_x = tuple(np.zeros(len(xy_for_exp.x_reg.name), dtype=int))
initial_node = p.Node(initial_x_coords, initial_r_x)
initial_node.x_fixed.set_fixed(initial_node,
fixed_cont=params['fixed_cont'],
fixed_disc_reg=params['fixed_disc_reg'],
fixed_disc=params['fixed_disc'],
fixed_zero_poi=params['fixed_zero_poi'])
p_class = initial_node.calc_p_class(args.update_flag, dict_emp_bayes)
initial_node.set_y_and_class_lp(xy_for_exp.model, params['y_type'],
params['k_class'], xy_for_exp,
dict_emp_bayes, params['sigma_y'],
p_class, model_type=model_type)
initial_node.tentative_distance = 0
current_node = initial_node
for i in range(len(list_move)):
n_r_x = tuple(np.array(current_node.r_x) + np.array(list_move[i]))
n_node = p.Node(initial_node.x, n_r_x, params['step'])
n_node.set_y_and_class_lp(xy_for_exp.model, params['y_type'], params['k_class'],
xy_for_exp, dict_emp_bayes, params['sigma_y'], p_class, model_type=model_type)
n_node.set_neg_logprob()
new_tentative_distance = current_node.tentative_distance + current_node.distance_to(n_node)
n_node.tentative_distance = new_tentative_distance
current_node = n_node
# brief check
if destination_node.r_x!=current_node.r_x:
print('[ERROR] arrived r_x is different from destination.')
df_result.loc[pp_i, f'rand_distance_{k}'] = current_node.tentative_distance
# calc actionability score
dif_distance = utils.calc_dif_distance(df_result)
# plot actionability score
utils.plot_dif_distance(dif_distance, ts_dir, args.n_bins, 0)
df_actionability_score = pd.DataFrame(dif_distance, columns=['Actionability score'], index=df_result.index)
# output actionability score
df_actionability_score.to_csv(ts_dir + 'df_actionability_score.csv')
# output_summary
with open(ts_dir+'distance_summary.txt', mode='w') as f:
f.write('Path-planned instances: {}\n'.format(len(params['list_pp_i_idx'])))
f.write('Detour instances: {}\n'.format(len(list_detour)))
f.write('Straight instances: {}\n'.format(len(list_line)))
f.write('Initial and destination consistent instances: {}\n'.format(len(list_noresult)))
f.close()
if __name__ == '__main__':
main()