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batch_stats.py
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batch_stats.py
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#!/usr/bin/python
import os
import numpy as np
from absl import app
from absl import flags
from absl import logging
FLAGS = flags.FLAGS
flags.DEFINE_string('tcs_dir', '', '...')
flags.DEFINE_integer('limit_ntcs', 10000, '...')
flags.DEFINE_string('single', '', '...')
class Problem:
def __init__(self, problem_path: str):
logging.info(f'Loading problem from: {problem_path}')
with open(problem_path, "r") as fin:
_, _, self.nd, self.t, self.m = [int(x) for x in next(fin).split()]
self.np = int(next(fin).split()[0])
self.product_weight = [int(x) for x in next(fin).split()]
self.nw = int(next(fin).split()[0])
self.warehouse_loc = []
self.warehouse_stock = []
for w in range(self.nw):
x, y = [int(x) for x in next(fin).split()]
self.warehouse_loc.append((x, y))
self.warehouse_stock.append(
[int(x) for x in next(fin).split()])
self.no = int(next(fin).split()[0])
self.order_loc = []
self.order_request = []
order_weights = []
all_ordered = np.zeros(self.np)
for o in range(self.no):
order_weight = 0
x, y = [int(x) for x in next(fin).split()]
self.order_loc.append((x, y))
# Eat one line - the one containing # of product items
next(fin)
curr_order_request = [0 for _ in range(self.np)]
for x in next(fin).split():
curr_order_request[int(x)] += 1
order_weight += self.product_weight[int(x)]
all_ordered[int(x)] += 1
order_weights.append(order_weight / self.m)
self.order_request.append(curr_order_request)
wcovers = []
wwcovers = []
for w in range(self.nw):
can = 0
canw = 0
total = 0
totalw = 0
for p in range(self.np):
can += min(all_ordered[p], self.warehouse_stock[w][p])
canw += self.product_weight[p] * min(
all_ordered[p], self.warehouse_stock[w][p])
total += all_ordered[p]
totalw += self.product_weight[p] * all_ordered[p]
curr_cover = can / total
wcovers.append(curr_cover)
curr_wcover = canw / totalw
wwcovers.append(curr_wcover)
ms = [x / self.m for x in self.product_weight]
self.min_pws = min(ms)
self.min_wcovers = min(wcovers)
self.min_wwcovers = min(wwcovers)
self.min_owcaps = min(order_weights)
self.max_pws = max(ms)
self.max_wcovers = max(wcovers)
self.max_wwcovers = max(wwcovers)
self.max_owcaps = max(order_weights)
self.mean_pws = np.mean(ms)
self.mean_wcovers = np.mean(wcovers)
self.mean_wwcovers = np.mean(wwcovers)
self.mean_owcaps = np.mean(order_weights)
self.std_pws = np.std(ms)
self.std_wcovers = np.std(wcovers)
self.std_wwcovers = np.std(wwcovers)
self.std_owcaps = np.std(order_weights)
logging.info('Loading done.')
def main(argv):
min_pws = []
min_wcovers = []
min_wwcovers = []
min_owcaps = []
max_pws = []
max_wcovers = []
max_wwcovers = []
max_owcaps = []
mean_pws = []
mean_wcovers = []
mean_wwcovers = []
mean_owcaps = []
std_pws = []
std_wcovers = []
std_wwcovers = []
std_owcaps = []
if FLAGS.single != '':
actual_path = FLAGS.single
logging.info(f'running: {actual_path}')
prob = Problem(actual_path)
min_pws.append(prob.min_pws)
min_wcovers.append(prob.min_wcovers)
min_wwcovers.append(prob.min_wwcovers)
min_owcaps.append(prob.min_owcaps)
max_pws.append(prob.max_pws)
max_wcovers.append(prob.max_wcovers)
max_wwcovers.append(prob.max_wwcovers)
max_owcaps.append(prob.max_owcaps)
mean_pws.append(prob.mean_pws)
mean_wcovers.append(prob.mean_wcovers)
mean_wwcovers.append(prob.mean_wwcovers)
mean_owcaps.append(prob.mean_owcaps)
std_pws.append(prob.std_pws)
std_wcovers.append(prob.std_wcovers)
std_wwcovers.append(prob.std_wwcovers)
std_owcaps.append(prob.std_owcaps)
else:
info_path = os.path.join(FLAGS.tcs_dir, 'tc_info.txt')
with open(info_path, 'r') as info:
num_tc = int(info.readline())
num_tc = min(num_tc, FLAGS.limit_ntcs)
logging.info(f'num_tc={num_tc}')
for tc in range(0, num_tc):
line = info.readline().split()
in_file = line[0]
actual_path = os.path.join(FLAGS.tcs_dir, in_file)
logging.info(f'running: {actual_path}')
prob = Problem(actual_path)
min_pws.append(prob.min_pws)
min_wcovers.append(prob.min_wcovers)
min_wwcovers.append(prob.min_wwcovers)
min_owcaps.append(prob.min_owcaps)
max_pws.append(prob.max_pws)
max_wcovers.append(prob.max_wcovers)
max_wwcovers.append(prob.max_wwcovers)
max_owcaps.append(prob.max_owcaps)
mean_pws.append(prob.mean_pws)
mean_wcovers.append(prob.mean_wcovers)
mean_wwcovers.append(prob.mean_wwcovers)
mean_owcaps.append(prob.mean_owcaps)
std_pws.append(prob.std_pws)
std_wcovers.append(prob.std_wcovers)
std_wwcovers.append(prob.std_wwcovers)
std_owcaps.append(prob.std_owcaps)
print(
f'pws: {np.mean(min_pws)} {np.mean(mean_pws)} {np.mean(max_pws)} / {np.mean(std_pws)}'
)
print(
f'wcovers: {np.mean(min_wcovers)} {np.mean(mean_wcovers)} {np.mean(max_wcovers)} / {np.mean(std_wcovers)}'
)
print(
f'wwcovers: {np.mean(min_wwcovers)} {np.mean(mean_wwcovers)} {np.mean(max_wwcovers)} / {np.mean(std_wwcovers)}'
)
print(
f'owcaps: {np.mean(min_owcaps)} {np.mean(mean_owcaps)} {np.mean(max_owcaps)} / {np.mean(std_owcaps)}'
)
if __name__ == "__main__":
app.run(main)