-
Notifications
You must be signed in to change notification settings - Fork 8
/
random_assignment.py
130 lines (94 loc) · 4.49 KB
/
random_assignment.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
import numpy as np
from tqdm import tqdm
from datetime import datetime
def count_pairwise_assignments(binary_matrix):
n_persons, n_tasks = binary_matrix.shape
n_pairwise_assignments = {}
n_pairwise_assignments_list = []
for person_1 in range(n_persons):
for person_2 in range(person_1 + 1, n_persons):
pairwise_tasks = [k for k in range(n_tasks) if binary_matrix[person_1, k] and binary_matrix[person_2, k]]
n_pairwise_assignments[(person_1, person_2)] = len(pairwise_tasks)
n_pairwise_assignments_list.append(len(pairwise_tasks))
avg_pairwise_assignments = np.mean(n_pairwise_assignments_list)
return n_pairwise_assignments, avg_pairwise_assignments
def main():
n_tasks = 28
n_persons = 14
tasks_per_person = 10
persons_per_task = 5
n_trials = int(1e7)
min_avg_pairwise_assignments = np.inf
max_3_count = 0
# n_valid_found = 0
# valid_assignments_list = []
for trials_id in tqdm(range(n_trials)):
person_to_n_tasks = np.zeros((n_persons,), dtype=np.ubyte)
available_persons = list(range(n_persons))
binary_matrix = np.zeros((n_persons, n_tasks), dtype=np.ubyte)
valid_found = 1
for task_id in range(n_tasks):
available_persons = [k for k in available_persons if person_to_n_tasks[k] < tasks_per_person]
n_available_persons = len(available_persons)
if n_available_persons < persons_per_task:
# print('Ran out of available_persons in task_id: {}'.format(task_id + 1))
valid_found = 0
break
person_idx = np.random.permutation(available_persons)[:persons_per_task]
for i, _idx in enumerate(person_idx):
person_to_n_tasks[_idx] += 1
binary_matrix[_idx, task_id] = 1
if not valid_found:
continue
# row_sum = np.count_nonzero(binary_matrix, axis=0)
col_sum = np.count_nonzero(binary_matrix, axis=1)
is_valid = np.all(col_sum == tasks_per_person)
if not is_valid:
continue
# is_new = all(not np.array_equal(k, binary_matrix) for k in valid_assignments_list)
# if not is_new:
# continue
#
# valid_assignments_list.append(binary_matrix)
#
# n_valid_found += 1
#
# valid_per_trial = float(n_valid_found) / (trials_id + 1)
n_pairwise_assignments, avg_pairwise_assignments = count_pairwise_assignments(binary_matrix)
n_pairwise_assignments_list = list(n_pairwise_assignments.values())
unique_values, unique_counts = np.unique(n_pairwise_assignments_list, return_counts=True)
unique_values = list(unique_values)
unique_counts = list(unique_counts)
prefix = 'binary_matrix'
curr_3_count = 0
if 3 in unique_values:
curr_3_count = unique_counts[unique_values.index(3)]
# print('\nfound new valid assignment {} in {} trials with {} valid/trial'.format(
# n_valid_found, trials_id + 1, valid_per_trial))
save = 0
if curr_3_count > max_3_count:
max_3_count = curr_3_count
prefix = 'max_3_count'
save = 1
if avg_pairwise_assignments < min_avg_pairwise_assignments:
min_avg_pairwise_assignments = avg_pairwise_assignments
prefix = 'min_avg_pairs'
unique_counts_str = '__'.join('{}-{}'.format(val, cnt) for val, cnt in zip(unique_values, unique_counts))
time_stamp = datetime.now().strftime("%y%m%d_%H%M%S")
out_fname = '{}___{}___{}.csv'.format(
prefix, unique_counts_str, time_stamp)
# out_fname = '{}_{}.csv'.format(out_fname, time_stamp)
# print('avg_pairwise_assignments: {}'.format(avg_pairwise_assignments))
# print('min_avg_pairwise_assignments: {}'.format(min_avg_pairwise_assignments))
# print('curr_3_count: {}'.format(curr_3_count))
if save:
print()
# print('\nn_pairwise_assignments: {}'.format(n_pairwise_assignments))
# print('n_pairwise_assignments_list: {}'.format(n_pairwise_assignments_list))
print('max_3_count: {}'.format(max_3_count))
print('unique_values: {}'.format(unique_values))
print('unique_counts: {}'.format(unique_counts))
print('out_fname: {}'.format(out_fname))
np.savetxt(out_fname, binary_matrix, fmt='%d', delimiter='\t')
if __name__ == '__main__':
main()