-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_alarm2_optimized_threshold_compensation.py
153 lines (125 loc) · 8.01 KB
/
test_alarm2_optimized_threshold_compensation.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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
import EncoderFactory
from DatasetManager import DatasetManager
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score, precision_recall_fscore_support, confusion_matrix
from sklearn.pipeline import FeatureUnion
import time
import os
import sys
import csv
from sys import argv
import pickle
from conf_constant_costfunctions import get_constant_costfunctions
def calculate_cost(x, costs):
return costs[int(x['prediction']), int(x['actual'])](x)
def calculate_cost_baseline(x, costs):
return costs[0, int(x['actual'])](x)
dataset_name = argv[1]
predictions_dir = argv[2]
conf_threshold_dir = argv[3]
results_dir = argv[4]
method = "alarm2"
# create results directory
if not os.path.exists(os.path.join(results_dir)):
os.makedirs(os.path.join(results_dir))
# load predictions
dt_preds = pd.read_csv(os.path.join(predictions_dir, "preds_%s.csv" % dataset_name), sep=";")
# write results to file
out_filename = os.path.join(results_dir, "results_%s_%s.csv" % (dataset_name, method))
# set nonomonotic constants
aConst, bConst, cConst, dConst, eConst, fConst = get_constant_costfunctions(dataset_name)
with open(out_filename, 'w') as fout:
writer = csv.writer(fout, delimiter=';', quotechar='', quoting=csv.QUOTE_NONE)
writer.writerow(["dataset", "method", "metric", "value", "c_miss", "c_action", "c_postpone", "c_com", "early_type",
"threshold"])
cost_weights = [(10, 1), (10, 2), (10, 3), (10, 4), (10, 5)]
c_com_weights = [1, 2, 3, 4, 5, 10, 20, 30, 40]
c_postpone_weight = 0
for c_miss_weight, c_action_weight in cost_weights:
for c_com_weight in c_com_weights:
for early_type in ["const"]:
c_miss = c_miss_weight / (c_miss_weight + c_action_weight + c_com_weight)
c_action = c_action_weight / (c_miss_weight + c_action_weight + c_com_weight)
c_com = c_com_weight / (c_miss_weight + c_action_weight + c_com_weight)
c_action = c_action * 1.2
c_com = c_com * 0.5
costs = np.matrix([[lambda x: 0,
lambda x: c_miss],
[lambda x: c_action + c_com,
lambda x: c_action
]])
# load the optimal confidence threshold
conf_file = os.path.join(conf_threshold_dir, "optimal_confs_%s_%s_%s_%s_%s_%s.pickle" % (
dataset_name, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight, early_type))
with open(conf_file, "rb") as fin:
pickle_file = pickle.load(fin)
conf_threshold = pickle_file['conf_threshold']
cost_training = pickle_file['cost_training']
# trigger alarms according to conf_threshold
dt_final = pd.DataFrame()
unprocessed_case_ids = set(dt_preds.case_id.unique())
for nr_events in range(1, dt_preds.prefix_nr.max() + 1):
tmp = dt_preds[(dt_preds.case_id.isin(unprocessed_case_ids)) & (dt_preds.prefix_nr == nr_events)]
tmp = tmp[tmp.predicted_proba >= conf_threshold]
tmp["prediction"] = 1
dt_final = pd.concat([dt_final, tmp], axis=0)
unprocessed_case_ids = unprocessed_case_ids.difference(tmp.case_id)
tmp = dt_preds[(dt_preds.case_id.isin(unprocessed_case_ids)) & (dt_preds.prefix_nr == 1)]
tmp["prediction"] = 0
dt_final = pd.concat([dt_final, tmp], axis=0)
case_lengths = dt_preds.groupby("case_id").prefix_nr.max().reset_index()
case_lengths.columns = ["case_id", "case_length"]
dt_final = dt_final.merge(case_lengths)
# calculate precision, recall etc.
prec, rec, fscore, _ = precision_recall_fscore_support(dt_final.actual, dt_final.prediction,
pos_label=1, average="binary")
tn, fp, fn, tp = confusion_matrix(dt_final.actual, dt_final.prediction).ravel()
# calculate earliness based on the "true alarms" only
tmp = dt_final[(dt_final.prediction == 1) & (dt_final.actual == 1)]
earliness = (1 - ((tmp.prefix_nr - 1) / tmp.case_length))
tmp = dt_final[(dt_final.prediction == 1)]
earliness_alarms = (1 - ((tmp.prefix_nr - 1) / tmp.case_length))
writer.writerow([dataset_name, method, "prec", prec, c_miss_weight, c_action_weight, c_postpone_weight,
c_com_weight, early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "rec", rec, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight,
early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "fscore", fscore, c_miss_weight, c_action_weight, c_postpone_weight,
c_com_weight, early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "tn", tn, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight,
early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "fp", fp, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight,
early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "fn", fn, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight,
early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "tp", tp, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight,
early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "cost_training", cost_training, c_miss_weight, c_action_weight, c_postpone_weight, c_com_weight,
early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "earliness_mean", earliness.mean(), c_miss_weight, c_action_weight,
c_postpone_weight, c_com_weight, early_type, conf_threshold])
writer.writerow([dataset_name, method, "earliness_std", earliness.std(), c_miss_weight, c_action_weight,
c_postpone_weight, c_com_weight, early_type, conf_threshold])
writer.writerow([dataset_name, method, "earliness_alarms_mean", earliness_alarms.mean(), c_miss_weight,
c_action_weight, c_postpone_weight, c_com_weight, early_type, conf_threshold])
writer.writerow([dataset_name, method, "earliness_alarms_std", earliness_alarms.std(), c_miss_weight,
c_action_weight, c_postpone_weight, c_com_weight, early_type, conf_threshold])
cost = dt_final.apply(calculate_cost, costs=costs, axis=1).sum()
writer.writerow([dataset_name, method, "cost", cost, c_miss_weight, c_action_weight, c_postpone_weight,
c_com_weight, early_type, conf_threshold])
writer.writerow([dataset_name, method, "cost_avg", cost / len(dt_final), c_miss_weight, c_action_weight,
c_postpone_weight, c_com_weight, early_type, conf_threshold])
cost_baseline = dt_final.apply(calculate_cost_baseline, costs=costs, axis=1).sum()
writer.writerow([dataset_name, method, "cost_baseline", cost_baseline, c_miss_weight, c_action_weight,
c_postpone_weight, c_com_weight, early_type, conf_threshold])
writer.writerow(
[dataset_name, method, "cost_avg_baseline", cost_baseline / len(dt_final), c_miss_weight,
c_action_weight, c_postpone_weight, c_com_weight, early_type, conf_threshold])