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Copy pathtest_2_thresholds_set_prefixes.py
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182 lines (152 loc) · 9.6 KB
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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
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)
def get_min_nonmonotonic(dataSetName):
if str.startswith(dataSetName, "traffic_fines"):
return 3
elif str.startswith(dataSetName, "bpic2017"):
return 18
def get_max_nonmonotonic(dataSetName):
if str.startswith(dataSetName,"traffic_fines"):
return 2
elif str.startswith(dataSetName,"bpic2017"):
return 12
dataset_name = argv[1]
predictions_dir = argv[2]
conf_threshold_dir = argv[3]
results_dir = argv[4]
method = "prefix_threshold"
# 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-threshold
nonmonotonic_threshold = get_min_nonmonotonic(dataset_name)
max_nonmonotonic_threshold = get_max_nonmonotonic(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 = [0, 1, 2, 3, 4, 5, 10, 20]
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", "linear","nonmonotonic"]:
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)
if early_type == "linear":
costs = np.matrix([[lambda x: 0,
lambda x: c_miss],
[lambda x: c_action * (x['prefix_nr'] - 1) / x['case_length'] + c_com,
lambda x: c_action * (x['prefix_nr'] - 1) / x['case_length'] + (
x['prefix_nr'] - 1) / x['case_length'] * c_miss
]])
elif early_type == "nonmonotonic":
costs = np.matrix([[lambda x: 0,
lambda x: c_miss],
[lambda x: (c_action * (
1 - min(x['prefix_nr'], nonmonotonic_threshold) / x[
'case_length'])) + (c_com * (
1 - x['prefix_nr'] / x['case_length'])),
lambda x: (c_action * (
1 - min(x['prefix_nr'], nonmonotonic_threshold) / x[
'case_length'])) + (max((x['prefix_nr'] - 1) / x['case_length'],min(max_nonmonotonic_threshold/ x['case_length'],1)) * c_miss)
]])
else:
costs = np.matrix([[lambda x: 0,
lambda x: c_miss],
[lambda x: c_action + c_com,
lambda x: c_action + (x['prefix_nr'] - 1) / x['case_length'] * c_miss
]])
# 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:
conf = pickle.load(fin)
conf_thresholds = conf['conf_threshold']
# 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):
if nr_events == 1:
conf_threshold = conf_thresholds[0]
else:
conf_threshold = conf_thresholds[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, "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])