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optimize_2_thresholds_prefix_set_second_prefix.py
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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
from sklearn.pipeline import FeatureUnion
import time
import os
import sys
from sys import argv
import pickle
import csv
from hyperopt import Trials, STATUS_OK, tpe, fmin, hp
import hyperopt
from multiprocessing import Process as Process
from conf_constant_costfunctions import get_constant_costfunctions
def calculate_cost(x, costs):
return costs[int(x['prediction']), int(x['actual'])](x)
def evaluate_model_cost(args):
conf_thresholds = args['conf_threshold']
c_action = args['c_action']
c_miss = args['c_miss']
c_com = args['c_com']
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']) / 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'], aConst) / bConst)) + (c_com * (
1 - min(x['prefix_nr'], cConst) / dConst)),
lambda x: (c_action * (
1 - min(x['prefix_nr'], aConst) / bConst)) + (1 - max(min(eConst,x['prefix_nr'])/fConst,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
]])
# 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)
cost = dt_final.apply(calculate_cost, costs=costs, axis=1).sum()
return {'loss': cost, 'status': STATUS_OK, 'model': dt_final}
def run_experiment(c_miss_weight, c_action_weight, c_com_weight, early_type):
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)
conf_thresholds = []
for i in range(2):
string_conf_threshold = "conf_threshold" + str(i)
conf_thresholds.append(hp.uniform(string_conf_threshold, 0, 1))
space = {'conf_threshold': conf_thresholds,
'c_action': c_action,
'c_miss': c_miss,
'c_com': c_com}
trials = Trials()
best = fmin(evaluate_model_cost, space, algo=tpe.suggest, max_evals=150, trials=trials)
best_params = hyperopt.space_eval(space, best)
outfile = os.path.join(params_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))
# write to file
with open(outfile, "wb") as fout:
print(outfile)
print(repr(best_params))
pickle.dump(best_params, fout)
print('Preparing data...')
start = time.time()
dataset_name = argv[1]
preds_dir = argv[2]
params_dir = argv[3]
# create output directory
if not os.path.exists(os.path.join(params_dir)):
os.makedirs(os.path.join(params_dir))
# read the data
dataset_manager = DatasetManager(dataset_name)
# prepare the dataset
dt_preds = pd.read_csv(os.path.join(preds_dir, "preds_val_%s.csv" % dataset_name), sep=";")
#set nonomonotic constants
aConst, bConst, cConst, dConst, eConst, fConst = get_constant_costfunctions(dataset_name)
print('Optimizing parameters...')
processes = []
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"]:
p = Process(target=run_experiment, args=(c_miss_weight, c_action_weight, c_com_weight, early_type))
p.start()
processes.append(p)
print(str(early_type))
for p in processes:
p.join()