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Incremental_predictive_monitoring_of_Business_Processes_with_a_priori_knowledge
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baseline_1_cf.py
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baseline_1_cf.py
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"""
this script takes as input the LSTM or RNN weights found by train.py
change the path in the shared_variables.py to point to the h5 file
with LSTM or RNN weights generated by train.py
Author: Niek Tax
"""
from __future__ import division
import csv
import os
import time
from datetime import timedelta
from itertools import izip
import distance
import numpy as np
from keras.models import load_model
from sklearn import metrics
import shared_variables
from evaluation.prepare_data_resource import select_declare_verified_traces, prepare_testing_data
def run_experiments(server_replayer, log_name, models_folder, fold):
model_filename = shared_variables.extract_last_model_checkpoint(log_name, models_folder, fold, 'CF')
declare_model_filename = shared_variables.extract_declare_model_filename(log_name)
log_settings_dictionary = shared_variables.log_settings[log_name]
prefix_size_pred_from = log_settings_dictionary['prefix_size_pred_from']
prefix_size_pred_to = log_settings_dictionary['prefix_size_pred_to']
start_time = time.time()
# prepare the data N.B. maxlen == predict_size
lines, \
lines_id, \
lines_group, \
lines_t, \
lines_t2, \
lines_t3, \
lines_t4, \
maxlen, \
chars, \
chars_group, \
char_indices, \
char_indices_group, \
divisor, \
divisor2, \
divisor3, \
predict_size, \
target_indices_char, \
target_indices_char_group, \
target_char_indices, \
target_char_indices_group = prepare_testing_data(log_name)
# load model, set this to the model generated by train.py
model = load_model(model_filename)
# define helper functions
# this one encodes the current sentence into the onehot encoding
def encode(sentence, times_enc, times3_enc, maxlen_enc=maxlen):
num_features = len(chars) + 5
x = np.zeros((1, maxlen_enc, num_features), dtype=np.float32)
leftpad = maxlen_enc - len(sentence)
times2_enc = np.cumsum(times_enc)
for v, char in enumerate(sentence):
midnight_enc = times3_enc[v].replace(hour=0, minute=0, second=0, microsecond=0)
timesincemidnight_enc = times3_enc[v] - midnight_enc
for c in chars:
if c == char:
x[0, v + leftpad, char_indices[c]] = 1
x[0, v + leftpad, len(chars)] = v + 1
x[0, v + leftpad, len(chars) + 1] = times_enc[v] / divisor
x[0, v + leftpad, len(chars) + 2] = times2_enc[v] / divisor2
x[0, v + leftpad, len(chars) + 3] = timesincemidnight_enc.seconds / 86400
x[0, v + leftpad, len(chars) + 4] = times3_enc[v].weekday() / 7
return x
# modify to be able to get second best prediction
def get_symbol(predictions, jth_best=0):
j = np.argsort(predictions)[len(predictions) - jth_best - 1]
return target_indices_char[j]
one_ahead_gt = []
one_ahead_pred = []
folder_path = shared_variables.outputs_folder + models_folder + '/' + str(fold) + '/results/baseline/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
output_filename = folder_path + '%s_%s.csv' % (log_name, 'CF')
with open(output_filename, 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(["Prefix length", "Ground truth", "Predicted", "Damerau-Levenshtein", "Jaccard",
"Ground truth times", "Predicted times", "RMSE", "MAE", "Median AE"])
for prefix_size in range(prefix_size_pred_from, prefix_size_pred_to):
print("prefix size: " + str(prefix_size))
curr_time = time.time()
lines_s, \
lines_id_s, \
lines_group_s, \
lines_t_s, \
lines_t2_s, \
lines_t3_s, \
lines_t4_s = select_declare_verified_traces(server_replayer,
declare_model_filename,
lines,
lines_id,
lines_group,
lines_t,
lines_t2,
lines_t3,
lines_t4,
prefix_size)
print("formulas verified: " + str(len(lines_s)) + " out of : " + str(len(lines)))
print('elapsed_time:', time.time() - curr_time)
for line, times, times2, times3 in izip(lines_s, lines_t_s, lines_t2_s, lines_t3_s):
times.append(0)
cropped_line = ''.join(line[:prefix_size])
cropped_times = times[:prefix_size]
cropped_times3 = times3[:prefix_size]
if len(times2) < prefix_size:
continue # make no prediction for this case, since this case has ended already
ground_truth = ''.join(line[prefix_size:prefix_size + predict_size])
ground_truth_t = times2[prefix_size - 1]
case_end_time = times2[len(times2) - 1]
ground_truth_t = case_end_time - ground_truth_t
predicted = ''
total_predicted_time = 0
for i in range(predict_size):
enc = encode(cropped_line, cropped_times, cropped_times3)
y = model.predict(enc, verbose=0) # make predictions
# split predictions into separate activity and time predictions
y_char = y[0][0]
y_t = y[1][0][0]
prediction = get_symbol(y_char) # undo one-hot encoding
cropped_line += prediction
if y_t < 0:
y_t = 0
cropped_times.append(y_t)
# end of case was just predicted, therefore, stop predicting further into the future
if prediction == '!':
one_ahead_pred.append(total_predicted_time)
one_ahead_gt.append(ground_truth_t)
# print('! predicted, end case')
break
y_t = y_t * divisor3
cropped_times3.append(cropped_times3[-1] + timedelta(seconds=y_t))
total_predicted_time = total_predicted_time + y_t
predicted += prediction
output = []
if len(ground_truth) > 0:
output.append(prefix_size)
output.append(unicode(ground_truth).encode("utf-8"))
output.append(unicode(predicted).encode("utf-8"))
output.append(1 - distance.nlevenshtein(predicted, ground_truth))
output.append(1 - distance.jaccard(predicted, ground_truth))
output.append(ground_truth_t)
output.append(total_predicted_time)
output.append('')
output.append(metrics.mean_absolute_error([ground_truth_t], [total_predicted_time]))
output.append(metrics.median_absolute_error([ground_truth_t], [total_predicted_time]))
spamwriter.writerow(output)
print("TIME TO FINISH --- %s seconds ---" % (time.time() - start_time))