def run_experiments(log_identificator, formula_type):
eventlog, path_to_model_file, beam_size, \
prefix_size_pred_from, prefix_size_pred_to, formula = activateSettings(log_identificator, formula_type)
current_path = os.path.abspath(getsourcefile(lambda: 0))
current_dir = os.path.dirname(current_path)
parent_dir = current_dir[:current_dir.rfind(os.path.sep)]
sys.path.insert(0, parent_dir)
start_time = time.time()
lines, lines_t, lines_t2, lines_t3, maxlen, chars, char_indices, divisor, divisor2, \
divisor3, predict_size, target_indices_char, target_char_indices = prepare_testing_data(eventlog)
# find cycles and modify the probability functionality goes here
stop_symbol_probability_amplifier_current = 1
# modify to be able to get second best prediction
def getSymbol(predictions, ith_best=0):
predictions[
0] = predictions[0] * stop_symbol_probability_amplifier_current
i = np.argsort(predictions)[len(predictions) - ith_best - 1]
return target_indices_char[i]
one_ahead_gt = []
one_ahead_pred = []
# load model, set this to the model generated by train.py
model = load_model(path_to_model_file)
stop_symbol_probability_amplifier_current = 1
# make predictions
with open(
'output_files/results/' + formula_type +
'/suffix_and_remaining_time2_%s' % eventlog, 'wb') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow([
"Prefix length", "Groud truth", "Predicted", "Levenshtein",
"Damerau", "Jaccard", "Ground truth times", "Predicted times",
"RMSE", "MAE", "Median AE"
])
for prefix_size in range(prefix_size_pred_from, prefix_size_pred_to):
# here we checkout the prefixes with formulas verified only on the suffix phase
lines_s, lines_t_s, lines_t2_s, lines_t3_s = selectFormulaVerifiedTraces(
lines, lines_t, lines_t2, lines_t3, formula, prefix_size)
print("prefix size: " + str(prefix_size))
print("formulas verifited: " + str(len(lines_s)) + " out of : " +
str(len(lines)))
for line, times, times2, times3 in zip(lines_s, lines_t_s,
lines_t2_s, lines_t3_s):
prediction_end_reached = False
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
# initialize root of the tree for beam search
total_predicted_time_initialization = 0
search_tree_root = MultileafTree(
beam_size,
encode(cropped_line, cropped_times, cropped_times3, maxlen,
chars, char_indices, divisor, divisor2),
cropped_line, total_predicted_time_initialization)
prediction_end_reached = False
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 = ''
for i in range(predict_size):
# here we will take data from the node in the tree used to prun
enc = search_tree_root.data # encode(cropped_line, cropped_times, cropped_times3)
y = model.predict(enc, verbose=0) # make predictions
# split predictions into seperate activity and time predictions
y_char = y[0][0]
y_t = y[1][0][0]
stop_symbol_probability_amplifier_current, \
start_of_the_cycle_symbol = amplify(search_tree_root.cropped_line)
# cropped_line += prediction
if y_t < 0:
y_t = 0
# TOO not normalizing here seems like a bug
cropped_times.append(y_t)
ma = False
for i in range(beam_size):
prediction = getSymbolAmpl(
y_char, target_indices_char, target_char_indices,
start_of_the_cycle_symbol,
stop_symbol_probability_amplifier_current, i)
# end of case was just predicted, therefore, stop predicting further into the future
if prediction == '!':
if verify_formula_as_compliant(
search_tree_root.cropped_line, formula,
prefix_size):
one_ahead_pred.append(
search_tree_root.total_predicted_time)
one_ahead_gt.append(ground_truth_t)
print('! predicted, end case')
ma = True
break
# else:
# prediction_end_reached = True;
if ma:
break
# if the end of prediction was not reached we continue as always, and then function :choose_next_
# top_descendant: will earch for future prediction
# in not reached, function :choose_next_top_descendant: will backtrack
y_t = y_t * divisor3
if not prediction_end_reached:
cropped_times3.append(cropped_times3[-1] +
timedelta(seconds=y_t))
for i in range(beam_size):
temp_prediction = getSymbolAmpl(
y_char, target_indices_char,
target_char_indices, start_of_the_cycle_symbol,
stop_symbol_probability_amplifier_current, i)
if temp_prediction == '!':
continue
temp_cropped_line = search_tree_root.cropped_line + temp_prediction
# this means that we found the end in one of the alternatives.
temp_total_predicted_time = search_tree_root.total_predicted_time + y_t
temp_state_data = encode(temp_cropped_line,
cropped_times,
cropped_times3, maxlen,
chars, char_indices,
divisor, divisor2)
search_tree_root.descendants[i] = MultileafTree(
beam_size, temp_state_data, temp_cropped_line,
temp_total_predicted_time, search_tree_root)
search_tree_root = search_tree_root.choose_next_top_descendant(
)
if prediction_end_reached:
prediction_end_reached = False
if search_tree_root is None:
print(
"Cannot find any trace that is compliant with formula given current beam size"
)
break
output = []
if search_tree_root is None:
predicted = u""
total_predicted_time = 0
else:
predicted = (search_tree_root.cropped_line[prefix_size:])
total_predicted_time = search_tree_root.total_predicted_time
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))
dls = 1 - (damerau_levenshtein_distance(
unicode(predicted), unicode(ground_truth)) /
max(len(predicted), len(ground_truth)))
if dls < 0:
dls = 0
# we encountered problems with Damerau-Levenshtein Similarity on some linux machines where the
# default character encoding of the operating system caused it to be negative, this should never
# be the case
output.append(dls)
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))
def runExperiments(logIdentificator, formulaType):
eventlog, path_to_model_file, beam_size, \
prefix_size_pred_from, prefix_size_pred_to, formula = activateSettings(logIdentificator, formulaType)
start_time = time.time()
csvfile = open('../data/%s' % eventlog, 'r')
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
next(spamreader, None) # skip the headers
lastcase = ''
line = ''
firstLine = True
lines = []
timeseqs = [] # relative time since previous event
timeseqs2 = [] # relative time since case start
timeseqs3 = [] # absolute time of previous event
times = []
times2 = []
times3 = []
numlines = 0
casestarttime = None
lasteventtime = None
for row in spamreader:
t = time.strptime(row[2], "%Y-%m-%d %H:%M:%S")
if row[0]!=lastcase:
casestarttime = t
lasteventtime = t
lastcase = row[0]
if not firstLine:
lines.append(line)
timeseqs.append(times)
timeseqs2.append(times2)
timeseqs3.append(times3)
line = ''
times = []
times2 = []
times3 = []
numlines+=1
line+= getUnicode_fromInt(row[1])
timesincelastevent = datetime.fromtimestamp(time.mktime(t))-datetime.fromtimestamp(time.mktime(lasteventtime))
timesincecasestart = datetime.fromtimestamp(time.mktime(t))-datetime.fromtimestamp(time.mktime(casestarttime))
midnight = datetime.fromtimestamp(time.mktime(t)).replace(hour=0, minute=0, second=0, microsecond=0)
timesincemidnight = datetime.fromtimestamp(time.mktime(t))-midnight
timediff = 86400 * timesincelastevent.days + timesincelastevent.seconds
timediff2 = 86400 * timesincecasestart.days + timesincecasestart.seconds
times.append(timediff)
times2.append(timediff2)
times3.append(datetime.fromtimestamp(time.mktime(t)))
lasteventtime = t
firstLine = False
# add last case
lines.append(line)
timeseqs.append(times)
timeseqs2.append(times2)
timeseqs3.append(times3)
numlines+=1
divisor = np.mean([item for sublist in timeseqs for item in sublist])
print('divisor: {}'.format(divisor))
divisor2 = np.mean([item for sublist in timeseqs2 for item in sublist])
print('divisor2: {}'.format(divisor2))
divisor3 = np.mean(map(lambda x: np.mean(map(lambda y: x[len(x)-1]-y, x)), timeseqs2))
print('divisor3: {}'.format(divisor3))
elems_per_fold = int(round(numlines/3))
fold1and2lines = lines[:2*elems_per_fold]
step = 1
sentences = []
softness = 0
next_chars = []
fold1and2lines = map(lambda x: x+'!',fold1and2lines)
maxlen = max(map(lambda x: len(x),fold1and2lines))
chars = map(lambda x : set(x),fold1and2lines)
chars = list(set().union(*chars))
chars.sort()
target_chars = copy.copy(chars)
chars.remove('!')
print('total chars: {}, target chars: {}'.format(len(chars), len(target_chars)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
target_char_indices = dict((c, i) for i, c in enumerate(target_chars))
target_indices_char = dict((i, c) for i, c in enumerate(target_chars))
print(indices_char)
#we only need the third fold, because first two were used for training
fold3 = lines[2*elems_per_fold:]
fold3_t = timeseqs[2*elems_per_fold:]
fold3_t2 = timeseqs2[2*elems_per_fold:]
fold3_t3 = timeseqs3[2*elems_per_fold:]
lines = fold3
lines_t = fold3_t
lines_t2 = fold3_t2
lines_t3 = fold3_t3
# set parameters
predict_size = maxlen
# load model, set this to the model generated by train.py
model = load_model(path_to_model_file)
# define helper functions
#this one encodes the current sentence into the onehot encoding
def encode(sentence, times, times3, maxlen=maxlen):
num_features = len(chars)+5
X = np.zeros((1, maxlen, num_features), dtype=np.float32)
leftpad = maxlen-len(sentence)
times2 = np.cumsum(times)
for t, char in enumerate(sentence):
midnight = times3[t].replace(hour=0, minute=0, second=0, microsecond=0)
timesincemidnight = times3[t]-midnight
multiset_abstraction = Counter(sentence[:t+1])
for c in chars:
if c==char:
X[0, t+leftpad, char_indices[c]] = 1
X[0, t+leftpad, len(chars)] = t+1
X[0, t+leftpad, len(chars)+1] = times[t]/divisor
X[0, t+leftpad, len(chars)+2] = times2[t]/divisor2
X[0, t+leftpad, len(chars)+3] = timesincemidnight.seconds/86400
X[0, t+leftpad, len(chars)+4] = times3[t].weekday()/7
return X
#find cycles and modify the probability functionality goes here
stop_symbol_probability_amplifier_current = 1
start_of_the_cycle_symbol = " "
one_ahead_gt = []
one_ahead_pred = []
two_ahead_gt = []
two_ahead_pred = []
three_ahead_gt = []
three_ahead_pred = []
with open('output_files/results/'+formulaType+'/suffix_and_remaining_time1_%s' % eventlog, 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(["Prefix length", "Groud truth", "Predicted", "Levenshtein", "Damerau", "Jaccard", "Ground truth times", "Predicted times", "RMSE", "MAE", "Median AE"])
for prefix_size in range(prefix_size_pred_from, prefix_size_pred_to):
lines_s, lines_t_s, lines_t2_s, lines_t3_s = selectFormulaVerifiedTraces(lines, lines_t, lines_t2, lines_t3,
formula, prefix_size)
print("prefix size: " + str(prefix_size))
print("formulas verifited: " + str(len(lines_s)) + " out of : " + str(len(lines)))
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 seperate activity and time predictions
y_char = y[0][0]
y_t = y[1][0][0]
prediction = getSymbolAmpl(y_char,target_indices_char,target_char_indices,
start_of_the_cycle_symbol,
stop_symbol_probability_amplifier_current) # undo one-hot encoding
cropped_line += prediction
stop_symbol_probability_amplifier_current, start_of_the_cycle_symbol = amplify(cropped_line)
if y_t<0:
y_t=0
cropped_times.append(y_t)
if prediction == '!': # end of case was just predicted, therefore, stop predicting further into the future
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))
dls = 1 - (damerau_levenshtein_distance(unicode(predicted), unicode(ground_truth)) / max(len(predicted),len(ground_truth)))
if dls<0:
dls=0 # we encountered problems with Damerau-Levenshtein Similarity on some linux machines where the default character encoding of the operating system caused it to be negative, this should never be the case
output.append(dls)
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))
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 seperate activity and time predictions
y_char = y[0][0]
y_t = y[1][0][0]
prediction = getSymbolAmpl(
y_char, target_indices_char, target_char_indices,
start_of_the_cycle_symbol,
stop_symbol_probability_amplifier_current
) # undo one-hot encoding
cropped_line += prediction
stop_symbol_probability_amplifier_current, start_of_the_cycle_symbol = amplify(
cropped_line)
if y_t < 0:
y_t = 0
cropped_times.append(y_t)
if prediction == '!': # end of case was just predicted, therefore, stop predicting further into the future
one_ahead_pred.append(total_predicted_time)
one_ahead_gt.append(ground_truth_t)
print('! predicted, end case')
break
if y_t < 0:
y_t = 0
cropped_times.append(y_t)
if not i == 0:
stop_symbol_probability_amplifier_current, start_of_the_cycle_symbol = amplify(
temp_cropped_line)
#in not reached, function :choose_next_top_descendant: will backtrack
y_t = y_t * divisor3
cropped_times3.append(cropped_times3[-1] +
timedelta(seconds=y_t))
for j in range(current_beam_size):
temp_prediction = getSymbolAmpl(
y_char, target_indices_char, target_char_indices,
start_of_the_cycle_symbol,
stop_symbol_probability_amplifier_current, j)
if temp_prediction == '!': # end of case was just predicted, therefore, stop predicting further into the future
if verify_formula_as_compliant(temp_cropped_line):
one_ahead_pred.append(
current_prediction_premis.
total_predicted_time)
one_ahead_gt.append(ground_truth_t)
stop_symbol_probability_amplifier_current = 1
print('! predicted, end case')
queue_next_steps = PriorityQueue()
break
else:
continue