def runOneWord():
(tag2tag, tag2word) = buildDataModels('data/at_unigram.json', 'data/at_tag_to_word.json', ["", ""])
devtestdata = ""
with open('devtest.txt') as f:
devtestdata = f.read()
dataarray = []
tagarray = []
c = 0
for word in devtestdata.split():
ws = word.split('_')
w = ws[0]
t = ws[1]
dataarray.append(w)
tagarray.append(t)
c+=1
if(c > 5):
break
devtestdata = ' '.join(dataarray)
v = Viterbi(tag2tag, tag2word)
singlePath = v.run(devtestdata)
confusionMatrix = ConfusionMatrix(tag2tag)
confusionMatrix.build(tagarray, singlePath)
results = confusionMatrix.getResults()
def addFormatedMatrix(res):
l = []
t2tkeys = [x for x in tag2tag.keys()]
l.append("".ljust(6))
for key in t2tkeys:
l.append(("%s" % (key, )).ljust(6))
l.append('\n')
c = 0
for row in res['confusion_matrix']:
l.append((t2tkeys[c]).ljust(6))
for col in row:
if(col > 0):
l.append(("%d" % (col, )).ljust(6))
else:
l.append("".ljust(6))
l.append("\n")
c+=1
res['formated_matrix'] = ''.join(l)
return res
results = addFormatedMatrix(results)
with open('results_one.txt', 'w') as f:
f.write("Path: %s" % (str([x.ljust(5) for x in singlePath]), ))
f.write('\r\n')
f.write(" %s" % (str([x.ljust(5) for x in results['results_matrix']]), ))
f.write('\r\n')
f.write(" %s" % (str([x.ljust(5) for x in tagarray]), ))
f.write('\r\n')
f.write("Tagging Accuracy: %f" % (results['accuracy'], ))
f.write('\r\n')
f.write(results['formated_matrix'])
def train_epoch(self, epoch):
if self.train_problems_loader is None:
self.train_problems_loader = init_problems_loader(
self.opts.train_dir)
epoch_start = time.clock()
epoch_train_cost = 0.0
epoch_train_mat = ConfusionMatrix()
train_problems, train_filename = self.train_problems_loader.get_next()
for problem in train_problems:
d = self.build_feed_dict(problem)
_, logits, cost = self.sess.run(
[self.apply_gradients, self.logits, self.cost], feed_dict=d)
epoch_train_cost += cost
epoch_train_mat.update(problem.is_sat, logits > 0)
epoch_train_cost /= len(train_problems)
epoch_train_mat = epoch_train_mat.get_percentages()
epoch_end = time.clock()
learning_rate = self.sess.run(self.learning_rate)
self.save(epoch)
return (train_filename, epoch_train_cost, epoch_train_mat,
learning_rate, epoch_end - epoch_start)
def test(self, test_data_dir):
test_problems_loader = init_problems_loader(test_data_dir)
results = []
while test_problems_loader.has_next():
test_problems, test_filename = test_problems_loader.get_next()
epoch_test_cost = 0.0
epoch_test_mat = ConfusionMatrix()
for problem in test_problems:
d = self.build_feed_dict(problem)
logits, cost = self.sess.run([self.logits, self.cost], feed_dict=d)
epoch_test_cost += cost
epoch_test_mat.update(problem.is_sat, logits > 0)
epoch_test_cost /= len(test_problems)
epoch_test_mat = epoch_test_mat.get_percentages()
results.append((test_filename, epoch_test_cost, epoch_test_mat))
return results
def train_epoch(self, epoch):
if self.train_problems_loader is None:
self.train_problems_loader = init_problems_loader(self.opts.train_dir)
epoch_start = time.clock()
epoch_train_cost = 0.0
epoch_train_mat = ConfusionMatrix()
train_problems, train_filename = self.train_problems_loader.get_next()
for problem in train_problems:
d = self.build_feed_dict(problem)
self.batchnum = problem.n_vars//len(problem.is_sat)
acc,feature_map_out, _, logits, cost, image, issat, XX = self.sess.run(
[self.accuracy, self.all_votes, self.apply_gradients, self.logits, self.cost, self.image, self.is_sat, self.merged], feed_dict=d)
epoch_train_cost += cost
epoch_train_mat.update(problem.is_sat, logits > 0)
# print(acc)
epoch_train_cost /= len(train_problems)
epoch_train_mat = epoch_train_mat.get_percentages()
epoch_end = time.clock()
learning_rate = self.sess.run(self.learning_rate)
self.save(epoch)
self.train_writer.add_summary(XX, epoch)
return (train_filename, epoch_train_cost, epoch_train_mat, learning_rate, epoch_end - epoch_start)
def get_individual_interstim_sensitivities_for_participants(
participants: List[Participant],
sector=None,
epsilon=0.001,
):
all_sensitivities = []
for part in participants:
confusion_matrix = ConfusionMatrix.of_indices(
part.get_responses(sector=sector))
participant_sensitivities = get_interstim_sensitivities_for_confusion_matrix(
confusion_matrix,
epsilon,
)
all_sensitivities += participant_sensitivities
return all_sensitivities
def train_epoch(self, epoch):
if self.train_problems_loader is None:
self.train_problems_loader = init_problems_loader(
self.opts.train_dir)
epoch_start = time.clock()
epoch_train_cost = 0.0
# accuracy_by_var = []
# accuracy_by_problem = []
epoch_train_mat = ConfusionMatrix()
train_problems, train_filename = self.train_problems_loader.get_next()
for problem in train_problems:
d = self.build_feed_dict(problem)
A_policy, L_policy = self.sess.run([self.A_policy, self.L_policy],
feed_dict=d)
A_move, L_move = self.sample_moves(A_policy, L_policy)
## TO HERE; need to evaluate is_sat
## serious concern: by random, only 4 percent of games are sat (the base is biased!!!)
_, logits, cost = self.sess.run(
[self.apply_gradients, self.logits, self.cost], feed_dict=d)
epoch_train_cost += cost
(av, ap) = self.accuracy(np.array(logits),
np.array(problem.labels))
accuracy_by_var.append(av)
accuracy_by_problem.append(ap)
# epoch_train_mat.update(problem.is_sat, logits > 0)
epoch_train_cost /= len(train_problems)
av_ac_by_var = np.mean(accuracy_by_var)
av_ac_by_pro = np.mean(accuracy_by_problem)
# epoch_train_mat = epoch_train_mat.get_percentages()
epoch_end = time.clock()
learning_rate = self.sess.run(self.learning_rate)
self.save(epoch)
return (train_filename, epoch_train_cost, av_ac_by_var, av_ac_by_pro,
learning_rate, epoch_end - epoch_start)
from confusion import ConfusionMatrix
from sensitivity import get_interstim_sensitivities_for_confusion_matrix, log_fit_sensitivities
def parse_str_example_data(str_data):
return np.array(
[[int(n_str) for n_str in row.split("\t")] for row in str_data.strip().splitlines()]
).T
cm_1 = ConfusionMatrix(
parse_str_example_data(
"""
39 4 1 0 0
135 107 26 3 0
24 87 146 108 15
1 2 27 81 98
0 0 0 8 87
"""
)
)
cm_2 = ConfusionMatrix(
parse_str_example_data(
"""
7 4 0 0 0
50 32 13 3 0
37 58 70 55 8
1 2 12 36 43
0 0 1 5 45
"""
columns = [
"participant_id",
"slowdown",
"compensation",
"sector",
"dist_positions",
"dist_degrees",
"d_prime",
]
with open(file_path, "w") as file:
writer = DictWriter(file, columns)
writer.writeheader()
for participant in echo_study.query_participants().get():
for sector in ("left", "center", "right"):
responses = participant.get_responses(sector=sector)
matrix = ConfusionMatrix.of_azimuths(responses)
sensitivities = get_interstim_sensitivities_for_confusion_matrix(
matrix)
for (dist, d_prime) in sensitivities:
writer.writerow({
"participant_id": participant.prolific_pid,
"slowdown": participant.slowdown,
"compensation": participant.compensation,
"sector": sector,
"dist_positions": dist,
"dist_degrees": 10 * dist,
"d_prime": d_prime,
})
