def evaluate(path):
cad = read_dataset('cad.csv')
rgbd = read_dataset('rgbd.csv')
freqs = freq_count(cad)
results = load_results(path, rgbd, cad)
mP = 0.0
mR = 0.0
mF = 0.0
mAP = 0.0
mNDCG = 0.0
mNNT1 = 0.0
mNNT2 = 0.0
for (queried, retrieved) in results:
f = freqs[queried[0]]
x = categories_to_rel(queried, retrieved)[:f]
# Sum up the retrieval scores
mP += precision(x)
mR += recall(x, f)
mF += f1score(x, f)
mNDCG += ndcg(x)
mAP += average_precision(x, f)
mNNT1 += nnt1(x, f)
mNNT2 += nnt2(x, f)
n = len(results)
print('num queries:', n)
print('mean precision:', mP / n)
print('mean recall:', mR / n)
print('mean F1:', mF / n)
print('mean AP:', mAP / n)
print('mean NDCG: ', mNDCG / n)
print('mean NNT1: ', mNNT1 / n)
print('mean NNT2: ', mNNT2 / n)
# Plot PR-curve
cutoff = 1000
mean_precisions = np.zeros(cutoff, np.float64)
mean_recalls = np.zeros(cutoff, np.float64)
for (queried, retrieved) in results:
x = categories_to_rel(queried, retrieved)[:cutoff]
x = np.pad(x, (0, cutoff - len(x)), 'constant', constant_values=(0))
precisions = []
recalls = []
for k, _ in enumerate(x):
p = precision(x[:k + 1])
r = recall(x[:k + 1], freqs[queried[0]])
precisions.append(p)
recalls.append(r)
mean_precisions += precisions
mean_recalls += recalls
mean_precisions /= len(results)
mean_recalls /= len(results)
plt.plot(mean_recalls, mean_precisions)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.axis([0, 1, 0, 1.05])
plt.show()
def test(test_set, model):
print("starting testing...")
start_time = time.time()
model.eval()
predictions, references = [], []
with torch.no_grad():
for i in range(len(test_set)):
Y, T, data = test_set.get_candidate(i)
Y = Y.to(device)
T = T.to(device)
ids = model.ranking(Y, T).data
candidate = []
comments = list(data['candidate'].keys())
for id in ids:
candidate.append(comments[id])
predictions.append(candidate)
references.append(data['candidate'])
if i % 100 == 0:
print(i)
recall_1 = recall(predictions, references, 1)
recall_5 = recall(predictions, references, 5)
recall_10 = recall(predictions, references, 10)
mr = mean_rank(predictions, references)
mrr = mean_reciprocal_rank(predictions, references)
s = "r1={}, r5={}, r10={}, mr={}, mrr={}"
print(s.format(recall_1, recall_5, recall_10, mr, mrr))
print("testing time:", time.time() - start_time)
def evaluate(path):
queries = read_dataset('queries.csv')
targets = read_dataset('targets.csv')
freqs = freq_count(targets)
results = load_results(path, queries, targets)
cutoff = 1000
precisions = []
recalls = []
f1scores = []
aps = []
gains = []
nnt1s = []
nnt2s = []
for (queried, retrieved) in results:
x = categories_to_rel(queried, retrieved)[:cutoff]
p = precision(x)
r = recall(x, freqs[queried[0]])
f = f1score(x, freqs[queried[0]])
g = ndcg(x)
ap = average_precision(x, freqs[queried[0]])
t1 = nnt1(x, freqs[queried[0]])
t2 = nnt2(x, freqs[queried[0]])
precisions.append(p)
recalls.append(r)
f1scores.append(f)
gains.append(g)
aps.append(ap)
nnt1s.append(t1)
nnt2s.append(t2)
print('mean precision:', numpy.mean(precisions))
print('mean recall:', numpy.mean(recalls))
print('mean F1 score:', numpy.mean(f1scores))
print('mAP:', numpy.mean(aps))
print('mean NDCG:', numpy.mean(gains))
print('mean nearest neighbor:', numpy.mean(nnt1s), numpy.mean(nnt2s))
# plot precision-recall curve
mean_precisions = numpy.zeros(cutoff, numpy.float64)
mean_recalls = numpy.zeros(cutoff, numpy.float64)
for (queried, retrieved) in results:
x = categories_to_rel(queried, retrieved)[:cutoff]
x = numpy.pad(x, (0, cutoff - len(x)), 'constant', constant_values=(0))
precisions = []
recalls = []
for k, _ in enumerate(x):
p = precision(x[:k + 1])
r = recall(x[:k + 1], freqs[queried[0]])
precisions.append(p)
recalls.append(r)
mean_precisions += precisions
mean_recalls += recalls
mean_precisions /= len(results)
mean_recalls /= len(results)
plt.plot(mean_recalls, mean_precisions)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.axis([0, 1, 0, 1.05])
plt.show()
def evaluate_test_set(session, tags, preds, fnames, lines, batch_limit=None):
batch_num = 0
num_sequences = 0
p_tp_total, p_fp_total, r_tp_total, r_fn_total = 0, 0, 0, 0
p_tp_total_binary, p_fp_total_binary, r_tp_total_binary, r_fn_total_binary = 0, 0, 0, 0
while True:
try:
#Train binary, eval binary setting
y, y_, filenames, line_nums = \
session.run([tags, preds, fnames, lines])
p_tp, p_fp = metrics.precision(reader, y, y_, counts=True)
r_tp, r_fn = metrics.recall(reader, y, y_, counts=True)
p_tp_total += p_tp
p_fp_total += p_fp
r_tp_total += r_tp
r_fn_total += r_fn
#Train All tags, eval binary setting
p_tp_binary, p_fp_binary = metrics.precision(reader, y, y_, binary=True, counts=True)
r_tp_binary, r_fn_binary = metrics.recall(reader, y, y_, binary=True , counts=True)
p_tp_total_binary += p_tp_binary
p_fp_total_binary += p_fp_binary
r_tp_total_binary += r_tp_binary
r_fn_total_binary += r_fn_binary
#TODO: Train binary, eval binary setting
num_sequences += len(y)
batch_num += 1
if batch_num == batch_limit:
break
except tf.errors.OutOfRangeError:
print 'test queue is empty'
break
if p_tp_total:
precision = p_tp_total / (p_tp_total + p_fp_total)
recall = r_tp_total / (r_tp_total + r_fn_total)
f1 = metrics.f1(precision, recall)
precision_binary = p_tp_total_binary / (p_tp_total_binary + p_fp_total_binary)
recall_binary = r_tp_total_binary / (r_tp_total_binary + r_fn_total_binary)
f1_binary = metrics.f1(precision_binary, recall_binary)
print 'Evaluated {} sequences from test set'.format(num_sequences)
print 'Precision: ', precision
print 'Recall: ', recall
print 'f1: ', f1
print 'Precision Binary: ', precision_binary
print 'Recall Binary: ', recall_binary
print 'f1 Binary: ', f1_binary
def classification_report(y_true, y_pred):
print('--------------------------------')
print('Accuracy -', metrics.accuracy(y_true, y_pred))
print('Recall -', metrics.recall(y_true, y_pred))
print('Precision -', metrics.precision(y_true, y_pred))
print('F1 score -', metrics.f1_score(y_true, y_pred))
print('--------------------------------')
def test(self):
tf.global_variables_initializer().run()
self.saver = tf.train.Saver()
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
test_feed_dict = {
self.test_inptus: self.test_x,
self.test_labels: self.test_y
}
summary_str, test_loss, test_accuracy, p, t = self.sess.run(
[
self.test_summary, self.test_loss, self.test_accuracy,
self.test_plab, self.test_tlab
],
feed_dict=test_feed_dict)
import metrics
print("test_accuracy: {}".format(test_accuracy))
with open('resnet.txt', 'a') as f: # 设置文件对象
f.write(
str(self.i) + '-' + str(self.j) + ',' +
str(metrics.accuracy(t, p)) + ',' +
str(metrics.precision(t, p)) + ',' +
str(metrics.recall(t, p)) + ',' + str(metrics.f1score(t, p)) +
',' + str(metrics.ft(t, p)) + '\n')
def f1_score(y_true, y_pred):
from metrics import precision, recall
y_true = K.cast(y_true, dtype='float32')
y_pred = K.cast(y_pred, dtype='float32')
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2 * ((precision * recall) / (precision + recall + K.epsilon()))
def evaluate(path):
queries = read_dataset('queries.csv')
targets = read_dataset('targets.csv')
freqs = freq_count(targets)
results = load_results(path, queries, targets)
cutoff = 1000
precisions = []
recalls = []
f1scores = []
aps = []
gains = []
nnt1s = []
nnt2s = []
for (queried, retrieved) in results:
x = categories_to_rel(queried, retrieved)[:cutoff]
p = precision(x)
r = recall(x, freqs[queried[0]])
f = f1score(x, freqs[queried[0]])
g = ndcg(x)
ap = average_precision(x, freqs[queried[0]])
t1 = nnt1(x, freqs[queried[0]])
t2 = nnt2(x, freqs[queried[0]])
precisions.append(p)
recalls.append(r)
f1scores.append(f)
gains.append(g)
aps.append(ap)
nnt1s.append(t1)
nnt2s.append(t2)
print('precision:', p)
print('recall:', r)
print('F1 score:', f)
print('average precision:', ap)
print('NDCG:', g)
print('nearest neighbor:', t1, t2)
def eval_classifier(classifier, x, y):
y_pred = classifier.predict(x)
conf = metrics.conf_matrix(y_pred, y)
accuracy = metrics.accuracy(y_pred, y)
precision = metrics.precision(y_pred, y)
recall = metrics.recall(y_pred, y)
f1_score = metrics.f_score(y_pred, y, beta=1)
avg_prec = np.mean(precision)
avg_rec = np.mean(recall)
avg_f1 = np.mean(f1_score)
print("Confusion Matrix: ")
print(conf)
print("Accuracy:")
print(accuracy)
print("Precision:")
print(precision)
print(f"Average Precision: {avg_prec}")
print("Recall:")
print(recall)
print(f"Average Recall: {avg_rec}")
print("F1_score:")
print(f1_score)
print(f"Average F1 Score: {avg_f1}")
def validation_end(self, outputs):
# OPTIONAL
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
avg_acc_loss = torch.stack([x['accuracy'] for x in outputs]).mean()
if self.mode == Train_Mode.TEACHER:
self.embeddings_all = torch.cat(self.embeddings_all).cpu()
self.labels_all = torch.cat(self.labels_all).cpu()
rec = recall(self.embeddings_all, self.labels_all, K=self.K)
log_metrics = {
"recall" : rec[0],
"val_loss": avg_loss.item(),
}
elif self.mode == Train_Mode.STUDENT:
avg_triplet_loss = torch.stack([x['val_triplet_loss'] for x in outputs]).mean()
avg_angle_loss = torch.stack([x['val_angle_loss'] for x in outputs]).mean()
avg_dist_loss = torch.stack([x['val_dist_loss'] for x in outputs]).mean()
log_metrics = {
"val_triplet_loss" : avg_triplet_loss.item(),
"val_angle_loss": avg_angle_loss.item(),
"val_dist_loss": avg_dist_loss.item(),
"val_accuracy": avg_acc_loss.item(),
"val_loss": avg_loss.item(),
}
self.embeddings_all, self.labels_all = [], []
self.train_step = 0
self.train_num_correct = 0
self.val_step = 0
self.val_num_correct = 0
return { 'val_loss': avg_loss, 'log': log_metrics}
def compute_all_metrics(execution_id, path_input, path_output, formula,
append):
from metrics import accuracy, precision, recall, f1, specificity
"""
Computes all metrics and persistes in a csv
Args:
execution_id (int): identifier of the execution
path_input (string): path of the file that contains the classifications
path_out (string): path of the file that will persist the metrics
formula (string): mean_max | mean_mean
append (boolean): true | false
"""
# loading results
with open(path_input) as data_file:
data = json.load(data_file)
# computing metrics
tp = tn = fp = fn = 0
for i in range(0, len(data)):
if (data[i]['values'][formula]['positive'] >=
data[i]['values'][formula]['negative']):
if data[i]['values']['label'] == 'positive':
tp += 1
else:
fp += 1
elif (data[i]['values'][formula]['positive'] <
data[i]['values'][formula]['negative']):
if (data[i]['values']['label'] == 'negative'):
tn += 1
else:
fn += 1
else:
raise Exception(
"Positive similarity equals to negative similarity to news " +
data[i]['id'])
accuracy = accuracy(tp, tn, fp, fn)
recall = recall(tp, fn)
precision = precision(tp, fp)
f1 = f1(precision, recall)
specificity = specificity(tn, fp)
# persiting the results
with open(path_output, 'a' if append else 'w') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',')
if (not append):
spamwriter.writerow([
'execution_id', 'tp', 'tn', 'fp', 'fn', 'accuracy',
'precision', 'recall', 'f1', 'specificity'
])
spamwriter.writerow([
execution_id, tp, tn, fp, fn, accuracy, precision, recall, f1,
specificity
])
def supervised_eval(self, train_or_valid):
data = self.dataset.get_labeled_data(train_or_valid)
if data == None:
raise ValueError('no labeled examples present in dataset')
X_labeled, y_true, _ = data
y_pred = self.model.predict(X_labeled)
p, r, ac, g, auc = metrics.precision(y_true, y_pred),metrics.recall(y_true, y_pred),\
metrics.accuracy(y_true, y_pred), metrics.g_means(y_true, y_pred),\
metrics.auc(y_true, y_pred)
self.metrics[train_or_valid].append((p, r, ac, g, auc))
def test_1(self): actual = [1, 1, 0, 1, 1, 1, 0, 0, 1, 1] predicted = [0, 1, 0, 1, 0, 1, 0, 1, 0, 0] tp, fn, fp, tn = metrics.confusion_matrix(actual, predicted) self.assertEqual(tp, 3) self.assertEqual(fn, 4) self.assertEqual(fp, 1) self.assertEqual(tn, 2) self.assertEqual(metrics.accuracy(actual, predicted), 0.5) self.assertEqual(metrics.precision(actual, predicted), 3/4) self.assertEqual(metrics.recall(actual, predicted), 3/7) self.assertEqual(metrics.f1(actual, predicted), 6/11)
def post(self):
global current_scene
score = 0
print('Submitted scene %s' % current_scene)
if self.scene_exists(current_scene):
return {
'message': "Scene {} already exist.".format(current_scene)
}, 400
correct_dict = self.fetch_correct_result()
if not correct_dict:
return {
'message':
"There is no reuslt dataexist for scene {}.".format(
current_scene)
}, 400
your_dict = request.get_json()
submission_time = datetime.datetime.now()
print(' Correct prediction', correct_dict)
your_dict = {str(k): int(v) for k, v in your_dict.items()}
print(' Your prediction', your_dict)
sys.stdout.flush()
score = 0
score2 = 0
score3 = 0
if your_dict:
#score = Benchmark.diff_dicts(correct_dict, your_dict)
score = metrics.accuracy(correct_dict, your_dict)
score2 = metrics.precision(correct_dict, your_dict)
score3 = metrics.recall(correct_dict, your_dict)
print("scene accuracy", score)
print("scene precision", score2)
print("scene recall", score3)
submission_result = {
'scene': current_scene,
'accuracy': score,
'precision': score2,
'recall': score3
}
try:
self.insert(submission_result, submission_time)
except:
return {
'message': 'An error occured while inserting the item'
}, 500
return {
'Your score for this scene is ': submission_result['accuracy']
}, 201
def recalls(self):
predictions = self.one_hot
size = len(self._classes)
with tf.compat.v1.name_scope("Recalls"):
rs = []
ops = []
for i, c in enumerate(self._classes):
mask = tf.one_hot([i], size, axis=-1)
r, op = recall(labels=self.target,
predictions=predictions,
weights=mask)
tf.compat.v1.summary.scalar("c{}_{}".format(i, c), r * 100)
rs.append(r)
ops.append(op)
return rs, ops
def results_to_metrics(results, methods, ref_motifs):
_, _, ref_labels = motif.unpack_motif(ref_motifs)
metric_dict = dict.fromkeys(methods)
for m in methods:
obs_motifs = results[m]
_, _, obs_labels = motif.unpack_motif(obs_motifs)
this_edit = metrics.edit_distance(obs_labels, ref_labels)
this_recall = metrics.recall(obs_motifs, ref_motifs)
this_precis = metrics.precision(obs_motifs, ref_motifs)
this_f = metrics.f_measure(obs_motifs, ref_motifs)
this_bm = metrics.boundary_distance(obs_motifs, ref_motifs)
metric_dict[m] = [this_edit, this_recall, this_precis, this_f, this_bm]
return metric_dict
def active_simulation_eval(self):
data = self.dataset.get_unlabeled_data()
if data == None:
UserWarning(
'all examples have been labeled; this eval mode works '
'if there is unlabeled pool of data in `simulate` mode'
)
return
X_unlabeled, unlabeled_indexes = data
# get unlabeled examples labels in simulation with `y_ideal`
y_true = self.dataset.y_ideal[unlabeled_indexes]
y_pred = self.model.predict(X_unlabeled)
p, r, ac, g, auc = metrics.precision(y_true, y_pred),metrics.recall(y_true, y_pred),\
metrics.accuracy(y_true, y_pred), metrics.g_means(y_true, y_pred),\
metrics.auc(y_true, y_pred)
self.metrics['simulate'].append((p, r, ac, g, auc))
def recalls(self):
predictions = self.one_hot
size = len(self._classes)
with tf.name_scope("Recalls"):
rs = []
ops = []
for i, c in enumerate(self._classes):
mask = tf.one_hot([i], size, axis=-1)
r, op = recall(labels=self.target,
predictions=predictions,
weights=mask,
updates_collections=tf.GraphKeys.UPDATE_OPS)
tf.summary.scalar("c_{}".format(c), r * 100)
rs.append(r)
ops.append(op)
return rs, ops
def validation_end(self, outputs):
# OPTIONAL
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
if self.mode == Train_Mode.TEACHER:
self.embeddings_all = torch.cat(self.embeddings_all).cpu()
self.labels_all = torch.cat(self.labels_all).cpu()
rec = recall(self.embeddings_all, self.labels_all, K=self.K)
log_metrics = {
"recall" : rec[0],
"val_loss": avg_loss.item(),
}
self.embeddings_all, self.labels_all = [], []
return { 'val_loss': avg_loss, 'log': log_metrics}
def compute_all_metrics(execution_id, path_input, path_output, formula, append):
from metrics import accuracy, precision, recall, f1, specificity
"""
Computes all metrics and persistes in a csv
Args:
execution_id (int): identifier of the execution
path_input (string): path of the file that contains the classifications
path_out (string): path of the file that will persist the metrics
formula (string): mean_max | mean_mean
append (boolean): true | false
"""
# loading results
with open(path_input) as data_file:
data = json.load(data_file)
# computing metrics
tp = tn = fp = fn = 0
for i in range(0, len(data)):
if (data[i]['values'][formula]['positive'] >= data[i]['values'][formula]['negative']):
if data[i]['values']['label'] == 'positive':
tp += 1
else:
fp += 1
elif (data[i]['values'][formula]['positive'] < data[i]['values'][formula]['negative']):
if (data[i]['values']['label'] == 'negative'):
tn += 1
else:
fn += 1
else:
raise Exception("Positive similarity equals to negative similarity to news " + data[i]['id'])
accuracy = accuracy(tp, tn, fp, fn)
recall = recall(tp, fn)
precision = precision(tp, fp)
f1 = f1(precision, recall);
specificity = specificity(tn, fp);
# persiting the results
with open(path_output, 'a' if append else 'w') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',')
if (not append):
spamwriter.writerow(
['execution_id', 'tp', 'tn', 'fp', 'fn', 'accuracy', 'precision', 'recall', 'f1', 'specificity'])
spamwriter.writerow([execution_id, tp, tn, fp, fn, accuracy, precision, recall, f1, specificity])
def test(engine, options):
queries = pd.read_csv(os.path.join('data', 'queries_train.tsv'), sep='\t')
bench_lbls = pd.read_csv(os.path.join('data', 'benchmark_lbls_train.csv'),
dtype={
'query': int,
'tweet': str,
'y_true': int
})
q2n_relevant = bench_lbls.groupby('query')['y_true'].sum().to_dict()
queries_results = []
q_times = []
for i, row in queries.iterrows():
q_id = row['query_id']
q_keywords = row['keywords']
start_time = time.time()
q_n_res, q_res = engine.search(q_keywords, options['methods'])
end_time = time.time()
q_time = end_time - start_time
q_times.append(q_time)
queries_results.extend([(q_id, str(doc_id)) for doc_id in q_res])
if q_time > 10:
print(f'Query time exceeded: {options}')
queries_results = pd.DataFrame(queries_results, columns=['query', 'tweet'])
q_results_labeled = pd.merge(queries_results,
bench_lbls,
on=['query', 'tweet'],
how='inner',
suffixes=('_result', '_bench'))
options['max_q_time'] = max(q_times)
options['avg_q_time'] = sum(q_times) / len(q_times)
options['MAP'] = metrics.map(q_results_labeled)
options['precision'] = metrics.precision(q_results_labeled)
options['precision@5'] = metrics.precision(
q_results_labeled.groupby('query').head(5))
options['precision@10'] = metrics.precision(
q_results_labeled.groupby('query').head(10))
options['precision@50'] = metrics.precision(
q_results_labeled.groupby('query').head(50))
options['recall'] = metrics.recall(q_results_labeled, q2n_relevant)
save_to_csv(options)
def metric_fn(label_ids, predict, num_labels, answer_num):
mask = tf.sequence_mask(answer_num, FLAGS.max_answer_num)
precision = metrics.precision(label_ids,
predict,
num_classes=num_labels,
weights=mask,
pos_indices=[1])
recall = metrics.recall(label_ids,
predict,
num_classes=num_labels,
weights=mask,
pos_indices=[1])
f1_score = metrics.f1(label_ids,
predict,
num_classes=num_labels,
weights=mask,
pos_indices=[1])
return {
"precision": precision,
"recall": recall,
"f1_score": f1_score
}
def calculate_metrics(patient_id, spacing, label_arr_org, pred_arr_org,
HAUSDORFF_PERCENT, OVERLAP_TOLERANCE,
SURFACE_DICE_TOLERANCE):
"""
metric calculation cleanup in test.py.
"""
result = {}
result["patient_id"] = patient_id
#
result["precision"] = precision(label_arr_org, pred_arr_org)
result["recall"] = recall(label_arr_org, pred_arr_org)
result["jaccard"] = jaccard(label_arr_org, pred_arr_org)
result["dice"] = dice(label_arr_org, pred_arr_org)
result["segmentation_score"] = segmentation_score(label_arr_org,
pred_arr_org, spacing)
bbox_metrics = calculate_bbox_metrics(label_arr_org, pred_arr_org, spacing)
result = append_helper(
result, ["x_distance", "y_distance", "z_distance", "distance"],
bbox_metrics)
surface_dice_metrics = surface_dice(label_arr_org, pred_arr_org, spacing,
HAUSDORFF_PERCENT, OVERLAP_TOLERANCE,
SURFACE_DICE_TOLERANCE)
result = append_helper(result, [
"average_surface_distance_gt_to_pr",
"average_surface_distance_pr_to_gt", "robust_hausdorff",
"overlap_fraction_gt_with_pr", "overlap_fraction_pr_with_gt",
"surface_dice"
], surface_dice_metrics)
# get bbox center (indices) of prediction for next segmentation step
for axes in ["X", "Y", "Z"]:
for location in ["min", "center", "max", "length"]:
result["prediction_{}_{}".format(
axes, location
)] = bbox_metrics["prediction_bbox_metrics"][axes][location]
return result, result["dice"], bbox_metrics
user_profile = train[test_user].indices #what is doing here?
relevant_items = test[test_user].indices
if len(relevant_items) > 0:
neval += 1
#
# TODO: Here you can write to file the recommendations for each user in the test split.
# WARNING: there is a catch with the item idx!
#
# this will rank *all* items
recommended_items = recommender.recommend(user_profile,
exclude_seen=True)
# use this to have the *top-k* recommended items (warning: this can underestimate ROC-AUC for small k)
# recommended_items = recommender.recommend(user_profile, k=at, exclude_seen=True)
roc_auc_ += roc_auc(recommended_items, relevant_items)
precision_ += precision(recommended_items, relevant_items, at=at)
recall_ += recall(recommended_items, relevant_items, at=at)
map_ += map(recommended_items, relevant_items, at=at)
mrr_ += rr(recommended_items, relevant_items, at=at)
ndcg_ += ndcg(recommended_items,
relevant_items,
relevance=test[test_user].data,
at=at)
roc_auc_ /= neval
precision_ /= neval
recall_ /= neval
map_ /= neval
mrr_ /= neval
ndcg_ /= neval
logger.info('Ranking quality')
logger.info('ROC-AUC: {:.4f}'.format(roc_auc_))
def evaluating(self, model, dataset, split):
"""
input:
model: (object) pytorch model
dataset: (object) dataset
split: (str) split of dataset in ['train', 'val', 'test']
return [overall_accuracy, precision, recall, f1-score, jaccard, kappa]
"""
args = self.args
oa, precision, recall, f1, jac, kappa = 0, 0, 0, 0, 0, 0
model.eval()
data_loader = DataLoader(dataset,
args.batch_size,
num_workers=4,
shuffle=False)
batch_iterator = iter(data_loader)
steps = len(dataset) // args.batch_size
start = time.time()
for step in range(steps):
x, y = next(batch_iterator)
x = Variable(x, volatile=True)
y = Variable(y, volatile=True)
if args.cuda:
x = x.cuda()
y = y.cuda()
# calculate pixel accuracy of generator
gen_y = model(x)
if self.is_multi:
gen_y = gen_y[0]
oa += metrics.overall_accuracy(gen_y.data, y.data)
precision += metrics.precision(gen_y.data, y.data)
recall += metrics.recall(gen_y.data, y.data)
f1 += metrics.f1_score(gen_y.data, y.data)
jac += metrics.jaccard(gen_y.data, y.data)
kappa += metrics.kappa(gen_y.data, y.data)
_time = time.time() - start
if not os.path.exists(os.path.join(Logs_DIR, 'statistic')):
os.makedirs(os.path.join(Logs_DIR, 'statistic'))
# recording performance of the model
nb_samples = steps * args.batch_size
basic_info = [
self.date, self.method, self.epoch, self.iter, nb_samples, _time
]
basic_info_names = [
'date', 'method', 'epochs', 'iters', 'nb_samples', 'time(sec)'
]
perform = [
round(idx / steps, 3)
for idx in [oa, precision, recall, f1, jac, kappa]
]
perform_names = [
"overall_accuracy", "precision", "recall", "f1-score", "jaccard",
"kappa"
]
cur_log = pd.DataFrame([basic_info + perform],
columns=basic_info_names + perform_names)
# save performance
if os.path.exists(
os.path.join(Logs_DIR, 'statistic', "{}.csv".format(split))):
logs = pd.read_csv(
os.path.join(Logs_DIR, 'statistic', "{}.csv".format(split)))
else:
logs = pd.DataFrame([])
logs = logs.append(cur_log, ignore_index=True)
logs.to_csv(os.path.join(Logs_DIR, 'statistic',
"{}.csv".format(split)),
index=False,
float_format='%.3f')
f"{engine_module} results have MAP value of {results_map}."
)
if results_map <= 0 or results_map > 1:
logging.error(
f'{engine_module} results MAP value is out of range (0,1).'
)
# test that the average across queries of precision,
# precision@5, precision@10, precision@50, and recall
# is in [0,1].
prec, p5, p10, p50, recall = \
metrics.precision(q_results_labeled), \
metrics.precision(q_results_labeled.groupby('query').head(5)), \
metrics.precision(q_results_labeled.groupby('query').head(10)), \
metrics.precision(q_results_labeled.groupby('query').head(50)), \
metrics.recall(q_results_labeled, q2n_relevant)
logging.debug(
f"{engine_module} results produced average precision of {prec}."
)
logging.debug(
f"{engine_module} results produced average precision@5 of {p5}."
)
logging.debug(
f"{engine_module} results produced average precision@10 of {p10}."
)
logging.debug(
f"{engine_module} results produced average precision@50 of {p50}."
)
logging.debug(
f"{engine_module} results produced average recall of {recall}."
)
def predict(loss_fn, model, data_set, data_loader, counting=False):
""" Validate after training an epoch
Note:
"""
model.eval()
true_positives = []
predicted_positives = []
possible_positives = []
union_areas = []
loss = []
for bc_cnt, bc_data in enumerate(data_loader):
if counting:
print('%d/%d' % (bc_cnt, len(data_set) // data_loader.batch_size))
imgs, masks, _ = bc_data
imgs = Variable(imgs).cuda()
masks = Variable(masks).cuda()
# labels = Variable(labels).cuda()
outputs = model(imgs)
# outputs = outputs.view(-1, outputs.size()[2], outputs.size()[3])
# print outputs.size(), masks.size()
# if outputs.size() != masks.size():
# outputs = F.upsample(outputs, size=masks.size()[-2:], mode='bilinear')
mask_loss = torch.zeros(1).cuda()
for o in outputs:
o = o.view(-1, o.size()[2], o.size()[3])
mask_loss = mask_loss + float(loss_fn(o, masks))
# mask_loss = mask_loss
# loss = criterion(outputs, masks)
loss.append(mask_loss)
# loss.append(loss_fn(outputs, masks))
# outputs = F.softmax(model(imgs), dim=1)
# if outputs.size() != masks.size():
# outputs = F.upsample(outputs, size=masks.size()[-2:], mode='bilinear')
#
# _, outputs = torch.max(outputs, dim=1)
output = outputs[-1]
output = output.view(-1, output.size()[2], output.size()[3])
output = output.cpu().data.numpy()
# labels = labels.cpu().data.numpy()
masks = masks.cpu().data.numpy()
imgs = imgs.cpu().data.numpy()
true_positive, predicted_positive, possible_positive, union_area = metrics_pred(
output, imgs, masks)
true_positives += true_positive
predicted_positives += predicted_positive
possible_positives += possible_positive
union_areas += union_area
precisions = precision(true_positives, predicted_positives)
recalls = recall(true_positives, possible_positives)
f1_scores = f1_score(recalls, precisions)
loss = torch.tensor(loss)
return precisions, recalls, f1_scores, loss.mean()
def run():
while True:
trial = pull_pending()
if trial is None:
break
params = eval(trial['Parameters'])
logging.info(trial)
dataset = load(trial['Dataset'])
fold = int(trial['Fold']) - 1
(X_train, y_train), (X_test,
y_test) = dataset[fold][0], dataset[fold][1]
n_minority = Counter(y_train).most_common()[1][1]
n_majority = Counter(y_train).most_common()[0][1]
imblearn_ratios = [
((n_majority - n_minority) * ratio + n_minority) / n_majority
for ratio in [0.5, 0.75, 1.0]
]
clf = {
'NB': NB(),
'KNN': KNN(),
'SVM': SVM(gamma='scale'),
'CART': CART()
}[params['classifier']]
if (trial['Algorithm'] is None) or (trial['Algorithm'] == 'None'):
algorithm = None
else:
algorithms = {
'AKNN':
ResamplingCV(AKNN, clf, n_neighbors=[1, 3, 5, 7]),
'Bord':
ResamplingCV(SMOTE,
clf,
kind=['borderline1'],
k_neighbors=[1, 3, 5, 7, 9],
m_neighbors=[5, 10, 15],
sampling_strategy=imblearn_ratios),
'CC':
ResamplingCV(CC, clf, sampling_strategy=imblearn_ratios),
'CNN':
ResamplingCV(CNN, clf, n_neighbors=[1, 3, 5, 7]),
'ENN':
ResamplingCV(ENN, clf, n_neighbors=[1, 3, 5, 7]),
'IHT':
ResamplingCV(IHT,
clf,
sampling_strategy=imblearn_ratios,
cv=[2]),
'NCL':
ResamplingCV(NCL, clf, n_neighbors=[1, 3, 5, 7]),
'NM':
ResamplingCV(NM, clf, n_neighbors=[1, 3, 5, 7]),
'OSS':
ResamplingCV(OSS, clf, n_neighbors=[1, 3, 5, 7]),
'RBO':
ResamplingCV(RBO,
clf,
gamma=[0.01, 0.1, 1.0, 10.0],
ratio=[0.5, 0.75, 1.0]),
'RBU':
ResamplingCV(RBU,
clf,
gamma=params.get('gamma'),
ratio=params.get('ratio')),
'RENN':
ResamplingCV(RENN, clf, n_neighbors=[1, 3, 5, 7]),
'ROS':
ResamplingCV(ROS, clf, sampling_strategy=imblearn_ratios),
'RUS':
ResamplingCV(RUS, clf, sampling_strategy=imblearn_ratios),
'SMOTE':
ResamplingCV(SMOTE,
clf,
k_neighbors=[1, 3, 5, 7, 9],
sampling_strategy=imblearn_ratios),
'SMOTE+ENN':
ResamplingCV(
SMOTEENN,
clf,
smote=[SMOTE(k_neighbors=k) for k in [1, 3, 5, 7, 9]],
sampling_strategy=imblearn_ratios),
'SMOTE+TL':
ResamplingCV(
SMOTETomek,
clf,
smote=[SMOTE(k_neighbors=k) for k in [1, 3, 5, 7, 9]],
sampling_strategy=imblearn_ratios),
'TL':
TL(),
}
algorithm = algorithms.get(trial['Algorithm'])
if algorithm is None:
raise NotImplementedError
if algorithm is not None:
X_train, y_train = algorithm.fit_sample(X_train, y_train)
clf = clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
scores = {
'Precision': metrics.precision(y_test, predictions),
'Recall': metrics.recall(y_test, predictions),
'F-measure': metrics.f_measure(y_test, predictions),
'AUC': metrics.auc(y_test, predictions),
'G-mean': metrics.g_mean(y_test, predictions)
}
submit_result(trial, scores)
def post(self):
#signal.alarm(0)
#signal.alarm(5)
global current_scene, latency, overall_latency
scene_latency = datetime.datetime.utcnow() - latency
if overall_latency:
overall_latency += scene_latency
else: #None
overall_latency = scene_latency
print("Latency for scene %s was %s" % (current_scene, scene_latency))
#timeout = int(os.getenv("BENCHMARK_POST_TIMEOUT", default=10))
#watchdog.reset_and_extend(timeout)
score = 0
print('Submitted scene %s' % current_scene)
if self.scene_exists(current_scene):
return {
'message': "Scene {} already exist.".format(current_scene)
}, 400
correct_dict = self.fetch_correct_result()
if not correct_dict:
return {"message": "Please request at least one scene first"}, 400
your_dict = request.get_json()
submission_time = datetime.datetime.utcnow()
try:
your_dict = {str(k): int(v) for k, v in your_dict.items()}
except ValueError:
return {
'message':
"Your result json should be in format {'object_name':'1'} with key as an object name."
}, 400
except AttributeError:
return {
'message':
"Your result json is incorrect. Specify it like: {'object_name:1'}"
}, 400
print(' Correct prediction', correct_dict)
print(' Your prediction', your_dict)
sys.stdout.flush()
score = 0
score2 = 0
score3 = 0
if your_dict:
#score = Benchmark.diff_dicts(correct_dict, your_dict)
score = metrics.accuracy(correct_dict, your_dict)
score2 = metrics.precision(correct_dict, your_dict)
score3 = metrics.recall(correct_dict, your_dict)
print("scene accuracy", score)
print("scene precision", score2)
print("scene recall", score3)
submission_result = {
'scene': current_scene,
'accuracy': score,
'precision': score2,
'recall': score3
}
try:
self.insert(submission_result, submission_time)
except:
return {
'message': 'An error occured while inserting the item'
}, 500
return {
'Your score for this scene is ': submission_result['accuracy']
}, 201
# Para cada porcentaje de confianza for i in xrange(100): # Obtengo las predicciones con una confianza mayor a cierto umbral porcentaje = float(i)/100 aux = result[result['trust'] > porcentaje] # matrix = metrics.confusion_matrix(aux) matrix = metrics.hard_matrix(aux) # Si la precision es menor que cero, es porque no habian datos que superaran tal nivel de confianza precision = metrics.accuracy(matrix, clase) if precision >= 0: valores_accuracy.append(precision) valores_recall.append(metrics.recall(matrix, clase)) x_values.append(porcentaje) # Si el f_score es menor que cero, es porque no habian datos que superaran tal nivel de confianza f_score = metrics.f_score(matrix, clase) if f_score >= 0: valores_fscore.append(f_score) x_values_fscore.append(porcentaje) #graf(clase, x_values, valores_accuracy, 'Accuracy') graf(clase, x_values, valores_recall, 'Recall') #graf(clase, x_values_fscore, valores_fscore, 'F-Score') print 'a' plt.show()
def test_precision_recall_treadoff(VGG11,CIFAR10):
assert ((precision(VGG11, CIFAR10, 1, 'cuda:0') >= 0.95) or (recall(VGG11, CIFAR10, 1, 'cuda:0') >= 0.95))
def model_save_load(name, model, x, y=None):
model_name = name + '.pkl'
if model_name not in os.listdir('data/model'):
model.fit(X=x, y=y)
joblib.dump(model, 'data/model/' + model_name)
return model
else:
model = joblib.load('data/model/' + model_name)
return model
dg = dg()
for i in range(10):
for j in range(10):
train_data, test_data, train_labels, test_labels = dg.dsift_only(j)
svm = SVC(kernel='poly', degree=3)
svm = model_save_load('svm' + str(i) + '-' + str(j), svm,
train_data.reshape([train_data.shape[0], -1]),
train_labels)
plab = p = svm.predict(test_data.reshape([test_data.shape[0],
-1])).reshape(-1,
1).tolist()
t = test_labels.reshape(-1, 1).tolist()
print(str(metrics.accuracy(t, p)))
with open('denseSIFTsvm.txt', 'a') as f: # 设置文件对象
f.write(
str(i) + '-' + str(j) + ',' + str(metrics.accuracy(t, p)) +
',' + str(metrics.precision(t, p)) + ',' +
str(metrics.recall(t, p)) + ',' + str(metrics.f1score(t, p)) +
',' + str(metrics.ft(t, p)) + '\n')
clf = None
clf = RandomForestClassifier(n_estimators=p, criterion='entropy',
max_depth=14, min_samples_split=20,
n_jobs=2)
clf.fit(train_X, train_y)
results.append(metrics.predict_table(clf, test_X, test_y))
result = pd.concat(results)
matrix = metrics.confusion_matrix(result)
clases = matrix.columns.tolist()
precisions = [metrics.precision(matrix, c) for c in clases]
recalls = [metrics.recall(matrix, c) for c in clases]
f_scores = [metrics.f_score(matrix, c) for c in clases]
w_score = metrics.weighted_f_score(matrix)
# f = open(result_dir + str(max_depth) + ' ' + str(min_samples_split) + '.txt', 'w')
f = open(result_dir + str(p) + '.txt', 'w')
f.write('F_score by class')
f.write('\n')
f.write(str(f_scores))
f.write('\n')
f.write('\n')
f.write('Weighted average: ')
f.write(str(w_score))