def search_spammer():
# here to detect if the new review is a spam
#return request.form["title"]
min_score = 1
dup_no = dup.dup(min_score, request.args.get["text"])
# userid = "ALYU98KL3VUGF"/A1GLC0S9H532IU
min_post = 1
early_constraint = 3600*24*180 # (sec*min)*hours*days*months
detection_dict = dev.spammer_detect(request.args.get["userid"], min_post, early_constraint)
#return str(devnrep_dict["time_diff"])
detection_dict["dup_no"] = dup_no
detection_dict["metric"] = dev.metrics(detection_dict)
#print detection_dict
return render_template("result.html", result = detection_dict)
def validation(loader):
loss = 0
prediction_list, target_list = [], []
for batch in loader:
inputs, targets = batch
predictions, targets, out = test_step(inputs, targets)
loss += out
prediction_list.append(predictions)
target_list.append(targets)
y_reco = tf.concat(prediction_list, axis=0)
y_true = tf.concat(target_list, axis=0)
y_true = tf.cast(y_true, tf.float32)
loss, loss_from = loss_func(y_reco, y_true, re=True)
energy, e_old, alpha, zeni, azi = metrics(y_reco, y_true)
return loss, loss_from, [energy, e_old, alpha, zeni, azi]