Here are the examples of the python api numpy.nanargmax taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
5 Examples
3
Example 1
Project: icu_rnn Source File: evaluation.py
def optimize_threshold_with_f1(f1c, thresholds, criterion='max'):
#f1c[np.isnan(f1c)] = 0
if criterion == 'max':
ti = np.nanargmax(f1c)
else:
ti = np.nanargmin(np.abs(thresholds-0.5*f1c))
#assert(np.all(thresholds>=0))
#idx = (thresholds>=f1c*0.5-mp) & (thresholds<=f1c*0.5+mp)
#assert(np.any(idx))
#ti = np.where(idx)[0][f1c[idx].argmax()]
return thresholds[ti], ti
0
Example 2
def guess(representation, sims, xi, a, a_, b):
sa = sims[xi[a]]
sa_ = sims[xi[a_]]
sb = sims[xi[b]]
add_sim = -sa+sa_+sb
if a in representation.wi:
add_sim[representation.wi[a]] = 0
if a_ in representation.wi:
add_sim[representation.wi[a_]] = 0
if b in representation.wi:
add_sim[representation.wi[b]] = 0
b_add = representation.iw[np.nanargmax(add_sim)]
mul_sim = sa_*sb*np.reciprocal(sa+0.01)
if a in representation.wi:
mul_sim[representation.wi[a]] = 0
if a_ in representation.wi:
mul_sim[representation.wi[a_]] = 0
if b in representation.wi:
mul_sim[representation.wi[b]] = 0
b_mul = representation.iw[np.nanargmax(mul_sim)]
return b_add, b_mul
0
Example 3
Project: GroundedTranslation Source File: Callbacks.py
def early_stop_decision(self, epoch, val_metric, val_loss):
'''
Stop training if validation loss has stopped decreasing and
validation BLEU score has not increased for --patience epochs.
WARNING: quits with sys.exit(0).
TODO: this doesn't yet support early stopping based on TER
'''
if val_loss < self.best_val_loss:
self.wait = 0
elif val_metric > self.best_val_metric or self.args.no_early_stopping:
self.wait = 0
else:
self.wait += 1
if self.wait >= self.patience:
# we have exceeded patience
if val_loss > self.best_val_loss:
# and loss is no longer decreasing
logger.info("Epoch %d: early stopping", epoch)
handle = open("checkpoints/%s/summary"
% self.args.run_string, "a")
handle.write("Early stopping because patience exceeded\n")
best_bleu = np.nanargmax(self.val_metric)
best_loss = np.nanargmin(self.val_loss)
logger.info("Best Metric: %d | val loss %.5f score %.2f",
best_bleu+1, self.val_loss[best_bleu],
self.val_metric[best_bleu])
logger.info("Best loss: %d | val loss %.5f score %.2f",
best_loss+1, self.val_loss[best_loss],
self.val_metric[best_loss])
handle.close()
sys.exit(0)
0
Example 4
def log_performance(self):
'''
Record model performance so far, based on validation loss.
'''
handle = open("checkpoints/%s/summary" % self.args.run_string, "w")
for epoch in range(len(self.val_loss)):
handle.write("Checkpoint %d | val loss: %.5f bleu %.2f\n"
% (epoch+1, self.val_loss[epoch],
self.val_metric[epoch]))
logger.info("---") # break up the presentation for clarity
# BLEU is the quickest indicator of performance for our task
# but loss is our objective function
best_bleu = np.nanargmax(self.val_metric)
best_loss = np.nanargmin(self.val_loss)
logger.info("Best Metric: %d | val loss %.5f score %.2f",
best_bleu+1, self.val_loss[best_bleu],
self.val_metric[best_bleu])
handle.write("Best Metric: %d | val loss %.5f score %.2f\n"
% (best_bleu+1, self.val_loss[best_bleu],
self.val_metric[best_bleu]))
logger.info("Best loss: %d | val loss %.5f score %.2f",
best_loss+1, self.val_loss[best_loss],
self.val_metric[best_loss])
handle.write("Best loss: %d | val loss %.5f score %.2f\n"
% (best_loss+1, self.val_loss[best_loss],
self.val_metric[best_loss]))
logger.info("Early stopping marker: wait/patience: %d/%d\n",
self.wait, self.patience)
handle.write("Early stopping marker: wait/patience: %d/%d\n" %
(self.wait, self.patience))
handle.close()
0
Example 5
def argf(self, *args, **kwargs): return np.nanargmax(*args, **kwargs)
class Extremum(ch.Ch):