def eval(self, predict, labels_map):
label = labels_map[self._label_name]
if np.sum(label) == 0 or np.sum(label) == label.size:
return MetricResult(result=float('nan'))
else:
auc = roc_auc_score(y_true=label, y_score=predict)
return MetricResult(result=auc, meta={'#': predict.size})
def eval(self, predict, labels_map):
label = labels_map[self._label_name]
if np.sum(label) == 0 or np.sum(label) == label.size:
return MetricResult(result=float('nan'))
else:
mse = mean_squared_error(y_true=label, y_pred=predict)
return MetricResult(result=mse, meta={'#': predict.size})
def eval(self, predict, labels_map):
label = labels_map[self._label_name]
group_key = labels_map[self._group_key_name]
weight = np.maximum(labels_map[self._weight_name], 1)
predict_groups, label_groups = defaultdict(list), defaultdict(list)
weight_groups = defaultdict(list)
for l, p, key, w in zip(label.flatten(), predict.flatten(),
group_key.flatten(), weight.flatten()):
predict_groups[key].append(p)
label_groups[key].append(l)
weight_groups[key].append(w)
weight_sum, auc_sum = 0, 0
for key in label_groups.keys():
if len(label_groups) >= 2:
n_pos = sum(label_groups[key])
n_neg = len(label_groups[key]) - n_pos
if n_pos > 0 and n_neg > 0:
weight_sum += n_pos * n_neg
cur_auc = roc_auc_score(y_true=label_groups[key],
y_score=predict_groups[key],
sample_weight=weight_groups[key])
auc_sum += n_pos * n_neg * cur_auc
if weight_sum == 0:
return MetricResult(result=float('nan'))
else:
wgauc = auc_sum / weight_sum
return MetricResult(result=wgauc)
def eval(self, predict, labels_map):
label = labels_map[self._label_name]
group_key = labels_map[self._group_key_name]
predict_groups, label_groups = defaultdict(list), defaultdict(list)
for l, p, key in zip(label.flatten(), predict.flatten(),
group_key.flatten()):
predict_groups[key].append(p)
label_groups[key].append(l)
weight_sum, auc_sum = 0, 0
for key in label_groups.keys():
if len(label_groups) >= 2:
n_pos = sum(label_groups[key])
n_neg = len(label_groups[key]) - n_pos
if n_pos > 0 and n_neg > 0:
weight_sum += n_pos * n_neg
cur_auc = roc_auc_score(y_true=label_groups[key],
y_score=predict_groups[key])
auc_sum += n_pos * n_neg * cur_auc
if weight_sum == 0:
return MetricResult(result=float('nan'))
else:
gauc = auc_sum / weight_sum
return MetricResult(result=gauc,
meta={
'#': predict.size,
'#pairs': weight_sum
})
def eval(self, predict, labels_map):
label = labels_map[self._label_name]
if np.sum(label) == 0 or np.sum(label) == label.size:
return MetricResult(result=float('nan'))
else:
weight=np.where(label>0,np.ones_like(label),np.zeros_like(label))
mse = mean_squared_error(y_true=label, y_pred=predict,sample_weight=weight)
return MetricResult(result=mse, meta={'#': predict.size})
def eval(self, predict, labels_map):
label = labels_map[self._label_name]
weight = np.maximum(labels_map[self._weight_name], 1)
if np.sum(label) == 0 or np.sum(label) == label.size:
return MetricResult(result=float('nan'))
else:
wauc = roc_auc_score(y_true=label,
y_score=predict,
sample_weight=weight)
return MetricResult(result=wauc)