def setUp(self):
self.op_type = "auc"
pred = np.random.random((128, 2)).astype("float32")
labels = np.random.randint(0, 2, (128, 1)).astype("int64")
num_thresholds = 200
slide_steps = 0
stat_pos = np.zeros((1, (num_thresholds + 1))).astype("int64")
stat_neg = np.zeros((1, (num_thresholds + 1))).astype("int64")
self.inputs = {
'Predict': pred,
'Label': labels,
"StatPos": stat_pos,
"StatNeg": stat_neg
}
self.attrs = {
'curve': 'ROC',
'num_thresholds': num_thresholds,
"slide_steps": slide_steps
}
python_auc = metrics.Auc(name="auc",
curve='ROC',
num_thresholds=num_thresholds)
python_auc.update(pred, labels)
pos = python_auc._stat_pos
neg = python_auc._stat_neg
self.outputs = {
'AUC': np.array(python_auc.eval()),
'StatPosOut': np.array(pos),
'StatNegOut': np.array(neg)
}
def setUp(self):
self.op_type = "auc"
pred = np.random.random((128, 2)).astype("float32")
pred0 = pred[:, 0].reshape(128, 1)
labels = np.random.randint(0, 2, (128, 1)).astype("int64")
num_thresholds = 200
stat_pos = np.zeros((num_thresholds + 1, )).astype("int64")
stat_neg = np.zeros((num_thresholds + 1, )).astype("int64")
self.inputs = {
'Predict': pred0,
'Label': labels,
"StatPos": stat_pos,
"StatNeg": stat_neg
}
self.attrs = {
'curve': 'ROC',
'num_thresholds': num_thresholds,
"slide_steps": 1
}
python_auc = metrics.Auc(name="auc",
curve='ROC',
num_thresholds=num_thresholds)
for i in range(128):
pred[i][1] = pred[i][0]
python_auc.update(pred, labels)
self.outputs = {
'AUC': np.array(python_auc.eval()),
'StatPosOut': np.array(python_auc._stat_pos),
'StatNegOut': np.array(python_auc._stat_neg)
}