microsoft/qlib · error · ValueError
unknown metric `%s`
Error message
unknown metric `%s`
What it means
Raised by GRUModelTS.metric_fn when self.metric is not "" or "loss". Validation scoring for the TS GRU is the negated training loss only; "ic" and other standard qlib metrics are not implemented in this class. Fires during the first validation epoch.
Source
Thrown at qlib/contrib/model/pytorch_gru_ts.py:162
def loss_fn(self, pred, label, weight=None):
mask = ~torch.isnan(label)
if weight is None:
weight = torch.ones_like(label)
if self.loss == "mse":
return self.mse(pred[mask], label[mask], weight[mask])
raise ValueError("unknown loss `%s`" % self.loss)
def metric_fn(self, pred, label):
mask = torch.isfinite(label)
if self.metric in ("", "loss"):
return -self.loss_fn(pred[mask], label[mask])
raise ValueError("unknown metric `%s`" % self.metric)
def train_epoch(self, data_loader):
self.GRU_model.train()
for data, weight in data_loader:
feature = data[:, :, 0:-1].to(self.device)
label = data[:, -1, -1].to(self.device)
pred = self.GRU_model(feature.float())
loss = self.loss_fn(pred, label, weight.to(self.device))
self.train_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_value_(self.GRU_model.parameters(), 3.0)
self.train_optimizer.step()
def test_epoch(self, data_loader):
self.GRU_model.eval()View on GitHub (pinned to 79633dd950)
Solutions
- Use metric="" or "loss".
- Subclass GRUModelTS, override metric_fn to add an "ic" branch (negate appropriately since higher scores are treated as better).
Example fix
# before GRUModelTS(metric="ic", ...) # ValueError # after GRUModelTS(metric="loss", ...)
Defensive patterns
Strategy: validation
Validate before calling
assert params.get("metric", "") in {"", "loss"}, "GRUModelTS metric must be '' or 'loss'" Type guard
def is_supported_metric(metric: str) -> bool:
return metric in {"", "loss"} Try / catch
try:
model.fit(dataset)
except ValueError as e:
if "unknown metric" in str(e):
params["metric"] = "loss"
model = GRUModelTS(**params)
model.fit(dataset)
else:
raise Prevention
- Check the concrete metric_fn dispatch of each model class before configuring metrics.
- Remember TS variants return -loss (higher-is-better) when adding custom metrics.
When it happens
Trigger: GRUModelTS(metric="ic") followed by fit() with a valid segment; also note the returned score is -loss_fn(...), i.e. higher-is-better convention, which matters when adding custom metrics.
Common situations: Copying benchmark configs that use metric="ic" with models that do support it; assuming metric names are uniform across qlib contrib models.
Related errors
AI-assisted analysis of microsoft/qlib@79633dd950 (2026-08-15).
Data as JSON: /api/errors/7a46820a43be44bb.
Report an issue: GitHub.