{"record":{"id":"0afcf051f0637ed5","repo":"microsoft/qlib","slug":"unknown-metric-s-0afcf0","errorCode":null,"errorMessage":"unknown metric `%s`","messagePattern":"unknown metric `(.+?)`","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"qlib/contrib/model/pytorch_tcn_ts.py","lineNumber":163,"sourceCode":"    def mse(self, pred, label):\n        loss = (pred - label) ** 2\n        return torch.mean(loss)\n\n    def loss_fn(self, pred, label):\n        mask = ~torch.isnan(label)\n\n        if self.loss == \"mse\":\n            return self.mse(pred[mask], label[mask])\n\n        raise ValueError(\"unknown loss `%s`\" % self.loss)\n\n    def metric_fn(self, pred, label):\n        mask = torch.isfinite(label)\n\n        if self.metric in (\"\", \"loss\"):\n            return -self.loss_fn(pred[mask], label[mask])\n\n        raise ValueError(\"unknown metric `%s`\" % self.metric)\n\n    def train_epoch(self, data_loader):\n        self.TCN_model.train()\n\n        for data in data_loader:\n            data = torch.transpose(data, 1, 2)\n            feature = data[:, 0:-1, :].to(self.device)\n            label = data[:, -1, -1].to(self.device)\n\n            pred = self.TCN_model(feature.float())\n            loss = self.loss_fn(pred, label)\n\n            self.train_optimizer.zero_grad()\n            loss.backward()\n            torch.nn.utils.clip_grad_value_(self.TCN_model.parameters(), 3.0)\n            self.train_optimizer.step()\n\n    def test_epoch(self, data_loader):","sourceCodeStart":145,"sourceCodeEnd":181,"githubUrl":"https://github.com/microsoft/qlib/blob/79633dd9506ea689e5400dea0197717b5b3d74b7/qlib/contrib/model/pytorch_tcn_ts.py#L145-L181","documentation":"Thrown by TCNTSModel.metric_fn, used to score each epoch for early stopping. Only '' and 'loss' (negated loss) are accepted for the `metric` hyperparameter; anything else raises before the first epoch completes.","triggerScenarios":"TCNTSModel(..., metric='ic') or any value other than ''/'loss', followed by fit(); validation calls metric_fn and hits the raise.","commonSituations":"Reusing a yaml/benchmark config written for models whose metric_fn supports 'ic' (e.g. some TRA/launcher workflows); typo such as 'Loss' or 'negloss'.","solutions":["Use metric='loss' (or '' to default to negative loss) in the TCNTSModel constructor.","For IC-based early stopping, subclass TCNTSModel and override metric_fn with a spearman-correlation implementation over the finite-label mask."],"exampleFix":"# before\nmodel = TCNTSModel(..., metric=\"ic\")\n\n# after\nmodel = TCNTSModel(..., metric=\"loss\")","handlingStrategy":"validation","validationCode":"assert model_kwargs.get(\"metric\", \"\") in (\"\", \"loss\"), \"TCNTSModel metric must be '' or 'loss'\"","typeGuard":null,"tryCatchPattern":"try:\n    model.fit(ds, valid)\nexcept ValueError as e:\n    if \"unknown metric\" in str(e):\n        model_kwargs[\"metric\"] = \"loss\"\n        model = TCNTSModel(**model_kwargs)\n        model.fit(ds, valid)\n    else:\n        raise","preventionTips":["Don't reuse IC-style metric names with TCN time-series models.","Assert supported enum values per model class before launching long benchmark runs."],"tags":["qlib","pytorch","tcn","hyperparameter","validation"],"backgroundTag":null,"analyzedSha":"79633dd9506ea689e5400dea0197717b5b3d74b7","analyzedAt":"2026-08-15T07:01:27.511Z","schemaVersion":2},"datasetVersion":"2026-08-15T17:31:12.345Z"}