{"record":{"id":"092291217d0499e5","repo":"microsoft/qlib","slug":"unknown-metric-s-092291","errorCode":null,"errorMessage":"unknown metric `%s`","messagePattern":"unknown metric `(.+?)`","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"qlib/contrib/model/pytorch_localformer.py","lineNumber":103,"sourceCode":"    def mse(self, pred, label):\r\n        loss = (pred.float() - label.float()) ** 2\r\n        return torch.mean(loss)\r\n\r\n    def loss_fn(self, pred, label):\r\n        mask = ~torch.isnan(label)\r\n\r\n        if self.loss == \"mse\":\r\n            return self.mse(pred[mask], label[mask])\r\n\r\n        raise ValueError(\"unknown loss `%s`\" % self.loss)\r\n\r\n    def metric_fn(self, pred, label):\r\n        mask = torch.isfinite(label)\r\n\r\n        if self.metric in (\"\", \"loss\"):\r\n            return -self.loss_fn(pred[mask], label[mask])\r\n\r\n        raise ValueError(\"unknown metric `%s`\" % self.metric)\r\n\r\n    def train_epoch(self, x_train, y_train):\r\n        x_train_values = x_train.values\r\n        y_train_values = np.squeeze(y_train.values)\r\n\r\n        self.model.train()\r\n\r\n        indices = np.arange(len(x_train_values))\r\n        np.random.shuffle(indices)\r\n\r\n        for i in range(len(indices))[:: self.batch_size]:\r\n            if len(indices) - i < self.batch_size:\r\n                break\r\n\r\n            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)\r\n            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)\r\n\r\n            pred = self.model(feature)\r","sourceCodeStart":85,"sourceCodeEnd":121,"githubUrl":"https://github.com/microsoft/qlib/blob/79633dd9506ea689e5400dea0197717b5b3d74b7/qlib/contrib/model/pytorch_localformer.py#L85-L121","documentation":"LocalTransformerModel.metric_fn supports metric in ('', 'loss') only, both meaning 'score = negative loss' over finite labels. Any other metric string (notably 'ic', which other qlib models support) raises ValueError during validation scoring in fit().","triggerScenarios":"LocalTransformerModel(metric='ic') or any value other than ''/'loss', then fit(); surfaces when the first validation score is computed.","commonSituations":"Defaults copied from models where 'ic' is valid; assuming every qlib torch model computes IC.","solutions":["Use metric='' or metric='loss'","Subclass and add an IC branch if IC-based early stopping is required"],"exampleFix":"# before\nLocalTransformerModel(metric=\"ic\")\n\n# after\nLocalTransformerModel(metric=\"loss\")","handlingStrategy":"validation","validationCode":"assert metric in (\"\", \"loss\"), \"LocalTransformerModel metric must be '' or 'loss'\"","typeGuard":"def is_supported_metric(name: str) -> bool:\n    return name in (\"\", \"loss\")","tryCatchPattern":null,"preventionTips":["Do not use metric='ic' with LocalTransformerModel","Maintain a per-model matrix of supported loss/metric/optimizer values"],"tags":["qlib","localformer","metric","invalid-argument"],"backgroundTag":null,"analyzedSha":"79633dd9506ea689e5400dea0197717b5b3d74b7","analyzedAt":"2026-08-15T07:01:27.511Z","schemaVersion":2},"datasetVersion":"2026-08-15T22:17:37.221Z"}