We choose another mode as the initial version

qlib/contrib/model/pytorch_general_nn.py

@@ -348,3 +348,285 @@ class AverageMeter:
         self.sum += val * n
         self.count += n
         self.avg = self.sum / self.count
+
+
+from ...model.utils import ConcatDataset
+
+class GeneralPTNN(Model):
+    """
+    Motivation:
+        We want to provide a Qlib General Pytorch Model Adaptor
+        You can reuse it for all kinds of Pytorch models.
+        It should include the training and predict process
+
+    Parameters
+    ----------
+    d_feat : int
+        input dimension for each time step
+    metric: str
+        the evaluation metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        d_feat=6,
+        hidden_size=64,
+        num_layers=2,
+        dropout=0.0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        n_jobs=10,
+        GPU=0,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("GRU")
+        self.logger.info("GRU pytorch version...")
+
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.metric = metric
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
+        self.n_jobs = n_jobs
+        self.seed = seed
+
+        self.logger.info(
+            "GRU parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\ndevice : {}"
+            "\nn_jobs : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                self.device,
+                n_jobs,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.GRU_model = GRUModel(
+            d_feat=self.d_feat,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+        )
+        self.logger.info("model:\n{:}".format(self.GRU_model))
+        self.logger.info("model size: {:.4f} MB".format(count_parameters(self.GRU_model)))
+
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.GRU_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self.fitted = False
+        self.GRU_model.to(self.device)
+
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
+    def mse(self, pred, label, weight):
+        loss = weight * (pred - label) ** 2
+        return torch.mean(loss)
+
+    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()
+
+        scores = []
+        losses = []
+
+        for data, weight in data_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+            # feature[torch.isnan(feature)] = 0
+            label = data[:, -1, -1].to(self.device)
+
+            with torch.no_grad():
+                pred = self.GRU_model(feature.float())
+                loss = self.loss_fn(pred, label, weight.to(self.device))
+                losses.append(loss.item())
+
+                score = self.metric_fn(pred, label)
+                scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def fit(
+        self,
+        dataset,
+        evals_result=dict(),
+        save_path=None,
+        reweighter=None,
+    ):
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        if dl_train.empty or dl_valid.empty:
+            raise ValueError("Empty data from dataset, please check your dataset config.")
+
+        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+
+        if reweighter is None:
+            wl_train = np.ones(len(dl_train))
+            wl_valid = np.ones(len(dl_valid))
+        elif isinstance(reweighter, Reweighter):
+            wl_train = reweighter.reweight(dl_train)
+            wl_valid = reweighter.reweight(dl_valid)
+        else:
+            raise ValueError("Unsupported reweighter type.")
+
+        train_loader = DataLoader(
+            ConcatDataset(dl_train, wl_train),
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.n_jobs,
+            drop_last=True,
+        )
+        valid_loader = DataLoader(
+            ConcatDataset(dl_valid, wl_valid),
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.n_jobs,
+            drop_last=True,
+        )
+
+        save_path = get_or_create_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # train
+        self.logger.info("training...")
+        self.fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.GRU_model.state_dict())
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.GRU_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset):
+        if not self.fitted:
+            raise ValueError("model is not fitted yet!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+        dl_test.config(fillna_type="ffill+bfill")
+        test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
+        self.GRU_model.eval()
+        preds = []
+
+        for data in test_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                pred = self.GRU_model(feature.float()).detach().cpu().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())