Successfully run training

examples/benchmarks/GeneralPtNN/README.md

@@ -0,0 +1,15 @@
+
+
+# Introduction
+
+What is GeneralPtNN
+- Fix previous design that fail to support both Time-series and tabular data
+- Now you can just replace the Pytorch model structure to run a NN model.
+
+We provide an example to demonstrate the effectiveness of the current design.
+- `workflow_config_gru.yaml` align with previous results [GRU(Kyunghyun Cho, et al.)](../README.md#Alpha158 dataset)
+- `workflow_config_mlp.yaml` align with previous results [MLP](../README.md#Alpha158 dataset)
+
+# TODO
+
+We will align existing models to current design.

examples/benchmarks/GeneralPtNN/workflow_config_gru.yaml

@@ -0,0 +1,97 @@
+qlib_init:
+    provider_uri: "~/.qlib/qlib_data/cn_data"
+    region: cn
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &data_handler_config
+    start_time: 2008-01-01
+    end_time: 2020-08-01
+    fit_start_time: 2008-01-01
+    fit_end_time: 2014-12-31
+    instruments: *market
+    infer_processors:
+        - class: FilterCol
+          kwargs:
+              fields_group: feature
+              col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
+                            "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
+                            "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
+                        ]
+        - class: RobustZScoreNorm
+          kwargs:
+              fields_group: feature
+              clip_outlier: true
+        - class: Fillna
+          kwargs:
+              fields_group: feature
+    learn_processors:
+        - class: DropnaLabel
+        - class: CSRankNorm
+          kwargs:
+              fields_group: label
+    label: ["Ref($close, -2) / Ref($close, -1) - 1"] 
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            signal: <PRED>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: GeneralPTNN
+        module_path: qlib.contrib.model.pytorch_general_nn
+        kwargs:
+            d_feat: 20
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.0
+            n_epochs: 200
+            lr: 2e-4
+            early_stop: 10
+            batch_size: 800
+            metric: loss
+            loss: mse
+            n_jobs: 20
+            GPU: 0
+    dataset:
+        class: TSDatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                module_path: qlib.contrib.data.handler
+                kwargs: *data_handler_config
+            segments:
+                train: [2008-01-01, 2014-12-31]
+                valid: [2015-01-01, 2016-12-31]
+                test: [2017-01-01, 2020-08-01]
+            step_len: 20
+    record: 
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            model: <MODEL>
+            dataset: <DATASET>
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            ana_long_short: False
+            ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config

examples/benchmarks/GeneralPtNN/workflow_config_mlp.yaml

@@ -0,0 +1,98 @@
+qlib_init:
+    provider_uri: "~/.qlib/qlib_data/cn_data"
+    region: cn
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &data_handler_config
+    start_time: 2008-01-01
+    end_time: 2020-08-01
+    fit_start_time: 2008-01-01
+    fit_end_time: 2014-12-31
+    instruments: *market
+    infer_processors: [
+        {
+            "class" : "DropCol", 
+            "kwargs":{"col_list": ["VWAP0"]}
+        },
+        {
+             "class" : "CSZFillna", 
+             "kwargs":{"fields_group": "feature"}
+        }
+    ]
+    learn_processors: [
+        {
+            "class" : "DropCol", 
+            "kwargs":{"col_list": ["VWAP0"]}
+        },
+        {
+            "class" : "DropnaProcessor", 
+            "kwargs":{"fields_group": "feature"}
+        },
+        "DropnaLabel",
+        {
+            "class": "CSZScoreNorm", 
+            "kwargs": {"fields_group": "label"}
+        }
+    ]
+    process_type: "independent"
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            signal: <PRED>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: GeneralPTNN
+        module_path: qlib.contrib.model.pytorch_general_nn
+        kwargs:
+            loss: mse
+            lr: 0.002
+            optimizer: adam
+            max_steps: 8000
+            batch_size: 8192
+            GPU: 0
+            weight_decay: 0.0002
+            pt_model_kwargs:
+              input_dim: 157
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                module_path: qlib.contrib.data.handler
+                kwargs: *data_handler_config
+            segments:
+                train: [2008-01-01, 2014-12-31]
+                valid: [2015-01-01, 2016-12-31]
+                test: [2017-01-01, 2020-08-01]
+    record: 
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            model: <MODEL>
+            dataset: <DATASET>
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            ana_long_short: False
+            ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config

qlib/contrib/model/pytorch_general_nn.py

@@ -0,0 +1,663 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import division
+from __future__ import print_function
+
+from torch.utils.data import DataLoader, RandomSampler, StackDataset
+
+
+import os
+import numpy as np
+import pandas as pd
+from typing import Callable, Optional, Text, Union
+from sklearn.metrics import roc_auc_score, mean_squared_error
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import StackDataset
+
+from qlib.data.dataset.weight import Reweighter
+
+from .pytorch_utils import count_parameters
+from ...model.base import Model
+from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset.handler import DataHandlerLP
+from ...utils import (
+    auto_filter_kwargs,
+    init_instance_by_config,
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    get_or_create_path,
+)
+from ...log import get_module_logger
+from ...workflow import R
+from qlib.contrib.meta.data_selection.utils import ICLoss
+from torch.nn import DataParallel
+
+
+class GeneralPTNN(Model):
+    """General Pytorch Neural Network Model
+    Parameters
+    ----------
+    input_dim : int
+        input dimension
+    output_dim : int
+        output dimension
+    layers : tuple
+        layer sizes
+    lr : float
+        learning rate
+    optimizer : str
+        optimizer name
+    GPU : int
+        the GPU ID used for training
+    """
+
+    def __init__(
+        self,
+        lr=0.001,
+        max_steps=300,
+        batch_size=2000,
+        early_stop_rounds=50,
+        eval_steps=20,
+        optimizer="gd",
+        loss="mse",
+        GPU=0,
+        seed=None,
+        weight_decay=0.0,
+        data_parall=False,
+        scheduler: Optional[Union[Callable]] = "default",  # when it is Callable, it accept one argument named optimizer
+        init_model=None,
+        eval_train_metric=False,
+        pt_model_uri="qlib.contrib.model.pytorch_nn.Net",
+        pt_model_kwargs={
+            "input_dim": 360,
+            "layers": (256,),
+        },
+        valid_key=DataHandlerLP.DK_L,
+        # TODO: Infer Key is a more reasonable key. But it requires more detailed processing on label processing
+    ):
+        # Set logger.
+        self.logger = get_module_logger("DNNModelPytorch")
+        self.logger.info("DNN pytorch version...")
+
+        # set hyper-parameters.
+        self.lr = lr
+        self.max_steps = max_steps
+        self.batch_size = batch_size
+        self.early_stop_rounds = early_stop_rounds
+        self.eval_steps = eval_steps
+        self.optimizer = optimizer.lower()
+        self.loss_type = loss
+        if isinstance(GPU, str):
+            self.device = torch.device(GPU)
+        else:
+            self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
+        self.seed = seed
+        self.weight_decay = weight_decay
+        self.data_parall = data_parall
+        self.eval_train_metric = eval_train_metric
+        self.valid_key = valid_key
+
+        self.best_step = None
+
+        self.logger.info(
+            "DNN parameters setting:"
+            f"\nlr : {lr}"
+            f"\nmax_steps : {max_steps}"
+            f"\nbatch_size : {batch_size}"
+            f"\nearly_stop_rounds : {early_stop_rounds}"
+            f"\neval_steps : {eval_steps}"
+            f"\noptimizer : {optimizer}"
+            f"\nloss_type : {loss}"
+            f"\nseed : {seed}"
+            f"\ndevice : {self.device}"
+            f"\nuse_GPU : {self.use_gpu}"
+            f"\nweight_decay : {weight_decay}"
+            f"\nenable data parall : {self.data_parall}"
+            f"\npt_model_uri: {pt_model_uri}"
+            f"\npt_model_kwargs: {pt_model_kwargs}"
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        if loss not in {"mse", "binary"}:
+            raise NotImplementedError("loss {} is not supported!".format(loss))
+        self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
+
+        if init_model is None:
+            self.dnn_model = init_instance_by_config({"class": pt_model_uri, "kwargs": pt_model_kwargs})
+
+            if self.data_parall:
+                self.dnn_model = DataParallel(self.dnn_model).to(self.device)
+        else:
+            self.dnn_model = init_model
+
+        self.logger.info("model:\n{:}".format(self.dnn_model))
+        self.logger.info("model size: {:.4f} MB".format(count_parameters(self.dnn_model)))
+
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        if scheduler == "default":
+            # Reduce learning rate when loss has stopped decrease
+            self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+                self.train_optimizer,
+                mode="min",
+                factor=0.5,
+                patience=10,
+                verbose=True,
+                threshold=0.0001,
+                threshold_mode="rel",
+                cooldown=0,
+                min_lr=0.00001,
+                eps=1e-08,
+            )
+        elif scheduler is None:
+            self.scheduler = None
+        else:
+            self.scheduler = scheduler(optimizer=self.train_optimizer)
+
+        self.dnn_model.to(self.device)
+
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
+    
+    def _eval_valid_dl(self, valid_loader, val_index):
+        with torch.no_grad():
+            self.dnn_model.eval()
+            val_loss = []
+            val_pred = []
+            val_label = []
+            for x_batch, y_batch in valid_loader:
+                x_batch = x_batch.to(self.device)
+                y_batch = y_batch.to(self.device)
+                cur_loss = self.get_loss(preds, y_batch, self.loss_type)
+                val_loss.append(cur_loss.detach().cpu().numpy().item())
+            val_loss = np.mean(val_loss)
+            val_pred = torch.cat(val_pred, axis=0).detach().cpu().numpy()
+            val_label = torch.cat(val_label, axis=0).detach().cpu().numpy()
+            val_metric = self.get_metric(val_pred, val_label, val_index).detach().cpu().numpy().item()
+        return val_loss, val_metric
+
+    def fit(
+        self,
+        dataset: Union[DatasetH, TSDatasetH],
+        verbose=True,
+        save_path=None,
+    ):
+
+        ists = isinstance(dataset, TSDatasetH)  # is this time series dataset
+
+        # prepare training
+        train_x = dataset.prepare("train", col_set="feature", data_key=DataHandlerLP.DK_L)
+        train_y = dataset.prepare("train", col_set="label", data_key=DataHandlerLP.DK_L)
+        train_ds = StackDataset(train_x, train_y)
+        train_sampler = RandomSampler(train_ds)
+        train_loader = DataLoader(train_ds, batch_size=self.batch_size, sampler=train_sampler)
+
+        # prepare validation
+        valid_x = dataset.prepare("train", col_set="feature", data_key=DataHandlerLP.DK_L)
+        valid_y = dataset.prepare("train", col_set="label", data_key=DataHandlerLP.DK_L)
+        valid_ds = StackDataset(valid_x, valid_y)
+        valid_loader = DataLoader(valid_ds, batch_size=self.batch_size, shuffle=False)
+        if ists:
+            val_index = valid_x.data_index
+        else:
+            val_index = valid_x.index
+
+
+        save_path = get_or_create_path(save_path)
+        stop_steps = 0
+        train_loss = 0
+        best_loss = np.inf
+        # train
+        self.logger.info("training...")
+
+
+        for step in range(1, self.max_steps + 1):
+            if stop_steps >= self.early_stop_rounds:
+                if verbose:
+                    self.logger.info("\tearly stop")
+                break
+            loss = AverageMeter()
+            self.dnn_model.train()
+            self.train_optimizer.zero_grad()
+
+            for x_batch, y_batch in train_loader:
+                x_batch = x_batch.to(self.device)
+                y_batch = y_batch.to(self.device)
+
+                # forward
+                preds = self.dnn_model(x_batch)
+                cur_loss = self.get_loss(preds, y_batch, self.loss_type)
+                cur_loss.backward()
+                self.train_optimizer.step()
+                loss.update(cur_loss.item())
+                R.log_metrics(train_loss=loss.avg, step=step)
+
+            # validation
+            train_loss += loss.val
+            # for every `eval_steps` steps or at the last steps, we will evaluate the model.
+            if step % self.eval_steps == 0 or step == self.max_steps:
+                stop_steps += 1
+                train_loss /= self.eval_steps
+
+                val_loss, val_metric = self._eval_valid_dl(valid_loader, val_index)
+                R.log_metrics(val_loss=val_loss, step=step)
+                R.log_metrics(val_metric=val_metric, step=step)
+
+                if val_loss < best_loss:
+                    if verbose:
+                        self.logger.info(
+                            "\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
+                                best_loss, val_loss
+                            )
+                        )
+                    best_loss = val_loss
+                    self.best_step = step
+                    R.log_metrics(best_step=self.best_step, step=step)
+                    stop_steps = 0
+                    torch.save(self.dnn_model.state_dict(), save_path)
+                train_loss = 0
+                # update learning rate
+                if self.scheduler is not None:
+                    auto_filter_kwargs(self.scheduler.step, warning=False)(metrics=val_loss, epoch=step)
+                R.log_metrics(lr=self.get_lr(), step=step)
+
+            # restore the optimal parameters after training
+            self.dnn_model.load_state_dict(torch.load(save_path, map_location=self.device))
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def get_lr(self):
+        assert len(self.train_optimizer.param_groups) == 1
+        return self.train_optimizer.param_groups[0]["lr"]
+
+    def get_loss(self, pred, target, loss_type, w=None):
+        pred, target = pred.reshape(-1), target.reshape(-1)
+        if w is None:
+            # make it ones and the same size with pred
+            w = torch.ones_like(pred).to(pred.device)
+
+        if loss_type == "mse":
+            sqr_loss = torch.mul(pred - target, pred - target)
+            loss = torch.mul(sqr_loss, w).mean()
+            return loss
+        elif loss_type == "binary":
+            loss = nn.BCEWithLogitsLoss(weight=w)
+            return loss(pred, target)
+        else:
+            raise NotImplementedError("loss {} is not supported!".format(loss_type))
+
+    def get_metric(self, pred, target, index):
+        # NOTE: the order of the index must follow <datetime, instrument> sorted order
+        return -ICLoss()(pred, target, index)  # pylint: disable=E1130
+
+    def _nn_predict(self, data, return_cpu=True):
+        """Reusing predicting NN.
+        Scenarios
+        1) test inference (data may come from CPU and expect the output data is on CPU)
+        2) evaluation on training (data may come from GPU)
+        """
+        if not isinstance(data, torch.Tensor):
+            if isinstance(data, pd.DataFrame):
+                data = data.values
+            data = torch.Tensor(data)
+        data = data.to(self.device)
+        preds = []
+        self.dnn_model.eval()
+        with torch.no_grad():
+            batch_size = 8096
+            for i in range(0, len(data), batch_size):
+                x = data[i : i + batch_size]
+                preds.append(self.dnn_model(x.to(self.device)).detach().reshape(-1))
+        if return_cpu:
+            preds = np.concatenate([pr.cpu().numpy() for pr in preds])
+        else:
+            preds = torch.cat(preds, axis=0)
+        return preds
+
+    def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
+        x_test_pd = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
+        preds = self._nn_predict(x_test_pd)
+        return pd.Series(preds.reshape(-1), index=x_test_pd.index)
+
+
+class AverageMeter:
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        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,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        n_jobs=10,
+        GPU=0,
+        seed=None,
+        pt_model_uri="qlib.contrib.model.pytorch_gru_ts.GRUModel",
+        pt_model_kwargs={
+            "d_feat":6,
+            "hidden_size":64,
+            "num_layers":2,
+            "dropout":0.,
+        },
+    ):
+        # Set logger.
+        self.logger = get_module_logger("GeneralPTNN")
+        self.logger.info("GeneralPTNN pytorch version...")
+
+        # set hyper-parameters.
+        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.pt_model_uri, self.pt_model_kwargs = pt_model_uri, pt_model_kwargs
+        self.dnn_model = init_instance_by_config({"class": pt_model_uri, "kwargs": pt_model_kwargs})
+
+        self.logger.info(
+            "GeneralPTNN parameters setting:"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\ndevice : {}"
+            "\nn_jobs : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}"
+            "\npt_model_uri: {}"
+            "\npt_model_kwargs: {}".format(
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                self.device,
+                n_jobs,
+                self.use_gpu,
+                seed,
+                pt_model_uri,
+                pt_model_kwargs,
+            )
+
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.logger.info("model:\n{:}".format(self.dnn_model))
+        self.logger.info("model size: {:.4f} MB".format(count_parameters(self.dnn_model)))
+
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self.fitted = False
+        self.dnn_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 _get_fl(self, data: torch.Tensor):
+        """
+        get feature and label from data
+        - Handle the different data shape of time series and tabular data
+
+        Parameters
+        ----------
+        data : torch.Tensor
+            input data which maybe 3 dimension or 2 dimension
+            - 3dim: [batch_size, time_step, feature_dim]
+            - 2dim: [batch_size, feature_dim]
+
+        Returns
+        -------
+        Tuple[torch.Tensor, torch.Tensor]
+        """
+        if data.dim() == 3:
+            # it is a time series dataset
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+        elif data.dim() == 2:
+            # it is a tabular dataset
+            feature = data[:, 0:-1].to(self.device)
+            label = data[:, -1].to(self.device)
+        else:
+            raise ValueError("Unsupported data shape.")
+        return feature, label
+
+    def train_epoch(self, data_loader):
+        self.dnn_model.train()
+
+        for data, weight in data_loader:
+            feature , label = self._get_fl(data)
+
+            pred = self.dnn_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.dnn_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+        self.dnn_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.dnn_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: Union[DatasetH, TSDatasetH],
+        evals_result=dict(),
+        save_path=None,
+        reweighter=None,
+    ):
+        ists = isinstance(dataset, TSDatasetH)  # is this time series dataset
+
+        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.")
+
+        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.")
+
+        # Preprocess for data.  To align to Dataset Interface for DataLoader
+        if ists:
+            dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+            dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        else:
+            # If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
+            dl_train = dl_train.values
+            dl_valid = dl_valid.values
+
+        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,
+        )
+        del dl_train, dl_valid, wl_train, wl_valid
+
+        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.dnn_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.dnn_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset: Union[DatasetH, TSDatasetH]):
+        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.dnn_model.eval()
+        preds = []
+
+        for data in test_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                pred = self.dnn_model(feature.float()).detach().cpu().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())

tests/model/test_general_nn.py

@@ -0,0 +1,86 @@
+
+import unittest
+
+from qlib.contrib.model.pytorch_general_nn import GeneralPTNN
+from qlib.data.dataset import DatasetH, TSDatasetH
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.tests import TestAutoData
+
+
+class TestNN(TestAutoData):
+
+    def test_both_dataset(self):
+        data_handler_config = {
+            "start_time": "2008-01-01",
+            "end_time": "2020-08-01",
+            "instruments": "csi300",
+            "data_loader": {
+                "class": "QlibDataLoader",  # Assuming QlibDataLoader is a string reference to the class
+                "kwargs": {
+                    "config": {
+                        "feature": [
+                            ["$high", "$close", "$low"],
+                            ["H", "C", "L"]
+                        ],
+                        "label": [
+                            ["Ref($close, -2)/Ref($close, -1) - 1"],
+                            ["LABEL0"]
+                        ]
+                    },
+                    "freq": "day"
+                }
+            },
+            # TODO: processors
+            "learn_processors": [
+                {
+                "class": "DropnaLabel",
+                },
+                {
+                    "class": "CSZScoreNorm",
+                    "kwargs": {
+                        "fields_group": "label"
+                    }
+                }
+            ]
+        }
+        segments = {
+            "train": ["2008-01-01", "2014-12-31"],
+            "valid": ["2015-01-01", "2016-12-31"],
+            "test": ["2017-01-01", "2020-08-01"]
+        }
+        data_handler = DataHandlerLP(**data_handler_config)
+
+        # time-series dataset
+        tsds = TSDatasetH(handler=data_handler, segments=segments)
+
+        # tabular dataset
+        tbds = DatasetH(handler=data_handler, segments=segments)
+
+        model_l = [
+            GeneralPTNN(
+                n_epochs=2,
+                pt_model_uri="qlib.contrib.model.pytorch_gru_ts.GRUModel",
+                pt_model_kwargs={
+                    "d_feat":3,
+                    "hidden_size":8,
+                    "num_layers":1,
+                    "dropout":0.,
+                },
+            ),
+            GeneralPTNN(
+                n_epochs=2,
+                pt_model_uri="qlib.contrib.model.pytorch_nn.Net",  # it is a MLP
+                pt_model_kwargs={
+                    "input_dim":3,
+                },
+            ),
+        ]
+        
+        for ds, model in reversed(list(zip((tsds, tbds), model_l))):
+            model.fit(ds)  # It works
+            model.predict(ds)  # It works
+            break
+
+
+if __name__ == "__main__":
+    unittest.main()