added KRNN and Sandwich models and their example results based on Alpha360 (#1414)

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---------

Co-authored-by: Young <afe.young@gmail.com>

qlib/contrib/model/pytorch_krnn.py

@@ -0,0 +1,511 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import pandas as pd
+from typing import Text, Union
+import copy
+from ...utils import get_or_create_path
+from ...log import get_module_logger
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from ...model.base import Model
+from ...data.dataset import DatasetH
+from ...data.dataset.handler import DataHandlerLP
+
+########################################################################
+########################################################################
+########################################################################
+
+
+class CNNEncoderBase(nn.Module):
+    def __init__(self, input_dim, output_dim, kernel_size, device):
+        """Build a basic CNN encoder
+
+        Parameters
+        ----------
+        input_dim : int
+            The input dimension
+        output_dim : int
+            The output dimension
+        kernel_size : int
+            The size of convolutional kernels
+        """
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.kernel_size = kernel_size
+        self.device = device
+
+        # set padding to ensure the same length
+        # it is correct only when kernel_size is odd, dilation is 1, stride is 1
+        self.conv = nn.Conv1d(input_dim, output_dim, kernel_size, padding=(kernel_size - 1) // 2)
+
+    def forward(self, x):
+        """
+        Parameters
+        ----------
+        x : torch.Tensor
+            input data
+
+        Returns
+        -------
+        torch.Tensor
+            Updated representations
+        """
+
+        # input shape: [batch_size, seq_len*input_dim]
+        # output shape: [batch_size, seq_len, input_dim]
+        x = x.view(x.shape[0], -1, self.input_dim).permute(0, 2, 1).to(self.device)
+        y = self.conv(x)  # [batch_size, output_dim, conved_seq_len]
+        y = y.permute(0, 2, 1)  # [batch_size, conved_seq_len, output_dim]
+
+        return y
+
+
+class KRNNEncoderBase(nn.Module):
+    def __init__(self, input_dim, output_dim, dup_num, rnn_layers, dropout, device):
+        """Build K parallel RNNs
+
+        Parameters
+        ----------
+        input_dim : int
+            The input dimension
+        output_dim : int
+            The output dimension
+        dup_num : int
+            The number of parallel RNNs
+        rnn_layers: int
+            The number of RNN layers
+        """
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.dup_num = dup_num
+        self.rnn_layers = rnn_layers
+        self.dropout = dropout
+        self.device = device
+
+        self.rnn_modules = nn.ModuleList()
+        for _ in range(dup_num):
+            self.rnn_modules.append(nn.GRU(input_dim, output_dim, num_layers=self.rnn_layers, dropout=dropout))
+
+    def forward(self, x):
+        """
+        Parameters
+        ----------
+        x : torch.Tensor
+            Input data
+        n_id : torch.Tensor
+            Node indices
+
+        Returns
+        -------
+        torch.Tensor
+            Updated representations
+        """
+
+        # input shape: [batch_size, seq_len, input_dim]
+        # output shape: [batch_size, seq_len, output_dim]
+        # [seq_len, batch_size, input_dim]
+        batch_size, seq_len, input_dim = x.shape
+        x = x.permute(1, 0, 2).to(self.device)
+
+        hids = []
+        for rnn in self.rnn_modules:
+            h, _ = rnn(x)  # [seq_len, batch_size, output_dim]
+            hids.append(h)
+        # [seq_len, batch_size, output_dim, num_dups]
+        hids = torch.stack(hids, dim=-1)
+        hids = hids.view(seq_len, batch_size, self.output_dim, self.dup_num)
+        hids = hids.mean(dim=3)
+        hids = hids.permute(1, 0, 2)
+
+        return hids
+
+
+class CNNKRNNEncoder(nn.Module):
+    def __init__(
+        self, cnn_input_dim, cnn_output_dim, cnn_kernel_size, rnn_output_dim, rnn_dup_num, rnn_layers, dropout, device
+    ):
+        """Build an encoder composed of CNN and KRNN
+
+        Parameters
+        ----------
+        cnn_input_dim : int
+            The input dimension of CNN
+        cnn_output_dim : int
+            The output dimension of CNN
+        cnn_kernel_size : int
+            The size of convolutional kernels
+        rnn_output_dim : int
+            The output dimension of KRNN
+        rnn_dup_num : int
+            The number of parallel duplicates for KRNN
+        rnn_layers : int
+            The number of RNN layers
+        """
+        super().__init__()
+
+        self.cnn_encoder = CNNEncoderBase(cnn_input_dim, cnn_output_dim, cnn_kernel_size, device)
+        self.krnn_encoder = KRNNEncoderBase(cnn_output_dim, rnn_output_dim, rnn_dup_num, rnn_layers, dropout, device)
+
+    def forward(self, x):
+        """
+        Parameters
+        ----------
+        x : torch.Tensor
+            Input data
+        n_id : torch.Tensor
+            Node indices
+
+        Returns
+        -------
+        torch.Tensor
+            Updated representations
+        """
+        cnn_out = self.cnn_encoder(x)
+        krnn_out = self.krnn_encoder(cnn_out)
+
+        return krnn_out
+
+
+class KRNNModel(nn.Module):
+    def __init__(self, fea_dim, cnn_dim, cnn_kernel_size, rnn_dim, rnn_dups, rnn_layers, dropout, device, **params):
+        """Build a KRNN model
+
+        Parameters
+        ----------
+        fea_dim : int
+            The feature dimension
+        cnn_dim : int
+            The hidden dimension of CNN
+        cnn_kernel_size : int
+            The size of convolutional kernels
+        rnn_dim : int
+            The hidden dimension of KRNN
+        rnn_dups : int
+            The number of parallel duplicates
+        rnn_layers: int
+            The number of RNN layers
+        """
+        super().__init__()
+
+        self.encoder = CNNKRNNEncoder(
+            cnn_input_dim=fea_dim,
+            cnn_output_dim=cnn_dim,
+            cnn_kernel_size=cnn_kernel_size,
+            rnn_output_dim=rnn_dim,
+            rnn_dup_num=rnn_dups,
+            rnn_layers=rnn_layers,
+            dropout=dropout,
+            device=device,
+        )
+
+        self.out_fc = nn.Linear(rnn_dim, 1)
+        self.device = device
+
+    def forward(self, x):
+        # x: [batch_size, node_num, seq_len, input_dim]
+        encode = self.encoder(x)
+        out = self.out_fc(encode[:, -1, :]).squeeze().to(self.device)
+
+        return out
+
+
+class KRNN(Model):
+    """KRNN Model
+
+    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,
+        fea_dim=6,
+        cnn_dim=64,
+        cnn_kernel_size=3,
+        rnn_dim=64,
+        rnn_dups=3,
+        rnn_layers=2,
+        dropout=0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        GPU=0,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("KRNN")
+        self.logger.info("KRNN pytorch version...")
+
+        # set hyper-parameters.
+        self.fea_dim = fea_dim
+        self.cnn_dim = cnn_dim
+        self.cnn_kernel_size = cnn_kernel_size
+        self.rnn_dim = rnn_dim
+        self.rnn_dups = rnn_dups
+        self.rnn_layers = rnn_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.seed = seed
+
+        self.logger.info(
+            "KRNN parameters setting:"
+            "\nfea_dim : {}"
+            "\ncnn_dim : {}"
+            "\ncnn_kernel_size : {}"
+            "\nrnn_dim : {}"
+            "\nrnn_dups : {}"
+            "\nrnn_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size: {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                fea_dim,
+                cnn_dim,
+                cnn_kernel_size,
+                rnn_dim,
+                rnn_dups,
+                rnn_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.krnn_model = KRNNModel(
+            fea_dim=self.fea_dim,
+            cnn_dim=self.cnn_dim,
+            cnn_kernel_size=self.cnn_kernel_size,
+            rnn_dim=self.rnn_dim,
+            rnn_dups=self.rnn_dups,
+            rnn_layers=self.rnn_layers,
+            dropout=self.dropout,
+            device=self.device,
+        )
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.krnn_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.krnn_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self.fitted = False
+        self.krnn_model.to(self.device)
+
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[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_daily_inter(self, df, shuffle=False):
+        # organize the train data into daily batches
+        daily_count = df.groupby(level=0).size().values
+        daily_index = np.roll(np.cumsum(daily_count), 1)
+        daily_index[0] = 0
+        if shuffle:
+            # shuffle data
+            daily_shuffle = list(zip(daily_index, daily_count))
+            np.random.shuffle(daily_shuffle)
+            daily_index, daily_count = zip(*daily_shuffle)
+        return daily_index, daily_count
+
+    def train_epoch(self, x_train, y_train):
+        x_train_values = x_train.values
+        y_train_values = np.squeeze(y_train.values)
+        self.krnn_model.train()
+
+        indices = np.arange(len(x_train_values))
+        np.random.shuffle(indices)
+
+        for i in range(len(indices))[:: self.batch_size]:
+            if len(indices) - i < self.batch_size:
+                break
+
+            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+            pred = self.krnn_model(feature)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.krnn_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_x, data_y):
+        # prepare training data
+        x_values = data_x.values
+        y_values = np.squeeze(data_y.values)
+
+        self.krnn_model.eval()
+
+        scores = []
+        losses = []
+
+        indices = np.arange(len(x_values))
+
+        for i in range(len(indices))[:: self.batch_size]:
+            if len(indices) - i < self.batch_size:
+                break
+
+            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+            pred = self.krnn_model(feature)
+            loss = self.loss_fn(pred, label)
+            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: DatasetH,
+        evals_result=dict(),
+        save_path=None,
+    ):
+        df_train, df_valid, df_test = dataset.prepare(
+            ["train", "valid", "test"],
+            col_set=["feature", "label"],
+            data_key=DataHandlerLP.DK_L,
+        )
+        if df_train.empty or df_valid.empty:
+            raise ValueError("Empty data from dataset, please check your dataset config.")
+
+        x_train, y_train = df_train["feature"], df_train["label"]
+        x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+        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(x_train, y_train)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(x_train, y_train)
+            val_loss, val_score = self.test_epoch(x_valid, y_valid)
+            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.krnn_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.krnn_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
+        if not self.fitted:
+            raise ValueError("model is not fitted yet!")
+
+        x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
+        index = x_test.index
+        self.krnn_model.eval()
+        x_values = x_test.values
+        sample_num = x_values.shape[0]
+        preds = []
+
+        for begin in range(sample_num)[:: self.batch_size]:
+            if sample_num - begin < self.batch_size:
+                end = sample_num
+            else:
+                end = begin + self.batch_size
+            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
+            with torch.no_grad():
+                pred = self.krnn_model(x_batch).detach().cpu().numpy()
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)

qlib/contrib/model/pytorch_sandwich.py

@@ -0,0 +1,376 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import pandas as pd
+from typing import Text, Union
+import copy
+from ...utils import get_or_create_path
+from ...log import get_module_logger
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from ...model.base import Model
+from ...data.dataset import DatasetH
+from ...data.dataset.handler import DataHandlerLP
+from .pytorch_krnn import CNNKRNNEncoder
+
+
+class SandwichModel(nn.Module):
+    def __init__(
+        self,
+        fea_dim,
+        cnn_dim_1,
+        cnn_dim_2,
+        cnn_kernel_size,
+        rnn_dim_1,
+        rnn_dim_2,
+        rnn_dups,
+        rnn_layers,
+        dropout,
+        device,
+        **params
+    ):
+        """Build a Sandwich model
+
+        Parameters
+        ----------
+        fea_dim : int
+            The feature dimension
+        cnn_dim_1 : int
+            The hidden dimension of the first CNN
+        cnn_dim_2 : int
+            The hidden dimension of the second CNN
+        cnn_kernel_size : int
+            The size of convolutional kernels
+        rnn_dim_1 : int
+            The hidden dimension of the first KRNN
+        rnn_dim_2 : int
+            The hidden dimension of the second KRNN
+        rnn_dups : int
+            The number of parallel duplicates
+        rnn_layers: int
+            The number of RNN layers
+        """
+        super().__init__()
+
+        self.first_encoder = CNNKRNNEncoder(
+            cnn_input_dim=fea_dim,
+            cnn_output_dim=cnn_dim_1,
+            cnn_kernel_size=cnn_kernel_size,
+            rnn_output_dim=rnn_dim_1,
+            rnn_dup_num=rnn_dups,
+            rnn_layers=rnn_layers,
+            dropout=dropout,
+            device=device,
+        )
+
+        self.second_encoder = CNNKRNNEncoder(
+            cnn_input_dim=rnn_dim_1,
+            cnn_output_dim=cnn_dim_2,
+            cnn_kernel_size=cnn_kernel_size,
+            rnn_output_dim=rnn_dim_2,
+            rnn_dup_num=rnn_dups,
+            rnn_layers=rnn_layers,
+            dropout=dropout,
+            device=device,
+        )
+
+        self.out_fc = nn.Linear(rnn_dim_2, 1)
+        self.device = device
+
+    def forward(self, x):
+        # x: [batch_size, node_num, seq_len, input_dim]
+        encode = self.first_encoder(x)
+        encode = self.second_encoder(encode)
+        out = self.out_fc(encode[:, -1, :]).squeeze().to(self.device)
+
+        return out
+
+
+class Sandwich(Model):
+    """Sandwich Model
+
+    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,
+        fea_dim=6,
+        cnn_dim_1=64,
+        cnn_dim_2=32,
+        cnn_kernel_size=3,
+        rnn_dim_1=16,
+        rnn_dim_2=8,
+        rnn_dups=3,
+        rnn_layers=2,
+        dropout=0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        GPU=0,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("Sandwich")
+        self.logger.info("Sandwich pytorch version...")
+
+        # set hyper-parameters.
+        self.fea_dim = fea_dim
+        self.cnn_dim_1 = cnn_dim_1
+        self.cnn_dim_2 = cnn_dim_2
+        self.cnn_kernel_size = cnn_kernel_size
+        self.rnn_dim_1 = rnn_dim_1
+        self.rnn_dim_2 = rnn_dim_2
+        self.rnn_dups = rnn_dups
+        self.rnn_layers = rnn_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.seed = seed
+
+        self.logger.info(
+            "Sandwich parameters setting:"
+            "\nfea_dim : {}"
+            "\ncnn_dim_1 : {}"
+            "\ncnn_dim_2 : {}"
+            "\ncnn_kernel_size : {}"
+            "\nrnn_dim_1 : {}"
+            "\nrnn_dim_2 : {}"
+            "\nrnn_dups : {}"
+            "\nrnn_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size: {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                fea_dim,
+                cnn_dim_1,
+                cnn_dim_2,
+                cnn_kernel_size,
+                rnn_dim_1,
+                rnn_dim_2,
+                rnn_dups,
+                rnn_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.sandwich_model = SandwichModel(
+            fea_dim=self.fea_dim,
+            cnn_dim_1=self.cnn_dim_1,
+            cnn_dim_2=self.cnn_dim_2,
+            cnn_kernel_size=self.cnn_kernel_size,
+            rnn_dim_1=self.rnn_dim_1,
+            rnn_dim_2=self.rnn_dim_2,
+            rnn_dups=self.rnn_dups,
+            rnn_layers=self.rnn_layers,
+            dropout=self.dropout,
+            device=self.device,
+        )
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.sandwich_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.sandwich_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self.fitted = False
+        self.sandwich_model.to(self.device)
+
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[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, x_train, y_train):
+        x_train_values = x_train.values
+        y_train_values = np.squeeze(y_train.values)
+        self.sandwich_model.train()
+
+        indices = np.arange(len(x_train_values))
+        np.random.shuffle(indices)
+
+        for i in range(len(indices))[:: self.batch_size]:
+            if len(indices) - i < self.batch_size:
+                break
+
+            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+            pred = self.sandwich_model(feature)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.sandwich_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_x, data_y):
+        # prepare training data
+        x_values = data_x.values
+        y_values = np.squeeze(data_y.values)
+
+        self.sandwich_model.eval()
+
+        scores = []
+        losses = []
+
+        indices = np.arange(len(x_values))
+
+        for i in range(len(indices))[:: self.batch_size]:
+            if len(indices) - i < self.batch_size:
+                break
+
+            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+            pred = self.sandwich_model(feature)
+            loss = self.loss_fn(pred, label)
+            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: DatasetH, evals_result=dict(), save_path=None,
+    ):
+        df_train, df_valid, df_test = dataset.prepare(
+            ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
+        )
+        if df_train.empty or df_valid.empty:
+            raise ValueError("Empty data from dataset, please check your dataset config.")
+
+        x_train, y_train = df_train["feature"], df_train["label"]
+        x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+        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(x_train, y_train)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(x_train, y_train)
+            val_loss, val_score = self.test_epoch(x_valid, y_valid)
+            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.sandwich_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.sandwich_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
+        if not self.fitted:
+            raise ValueError("model is not fitted yet!")
+
+        x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
+        index = x_test.index
+        self.sandwich_model.eval()
+        x_values = x_test.values
+        sample_num = x_values.shape[0]
+        preds = []
+
+        for begin in range(sample_num)[:: self.batch_size]:
+            if sample_num - begin < self.batch_size:
+                end = sample_num
+            else:
+                end = begin + self.batch_size
+            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
+            with torch.no_grad():
+                pred = self.sandwich_model(x_batch).detach().cpu().numpy()
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)