Add LSTM and Gats

examples/workflow_by_code_gats.py

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+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+
+import sys
+from pathlib import Path
+
+import qlib
+import pandas as pd
+from qlib.config import REG_CN
+from qlib.contrib.model.pytorch_gats import GAT
+from qlib.contrib.data.handler import ALPHA360_Denoise
+from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+from qlib.contrib.evaluate import (
+    backtest as normal_backtest,
+    risk_analysis,
+)
+from qlib.utils import exists_qlib_data
+
+# from qlib.model.learner import train_model
+from qlib.utils import init_instance_by_config
+
+import pickle
+
+if __name__ == "__main__":
+
+    # use default data
+    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+    if not exists_qlib_data(provider_uri):
+        print(f"Qlib data is not found in {provider_uri}")
+        sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
+        from get_data import GetData
+
+        GetData().qlib_data_cn(target_dir=provider_uri)
+
+    qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+    MARKET = "csi300"
+    BENCHMARK = "SH000300"
+
+    ###################################
+    # train model
+    ###################################
+    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,
+    }
+
+    TRAINER_CONFIG = {
+        "train_start_time": "2008-01-01",
+        "train_end_time": "2014-12-31",
+        "validate_start_time": "2015-01-01",
+        "validate_end_time": "2016-12-31",
+        "test_start_time": "2017-01-01",
+        "test_end_time": "2020-08-01",
+    }
+
+    task = {
+        "model": {
+            "class": "GAT",
+            "module_path": "qlib.contrib.model.pytorch_gats",
+            "kwargs": {
+                "d_feat": 6,
+                "hidden_size": 64,
+                "num_layers": 2,
+                "dropout": 0.0,
+                "n_epochs": 200,
+                "lr": 1e-3,
+                "early_stop": 20,
+                "batch_size": 800,
+                "metric": "IC",
+                "loss": "mse",
+                "base_model":"GRU",
+                "seed": 0,
+                "GPU": 0,
+            },
+        },
+        "dataset": {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "ALPHA360_Denoise",
+                    "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"),
+                },
+            },
+        }
+        # You shoud record the data in specific sequence
+        # "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
+    }
+
+    # model = train_model(task)
+    model = init_instance_by_config(task["model"])
+    dataset = init_instance_by_config(task["dataset"])
+    model.fit(dataset)
+
+    pred_score = model.predict(dataset)
+
+    # save pred_score to file
+    pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
+    pred_score_path.parent.mkdir(exist_ok=True, parents=True)
+    pred_score.to_pickle(pred_score_path)
+
+    ###################################
+    # backtest
+    ###################################
+    STRATEGY_CONFIG = {
+        "topk": 50,
+        "n_drop": 5,
+    }
+    BACKTEST_CONFIG = {
+        "verbose": False,
+        "limit_threshold": 0.095,
+        "account": 100000000,
+        "benchmark": BENCHMARK,
+        "deal_price": "close",
+        "open_cost": 0.0005,
+        "close_cost": 0.0015,
+        "min_cost": 5,
+    }
+
+    # use default strategy
+    # custom Strategy, refer to: TODO: Strategy API url
+    strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
+    report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
+
+    ###################################
+    # analyze
+    # If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
+    ###################################
+    analysis = dict()
+    analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
+    analysis["excess_return_with_cost"] = risk_analysis(
+        report_normal["return"] - report_normal["bench"] - report_normal["cost"]
+    )
+    analysis_df = pd.concat(analysis)  # type: pd.DataFrame
+    print(analysis_df)

examples/workflow_by_code_lstm.py

@@ -0,0 +1,144 @@
+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+
+import sys
+from pathlib import Path
+
+import qlib
+import pandas as pd
+from qlib.config import REG_CN
+from qlib.contrib.model.pytorch_lstm import LSTM
+from qlib.contrib.data.handler import ALPHA360_Denoise
+from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+from qlib.contrib.evaluate import (
+    backtest as normal_backtest,
+    risk_analysis,
+)
+from qlib.utils import exists_qlib_data
+
+# from qlib.model.learner import train_model
+from qlib.utils import init_instance_by_config
+
+import pickle
+
+if __name__ == "__main__":
+
+    # use default data
+    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+    if not exists_qlib_data(provider_uri):
+        print(f"Qlib data is not found in {provider_uri}")
+        sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
+        from get_data import GetData
+
+        GetData().qlib_data_cn(target_dir=provider_uri)
+
+    qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+    MARKET = "csi300"
+    BENCHMARK = "SH000300"
+
+    ###################################
+    # train model
+    ###################################
+    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,
+    }
+
+    TRAINER_CONFIG = {
+        "train_start_time": "2008-01-01",
+        "train_end_time": "2014-12-31",
+        "validate_start_time": "2015-01-01",
+        "validate_end_time": "2016-12-31",
+        "test_start_time": "2017-01-01",
+        "test_end_time": "2020-08-01",
+    }
+
+    task = {
+        "model": {
+            "class": "LSTM",
+            "module_path": "qlib.contrib.model.pytorch_lstm",
+            "kwargs": {
+                "d_feat": 6,
+                "hidden_size": 64,
+                "num_layers": 2,
+                "dropout": 0.0,
+                "n_epochs": 200,
+                "lr": 1e-3,
+                "early_stop": 20,
+                "batch_size": 800,
+                "metric": "IC",
+                "loss": "mse",
+                "seed": 0,
+                "GPU": 0,
+            },
+        },
+        "dataset": {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "ALPHA360_Denoise",
+                    "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"),
+                },
+            },
+        }
+        # You shoud record the data in specific sequence
+        # "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
+    }
+
+    # model = train_model(task)
+    model = init_instance_by_config(task["model"])
+    dataset = init_instance_by_config(task["dataset"])
+    model.fit(dataset)
+
+    pred_score = model.predict(dataset)
+
+    # save pred_score to file
+    pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
+    pred_score_path.parent.mkdir(exist_ok=True, parents=True)
+    pred_score.to_pickle(pred_score_path)
+
+    ###################################
+    # backtest
+    ###################################
+    STRATEGY_CONFIG = {
+        "topk": 50,
+        "n_drop": 5,
+    }
+    BACKTEST_CONFIG = {
+        "verbose": False,
+        "limit_threshold": 0.095,
+        "account": 100000000,
+        "benchmark": BENCHMARK,
+        "deal_price": "close",
+        "open_cost": 0.0005,
+        "close_cost": 0.0015,
+        "min_cost": 5,
+    }
+
+    # use default strategy
+    # custom Strategy, refer to: TODO: Strategy API url
+    strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
+    report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
+
+    ###################################
+    # analyze
+    # If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
+    ###################################
+    analysis = dict()
+    analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
+    analysis["excess_return_with_cost"] = risk_analysis(
+        report_normal["return"] - report_normal["bench"] - report_normal["cost"]
+    )
+    analysis_df = pd.concat(analysis)  # type: pd.DataFrame
+    print(analysis_df)

qlib/contrib/model/pytorch_gats.py

@@ -0,0 +1,383 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
+from ...log import get_module_logger, TimeInspector
+
+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 GAT(Model):
+    """GAT 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 : 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="IC",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        base_model="GRU",
+        optimizer="adam",
+        GPU="0",
+        seed=0,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("GAT")
+        self.logger.info("GAT 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.base_model = base_model
+        self.visible_GPU = GPU
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "GAT parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nbase_model : {}"
+            "\nvisible_GPU : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                base_model,
+                GPU,
+                self.use_gpu,
+                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
+
+        self.GAT_model = GATModel(
+            d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, base_model=self.base_model
+        )
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.GAT_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        if self.use_gpu:
+            self.GAT_model.cuda()
+            # set the visible GPU
+            if self.visible_GPU:
+                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+
+    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 == "IC":
+            return self.cal_ic(pred[mask], label[mask])
+
+        if self.metric == "" or self.metric == "loss":  # use loss
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def cal_ic(self, pred, label):
+        return torch.mean(pred * label)
+
+    def train_epoch(self, x_train, y_train):
+
+        x_train_values = x_train.values
+        y_train_values = np.squeeze(y_train.values) * 100
+
+        self.GAT_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()
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
+
+            if self.use_gpu:
+                feature = feature.cuda()
+                label = label.cuda()
+
+            pred = self.GAT_model(feature)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.GAT_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.GAT_model.eval()
+
+        scores = []
+        losses = []
+
+        indices = np.arange(len(x_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_values[indices[i : i + self.batch_size]]).float()
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
+
+            if self.use_gpu:
+                feature = feature.cuda()
+                label = label.cuda()
+
+            pred = self.GAT_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(),
+        verbose=True,
+        save_path=None,
+    ):
+
+        df_train, df_valid, df_test = dataset.prepare(
+            ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
+        )
+
+        x_train, y_train = df_train["feature"], df_train["label"]
+        x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+        if save_path == None:
+            save_path = create_save_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
+        # return
+
+        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.GAT_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.GAT_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!")
+
+        x_test = dataset.prepare("test", col_set="feature")
+        index = x_test.index
+        self.GAT_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()
+
+            if self.use_gpu:
+                x_batch = x_batch.cuda()
+
+            with torch.no_grad():
+                if self.use_gpu:
+                    pred = self.GAT_model(x_batch).detach().cpu().numpy()
+                else:
+                    pred = self.GAT_model(x_batch).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)
+
+
+class GATModel(nn.Module):
+    
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model='GRU'):
+        super().__init__()
+
+        if base_model == 'GRU':
+            self.rnn = nn.GRU(
+                input_size=d_feat,
+                hidden_size=hidden_size,
+                num_layers=num_layers,
+                batch_first=True,
+                dropout=dropout,
+            )
+        elif base_model == 'LSTM':
+            self.rnn = nn.LSTM(
+                input_size=d_feat,
+                hidden_size=hidden_size,
+                num_layers=num_layers,
+                batch_first=True,
+                dropout=dropout,
+            )
+        else:
+            raise ValueError('unknown base model name `%s`'%base_model) 
+
+        self.hidden_size = hidden_size
+        self.bn1 = nn.BatchNorm1d(num_features=hidden_size, track_running_stats=False)
+        self.fc = nn.Linear(hidden_size, hidden_size)
+        self.bn2 = nn.BatchNorm1d(num_features=hidden_size, track_running_stats=False)
+        self.fc_out = nn.Linear(hidden_size, 1)
+        self.leaky_relu = nn.LeakyReLU()
+        self.softmax = nn.Softmax(dim=1)
+
+        self.d_feat = d_feat
+
+    def cal_convariance(self, x, y): # the 2nd dimension of x and y are the same
+        e_x = torch.mean(x, dim = 1).reshape(-1, 1)
+        e_y = torch.mean(y, dim = 1).reshape(-1, 1)
+        e_x_e_y = e_x.mm(torch.t(e_y))
+        x_extend = x.reshape(x.shape[0], 1, x.shape[1]).repeat(1, y.shape[0], 1)
+        y_extend = y.reshape(1, y.shape[0], y.shape[1]).repeat(x.shape[0], 1, 1)
+        e_xy = torch.mean(x_extend*y_extend, dim = 2)
+        return e_xy - e_x_e_y
+
+    def forward(self, x):
+        # x: [N, F*T]
+        x = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
+        x = x.permute(0, 2, 1) # [N, T, F]
+        out, _ = self.rnn(x)
+        hidden = out[:, -1, :]
+        hidden = self.bn1(hidden)
+
+        gamma = self.cal_convariance(hidden, hidden)
+        # gamma = hidden.mm(torch.t(hidden))
+        # gamma = self.leaky_relu(gamma)
+        # gamma = self.softmax(gamma)
+        # gamma = gamma * (torch.ones(x.shape[0], x.shape[0]).to(device) - torch.diag(torch.ones(x.shape[0])).to(device))
+        output = gamma.mm(hidden)
+        output = self.fc(output)
+        output = self.bn2(output)
+        output = self.leaky_relu(output)
+        return self.fc_out(output).squeeze()

qlib/contrib/model/pytorch_lstm.py

@@ -0,0 +1,340 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
+from ...log import get_module_logger, TimeInspector
+
+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 LSTM(Model):
+    """LSTM 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 : 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="IC",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        GPU="0",
+        seed=0,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("LSTM")
+        self.logger.info("LSTM 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.visible_GPU = GPU
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "LSTM parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                self.use_gpu,
+                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
+
+        self.lstm_model = LSTMModel(
+            d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
+        )
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.lstm_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        if self.use_gpu:
+            self.lstm_model.cuda()
+            # set the visible GPU
+            if self.visible_GPU:
+                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+
+    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 == "IC":
+            return self.cal_ic(pred[mask], label[mask])
+
+        if self.metric == "" or self.metric == "loss":  # use loss
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def cal_ic(self, pred, label):
+        return torch.mean(pred * label)
+
+    def train_epoch(self, x_train, y_train):
+
+        x_train_values = x_train.values
+        y_train_values = np.squeeze(y_train.values) * 100
+
+        self.lstm_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()
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
+
+            if self.use_gpu:
+                feature = feature.cuda()
+                label = label.cuda()
+
+            pred = self.lstm_model(feature)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.lstm_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.lstm_model.eval()
+
+        scores = []
+        losses = []
+
+        indices = np.arange(len(x_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_values[indices[i : i + self.batch_size]]).float()
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
+
+            if self.use_gpu:
+                feature = feature.cuda()
+                label = label.cuda()
+
+            pred = self.lstm_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(),
+        verbose=True,
+        save_path=None,
+    ):
+
+        df_train, df_valid, df_test = dataset.prepare(
+            ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
+        )
+
+        x_train, y_train = df_train["feature"], df_train["label"]
+        x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+        if save_path == None:
+            save_path = create_save_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
+        # return
+
+        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.lstm_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.lstm_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!")
+
+        x_test = dataset.prepare("test", col_set="feature")
+        index = x_test.index
+        self.lstm_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()
+
+            if self.use_gpu:
+                x_batch = x_batch.cuda()
+
+            with torch.no_grad():
+                if self.use_gpu:
+                    pred = self.lstm_model(x_batch).detach().cpu().numpy()
+                else:
+                    pred = self.lstm_model(x_batch).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)
+
+
+class LSTMModel(nn.Module):
+
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0):
+        super().__init__()
+
+        self.rnn = nn.LSTM(
+            input_size=d_feat,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout,
+        )
+        self.fc_out = nn.Linear(hidden_size, 1)
+
+        self.d_feat = d_feat
+
+    def forward(self, x):
+        # x: [N, F*T]
+        x = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
+        x = x.permute(0, 2, 1) # [N, T, F]
+        out, _ = self.rnn(x)
+        return self.fc_out(out[:, -1, :]).squeeze()