hats3

examples/benchmarks/HATS/requirements.txt

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+pandas==1.1.2
+numpy==1.17.4
+scikit_learn==0.23.2
+torch==1.7.0

examples/benchmarks/HATS/worflow_config_hats.yaml

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+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
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy.strategy
+        kwargs:
+            topk: 50
+            n_drop: 5
+    backtest:
+        verbose: False
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+task:
+    model:
+        class: HATS
+        module_path: qlib.contrib.model.pytorch_gats
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.6
+            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]
+    record: 
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config

examples/workflow_by_code_hats.py

@@ -0,0 +1,145 @@
+#  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_hats import HATS
+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": "HATS",
+            "module_path": "qlib.contrib.model.pytorch_hats",
+            "kwargs": {
+                "d_feat": 6,
+                "hidden_size": 64,
+                "num_layers": 2,
+                "dropout": 0.6,
+                "n_epochs": 200,
+                "lr": 1e-3,
+                "early_stop": 20,
+                "batch_size": 800,
+                "metric": "IC",
+                "loss": "mse",
+                "base_model": "LSTM",
+                "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,save_path='benchmarks/HATS/model_hat.pkl')
+
+    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_hats.py

@@ -0,0 +1,497 @@
+# 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 HATS(Model):
+    """HATS 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=0.5,
+        n_epochs=200,
+        lr=0.01,
+        metric="IC",
+        batch_size=800,
+        early_stop=20,
+        loss="mse",
+        base_model="GRU",
+        with_pretrain=True,
+        optimizer="adam",
+        GPU="0",
+        seed=0,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("HATS")
+        self.logger.info("HATS 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.with_pretrain = with_pretrain #### True if train HATS with pretrained base model
+        self.visible_GPU = GPU
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "HATS parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nbase_model : {}"
+            "\nwith_pretrain : {}" ##### debug
+            "\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,
+                with_pretrain, ### debug
+                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.HATS_model = HATSModel(
+            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.HATS_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.HATS_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        if self.use_gpu:
+            self.HATS_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.HATS_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.HATS_model(feature)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.HATS_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.HATS_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.HATS_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"] = []
+
+        # load pretrained base_model
+        if self.with_pretrain:
+            self.logger.info("loading pretrained model...")
+            if self.base_model == "LSTM":
+                from ...contrib.model.pytorch_lstm import LSTMModel
+                pretrained_model = LSTMModel()
+                pretrained_model.load_state_dict(torch.load('benchmarks/LSTM/model_lstm_csi300.pkl'))
+            elif self.base_model == "GRU":
+                from ...contrib.model.pytorch_gru import GRUModel
+                pretrained_model = GRUModel()
+                pretrained_model.load_state_dict(torch.load('benchmarks/GRU/model_gru_csi300.pkl'))
+            model_dict = self.HATS_model.state_dict()
+
+            # filter unnecessary parameters
+            pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in model_dict}
+            # overwrite entries in the existing state dict
+            model_dict.update(pretrained_dict)
+            # load the new state dict
+            self.HATS_model.load_state_dict(model_dict)
+            self.logger.info("loading pretrained model Done...")
+
+
+        # 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.HATS_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.HATS_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.HATS_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.HATS_model(x_batch).detach().cpu().numpy()
+                else:
+                    pred = self.HATS_model(x_batch).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)
+
+
+class HATSModel(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.model = nn.GRU(
+                input_size=d_feat,
+                hidden_size=hidden_size,
+                num_layers=num_layers,
+                batch_first=True,
+                dropout=dropout,
+            )
+        elif base_model == "LSTM":
+            self.model = 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
+
+        num_head_att = [1]*num_layers
+        hidden_dim = [hidden_size]*num_layers
+        dims = [d_feat] + [d*nh for (d, nh) in zip(hidden_dim, num_head_att[:-1])] + [num_head_att[-1]]
+        in_dims = dims[:-1]
+        out_dims = [d // nh for (d, nh) in zip(dims[1:], num_head_att)]
+        self.attn = nn.ModuleList([GraphAttention(i, o, nh, dropout) for (i, o, nh) in zip(in_dims, out_dims,num_head_att)])
+        self.bns = nn.ModuleList([nn.BatchNorm1d(dim) for dim in dims[1:-1]])
+        self.dropout = nn.Dropout(dropout)
+        self.elu = nn.ELU()
+
+    def forward(self, x):
+        x = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
+        x = x.permute(0, 2, 1) # [N, T, F]
+        out,_ = self.model(x)
+        hidden = out[:, -1, :]
+        hidden = self.bn1(hidden) 
+        attention = GraphAttention.cal_attention(hidden, hidden)
+        output = attention.mm(hidden)
+        output = self.fc(output)
+        output = self.bn2(output)
+        output = self.leaky_relu(output)
+        return self.fc_out(output).squeeze()
+
+
+
+class GraphAttention(nn.Module):
+
+    def __init__(self, input_dim, output_dim, num_heads, dropout=0.5):
+
+        super().__init__()
+
+        """
+        Parameters
+        ----------
+        input_dim : int
+            Dimension of input node features.
+        output_dim : int
+            Dimension of output node features.
+        num_heads : list of ints
+            Number of attention heads in each hidden layer and output layer. Must be non empty. Note that len(num_heads) = len(hidden_dims)+1.
+        dropout : float
+            Dropout rate. Default: 0.5.
+        """
+
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.num_heads = num_heads
+
+        self.fcs = nn.ModuleList([nn.Linear(input_dim, output_dim) for _ in range(num_heads)])
+        self.a = nn.ModuleList([nn.Linear(2*output_dim, 1) for _ in range(num_heads)])
+
+        self.dropout = nn.Dropout(dropout)
+        self.softmax = nn.Softmax(dim=0)
+        self.leakyrelu = nn.LeakyReLU()
+
+    def forward(self, features, nodes, mapping, rows):
+
+        """
+        Parameters
+        ----------
+        features : torch.Tensor
+            An (n' x input_dim) tensor of input node features.
+        node_layers : list of numpy array
+            node_layers[i] is an array of the nodes in the ith layer of the
+            computation graph.
+        mappings : list of dictionary
+            mappings[i] is a dictionary mapping node v (labelled 0 to |V|-1)
+            in node_layers[i] to its position in node_layers[i]. For example,
+            if node_layers[i] = [2,5], then mappings[i][2] = 0 and
+            mappings[i][5] = 1.
+        rows : numpy array
+            rows[i] is an array of neighbors of node i.
+        Returns
+        -------
+        out : torch.Tensor
+            An (len(node_layers[-1]) x output_dim) tensor of output node features.
+        """
+
+        nprime = features.shape[0]
+        rows = [np.array([mapping[v] for v in row], dtype=np.int64) for row in rows]
+        sum_degs = np.hstack(([0], np.cumsum([len(row) for row in rows])))
+        mapped_nodes = [mapping[v] for v in nodes]
+        indices = torch.LongTensor([[v, c] for (v, row) in zip(mapped_nodes, rows) for c in row]).t()
+        
+
+        out = []
+        for k in range(self.num_heads):
+            h = self.fcs[k](features)
+
+            nbr_h = torch.cat(tuple([h[row] for row in rows]), dim=0)
+            self_h = torch.cat(tuple([h[mapping[nodes[i]]].repeat(len(row), 1) for (i, row) in enumerate(rows)]), dim=0)
+            cat_h = torch.cat((self_h, nbr_h), dim=1)
+
+            e = self.leakyrelu(self.a[k](cat_h))
+
+            alpha = [self.softmax(e[lo : hi]) for (lo, hi) in zip(sum_degs, sum_degs[1:])]
+            alpha = torch.cat(tuple(alpha), dim=0)
+            alpha = alpha.squeeze(1)
+            alpha = self.dropout(alpha)
+
+            adj = torch.sparse.FloatTensor(indices, alpha, torch.Size([nprime, nprime]))
+            out.append(torch.sparse.mm(adj, h)[mapped_nodes])
+
+        return out
+
+    def cal_attention(x, y): 
+     
+        att_x = torch.mean(x, dim = 1).reshape(-1, 1)
+        att_y = torch.mean(y, dim = 1).reshape(-1, 1)
+        att = att_x.mm(torch.t(att_y))
+        x_att = x.reshape(x.shape[0], 1, x.shape[1]).repeat(1, y.shape[0], 1)
+        y_att = y.reshape(1, y.shape[0], y.shape[1]).repeat(x.shape[0], 1, 1)
+        return torch.mean(x.reshape(x.shape[0], 1, x.shape[1]).repeat(1, y.shape[0], 1)*y.reshape(1, y.shape[0], y.shape[1]).repeat(x.shape[0], 1, 1), dim = 2)-att