From 05599d1de8ec7b0e48ba7b71a2905027d40f2666 Mon Sep 17 00:00:00 2001
From: Don-ustc <43958178+Don-ustc@users.noreply.github.com>
Date: Wed, 25 Nov 2020 19:13:53 +0800
Subject: [PATCH] Update about ALSTM

---
 qlib/contrib/model/pytorch_alstm.py | 392 ++++++++++++++++++++++++++++
 1 file changed, 392 insertions(+)
 create mode 100644 qlib/contrib/model/pytorch_alstm.py

diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py
new file mode 100644
index 000000000..b302925ec
--- /dev/null
+++ b/qlib/contrib/model/pytorch_alstm.py
@@ -0,0 +1,392 @@
+# 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 ALSTM(Model):
+    """ALSTM 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,
+        rnn_type="GRU",
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("ALSTM")
+        self.logger.info("ALSTM 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.rnn_type = rnn_type
+
+        self.logger.info(
+            "ALSTM 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 : {}"
+            "\nrnn_type : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                self.use_gpu,
+                seed,
+                self.rnn_type,
+            )
+        )
+
+        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.alstm_model = ALSTMModel(
+            d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
+        )
+        # def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, input_day=20, rnn_type="GRU"):
+
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.alstm_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.alstm_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        if self.use_gpu:
+            self.alstm_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.alstm_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.alstm_model(feature)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.alstm_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.alstm_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.alstm_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.alstm_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.alstm_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.alstm_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.alstm_model(x_batch).detach().cpu().numpy()
+                else:
+                    pred = self.alstm_model(x_batch).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)
+
+
+class GRUModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0):
+        super().__init__()
+
+        self.rnn = nn.GRU(
+            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()
+
+
+
+class ALSTMModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, rnn_type="GRU"):
+        super().__init__()
+        self.hid_size = hidden_size
+        self.input_size = d_feat
+        self.dropout = dropout
+        self.rnn_type = rnn_type
+        self.rnn_layer = num_layers
+        self._build_model()
+
+    def _build_model(self):
+        try:
+            klass = getattr(nn, self.rnn_type.upper())
+        except:
+            raise ValueError('unknown rnn_type `%s`' % self.rnn_type)
+        self.net = nn.Sequential()
+        self.net.add_module('fc_in', nn.Linear(in_features=self.input_size, out_features=self.hid_size))
+        self.net.add_module('act', nn.Tanh())
+        self.rnn = klass(input_size=self.hid_size,
+                         hidden_size=self.hid_size,
+                         num_layers=self.rnn_layer,
+                         batch_first=True,
+                         dropout=self.dropout)
+        self.fc_out = nn.Linear(in_features=self.hid_size*2, out_features=1)
+        # self.fc_out = nn.Linear(in_features=self.hid_size, out_features=1)
+        self.att_net = nn.Sequential()
+        self.att_net.add_module('att_fc_in', nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size/2)))
+        self.att_net.add_module('att_dropout', torch.nn.Dropout(self.dropout))
+        self.att_net.add_module('att_act', nn.Tanh())
+        self.att_net.add_module('att_fc_out', nn.Linear(in_features=int(self.hid_size/2), out_features=1, bias=False))
+        self.att_net.add_module('att_softmax', nn.Softmax(dim=1))
+
+    def forward(self, inputs):
+        # inputs: [batch_size, input_size*input_day]
+        inputs = inputs.view(len(inputs), self.input_size, -1)
+        inputs = inputs.permute(0, 2, 1)        # [batch, input_size, seq_len] -> [batch, seq_len, input_size]
+        rnn_out, _ = self.rnn(self.net(inputs)) # [batch, seq_len, num_directions * hidden_size]
+        attention_score = self.att_net(rnn_out) # [batch, seq_len, 1]
+        out_att = torch.mul(rnn_out, attention_score)
+        out_att = torch.sum(out_att, dim=1)
+        out = self.fc_out(torch.cat((rnn_out[:, -1, :], out_att), dim=1))  # [batch, seq_len, num_directions * hidden_size] -> [batch, 1]
+        # out = self.fc_out(rnn_out[:, -1, :] + out_att)
+        return out[..., 0]
+