Add two transformer models via upload

qlib/contrib/model/pytorch_localformer.py

@@ -0,0 +1,341 @@
+# 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
+import math
+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 torch.utils.data import DataLoader
+
+from .pytorch_utils import count_parameters
+from ...model.base import Model
+from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset.handler import DataHandlerLP
+from torch.nn.modules.container import ModuleList
+
+import pdb
+
+# qrun benchmarks/Transformer/workflow_config_localformer_Alpha158.yaml
+#  0.992366, @13,
+'''
+{'IC': 0.037426503365732174,
+ 'ICIR': 0.28977883455541603,
+ 'Rank IC': 0.04659889541774283,
+ 'Rank ICIR': 0.373569340092482}
+
+'The following are analysis results of the excess return without cost.'
+                       risk
+mean               0.000381
+std                0.004109
+annualized_return  0.096066
+information_ratio  1.472729
+max_drawdown      -0.094917
+'The following are analysis results of the excess return with cost.'
+                       risk
+mean               0.000213
+std                0.004111
+annualized_return  0.053630
+information_ratio  0.821711
+max_drawdown      -0.113694
+'''
+
+
+class LocalformerModel(Model):
+    def __init__(
+        self,
+        d_feat: int = 20,
+        d_model: int = 64,
+        batch_size: int = 8192,
+        nhead: int = 2,
+        num_layers: int = 2,
+        dropout: float = 0,
+        n_epochs=100,
+        lr=0.0001,
+        metric="",
+        early_stop=5,
+        loss="mse",
+        optimizer="adam",
+        reg=1e-3,
+        n_jobs=10,
+        GPU=2,
+        seed=None,
+        **kwargs
+    ):
+
+        # set hyper-parameters.
+        self.d_model = d_model
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.reg = reg
+        self.metric = metric
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.n_jobs = n_jobs
+        self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
+        self.seed = seed
+        self.logger = get_module_logger("TransformerModel")
+        print('do we have gpu?{}'.format(torch.cuda.is_available()))
+        self.logger.info(
+            "Improved Transformer:"
+            "\nbatch_size : {}"
+            "\ndevice : {}".format(self.batch_size, self.device)
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self.fitted = False
+        self.model.to(self.device)
+
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
+    def mse(self, pred, label):
+        loss = (pred.float() - label.float()) ** 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 == "" or self.metric == "loss":
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def train_epoch(self, data_loader):
+
+        self.model.train()
+
+        for data in data_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.model(feature.float())  # .float()
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+
+        self.model.eval()
+
+        scores = []
+        losses = []
+
+        for data in data_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+            # feature[torch.isnan(feature)] = 0
+            label = data[:, -1, -1].to(self.device)
+
+            with torch.no_grad():
+                pred = self.model(feature.float())  # .float()
+                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,
+    ):
+
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+
+        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+
+        train_loader = DataLoader(
+            dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
+        )
+        valid_loader = DataLoader(
+            dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
+        )
+
+        save_path = get_or_create_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # train
+        self.logger.info("training...")
+        self.fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.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.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!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+        dl_test.config(fillna_type="ffill+bfill")
+        test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
+        self.model.eval()
+        preds = []
+
+        for data in test_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                pred = self.model(feature.float()).detach().cpu().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=1000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        # [T, N, F]
+        return x + self.pe[:x.size(0), :]
+
+
+def _get_clones(module, N):
+    return ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+class LocalformerEncoder(nn.Module):
+    __constants__ = ['norm']
+
+    def __init__(self, encoder_layer, num_layers, d_model):
+        super(LocalformerEncoder, self).__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.conv = _get_clones(nn.Conv1d(d_model, d_model, 3, 1, 1), num_layers)
+        self.num_layers = num_layers
+
+    def forward(self, src, mask):
+        output = src
+        out = src
+
+        for i, mod in enumerate(self.layers):
+            # [T, N, F] --> [N, T, F] --> [N, F, T]
+            out = output.transpose(1, 0).transpose(2, 1)
+            out = self.conv[i](out).transpose(2, 1).transpose(1, 0)
+
+            output = mod(output+out, src_mask=mask)
+
+        return output + out
+
+
+class Transformer(nn.Module):
+    def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
+        super(Transformer, self).__init__()
+        self.rnn = nn.GRU(
+            input_size=d_model,
+            hidden_size=d_model,
+            num_layers=num_layers,
+            batch_first=False,
+            dropout=dropout,
+        )
+        self.feature_layer = nn.Linear(d_feat, d_model)
+        self.pos_encoder = PositionalEncoding(d_model)
+        self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
+        self.transformer_encoder = LocalformerEncoder(self.encoder_layer, num_layers=num_layers, d_model=d_model)
+        self.decoder_layer = nn.Linear(d_model, 1)
+        self.device = device
+        self.d_feat = d_feat
+
+    def forward(self, src):
+        # pdb.set_trace()
+        # src [N, T, F], [512, 60, 6]
+
+        src = self.feature_layer(src)  # [512, 60, 8]
+
+        # src [N, T, F] --> [T, N, F], [60, 512, 8]
+        src = src.transpose(1, 0)  # not batch first
+
+        mask = None
+
+        src = self.pos_encoder(src)
+        output = self.transformer_encoder(src, mask)  # [60, 512, 8]
+
+        output, _ = self.rnn(output)
+
+        # [T, N, F] --> [N, T*F]
+        output = self.decoder_layer(output.transpose(1, 0)[:, -1, :])  # [512, 1]
+
+        return output.squeeze()
+