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Add files via upload
Add naive transformer model and a improved transformer model.
This commit is contained in:
Ying-Tao Luo
341
qlib/contrib/model/pytorch_localformer.py
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341
qlib/contrib/model/pytorch_localformer.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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from __future__ import division
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from __future__ import print_function
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import os
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import numpy as np
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import pandas as pd
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import copy
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import math
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from ...utils import get_or_create_path
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from ...log import get_module_logger
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from .pytorch_utils import count_parameters
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from ...model.base import Model
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from ...data.dataset import DatasetH, TSDatasetH
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from ...data.dataset.handler import DataHandlerLP
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from torch.nn.modules.container import ModuleList
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import pdb
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# qrun benchmarks/Transformer/workflow_config_localformer_Alpha158.yaml
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# 0.992366, @13,
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'''
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{'IC': 0.037426503365732174,
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'ICIR': 0.28977883455541603,
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'Rank IC': 0.04659889541774283,
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'Rank ICIR': 0.373569340092482}
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'The following are analysis results of the excess return without cost.'
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risk
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mean 0.000381
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std 0.004109
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annualized_return 0.096066
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information_ratio 1.472729
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max_drawdown -0.094917
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'The following are analysis results of the excess return with cost.'
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risk
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mean 0.000213
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std 0.004111
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annualized_return 0.053630
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information_ratio 0.821711
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max_drawdown -0.113694
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'''
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class LocalformerModel(Model):
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def __init__(
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self,
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d_feat: int = 20,
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d_model: int = 64,
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batch_size: int = 8192,
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nhead: int = 2,
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num_layers: int = 2,
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dropout: float = 0,
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n_epochs=100,
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lr=0.0001,
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metric="",
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early_stop=5,
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loss="mse",
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optimizer="adam",
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reg=1e-3,
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n_jobs=10,
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GPU=2,
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seed=None,
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**kwargs
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):
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# set hyper-parameters.
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self.d_model = d_model
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self.dropout = dropout
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self.n_epochs = n_epochs
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self.lr = lr
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self.reg = reg
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self.metric = metric
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.n_jobs = n_jobs
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self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
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self.seed = seed
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self.logger = get_module_logger("TransformerModel")
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print('do we have gpu?{}'.format(torch.cuda.is_available()))
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self.logger.info(
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"Improved Transformer:"
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"\nbatch_size : {}"
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"\ndevice : {}".format(self.batch_size, self.device)
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)
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if self.seed is not None:
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np.random.seed(self.seed)
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torch.manual_seed(self.seed)
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self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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self.fitted = False
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self.model.to(self.device)
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@property
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def use_gpu(self):
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred.float() - label.float()) ** 2
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return torch.mean(loss)
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def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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if self.loss == "mse":
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return self.mse(pred[mask], label[mask])
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raise ValueError("unknown loss `%s`" % self.loss)
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def metric_fn(self, pred, label):
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mask = torch.isfinite(label)
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if self.metric == "" or self.metric == "loss":
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return -self.loss_fn(pred[mask], label[mask])
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raise ValueError("unknown metric `%s`" % self.metric)
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def train_epoch(self, data_loader):
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self.model.train()
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for data in data_loader:
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feature = data[:, :, 0:-1].to(self.device)
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label = data[:, -1, -1].to(self.device)
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pred = self.model(feature.float()) # .float()
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loss = self.loss_fn(pred, label)
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self.train_optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
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self.train_optimizer.step()
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def test_epoch(self, data_loader):
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self.model.eval()
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scores = []
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losses = []
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for data in data_loader:
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feature = data[:, :, 0:-1].to(self.device)
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# feature[torch.isnan(feature)] = 0
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label = data[:, -1, -1].to(self.device)
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with torch.no_grad():
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pred = self.model(feature.float()) # .float()
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loss = self.loss_fn(pred, label)
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losses.append(loss.item())
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score = self.metric_fn(pred, label)
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scores.append(score.item())
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return np.mean(losses), np.mean(scores)
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def fit(
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self,
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dataset: DatasetH,
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evals_result=dict(),
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save_path=None,
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):
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dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
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dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
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dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
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dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
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train_loader = DataLoader(
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dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
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)
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valid_loader = DataLoader(
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dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
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)
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save_path = get_or_create_path(save_path)
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stop_steps = 0
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train_loss = 0
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best_score = -np.inf
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best_epoch = 0
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evals_result["train"] = []
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evals_result["valid"] = []
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# train
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self.logger.info("training...")
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self.fitted = True
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for step in range(self.n_epochs):
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self.logger.info("Epoch%d:", step)
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self.logger.info("training...")
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self.train_epoch(train_loader)
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self.logger.info("evaluating...")
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train_loss, train_score = self.test_epoch(train_loader)
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val_loss, val_score = self.test_epoch(valid_loader)
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self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
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evals_result["train"].append(train_score)
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evals_result["valid"].append(val_score)
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if val_score > best_score:
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best_score = val_score
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stop_steps = 0
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best_epoch = step
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best_param = copy.deepcopy(self.model.state_dict())
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else:
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stop_steps += 1
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if stop_steps >= self.early_stop:
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self.logger.info("early stop")
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break
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self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
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self.model.load_state_dict(best_param)
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torch.save(best_param, save_path)
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if self.use_gpu:
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torch.cuda.empty_cache()
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def predict(self, dataset):
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if not self.fitted:
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raise ValueError("model is not fitted yet!")
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dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
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dl_test.config(fillna_type="ffill+bfill")
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test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
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self.model.eval()
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preds = []
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for data in test_loader:
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feature = data[:, :, 0:-1].to(self.device)
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with torch.no_grad():
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pred = self.model(feature.float()).detach().cpu().numpy()
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preds.append(pred)
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return pd.Series(np.concatenate(preds), index=dl_test.get_index())
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=1000):
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super(PositionalEncoding, self).__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0).transpose(0, 1)
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self.register_buffer('pe', pe)
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def forward(self, x):
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# [T, N, F]
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return x + self.pe[:x.size(0), :]
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def _get_clones(module, N):
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return ModuleList([copy.deepcopy(module) for i in range(N)])
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class LocalformerEncoder(nn.Module):
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__constants__ = ['norm']
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def __init__(self, encoder_layer, num_layers, d_model):
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super(LocalformerEncoder, self).__init__()
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self.layers = _get_clones(encoder_layer, num_layers)
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self.conv = _get_clones(nn.Conv1d(d_model, d_model, 3, 1, 1), num_layers)
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self.num_layers = num_layers
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def forward(self, src, mask):
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output = src
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out = src
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for i, mod in enumerate(self.layers):
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# [T, N, F] --> [N, T, F] --> [N, F, T]
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out = output.transpose(1, 0).transpose(2, 1)
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out = self.conv[i](out).transpose(2, 1).transpose(1, 0)
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output = mod(output+out, src_mask=mask)
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return output + out
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class Transformer(nn.Module):
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def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
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super(Transformer, self).__init__()
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self.rnn = nn.GRU(
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input_size=d_model,
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hidden_size=d_model,
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num_layers=num_layers,
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batch_first=False,
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dropout=dropout,
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)
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self.feature_layer = nn.Linear(d_feat, d_model)
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self.pos_encoder = PositionalEncoding(d_model)
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self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
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self.transformer_encoder = LocalformerEncoder(self.encoder_layer, num_layers=num_layers, d_model=d_model)
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self.decoder_layer = nn.Linear(d_model, 1)
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self.device = device
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self.d_feat = d_feat
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def forward(self, src):
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# pdb.set_trace()
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# src [N, T, F], [512, 60, 6]
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src = self.feature_layer(src) # [512, 60, 8]
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# src [N, T, F] --> [T, N, F], [60, 512, 8]
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src = src.transpose(1, 0) # not batch first
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mask = None
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src = self.pos_encoder(src)
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output = self.transformer_encoder(src, mask) # [60, 512, 8]
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output, _ = self.rnn(output)
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# [T, N, F] --> [N, T*F]
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output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
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return output.squeeze()
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312
qlib/contrib/model/pytorch_transformer.py
Normal file
312
qlib/contrib/model/pytorch_transformer.py
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@@ -0,0 +1,312 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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from __future__ import division
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from __future__ import print_function
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import os
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import numpy as np
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import pandas as pd
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import copy
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import math
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from ...utils import get_or_create_path
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from ...log import get_module_logger
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from .pytorch_utils import count_parameters
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from ...model.base import Model
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from ...data.dataset import DatasetH, TSDatasetH
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from ...data.dataset.handler import DataHandlerLP
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import pdb
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# qrun benchmarks/Transformer/workflow_config_transformer_Alpha158.yaml
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# 0.993681, @11,
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'''
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'IC': 0.03186587768611013,
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'ICIR': 0.2556910881045764,
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'Rank IC': 0.04735251936658551,
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'Rank ICIR': 0.388378955424602
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'The following are analysis results of the excess return without cost.'
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risk
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mean 0.000309
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std 0.004209
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annualized_return 0.077839
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information_ratio 1.164993
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max_drawdown -0.106215
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'The following are analysis results of the excess return with cost.'
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risk
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mean 0.000126
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std 0.004209
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annualized_return 0.031707
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information_ratio 0.474567
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max_drawdown -0.131948
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'''
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class TransformerModel(Model):
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def __init__(
|
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self,
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d_feat: int = 20,
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d_model: int = 64,
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batch_size: int = 8192,
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nhead: int = 2,
|
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num_layers: int = 2,
|
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dropout: float = 0,
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n_epochs=100,
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lr=0.0001,
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metric="",
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early_stop=5,
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loss="mse",
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optimizer="adam",
|
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reg=1e-3,
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n_jobs=10,
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GPU=0,
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seed=None,
|
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**kwargs
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):
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# set hyper-parameters.
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self.d_model = d_model
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self.dropout = dropout
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self.n_epochs = n_epochs
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self.lr = lr
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self.reg = reg
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self.metric = metric
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.n_jobs = n_jobs
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self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
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self.seed = seed
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self.logger = get_module_logger("TransformerModel")
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print('do we have gpu?{}'.format(torch.cuda.is_available()))
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self.logger.info(
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"Naive Transformer:"
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"\nbatch_size : {}"
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"\ndevice : {}".format(self.batch_size, self.device)
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)
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if self.seed is not None:
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np.random.seed(self.seed)
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torch.manual_seed(self.seed)
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self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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self.fitted = False
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self.model.to(self.device)
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@property
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def use_gpu(self):
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return self.device != torch.device("cpu")
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|
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def mse(self, pred, label):
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loss = (pred.float() - label.float()) ** 2
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return torch.mean(loss)
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||||
def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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|
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if self.loss == "mse":
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return self.mse(pred[mask], label[mask])
|
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|
||||
raise ValueError("unknown loss `%s`" % self.loss)
|
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|
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def metric_fn(self, pred, label):
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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), :]
|
||||
|
||||
|
||||
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_feat,
|
||||
hidden_size=d_model,
|
||||
num_layers=num_layers,
|
||||
batch_first=True,
|
||||
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 = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
|
||||
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]
|
||||
|
||||
# out, _ = self.rnn(src)
|
||||
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]
|
||||
|
||||
# [T, N, F] --> [N, T*F]
|
||||
output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
|
||||
|
||||
return output.squeeze()
|
||||
|
||||
Reference in New Issue
Block a user