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4b8d70df1b
* update qlib general_nn for rdagent_qlib * fix install lightgbm error * fix install lightgbm error & format with black --------- Co-authored-by: Linlang <Lv.Linlang@hotmail.com>
372 lines
12 KiB
Python
372 lines
12 KiB
Python
# 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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from torch.utils.data import DataLoader
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import numpy as np
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import pandas as pd
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from typing import Union
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import copy
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import torch
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import torch.optim as optim
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from torch.optim.lr_scheduler import ReduceLROnPlateau
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from qlib.data.dataset.weight import Reweighter
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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 ...utils import (
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init_instance_by_config,
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get_or_create_path,
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)
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from ...log import get_module_logger
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from ...model.utils import ConcatDataset
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class GeneralPTNN(Model):
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"""
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Motivation:
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We want to provide a Qlib General Pytorch Model Adaptor
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You can reuse it for all kinds of Pytorch models.
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It should include the training and predict process
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Parameters
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----------
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d_feat : int
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input dimension for each time step
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metric: str
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the evaluation metric used in early stop
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optimizer : str
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optimizer name
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GPU : str
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the GPU ID(s) used for training
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"""
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def __init__(
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self,
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n_epochs=200,
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lr=0.001,
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metric="",
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batch_size=2000,
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early_stop=20,
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loss="mse",
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weight_decay=0.0,
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optimizer="adam",
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n_jobs=10,
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GPU=0,
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seed=None,
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pt_model_uri="qlib.contrib.model.pytorch_gru_ts.GRUModel",
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pt_model_kwargs={
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"d_feat": 6,
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"hidden_size": 64,
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"num_layers": 2,
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"dropout": 0.0,
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},
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):
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# Set logger.
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self.logger = get_module_logger("GeneralPTNN")
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self.logger.info("GeneralPTNN pytorch version...")
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# set hyper-parameters.
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self.n_epochs = n_epochs
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self.lr = lr
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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.weight_decay = weight_decay
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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.n_jobs = n_jobs
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self.seed = seed
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self.pt_model_uri, self.pt_model_kwargs = pt_model_uri, pt_model_kwargs
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self.dnn_model = init_instance_by_config({"class": pt_model_uri, "kwargs": pt_model_kwargs})
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self.logger.info(
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"GeneralPTNN parameters setting:"
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"\nn_epochs : {}"
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"\nlr : {}"
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"\nmetric : {}"
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"\nbatch_size : {}"
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\ndevice : {}"
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"\nn_jobs : {}"
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"\nuse_GPU : {}"
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"\nweight_decay : {}"
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"\nseed : {}"
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"\npt_model_uri: {}"
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"\npt_model_kwargs: {}".format(
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n_epochs,
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lr,
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metric,
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batch_size,
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early_stop,
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optimizer.lower(),
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loss,
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self.device,
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n_jobs,
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self.use_gpu,
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weight_decay,
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seed,
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pt_model_uri,
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pt_model_kwargs,
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)
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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.logger.info("model:\n{:}".format(self.dnn_model))
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self.logger.info("model size: {:.4f} MB".format(count_parameters(self.dnn_model)))
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=weight_decay)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=weight_decay)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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# === ReduceLROnPlateau learning rate scheduler ===
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self.lr_scheduler = ReduceLROnPlateau(
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self.train_optimizer, mode="min", factor=0.5, patience=5, min_lr=1e-6, threshold=1e-5
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)
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self.fitted = False
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self.dnn_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, weight):
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loss = weight * (pred - label) ** 2
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return torch.mean(loss)
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def loss_fn(self, pred, label, weight=None):
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mask = ~torch.isnan(label)
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if weight is None:
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weight = torch.ones_like(label)
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if self.loss == "mse":
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return self.mse(pred[mask], label[mask].view(-1, 1), weight[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 in ("", "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 _get_fl(self, data: torch.Tensor):
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"""
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get feature and label from data
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- Handle the different data shape of time series and tabular data
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Parameters
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----------
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data : torch.Tensor
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input data which maybe 3 dimension or 2 dimension
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- 3dim: [batch_size, time_step, feature_dim]
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- 2dim: [batch_size, feature_dim]
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Returns
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-------
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Tuple[torch.Tensor, torch.Tensor]
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"""
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if data.dim() == 3:
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# it is a time series dataset
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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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elif data.dim() == 2:
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# it is a tabular dataset
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feature = data[:, 0:-1].to(self.device)
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label = data[:, -1].to(self.device)
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else:
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raise ValueError("Unsupported data shape.")
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return feature, label
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def train_epoch(self, data_loader):
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self.dnn_model.train()
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for data, weight in data_loader:
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feature, label = self._get_fl(data)
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pred = self.dnn_model(feature.float())
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loss = self.loss_fn(pred, label, weight.to(self.device))
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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.dnn_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.dnn_model.eval()
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scores = []
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losses = []
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for data, weight in data_loader:
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feature, label = self._get_fl(data)
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with torch.no_grad():
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pred = self.dnn_model(feature.float())
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loss = self.loss_fn(pred, label, weight.to(self.device))
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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: Union[DatasetH, TSDatasetH],
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evals_result=dict(),
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save_path=None,
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reweighter=None,
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):
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ists = isinstance(dataset, TSDatasetH) # is this time series dataset
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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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self.logger.info(f"Train samples: {len(dl_train)}")
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self.logger.info(f"Valid samples: {len(dl_valid)}")
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if dl_train.empty or dl_valid.empty:
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raise ValueError("Empty data from dataset, please check your dataset config.")
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if reweighter is None:
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wl_train = np.ones(len(dl_train))
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wl_valid = np.ones(len(dl_valid))
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elif isinstance(reweighter, Reweighter):
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wl_train = reweighter.reweight(dl_train)
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wl_valid = reweighter.reweight(dl_valid)
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else:
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raise ValueError("Unsupported reweighter type.")
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# Preprocess for data. To align to Dataset Interface for DataLoader
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if ists:
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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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else:
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# If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
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dl_train = dl_train.values
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dl_valid = dl_valid.values
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train_loader = DataLoader(
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ConcatDataset(dl_train, wl_train),
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batch_size=self.batch_size,
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shuffle=True,
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num_workers=self.n_jobs,
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drop_last=True,
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)
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valid_loader = DataLoader(
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ConcatDataset(dl_valid, wl_valid),
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batch_size=self.batch_size,
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shuffle=False,
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num_workers=self.n_jobs,
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drop_last=True,
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)
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del dl_train, dl_valid, wl_train, wl_valid
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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("Epoch%d: train %.6f, valid %.6f" % (step, 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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# current_lr = self.train_optimizer.param_groups[0]["lr"]
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# self.logger.info("Current learning rate: %.6e" % current_lr)
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self.lr_scheduler.step(val_score)
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if step == 0:
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best_param = copy.deepcopy(self.dnn_model.state_dict())
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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.dnn_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 epoch" % (best_score, best_epoch))
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self.dnn_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(
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self,
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dataset: Union[DatasetH, TSDatasetH],
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batch_size=None,
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n_jobs=None,
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):
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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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self.logger.info(f"Test samples: {len(dl_test)}")
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if isinstance(dataset, TSDatasetH):
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dl_test.config(fillna_type="ffill+bfill") # process nan brought by dataloader
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index = dl_test.get_index()
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else:
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# If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
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index = dl_test.index
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dl_test = dl_test.values
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test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
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self.dnn_model.eval()
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preds = []
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for data in test_loader:
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feature, _ = self._get_fl(data)
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feature = feature.to(self.device)
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with torch.no_grad():
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pred = self.dnn_model(feature.float()).detach().cpu().numpy()
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preds.append(pred)
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preds_concat = np.concatenate(preds)
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if preds_concat.ndim != 1:
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preds_concat = preds_concat.ravel()
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return pd.Series(preds_concat, index=index)
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