mirror of
https://github.com/microsoft/qlib.git
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664 lines
22 KiB
Python
664 lines
22 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, RandomSampler, StackDataset
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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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from typing import Callable, Optional, Text, Union
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from sklearn.metrics import roc_auc_score, mean_squared_error
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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 StackDataset
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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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auto_filter_kwargs,
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init_instance_by_config,
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unpack_archive_with_buffer,
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save_multiple_parts_file,
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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 ...workflow import R
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from qlib.contrib.meta.data_selection.utils import ICLoss
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from torch.nn import DataParallel
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class GeneralPTNN(Model):
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"""General Pytorch Neural Network Model
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Parameters
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----------
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input_dim : int
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input dimension
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output_dim : int
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output dimension
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layers : tuple
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layer sizes
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lr : float
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learning rate
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optimizer : str
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optimizer name
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GPU : int
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the GPU ID used for training
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"""
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def __init__(
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self,
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lr=0.001,
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max_steps=300,
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batch_size=2000,
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early_stop_rounds=50,
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eval_steps=20,
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optimizer="gd",
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loss="mse",
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GPU=0,
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seed=None,
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weight_decay=0.0,
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data_parall=False,
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scheduler: Optional[Union[Callable]] = "default", # when it is Callable, it accept one argument named optimizer
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init_model=None,
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eval_train_metric=False,
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pt_model_uri="qlib.contrib.model.pytorch_nn.Net",
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pt_model_kwargs={
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"input_dim": 360,
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"layers": (256,),
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},
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valid_key=DataHandlerLP.DK_L,
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# TODO: Infer Key is a more reasonable key. But it requires more detailed processing on label processing
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):
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# Set logger.
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self.logger = get_module_logger("DNNModelPytorch")
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self.logger.info("DNN pytorch version...")
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# set hyper-parameters.
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self.lr = lr
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self.max_steps = max_steps
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self.batch_size = batch_size
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self.early_stop_rounds = early_stop_rounds
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self.eval_steps = eval_steps
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self.optimizer = optimizer.lower()
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self.loss_type = loss
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if isinstance(GPU, str):
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self.device = torch.device(GPU)
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else:
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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.weight_decay = weight_decay
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self.data_parall = data_parall
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self.eval_train_metric = eval_train_metric
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self.valid_key = valid_key
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self.best_step = None
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self.logger.info(
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"DNN parameters setting:"
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f"\nlr : {lr}"
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f"\nmax_steps : {max_steps}"
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f"\nbatch_size : {batch_size}"
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f"\nearly_stop_rounds : {early_stop_rounds}"
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f"\neval_steps : {eval_steps}"
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f"\noptimizer : {optimizer}"
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f"\nloss_type : {loss}"
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f"\nseed : {seed}"
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f"\ndevice : {self.device}"
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f"\nuse_GPU : {self.use_gpu}"
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f"\nweight_decay : {weight_decay}"
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f"\nenable data parall : {self.data_parall}"
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f"\npt_model_uri: {pt_model_uri}"
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f"\npt_model_kwargs: {pt_model_kwargs}"
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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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if loss not in {"mse", "binary"}:
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raise NotImplementedError("loss {} is not supported!".format(loss))
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self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
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if init_model is None:
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self.dnn_model = init_instance_by_config({"class": pt_model_uri, "kwargs": pt_model_kwargs})
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if self.data_parall:
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self.dnn_model = DataParallel(self.dnn_model).to(self.device)
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else:
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self.dnn_model = init_model
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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=self.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=self.weight_decay)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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if scheduler == "default":
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# Reduce learning rate when loss has stopped decrease
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self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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self.train_optimizer,
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mode="min",
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factor=0.5,
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patience=10,
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verbose=True,
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threshold=0.0001,
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threshold_mode="rel",
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cooldown=0,
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min_lr=0.00001,
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eps=1e-08,
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)
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elif scheduler is None:
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self.scheduler = None
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else:
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self.scheduler = scheduler(optimizer=self.train_optimizer)
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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 _eval_valid_dl(self, valid_loader, val_index):
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with torch.no_grad():
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self.dnn_model.eval()
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val_loss = []
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val_pred = []
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val_label = []
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for x_batch, y_batch in valid_loader:
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x_batch = x_batch.to(self.device)
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y_batch = y_batch.to(self.device)
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cur_loss = self.get_loss(preds, y_batch, self.loss_type)
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val_loss.append(cur_loss.detach().cpu().numpy().item())
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val_loss = np.mean(val_loss)
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val_pred = torch.cat(val_pred, axis=0).detach().cpu().numpy()
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val_label = torch.cat(val_label, axis=0).detach().cpu().numpy()
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val_metric = self.get_metric(val_pred, val_label, val_index).detach().cpu().numpy().item()
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return val_loss, val_metric
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def fit(
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self,
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dataset: Union[DatasetH, TSDatasetH],
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verbose=True,
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save_path=None,
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):
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ists = isinstance(dataset, TSDatasetH) # is this time series dataset
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# prepare training
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train_x = dataset.prepare("train", col_set="feature", data_key=DataHandlerLP.DK_L)
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train_y = dataset.prepare("train", col_set="label", data_key=DataHandlerLP.DK_L)
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train_ds = StackDataset(train_x, train_y)
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train_sampler = RandomSampler(train_ds)
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train_loader = DataLoader(train_ds, batch_size=self.batch_size, sampler=train_sampler)
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# prepare validation
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valid_x = dataset.prepare("train", col_set="feature", data_key=DataHandlerLP.DK_L)
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valid_y = dataset.prepare("train", col_set="label", data_key=DataHandlerLP.DK_L)
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valid_ds = StackDataset(valid_x, valid_y)
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valid_loader = DataLoader(valid_ds, batch_size=self.batch_size, shuffle=False)
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if ists:
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val_index = valid_x.data_index
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else:
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val_index = valid_x.index
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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_loss = np.inf
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# train
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self.logger.info("training...")
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for step in range(1, self.max_steps + 1):
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if stop_steps >= self.early_stop_rounds:
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if verbose:
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self.logger.info("\tearly stop")
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break
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loss = AverageMeter()
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self.dnn_model.train()
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self.train_optimizer.zero_grad()
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for x_batch, y_batch in train_loader:
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x_batch = x_batch.to(self.device)
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y_batch = y_batch.to(self.device)
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# forward
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preds = self.dnn_model(x_batch)
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cur_loss = self.get_loss(preds, y_batch, self.loss_type)
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cur_loss.backward()
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self.train_optimizer.step()
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loss.update(cur_loss.item())
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R.log_metrics(train_loss=loss.avg, step=step)
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# validation
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train_loss += loss.val
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# for every `eval_steps` steps or at the last steps, we will evaluate the model.
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if step % self.eval_steps == 0 or step == self.max_steps:
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stop_steps += 1
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train_loss /= self.eval_steps
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val_loss, val_metric = self._eval_valid_dl(valid_loader, val_index)
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R.log_metrics(val_loss=val_loss, step=step)
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R.log_metrics(val_metric=val_metric, step=step)
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if val_loss < best_loss:
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if verbose:
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self.logger.info(
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"\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
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best_loss, val_loss
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)
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)
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best_loss = val_loss
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self.best_step = step
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R.log_metrics(best_step=self.best_step, step=step)
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stop_steps = 0
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torch.save(self.dnn_model.state_dict(), save_path)
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train_loss = 0
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# update learning rate
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if self.scheduler is not None:
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auto_filter_kwargs(self.scheduler.step, warning=False)(metrics=val_loss, epoch=step)
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R.log_metrics(lr=self.get_lr(), step=step)
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# restore the optimal parameters after training
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self.dnn_model.load_state_dict(torch.load(save_path, map_location=self.device))
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if self.use_gpu:
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torch.cuda.empty_cache()
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def get_lr(self):
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assert len(self.train_optimizer.param_groups) == 1
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return self.train_optimizer.param_groups[0]["lr"]
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def get_loss(self, pred, target, loss_type, w=None):
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pred, target = pred.reshape(-1), target.reshape(-1)
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if w is None:
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# make it ones and the same size with pred
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w = torch.ones_like(pred).to(pred.device)
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if loss_type == "mse":
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sqr_loss = torch.mul(pred - target, pred - target)
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loss = torch.mul(sqr_loss, w).mean()
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return loss
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elif loss_type == "binary":
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loss = nn.BCEWithLogitsLoss(weight=w)
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return loss(pred, target)
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else:
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raise NotImplementedError("loss {} is not supported!".format(loss_type))
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def get_metric(self, pred, target, index):
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# NOTE: the order of the index must follow <datetime, instrument> sorted order
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return -ICLoss()(pred, target, index) # pylint: disable=E1130
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def _nn_predict(self, data, return_cpu=True):
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"""Reusing predicting NN.
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Scenarios
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1) test inference (data may come from CPU and expect the output data is on CPU)
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2) evaluation on training (data may come from GPU)
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"""
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if not isinstance(data, torch.Tensor):
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if isinstance(data, pd.DataFrame):
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data = data.values
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data = torch.Tensor(data)
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data = data.to(self.device)
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preds = []
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self.dnn_model.eval()
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with torch.no_grad():
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batch_size = 8096
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for i in range(0, len(data), batch_size):
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x = data[i : i + batch_size]
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preds.append(self.dnn_model(x.to(self.device)).detach().reshape(-1))
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if return_cpu:
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preds = np.concatenate([pr.cpu().numpy() for pr in preds])
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else:
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preds = torch.cat(preds, axis=0)
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return preds
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def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
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x_test_pd = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
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preds = self._nn_predict(x_test_pd)
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return pd.Series(preds.reshape(-1), index=x_test_pd.index)
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class AverageMeter:
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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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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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.,
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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.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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"\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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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)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr)
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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.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], 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
|
|
-------
|
|
Tuple[torch.Tensor, torch.Tensor]
|
|
"""
|
|
if data.dim() == 3:
|
|
# it is a time series dataset
|
|
feature = data[:, :, 0:-1].to(self.device)
|
|
label = data[:, -1, -1].to(self.device)
|
|
elif data.dim() == 2:
|
|
# it is a tabular dataset
|
|
feature = data[:, 0:-1].to(self.device)
|
|
label = data[:, -1].to(self.device)
|
|
else:
|
|
raise ValueError("Unsupported data shape.")
|
|
return feature, label
|
|
|
|
def train_epoch(self, data_loader):
|
|
self.dnn_model.train()
|
|
|
|
for data, weight in data_loader:
|
|
feature , label = self._get_fl(data)
|
|
|
|
pred = self.dnn_model(feature.float())
|
|
loss = self.loss_fn(pred, label, weight.to(self.device))
|
|
|
|
self.train_optimizer.zero_grad()
|
|
loss.backward()
|
|
torch.nn.utils.clip_grad_value_(self.dnn_model.parameters(), 3.0)
|
|
self.train_optimizer.step()
|
|
|
|
def test_epoch(self, data_loader):
|
|
self.dnn_model.eval()
|
|
|
|
scores = []
|
|
losses = []
|
|
|
|
for data, weight 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.dnn_model(feature.float())
|
|
loss = self.loss_fn(pred, label, weight.to(self.device))
|
|
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: Union[DatasetH, TSDatasetH],
|
|
evals_result=dict(),
|
|
save_path=None,
|
|
reweighter=None,
|
|
):
|
|
ists = isinstance(dataset, TSDatasetH) # is this time series dataset
|
|
|
|
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)
|
|
if dl_train.empty or dl_valid.empty:
|
|
raise ValueError("Empty data from dataset, please check your dataset config.")
|
|
|
|
if reweighter is None:
|
|
wl_train = np.ones(len(dl_train))
|
|
wl_valid = np.ones(len(dl_valid))
|
|
elif isinstance(reweighter, Reweighter):
|
|
wl_train = reweighter.reweight(dl_train)
|
|
wl_valid = reweighter.reweight(dl_valid)
|
|
else:
|
|
raise ValueError("Unsupported reweighter type.")
|
|
|
|
# Preprocess for data. To align to Dataset Interface for DataLoader
|
|
if ists:
|
|
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
|
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
|
else:
|
|
# If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
|
|
dl_train = dl_train.values
|
|
dl_valid = dl_valid.values
|
|
|
|
train_loader = DataLoader(
|
|
ConcatDataset(dl_train, wl_train),
|
|
batch_size=self.batch_size,
|
|
shuffle=True,
|
|
num_workers=self.n_jobs,
|
|
drop_last=True,
|
|
)
|
|
valid_loader = DataLoader(
|
|
ConcatDataset(dl_valid, wl_valid),
|
|
batch_size=self.batch_size,
|
|
shuffle=False,
|
|
num_workers=self.n_jobs,
|
|
drop_last=True,
|
|
)
|
|
del dl_train, dl_valid, wl_train, wl_valid
|
|
|
|
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.dnn_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.dnn_model.load_state_dict(best_param)
|
|
torch.save(best_param, save_path)
|
|
|
|
if self.use_gpu:
|
|
torch.cuda.empty_cache()
|
|
|
|
def predict(self, dataset: Union[DatasetH, TSDatasetH]):
|
|
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.dnn_model.eval()
|
|
preds = []
|
|
|
|
for data in test_loader:
|
|
feature = data[:, :, 0:-1].to(self.device)
|
|
|
|
with torch.no_grad():
|
|
pred = self.dnn_model(feature.float()).detach().cpu().numpy()
|
|
|
|
preds.append(pred)
|
|
|
|
return pd.Series(np.concatenate(preds), index=dl_test.get_index())
|