mirror of
https://github.com/microsoft/qlib.git
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414 lines
13 KiB
Python
414 lines
13 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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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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from sklearn.metrics import roc_auc_score, mean_squared_error
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import logging
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from ...utils import (
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unpack_archive_with_buffer,
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save_multiple_parts_file,
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create_save_path,
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drop_nan_by_y_index,
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)
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from ...log import get_module_logger, TimeInspector
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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 torch.utils.data import Sampler
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from ...model.base import Model
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from ...data.dataset import DatasetH
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from ...data.dataset.handler import DataHandlerLP
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from ...contrib.model.pytorch_lstm import LSTMModel
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from ...contrib.model.pytorch_gru import GRUModel
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class DailyBatchSampler(Sampler):
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def __init__(self, data_source):
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self.data_source = data_source
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self.data = self.data_source.data.loc[self.data_source.get_index()]
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self.daily_count = self.data.groupby(level=0).size().values # calculate number of samples in each batch
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self.daily_index = np.roll(np.cumsum(self.daily_count), 1) # calculate begin index of each batch
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self.daily_index[0] = 0
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def __iter__(self):
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for idx, count in zip(self.daily_index, self.daily_count):
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yield np.arange(idx, idx + count)
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def __len__(self):
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return len(self.data_source)
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class GATs(Model):
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"""GATs Model
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Parameters
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----------
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lr : float
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learning rate
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d_feat : int
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input dimensions for each time step
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metric : str
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the evaluate 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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d_feat=20,
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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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n_epochs=200,
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lr=0.001,
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metric="",
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early_stop=20,
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loss="mse",
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base_model="GRU",
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with_pretrain=True,
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model_path=None,
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optimizer="adam",
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GPU="0",
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n_jobs=10,
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seed=None,
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**kwargs
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):
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# Set logger.
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self.logger = get_module_logger("GATs")
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self.logger.info("GATs pytorch version...")
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# set hyper-parameters.
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self.d_feat = d_feat
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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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.metric = metric
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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.base_model = base_model
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self.with_pretrain = with_pretrain
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self.model_path = model_path
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self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
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self.n_jobs = n_jobs
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
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"GATs parameters setting:"
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"\nd_feat : {}"
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"\nhidden_size : {}"
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"\nnum_layers : {}"
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"\ndropout : {}"
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"\nn_epochs : {}"
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"\nlr : {}"
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"\nmetric : {}"
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nbase_model : {}"
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"\nwith_pretrain : {}"
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"\nmodel_path : {}"
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"\nvisible_GPU : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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hidden_size,
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num_layers,
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dropout,
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n_epochs,
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lr,
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metric,
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early_stop,
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optimizer.lower(),
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loss,
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base_model,
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with_pretrain,
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model_path,
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GPU,
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self.use_gpu,
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seed,
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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.GAT_model = GATModel(
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d_feat=self.d_feat,
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hidden_size=self.hidden_size,
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num_layers=self.num_layers,
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dropout=self.dropout,
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base_model=self.base_model,
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)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.GAT_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.GAT_model.to(self.device)
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def mse(self, pred, label):
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loss = (pred - label) ** 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 get_daily_inter(self, df, shuffle=False):
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# organize the train data into daily batches
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daily_count = df.groupby(level=0).size().values
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daily_index = np.roll(np.cumsum(daily_count), 1)
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daily_index[0] = 0
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if shuffle:
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# shuffle data
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daily_shuffle = list(zip(daily_index, daily_count))
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np.random.shuffle(daily_shuffle)
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daily_index, daily_count = zip(*daily_shuffle)
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return daily_index, daily_count
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def train_epoch(self, data_loader):
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self.GAT_model.train()
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for data in data_loader:
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data = data.squeeze()
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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.GAT_model(feature.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.GAT_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.GAT_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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data = data.squeeze()
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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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pred = self.GAT_model(feature.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,
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evals_result=dict(),
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verbose=True,
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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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sampler_train = DailyBatchSampler(dl_train)
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sampler_valid = DailyBatchSampler(dl_valid)
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train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs)
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valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs)
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if save_path == None:
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save_path = create_save_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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# load pretrained base_model
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if self.with_pretrain:
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if self.model_path == None:
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raise ValueError("the path of the pretrained model should be given first!")
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self.logger.info("Loading pretrained model...")
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if self.base_model == "LSTM":
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pretrained_model = LSTMModel(
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d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers
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)
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pretrained_model.load_state_dict(torch.load(self.model_path))
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elif self.base_model == "GRU":
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pretrained_model = GRUModel(
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d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers
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)
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pretrained_model.load_state_dict(torch.load(self.model_path))
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else:
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raise ValueError("unknown base model name `%s`" % self.base_model)
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model_dict = self.GAT_model.state_dict()
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pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in model_dict}
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model_dict.update(pretrained_dict)
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self.GAT_model.load_state_dict(model_dict)
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self.logger.info("Loading pretrained model Done...")
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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.GAT_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.GAT_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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sampler_test = DailyBatchSampler(dl_test)
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test_loader = DataLoader(dl_test, sampler=sampler_test, num_workers=self.n_jobs)
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self.GAT_model.eval()
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preds = []
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for data in test_loader:
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data = data.squeeze()
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feature = data[:, :, 0:-1].to(self.device)
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with torch.no_grad():
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if self.use_gpu:
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pred = self.GAT_model(feature.float()).detach().cpu().numpy()
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else:
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pred = self.GAT_model(feature.float()).detach().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 GATModel(nn.Module):
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def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model="GRU"):
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super().__init__()
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if base_model == "GRU":
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self.rnn = nn.GRU(
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input_size=d_feat,
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hidden_size=hidden_size,
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num_layers=num_layers,
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batch_first=True,
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dropout=dropout,
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)
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elif base_model == "LSTM":
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self.rnn = nn.LSTM(
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input_size=d_feat,
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hidden_size=hidden_size,
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num_layers=num_layers,
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batch_first=True,
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dropout=dropout,
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)
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else:
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raise ValueError("unknown base model name `%s`" % base_model)
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self.hidden_size = hidden_size
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self.d_feat = d_feat
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self.transformation = nn.Linear(self.hidden_size, self.hidden_size)
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self.a = nn.Parameter(torch.randn(self.hidden_size * 2, 1))
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self.a.requires_grad = True
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self.fc = nn.Linear(self.hidden_size, self.hidden_size)
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self.fc_out = nn.Linear(hidden_size, 1)
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self.leaky_relu = nn.LeakyReLU()
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self.softmax = nn.Softmax(dim=1)
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def cal_attention(self, x, y):
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x = self.transformation(x)
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y = self.transformation(y)
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sample_num = x.shape[0]
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dim = x.shape[1]
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e_x = x.expand(sample_num, sample_num, dim)
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e_y = torch.transpose(e_x, 0, 1)
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attention_in = torch.cat((e_x, e_y), 2).view(-1, dim * 2)
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self.a_t = torch.t(self.a)
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attention_out = self.a_t.mm(torch.t(attention_in)).view(sample_num, sample_num)
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attention_out = self.leaky_relu(attention_out)
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att_weight = self.softmax(attention_out)
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return att_weight
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def forward(self, x):
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out, _ = self.rnn(x)
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hidden = out[:, -1, :]
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att_weight = self.cal_attention(hidden, hidden)
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hidden = att_weight.mm(hidden) + hidden
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hidden = self.fc(hidden)
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hidden = self.leaky_relu(hidden)
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return self.fc_out(hidden).squeeze()
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