mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-05 12:00:58 +08:00
fbba768006
* fixed a problem with multi index caused by the default value of groupkey * modify group_key default value * limit pandas verion * format with black * fix docs error * fix docs error * fixed bugs caused by pandas upgrade * remove needless code * reformat with black * limit version & add docs
501 lines
18 KiB
Python
501 lines
18 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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from typing import Text, Union
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import urllib.request
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import copy
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from ...utils import get_or_create_path
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from ...log import get_module_logger
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from .pytorch_utils import count_parameters
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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 HIST(Model):
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"""HIST 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 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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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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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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model_path=None,
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stock2concept=None,
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stock_index=None,
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optimizer="adam",
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GPU=0,
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seed=None,
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**kwargs,
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):
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# Set logger.
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self.logger = get_module_logger("HIST")
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self.logger.info("HIST 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.model_path = model_path
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self.stock2concept = stock2concept
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self.stock_index = stock_index
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self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
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self.seed = seed
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self.logger.info(
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"HIST 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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"\nmodel_path : {}"
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"\nstock2concept : {}"
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"\nstock_index : {}"
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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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model_path,
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stock2concept,
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stock_index,
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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.HIST_model = HISTModel(
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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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self.logger.info("model:\n{:}".format(self.HIST_model))
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self.logger.info("model size: {:.4f} MB".format(count_parameters(self.HIST_model)))
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.HIST_model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.HIST_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.HIST_model.to(self.device)
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@property
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def use_gpu(self):
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - 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 == "ic":
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x = pred[mask]
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y = label[mask]
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vx = x - torch.mean(x)
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vy = y - torch.mean(y)
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return torch.sum(vx * vy) / (torch.sqrt(torch.sum(vx**2)) * torch.sqrt(torch.sum(vy**2)))
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if 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, group_keys=False).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, x_train, y_train, stock_index):
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stock2concept_matrix = np.load(self.stock2concept)
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x_train_values = x_train.values
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y_train_values = np.squeeze(y_train.values)
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stock_index = stock_index.values
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stock_index[np.isnan(stock_index)] = 733
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self.HIST_model.train()
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# organize the train data into daily batches
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daily_index, daily_count = self.get_daily_inter(x_train, shuffle=True)
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for idx, count in zip(daily_index, daily_count):
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batch = slice(idx, idx + count)
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feature = torch.from_numpy(x_train_values[batch]).float().to(self.device)
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concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index[batch]]).float().to(self.device)
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label = torch.from_numpy(y_train_values[batch]).float().to(self.device)
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pred = self.HIST_model(feature, concept_matrix)
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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.HIST_model.parameters(), 3.0)
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self.train_optimizer.step()
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def test_epoch(self, data_x, data_y, stock_index):
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# prepare training data
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stock2concept_matrix = np.load(self.stock2concept)
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x_values = data_x.values
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y_values = np.squeeze(data_y.values)
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stock_index = stock_index.values
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stock_index[np.isnan(stock_index)] = 733
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self.HIST_model.eval()
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scores = []
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losses = []
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# organize the test data into daily batches
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daily_index, daily_count = self.get_daily_inter(data_x, shuffle=False)
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for idx, count in zip(daily_index, daily_count):
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batch = slice(idx, idx + count)
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feature = torch.from_numpy(x_values[batch]).float().to(self.device)
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concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index[batch]]).float().to(self.device)
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label = torch.from_numpy(y_values[batch]).float().to(self.device)
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with torch.no_grad():
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pred = self.HIST_model(feature, concept_matrix)
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loss = self.loss_fn(pred, label)
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losses.append(loss.item())
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score = self.metric_fn(pred, label)
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scores.append(score.item())
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return np.mean(losses), np.mean(scores)
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def fit(
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self,
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dataset: DatasetH,
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evals_result=dict(),
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save_path=None,
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):
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df_train, df_valid, df_test = dataset.prepare(
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["train", "valid", "test"],
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col_set=["feature", "label"],
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data_key=DataHandlerLP.DK_L,
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)
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if df_train.empty or df_valid.empty:
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raise ValueError("Empty data from dataset, please check your dataset config.")
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if not os.path.exists(self.stock2concept):
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url = "https://github.com/SunsetWolf/qlib_dataset/releases/download/v0/qlib_csi300_stock2concept.npy"
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urllib.request.urlretrieve(url, self.stock2concept)
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stock_index = np.load(self.stock_index, allow_pickle=True).item()
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df_train["stock_index"] = 733
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df_train["stock_index"] = df_train.index.get_level_values("instrument").map(stock_index)
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df_valid["stock_index"] = 733
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df_valid["stock_index"] = df_valid.index.get_level_values("instrument").map(stock_index)
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x_train, y_train, stock_index_train = df_train["feature"], df_train["label"], df_train["stock_index"]
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x_valid, y_valid, stock_index_valid = df_valid["feature"], df_valid["label"], df_valid["stock_index"]
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save_path = get_or_create_path(save_path)
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stop_steps = 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.base_model == "LSTM":
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pretrained_model = LSTMModel()
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elif self.base_model == "GRU":
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pretrained_model = GRUModel()
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else:
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raise ValueError("unknown base model name `%s`" % self.base_model)
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if self.model_path is not None:
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self.logger.info("Loading pretrained model...")
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pretrained_model.load_state_dict(torch.load(self.model_path))
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model_dict = self.HIST_model.state_dict()
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pretrained_dict = {
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k: v for k, v in pretrained_model.state_dict().items() if k in model_dict # pylint: disable=E1135
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}
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model_dict.update(pretrained_dict)
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self.HIST_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(x_train, y_train, stock_index_train)
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self.logger.info("evaluating...")
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train_loss, train_score = self.test_epoch(x_train, y_train, stock_index_train)
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val_loss, val_score = self.test_epoch(x_valid, y_valid, stock_index_valid)
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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.HIST_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.HIST_model.load_state_dict(best_param)
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torch.save(best_param, save_path)
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def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
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if not self.fitted:
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raise ValueError("model is not fitted yet!")
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stock2concept_matrix = np.load(self.stock2concept)
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stock_index = np.load(self.stock_index, allow_pickle=True).item()
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df_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
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df_test["stock_index"] = 733
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df_test["stock_index"] = df_test.index.get_level_values("instrument").map(stock_index)
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stock_index_test = df_test["stock_index"].values
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stock_index_test[np.isnan(stock_index_test)] = 733
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stock_index_test = stock_index_test.astype("int")
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df_test = df_test.drop(["stock_index"], axis=1)
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index = df_test.index
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self.HIST_model.eval()
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x_values = df_test.values
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preds = []
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# organize the data into daily batches
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daily_index, daily_count = self.get_daily_inter(df_test, shuffle=False)
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for idx, count in zip(daily_index, daily_count):
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batch = slice(idx, idx + count)
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x_batch = torch.from_numpy(x_values[batch]).float().to(self.device)
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concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index_test[batch]]).float().to(self.device)
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with torch.no_grad():
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pred = self.HIST_model(x_batch, concept_matrix).detach().cpu().numpy()
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preds.append(pred)
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return pd.Series(np.concatenate(preds), index=index)
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class HISTModel(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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self.d_feat = d_feat
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self.hidden_size = hidden_size
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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.fc_es = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_es.weight)
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self.fc_is = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_is.weight)
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self.fc_es_middle = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_es_middle.weight)
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self.fc_is_middle = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_is_middle.weight)
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self.fc_es_fore = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_es_fore.weight)
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self.fc_is_fore = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_is_fore.weight)
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self.fc_indi_fore = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_indi_fore.weight)
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self.fc_es_back = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_es_back.weight)
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self.fc_is_back = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_is_back.weight)
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self.fc_indi = nn.Linear(hidden_size, hidden_size)
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torch.nn.init.xavier_uniform_(self.fc_indi.weight)
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self.leaky_relu = nn.LeakyReLU()
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self.softmax_s2t = torch.nn.Softmax(dim=0)
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self.softmax_t2s = torch.nn.Softmax(dim=1)
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self.fc_out_es = nn.Linear(hidden_size, 1)
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self.fc_out_is = nn.Linear(hidden_size, 1)
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self.fc_out_indi = nn.Linear(hidden_size, 1)
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self.fc_out = nn.Linear(hidden_size, 1)
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def cal_cos_similarity(self, x, y): # the 2nd dimension of x and y are the same
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xy = x.mm(torch.t(y))
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x_norm = torch.sqrt(torch.sum(x * x, dim=1)).reshape(-1, 1)
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y_norm = torch.sqrt(torch.sum(y * y, dim=1)).reshape(-1, 1)
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cos_similarity = xy / (x_norm.mm(torch.t(y_norm)) + 1e-6)
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return cos_similarity
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def forward(self, x, concept_matrix):
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device = torch.device(torch.get_device(x))
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x_hidden = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
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x_hidden = x_hidden.permute(0, 2, 1) # [N, T, F]
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x_hidden, _ = self.rnn(x_hidden)
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x_hidden = x_hidden[:, -1, :]
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# Predefined Concept Module
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stock_to_concept = concept_matrix
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stock_to_concept_sum = torch.sum(stock_to_concept, 0).reshape(1, -1).repeat(stock_to_concept.shape[0], 1)
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stock_to_concept_sum = stock_to_concept_sum.mul(concept_matrix)
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stock_to_concept_sum = stock_to_concept_sum + (
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torch.ones(stock_to_concept.shape[0], stock_to_concept.shape[1]).to(device)
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)
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stock_to_concept = stock_to_concept / stock_to_concept_sum
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hidden = torch.t(stock_to_concept).mm(x_hidden)
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hidden = hidden[hidden.sum(1) != 0]
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concept_to_stock = self.cal_cos_similarity(x_hidden, hidden)
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concept_to_stock = self.softmax_t2s(concept_to_stock)
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e_shared_info = concept_to_stock.mm(hidden)
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e_shared_info = self.fc_es(e_shared_info)
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e_shared_back = self.fc_es_back(e_shared_info)
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output_es = self.fc_es_fore(e_shared_info)
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output_es = self.leaky_relu(output_es)
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# Hidden Concept Module
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i_shared_info = x_hidden - e_shared_back
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hidden = i_shared_info
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i_stock_to_concept = self.cal_cos_similarity(i_shared_info, hidden)
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dim = i_stock_to_concept.shape[0]
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diag = i_stock_to_concept.diagonal(0)
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i_stock_to_concept = i_stock_to_concept * (torch.ones(dim, dim) - torch.eye(dim)).to(device)
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row = torch.linspace(0, dim - 1, dim).to(device).long()
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column = i_stock_to_concept.max(1)[1].long()
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value = i_stock_to_concept.max(1)[0]
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i_stock_to_concept[row, column] = 10
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i_stock_to_concept[i_stock_to_concept != 10] = 0
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i_stock_to_concept[row, column] = value
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i_stock_to_concept = i_stock_to_concept + torch.diag_embed((i_stock_to_concept.sum(0) != 0).float() * diag)
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hidden = torch.t(i_shared_info).mm(i_stock_to_concept).t()
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hidden = hidden[hidden.sum(1) != 0]
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i_concept_to_stock = self.cal_cos_similarity(i_shared_info, hidden)
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i_concept_to_stock = self.softmax_t2s(i_concept_to_stock)
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i_shared_info = i_concept_to_stock.mm(hidden)
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i_shared_info = self.fc_is(i_shared_info)
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i_shared_back = self.fc_is_back(i_shared_info)
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output_is = self.fc_is_fore(i_shared_info)
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output_is = self.leaky_relu(output_is)
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# Individual Information Module
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individual_info = x_hidden - e_shared_back - i_shared_back
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output_indi = individual_info
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output_indi = self.fc_indi(output_indi)
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output_indi = self.leaky_relu(output_indi)
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# Stock Trend Prediction
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all_info = output_es + output_is + output_indi
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pred_all = self.fc_out(all_info).squeeze()
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return pred_all
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