mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-05 20:11:08 +08:00
484 lines
15 KiB
Python
484 lines
15 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.nn.init as init
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import torch.optim as optim
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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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class SFM_Model(nn.Module):
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def __init__(
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self,
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d_feat=6,
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output_dim=1,
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freq_dim=10,
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hidden_size=64,
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dropout_W=0.0,
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dropout_U=0.0,
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device="cpu",
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):
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super().__init__()
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self.input_dim = d_feat
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self.output_dim = output_dim
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self.freq_dim = freq_dim
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self.hidden_dim = hidden_size
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self.device = device
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self.W_i = nn.Parameter(init.xavier_uniform_(torch.empty((self.input_dim, self.hidden_dim))))
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self.U_i = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
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self.b_i = nn.Parameter(torch.zeros(self.hidden_dim))
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self.W_ste = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
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self.U_ste = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
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self.b_ste = nn.Parameter(torch.ones(self.hidden_dim))
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self.W_fre = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.freq_dim)))
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self.U_fre = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.freq_dim)))
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self.b_fre = nn.Parameter(torch.ones(self.freq_dim))
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self.W_c = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
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self.U_c = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
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self.b_c = nn.Parameter(torch.zeros(self.hidden_dim))
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self.W_o = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
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self.U_o = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
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self.b_o = nn.Parameter(torch.zeros(self.hidden_dim))
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self.U_a = nn.Parameter(init.orthogonal_(torch.empty(self.freq_dim, 1)))
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self.b_a = nn.Parameter(torch.zeros(self.hidden_dim))
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self.W_p = nn.Parameter(init.xavier_uniform_(torch.empty(self.hidden_dim, self.output_dim)))
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self.b_p = nn.Parameter(torch.zeros(self.output_dim))
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self.activation = nn.Tanh()
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self.inner_activation = nn.Hardsigmoid()
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self.dropout_W, self.dropout_U = (dropout_W, dropout_U)
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self.fc_out = nn.Linear(self.output_dim, 1)
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self.states = []
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def forward(self, input):
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input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
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input = input.permute(0, 2, 1) # [N, T, F]
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time_step = input.shape[1]
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for ts in range(time_step):
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x = input[:, ts, :]
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if len(self.states) == 0: # hasn't initialized yet
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self.init_states(x)
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self.get_constants(x)
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p_tm1 = self.states[0]
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h_tm1 = self.states[1]
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S_re_tm1 = self.states[2]
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S_im_tm1 = self.states[3]
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time_tm1 = self.states[4]
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B_U = self.states[5]
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B_W = self.states[6]
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frequency = self.states[7]
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x_i = torch.matmul(x * B_W[0], self.W_i) + self.b_i
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x_ste = torch.matmul(x * B_W[0], self.W_ste) + self.b_ste
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x_fre = torch.matmul(x * B_W[0], self.W_fre) + self.b_fre
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x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
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x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
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i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i))
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ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
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fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
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ste = torch.reshape(ste, (-1, self.hidden_dim, 1))
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fre = torch.reshape(fre, (-1, 1, self.freq_dim))
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f = ste * fre
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c = i * self.activation(x_c + torch.matmul(h_tm1 * B_U[0], self.U_c))
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time = time_tm1 + 1
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omega = torch.tensor(2 * np.pi) * time * frequency
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re = torch.cos(omega)
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im = torch.sin(omega)
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c = torch.reshape(c, (-1, self.hidden_dim, 1))
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S_re = f * S_re_tm1 + c * re
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S_im = f * S_im_tm1 + c * im
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A = torch.square(S_re) + torch.square(S_im)
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A = torch.reshape(A, (-1, self.freq_dim)).float()
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A_a = torch.matmul(A * B_U[0], self.U_a)
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A_a = torch.reshape(A_a, (-1, self.hidden_dim))
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a = self.activation(A_a + self.b_a)
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o = self.inner_activation(x_o + torch.matmul(h_tm1 * B_U[0], self.U_o))
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h = o * a
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p = torch.matmul(h, self.W_p) + self.b_p
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self.states = [p, h, S_re, S_im, time, None, None, None]
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self.states = []
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return self.fc_out(p).squeeze()
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def init_states(self, x):
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reducer_f = torch.zeros((self.hidden_dim, self.freq_dim)).to(self.device)
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reducer_p = torch.zeros((self.hidden_dim, self.output_dim)).to(self.device)
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init_state_h = torch.zeros(self.hidden_dim).to(self.device)
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init_state_p = torch.matmul(init_state_h, reducer_p)
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init_state = torch.zeros_like(init_state_h).to(self.device)
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init_freq = torch.matmul(init_state_h, reducer_f)
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init_state = torch.reshape(init_state, (-1, self.hidden_dim, 1))
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init_freq = torch.reshape(init_freq, (-1, 1, self.freq_dim))
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init_state_S_re = init_state * init_freq
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init_state_S_im = init_state * init_freq
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init_state_time = torch.tensor(0).to(self.device)
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self.states = [
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init_state_p,
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init_state_h,
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init_state_S_re,
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init_state_S_im,
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init_state_time,
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None,
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None,
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None,
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]
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def get_constants(self, x):
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constants = []
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constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)])
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constants.append([torch.tensor(1.0).to(self.device) for _ in range(7)])
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array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)])
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constants.append(torch.tensor(array).to(self.device))
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self.states[5:] = constants
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class SFM(Model):
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"""SFM 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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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 : 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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output_dim=1,
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freq_dim=10,
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dropout_W=0.0,
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dropout_U=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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batch_size=2000,
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early_stop=20,
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eval_steps=5,
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loss="mse",
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optimizer="gd",
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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("SFM")
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self.logger.info("SFM 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.output_dim = output_dim
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self.freq_dim = freq_dim
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self.dropout_W = dropout_W
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self.dropout_U = dropout_U
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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.eval_steps = eval_steps
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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() else "cpu")
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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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"SFM parameters setting:"
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"\nd_feat : {}"
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"\nhidden_size : {}"
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"\noutput_size : {}"
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"\nfrequency_dimension : {}"
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"\ndropout_W: {}"
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"\ndropout_U: {}"
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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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"\neval_steps : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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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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output_dim,
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freq_dim,
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dropout_W,
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dropout_U,
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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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eval_steps,
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optimizer.lower(),
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loss,
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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.sfm_model = SFM_Model(
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d_feat=self.d_feat,
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output_dim=self.output_dim,
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hidden_size=self.hidden_size,
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freq_dim=self.freq_dim,
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dropout_W=self.dropout_W,
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dropout_U=self.dropout_U,
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device=self.device,
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)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.sfm_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.sfm_model.to(self.device)
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def test_epoch(self, data_x, data_y):
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# prepare training data
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x_values = data_x.values
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y_values = np.squeeze(data_y.values)
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self.sfm_model.eval()
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scores = []
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losses = []
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indices = np.arange(len(x_values))
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for i in range(len(indices))[:: self.batch_size]:
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if len(indices) - i < self.batch_size:
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break
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feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
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label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
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pred = self.sfm_model(feature)
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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 train_epoch(self, x_train, y_train):
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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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self.sfm_model.train()
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indices = np.arange(len(x_train_values))
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np.random.shuffle(indices)
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for i in range(len(indices))[:: self.batch_size]:
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if len(indices) - i < self.batch_size:
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break
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feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
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label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
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pred = self.sfm_model(feature)
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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.sfm_model.parameters(), 3.0)
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self.train_optimizer.step()
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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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verbose=True,
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save_path=None,
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):
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df_train, df_valid = dataset.prepare(
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["train", "valid"],
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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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x_train, y_train = df_train["feature"], df_train["label"]
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x_valid, y_valid = df_valid["feature"], df_valid["label"]
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stop_steps = 0
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train_loss = 0
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best_score = -np.inf
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best_epoch = 0
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evals_result["train"] = []
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evals_result["valid"] = []
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# train
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self.logger.info("training...")
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self._fitted = True
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for step in range(self.n_epochs):
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self.logger.info("Epoch%d:", step)
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self.logger.info("training...")
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self.train_epoch(x_train, y_train)
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self.logger.info("evaluating...")
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train_loss, train_score = self.test_epoch(x_train, y_train)
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val_loss, val_score = self.test_epoch(x_valid, y_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.sfm_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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if self.device != "cpu":
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torch.cuda.empty_cache()
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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 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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x_test = dataset.prepare("test", col_set="feature")
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index = x_test.index
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self.sfm_model.eval()
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x_values = x_test.values
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sample_num = x_values.shape[0]
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preds = []
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for begin in range(sample_num)[:: self.batch_size]:
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if sample_num - begin < self.batch_size:
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end = sample_num
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else:
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end = begin + self.batch_size
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x_batch = torch.from_numpy(x_values[begin:end]).float()
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if self.device != "cpu":
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x_batch = x_batch.to(self.device)
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with torch.no_grad():
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pred = self.sfm_model(x_batch).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 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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