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include SFM model
This commit is contained in:
Alex Wang
465
qlib/contrib/model/pytorch_sfm.py
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465
qlib/contrib/model/pytorch_sfm.py
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# 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 unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
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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__(self, d_feat=6, output_dim = 1, freq_dim = 10, hidden_size = 64, dropout_W = 0.0, dropout_U = 0.0, device = "cpu"):
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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)) # not sure whether I am doing in the right unsquuze
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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 = [init_state_p, init_state_h, init_state_S_re, init_state_S_im, init_state_time, None, None, None]
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def get_constants(self, x):
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constants = []
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constants.append([torch.tensor(1.).to(self.device) for _ in range(6)])
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constants.append([torch.tensor(1.).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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lr_decay : float
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learning rate decay
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lr_decay_steps : int
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learning rate decay steps
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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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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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lr_decay=0.96,
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lr_decay_steps=100,
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optimizer="gd",
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GPU="0",
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seed=0,
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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.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.lr_decay = lr_decay
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self.lr_decay_steps = lr_decay_steps
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self.optimizer = optimizer.lower()
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self.loss_type = loss
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self.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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"\nfreqency_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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"\nbatch_size : {}"
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"\nearly_stop : {}"
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"\neval_steps : {}"
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"\nlr_decay : {}"
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"\nlr_decay_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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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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batch_size,
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early_stop,
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eval_steps,
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lr_decay,
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lr_decay_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 loss not in {"mse", "binary"}:
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raise NotImplementedError("loss {} is not supported!".format(loss))
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self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
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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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# Reduce learning rate when loss has stopped decrease
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self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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self.train_optimizer,
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mode="min",
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factor=0.5,
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patience=10,
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verbose=True,
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threshold=0.0001,
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threshold_mode="rel",
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cooldown=0,
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min_lr=0.00001,
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eps=1e-08,
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)
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self._fitted = False
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self.sfm_model.to(self.device)
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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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**kwargs
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):
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df_train, df_valid = dataset.prepare(
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["train", "valid"], col_set=["feature", "label"], 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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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_loss = np.inf
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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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# prepare training data
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x_train_values = torch.from_numpy(x_train.values).float()
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y_train_values = torch.from_numpy(np.squeeze(y_train.values)).float()
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train_num = y_train_values.shape[0]
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# prepare validation data
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x_val_auto = torch.from_numpy(x_valid.values).float()
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y_val_auto = torch.from_numpy(np.squeeze(y_valid.values)).float()
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x_val_auto = x_val_auto.to(self.device)
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y_val_auto = y_val_auto.to(self.device)
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for step in range(self.n_epochs):
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if stop_steps >= self.early_stop:
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if verbose:
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self.logger.info("\tearly stop")
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break
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loss = AverageMeter()
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self.sfm_model.train()
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self.train_optimizer.zero_grad()
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choice = np.random.choice(train_num, self.batch_size)
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x_batch_auto = x_train_values[choice]
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y_batch_auto = y_train_values[choice]
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x_batch_auto = x_batch_auto.to(self.device)
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y_batch_auto = y_batch_auto.to(self.device)
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# forward
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preds = self.sfm_model(x_batch_auto)
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cur_loss = self.get_loss(preds, y_batch_auto, self.loss_type)
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cur_loss.backward()
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self.train_optimizer.step()
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loss.update(cur_loss.item())
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# validation
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train_loss += loss.val
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# print(loss.val)
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if step and step % self.eval_steps == 0:
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stop_steps += 1
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train_loss /= self.eval_steps
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with torch.no_grad():
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self.sfm_model.eval()
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loss_val = AverageMeter()
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# forward
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preds = self.sfm_model(x_val_auto)
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cur_loss_val = self.get_loss(preds, y_val_auto, self.loss_type)
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loss_val.update(cur_loss_val.item())
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if verbose:
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self.logger.info(
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"[Epoch {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val)
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)
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evals_result["train"].append(train_loss)
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evals_result["valid"].append(loss_val.val)
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if loss_val.val < best_loss:
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if verbose:
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self.logger.info(
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"\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
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best_loss, loss_val.val
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)
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)
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best_loss = loss_val.val
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stop_steps = 0
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torch.save(self.sfm_model.state_dict(), save_path)
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train_loss = 0
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# update learning rate
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self.scheduler.step(cur_loss_val)
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if device != 'cpu':
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torch.cuda.empty_cache()
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def get_loss(self, pred, target, loss_type):
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if loss_type == "mse":
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sqr_loss = (pred - target)**2
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loss = sqr_loss.mean()
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return loss
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elif loss_type == "binary":
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loss = nn.BCELoss()
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return loss(pred, target)
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else:
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raise NotImplementedError("loss {} is not supported!".format(loss_type))
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def 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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x_test = torch.from_numpy(x_test.values).float()
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x_test = x_test.to(device)
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self.sfm_model.eval()
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with torch.no_grad():
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if device != 'cpu':
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preds = self.sfm_model(x_test).detach().cpu().numpy()
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else:
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preds = self.sfm_model(x_test).detach().numpy()
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return pd.Series(preds, index=index)
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def save(self, filename, **kwargs):
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with save_multiple_parts_file(filename) as model_dir:
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||||
model_path = os.path.join(model_dir, os.path.split(model_dir)[-1])
|
||||
# Save model
|
||||
torch.save(self.sfm_model.state_dict(), model_path)
|
||||
|
||||
def load(self, buffer, **kwargs):
|
||||
with unpack_archive_with_buffer(buffer) as model_dir:
|
||||
# Get model name
|
||||
_model_name = os.path.splitext(list(filter(lambda x: x.startswith("model.bin"), os.listdir(model_dir)))[0])[
|
||||
0
|
||||
]
|
||||
_model_path = os.path.join(model_dir, _model_name)
|
||||
# Load model
|
||||
self.sfm_model.load_state_dict(torch.load(_model_path))
|
||||
self._fitted = True
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0
|
||||
self.avg = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
Reference in New Issue
Block a user