mirror of
https://github.com/microsoft/qlib.git
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357 lines
12 KiB
Python
357 lines
12 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 logging
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import numpy as np
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import pandas as pd
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from sklearn.metrics import roc_auc_score, mean_squared_error
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from ...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 ...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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from ...workflow import R
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class DNNModelPytorch(Model):
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"""DNN Model
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Parameters
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----------
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input_dim : int
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input dimension
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output_dim : int
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output dimension
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layers : tuple
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layer sizes
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lr : float
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learning rate
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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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input_dim,
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output_dim,
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layers=(256,),
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lr=0.001,
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max_steps=300,
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batch_size=2000,
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early_stop_rounds=50,
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eval_steps=20,
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lr_decay=0.96,
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lr_decay_steps=100,
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optimizer="gd",
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loss="mse",
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GPU="0",
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seed=None,
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weight_decay=0.0,
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**kwargs
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):
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# Set logger.
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self.logger = get_module_logger("DNNModelPytorch")
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self.logger.info("DNN pytorch version...")
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# set hyper-parameters.
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self.layers = layers
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self.lr = lr
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self.max_steps = max_steps
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self.batch_size = batch_size
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self.early_stop_rounds = early_stop_rounds
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self.eval_steps = eval_steps
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self.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 = 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.weight_decay = weight_decay
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self.logger.info(
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"DNN parameters setting:"
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"\nlayers : {}"
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"\nlr : {}"
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"\nmax_steps : {}"
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"\nbatch_size : {}"
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"\nearly_stop_rounds : {}"
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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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"\neval_steps : {}"
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"\nseed : {}"
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"\nvisible_GPU : {}"
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"\nuse_GPU : {}"
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"\nweight_decay : {}".format(
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layers,
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lr,
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max_steps,
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batch_size,
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early_stop_rounds,
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eval_steps,
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lr_decay,
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lr_decay_steps,
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optimizer,
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loss,
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eval_steps,
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seed,
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GPU,
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self.use_GPU,
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weight_decay,
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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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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.dnn_model = Net(input_dim, output_dim, layers, loss=self.loss_type)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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# 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.dnn_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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):
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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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try:
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wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], data_key=DataHandlerLP.DK_L)
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w_train, w_valid = wdf_train["weight"], wdf_valid["weight"]
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except KeyError as e:
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w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
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w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
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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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# return
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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(y_train.values).float()
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w_train_values = torch.from_numpy(w_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().to(self.device)
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y_val_auto = torch.from_numpy(y_valid.values).float().to(self.device)
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w_val_auto = torch.from_numpy(w_valid.values).float().to(self.device)
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for step in range(self.max_steps):
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if stop_steps >= self.early_stop_rounds:
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if verbose:
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self.logger.info("\tearly stop")
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break
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loss = AverageMeter()
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self.dnn_model.train()
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self.train_optimizer.zero_grad()
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choice = np.random.choice(train_num, self.batch_size)
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x_batch_auto = x_train_values[choice].to(self.device)
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y_batch_auto = y_train_values[choice].to(self.device)
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w_batch_auto = w_train_values[choice].to(self.device)
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# forward
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preds = self.dnn_model(x_batch_auto)
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cur_loss = self.get_loss(preds, w_batch_auto, 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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R.log_metrics(train_loss=loss.avg, step=step)
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# validation
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train_loss += loss.val
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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.dnn_model.eval()
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loss_val = AverageMeter()
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# forward
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preds = self.dnn_model(x_val_auto)
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cur_loss_val = self.get_loss(preds, w_val_auto, y_val_auto, self.loss_type)
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loss_val.update(cur_loss_val.item())
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R.log_metrics(val_loss=loss_val.val, step=step)
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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.dnn_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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# restore the optimal parameters after training ??
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self.dnn_model.load_state_dict(torch.load(save_path))
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if self.use_GPU:
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torch.cuda.empty_cache()
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def get_loss(self, pred, w, target, loss_type):
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if loss_type == "mse":
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sqr_loss = torch.mul(pred - target, pred - target)
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loss = torch.mul(sqr_loss, w).mean()
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return loss
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elif loss_type == "binary":
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loss = nn.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_pd = dataset.prepare("test", col_set="feature")
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x_test = torch.from_numpy(x_test_pd.values).float().to(self.device)
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self.dnn_model.eval()
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with torch.no_grad():
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if self.use_GPU:
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preds = self.dnn_model(x_test).detach().cpu().numpy()
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else:
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preds = self.dnn_model(x_test).detach().numpy()
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return pd.Series(np.squeeze(preds), index=x_test_pd.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])
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# Save model
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torch.save(self.dnn_model.state_dict(), model_path)
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def load(self, buffer, **kwargs):
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with unpack_archive_with_buffer(buffer) as model_dir:
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# Get model name
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_model_name = os.path.splitext(list(filter(lambda x: x.startswith("model.bin"), os.listdir(model_dir)))[0])[
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0
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]
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_model_path = os.path.join(model_dir, _model_name)
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# Load model
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self.dnn_model.load_state_dict(torch.load(_model_path))
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self._fitted = True
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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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class Net(nn.Module):
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def __init__(self, input_dim, output_dim, layers=(256, 512, 768, 512, 256, 128, 64), loss="mse"):
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super(Net, self).__init__()
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layers = [input_dim] + list(layers)
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dnn_layers = []
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drop_input = nn.Dropout(0.05)
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dnn_layers.append(drop_input)
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for i, (input_dim, hidden_units) in enumerate(zip(layers[:-1], layers[1:])):
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fc = nn.Linear(input_dim, hidden_units)
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activation = nn.LeakyReLU(negative_slope=0.1, inplace=False)
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bn = nn.BatchNorm1d(hidden_units)
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seq = nn.Sequential(fc, bn, activation)
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dnn_layers.append(seq)
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drop_input = nn.Dropout(0.05)
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dnn_layers.append(drop_input)
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if loss == "mse":
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fc = nn.Linear(hidden_units, output_dim)
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dnn_layers.append(fc)
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elif loss == "binary":
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fc = nn.Linear(hidden_units, output_dim)
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sigmoid = nn.Sigmoid()
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dnn_layers.append(nn.Sequential(fc, sigmoid))
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else:
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raise NotImplementedError("loss {} is not supported!".format(loss))
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# optimizer
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self.dnn_layers = nn.ModuleList(dnn_layers)
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self._weight_init()
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def _weight_init(self):
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for m in self.modules():
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if isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight, a=0.1, mode="fan_in", nonlinearity="leaky_relu")
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def forward(self, x):
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cur_output = x
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for i, now_layer in enumerate(self.dnn_layers):
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cur_output = now_layer(cur_output)
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return cur_output
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