# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from __future__ import division from __future__ import print_function import os import numpy as np import pandas as pd import copy from ...utils import get_or_create_path from ...log import get_module_logger import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH, TSDatasetH from ...data.dataset.handler import DataHandlerLP class GRU(Model): """GRU Model Parameters ---------- d_feat : int input dimension for each time step metric: str the evaluate metric used in early stop optimizer : str optimizer name GPU : str the GPU ID(s) used for training """ def __init__( self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, n_epochs=200, lr=0.001, metric="", batch_size=2000, early_stop=20, loss="mse", optimizer="adam", n_jobs=10, GPU=0, seed=None, **kwargs ): # Set logger. self.logger = get_module_logger("GRU") self.logger.info("GRU pytorch version...") # set hyper-parameters. self.d_feat = d_feat self.hidden_size = hidden_size self.num_layers = num_layers self.dropout = dropout self.n_epochs = n_epochs self.lr = lr self.metric = metric self.batch_size = batch_size self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.n_jobs = n_jobs self.seed = seed self.logger.info( "GRU parameters setting:" "\nd_feat : {}" "\nhidden_size : {}" "\nnum_layers : {}" "\ndropout : {}" "\nn_epochs : {}" "\nlr : {}" "\nmetric : {}" "\nbatch_size : {}" "\nearly_stop : {}" "\noptimizer : {}" "\nloss_type : {}" "\ndevice : {}" "\nn_jobs : {}" "\nuse_GPU : {}" "\nseed : {}".format( d_feat, hidden_size, num_layers, dropout, n_epochs, lr, metric, batch_size, early_stop, optimizer.lower(), loss, self.device, n_jobs, self.use_gpu, seed, ) ) if self.seed is not None: np.random.seed(self.seed) torch.manual_seed(self.seed) self.GRU_model = GRUModel( d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) self.logger.info("model:\n{:}".format(self.GRU_model)) self.logger.info("model size: {:.4f} MB".format(count_parameters(self.GRU_model))) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": self.train_optimizer = optim.SGD(self.GRU_model.parameters(), lr=self.lr) else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) self.fitted = False self.GRU_model.to(self.device) @property def use_gpu(self): return self.device != torch.device("cpu") def mse(self, pred, label): loss = (pred - label) ** 2 return torch.mean(loss) def loss_fn(self, pred, label): mask = ~torch.isnan(label) if self.loss == "mse": return self.mse(pred[mask], label[mask]) raise ValueError("unknown loss `%s`" % self.loss) def metric_fn(self, pred, label): mask = torch.isfinite(label) if self.metric == "" or self.metric == "loss": return -self.loss_fn(pred[mask], label[mask]) raise ValueError("unknown metric `%s`" % self.metric) def train_epoch(self, data_loader): self.GRU_model.train() for data in data_loader: feature = data[:, :, 0:-1].to(self.device) label = data[:, -1, -1].to(self.device) pred = self.GRU_model(feature.float()) loss = self.loss_fn(pred, label) self.train_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.GRU_model.parameters(), 3.0) self.train_optimizer.step() def test_epoch(self, data_loader): self.GRU_model.eval() scores = [] losses = [] for data in data_loader: feature = data[:, :, 0:-1].to(self.device) # feature[torch.isnan(feature)] = 0 label = data[:, -1, -1].to(self.device) with torch.no_grad(): pred = self.GRU_model(feature.float()) loss = self.loss_fn(pred, label) losses.append(loss.item()) score = self.metric_fn(pred, label) scores.append(score.item()) return np.mean(losses), np.mean(scores) def fit( self, dataset, evals_result=dict(), save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader train_loader = DataLoader( dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True ) valid_loader = DataLoader( dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True ) save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 best_score = -np.inf best_epoch = 0 evals_result["train"] = [] evals_result["valid"] = [] # train self.logger.info("training...") self.fitted = True for step in range(self.n_epochs): self.logger.info("Epoch%d:", step) self.logger.info("training...") self.train_epoch(train_loader) self.logger.info("evaluating...") train_loss, train_score = self.test_epoch(train_loader) val_loss, val_score = self.test_epoch(valid_loader) self.logger.info("train %.6f, valid %.6f" % (train_score, val_score)) evals_result["train"].append(train_score) evals_result["valid"].append(val_score) if val_score > best_score: best_score = val_score stop_steps = 0 best_epoch = step best_param = copy.deepcopy(self.GRU_model.state_dict()) else: stop_steps += 1 if stop_steps >= self.early_stop: self.logger.info("early stop") break self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch)) self.GRU_model.load_state_dict(best_param) torch.save(best_param, save_path) if self.use_gpu: torch.cuda.empty_cache() def predict(self, dataset): if not self.fitted: raise ValueError("model is not fitted yet!") dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I) dl_test.config(fillna_type="ffill+bfill") test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs) self.GRU_model.eval() preds = [] for data in test_loader: feature = data[:, :, 0:-1].to(self.device) with torch.no_grad(): pred = self.GRU_model(feature.float()).detach().cpu().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=dl_test.get_index()) class GRUModel(nn.Module): def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0): super().__init__() self.rnn = nn.GRU( input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) self.d_feat = d_feat def forward(self, x): out, _ = self.rnn(x) return self.fc_out(out[:, -1, :]).squeeze()