# 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 from typing import Text, Union 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 ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP class LSTM(Model): """LSTM 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", GPU=0, seed=None, **kwargs ): # Set logger. self.logger = get_module_logger("LSTM") self.logger.info("LSTM 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.seed = seed self.logger.info( "LSTM parameters setting:" "\nd_feat : {}" "\nhidden_size : {}" "\nnum_layers : {}" "\ndropout : {}" "\nn_epochs : {}" "\nlr : {}" "\nmetric : {}" "\nbatch_size : {}" "\nearly_stop : {}" "\noptimizer : {}" "\nloss_type : {}" "\nvisible_GPU : {}" "\nuse_GPU : {}" "\nseed : {}".format( d_feat, hidden_size, num_layers, dropout, n_epochs, lr, metric, batch_size, early_stop, optimizer.lower(), loss, GPU, self.use_gpu, seed, ) ) if self.seed is not None: np.random.seed(self.seed) torch.manual_seed(self.seed) self.lstm_model = LSTMModel( d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": self.train_optimizer = optim.SGD(self.lstm_model.parameters(), lr=self.lr) else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) self.fitted = False self.lstm_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, x_train, y_train): x_train_values = x_train.values y_train_values = np.squeeze(y_train.values) self.lstm_model.train() indices = np.arange(len(x_train_values)) np.random.shuffle(indices) for i in range(len(indices))[:: self.batch_size]: if len(indices) - i < self.batch_size: break feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device) label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device) pred = self.lstm_model(feature) loss = self.loss_fn(pred, label) self.train_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.lstm_model.parameters(), 3.0) self.train_optimizer.step() def test_epoch(self, data_x, data_y): # prepare training data x_values = data_x.values y_values = np.squeeze(data_y.values) self.lstm_model.eval() scores = [] losses = [] indices = np.arange(len(x_values)) for i in range(len(indices))[:: self.batch_size]: if len(indices) - i < self.batch_size: break feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device) label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device) pred = self.lstm_model(feature) 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: DatasetH, evals_result=dict(), save_path=None, ): df_train, df_valid, df_test = dataset.prepare( ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] 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(x_train, y_train) self.logger.info("evaluating...") train_loss, train_score = self.test_epoch(x_train, y_train) val_loss, val_score = self.test_epoch(x_valid, y_valid) 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.lstm_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.lstm_model.load_state_dict(best_param) torch.save(best_param, save_path) if self.use_gpu: torch.cuda.empty_cache() def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"): if not self.fitted: raise ValueError("model is not fitted yet!") x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I) index = x_test.index self.lstm_model.eval() x_values = x_test.values sample_num = x_values.shape[0] preds = [] for begin in range(sample_num)[:: self.batch_size]: if sample_num - begin < self.batch_size: end = sample_num else: end = begin + self.batch_size x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device) with torch.no_grad(): pred = self.lstm_model(x_batch).detach().cpu().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=index) class LSTMModel(nn.Module): def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0): super().__init__() self.rnn = nn.LSTM( 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): # x: [N, F*T] x = x.reshape(len(x), self.d_feat, -1) # [N, F, T] x = x.permute(0, 2, 1) # [N, T, F] out, _ = self.rnn(x) return self.fc_out(out[:, -1, :]).squeeze()