# 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 import math 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 from torch.nn.modules.container import ModuleList class LocalformerModel(Model): def __init__( self, d_feat: int = 20, d_model: int = 64, batch_size: int = 8192, nhead: int = 2, num_layers: int = 2, dropout: float = 0, n_epochs=100, lr=0.0001, metric="", early_stop=5, loss="mse", optimizer="adam", reg=1e-3, n_jobs=10, GPU=0, seed=None, **kwargs ): # set hyper-parameters. self.d_model = d_model self.dropout = dropout self.n_epochs = n_epochs self.lr = lr self.reg = reg self.metric = metric self.batch_size = batch_size self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss self.n_jobs = n_jobs self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu") self.seed = seed self.logger = get_module_logger("TransformerModel") self.logger.info( "Improved Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device) ) if self.seed is not None: np.random.seed(self.seed) torch.manual_seed(self.seed) self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg) elif optimizer.lower() == "gd": self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg) else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) self.fitted = False self.model.to(self.device) @property def use_gpu(self): return self.device != torch.device("cpu") def mse(self, pred, label): loss = (pred.float() - label.float()) ** 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.model.train() for data in data_loader: feature = data[:, :, 0:-1].to(self.device) label = data[:, -1, -1].to(self.device) pred = self.model(feature.float()) # .float() loss = self.loss_fn(pred, label) self.train_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0) self.train_optimizer.step() def test_epoch(self, data_loader): self.model.eval() scores = [] losses = [] for data in data_loader: feature = data[:, :, 0:-1].to(self.device) label = data[:, -1, -1].to(self.device) with torch.no_grad(): pred = self.model(feature.float()) # .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: DatasetH, 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.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.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.model.eval() preds = [] for data in test_loader: feature = data[:, :, 0:-1].to(self.device) with torch.no_grad(): pred = self.model(feature.float()).detach().cpu().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=dl_test.get_index()) class PositionalEncoding(nn.Module): def __init__(self, d_model, max_len=1000): super(PositionalEncoding, self).__init__() pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) self.register_buffer("pe", pe) def forward(self, x): # [T, N, F] return x + self.pe[: x.size(0), :] def _get_clones(module, N): return ModuleList([copy.deepcopy(module) for i in range(N)]) class LocalformerEncoder(nn.Module): __constants__ = ["norm"] def __init__(self, encoder_layer, num_layers, d_model): super(LocalformerEncoder, self).__init__() self.layers = _get_clones(encoder_layer, num_layers) self.conv = _get_clones(nn.Conv1d(d_model, d_model, 3, 1, 1), num_layers) self.num_layers = num_layers def forward(self, src, mask): output = src out = src for i, mod in enumerate(self.layers): # [T, N, F] --> [N, T, F] --> [N, F, T] out = output.transpose(1, 0).transpose(2, 1) out = self.conv[i](out).transpose(2, 1).transpose(1, 0) output = mod(output + out, src_mask=mask) return output + out class Transformer(nn.Module): def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None): super(Transformer, self).__init__() self.rnn = nn.GRU( input_size=d_model, hidden_size=d_model, num_layers=num_layers, batch_first=False, dropout=dropout, ) self.feature_layer = nn.Linear(d_feat, d_model) self.pos_encoder = PositionalEncoding(d_model) self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout) self.transformer_encoder = LocalformerEncoder(self.encoder_layer, num_layers=num_layers, d_model=d_model) self.decoder_layer = nn.Linear(d_model, 1) self.device = device self.d_feat = d_feat def forward(self, src): # src [N, T, F], [512, 60, 6] src = self.feature_layer(src) # [512, 60, 8] # src [N, T, F] --> [T, N, F], [60, 512, 8] src = src.transpose(1, 0) # not batch first mask = None src = self.pos_encoder(src) output = self.transformer_encoder(src, mask) # [60, 512, 8] output, _ = self.rnn(output) # [T, N, F] --> [N, T*F] output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1] return output.squeeze()