# 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 random from sklearn.metrics import roc_auc_score, mean_squared_error import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector 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 TCTS(Model): """TCTS 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, batch_size=2000, early_stop=20, loss="mse", fore_optimizer="adam", weight_optimizer="adam", output_dim=5, fore_lr=5e-7, weight_lr=5e-7, steps=3, GPU=0, seed=0, target_label=0, lowest_valid_performance=0.993, **kwargs ): # Set logger. self.logger = get_module_logger("TCTS") self.logger.info("TCTS 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.batch_size = batch_size self.early_stop = early_stop self.loss = loss self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed self.output_dim = output_dim self.fore_lr = fore_lr self.weight_lr = weight_lr self.steps = steps self.target_label = target_label self.lowest_valid_performance = lowest_valid_performance self._fore_optimizer = fore_optimizer self._weight_optimizer = weight_optimizer self.logger.info( "TCTS parameters setting:" "\nd_feat : {}" "\nhidden_size : {}" "\nnum_layers : {}" "\ndropout : {}" "\nn_epochs : {}" "\nbatch_size : {}" "\nearly_stop : {}" "\nloss_type : {}" "\nvisible_GPU : {}" "\nuse_GPU : {}" "\nseed : {}".format( d_feat, hidden_size, num_layers, dropout, n_epochs, batch_size, early_stop, loss, GPU, self.use_gpu, seed, ) ) def loss_fn(self, pred, label, weight): loc = torch.argmax(weight, 1) loss = (pred - label[np.arange(weight.shape[0]), loc]) ** 2 return torch.mean(loss) def train_epoch(self, x_train, y_train, x_valid, y_valid): x_train_values = x_train.values y_train_values = np.squeeze(y_train.values) indices = np.arange(len(x_train_values)) np.random.shuffle(indices) init_fore_model = copy.deepcopy(self.fore_model) for p in init_fore_model.parameters(): p.init_fore_model = False self.fore_model.train() self.weight_model.train() for p in self.weight_model.parameters(): p.requires_grad = False for p in self.fore_model.parameters(): p.requires_grad = True for i in range(self.steps): 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) init_pred = init_fore_model(feature) pred = self.fore_model(feature) dis = init_pred - label.transpose(0, 1) weight_feature = torch.cat((feature, dis.transpose(0, 1), label, init_pred.view(-1, 1)), 1) weight = self.weight_model(weight_feature) loss = self.loss_fn(pred, label, weight) # hard self.fore_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.fore_model.parameters(), 3.0) self.fore_optimizer.step() x_valid_values = x_valid.values y_valid_values = np.squeeze(y_valid.values) indices = np.arange(len(x_valid_values)) np.random.shuffle(indices) for p in self.weight_model.parameters(): p.requires_grad = True for p in self.fore_model.parameters(): p.requires_grad = False # fix forecasting model and valid weight model for i in range(len(indices))[:: self.batch_size]: if len(indices) - i < self.batch_size: break feature = torch.from_numpy(x_valid_values[indices[i : i + self.batch_size]]).float().to(self.device) label = torch.from_numpy(y_valid_values[indices[i : i + self.batch_size]]).float().to(self.device) pred = self.fore_model(feature) dis = pred - label.transpose(0, 1) weight_feature = torch.cat((feature, dis.transpose(0, 1), label, pred.view(-1, 1)), 1) weight = self.weight_model(weight_feature) loc = torch.argmax(weight, 1) valid_loss = torch.mean((pred - label[:, 0]) ** 2) loss = torch.mean(-valid_loss * torch.log(weight[np.arange(weight.shape[0]), loc])) self.weight_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.weight_model.parameters(), 3.0) self.weight_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.fore_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.fore_model(feature) loss = torch.mean((pred - label[:, abs(self.target_label)]) ** 2) losses.append(loss.item()) return np.mean(losses) def fit( self, dataset: DatasetH, verbose=True, 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"] x_test, y_test = df_test["feature"], df_test["label"] if save_path == None: save_path = get_or_create_path(save_path) best_loss = np.inf while best_loss > self.lowest_valid_performance: if best_loss < np.inf: print("Failed! Start retraining.") self.seed = random.randint(0, 1000) # reset random seed if self.seed is not None: np.random.seed(self.seed) torch.manual_seed(self.seed) best_loss = self.training( x_train, y_train, x_valid, y_valid, x_test, y_test, verbose=verbose, save_path=save_path ) def training( self, x_train, y_train, x_valid, y_valid, x_test, y_test, verbose=True, save_path=None, ): self.fore_model = GRUModel( d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) self.weight_model = MLPModel( d_feat=360 + 2 * self.output_dim + 1, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, output_dim=self.output_dim, ) if self._fore_optimizer.lower() == "adam": self.fore_optimizer = optim.Adam(self.fore_model.parameters(), lr=self.fore_lr) elif self._fore_optimizer.lower() == "gd": self.fore_optimizer = optim.SGD(self.fore_model.parameters(), lr=self.fore_lr) else: raise NotImplementedError("optimizer {} is not supported!".format(self._fore_optimizer)) if self._weight_optimizer.lower() == "adam": self.weight_optimizer = optim.Adam(self.weight_model.parameters(), lr=self.weight_lr) elif self._weight_optimizer.lower() == "gd": self.weight_optimizer = optim.SGD(self.weight_model.parameters(), lr=self.weight_lr) else: raise NotImplementedError("optimizer {} is not supported!".format(self._weight_optimizer)) self.fitted = False self.fore_model.to(self.device) self.weight_model.to(self.device) best_loss = np.inf best_epoch = 0 stop_round = 0 fore_best_param = copy.deepcopy(self.fore_optimizer.state_dict()) weight_best_param = copy.deepcopy(self.weight_optimizer.state_dict()) for epoch in range(self.n_epochs): print("Epoch:", epoch) print("training...") self.train_epoch(x_train, y_train, x_valid, y_valid) print("evaluating...") val_loss = self.test_epoch(x_valid, y_valid) test_loss = self.test_epoch(x_test, y_test) if verbose: print("valid %.6f, test %.6f" % (val_loss, test_loss)) if val_loss < best_loss: best_loss = val_loss stop_round = 0 best_epoch = epoch torch.save(copy.deepcopy(self.fore_model.state_dict()), save_path + "_fore_model.bin") torch.save(copy.deepcopy(self.weight_model.state_dict()), save_path + "_weight_model.bin") else: stop_round += 1 if stop_round >= self.early_stop: print("early stop") break print("best loss:", best_loss, "@", best_epoch) best_param = torch.load(save_path + "_fore_model.bin") self.fore_model.load_state_dict(best_param) best_param = torch.load(save_path + "_weight_model.bin") self.weight_model.load_state_dict(best_param) self.fitted = True if self.use_gpu: torch.cuda.empty_cache() return best_loss def predict(self, dataset): if not self.fitted: raise ValueError("model is not fitted yet!") x_test = dataset.prepare("test", col_set="feature") index = x_test.index self.fore_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(): if self.use_gpu: pred = self.fore_model(x_batch).detach().cpu().numpy() else: pred = self.fore_model(x_batch).detach().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=index) class MLPModel(nn.Module): def __init__(self, d_feat, hidden_size=256, num_layers=3, dropout=0.0, output_dim=1): super().__init__() self.mlp = nn.Sequential() self.softmax = nn.Softmax(dim=1) for i in range(num_layers): if i > 0: self.mlp.add_module("drop_%d" % i, nn.Dropout(dropout)) self.mlp.add_module("fc_%d" % i, nn.Linear(d_feat if i == 0 else hidden_size, hidden_size)) self.mlp.add_module("relu_%d" % i, nn.ReLU()) self.mlp.add_module("fc_out", nn.Linear(hidden_size, output_dim)) def forward(self, x): # feature # [N, F] out = self.mlp(x).squeeze() out = self.softmax(out) return out 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): # 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()