# 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 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 .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP from ...contrib.model.pytorch_lstm import LSTMModel from ...contrib.model.pytorch_gru import GRUModel class GATs(Model): """GATs Model Parameters ---------- lr : float learning rate d_feat : int input dimensions for each time step metric : str the evaluate metric used in early stop optimizer : str optimizer name GPU : int the GPU ID 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="", early_stop=20, loss="mse", base_model="GRU", with_pretrain=True, model_path=None, optimizer="adam", GPU=0, seed=None, **kwargs ): # Set logger. self.logger = get_module_logger("GATs") self.logger.info("GATs 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.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss self.base_model = base_model self.with_pretrain = with_pretrain self.model_path = model_path self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed self.logger.info( "GATs parameters setting:" "\nd_feat : {}" "\nhidden_size : {}" "\nnum_layers : {}" "\ndropout : {}" "\nn_epochs : {}" "\nlr : {}" "\nmetric : {}" "\nearly_stop : {}" "\noptimizer : {}" "\nloss_type : {}" "\nbase_model : {}" "\nwith_pretrain : {}" "\nmodel_path : {}" "\ndevice : {}" "\nuse_GPU : {}" "\nseed : {}".format( d_feat, hidden_size, num_layers, dropout, n_epochs, lr, metric, early_stop, optimizer.lower(), loss, base_model, with_pretrain, model_path, self.device, self.use_gpu, seed, ) ) if self.seed is not None: np.random.seed(self.seed) torch.manual_seed(self.seed) self.GAT_model = GATModel( d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, base_model=self.base_model, ) self.logger.info("model:\n{:}".format(self.GAT_model)) self.logger.info("model size: {:.4f} MB".format(count_parameters(self.GAT_model))) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": self.train_optimizer = optim.SGD(self.GAT_model.parameters(), lr=self.lr) else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) self.fitted = False self.GAT_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 get_daily_inter(self, df, shuffle=False): # organize the train data into daily batches daily_count = df.groupby(level=0).size().values daily_index = np.roll(np.cumsum(daily_count), 1) daily_index[0] = 0 if shuffle: # shuffle data daily_shuffle = list(zip(daily_index, daily_count)) np.random.shuffle(daily_shuffle) daily_index, daily_count = zip(*daily_shuffle) return daily_index, daily_count def train_epoch(self, x_train, y_train): x_train_values = x_train.values y_train_values = np.squeeze(y_train.values) self.GAT_model.train() # organize the train data into daily batches daily_index, daily_count = self.get_daily_inter(x_train, shuffle=True) for idx, count in zip(daily_index, daily_count): batch = slice(idx, idx + count) feature = torch.from_numpy(x_train_values[batch]).float().to(self.device) label = torch.from_numpy(y_train_values[batch]).float().to(self.device) pred = self.GAT_model(feature) loss = self.loss_fn(pred, label) self.train_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.GAT_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.GAT_model.eval() scores = [] losses = [] # organize the test data into daily batches daily_index, daily_count = self.get_daily_inter(data_x, shuffle=False) for idx, count in zip(daily_index, daily_count): batch = slice(idx, idx + count) feature = torch.from_numpy(x_values[batch]).float().to(self.device) label = torch.from_numpy(y_values[batch]).float().to(self.device) pred = self.GAT_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 best_score = -np.inf best_epoch = 0 evals_result["train"] = [] evals_result["valid"] = [] # load pretrained base_model if self.with_pretrain: if self.model_path == None: raise ValueError("the path of the pretrained model should be given first!") self.logger.info("Loading pretrained model...") if self.base_model == "LSTM": pretrained_model = LSTMModel() pretrained_model.load_state_dict(torch.load(self.model_path)) elif self.base_model == "GRU": pretrained_model = GRUModel() pretrained_model.load_state_dict(torch.load(self.model_path)) else: raise ValueError("unknown base model name `%s`" % self.base_model) model_dict = self.GAT_model.state_dict() pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in model_dict} model_dict.update(pretrained_dict) self.GAT_model.load_state_dict(model_dict) self.logger.info("Loading pretrained model Done...") # 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.GAT_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.GAT_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!") x_test = dataset.prepare("test", col_set="feature") index = x_test.index self.GAT_model.eval() x_values = x_test.values preds = [] # organize the data into daily batches daily_index, daily_count = self.get_daily_inter(x_test, shuffle=False) for idx, count in zip(daily_index, daily_count): batch = slice(idx, idx + count) x_batch = torch.from_numpy(x_values[batch]).float().to(self.device) with torch.no_grad(): pred = self.GAT_model(x_batch).detach().cpu().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=index) class GATModel(nn.Module): def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model="GRU"): super().__init__() if base_model == "GRU": self.rnn = nn.GRU( input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) self.hidden_size = hidden_size self.d_feat = d_feat self.transformation = nn.Linear(self.hidden_size, self.hidden_size) self.a = nn.Parameter(torch.randn(self.hidden_size * 2, 1)) self.a.requires_grad = True self.fc = nn.Linear(self.hidden_size, self.hidden_size) self.fc_out = nn.Linear(hidden_size, 1) self.leaky_relu = nn.LeakyReLU() self.softmax = nn.Softmax(dim=1) def cal_attention(self, x, y): x = self.transformation(x) y = self.transformation(y) sample_num = x.shape[0] dim = x.shape[1] e_x = x.expand(sample_num, sample_num, dim) e_y = torch.transpose(e_x, 0, 1) attention_in = torch.cat((e_x, e_y), 2).view(-1, dim * 2) self.a_t = torch.t(self.a) attention_out = self.a_t.mm(torch.t(attention_in)).view(sample_num, sample_num) attention_out = self.leaky_relu(attention_out) att_weight = self.softmax(attention_out) return att_weight 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) hidden = out[:, -1, :] att_weight = self.cal_attention(hidden, hidden) hidden = att_weight.mm(hidden) + hidden hidden = self.fc(hidden) hidden = self.leaky_relu(hidden) return self.fc_out(hidden).squeeze()