# 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, create_save_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 ALSTM(Model): """ALSTM Model Parameters ---------- input_dim : int input dimension output_dim : int output dimension layers : tuple layer sizes lr : float learning rate 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="IC", batch_size=2000, early_stop=20, loss="mse", optimizer="adam", GPU="0", seed=0, rnn_type="GRU", **kwargs ): # Set logger. self.logger = get_module_logger("ALSTM") self.logger.info("ALSTM 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.visible_GPU = GPU self.use_gpu = torch.cuda.is_available() self.seed = seed self.rnn_type = rnn_type self.logger.info( "ALSTM 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 : {}" "\nrnn_type : {}".format( d_feat, hidden_size, num_layers, dropout, n_epochs, lr, metric, batch_size, early_stop, optimizer.lower(), loss, GPU, self.use_gpu, seed, self.rnn_type, ) ) if loss not in {"mse", "binary"}: raise NotImplementedError("loss {} is not supported!".format(loss)) self._scorer = mean_squared_error if loss == "mse" else roc_auc_score self.alstm_model = ALSTMModel( d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout ) # def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, input_day=20, rnn_type="GRU"): if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.alstm_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": self.train_optimizer = optim.SGD(self.alstm_model.parameters(), lr=self.lr) else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) self._fitted = False if self.use_gpu: self.alstm_model.cuda() # set the visible GPU if self.visible_GPU: os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU 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 == "IC": return self.cal_ic(pred[mask], label[mask]) if self.metric == "" or self.metric == "loss": # use loss return -self.loss_fn(pred[mask], label[mask]) raise ValueError("unknown metric `%s`" % self.metric) def cal_ic(self, pred, label): return torch.mean(pred * label) def train_epoch(self, x_train, y_train): x_train_values = x_train.values y_train_values = np.squeeze(y_train.values) * 100 self.alstm_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() label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float() if self.use_gpu: feature = feature.cuda() label = label.cuda() pred = self.alstm_model(feature) loss = self.loss_fn(pred, label) self.train_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.alstm_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.alstm_model.eval() scores = [] losses = [] indices = np.arange(len(x_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_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float() if self.use_gpu: feature = feature.cuda() label = label.cuda() pred = self.alstm_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(), 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"] if save_path == None: save_path = create_save_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 # return 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.alstm_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.alstm_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.alstm_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() if self.use_gpu: x_batch = x_batch.cuda() with torch.no_grad(): if self.use_gpu: pred = self.alstm_model(x_batch).detach().cpu().numpy() else: pred = self.alstm_model(x_batch).detach().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=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): # 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() class ALSTMModel(nn.Module): def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, rnn_type="GRU"): super().__init__() self.hid_size = hidden_size self.input_size = d_feat self.dropout = dropout self.rnn_type = rnn_type self.rnn_layer = num_layers self._build_model() def _build_model(self): try: klass = getattr(nn, self.rnn_type.upper()) except: raise ValueError("unknown rnn_type `%s`" % self.rnn_type) self.net = nn.Sequential() self.net.add_module("fc_in", nn.Linear(in_features=self.input_size, out_features=self.hid_size)) self.net.add_module("act", nn.Tanh()) self.rnn = klass( input_size=self.hid_size, hidden_size=self.hid_size, num_layers=self.rnn_layer, batch_first=True, dropout=self.dropout, ) self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) # self.fc_out = nn.Linear(in_features=self.hid_size, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module("att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2))) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module("att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False)) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) def forward(self, inputs): # inputs: [batch_size, input_size*input_day] inputs = inputs.view(len(inputs), self.input_size, -1) inputs = inputs.permute(0, 2, 1) # [batch, input_size, seq_len] -> [batch, seq_len, input_size] rnn_out, _ = self.rnn(self.net(inputs)) # [batch, seq_len, num_directions * hidden_size] attention_score = self.att_net(rnn_out) # [batch, seq_len, 1] out_att = torch.mul(rnn_out, attention_score) out_att = torch.sum(out_att, dim=1) out = self.fc_out( torch.cat((rnn_out[:, -1, :], out_att), dim=1) ) # [batch, seq_len, num_directions * hidden_size] -> [batch, 1] # out = self.fc_out(rnn_out[:, -1, :] + out_att) return out[..., 0]