# 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.nn.init as init import torch.optim as optim from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP class SFM_Model(nn.Module): def __init__( self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu", ): super().__init__() self.input_dim = d_feat self.output_dim = output_dim self.freq_dim = freq_dim self.hidden_dim = hidden_size self.device = device self.W_i = nn.Parameter(init.xavier_uniform_(torch.empty((self.input_dim, self.hidden_dim)))) self.U_i = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))) self.b_i = nn.Parameter(torch.zeros(self.hidden_dim)) self.W_ste = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim))) self.U_ste = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))) self.b_ste = nn.Parameter(torch.ones(self.hidden_dim)) self.W_fre = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.freq_dim))) self.U_fre = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.freq_dim))) self.b_fre = nn.Parameter(torch.ones(self.freq_dim)) self.W_c = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim))) self.U_c = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))) self.b_c = nn.Parameter(torch.zeros(self.hidden_dim)) self.W_o = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim))) self.U_o = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))) self.b_o = nn.Parameter(torch.zeros(self.hidden_dim)) self.U_a = nn.Parameter(init.orthogonal_(torch.empty(self.freq_dim, 1))) self.b_a = nn.Parameter(torch.zeros(self.hidden_dim)) self.W_p = nn.Parameter(init.xavier_uniform_(torch.empty(self.hidden_dim, self.output_dim))) self.b_p = nn.Parameter(torch.zeros(self.output_dim)) self.activation = nn.Tanh() self.inner_activation = nn.Hardsigmoid() self.dropout_W, self.dropout_U = (dropout_W, dropout_U) self.fc_out = nn.Linear(self.output_dim, 1) self.states = [] def forward(self, input): input = input.reshape(len(input), self.input_dim, -1) # [N, F, T] input = input.permute(0, 2, 1) # [N, T, F] time_step = input.shape[1] for ts in range(time_step): x = input[:, ts, :] if len(self.states) == 0: # hasn't initialized yet self.init_states(x) self.get_constants(x) p_tm1 = self.states[0] h_tm1 = self.states[1] S_re_tm1 = self.states[2] S_im_tm1 = self.states[3] time_tm1 = self.states[4] B_U = self.states[5] B_W = self.states[6] frequency = self.states[7] x_i = torch.matmul(x * B_W[0], self.W_i) + self.b_i x_ste = torch.matmul(x * B_W[0], self.W_ste) + self.b_ste x_fre = torch.matmul(x * B_W[0], self.W_fre) + self.b_fre x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste)) fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre)) ste = torch.reshape(ste, (-1, self.hidden_dim, 1)) fre = torch.reshape(fre, (-1, 1, self.freq_dim)) f = ste * fre c = i * self.activation(x_c + torch.matmul(h_tm1 * B_U[0], self.U_c)) time = time_tm1 + 1 omega = torch.tensor(2 * np.pi) * time * frequency re = torch.cos(omega) im = torch.sin(omega) c = torch.reshape(c, (-1, self.hidden_dim, 1)) S_re = f * S_re_tm1 + c * re S_im = f * S_im_tm1 + c * im A = torch.square(S_re) + torch.square(S_im) A = torch.reshape(A, (-1, self.freq_dim)).float() A_a = torch.matmul(A * B_U[0], self.U_a) A_a = torch.reshape(A_a, (-1, self.hidden_dim)) a = self.activation(A_a + self.b_a) o = self.inner_activation(x_o + torch.matmul(h_tm1 * B_U[0], self.U_o)) h = o * a p = torch.matmul(h, self.W_p) + self.b_p self.states = [p, h, S_re, S_im, time, None, None, None] self.states = [] return self.fc_out(p).squeeze() def init_states(self, x): reducer_f = torch.zeros((self.hidden_dim, self.freq_dim)).to(self.device) reducer_p = torch.zeros((self.hidden_dim, self.output_dim)).to(self.device) init_state_h = torch.zeros(self.hidden_dim).to(self.device) init_state_p = torch.matmul(init_state_h, reducer_p) init_state = torch.zeros_like(init_state_h).to(self.device) init_freq = torch.matmul(init_state_h, reducer_f) init_state = torch.reshape(init_state, (-1, self.hidden_dim, 1)) init_freq = torch.reshape(init_freq, (-1, 1, self.freq_dim)) init_state_S_re = init_state * init_freq init_state_S_im = init_state * init_freq init_state_time = torch.tensor(0).to(self.device) self.states = [ init_state_p, init_state_h, init_state_S_re, init_state_S_im, init_state_time, None, None, None, ] def get_constants(self, x): constants = [] constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)]) constants.append([torch.tensor(1.0).to(self.device) for _ in range(7)]) array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)]) constants.append(torch.tensor(array).to(self.device)) self.states[5:] = constants class SFM(Model): """SFM Model Parameters ---------- input_dim : int input dimension output_dim : int output dimension 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, output_dim=1, freq_dim=10, dropout_W=0.0, dropout_U=0.0, n_epochs=200, lr=0.001, metric="", batch_size=2000, early_stop=20, eval_steps=5, loss="mse", optimizer="gd", GPU="0", seed=None, **kwargs ): # Set logger. self.logger = get_module_logger("SFM") self.logger.info("SFM pytorch version...") # set hyper-parameters. self.d_feat = d_feat self.hidden_size = hidden_size self.output_dim = output_dim self.freq_dim = freq_dim self.dropout_W = dropout_W self.dropout_U = dropout_U self.n_epochs = n_epochs self.lr = lr self.metric = metric self.batch_size = batch_size self.early_stop = early_stop self.eval_steps = eval_steps self.optimizer = optimizer.lower() 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.logger.info( "SFM parameters setting:" "\nd_feat : {}" "\nhidden_size : {}" "\noutput_size : {}" "\nfrequency_dimension : {}" "\ndropout_W: {}" "\ndropout_U: {}" "\nn_epochs : {}" "\nlr : {}" "\nmetric : {}" "\nbatch_size : {}" "\nearly_stop : {}" "\neval_steps : {}" "\noptimizer : {}" "\nloss_type : {}" "\nvisible_GPU : {}" "\nuse_GPU : {}" "\nseed : {}".format( d_feat, hidden_size, output_dim, freq_dim, dropout_W, dropout_U, n_epochs, lr, metric, batch_size, early_stop, eval_steps, 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.sfm_model = SFM_Model( d_feat=self.d_feat, output_dim=self.output_dim, hidden_size=self.hidden_size, freq_dim=self.freq_dim, dropout_W=self.dropout_W, dropout_U=self.dropout_U, device=self.device, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": self.train_optimizer = optim.SGD(self.sfm_model.parameters(), lr=self.lr) else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) self.fitted = False self.sfm_model.to(self.device) def test_epoch(self, data_x, data_y): # prepare training data x_values = data_x.values y_values = np.squeeze(data_y.values) self.sfm_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.sfm_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 train_epoch(self, x_train, y_train): x_train_values = x_train.values y_train_values = np.squeeze(y_train.values) self.sfm_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.sfm_model(feature) loss = self.loss_fn(pred, label) self.train_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_value_(self.sfm_model.parameters(), 3.0) self.train_optimizer.step() def fit( self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid = dataset.prepare( ["train", "valid"], 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"] 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.sfm_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)) if self.device != "cpu": torch.cuda.empty_cache() 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 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.sfm_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.device != "cpu": x_batch = x_batch.to(self.device) with torch.no_grad(): pred = self.sfm_model(x_batch).detach().cpu().numpy() preds.append(pred) return pd.Series(np.concatenate(preds), index=index) class AverageMeter: """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count