From 28b11886dd47f9304406167f934875922bd28c65 Mon Sep 17 00:00:00 2001 From: Alex Wang Date: Thu, 26 Nov 2020 14:35:16 +0800 Subject: [PATCH] update --- examples/workflow_by_code_sfm.py | 21 ++- qlib/contrib/model/pytorch_sfm.py | 298 +++++++++++++++--------------- 2 files changed, 158 insertions(+), 161 deletions(-) diff --git a/examples/workflow_by_code_sfm.py b/examples/workflow_by_code_sfm.py index ccc2d412c..4de79e075 100644 --- a/examples/workflow_by_code_sfm.py +++ b/examples/workflow_by_code_sfm.py @@ -71,21 +71,22 @@ if __name__ == "__main__": "module_path": "qlib.contrib.model.pytorch_sfm", "kwargs": { "d_feat": 6, - "hidden_size": 32, - "output_dim": 16, - "freq_dim": 25, + "hidden_size": 64, + "output_dim" : 32, + "freq_dim" : 25, "dropout_W": 0.5, "dropout_U": 0.5, - "n_epochs": 200, - "lr": 1e-3, - "batch_size": 200, + "n_epochs": 15, + "lr": 1e-2, + "metric": "", + "batch_size": 1600, "early_stop": 20, "eval_steps": 5, "loss": "mse", - "lr_decay": 0.96, - "lr_decay_steps": 100, - "optimizer": "adam", - "GPU": 1, + "lr_decay" : 0.96, + "lr_decay_steps" : 100, + "optimizer" : "adam", + "GPU": 3, "seed": 710, }, }, diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index d8baa9cb2..4ec61430e 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -31,7 +31,6 @@ 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__() @@ -76,13 +75,13 @@ class SFM_Model(nn.Module): 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] + 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 + 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] @@ -99,65 +98,64 @@ class SFM_Model(nn.Module): 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) - ) # not sure whether I am doing in the right unsquuze + + i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) # not sure whether I am doing in the right unsquuze + 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) + 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 = [] + 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] @@ -203,6 +201,7 @@ class SFM(Model): dropout_U=0.0, n_epochs=200, lr=0.001, + metric = "", batch_size=2000, early_stop=20, eval_steps=5, @@ -227,14 +226,15 @@ class SFM(Model): 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.lr_decay = lr_decay self.lr_decay_steps = lr_decay_steps self.optimizer = optimizer.lower() - self.loss_type = loss - self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu" + self.loss = loss + self.device = "cuda:%d"%(GPU) if torch.cuda.is_available() else "cpu" self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -243,11 +243,12 @@ class SFM(Model): "\nd_feat : {}" "\nhidden_size : {}" "\noutput_size : {}" - "\nfrequency_dimension : {}" + "\nfrequency_dimension : {}" "\ndropout_W: {}" "\ndropout_U: {}" "\nn_epochs : {}" "\nlr : {}" + "\nmetric : {}" "\nbatch_size : {}" "\nearly_stop : {}" "\neval_steps : {}" @@ -266,6 +267,7 @@ class SFM(Model): dropout_U, n_epochs, lr, + metric, batch_size, early_stop, eval_steps, @@ -284,14 +286,14 @@ class SFM(Model): self._scorer = mean_squared_error if loss == "mse" else roc_auc_score self.sfm_model = SFM_Model( - d_feat=self.d_feat, + 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, - ) + 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": @@ -299,24 +301,73 @@ class SFM(Model): else: raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) - # Reduce learning rate when loss has stopped decrease - self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.train_optimizer, - mode="min", - factor=0.5, - patience=10, - verbose=True, - threshold=0.0001, - threshold_mode="rel", - cooldown=0, - min_lr=0.00001, - eps=1e-08, - ) - self._fitted = False self.sfm_model.to(self.device) - def fit(self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, **kwargs): + 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)) + 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().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) * 100 + + 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 @@ -324,10 +375,10 @@ class SFM(Model): x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] - save_path = create_save_path(save_path) stop_steps = 0 train_loss = 0 - best_loss = np.inf + best_score = -np.inf + best_epoch = 0 evals_result["train"] = [] evals_result["valid"] = [] @@ -335,90 +386,56 @@ class SFM(Model): self.logger.info("training...") self._fitted = True - # prepare training data - x_train_values = torch.from_numpy(x_train.values).float() - y_train_values = torch.from_numpy(np.squeeze(y_train.values)).float() - train_num = y_train_values.shape[0] - - # prepare validation data - x_val_auto = torch.from_numpy(x_valid.values).float() - y_val_auto = torch.from_numpy(np.squeeze(y_valid.values)).float() - - x_val_auto = x_val_auto.to(self.device) - y_val_auto = y_val_auto.to(self.device) - for step in range(self.n_epochs): - if stop_steps >= self.early_stop: - if verbose: - self.logger.info("\tearly stop") - break - loss = AverageMeter() - self.sfm_model.train() - self.train_optimizer.zero_grad() + 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) - choice = np.random.choice(train_num, self.batch_size) - x_batch_auto = x_train_values[choice] - y_batch_auto = y_train_values[choice] - - x_batch_auto = x_batch_auto.to(self.device) - y_batch_auto = y_batch_auto.to(self.device) - - # forward - preds = self.sfm_model(x_batch_auto) - cur_loss = self.get_loss(preds, y_batch_auto, self.loss_type) - cur_loss.backward() - self.train_optimizer.step() - loss.update(cur_loss.item()) - - # validation - train_loss += loss.val - if step and step % self.eval_steps == 0: + 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 - train_loss /= self.eval_steps - - with torch.no_grad(): - self.sfm_model.eval() - loss_val = AverageMeter() - - # forward - preds = self.sfm_model(x_val_auto) - cur_loss_val = self.get_loss(preds, y_val_auto, self.loss_type) - loss_val.update(cur_loss_val.item()) - - if verbose: - self.logger.info( - "[Epoch {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val) - ) - evals_result["train"].append(train_loss) - evals_result["valid"].append(loss_val.val) - if loss_val.val < best_loss: - if verbose: - self.logger.info( - "\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format( - best_loss, loss_val.val - ) - ) - best_loss = loss_val.val - stop_steps = 0 - torch.save(self.sfm_model.state_dict(), save_path) - train_loss = 0 - # update learning rate - self.scheduler.step(cur_loss_val) - + 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 get_loss(self, pred, target, loss_type): - if loss_type == "mse": - sqr_loss = (pred - target) ** 2 - loss = sqr_loss.mean() - return loss - elif loss_type == "binary": - loss = nn.BCELoss() - return loss(pred, target) - else: - raise NotImplementedError("loss {} is not supported!".format(loss_type)) + 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 predict(self, dataset): if not self._fitted: raise ValueError("model is not fitted yet!") @@ -430,7 +447,7 @@ class SFM(Model): sample_num = x_values.shape[0] preds = [] - for begin in range(sample_num)[:: self.batch_size]: + for begin in range(sample_num)[::self.batch_size]: if sample_num - begin < self.batch_size: end = sample_num else: @@ -440,37 +457,16 @@ class SFM(Model): if self.device != "cpu": x_batch = x_batch.to(self.device) - + with torch.no_grad(): - if self.device != "cpu": - pred = self.sfm_model(x_batch).detach().cpu().numpy() - else: - pred = self.sfm_model(x_batch).detach().cpu().numpy() + pred = self.sfm_model(x_batch).detach().cpu().numpy() + preds.append(pred) - + return pd.Series(np.concatenate(preds), index=index) - def save(self, filename, **kwargs): - with save_multiple_parts_file(filename) as model_dir: - model_path = os.path.join(model_dir, os.path.split(model_dir)[-1]) - # Save model - torch.save(self.sfm_model.state_dict(), model_path) - - def load(self, buffer, **kwargs): - with unpack_archive_with_buffer(buffer) as model_dir: - # Get model name - _model_name = os.path.splitext(list(filter(lambda x: x.startswith("model.bin"), os.listdir(model_dir)))[0])[ - 0 - ] - _model_path = os.path.join(model_dir, _model_name) - # Load model - self.sfm_model.load_state_dict(torch.load(_model_path)) - self._fitted = True - - class AverageMeter(object): """Computes and stores the average and current value""" - def __init__(self): self.reset()