diff --git a/README.md b/README.md index d3f8170bc52..b9d84d1fa5f 100644 --- a/README.md +++ b/README.md @@ -231,6 +231,7 @@ Here is a list of models built on `Qlib`. - [GATs based on pytorch (Petar Velickovic, et al.)](qlib/contrib/model/pytorch_gats.py) - [SFM based on pytorch (Liheng Zhang, et al.)](qlib/contrib/model/pytorch_sfm.py) - [TFT based on tensorflow (Bryan Lim, et al.)](examples/benchmarks/TFT/tft.py) +- [TabNet based on pytorch (Sercan O. Arik, et al.)](qlib/contrib/model/pytorch_tabnet.py) Your PR of new Quant models is highly welcomed. diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md index d440a967ebb..bcd2279b33f 100644 --- a/examples/benchmarks/README.md +++ b/examples/benchmarks/README.md @@ -25,6 +25,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | XGBoost (Tianqi Chen, et al.) | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 | | LightGBM (Guolin Ke, et al.) | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 | | MLP | Alpha158 | 0.0358±0.00 | 0.2738±0.03| 0.0425±0.00 | 0.3221±0.01 | 0.0836±0.02 | 1.0323±0.25| -0.1127±0.02 | +| TabNet with pretrain (Sercan O. Arikm et al) | Alpha158 | 0.0344±0.00|0.205±0.11|0.0398±0.00 |0.3479±0.01|0.0827±0.02|1.1141±0.32 |-0.0925±0.02 | | TFT (Bryan Lim, et al.) | Alpha158 (with selected 20 features) | 0.0343±0.00 | 0.2071±0.02| 0.0107±0.00 | 0.0660±0.02 | 0.0623±0.02 | 0.5818±0.20| -0.1762±0.01 | | GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 | | LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 | diff --git a/examples/benchmarks/TabNet/pretrain/best.model b/examples/benchmarks/TabNet/pretrain/best.model new file mode 100644 index 00000000000..a85cbe392a4 Binary files /dev/null and b/examples/benchmarks/TabNet/pretrain/best.model differ diff --git a/examples/benchmarks/TabNet/requirements.txt b/examples/benchmarks/TabNet/requirements.txt new file mode 100644 index 00000000000..6a3d13097a0 --- /dev/null +++ b/examples/benchmarks/TabNet/requirements.txt @@ -0,0 +1,4 @@ +pandas==1.1.2 +numpy==1.17.4 +scikit_learn==0.23.2 +torch==1.7.0 \ No newline at end of file diff --git a/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml b/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml new file mode 100644 index 00000000000..4e9f0e7e940 --- /dev/null +++ b/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml @@ -0,0 +1,74 @@ +qlib_init: + provider_uri: "~/.qlib/qlib_data/cn_data" + region: cn +market: &market csi300 +benchmark: &benchmark SH000300 +data_handler_config: &data_handler_config + start_time: 2008-01-01 + end_time: 2020-08-01 + fit_start_time: 2008-01-01 + fit_end_time: 2014-12-31 + instruments: *market + infer_processors: + - class: RobustZScoreNorm + kwargs: + fields_group: feature + clip_outlier: true + - class: Fillna + kwargs: + fields_group: feature + learn_processors: + - class: DropnaLabel + - class: CSRankNorm + kwargs: + fields_group: label + label: ["Ref($close, -2) / Ref($close, -1) - 1"] +port_analysis_config: &port_analysis_config + strategy: + class: TopkDropoutStrategy + module_path: qlib.contrib.strategy.strategy + kwargs: + topk: 50 + n_drop: 5 + backtest: + verbose: False + limit_threshold: 0.095 + account: 100000000 + benchmark: *benchmark + deal_price: close + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5 +task: + model: + class: TabnetModel + module_path: qlib.contrib.model.pytorch_tabnet + kwargs: + pretrain: True + dataset: + class: DatasetH + module_path: qlib.data.dataset + kwargs: + handler: + class: Alpha158 + module_path: qlib.contrib.data.handler + kwargs: *data_handler_config + segments: + pretrain: [2008-01-01, 2014-12-31] + pretrain_validation: [2015-01-01, 2020-08-01] + train: [2008-01-01, 2014-12-31] + valid: [2015-01-01, 2016-12-31] + test: [2017-01-01, 2020-08-01] + record: + - class: SignalRecord + module_path: qlib.workflow.record_temp + kwargs: {} + - class: SigAnaRecord + module_path: qlib.workflow.record_temp + kwargs: + ana_long_short: False + ann_scaler: 252 + - class: PortAnaRecord + module_path: qlib.workflow.record_temp + kwargs: + config: *port_analysis_config diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py new file mode 100644 index 00000000000..ef1c8e2a8a5 --- /dev/null +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -0,0 +1,642 @@ +# 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 +import torch.nn.functional as F +from torch.autograd import Function + +from ...model.base import Model +from ...data.dataset import DatasetH +from ...data.dataset.handler import DataHandlerLP + + +class TabnetModel(Model): + def __init__( + self, + d_feat=158, + out_dim=64, + final_out_dim=1, + batch_size=4096, + n_d=64, + n_a=64, + n_shared=2, + n_ind=2, + n_steps=5, + n_epochs=100, + pretrain_n_epochs=50, + relax=1.3, + vbs=2048, + seed=993, + optimizer="adam", + loss="mse", + metric="", + early_stop=20, + GPU="1", + pretrain_loss="custom", + ps=0.3, + lr=0.01, + pretrain=True, + pretrain_file="./pretrain/best.model", + ): + """ + TabNet model for Qlib + + Args： + ps: probability to generate the bernoulli mask + """ + # set hyper-parameters. + self.d_feat = d_feat + self.out_dim = out_dim + self.final_out_dim = final_out_dim + self.lr = lr + self.batch_size = batch_size + self.optimizer = optimizer.lower() + self.pretrain_loss = pretrain_loss + self.seed = seed + self.ps = ps + self.n_epochs = n_epochs + self.logger = get_module_logger("TabNet") + self.pretrain_n_epochs = pretrain_n_epochs + self.device = "cuda:%s" % (GPU) if torch.cuda.is_available() else "cpu" + self.loss = loss + self.metric = metric + self.early_stop = early_stop + self.pretrain = pretrain + self.pretrain_file = pretrain_file + self.logger.info( + "TabNet:" + "\nbatch_size : {}" + "\nvirtual bs : {}" + "\nGPU : {}" + "\npretrain: {}".format(self.batch_size, vbs, GPU, pretrain) + ) + np.random.seed(self.seed) + torch.manual_seed(self.seed) + + self.tabnet_model = TabNet( + inp_dim=self.d_feat, out_dim=self.out_dim, vbs=vbs, relax=relax, device=self.device + ).to(self.device) + self.tabnet_decoder = TabNet_Decoder(self.out_dim, self.d_feat, n_shared, n_ind, vbs, n_steps, self.device).to( + self.device + ) + + if optimizer.lower() == "adam": + self.pretrain_optimizer = optim.Adam( + list(self.tabnet_model.parameters()) + list(self.tabnet_decoder.parameters()), lr=self.lr + ) + self.train_optimizer = optim.Adam(self.tabnet_model.parameters(), lr=self.lr) + + elif optimizer.lower() == "gd": + self.pretrain_optimizer = optim.SGD( + list(self.tabnet_model.parameters()) + list(self.tabnet_decoder.parameters()), lr=self.lr + ) + self.train_optimizer = optim.SGD(self.tabnet_model.parameters(), lr=self.lr) + else: + raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) + + def pretrain_fn(self, dataset=DatasetH, pretrain_file="./pretrain/best.model"): + # make a directory if pretrian director does not exist + if pretrain_file.startswith("./pretrain") and not os.path.exists("pretrain"): + self.logger.info("make folder to store model...") + os.makedirs("pretrain") + + [df_train, df_valid] = dataset.prepare( + ["pretrain", "pretrain_validation"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, + ) + + df_train.fillna(df_train.mean(), inplace=True) + df_valid.fillna(df_valid.mean(), inplace=True) + + x_train = df_train["feature"] + x_valid = df_valid["feature"] + + # Early stop setup + stop_steps = 0 + train_loss = 0 + best_loss = np.inf + + for epoch_idx in range(self.pretrain_n_epochs): + self.logger.info("epoch: %s" % (epoch_idx)) + self.logger.info("pre-training...") + self.pretrain_epoch(x_train) + self.logger.info("evaluating...") + train_loss = self.pretrain_test_epoch(x_train) + valid_loss = self.pretrain_test_epoch(x_valid) + self.logger.info("train %.6f, valid %.6f" % (train_loss, valid_loss)) + + if valid_loss < best_loss: + self.logger.info("Save Model...") + torch.save(self.tabnet_model.state_dict(), pretrain_file) + best_loss = valid_loss + else: + stop_steps += 1 + if stop_steps >= self.early_stop: + self.logger.info("early stop") + break + + def fit( + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, + ): + if self.pretrain: + # there is a pretrained model, load the model + self.logger.info("Pretrain...") + self.pretrain_fn(dataset, self.pretrain_file) + self.logger.info("Load Pretrain model") + self.tabnet_model.load_state_dict(torch.load(self.pretrain_file)) + + # adding one more linear layer to fit the final output dimension + self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device) + df_train, df_valid = dataset.prepare( + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, + ) + df_train.fillna(df_train.mean(), inplace=True) + 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"] = [] + + self.logger.info("training...") + self._fitted = True + + for epoch_idx in range(self.n_epochs): + self.logger.info("epoch: %s" % (epoch_idx)) + 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) + valid_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 = epoch_idx + 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)) + + def predict(self, dataset): + if not self._fitted: + raise ValueError("model is not fitted yet!") + + x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I) + index = x_test.index + self.tabnet_model.eval() + x_values = torch.from_numpy(x_test.values) + x_values[torch.isnan(x_values)] = 0 + 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 = x_values[begin:end].float().to(self.device) + priors = torch.ones(end - begin, self.d_feat).to(self.device) + + with torch.no_grad(): + pred = self.tabnet_model(x_batch, priors).detach().cpu().numpy() + + preds.append(pred) + + return pd.Series(np.concatenate(preds), index=index) + + def test_epoch(self, data_x, data_y): + # prepare training data + x_values = torch.from_numpy(data_x.values) + y_values = torch.from_numpy(np.squeeze(data_y.values)) + x_values[torch.isnan(x_values)] = 0 + y_values[torch.isnan(y_values)] = 0 + self.tabnet_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 = x_values[indices[i : i + self.batch_size]].float().to(self.device) + label = y_values[indices[i : i + self.batch_size]].float().to(self.device) + priors = torch.ones(self.batch_size, self.d_feat).to(self.device) + pred = self.tabnet_model(feature, priors) + 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 = torch.from_numpy(x_train.values) + y_train_values = torch.from_numpy(np.squeeze(y_train.values)) + x_train_values[torch.isnan(x_train_values)] = 0 + y_train_values[torch.isnan(y_train_values)] = 0 + self.tabnet_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 = x_train_values[indices[i : i + self.batch_size]].float().to(self.device) + label = y_train_values[indices[i : i + self.batch_size]].float().to(self.device) + priors = torch.ones(self.batch_size, self.d_feat).to(self.device) + pred = self.tabnet_model(feature, priors) + loss = self.loss_fn(pred, label) + + self.train_optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_value_(self.tabnet_model.parameters(), 3.0) + self.train_optimizer.step() + + def pretrain_epoch(self, x_train): + train_set = torch.from_numpy(x_train.values) + train_set[torch.isnan(train_set)] = 0 + indices = np.arange(len(train_set)) + np.random.shuffle(indices) + + self.tabnet_model.train() + self.tabnet_decoder.train() + + for i in range(len(indices))[:: self.batch_size]: + + if len(indices) - i < self.batch_size: + break + + S_mask = torch.bernoulli(torch.empty(self.batch_size, self.d_feat).fill_(self.ps)) + x_train_values = train_set[indices[i : i + self.batch_size]] * (1 - S_mask) + y_train_values = train_set[indices[i : i + self.batch_size]] * (S_mask) + + S_mask = S_mask.to(self.device) + feature = x_train_values.float().to(self.device) + label = y_train_values.float().to(self.device) + priors = 1 - S_mask + (vec, sparse_loss) = self.tabnet_model(feature, priors) + f = self.tabnet_decoder(vec) + loss = self.pretrain_loss_fn(label, f, S_mask) + + self.pretrain_optimizer.zero_grad() + loss.backward() + self.pretrain_optimizer.step() + + def pretrain_test_epoch(self, x_train): + train_set = torch.from_numpy(x_train.values) + train_set[torch.isnan(train_set)] = 0 + indices = np.arange(len(train_set)) + + self.tabnet_model.eval() + self.tabnet_decoder.eval() + + losses = [] + + for i in range(len(indices))[:: self.batch_size]: + + if len(indices) - i < self.batch_size: + break + + S_mask = torch.bernoulli(torch.empty(self.batch_size, self.d_feat).fill_(self.ps)) + x_train_values = train_set[indices[i : i + self.batch_size]] * (1 - S_mask) + y_train_values = train_set[indices[i : i + self.batch_size]] * (S_mask) + + feature = x_train_values.float().to(self.device) + label = y_train_values.float().to(self.device) + S_mask = S_mask.to(self.device) + priors = 1 - S_mask + (vec, sparse_loss) = self.tabnet_model(feature, priors) + f = self.tabnet_decoder(vec) + + loss = self.pretrain_loss_fn(label, f, S_mask) + losses.append(loss.item()) + + return np.mean(losses) + + def pretrain_loss_fn(self, f_hat, f, S): + """ + Pretrain loss function defined in the original paper, read "Tabular self-supervised learning" in https://arxiv.org/pdf/1908.07442.pdf + """ + down_mean = torch.mean(f, dim=0) + down = torch.sqrt(torch.sum(torch.square(f - down_mean), dim=0)) + up = (f_hat - f) * S + return torch.sum(torch.square(up / down)) + + 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 mse(self, pred, label): + loss = (pred - label) ** 2 + return torch.mean(loss) + + +class FinetuneModel(nn.Module): + """ + FinuetuneModel for adding a layer by the end + """ + + def __init__(self, input_dim, output_dim, trained_model): + super().__init__() + self.model = trained_model + self.fc = nn.Linear(input_dim, output_dim) + + def forward(self, x, priors): + return self.fc(self.model(x, priors)[0]).squeeze() # take the vec out + + +class DecoderStep(nn.Module): + def __init__(self, inp_dim, out_dim, shared, n_ind, vbs, device): + super().__init__() + self.fea_tran = FeatureTransformer(inp_dim, out_dim, shared, n_ind, vbs, device) + self.fc = nn.Linear(out_dim, out_dim) + + def forward(self, x): + x = self.fea_tran(x) + return self.fc(x) + + +class TabNet_Decoder(nn.Module): + def __init__(self, inp_dim, out_dim, n_shared, n_ind, vbs, n_steps, device): + """ + TabNet decoder that is used in pre-training + """ + self.out_dim = out_dim + + super().__init__() + if n_shared > 0: + self.shared = nn.ModuleList() + self.shared.append(nn.Linear(inp_dim, 2 * out_dim)) + for x in range(n_shared - 1): + self.shared.append(nn.Linear(out_dim, 2 * out_dim)) # preset the linear function we will use + else: + self.shared = None + self.n_steps = n_steps + self.steps = nn.ModuleList() + for x in range(n_steps): + self.steps.append(DecoderStep(inp_dim, out_dim, self.shared, n_ind, vbs, device)) + + def forward(self, x): + out = torch.zeros(x.size(0), self.out_dim).to(x.device) + for step in self.steps: + out += step(x) + return out + + +class TabNet(nn.Module): + def __init__( + self, inp_dim=6, out_dim=6, n_d=64, n_a=64, n_shared=2, n_ind=2, n_steps=5, relax=1.2, vbs=1024, device="cpu" + ): + """ + TabNet AKA the original encoder + + Args: + n_d: dimension of the features used to calculate the final results + n_a: dimension of the features input to the attention transformer of the next step + n_shared: numbr of shared steps in feature transfomer(optional) + n_ind: number of independent steps in feature transformer + n_steps: number of steps of pass through tabbet + relax coefficient: + virtual batch size: + """ + super().__init__() + + # set the number of shared step in feature transformer + if n_shared > 0: + self.shared = nn.ModuleList() + self.shared.append(nn.Linear(inp_dim, 2 * (n_d + n_a))) + for x in range(n_shared - 1): + self.shared.append(nn.Linear(n_d + n_a, 2 * (n_d + n_a))) # preset the linear function we will use + else: + self.shared = None + + self.first_step = FeatureTransformer(inp_dim, n_d + n_a, self.shared, n_ind, vbs, device) + self.steps = nn.ModuleList() + for x in range(n_steps - 1): + self.steps.append(DecisionStep(inp_dim, n_d, n_a, self.shared, n_ind, relax, vbs, device)) + self.fc = nn.Linear(n_d, out_dim) + self.bn = nn.BatchNorm1d(inp_dim, momentum=0.01) + self.n_d = n_d + + def forward(self, x, priors): + assert not torch.isnan(x).any() + x = self.bn(x) + x_a = self.first_step(x)[:, self.n_d :] + sparse_loss = torch.zeros(1).to(x.device) + out = torch.zeros(x.size(0), self.n_d).to(x.device) + for step in self.steps: + x_te, l = step(x, x_a, priors) + out += F.relu(x_te[:, : self.n_d]) # split the feautre from feat_transformer + x_a = x_te[:, self.n_d :] + sparse_loss += l + return self.fc(out), sparse_loss + + +class GBN(nn.Module): + """ + Ghost Batch Normalization + an efficient way of doing batch normalization + + Args: + vbs: virtual batch size + """ + + def __init__(self, inp, vbs=1024, momentum=0.01): + super().__init__() + self.bn = nn.BatchNorm1d(inp, momentum=momentum) + self.vbs = vbs + + def forward(self, x): + chunk = torch.chunk(x, x.size(0) // self.vbs, 0) + res = [self.bn(y) for y in chunk] + return torch.cat(res, 0) + + +class GLU(nn.Module): + """ + GLU block that extracts only the most essential information + + Args: + vbs: virtual batch size + """ + + def __init__(self, inp_dim, out_dim, fc=None, vbs=1024): + super().__init__() + if fc: + self.fc = fc + else: + self.fc = nn.Linear(inp_dim, out_dim * 2) + self.bn = GBN(out_dim * 2, vbs=vbs) + self.od = out_dim + + def forward(self, x): + x = self.bn(self.fc(x)) + return torch.mul(x[:, : self.od], torch.sigmoid(x[:, self.od :])) + + +class AttentionTransformer(nn.Module): + """ + Args: + relax: relax coefficient. The greater it is, we can + use the same features more. When it is set to 1 + we can use every feature only once + """ + + def __init__(self, d_a, inp_dim, relax, vbs=1024): + super().__init__() + self.fc = nn.Linear(d_a, inp_dim) + self.bn = GBN(inp_dim, vbs=vbs) + self.r = relax + + # a:feature from previous decision step + def forward(self, a, priors): + a = self.bn(self.fc(a)) + mask = SparsemaxFunction.apply(a * priors) + priors = priors * (self.r - mask) # updating the prior + return mask + + +class FeatureTransformer(nn.Module): + def __init__(self, inp_dim, out_dim, shared, n_ind, vbs, device): + super().__init__() + first = True + self.shared = nn.ModuleList() + if shared: + self.shared.append(GLU(inp_dim, out_dim, shared[0], vbs=vbs)) + first = False + for fc in shared[1:]: + self.shared.append(GLU(out_dim, out_dim, fc, vbs=vbs)) + else: + self.shared = None + self.independ = nn.ModuleList() + if first: + self.independ.append(GLU(inp, out_dim, vbs=vbs)) + for x in range(first, n_ind): + self.independ.append(GLU(out_dim, out_dim, vbs=vbs)) + self.scale = torch.sqrt(torch.tensor([0.5], device=device)) + + def forward(self, x): + if self.shared: + x = self.shared[0](x) + for glu in self.shared[1:]: + x = torch.add(x, glu(x)) + x = x * self.scale + for glu in self.independ: + x = torch.add(x, glu(x)) + x = x * self.scale + return x + + +class DecisionStep(nn.Module): + """ + One step for the TabNet + """ + + def __init__(self, inp_dim, n_d, n_a, shared, n_ind, relax, vbs, device): + super().__init__() + self.atten_tran = AttentionTransformer(n_a, inp_dim, relax, vbs) + self.fea_tran = FeatureTransformer(inp_dim, n_d + n_a, shared, n_ind, vbs, device) + + def forward(self, x, a, priors): + mask = self.atten_tran(a, priors) + sparse_loss = ((-1) * mask * torch.log(mask + 1e-10)).mean() + x = self.fea_tran(x * mask) + return x, sparse_loss + + +def make_ix_like(input, dim=0): + d = input.size(dim) + rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype) + view = [1] * input.dim() + view[0] = -1 + return rho.view(view).transpose(0, dim) + + +class SparsemaxFunction(Function): + """ + SparseMax function for replacing reLU + """ + + @staticmethod + def forward(ctx, input, dim=-1): + ctx.dim = dim + max_val, _ = input.max(dim=dim, keepdim=True) + input -= max_val # same numerical stability trick as for softmax + tau, supp_size = SparsemaxFunction.threshold_and_support(input, dim=dim) + output = torch.clamp(input - tau, min=0) + ctx.save_for_backward(supp_size, output) + return output + + @staticmethod + def backward(ctx, grad_output): + supp_size, output = ctx.saved_tensors + dim = ctx.dim + grad_input = grad_output.clone() + grad_input[output == 0] = 0 + + v_hat = grad_input.sum(dim=dim) / supp_size.to(output.dtype).squeeze() + v_hat = v_hat.unsqueeze(dim) + grad_input = torch.where(output != 0, grad_input - v_hat, grad_input) + return grad_input, None + + @staticmethod + def threshold_and_support(input, dim=-1): + input_srt, _ = torch.sort(input, descending=True, dim=dim) + input_cumsum = input_srt.cumsum(dim) - 1 + rhos = make_ix_like(input, dim) + support = rhos * input_srt > input_cumsum + + support_size = support.sum(dim=dim).unsqueeze(dim) + tau = input_cumsum.gather(dim, support_size - 1) + tau /= support_size.to(input.dtype) + return tau, support_size