Merge github.com:microsoft/qlib into qlib_register_ops

qlib/contrib/model/pytorch_nn.py

@@ -259,7 +259,7 @@ class DNNModelPytorch(Model):
             loss = torch.mul(sqr_loss, w).mean()
             return loss
         elif loss_type == "binary":
-            loss = nn.BCELoss()
+            loss = nn.BCELoss(weight=w)
             return loss(pred, target)
         else:
             raise NotImplementedError("loss {} is not supported!".format(loss_type))

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