Enhance pytorch nn (#917)

* enhance pytorch_nn

* fix dim bug

* Black format

* Fix pylint error

.pylintrc

@@ -0,0 +1,5 @@
+[TYPECHECK]
+# https://stackoverflow.com/a/53572939 
+# List of members which are set dynamically and missed by Pylint inference
+# system, and so shouldn't trigger E1101 when accessed.
+generated-members=numpy.*, torch.*

docs/developer/code_standard.rst

@@ -14,9 +14,19 @@ Continuous Integration (CI) tools help you stick to the quality standards by run
 
 When you submit a PR request, you can check whether your code passes the CI tests in the "check" section at the bottom of the web page.
 
-A common error is the mixed use of space and tab. You can fix the bug by inputing the following code in the command line.
+1. Qlib will check the code format with black. The PR will raise error if your code does not align to the standard of Qlib(e.g. a common error is the mixed use of space and tab).
+ You can fix the bug by inputing the following code in the command line.
 
 .. code-block:: python
 
     pip install black
     python -m black . -l 120
+
+
+2. Qlib will check your code style pylint. The checking command is implemented in [github action workflow](https://github.com/microsoft/qlib/blob/0e8b94a552f1c457cfa6cd2c1bb3b87ebb3fb279/.github/workflows/test.yml#L66). 
+   Sometime pylint's restrictions are not that reasonable. You can ignore specific errors like this
+
+.. code-block:: python
+
+    return -ICLoss()(pred, target, index)  # pylint: disable=E1130
+

examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml

@@ -63,8 +63,6 @@ task:
         module_path: qlib.contrib.model.pytorch_nn
         kwargs:
             loss: mse
-            input_dim: 157
-            output_dim: 1
             lr: 0.002
             lr_decay: 0.96
             lr_decay_steps: 100
@@ -73,6 +71,8 @@ task:
             batch_size: 8192
             GPU: 0
             weight_decay: 0.0002
+            pt_model_kwargs:
+              input_dim: 157
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml

@@ -51,8 +51,6 @@ task:
         module_path: qlib.contrib.model.pytorch_nn
         kwargs:
             loss: mse
-            input_dim: 360
-            output_dim: 1
             lr: 0.002
             lr_decay: 0.96
             lr_decay_steps: 100
@@ -60,6 +58,8 @@ task:
             max_steps: 8000
             batch_size: 4096
             GPU: 0
+            pt_model_kwargs:
+              input_dim: 360
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

qlib/contrib/meta/data_selection/utils.py

@@ -9,6 +9,9 @@ from torch import nn
 class ICLoss(nn.Module):
     def forward(self, pred, y, idx, skip_size=50):
         """forward.
+        FIXME:
+        - Some times it will be a slightly different from the result from `pandas.corr()`
+        - It may be caused by the precision problem of model;
 
         :param pred:
         :param y:

qlib/contrib/model/gbdt.py

@@ -10,6 +10,7 @@ from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 from ...model.interpret.base import LightGBMFInt
 from ...data.dataset.weight import Reweighter
+from qlib.workflow import R
 
 
 class LGBModel(ModelFT, LightGBMFInt):
@@ -59,10 +60,12 @@ class LGBModel(ModelFT, LightGBMFInt):
         num_boost_round=None,
         early_stopping_rounds=None,
         verbose_eval=20,
-        evals_result=dict(),
+        evals_result=None,
         reweighter=None,
-        **kwargs
+        **kwargs,
     ):
+        if evals_result is None:
+            evals_result = {}  # in case of unsafety of Python default values
         ds_l = self._prepare_data(dataset, reweighter)
         ds, names = list(zip(*ds_l))
         self.model = lgb.train(
@@ -76,10 +79,13 @@ class LGBModel(ModelFT, LightGBMFInt):
             ),
             verbose_eval=verbose_eval,
             evals_result=evals_result,
-            **kwargs
+            **kwargs,
         )
         for k in names:
-            evals_result[k] = list(evals_result[k].values())[0]
+            for key, val in evals_result[k].items():
+                name = f"{key}.{k}"
+                for epoch, m in enumerate(val):
+                    R.log_metrics(**{name.replace("@", "_"): m}, step=epoch)
 
     def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
         if self.model is None:

qlib/contrib/model/pytorch_gats.py

@@ -263,8 +263,8 @@ class GATs(Model):
 
         model_dict = self.GAT_model.state_dict()
         pretrained_dict = {
-            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict
-        }  # pylint: disable=E1135
+            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict  # pylint: disable=E1135
+        }
         model_dict.update(pretrained_dict)
         self.GAT_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_gats_ts.py

@@ -278,8 +278,8 @@ class GATs(Model):
 
         model_dict = self.GAT_model.state_dict()
         pretrained_dict = {
-            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict
-        }  # pylint: disable=E1135
+            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict  # pylint: disable=E1135
+        }
         model_dict.update(pretrained_dict)
         self.GAT_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_nn.py

@@ -4,11 +4,12 @@
 
 from __future__ import division
 from __future__ import print_function
+from collections import defaultdict
 
 import os
 import numpy as np
 import pandas as pd
-from typing import Text, Union
+from typing import Callable, Optional, Text, Union
 from sklearn.metrics import roc_auc_score, mean_squared_error
 
 import torch
@@ -20,9 +21,18 @@ from ...model.base import Model
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 from ...data.dataset.weight import Reweighter
-from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path
+from ...utils import (
+    auto_filter_kwargs,
+    init_instance_by_config,
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    get_or_create_path,
+)
 from ...log import get_module_logger
 from ...workflow import R
+from qlib.contrib.meta.data_selection.utils import ICLoss
+from torch.nn import DataParallel
+from torch.utils.data import DataLoader, SequentialSampler
 
 
 class DNNModelPytorch(Model):
@@ -49,9 +59,6 @@ class DNNModelPytorch(Model):
 
     def __init__(
         self,
-        input_dim=360,
-        output_dim=1,
-        layers=(256,),
         lr=0.001,
         max_steps=300,
         batch_size=2000,
@@ -64,14 +71,23 @@ class DNNModelPytorch(Model):
         GPU=0,
         seed=None,
         weight_decay=0.0,
-        **kwargs
+        data_parall=False,
+        scheduler: Optional[Union[Callable]] = "default",  # when it is Callable, it accept one argument named optimizer
+        init_model=None,
+        eval_train_metric=True,
+        pt_model_uri="qlib.contrib.model.pytorch_nn.Net",
+        pt_model_kwargs={
+            "input_dim": 360,
+            "layers": (256,),
+        },
+        valid_key=DataHandlerLP.DK_L,
+        # TODO: Infer Key is a more reasonable key. But it requires more detailed processing on label processing
     ):
         # Set logger.
         self.logger = get_module_logger("DNNModelPytorch")
         self.logger.info("DNN pytorch version...")
 
         # set hyper-parameters.
-        self.layers = layers
         self.lr = lr
         self.max_steps = max_steps
         self.batch_size = batch_size
@@ -81,41 +97,36 @@ class DNNModelPytorch(Model):
         self.lr_decay_steps = lr_decay_steps
         self.optimizer = optimizer.lower()
         self.loss_type = loss
-        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
+        if isinstance(GPU, str):
+            self.device = torch.device(GPU)
+        else:
+            self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
         self.seed = seed
         self.weight_decay = weight_decay
+        self.data_parall = data_parall
+        self.eval_train_metric = eval_train_metric
+        self.valid_key = valid_key
+
+        self.best_step = None
 
         self.logger.info(
             "DNN parameters setting:"
-            "\nlayers : {}"
-            "\nlr : {}"
-            "\nmax_steps : {}"
-            "\nbatch_size : {}"
-            "\nearly_stop_rounds : {}"
-            "\neval_steps : {}"
-            "\nlr_decay : {}"
-            "\nlr_decay_steps : {}"
-            "\noptimizer : {}"
-            "\nloss_type : {}"
-            "\nseed : {}"
-            "\ndevice : {}"
-            "\nuse_GPU : {}"
-            "\nweight_decay : {}".format(
-                layers,
-                lr,
-                max_steps,
-                batch_size,
-                early_stop_rounds,
-                eval_steps,
-                lr_decay,
-                lr_decay_steps,
-                optimizer,
-                loss,
-                seed,
-                self.device,
-                self.use_gpu,
-                weight_decay,
-            )
+            f"\nlr : {lr}"
+            f"\nmax_steps : {max_steps}"
+            f"\nbatch_size : {batch_size}"
+            f"\nearly_stop_rounds : {early_stop_rounds}"
+            f"\neval_steps : {eval_steps}"
+            f"\nlr_decay : {lr_decay}"
+            f"\nlr_decay_steps : {lr_decay_steps}"
+            f"\noptimizer : {optimizer}"
+            f"\nloss_type : {loss}"
+            f"\nseed : {seed}"
+            f"\ndevice : {self.device}"
+            f"\nuse_GPU : {self.use_gpu}"
+            f"\nweight_decay : {weight_decay}"
+            f"\nenable data parall : {self.data_parall}"
+            f"\npt_model_uri: {pt_model_uri}"
+            f"\npt_model_kwargs: {pt_model_kwargs}"
         )
 
         if self.seed is not None:
@@ -126,7 +137,14 @@ class DNNModelPytorch(Model):
             raise NotImplementedError("loss {} is not supported!".format(loss))
         self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
 
-        self.dnn_model = Net(input_dim, output_dim, layers, loss=self.loss_type)
+        if init_model is None:
+            self.dnn_model = init_instance_by_config({"class": pt_model_uri, "kwargs": pt_model_kwargs})
+
+            if self.data_parall:
+                self.dnn_model = DataParallel(self.dnn_model).to(self.device)
+        else:
+            self.dnn_model = init_model
+
         self.logger.info("model:\n{:}".format(self.dnn_model))
         self.logger.info("model size: {:.4f} MB".format(count_parameters(self.dnn_model)))
 
@@ -137,19 +155,24 @@ class DNNModelPytorch(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,
-        )
+        if scheduler == "default":
+            # 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,
+            )
+        elif scheduler is None:
+            self.scheduler = None
+        else:
+            self.scheduler = scheduler(optimizer=self.train_optimizer)
 
         self.fitted = False
         self.dnn_model.to(self.device)
@@ -166,40 +189,44 @@ class DNNModelPytorch(Model):
         save_path=None,
         reweighter=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"]
+        has_valid = "valid" in dataset.segments
+        segments = ["train", "valid"]
+        vars = ["x", "y", "w"]
+        all_df = defaultdict(dict)  # x_train, x_valid y_train, y_valid w_train, w_valid
+        all_t = defaultdict(dict)  # tensors
+        for seg in segments:
+            if seg in dataset.segments:
+                # df_train df_valid
+                df = dataset.prepare(
+                    seg, col_set=["feature", "label"], data_key=self.valid_key if seg == "valid" else DataHandlerLP.DK_L
+                )
+                all_df["x"][seg] = df["feature"]
+                all_df["y"][seg] = df["label"]
+                if reweighter is None:
+                    all_df["w"][seg] = pd.DataFrame(np.ones_like(all_df["y"][seg].values), index=df.index)
+                elif isinstance(reweighter, Reweighter):
+                    all_df["w"][seg] = pd.DataFrame(reweighter.reweight(df))
+                else:
+                    raise ValueError("Unsupported reweighter type.")
 
-        if reweighter is None:
-            w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
-            w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
-        elif isinstance(reweighter, Reweighter):
-            w_train = pd.DataFrame(reweighter.reweight(df_train))
-            w_valid = pd.DataFrame(reweighter.reweight(df_valid))
-        else:
-            raise ValueError("Unsupported reweighter type.")
+                # get tensors
+                for v in vars:
+                    all_t[v][seg] = torch.from_numpy(all_df[v][seg].values).float()
+                    # if seg == "valid": # accelerate the eval of validation
+                    all_t[v][seg] = all_t[v][seg].to(self.device)  # This will consume a lot of memory !!!!
+
+                evals_result[seg] = []
 
         save_path = get_or_create_path(save_path)
         stop_steps = 0
         train_loss = 0
         best_loss = np.inf
-        evals_result["train"] = []
-        evals_result["valid"] = []
         # train
         self.logger.info("training...")
         self.fitted = True
         # return
         # prepare training data
-        x_train_values = torch.from_numpy(x_train.values).float()
-        y_train_values = torch.from_numpy(y_train.values).float()
-        w_train_values = torch.from_numpy(w_train.values).float()
-        train_num = y_train_values.shape[0]
-        # prepare validation data
-        x_val_auto = torch.from_numpy(x_valid.values).float().to(self.device)
-        y_val_auto = torch.from_numpy(y_valid.values).float().to(self.device)
-        w_val_auto = torch.from_numpy(w_valid.values).float().to(self.device)
+        train_num = all_t["y"]["train"].shape[0]
 
         for step in range(1, self.max_steps + 1):
             if stop_steps >= self.early_stop_rounds:
@@ -210,9 +237,9 @@ class DNNModelPytorch(Model):
             self.dnn_model.train()
             self.train_optimizer.zero_grad()
             choice = np.random.choice(train_num, self.batch_size)
-            x_batch_auto = x_train_values[choice].to(self.device)
-            y_batch_auto = y_train_values[choice].to(self.device)
-            w_batch_auto = w_train_values[choice].to(self.device)
+            x_batch_auto = all_t["x"]["train"][choice].to(self.device)
+            y_batch_auto = all_t["y"]["train"][choice].to(self.device)
+            w_batch_auto = all_t["w"]["train"][choice].to(self.device)
 
             # forward
             preds = self.dnn_model(x_batch_auto)
@@ -226,44 +253,84 @@ class DNNModelPytorch(Model):
             train_loss += loss.val
             # for evert `eval_steps` steps or at the last steps, we will evaluate the model.
             if step % self.eval_steps == 0 or step == self.max_steps:
-                stop_steps += 1
-                train_loss /= self.eval_steps
+                if has_valid:
+                    stop_steps += 1
+                    train_loss /= self.eval_steps
 
-                with torch.no_grad():
-                    self.dnn_model.eval()
-                    loss_val = AverageMeter()
+                    with torch.no_grad():
+                        self.dnn_model.eval()
 
-                    # forward
-                    preds = self.dnn_model(x_val_auto)
-                    cur_loss_val = self.get_loss(preds, w_val_auto, y_val_auto, self.loss_type)
-                    loss_val.update(cur_loss_val.item())
-                R.log_metrics(val_loss=loss_val.val, step=step)
-                if verbose:
-                    self.logger.info(
-                        "[Step {}]: 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:
+                        # forward
+                        preds = self._nn_predict(all_t["x"]["valid"], return_cpu=False)
+                        cur_loss_val = self.get_loss(preds, all_t["w"]["valid"], all_t["y"]["valid"], self.loss_type)
+                        loss_val = cur_loss_val.item()
+                        metric_val = (
+                            self.get_metric(
+                                preds.reshape(-1), all_t["y"]["valid"].reshape(-1), all_df["x"]["valid"].index
+                            )
+                            .detach()
+                            .cpu()
+                            .numpy()
+                            .item()
+                        )
+                        R.log_metrics(val_loss=loss_val, step=step)
+                        R.log_metrics(val_metric=metric_val, step=step)
+
+                        if self.eval_train_metric:
+                            metric_train = (
+                                self.get_metric(
+                                    self._nn_predict(all_t["x"]["train"], return_cpu=False),
+                                    all_t["y"]["train"].reshape(-1),
+                                    all_df["x"]["train"].index,
+                                )
+                                .detach()
+                                .cpu()
+                                .numpy()
+                                .item()
+                            )
+                            R.log_metrics(train_metric=metric_train, step=step)
+                        else:
+                            metric_train = -1
                     if verbose:
                         self.logger.info(
-                            "\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
-                                best_loss, loss_val.val
-                            )
+                            f"[Step {step}]: train_loss {train_loss:.6f}, valid_loss {loss_val:.6f}, train_metric {metric_train:.6f}, valid_metric {metric_val:.6f}"
                         )
-                    best_loss = loss_val.val
-                    stop_steps = 0
-                    torch.save(self.dnn_model.state_dict(), save_path)
-                train_loss = 0
-                # update learning rate
-                self.scheduler.step(cur_loss_val)
+                    evals_result["train"].append(train_loss)
+                    evals_result["valid"].append(loss_val)
+                    if loss_val < best_loss:
+                        if verbose:
+                            self.logger.info(
+                                "\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
+                                    best_loss, loss_val
+                                )
+                            )
+                        best_loss = loss_val
+                        self.best_step = step
+                        R.log_metrics(best_step=self.best_step, step=step)
+                        stop_steps = 0
+                        torch.save(self.dnn_model.state_dict(), save_path)
+                    train_loss = 0
+                    # update learning rate
+                    if self.scheduler is not None:
+                        auto_filter_kwargs(self.scheduler.step, warning=False)(metrics=cur_loss_val, epoch=step)
+                    R.log_metrics(lr=self.get_lr(), step=step)
+                else:
+                    # retraining mode
+                    if self.scheduler is not None:
+                        self.scheduler.step(epoch=step)
 
-        # restore the optimal parameters after training
-        self.dnn_model.load_state_dict(torch.load(save_path, map_location=self.device))
+        if has_valid:
+            # restore the optimal parameters after training
+            self.dnn_model.load_state_dict(torch.load(save_path, map_location=self.device))
         if self.use_gpu:
             torch.cuda.empty_cache()
 
+    def get_lr(self):
+        assert len(self.train_optimizer.param_groups) == 1
+        return self.train_optimizer.param_groups[0]["lr"]
+
     def get_loss(self, pred, w, target, loss_type):
+        pred, w, target = pred.reshape(-1), w.reshape(-1), target.reshape(-1)
         if loss_type == "mse":
             sqr_loss = torch.mul(pred - target, pred - target)
             loss = torch.mul(sqr_loss, w).mean()
@@ -274,15 +341,54 @@ class DNNModelPytorch(Model):
         else:
             raise NotImplementedError("loss {} is not supported!".format(loss_type))
 
+    def get_metric(self, pred, target, index):
+        # NOTE: the order of the index must follow <datetime, instrument> sorted order
+        return -ICLoss()(pred, target, index)  # pylint: disable=E1130
+
+    def _nn_predict(self, data, return_cpu=True):
+        """Reusing predicting NN.
+        Scenarios
+        1) test inference (data may come from CPU and expect the output data is on CPU)
+        2) evaluation on training (data may come from GPU)
+        """
+        if isinstance(data, torch.Tensor) and data.device.type != "cpu":
+            # GPU data
+            # CUDA data don't support pin_memory and multi-processing workers
+            num_workers = 0
+            pin_memory = False
+        else:
+            # CPU data
+            if not isinstance(data, torch.Tensor):
+                if isinstance(data, pd.DataFrame):
+                    data = data.values
+            # else: CPU Tensor
+            num_workers = 8
+            pin_memory = True
+        data_loader = DataLoader(
+            data,
+            sampler=SequentialSampler(data),
+            batch_size=self.batch_size,
+            drop_last=False,
+            num_workers=num_workers,
+            pin_memory=pin_memory,
+        )
+        preds = []
+        self.dnn_model.eval()
+        with torch.no_grad():
+            for x in data_loader:
+                preds.append(self.dnn_model(x.to(self.device)).detach().reshape(-1))
+        if return_cpu:
+            preds = np.concatenate([pr.cpu().numpy() for pr in preds])
+        else:
+            preds = torch.cat(preds, axis=0)
+        return preds
+
     def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
         if not self.fitted:
             raise ValueError("model is not fitted yet!")
         x_test_pd = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
-        x_test = torch.from_numpy(x_test_pd.values).float().to(self.device)
-        self.dnn_model.eval()
-        with torch.no_grad():
-            preds = self.dnn_model(x_test).detach().cpu().numpy()
-        return pd.Series(np.squeeze(preds), index=x_test_pd.index)
+        preds = self._nn_predict(x_test_pd)
+        return pd.Series(preds.reshape(-1), index=x_test_pd.index)
 
     def save(self, filename, **kwargs):
         with save_multiple_parts_file(filename) as model_dir:
@@ -322,16 +428,22 @@ class AverageMeter:
 
 
 class Net(nn.Module):
-    def __init__(self, input_dim, output_dim, layers=(256, 512, 768, 512, 256, 128, 64), loss="mse"):
+    def __init__(self, input_dim, output_dim=1, layers=(256,), act="LeakyReLU"):
         super(Net, self).__init__()
+
         layers = [input_dim] + list(layers)
         dnn_layers = []
         drop_input = nn.Dropout(0.05)
         dnn_layers.append(drop_input)
-        hidden_units = None
+        hidden_units = input_dim
         for i, (_input_dim, hidden_units) in enumerate(zip(layers[:-1], layers[1:])):
             fc = nn.Linear(_input_dim, hidden_units)
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=False)
+            if act == "LeakyReLU":
+                activation = nn.LeakyReLU(negative_slope=0.1, inplace=False)
+            elif act == "SiLU":
+                activation = nn.SiLU()
+            else:
+                raise NotImplementedError(f"This type of input is not supported")
             bn = nn.BatchNorm1d(hidden_units)
             seq = nn.Sequential(fc, bn, activation)
             dnn_layers.append(seq)

qlib/model/utils.py

@@ -13,3 +13,14 @@ class ConcatDataset(Dataset):
 
     def __len__(self):
         return min(len(d) for d in self.datasets)
+
+
+class IndexSampler:
+    def __init__(self, sampler):
+        self.sampler = sampler
+
+    def __getitem__(self, i: int):
+        return self.sampler[i], i
+
+    def __len__(self):
+        return len(self.sampler)

qlib/utils/__init__.py

@@ -823,7 +823,7 @@ def fill_placeholder(config: dict, config_extend: dict):
     return config
 
 
-def auto_filter_kwargs(func: Callable) -> Callable:
+def auto_filter_kwargs(func: Callable, warning=True) -> Callable:
     """
     this will work like a decoration function
 
@@ -846,7 +846,8 @@ def auto_filter_kwargs(func: Callable) -> Callable:
         for k, v in kwargs.items():
             # if `func` don't accept variable keyword arguments like `**kwargs` and have not according named arguments
             if spec.varkw is None and k not in spec.args:
-                log.warning(f"The parameter `{k}` with value `{v}` is ignored.")
+                if warning:
+                    log.warning(f"The parameter `{k}` with value `{v}` is ignored.")
             else:
                 new_kwargs[k] = v
         return func(*args, **new_kwargs)

qlib/workflow/utils.py

@@ -20,6 +20,9 @@ def experiment_exit_handler():
     The `atexit` handler should be put in the last, since, as long as the program ends, it will be called.
     Thus, if any exception or user interuption occurs beforehead, we should handle them first. Once `R` is
     ended, another call of `R.end_exp` will not take effect.
+
+    Limitations:
+    - If pdb is used in the your program, excepthook will not be triggered when it ends.  The status will be finished
     """
     sys.excepthook = experiment_exception_hook  # handle uncaught exception
     atexit.register(R.end_exp, recorder_status=Recorder.STATUS_FI)  # will not take effect if experiment ends

tests/test_dataset.py

@@ -79,23 +79,14 @@ class TestDataset(TestAutoData):
             # 3) get both index and data
             # NOTE: We don't want to reply on pytorch, so this test can't be included. It is just a example
             from torch.utils.data import DataLoader
-
-            class IdxSampler:
-                def __init__(self, sampler):
-                    self.sampler = sampler
-
-                def __getitem__(self, i: int):
-                    return self.sampler[i], i
-
-                def __len__(self):
-                    return len(self.sampler)
+            from qlib.model.utils import IndexSampler
 
             i = len(tsds) - 1
             idx = tsds.get_index()
             tsds[i]
             idx[i]
 
-            s_w_i = IdxSampler(tsds)
+            s_w_i = IndexSampler(tsds)
             test_loader = DataLoader(s_w_i)
 
             s_w_i[3]