pylint code refine & Fix nested example (#848)

* refine code by CI

* fix argument error

* fix nested eample

examples/nested_decision_execution/workflow.py

@@ -155,6 +155,8 @@ class NestedDecisionExecutionWorkflow:
         },
     }
 
+    exp_name = "nested"
+
     port_analysis_config = {
         "executor": {
             "class": "NestedExecutor",
@@ -230,7 +232,7 @@ class NestedDecisionExecutionWorkflow:
         qlib.init(provider_uri=provider_uri_map, dataset_cache=None, expression_cache=None)
 
     def _train_model(self, model, dataset):
-        with R.start(experiment_name="train"):
+        with R.start(experiment_name=self.exp_name):
             R.log_params(**flatten_dict(self.task))
             model.fit(dataset)
             R.save_objects(**{"params.pkl": model})
@@ -257,7 +259,7 @@ class NestedDecisionExecutionWorkflow:
         self.port_analysis_config["strategy"] = strategy_config
         self.port_analysis_config["backtest"]["benchmark"] = self.benchmark
 
-        with R.start(experiment_name="backtest"):
+        with R.start(experiment_name=self.exp_name, resume=True):
             recorder = R.get_recorder()
             par = PortAnaRecord(
                 recorder,
@@ -382,7 +384,7 @@ class NestedDecisionExecutionWorkflow:
         }
         pa_conf["backtest"]["benchmark"] = self.benchmark
 
-        with R.start(experiment_name="backtest"):
+        with R.start(experiment_name=self.exp_name, resume=True):
             recorder = R.get_recorder()
             par = PortAnaRecord(recorder, pa_conf)
             par.generate()

qlib/backtest/exchange.py

@@ -536,7 +536,7 @@ class Exchange:
             deal_amount = self.get_real_deal_amount(current_amount, target_amount, factor)
             if deal_amount == 0:
                 continue
-            elif deal_amount > 0:
+            if deal_amount > 0:
                 # buy stock
                 buy_order_list.append(
                     Order(
@@ -687,9 +687,7 @@ class Exchange:
         orig_deal_amount = order.deal_amount
         order.deal_amount = max(min(vol_limit_min, orig_deal_amount), 0)
         if vol_limit_min < orig_deal_amount:
-            self.logger.debug(
-                f"Order clipped due to volume limitation: {order}, {[(vol, rule) for vol, rule in zip(vol_limit_num, vol_limit)]}"
-            )
+            self.logger.debug(f"Order clipped due to volume limitation: {order}, {list(zip(vol_limit_num, vol_limit))}")
 
     def _get_buy_amount_by_cash_limit(self, trade_price, cash, cost_ratio):
         """return the real order amount after cash limit for buying.

qlib/backtest/executor.py

@@ -194,7 +194,7 @@ class BaseExecutor:
         return return_value.get("execute_result")
 
     @abstractclassmethod
-    def _collect_data(self, trade_decision: BaseTradeDecision, level: int = 0) -> Tuple[List[object], dict]:
+    def _collect_data(cls, trade_decision: BaseTradeDecision, level: int = 0) -> Tuple[List[object], dict]:
         """
         Please refer to the doc of collect_data
         The only difference between `_collect_data` and `collect_data` is that some common steps are moved into

qlib/backtest/position.py

@@ -20,7 +20,7 @@ class BasePosition:
     Please refer to the `Position` class for the position
     """
 
-    def __init__(self, cash=0.0, *args, **kwargs):
+    def __init__(self, *args, cash=0.0, **kwargs):
         self._settle_type = self.ST_NO
 
     def skip_update(self) -> bool:

qlib/backtest/profit_attribution.py

@@ -156,16 +156,16 @@ def decompose_portofolio(stock_weight_df, stock_group_df, stock_ret_df):
     group_weight, stock_weight_in_group = decompose_portofolio_weight(stock_weight_df, stock_group_df)
 
     group_ret = {}
-    for group_key in stock_weight_in_group:
-        stock_weight_in_group_start_date = min(stock_weight_in_group[group_key].index)
-        stock_weight_in_group_end_date = max(stock_weight_in_group[group_key].index)
+    for group_key, val in stock_weight_in_group.items():
+        stock_weight_in_group_start_date = min(val.index)
+        stock_weight_in_group_end_date = max(val.index)
 
         temp_stock_ret_df = stock_ret_df[
             (stock_ret_df.index >= stock_weight_in_group_start_date)
             & (stock_ret_df.index <= stock_weight_in_group_end_date)
         ]
 
-        group_ret[group_key] = (temp_stock_ret_df * stock_weight_in_group[group_key]).sum(axis=1)
+        group_ret[group_key] = (temp_stock_ret_df * val).sum(axis=1)
         # If no weight is assigned, then the return of group will be np.nan
         group_ret[group_key][group_weight[group_key] == 0.0] = np.nan
 

qlib/backtest/report.py

@@ -212,7 +212,8 @@ class PortfolioMetrics:
                 path: str/ pathlib.Path()
         """
         path = pathlib.Path(path)
-        r = pd.read_csv(open(path, "rb"), index_col=0)
+        with path.open("rb") as f:
+            r = pd.read_csv(f, index_col=0)
         r.index = pd.DatetimeIndex(r.index)
 
         index = r.index

qlib/backtest/utils.py

@@ -205,10 +205,7 @@ class BaseInfrastructure:
             warnings.warn(f"infra {infra_name} is not found!")
 
     def has(self, infra_name):
-        if infra_name in self.get_support_infra() and hasattr(self, infra_name):
-            return True
-        else:
-            return False
+        return infra_name in self.get_support_infra() and hasattr(self, infra_name)
 
     def update(self, other):
         support_infra = other.get_support_infra()

qlib/contrib/data/dataset.py

@@ -63,9 +63,7 @@ def _get_date_parse_fn(target):
         get_date_parse_fn('20120101')('2017-01-01') => '20170101'
         get_date_parse_fn(20120101)('2017-01-01') => 20170101
     """
-    if isinstance(target, pd.Timestamp):
-        _fn = lambda x: pd.Timestamp(x)  # Timestamp('2020-01-01')
-    elif isinstance(target, int):
+    if isinstance(target, int):
         _fn = lambda x: int(str(x).replace("-", "")[:8])  # 20200201
     elif isinstance(target, str) and len(target) == 8:
         _fn = lambda x: str(x).replace("-", "")[:8]  # '20200201'
@@ -158,7 +156,7 @@ class MTSDatasetH(DatasetH):
         try:
             df = self.handler._learn.copy()  # use copy otherwise recorder will fail
             # FIXME: currently we cannot support switching from `_learn` to `_infer` for inference
-        except:
+        except Exception:
             warnings.warn("cannot access `_learn`, will load raw data")
             df = self.handler._data.copy()
         df.index = df.index.swaplevel()

qlib/contrib/evaluate.py

@@ -371,7 +371,7 @@ def long_short_backtest(
 
 def t_run():
     pred_FN = "./check_pred.csv"
-    pred = pd.read_csv(pred_FN)
+    pred: pd.DataFrame = pd.read_csv(pred_FN)
     pred["datetime"] = pd.to_datetime(pred["datetime"])
     pred = pred.set_index([pred.columns[0], pred.columns[1]])
     pred = pred.iloc[:9000]

qlib/contrib/model/pytorch_adarnn.py

@@ -554,7 +554,7 @@ class AdaRNN(nn.Module):
         return fc_out
 
 
-class TransferLoss(object):
+class TransferLoss:
     def __init__(self, loss_type="cosine", input_dim=512):
         """
         Supported loss_type: mmd(mmd_lin), mmd_rbf, coral, cosine, kl, js, mine, adv

qlib/contrib/model/pytorch_nn.py

@@ -98,7 +98,6 @@ class DNNModelPytorch(Model):
             "\nlr_decay_steps : {}"
             "\noptimizer : {}"
             "\nloss_type : {}"
-            "\neval_steps : {}"
             "\nseed : {}"
             "\ndevice : {}"
             "\nuse_GPU : {}"
@@ -113,7 +112,6 @@ class DNNModelPytorch(Model):
                 lr_decay_steps,
                 optimizer,
                 loss,
-                eval_steps,
                 seed,
                 self.device,
                 self.use_gpu,
@@ -331,8 +329,8 @@ class Net(nn.Module):
         dnn_layers = []
         drop_input = nn.Dropout(0.05)
         dnn_layers.append(drop_input)
-        for i, (input_dim, hidden_units) in enumerate(zip(layers[:-1], layers[1:])):
-            fc = nn.Linear(input_dim, hidden_units)
+        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)
             bn = nn.BatchNorm1d(hidden_units)
             seq = nn.Sequential(fc, bn, activation)

qlib/contrib/model/pytorch_tra.py

@@ -19,7 +19,7 @@ import torch.nn.functional as F
 
 try:
     from torch.utils.tensorboard import SummaryWriter
-except:
+except ImportError:
     SummaryWriter = None
 
 from tqdm import tqdm
@@ -257,7 +257,7 @@ class TRAModel(Model):
             total_loss += loss.item()
             total_count += 1
 
-        if self.use_daily_transport and len(P_all):
+        if self.use_daily_transport and len(P_all) > 0:
             P_all = pd.concat(P_all, axis=0)
             prob_all = pd.concat(prob_all, axis=0)
             choice_all = pd.concat(choice_all, axis=0)

qlib/contrib/report/graph.py

@@ -15,7 +15,6 @@ from plotly.figure_factory import create_distplot
 
 
 class BaseGraph:
-    """ """
 
     _name = None
 
@@ -297,8 +296,8 @@ class SubplotsGraph:
 
         :return:
         """
-        self._sub_graph_data = list()
-        self._subplot_titles = list()
+        self._sub_graph_data = []
+        self._subplot_titles = []
 
         for i, column_name in enumerate(self._df.columns):
             row = math.ceil((i + 1) / self.__cols)

qlib/data/dataset/__init__.py

@@ -594,7 +594,7 @@ class TSDatasetH(DatasetH):
         flt_kwargs = deepcopy(kwargs)
         if flt_col is not None:
             flt_kwargs["col_set"] = flt_col
-            flt_data = self._prepare_seg(ext_slice, **flt_kwargs)
+            flt_data = super()._prepare_seg(ext_slice, **flt_kwargs)
             assert len(flt_data.columns) == 1
         else:
             flt_data = None

qlib/data/ops.py

@@ -1407,14 +1407,14 @@ class PairRolling(ExpressionOps):
         )
 
     def get_extended_window_size(self):
+        ll, lr = self.feature_left.get_extended_window_size()
+        rl, rr = self.feature_right.get_extended_window_size()
         if self.N == 0:
             get_module_logger(self.__class__.__name__).warning(
                 "The PairRolling(ATTR, 0) will not be accurately calculated"
             )
-            return self.feature.get_extended_window_size()
+            return -np.inf, max(lr, rr)
         else:
-            ll, lr = self.feature_left.get_extended_window_size()
-            rl, rr = self.feature_right.get_extended_window_size()
             return max(ll, rl) + self.N - 1, max(lr, rr)
 
 

qlib/data/storage/file_storage.py

@@ -120,7 +120,7 @@ class FileCalendarStorage(FileStorageMixin, CalendarStorage):
         # If cache is enabled, then return cache directly
         if self.enable_read_cache:
             key = "orig_file" + str(self.uri)
-            if not key in H["c"]:
+            if key not in H["c"]:
                 H["c"][key] = self._read_calendar()
             _calendar = H["c"][key]
         else:

qlib/model/riskmodel/structured.py

@@ -50,7 +50,7 @@ class StructuredCovEstimator(RiskModel):
             num_factors (int): number of components to keep.
             kwargs: see `RiskModel` for more information
         """
-        if "nan_option" in kwargs.keys():
+        if "nan_option" in kwargs:
             assert kwargs["nan_option"] in [self.DEFAULT_NAN_OPTION], "nan_option={} is not supported".format(
                 kwargs["nan_option"]
             )

qlib/model/trainer.py

@@ -254,21 +254,21 @@ class TrainerR(Trainer):
             recs.append(rec)
         return recs
 
-    def end_train(self, recs: list, **kwargs) -> List[Recorder]:
+    def end_train(self, models: list, **kwargs) -> List[Recorder]:
         """
         Set STATUS_END tag to the recorders.
 
         Args:
-            recs (list): a list of trained recorders.
+            models (list): a list of trained recorders.
 
         Returns:
             List[Recorder]: the same list as the param.
         """
-        if isinstance(recs, Recorder):
-            recs = [recs]
-        for rec in recs:
+        if isinstance(models, Recorder):
+            models = [models]
+        for rec in models:
             rec.set_tags(**{self.STATUS_KEY: self.STATUS_END})
-        return recs
+        return models
 
 
 class DelayTrainerR(TrainerR):
@@ -289,13 +289,13 @@ class DelayTrainerR(TrainerR):
         self.end_train_func = end_train_func
         self.delay = True
 
-    def end_train(self, recs, end_train_func=None, experiment_name: str = None, **kwargs) -> List[Recorder]:
+    def end_train(self, models, end_train_func=None, experiment_name: str = None, **kwargs) -> List[Recorder]:
         """
         Given a list of Recorder and return a list of trained Recorder.
         This class will finish real data loading and model fitting.
 
         Args:
-            recs (list): a list of Recorder, the tasks have been saved to them
+            models (list): a list of Recorder, the tasks have been saved to them
             end_train_func (Callable, optional): the end_train method which needs at least `recorder`s and `experiment_name`. Defaults to None for using self.end_train_func.
             experiment_name (str): the experiment name, None for use default name.
             kwargs: the params for end_train_func.
@@ -303,18 +303,18 @@ class DelayTrainerR(TrainerR):
         Returns:
             List[Recorder]: a list of Recorders
         """
-        if isinstance(recs, Recorder):
-            recs = [recs]
+        if isinstance(models, Recorder):
+            models = [models]
         if end_train_func is None:
             end_train_func = self.end_train_func
         if experiment_name is None:
             experiment_name = self.experiment_name
-        for rec in recs:
+        for rec in models:
             if rec.list_tags()[self.STATUS_KEY] == self.STATUS_END:
                 continue
             end_train_func(rec, experiment_name, **kwargs)
             rec.set_tags(**{self.STATUS_KEY: self.STATUS_END})
-        return recs
+        return models
 
 
 class TrainerRM(Trainer):

qlib/tests/data.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 
 import re
+import sys
 import qlib
 import shutil
 import zipfile
@@ -101,7 +102,7 @@ class GetData:
                 f"\nAre you sure you want to delete, yes(Y/y), no (N/n):"
             )
             if str(flag) not in ["Y", "y"]:
-                exit()
+                sys.exit()
             for _p in rm_dirs:
                 logger.warning(f"delete: {_p}")
                 shutil.rmtree(_p)

qlib/utils/__init__.py

@@ -654,16 +654,13 @@ def exists_qlib_data(qlib_dir):
 def check_qlib_data(qlib_config):
     inst_dir = Path(qlib_config["provider_uri"]).joinpath("instruments")
     for _p in inst_dir.glob("*.txt"):
-        try:
-            assert len(pd.read_csv(_p, sep="\t", nrows=0, header=None).columns) == 3, (
-                f"\nThe {str(_p.resolve())} of qlib data is not equal to 3 columns:"
-                f"\n\tIf you are using the data provided by qlib: "
-                f"https://qlib.readthedocs.io/en/latest/component/data.html#qlib-format-dataset"
-                f"\n\tIf you are using your own data, please dump the data again: "
-                f"https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format"
-            )
-        except AssertionError:
-            raise
+        assert len(pd.read_csv(_p, sep="\t", nrows=0, header=None).columns) == 3, (
+            f"\nThe {str(_p.resolve())} of qlib data is not equal to 3 columns:"
+            f"\n\tIf you are using the data provided by qlib: "
+            f"https://qlib.readthedocs.io/en/latest/component/data.html#qlib-format-dataset"
+            f"\n\tIf you are using your own data, please dump the data again: "
+            f"https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format"
+        )
 
 
 def lazy_sort_index(df: pd.DataFrame, axis=0) -> pd.DataFrame:

qlib/utils/exceptions.py

@@ -4,8 +4,7 @@
 
 # Base exception class
 class QlibException(Exception):
-    def __init__(self, message):
-        super(QlibException, self).__init__(message)
+    pass
 
 
 class RecorderInitializationError(QlibException):

qlib/utils/paral.py

@@ -80,8 +80,7 @@ class AsyncCaller:
             data = self._q.get()
             if data == self.STOP_MARK:
                 break
-            else:
-                data()
+            data()
 
     def __call__(self, func, *args, **kwargs):
         self._q.put(partial(func, *args, **kwargs))

qlib/utils/resam.py

@@ -187,7 +187,7 @@ def resam_ts_data(
     if isinstance(feature.index, pd.MultiIndex):
         if callable(method):
             method_func = method
-            return feature.groupby(level="instrument").apply(lambda x: method_func(x, **method_kwargs))
+            return feature.groupby(level="instrument").apply(method_func, **method_kwargs)
         elif isinstance(method, str):
             return getattr(feature.groupby(level="instrument"), method)(**method_kwargs)
     else:

qlib/workflow/__init__.py

@@ -416,6 +416,11 @@ class QlibRecorder:
             # Case 5
             recorder = R.get_recorder(recorder_id='2e7a4efd66574fa49039e00ffaefa99d', experiment_name='test')
 
+
+        Here are some things users may concern
+        - Q: What recorder will it return if multiple recorder meets the query (e.g. query with experiment_name)
+        - A: If mlflow backend is used, then the recorder with the latest `start_time` will be returned. Because MLflow's `search_runs` function guarantee it
+
         Parameters
         ----------
         recorder_id : str

qlib/workflow/exp.py

@@ -287,6 +287,9 @@ class MLflowExperiment(Experiment):
         """
         Method for getting or creating a recorder. It will try to first get a valid recorder, if exception occurs, it will
         raise errors.
+
+        Quoting docs of search_runs from MLflow
+        > The default ordering is to sort by start_time DESC, then run_id.
         """
         assert (
             recorder_id is not None or recorder_name is not None

qlib/workflow/recorder.py

@@ -355,7 +355,7 @@ class MLflowRecorder(Recorder):
                 shutil.rmtree(Path(path).absolute().parent)
             return data
         except Exception as e:
-            raise LoadObjectError(message=str(e))
+            raise LoadObjectError(str(e))
 
     @AsyncCaller.async_dec(ac_attr="async_log")
     def log_params(self, **kwargs):