fix_typo (#790)

Signed-off-by: unknown <lv.linlang@qq.com>

qlib/contrib/data/utils/sepdf.py

@@ -18,8 +18,8 @@ class SepDataFrame:
     """
     (Sep)erate DataFrame
     We usually concat multiple dataframe to be processed together(Such as feature, label, weight, filter).
-    However, they are usally be used seperately at last.
-    This will result in extra cost for concating and spliting data(reshaping and copying data in the memory is very expensive)
+    However, they are usually be used separately at last.
+    This will result in extra cost for concatenating and splitting data(reshaping and copying data in the memory is very expensive)
 
     SepDataFrame tries to act like a DataFrame whose column with multiindex
     """

qlib/contrib/evaluate_portfolio.py

@@ -38,11 +38,11 @@ def _get_position_value_from_df(evaluate_date, position, close_data_df):
 def get_position_value(evaluate_date, position):
     """sum of close*amount
 
-    get value of postion
+    get value of position
 
     use close price
 
-        postions:
+        positions:
         {
             Timestamp('2016-01-05 00:00:00'):
             {

qlib/contrib/model/highfreq_gdbt_model.py

@@ -56,7 +56,7 @@ class HFLGBModel(ModelFT, LightGBMFInt):
 
     def hf_signal_test(self, dataset: DatasetH, threhold=0.2):
         """
-        Test the sigal in high frequency test set
+        Test the signal in high frequency test set
         """
         if self.model == None:
             raise ValueError("Model hasn't been trained yet")

qlib/contrib/model/pytorch_tabnet.py

@@ -446,7 +446,7 @@ class TabNet(nn.Module):
         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_shared: numbr of shared steps in feature transformer(optional)
             n_ind: number of independent steps in feature transformer
             n_steps: number of steps of pass through tabbet
             relax coefficient:
@@ -479,7 +479,7 @@ class TabNet(nn.Module):
         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
+            out += F.relu(x_te[:, : self.n_d])  # split the feature from feat_transformer
             x_a = x_te[:, self.n_d :]
             sparse_loss.append(l)
         return self.fc(out), sum(sparse_loss)

qlib/contrib/model/pytorch_tra.py

@@ -232,7 +232,7 @@ class TRAModel(Model):
                     choice_all.append(pd.DataFrame(choice.detach().cpu().numpy(), index=index))
                 decay = self.rho ** (self.global_step // 100)  # decay every 100 steps
                 lamb = 0 if is_pretrain else self.lamb * decay
-                reg = prob.log().mul(P).sum(dim=1).mean()  # train router to predict OT assignment
+                reg = prob.log().mul(P).sum(dim=1).mean()  # train router to predict TO assignment
                 if self._writer is not None and not is_pretrain:
                     self._writer.add_scalar("training/router_loss", -reg.item(), self.global_step)
                     self._writer.add_scalar("training/reg_loss", loss.item(), self.global_step)
@@ -663,7 +663,7 @@ class TRA(nn.Module):
 
     """Temporal Routing Adaptor (TRA)
 
-    TRA takes historical prediction erros & latent representation as inputs,
+    TRA takes historical prediction errors & latent representation as inputs,
     then routes the input sample to a specific predictor for training & inference.
 
     Args:

qlib/contrib/model/pytorch_utils.py

@@ -33,5 +33,5 @@ def count_parameters(models_or_parameters, unit="m"):
     elif unit == "gb" or unit == "g":
         counts /= 2 ** 30
     elif unit is not None:
-        raise ValueError("Unknow unit: {:}".format(unit))
+        raise ValueError("Unknown unit: {:}".format(unit))
     return counts

qlib/contrib/online/utils.py

@@ -36,7 +36,7 @@ def save_instance(instance, file_path):
     save(dump) an instance to a pickle file
         Parameter
             instance :
-                data to te dumped
+                data to be dumped
             file_path : string / pathlib.Path()
                 path of file to be dumped
     """

qlib/contrib/strategy/cost_control.py

@@ -47,7 +47,7 @@ class SoftTopkStrategy(WeightStrategyBase):
         Return the proportion of your total value you will used in investment.
         Dynamically risk_degree will result in Market timing
         """
-        # It will use 95% amoutn of your total value by default
+        # It will use 95% amount of your total value by default
         return self.risk_degree
 
     def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time):

qlib/contrib/strategy/rule_strategy.py

@@ -24,7 +24,7 @@ class TWAPStrategy(BaseStrategy):
 
     NOTE:
         - This TWAP strategy will celling round when trading. This will make the TWAP trading strategy produce the order
-          ealier when the total trade unit of amount is less than the trading step
+          earlier when the total trade unit of amount is less than the trading step
     """
 
     def reset(self, outer_trade_decision: BaseTradeDecision = None, **kwargs):
@@ -43,8 +43,8 @@ class TWAPStrategy(BaseStrategy):
     def generate_trade_decision(self, execute_result=None):
         # NOTE:  corner cases!!!
         # - If using upperbound round, please don't sell the amount which should in next step
-        #   - the coordinate of the amount between steps is hard to be dealed between steps in the same level. It
-        #     is easier to be dealed in upper steps
+        #   - the coordinate of the amount between steps is hard to be dealt between steps in the same level. It
+        #     is easier to be dealt in upper steps
 
         # strategy is not available. Give an empty decision
         if len(self.outer_trade_decision.get_decision()) == 0:

qlib/contrib/strategy/signal_strategy.py

@@ -69,7 +69,7 @@ class BaseSignalStrategy(BaseStrategy):
         Return the proportion of your total value you will used in investment.
         Dynamically risk_degree will result in Market timing.
         """
-        # It will use 95% amoutn of your total value by default
+        # It will use 95% amount of your total value by default
         return self.risk_degree
 
 

qlib/contrib/tuner/tuner.py

@@ -90,7 +90,7 @@ class QLibTuner(Tuner):
 
     def objective(self, params):
 
-        # 1. Setup an config for a spcific estimator process
+        # 1. Setup an config for a specific estimator process
         estimator_path = self.setup_estimator_config(params)
         self.logger.info("Searching params: {} ".format(params))