More docstring changes

freqtrade/freqai/freqai_interface.py

@@ -92,11 +92,11 @@ class IFreqaiModel(ABC):
         Entry point to the FreqaiModel from a specific pair, it will train a new model if
         necessary before making the prediction.
 
-        :params:
-        :dataframe: Full dataframe coming from strategy - it contains entire
-        backtesting timerange + additional historical data necessary to train
+        :param dataframe: Full dataframe coming from strategy - it contains entire
+                           backtesting timerange + additional historical data necessary to train
         the model.
-        :metadata: pair metadata coming from strategy.
+        :param metadata: pair metadata coming from strategy.
+        :param strategy: Strategy to train on
         """
 
         self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)

freqtrade/freqai/prediction_models/BaseRegressionModel.py

@@ -32,10 +32,9 @@ class BaseRegressionModel(IFreqaiModel):
         """
         Filter the training data and train a model to it. Train makes heavy use of the datakitchen
         for storing, saving, loading, and analyzing the data.
-        :params:
-        :unfiltered_dataframe: Full dataframe for the current training period
-        :metadata: pair metadata from strategy.
-        :returns:
+        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :return:
         :model: Trained model which can be used to inference (self.predict)
         """
 

freqtrade/freqai/prediction_models/BaseTensorFlowModel.py

@@ -31,9 +31,8 @@ class BaseTensorFlowModel(IFreqaiModel):
         """
         Filter the training data and train a model to it. Train makes heavy use of the datakitchen
         for storing, saving, loading, and analyzing the data.
-        :params:
-        :unfiltered_dataframe: Full dataframe for the current training period
-        :metadata: pair metadata from strategy.
+        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
         :returns:
         :model: Trained model which can be used to inference (self.predict)
         """

freqtrade/freqai/prediction_models/CatboostPredictionModel.py

@@ -19,9 +19,8 @@ class CatboostPredictionModel(BaseRegressionModel):
     def fit(self, data_dictionary: Dict) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         train_data = Pool(

freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py

@@ -20,9 +20,8 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
     def fit(self, data_dictionary: Dict) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         cbr = CatBoostRegressor(

freqtrade/freqai/prediction_models/LightGBMPredictionModel.py

@@ -21,9 +21,8 @@ class LightGBMPredictionModel(BaseRegressionModel):
         Most regressors use the same function names and arguments e.g. user
         can drop in LGBMRegressor in place of CatBoostRegressor and all data
         management will be properly handled by Freqai.
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])

freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py

@@ -20,9 +20,8 @@ class LightGBMPredictionMultiModel(BaseRegressionModel):
     def fit(self, data_dictionary: Dict) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         lgb = LGBMRegressor(**self.model_training_parameters)