throw user error if user tries to load models but feeds the wrong features (while using PCA)

docs/freqai.md

@@ -313,6 +313,8 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$
 
 where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._
 
+![weight-factor](assets/weights_factor.png)
+
 Finally, `period` defines the offset used for the `labels`. In the present example,
 the user is asking for `labels` that are 24 candles in the future.
 

freqtrade/freqai/data_kitchen.py

@@ -477,6 +477,11 @@ class FreqaiDataKitchen:
             index=self.data_dictionary["train_features"].index,
         )
 
+        # keeping a copy of the non-transformed features so we can check for errors during
+        # model load from disk
+        self.data['training_features_list_raw'] = copy.deepcopy(self.training_features_list)
+        self.training_features_list = self.data_dictionary["train_features"].columns
+
         self.data_dictionary["test_features"] = pd.DataFrame(
             data=test_components,
             columns=["PC" + str(i) for i in range(0, n_keep_components)],
@@ -563,7 +568,8 @@ class FreqaiDataKitchen:
     def find_features(self, dataframe: DataFrame) -> list:
         column_names = dataframe.columns
         features = [c for c in column_names if '%' in c]
-        assert features, ("Could not find any features!")
+        if not features:
+            raise OperationalException("Could not find any features!")
         return features
 
     def check_if_pred_in_training_spaces(self) -> None:

freqtrade/freqai/freqai_interface.py

@@ -225,7 +225,11 @@ class IFreqaiModel(ABC):
     def check_if_feature_list_matches_strategy(self, dataframe: DataFrame,
                                                dh: FreqaiDataKitchen) -> None:
         strategy_provided_features = dh.find_features(dataframe)
-        if strategy_provided_features != dh.training_features_list:
+        if dh.data['training_features_list_raw']:
+            feature_list = dh.data['training_features_list_raw']
+        else:
+            feature_list = dh.training_features_list
+        if strategy_provided_features != feature_list:
             raise OperationalException("Trying to access pretrained model with `identifier` "
                                        "but found different features furnished by current strategy."
                                        "Change `identifer` to train from scratch, or ensure the"
@@ -254,7 +258,7 @@ class IFreqaiModel(ABC):
         # if self.feature_parameters["remove_outliers"]:
         #     dh.remove_outliers(predict=False)
 
-    def data_cleaning_predict(self, dh: FreqaiDataKitchen) -> None:
+    def data_cleaning_predict(self, dh: FreqaiDataKitchen, dataframe: DataFrame) -> None:
         """
         Base data cleaning method for predict.
         These functions each modify dh.do_predict, which is a dataframe with equal length
@@ -266,7 +270,7 @@ class IFreqaiModel(ABC):
         for buy signals.
         """
         if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
-            dh.pca_transform()
+            dh.pca_transform(dataframe)
 
         if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
             dh.use_SVM_to_remove_outliers(predict=True)

freqtrade/freqai/prediction_models/CatboostPredictionModel.py

@@ -71,7 +71,8 @@ class CatboostPredictionModel(IFreqaiModel):
         # optional additional data cleaning/analysis
         self.data_cleaning_train(dh)
 
-        logger.info(f'Training model on {len(dh.training_features_list)} features')
+        logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
+                    'features')
         logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
 
         model = self.fit(data_dictionary)
@@ -129,7 +130,7 @@ class CatboostPredictionModel(IFreqaiModel):
         dh.data_dictionary["prediction_features"] = filtered_dataframe
 
         # optional additional data cleaning/analysis
-        self.data_cleaning_predict(dh)
+        self.data_cleaning_predict(dh, filtered_dataframe)
 
         predictions = self.model.predict(dh.data_dictionary["prediction_features"])