fix tests

docs/freqai-reinforcement-learning.md

@@ -197,4 +197,4 @@ tensorboard --logdir user_data/models/unique-id
 
 where `unique-id` is the `identifier` set in the `freqai` configuration file. 
 
-![tensorboard](assets/tensorboard.png)
+![tensorboard](assets/tensorboard.png)

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -213,7 +213,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         dk.data_dictionary["prediction_features"] = filtered_dataframe
 
         # optional additional data cleaning/analysis
-        self.data_cleaning_predict(dk, filtered_dataframe)
+        self.data_cleaning_predict(dk)
 
         pred_df = self.rl_model_predict(
             dk.data_dictionary["prediction_features"], dk, self.model)

tests/freqai/test_freqai_datakitchen.py

@@ -71,7 +71,7 @@ def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
     freqai = make_data_dictionary(mocker, freqai_conf)
     # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
     freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
-    assert log_has_re(r"DBSCAN found eps of 2\.3\d\.", caplog)
+    assert log_has_re(r"DBSCAN found eps of 1.75", caplog)
 
 
 def test_compute_distances(mocker, freqai_conf):
@@ -86,7 +86,7 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
     freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
     freqai.dk.use_SVM_to_remove_outliers(predict=False)
     assert log_has_re(
-        "SVM detected 8.66%",
+        "SVM detected 7.36%",
         caplog,
     )