fix: use .shape instead of index for outliers

3 years ago · fca73531cf
parent 9da28e5328
commit fca73531cf
7 changed files with 7 additions and 7 deletions
--- a/docs/strategy_migration.md
+++ b/docs/strategy_migration.md
@ -800,7 +800,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
        if self.freqai_info.get("DI_threshold", 0) > 0:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        # ... your custom code
--- a/freqtrade/freqai/base_models/BaseClassifierModel.py
+++ b/freqtrade/freqai/base_models/BaseClassifierModel.py
@ -120,7 +120,7 @@ class BaseClassifierModel(IFreqaiModel):
        if dk.feature_pipeline["di"]:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        return (pred_df, dk.do_predict)
--- a/freqtrade/freqai/base_models/BasePyTorchClassifier.py
+++ b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
@ -94,7 +94,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
        if dk.feature_pipeline["di"]:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        return (pred_df, dk.do_predict)
--- a/freqtrade/freqai/base_models/BasePyTorchRegressor.py
+++ b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
@ -55,7 +55,7 @@ class BasePyTorchRegressor(BasePyTorchModel):
        if dk.feature_pipeline["di"]:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers
        return (pred_df, dk.do_predict)

--- a/freqtrade/freqai/base_models/BaseRegressionModel.py
+++ b/freqtrade/freqai/base_models/BaseRegressionModel.py
@ -114,7 +114,7 @@ class BaseRegressionModel(IFreqaiModel):
        if dk.feature_pipeline["di"]:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        return (pred_df, dk.do_predict)
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@ -1012,6 +1012,6 @@ class IFreqaiModel(ABC):
        if self.freqai_info.get("DI_threshold", 0) > 0:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers
        return
--- a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
@ -136,7 +136,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
        if self.freqai_info.get("DI_threshold", 0) > 0:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
-            dk.DI_values = np.zeros(len(outliers.index))
+            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        if x.shape[1] > 1: