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fix: use .shape instead of index for outliers

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robcaulk
2023-06-25 16:34:44 +02:00
parent 9da28e5328
commit fca73531cf
7 changed files with 7 additions and 7 deletions
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2
docs/strategy_migration.md
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@@ -800,7 +800,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
if self.freqai_info.get("DI_threshold", 0) > 0: if self.freqai_info.get("DI_threshold", 0) > 0:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
# ... your custom code # ... your custom code

2
freqtrade/freqai/base_models/BaseClassifierModel.py
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@@ -120,7 +120,7 @@ class BaseClassifierModel(IFreqaiModel):
if dk.feature_pipeline["di"]: if dk.feature_pipeline["di"]:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
return (pred_df, dk.do_predict) return (pred_df, dk.do_predict)

2
freqtrade/freqai/base_models/BasePyTorchClassifier.py
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@@ -94,7 +94,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
if dk.feature_pipeline["di"]: if dk.feature_pipeline["di"]:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
return (pred_df, dk.do_predict) return (pred_df, dk.do_predict)

2
freqtrade/freqai/base_models/BasePyTorchRegressor.py
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@@ -55,7 +55,7 @@ class BasePyTorchRegressor(BasePyTorchModel):
if dk.feature_pipeline["di"]: if dk.feature_pipeline["di"]:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
return (pred_df, dk.do_predict) return (pred_df, dk.do_predict)

2
freqtrade/freqai/base_models/BaseRegressionModel.py
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@@ -114,7 +114,7 @@ class BaseRegressionModel(IFreqaiModel):
if dk.feature_pipeline["di"]: if dk.feature_pipeline["di"]:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
return (pred_df, dk.do_predict) return (pred_df, dk.do_predict)

2
freqtrade/freqai/freqai_interface.py
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@@ -1012,6 +1012,6 @@ class IFreqaiModel(ABC):
if self.freqai_info.get("DI_threshold", 0) > 0: if self.freqai_info.get("DI_threshold", 0) > 0:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
return return

2
freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
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@@ -136,7 +136,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
if self.freqai_info.get("DI_threshold", 0) > 0: if self.freqai_info.get("DI_threshold", 0) > 0:
dk.DI_values = dk.feature_pipeline["di"].di_values dk.DI_values = dk.feature_pipeline["di"].di_values
else: else:
dk.DI_values = np.zeros(len(outliers.index)) dk.DI_values = np.zeros(outliers.shape[0])
dk.do_predict = outliers dk.do_predict = outliers
if x.shape[1] > 1: if x.shape[1] > 1: