]> Piment Noir Git Repositories - freqai-strategies.git/commitdiff
fix: readd outlier_threshold default value
authorJérôme Benoit <jerome.benoit@piment-noir.org>
Wed, 29 Jan 2025 12:37:35 +0000 (13:37 +0100)
committerJérôme Benoit <jerome.benoit@piment-noir.org>
Wed, 29 Jan 2025 12:37:35 +0000 (13:37 +0100)
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py
quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py

index 3316c6c432d97c86964b4bd9eecabfc2c012ca0a..c3273eb551929230fd599c71f030903ef7b67bb9 100644 (file)
@@ -182,7 +182,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             di_values = di_values.dropna()
             f = spy.stats.genextreme.fit(di_values)
             cutoff = spy.stats.genextreme.ppf(
-                self.freqai_info.get("outlier_threshold"), *f
+                self.freqai_info.get("outlier_threshold", 0.999), *f
             )
 
         dk.data["DI_value_mean"] = pred_df_full["DI_values"].mean()
index 3ab27f7d7509987d82ade19ed02bc557b6d59a35..8538e3be5c60dbda67dcce292d16bf6252cd8e24 100644 (file)
@@ -129,7 +129,7 @@ class XGBoostRegressorQuickAdapterV3(BaseRegressionModel):
             di_values = di_values.dropna()
             f = spy.stats.genextreme.fit(di_values)
             cutoff = spy.stats.genextreme.ppf(
-                self.freqai_info.get("outlier_threshold"), *f
+                self.freqai_info.get("outlier_threshold", 0.999), *f
             )
 
         dk.data["DI_value_mean"] = pred_df_full["DI_values"].mean()
index 646492031f11afc07da6aa86a891bf9f967fc252..222b2a7aa18f2fd900bf32ba557b4abe445d126c 100644 (file)
@@ -179,7 +179,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             di_values = di_values.dropna()
             f = spy.stats.genextreme.fit(di_values)
             cutoff = spy.stats.genextreme.ppf(
-                self.freqai_info.get("outlier_threshold"), *f
+                self.freqai_info.get("outlier_threshold", 0.999), *f
             )
 
         dk.data["DI_value_mean"] = pred_df_full["DI_values"].mean()