From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Mon, 10 Feb 2025 20:21:40 +0000 (+0100)
Subject: refactor(qav3): fit label mean and std
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=59b94bedd8a37bd7972dfe9875017b39b1326299;p=freqai-strategies.git

refactor(qav3): fit label mean and std

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
---

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index c1af399..cdf4a34 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -199,10 +199,15 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             ]
 
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
-        for ft in dk.label_list:
-            # f = spy.stats.norm.fit(pred_df_full[ft])
-            dk.data["labels_std"][ft] = 0  # f[1]
-            dk.data["labels_mean"][ft] = 0  # f[0]
+        for label in dk.label_list + dk.unique_class_list:
+            if pred_df_full[label].dtype == object:
+                continue
+            if not warmed_up:
+                f = [0, 0]
+            else:
+                f = spy.stats.norm.fit(pred_df_full[label])
+            dk.data["labels_mean"][label] = f[0]
+            dk.data["labels_std"][label] = f[1]
 
         # fit the DI_threshold
         if not warmed_up:
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index f2c28f8..9b8d629 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -199,10 +199,15 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             ]
 
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
-        for ft in dk.label_list:
-            # f = spy.stats.norm.fit(pred_df_full[ft])
-            dk.data["labels_std"][ft] = 0  # f[1]
-            dk.data["labels_mean"][ft] = 0  # f[0]
+        for label in dk.label_list + dk.unique_class_list:
+            if pred_df_full[label].dtype == object:
+                continue
+            if not warmed_up:
+                f = [0, 0]
+            else:
+                f = spy.stats.norm.fit(pred_df_full[label])
+            dk.data["labels_mean"][label] = f[0]
+            dk.data["labels_std"][label] = f[1]
 
         # fit the DI_threshold
         if not warmed_up: