fix(qav3): properly size `label_period_candles`

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

diff --git a/.gitignore b/.gitignore
index 1bdc5853b321de85fa5232233c1d05ac98b1f960..b54c135012821dd93ac3694dcdfc6f21dffc9f29 100644 (file)
--- a/.gitignore
+++ b/.gitignore
@@ -372,8 +372,9 @@ $RECYCLE.BIN/
 
 # End of https://www.toptal.com/developers/gitignore/api/vim,visualstudiocode,pycharm+all,linux,osx,windows,python
 
+*.log.*
 config.json
 *.sqlite*
-models/
-data/
+**/user_data/models/**
+**/user_data/data/**
 !.gitkeep

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index c910fa9d6953d4197caf5a9903ffb4eb37e064a4..a8ab0d6206b9fa22564236ae3a8519f34ae4396a 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -259,19 +259,18 @@ def objective(
     y_pred = model.predict(X_test)
 
     label_period_candles = trial.suggest_int(
-        "label_period_candles", 1, fit_live_predictions_candles // 2
-    )
-    y_pred_min, y_pred_max = min_max_pred(
-        pd.DataFrame(y_pred), fit_live_predictions_candles, label_period_candles
-    )
-    y_test_min, y_test_max = min_max_pred(
-        pd.DataFrame(y_test), fit_live_predictions_candles, label_period_candles
+        "label_period_candles",
+        int(fit_live_predictions_candles / 20)
+        if fit_live_predictions_candles > 20
+        else 1,
+        int(fit_live_predictions_candles / 2)
+        if fit_live_predictions_candles > 2
+        else fit_live_predictions_candles,
     )
+    y_test = y_test.tail(label_period_candles)
+    y_pred = y_pred[-label_period_candles:]
 
-    error = sklearn.metrics.root_mean_squared_error(
-        pd.concat([y_test_min, y_test_max]).reset_index(drop=True),
-        pd.concat([y_pred_min, y_pred_max]).reset_index(drop=True),
-    )
+    error = sklearn.metrics.root_mean_squared_error(y_test, y_pred)
 
     return error
 

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 992696bf552bba1833563887491e6bc22733a1d1..6017b0ab3c6fa48d1f6ad2b9105650d3173d5544 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -264,19 +264,18 @@ def objective(
     y_pred = model.predict(X_test)
 
     label_period_candles = trial.suggest_int(
-        "label_period_candles", 1, fit_live_predictions_candles // 2
-    )
-    y_pred_min, y_pred_max = min_max_pred(
-        pd.DataFrame(y_pred), fit_live_predictions_candles, label_period_candles
-    )
-    y_test_min, y_test_max = min_max_pred(
-        pd.DataFrame(y_test), fit_live_predictions_candles, label_period_candles
+        "label_period_candles",
+        int(fit_live_predictions_candles / 20)
+        if fit_live_predictions_candles > 20
+        else 1,
+        int(fit_live_predictions_candles / 2)
+        if fit_live_predictions_candles > 2
+        else fit_live_predictions_candles,
     )
+    y_test = y_test.tail(label_period_candles)
+    y_pred = y_pred[-label_period_candles:]
 
-    error = sklearn.metrics.root_mean_squared_error(
-        pd.concat([y_test_min, y_test_max]).reset_index(drop=True),
-        pd.concat([y_pred_min, y_pred_max]).reset_index(drop=True),
-    )
+    error = sklearn.metrics.root_mean_squared_error(y_test, y_pred)
 
     return error