From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Wed, 19 Feb 2025 22:27:42 +0000 (+0100)
Subject: fix(qav3): pad not evenly shapped chunks
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=4c16156b779a7f8ee6d8a0545c297c58c2e69782;p=freqai-strategies.git

fix(qav3): pad not evenly shapped chunks

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 dc09d62..6cf2c96 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -588,6 +588,25 @@ def period_objective(
         y_pred.iloc[i : i + label_period_candles]
         for i in range(0, len(y_pred), label_period_candles)
     ]
+    max_chunk_length = max(len(chunk) for chunk in y_test + y_pred)
+    y_test = [
+        np.pad(
+            chunk,
+            (0, max_chunk_length - len(chunk)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        for chunk in y_test
+    ]
+    y_pred = [
+        np.pad(
+            chunk,
+            (0, max_chunk_length - len(chunk)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        for chunk in y_pred
+    ]
 
     error = sklearn.metrics.root_mean_squared_error(y_test, y_pred)
 
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 48d2e6b..3709690 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -592,6 +592,25 @@ def period_objective(
         y_pred.iloc[i : i + label_period_candles]
         for i in range(0, len(y_pred), label_period_candles)
     ]
+    max_chunk_length = max(len(chunk) for chunk in y_test + y_pred)
+    y_test = [
+        np.pad(
+            chunk,
+            (0, max_chunk_length - len(chunk)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        for chunk in y_test
+    ]
+    y_pred = [
+        np.pad(
+            chunk,
+            (0, max_chunk_length - len(chunk)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        for chunk in y_pred
+    ]
 
     error = sklearn.metrics.root_mean_squared_error(y_test, y_pred)