From 00dec1d124f8312d6684864cb2282fb45bd3df76 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Wed, 12 Mar 2025 00:16:37 +0100 Subject: [PATCH] fix(qav3): fix array like shape for rmse MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 6 +++--- .../freqaimodels/XGBoostRegressorQuickAdapterV35.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index 602b6ab..8a2c9c7 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -614,9 +614,9 @@ def period_objective( y_pred[i : i + label_period_candles] for i in np.arange(0, label_period_candles * n_windows, label_period_candles) ] - y_test = np.array([window for window in y_test_windows]) - test_weights = np.concatenate(np.array([window for window in test_weights_windows])) - y_pred = np.array([window for window in y_pred_windows]) + y_test = [window for window in y_test_windows] + test_weights = np.concatenate([window for window in test_weights_windows]) + y_pred = [window for window in y_pred_windows] error = sklearn.metrics.root_mean_squared_error( y_test, y_pred, sample_weight=test_weights diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index a9e0384..54a7ca3 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -622,9 +622,9 @@ def period_objective( y_pred[i : i + label_period_candles] for i in np.arange(0, label_period_candles * n_windows, label_period_candles) ] - y_test = np.array([window for window in y_test_windows]) - test_weights = np.concatenate(np.array([window for window in test_weights_windows])) - y_pred = np.array([window for window in y_pred_windows]) + y_test = [window for window in y_test_windows] + test_weights = np.concatenate([window for window in test_weights_windows]) + y_pred = [window for window in y_pred_windows] error = sklearn.metrics.root_mean_squared_error( y_test, y_pred, sample_weight=test_weights -- 2.43.0