From: Jérôme Benoit Date: Tue, 11 Mar 2025 23:16:37 +0000 (+0100) Subject: fix(qav3): fix array like shape for rmse X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=00dec1d124f8312d6684864cb2282fb45bd3df76;p=freqai-strategies.git fix(qav3): fix array like shape for rmse Signed-off-by: Jérôme Benoit --- 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