From a3471132977edafba5085ade9c92a19ef3ad5f0e Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Sat, 26 Apr 2025 22:25:48 +0200 Subject: [PATCH] refactor(qav3): cleanup variables namespace MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/QuickAdapterRegressorV3.py | 38 +++++++++---------- quickadapter/user_data/strategies/Utils.py | 2 +- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index 11ca199..d13441c 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -401,43 +401,43 @@ class QuickAdapterRegressorV3(BaseRegressionModel): return None best_trials = study.best_trials if namespace == "label": - peaks_sizes = [trial.values[1] for trial in best_trials] - quantile_peaks_size = np.quantile( - peaks_sizes, self.ft_params.get("label_quantile", 0.75) + pivots_sizes = [trial.values[1] for trial in best_trials] + quantile_pivots_size = np.quantile( + pivots_sizes, self.ft_params.get("label_quantile", 0.75) ) - equal_quantile_peaks_size_trials = [ + equal_quantile_pivots_size_trials = [ trial for trial in best_trials - if np.isclose(trial.values[1], quantile_peaks_size) + if np.isclose(trial.values[1], quantile_pivots_size) ] - if equal_quantile_peaks_size_trials: + if equal_quantile_pivots_size_trials: return max( - equal_quantile_peaks_size_trials, key=lambda trial: trial.values[0] + equal_quantile_pivots_size_trials, key=lambda trial: trial.values[0] ) nearest_above_quantile = ( np.inf, -np.inf, None, - ) # (trial_peaks_size, trial_scaled_natr, trial_index) + ) # (trial_pivots_size, trial_scaled_natr, trial_index) nearest_below_quantile = ( -np.inf, -np.inf, None, - ) # (trial_peaks_size, trial_scaled_natr, trial_index) + ) # (trial_pivots_size, trial_scaled_natr, trial_index) for idx, trial in enumerate(best_trials): - peaks_size = trial.values[1] - if peaks_size >= quantile_peaks_size: - if peaks_size < nearest_above_quantile[0] or ( - peaks_size == nearest_above_quantile[0] + pivots_size = trial.values[1] + if pivots_size >= quantile_pivots_size: + if pivots_size < nearest_above_quantile[0] or ( + pivots_size == nearest_above_quantile[0] and trial.values[0] > nearest_above_quantile[1] ): - nearest_above_quantile = (peaks_size, trial.values[0], idx) - if peaks_size <= quantile_peaks_size: - if peaks_size > nearest_below_quantile[0] or ( - peaks_size == nearest_below_quantile[0] + nearest_above_quantile = (pivots_size, trial.values[0], idx) + if pivots_size <= quantile_pivots_size: + if pivots_size > nearest_below_quantile[0] or ( + pivots_size == nearest_below_quantile[0] and trial.values[0] > nearest_below_quantile[1] ): - nearest_below_quantile = (peaks_size, trial.values[0], idx) + nearest_below_quantile = (pivots_size, trial.values[0], idx) if nearest_above_quantile[2] is None or nearest_below_quantile[2] is None: return None above_quantile_trial = best_trials[nearest_above_quantile[2]] @@ -847,7 +847,7 @@ def zigzag( df: pd.DataFrame, period: int = 14, ratio: float = 1.0, - depth: int = 7, + depth: int = 12, ) -> tuple[list[int], list[float], list[int]]: if df.empty or len(df) < 2: return [], [], [] diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py index 4759073..3d44ae9 100644 --- a/quickadapter/user_data/strategies/Utils.py +++ b/quickadapter/user_data/strategies/Utils.py @@ -302,7 +302,7 @@ def zigzag( df: pd.DataFrame, period: int = 14, ratio: float = 1.0, - depth: int = 7, + depth: int = 12, ) -> tuple[list[int], list[float], list[int]]: if df.empty or len(df) < 2: return [], [], [] -- 2.43.0