From 933c22ec4ebe34241ef0034b704e5840a1044883 Mon Sep 17 00:00:00 2001 From: Lukas Beiske Date: Fri, 17 Nov 2023 12:53:15 +0100 Subject: [PATCH] Do not duplicate docs in config files --- .../resources/train_disp_reconstructor.yaml | 28 ++++++++++--------- ctapipe/resources/train_energy_regressor.yaml | 24 ++++++++-------- .../resources/train_particle_classifier.yaml | 19 +++++++------ 3 files changed, 37 insertions(+), 34 deletions(-) diff --git a/ctapipe/resources/train_disp_reconstructor.yaml b/ctapipe/resources/train_disp_reconstructor.yaml index f13ecf24f83..499c068491b 100644 --- a/ctapipe/resources/train_disp_reconstructor.yaml +++ b/ctapipe/resources/train_disp_reconstructor.yaml @@ -16,21 +16,23 @@ TrainDispReconstructor: rng_seed: 1337 DispReconstructor: - prefix: disp # Prefix of the output generated by the DispReconstructor (Default: "disp") + # prefix: # Add a prefix of the output here, if you want to apply multiple + # DispReconstructors on the same file (e.g. for comparing different settings) - # All regression algorithms in scikit-learn are supported (https://scikit-learn.org/stable/modules/classes.html) - norm_cls: ExtraTreesRegressor # scikit-learn algorithm to be used for the norm(disp) models - norm_config: # Settings passed to the scikit-learn algorithm + # All regression algorithms in scikit-learn are supported + # (https://scikit-learn.org/stable/modules/classes.html) + norm_cls: ExtraTreesRegressor + norm_config: n_estimators: 10 max_depth: 10 n_jobs: -1 - log_target: True # If true, norm(disp) models predict log(norm(disp)) and the output is - # prefix_parameter = sign_prediction * exp(norm_prediction) + log_target: True - # All classification algorithms in scikit-learn are supported (https://scikit-learn.org/stable/modules/classes.html) - sign_cls: ExtraTreesClassifier # scikit-learn algorithm to be used for the sign(disp) models - sign_config: # Settings passed to the scikit-learn algorithm + # All classification algorithms in scikit-learn are supported + # (https://scikit-learn.org/stable/modules/classes.html) + sign_cls: ExtraTreesClassifier + sign_config: n_estimators: 10 max_depth: 10 n_jobs: -1 @@ -45,7 +47,7 @@ TrainDispReconstructor: - ["not clipped", "leakage_intensity_width_2 < 0.5"] FeatureGenerator: # On-the-fly generation of additional features - features: # Supported functions: "sin", "cos", "tan", "arctan2", "log", "log10", "exp", "sqrt" + features: # - [, ] - ["area", "hillas_width * hillas_length"] @@ -82,7 +84,7 @@ TrainDispReconstructor: - area # Features generated by the FeatureGenerator still have to be listed here # for the models to use them. - stereo_combiner_cls: "StereoMeanCombiner" # No other combiners are implemented atm. - StereoMeanCombiner: - weights: None # Options: None, intensity, konrad + # stereo_combiner_cls: "StereoMeanCombiner" + # StereoMeanCombiner: # Here you can set options for StereoMeanCombiner + # weights: # (e.g. which kind of weights to use). diff --git a/ctapipe/resources/train_energy_regressor.yaml b/ctapipe/resources/train_energy_regressor.yaml index 8a0f7458345..a2ecdcfa9c2 100644 --- a/ctapipe/resources/train_energy_regressor.yaml +++ b/ctapipe/resources/train_energy_regressor.yaml @@ -16,14 +16,15 @@ TrainEnergyRegressor: rng_seed: 1337 EnergyRegressor: - prefix: ExtraTreesRegressor # Prefix of the output generated by the EnergyRegressor - # (Default: Name of the used scikit-learn algorithm). - log_target: True # If true, models predict log(energy) and the output is - # prefix_energy = exp(model_prediction). + # prefix: # Add a prefix of the output here, if you want to apply multiple + # EnergyRegressors on the same file (e.g. for comparing different settings) - # All regression algorithms in scikit-learn are supported (https://scikit-learn.org/stable/modules/classes.html) - model_cls: ExtraTreesRegressor # scikit-learn algorithm to be used for the models - model_config: # Settings passed to the scikit-learn algorithm + log_target: True + + # All regression algorithms in scikit-learn are supported + # (https://scikit-learn.org/stable/modules/classes.html) + model_cls: ExtraTreesRegressor + model_config: n_estimators: 10 max_depth: 10 n_jobs: -1 @@ -38,7 +39,7 @@ TrainEnergyRegressor: - ["not clipped", "leakage_intensity_width_2 < 0.5"] FeatureGenerator: # On-the-fly generation of additional features - features: # Supported functions: "sin", "cos", "tan", "arctan2", "log", "log10", "exp", "sqrt" + features: # - [, ] - ["area", "hillas_width * hillas_length"] - ["n_telescopes_triggered", "subarray.multiplicity(tels_with_trigger)"] @@ -79,8 +80,7 @@ TrainEnergyRegressor: - n_telescopes_triggered # still have to be listed here for the models - n_telescopes_hillas_reconstructor # to use them. - stereo_combiner_cls: "StereoMeanCombiner" # No other combiners are implemented atm. - StereoMeanCombiner: - weights: None # Options: None, intensity, konrad - log_target: true # If true, calculate exp(mean(log(values))) + # stereo_combiner_cls: "StereoMeanCombiner" + # StereoMeanCombiner: # Here you can set options for StereoMeanCombiner + # weights: # (e.g. which kind of weights to use). diff --git a/ctapipe/resources/train_particle_classifier.yaml b/ctapipe/resources/train_particle_classifier.yaml index 687cc4434f9..ae4a81272e0 100644 --- a/ctapipe/resources/train_particle_classifier.yaml +++ b/ctapipe/resources/train_particle_classifier.yaml @@ -19,12 +19,13 @@ TrainParticleClassifier: rng_seed: 1337 ParticleClassifier: - prefix: ExtraTreesClassifier # Prefix of the output generated by the ParticleClassifier - # (Default: Name of the used scikit-learn algorithm). + # prefix: # Add a prefix of the output here, if you want to apply multiple + # ParticleClassifiers on the same file (e.g. for comparing different settings) - # All classification algorithms in scikit-learn are supported (https://scikit-learn.org/stable/modules/classes.html) - model_cls: ExtraTreesClassifier # scikit-learn algorithm to be used for the models - model_config: # Settings passed to the scikit-learn algorithm + # All classification algorithms in scikit-learn are supported + # (https://scikit-learn.org/stable/modules/classes.html) + model_cls: ExtraTreesClassifier + model_config: n_estimators: 10 max_depth: 10 n_jobs: -1 @@ -39,7 +40,7 @@ TrainParticleClassifier: - ["not clipped", "leakage_intensity_width_2 < 0.5"] FeatureGenerator: # On-the-fly generation of additional features - features: # Supported functions: "sin", "cos", "tan", "arctan2", "log", "log10", "exp", "sqrt" + features: # - [, ] - ["area", "hillas_width * hillas_length"] @@ -79,7 +80,7 @@ TrainParticleClassifier: - area # Features generated by the FeatureGenerator still have to be listed here # for the models to use them. - stereo_combiner_cls: "StereoMeanCombiner" # No other combiners are implemented atm. - StereoMeanCombiner: - weights: None # Options: None, intensity, konrad + # stereo_combiner_cls: "StereoMeanCombiner" + # StereoMeanCombiner: # Here you can set options for StereoMeanCombiner + # weights: # (e.g. which kind of weights to use).