def main():
parser = argparse.ArgumentParser(
description=
"Plot features importance from trained random forests models")
parser.add_argument("models_path", help="path to the trained models")
parser.add_argument("--config_file",
"-cf",
default=None,
help="Path to the config file. Optional."),
args = parser.parse_args()
plot_models_features_importances(args.models_path,
config_file=args.config_file)
plt.savefig(os.path.join(args.models_path, "features_importance.png"))
def main():
custom_config = {}
if args.config_file is not None:
try:
custom_config = read_configuration_file(args.config_file)
except ("Custom configuration could not be loaded !!!"):
pass
config = replace_config(standard_config, custom_config)
reg_energy, reg_disp_vector, cls_gh = dl1_to_dl2.build_models(
args.gammafile,
args.protonfile,
save_models=args.storerf,
path_models=args.path_models,
custom_config=config,
)
gammas = filter_events(
pd.read_hdf(args.gammatest, key=dl1_params_lstcam_key),
config["events_filters"],
)
proton = filter_events(
pd.read_hdf(args.protontest, key=dl1_params_lstcam_key),
config["events_filters"],
)
data = pd.concat([gammas, proton], ignore_index=True)
dl2 = dl1_to_dl2.apply_models(data,
cls_gh,
reg_energy,
reg_disp_vector,
custom_config=config)
####PLOT SOME RESULTS#####
selected_gammas = dl2.query('reco_type==0 & mc_type==0')
if (len(selected_gammas) == 0):
log.warning('No gammas selected, I will not plot any output')
sys.exit()
plot_dl2.plot_features(dl2)
if not args.batch:
plt.show()
plot_dl2.energy_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.direction_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_disp_vector(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_pos(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_roc_gamma(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_models_features_importances(args.path_models,
args.config_file)
if not args.batch:
plt.show()
plt.hist(dl2[dl2['mc_type'] == 101]['gammaness'], bins=100)
plt.hist(dl2[dl2['mc_type'] == 0]['gammaness'], bins=100)
if not args.batch:
plt.show()
def test_plot_models_features_importances():
plot_dl2.plot_models_features_importances(output_dir)
def test_plot_models_features_importances(rf_models):
fig, axes = plt.subplots(1, 3, figsize=(15, 10))
plot_dl2.plot_models_features_importances(rf_models["path"],
axes=axes,
alpha=0.5,
fill=False)
def test_plot_models_features_importances():
fig, axes = plt.subplots(1, 3, figsize=(15, 10))
plot_dl2.plot_models_features_importances(output_dir,
axes=axes,
alpha=0.5,
fill=False)
def main():
args = parser.parse_args()
custom_config = {}
if args.config_file is not None:
custom_config = read_configuration_file(args.config_file)
config = replace_config(standard_config, custom_config)
subarray_info = SubarrayDescription.from_hdf(args.gammatest)
tel_id = config["allowed_tels"][0] if "allowed_tels" in config else 1
focal_length = subarray_info.tel[tel_id].optics.equivalent_focal_length
reg_energy, reg_disp_norm, cls_disp_sign, cls_gh = dl1_to_dl2.build_models(
args.gammafile,
args.protonfile,
save_models=args.save_models,
path_models=args.path_models,
custom_config=config,
)
gammas = filter_events(
pd.read_hdf(args.gammatest, key=dl1_params_lstcam_key),
config["events_filters"],
)
proton = filter_events(
pd.read_hdf(args.protontest, key=dl1_params_lstcam_key),
config["events_filters"],
)
data = pd.concat([gammas, proton], ignore_index=True)
dl2 = dl1_to_dl2.apply_models(data,
cls_gh,
reg_energy,
reg_disp_norm=reg_disp_norm,
cls_disp_sign=cls_disp_sign,
focal_length=focal_length,
custom_config=config)
####PLOT SOME RESULTS#####
selected_gammas = dl2.query('reco_type==0 & mc_type==0')
if (len(selected_gammas) == 0):
log.warning('No gammas selected, I will not plot any output')
sys.exit()
plot_dl2.plot_features(dl2)
if not args.batch:
plt.show()
plot_dl2.energy_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.direction_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_disp_vector(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_pos(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_roc_gamma(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_models_features_importances(args.path_models,
args.config_file)
if not args.batch:
plt.show()
plt.hist(dl2[dl2['mc_type'] == 101]['gammaness'], bins=100)
plt.hist(dl2[dl2['mc_type'] == 0]['gammaness'], bins=100)
if not args.batch:
plt.show()