def main():
ctrl = Controller()
ctrl.load_dataset_from_file(DATASET_FILE_PATH)
if MODE == MODE_TRAIN:
from scripts.test_model import BHSMMTestModel
from hassbrain_algorithm.models.hmm.bhmm_hp import BernoulliHMM_HandcraftedPriors
from hassbrain_algorithm.models.tads import TADS
if MODEL_CLASS == BHMM:
hmm_model = BHMMTestModel(ctrl)
elif MODEL_CLASS == BHSMM:
hmm_model = BHSMMTestModel(ctrl)
hmm_model.set_training_steps(50)
elif MODEL_CLASS == BHMMPC:
hmm_model = BernoulliHMM_HandcraftedPriors(ctrl)
elif MODEL_CLASS == MCTADS:
hmm_model = TADS(ctrl)
else:
raise ValueError
ctrl.register_model(hmm_model, MODEL_NAME)
# load domain knowledge
if MODEL_CLASS == BHMMPC:
path = '/home/cmeier/code/data/hassbrain/datasets/hass_chris_final/data/domain_knowledge.json'
act_data, loc_data = load_domain_knowledge(path)
ctrl.register_location_info(MODEL_NAME, loc_data)
ctrl.register_activity_info(MODEL_NAME, act_data)
# load model
elif MODE == MODE_BENCH:
ctrl.load_model(MODEL_FILE_PATH, MODEL_NAME)
else:
raise ValueError
ctrl.register_benchmark(MODEL_NAME)
ctrl.init_model_on_dataset(MODEL_NAME)
if MODE == MODE_TRAIN:
ctrl.register_loss_file_path(MD_LOSS_FILE_PATH, MODEL_NAME)
ctrl.train_model(MODEL_NAME)
ctrl.save_model(MODEL_FILE_PATH, MODEL_NAME)
# bench the model
reports = ctrl.bench_models()
# save metrics
ctrl.save_df_metrics_to_file(MODEL_NAME, MD_METRICS_FILE_PATH)
ctrl.save_df_confusion(MODEL_NAME, MD_CONF_MAT_FILE_PATH)
ctrl.save_df_act_dur_dists(MODEL_NAME, MD_ACT_DUR_DISTS_DF_FILE_PATH,
DATA_ACT_DUR_DISTS_DF_FILE_PATH)
ctrl.save_df_class_accs(MODEL_NAME, MD_CLASS_ACTS_FILE_PATH)
# plots
if MODE == MODE_TRAIN and MODEL_CLASS != MCTADS:
ctrl.save_plot_trainloss(MODEL_NAME, MD_LOSS_IMG_FILE_PATH)
ctrl.save_plot_inferred_states(MODEL_NAME, MD_INFST_IMG_FILE_PATH)
ctrl.save_plot_act_dur_dists([MODEL_NAME], MD_ACT_DUR_DISTS_IMG_FILE_PATH)
def main():
ctrl = Controller()
ctrl.load_dataset_from_file(DATASET_FILE_PATH)
#hmm_model = BHMMTestModel(ctrl)
#hmm_model = BHSMMTestModel(ctrl)
from hassbrain_algorithm.models.hmm.bhmm_hp import BernoulliHMM_HandcraftedPriors
hmm_model = BernoulliHMM_HandcraftedPriors(ctrl)
ctrl.register_model(hmm_model, MODEL_NAME)
# load domain knowledge
path = '/home/cmeier/code/data/hassbrain/datasets/hass_chris_final/data/domain_knowledge.json'
act_data, loc_data = load_domain_knowledge(path)
ctrl.register_location_info(MODEL_NAME, loc_data)
ctrl.register_activity_info(MODEL_NAME, act_data)
# load model
#ctrl.load_model(MODEL_FILE_PATH, MODEL_NAME)
from scripts.test_model import BHSMMTestModel
ctrl.register_benchmark(MODEL_NAME)
ctrl.init_model_on_dataset(MODEL_NAME)
ctrl.register_loss_file_path(MD_LOSS_FILE_PATH, MODEL_NAME)
ctrl.train_model(MODEL_NAME)
# bench the model
reports = ctrl.bench_models()
# save metrics
ctrl.save_df_metrics_to_file(MODEL_NAME, MD_METRICS_FILE_PATH)
ctrl.save_df_confusion(MODEL_NAME, MD_CONF_MAT_FILE_PATH)
ctrl.save_df_act_dur_dists(MODEL_NAME, MD_ACT_DUR_DISTS_DF_FILE_PATH,
DATA_ACT_DUR_DISTS_DF_FILE_PATH)
# plots
ctrl.save_plot_trainloss(MD_LOSS_IMG_FILE_PATH, MODEL_NAME)
ctrl.plot_and_save_inferred_states(MD_INFST_IMG_FILE_PATH, MODEL_NAME)
ctrl.save_plot_act_dur_dists(MODEL_NAME, MD_ACT_DUR_DISTS_IMG_FILE_PATH,
DATA_ACT_DUR_DISTS_IMG_FILE_PATH)