def setUp(self):
# set of observations
self.ctrl = Controller() # type: Controller
#self.hmm_model = ModelHMM(self.ctrl)
#self.hmm_model = ModelHMM_scaled(self.ctrl)
#self.hmm_model = ModelHMM_log(self.ctrl)
self.hmm_model = ModelHMM_log_scaled(self.ctrl)
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)
# load model
model_name = 'bhmm'
ctrl.load_model(MODEL_FILE_PATH, model_name)
ctrl.create_model_agnostics(model_name)
hm = ctrl.get_model(model_name)._obs_lbl_hashmap
file_path = MODEL_FOLDER_PATH + '/' + MODEL_NAME + '.lime_sleeping.png'
file_paths = [file_path]
labels_to_explain=['sleeping']
import numpy as np
# typical for activation pattern for sleeping
raw_feature_sleeping = np.array(
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=bool)
ch_feature_sleeping = np.array(
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=bool)
lf_feature_sleeping = np.array(
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=bool)
data = ctrl._dataset # type: _Dataset
ctrl.plot_and_save_explanation(
model_name,
ch_feature_sleeping,
labels_to_explain,
file_paths
)
def setUp(self):
self.ctrl = Controller()
self.ctrl.load_dataset(Dataset.PENDIGITS)
pd_model = ModelPendigits(self.ctrl, "test")
self.ctrl.register_model(pd_model)
self.dset = self.ctrl._dataset # type: DatasetPendigits
self.pd_model = self.ctrl._model
def main():
ctrl = Controller()
ctrl.load_dataset_from_file(DATASET_FILE_PATH)
# load model
model_name = 'bhmm'
ctrl.load_model(MODEL_FILE_PATH, model_name)
ctrl.create_model_agnostics(model_name)
file_path = MODEL_FOLDER_PATH + '/' + MODEL_NAME + '.feature_importance.png'
ctrl.save_plot_feature_importance(model_name, file_path)
def main():
ctrl = Controller()
ctrl.load_dataset_from_file(DATASET_FILE_PATH)
print()
# load model
ctrl.load_model(MODEL_FILE_PATH, 'bhmm')
ctrl.load_model(MODEL_FILE_PATH2, 'bhsmm')
state_sel = ['dental_care', 'outside_activity', 'learning']
#state_sel = ['dental_care', 'enter_home', 'kitchen_social_activity']
img_file_path = MODEL_FOLDER_PATH + '/' + 'comparision.png'
ctrl.compare_dur_dists_and_save('bhmm', 'bhsmm', state_sel, img_file_path)
def main():
ctrl = Controller()
ctrl.load_dataset_from_file(DATASET_FILE_PATH)
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)
hmm_model.set_training_steps(10)
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)
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)
ctrl.save_model(MODEL_FILE_PATH, MODEL_NAME)
# bench the model
params = {
'metrics': False,
'act_dur_dist': True,
'feature_importance': False
}
reports = ctrl.bench_models(**params)
# 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)
dict = {
#'feature_importance' : MD_FEATURE_IMP_PLT_FILE_PATH,
'train_loss': MD_LOSS_IMG_FILE_PATH,
#'inf_states' : MD_INFST_IMG_FILE_PATH,
'act_dur': MD_ACT_DUR_DISTS_IMG_FILE_PATH
}
ctrl.save_plots(MODEL_NAME, dict)
def _create_ctrl_for_normal_dataset(self, algorithm, dataset):
"""
generates an instance of controller and model
:param algorithm:
:param dataset:
:return:
"""
from hassbrain_algorithm.controller import Controller
ctrl = Controller(settings.HASSBRAIN_ALGO_CONFIG) # type: Controller
dk = self.train_get_ds_type_by_name(dataset.class_name)
ctrl.set_dataset(dk)
model_object = self._create_class_from_name(ctrl, algorithm)
ctrl.load_dataset()
ctrl.register_model(model_object)
return ctrl
def main():
dk = DK
data_name = DATA_NAME
# set of observations
ctrl_config = {
'datasets': {
'kasteren': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/kasteren/config.yaml'
},
'hass_testing': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing/config.yaml'
},
'hass_testing2': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing2/config.yaml'
},
'hass_chris': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_chris/config.yaml'
},
'hass_chris_final': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_chris_final/config.yaml'
}
}
}
ctrl = Controller(config=ctrl_config)
params = {
'repr': DATA_TYPE,
'data_format': 'bernoulli',
'test_selection': TEST_SEL,
'test_day': TEST_DAY,
'freq': TIME_DIFF,
'include_idle': False
}
ctrl.set_dataset(data_name=data_name, data_type=dk, params=params)
dataset = ctrl._dataset # type: _Dataset
join_two_seperate_databses(dataset)
load_rest(dataset)
#act_stats = dataset.get_act_stats()
#dev_stats = dataset.get_dev_stats()
ctrl.save_dataset(DATASET_FILE_PATH)
#print('filepath: ', DATASET_FILE_PATH)
print('*' * 10)
def _create_dataset(self, datainstance):
db_path = self._create_hass_db_path()
activity_file_path = self._create_activity_file_path(person)
ctrl = Controller(path_to_config=settings.HASSBRAIN_ALGO_CONFIG,
config={'datasets': {
datainstance.name: db_path
}})
params = {
'repr': DataRep(datainstance.data_rep),
'data_format': 'bernoulli',
'test_selection': datainstance.test_sel,
'freq': datainstance.timeslicelength
}
ctrl.set_dataset(data_name=datainstance.name,
data_type=Datasettype.HASS,
params=params)
def main():
# set of observations
ctrl = Controller()
# todo this is only for testing interpretability
from scripts.test_model import BHMMTestModel
from scripts.test_model import BHSMMTestModel
#hmm_model = BHMMTestModel(ctrl)
hmm_model = BHSMMTestModel(ctrl)
ctrl.load_dataset_from_file(DATASET_FILE_PATH)
ctrl.register_model(hmm_model, MODEL_NAME)
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)
print(MODEL_FILE_PATH)
ctrl.save_model(MODEL_FILE_PATH, MODEL_NAME)
print()
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)
def main():
dk = DK
data_name = DATA_NAME
# set of observations
ctrl_config = {
'datasets': {
'kasteren': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/kasteren/config.yaml'
},
'hass_testing': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing/config.yaml'
},
'hass_testing2': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing2/config.yaml'
},
'hass_chris': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_chris/config.yaml'
}
}
}
ctrl = Controller(config=ctrl_config)
params = {
'repr': DATA_TYPE,
'data_format': 'bernoulli',
'test_selection': TEST_SEL,
'test_day': TEST_DAY,
'freq': TIME_DIFF
}
ctrl.set_dataset(data_name=data_name, data_type=dk, params=params)
ctrl.load_dataset()
tmp1 = ctrl._dataset.get_dev_stats()
tmp2 = ctrl._dataset.get_act_stats()
#ctrl.save_dataset(DATASET_FILE_PATH)
#print('filepath: ', DATASET_FILE_PATH)
print('*' * 10)
def _create_ctrl_for_hass_instance(self, algorithm, person, datainstance,
model_name):
from hassbrain_algorithm.controller import Controller
"""
the case where the data should be loaded from the active homeassistant instance
"""
ctrl = Controller(path_to_config=settings.HASSBRAIN_ALGO_CONFIG)
ctrl.load_dataset_from_file(datainstance.data_file)
model_object = self._create_class_from_name(ctrl, algorithm)
self._set_custom_act_n_dev(ctrl)
ctrl.register_model(model_object, model_name)
#if algorithm.location:
# loc_data = AlgorithmView.get_location_data()
# ctrl.register_location_info(loc_data)
#if algorithm.synthetic_activities:
# act_data = AlgorithmView.get_activity_data(algorithm.selected_person)
# ctrl.register_activity_info(act_data)
return ctrl
def setUp(self):
# set of observations
self.ctrl = Controller()
self.ctrl.set_dataset(Dataset.HASS)
self.ctrl.load_dataset()
self.hass_obj = self.ctrl._dataset #type: DatasetHomeassistant
def setUp(self):
# set of observations
self.ctrl = Controller() # type: Controller
self.hmm_model = PomHMM(self.ctrl)
def setUp(self):
# set of observations
self.cm = Controller()
self.cm.load_dataset(Dataset.PENDIGITS)
self.pd = self.cm._dataset # type: DatasetPendigits
def setUp(self):
# set of observations
self.ctrl = Controller()
self.ctrl.set_dataset(Dataset.HASS_TESTING)
self.hass_obj = self.ctrl._dataset #type: DatasetHomeassistant
self.hmm_model = HMMForward(self.ctrl)