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)
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 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)
class TestPendigitsModel(unittest.TestCase):
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 test_init_hmms(self):
self.ctrl.init_model_on_dataset()
def test_save_hmms(self):
self.ctrl.init_model_on_dataset()
self.ctrl.save_model()
def test_load_hmms(self):
self.ctrl.load_model()
# attention manually refresh reference
self.pd_model = self.ctrl._model
def test_train_hmms(self):
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
def test_train_n_save_hmms(self):
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
self.ctrl.save_model()
def test_pre_bench_hmms(self):
self.ctrl.load_model()
self.pd_model = self.ctrl._model
y_true, y_pred = self.pd_model.create_pred_act_seqs(self.dset)
def test_bench_hmms(self):
self.ctrl.load_model()
self.pd_model = self.ctrl._model
self.ctrl.register_benchmark()
rep = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(rep)
def test_generate_obs(self):
self.ctrl.load_model()
self.pd_model = self.ctrl._model
self.ctrl.generate_observations()
def test_generate_obs_n_plot(self):
# numbers that were good in benchmark (desc):
# [0,8,4,9,3,7]
self.ctrl.load_model()
ds = self.ctrl._dataset # type: DatasetPendigits
pdmod = self.ctrl._model # type: ModelPendigits
num = 1
zero_start = [8, 1, 1]
one_start = [8, 2, 1, 1, 1]
start_seq = one_start
hmm = pdmod._model_dict[num]
hmm.set_format_full(True)
print(hmm)
pdmod.select_number(num)
seq = pdmod.generate_observations(start_seq)
ds.plot_obs_seq(seq, num)
def tearDown(self):
pass
class TestHomeassistantModelHMMLogScaled(unittest.TestCase):
# Model part
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)
def tearDown(self):
pass
def test_load_custom_lists_modelHMM(self):
custom_state_list = ['sleeping', 'cooking']
custom_obs_list = [
'binary_sensor.motion_bed', 'binary_sensor.motion_mirror',
'binary_sensor.motion_pc', 'switch.test_switch_1',
'light.test_light'
]
hmm_model = self.hmm_model
self.ctrl.set_custom_state_list(custom_state_list)
self.ctrl.set_custom_obs_list(custom_obs_list)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
def test_load_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
print(self.hass_obj.get_obs_lbl_hashmap())
print(self.hass_obj.get_state_lbl_hashmap())
def test_train_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
self.ctrl.train_model()
print(self.ctrl._model)
def test_bench_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
self.ctrl.register_benchmark()
self.ctrl.train_model()
print(self.ctrl._model)
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(report)
def test_classify(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('-' * 10)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1),
('binary_sensor.motion_bed', 0)]
pred_state = hmm_model.classify(obs_seq)
print('#' * 100)
print(pred_state)
def test_classify_multi(self):
"""
used to test for classification of multiple labels
"""
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('-' * 10)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1)
] #, ('binary_sensor.motion_bed', 0)]
act_state_dict = hmm_model.classify_multi(obs_seq)
print('#' * 100)
print(act_state_dict)
#print(act_state_dict)
#print(hmm_model.get_state_label_list())
def test_pred_next_obs_single(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
#print(hmm_model)
print('#' * 100)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1),
('binary_sensor.motion_bed', 0)]
tupel = hmm_model.predict_next_obs(obs_seq)
print(tupel)
def test_pred_next_obs_multi(self):
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('#' * 100)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1),
('binary_sensor.motion_bed', 0)]
#arr = hmm_model.predict_next_obs_arr(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
res_dict = hmm_model.predict_prob_xnp1(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
res_dict = hmm_model.predict_prob_xnp1(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
print('#' * 100)
print(res_dict)
class TestController(unittest.TestCase):
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 tearDown(self):
pass
def test_ctrl_get_bench_metrics(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.register
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
self.ctrl.train_model(True)
print(self.ctrl.get_bench_metrics())
def test_ctrl_train_seqs(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
self.ctrl.train_model()
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(self.ctrl._model)
print(report)
#self.ctrl.show_plot()
def test_ctrl_presentation(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
self.ctrl.train_model(True)
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(self.ctrl._model)
print(report)
#self.ctrl.show_plot()
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
def test_generate_visualization(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print('state_label_hm: ', hmm_model._state_lbl_hashmap)
print('state_label_rev_hm: ', hmm_model._state_lbl_rev_hashmap)
self.ctrl.save_visualization_to_file(
'/home/cmeier/code/tmp/visualization.png')
def test_bench_train_loss(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.register_loss_file_path(
'/home/cmeier/code/tmp/kasteren/train_loss.log')
self.ctrl.train_model()
self.ctrl.save_model(
'/home/cmeier/code/tmp/kasteren/kasteren_model.joblib')
def test_bench_reports_conf_matrix(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.train_model()
report = self.ctrl.create_report(conf_matrix=True)
print(report)
def test_bench_reports(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.train_model()
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(report)
#self.ctrl.show_plot()
def test_bench_q_fct(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
print(self.ctrl._model)
# use dataset Kasteren and q_fct
self.ctrl.train_model(True)
report = self.ctrl.create_report()
print(self.ctrl._model)
print(report)
#self.ctrl.show_plot()
def test_om(self):
#plt.figure(figsize=(10,6))
#self.pom.plot()
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
dot = self.ctrl.render_model()
dot.render('test.gv', view=True)
def test_train_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
#print(hmm_model._hmm)
self.ctrl.train_model()
print('#' * 200)
print(hmm_model)
hmm_model._hmm.set_str_exp(True)
hmm_model._hmm.set_format_full(True)
print(hmm_model)
#print(hmm_model._hmm.verify_transition_matrix())
#print(hmm_model._hmm.verify_emission_matrix())
#self._bench._model.draw()
#self.ctrl.register_benchmark()
#report = self.ctrl.create_report(True, True, True, True, True)
#print(report)
#self._bench.show_plot()
def test_save_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
self.ctrl.train_model()
self.ctrl.save_model()
def test_init_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
def test_load_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.load_model()
print(self.ctrl._model)
class TestHomeassistantModelHMMLogScaled(unittest.TestCase):
# Model part
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 = PreConfHMM(self.ctrl)
def tearDown(self):
pass
def test_load_custom_lists_modelHMM(self):
custom_state_list = ['sleeping', 'cooking']
custom_obs_list = [
'binary_sensor.motion_bed',
'binary_sensor.motion_mirror',
'binary_sensor.motion_pc',
'switch.test_switch_1',
'light.test_light'
]
hmm_model = self.hmm_model
self.ctrl.set_custom_state_list(custom_state_list)
self.ctrl.set_custom_obs_list(custom_obs_list)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
def test_load_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
print(self.hass_obj.get_obs_lbl_hashmap())
print(self.hass_obj.get_state_lbl_hashmap())
def test_train_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
self.ctrl.train_model()
print(self.ctrl._model)
def test_bench_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
self.ctrl.register_benchmark()
self.ctrl.train_model()
print(self.ctrl._model)
report = self.ctrl.create_report(
conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True
)
print(report)
def test_classify(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('-'*10)
obs_seq = [('binary_sensor.motion_bed', 0), ('binary_sensor.motion_mirror', 1), ('binary_sensor.motion_bed', 0)]
pred_state = hmm_model.classify(obs_seq)
print('#'*100)
print(pred_state)
def test_classify_multi(self):
"""
used to test for classification of multiple labels
"""
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('-'*10)
obs_seq = [('binary_sensor.motion_bed', 0), ('binary_sensor.motion_mirror', 1)]#, ('binary_sensor.motion_bed', 0)]
act_state_dict = hmm_model.classify_multi(obs_seq)
print('#'*100)
print(act_state_dict)
#print(act_state_dict)
#print(hmm_model.get_state_label_list())
def test_pred_next_obs_single(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
#print(hmm_model)
print('#'*100)
obs_seq = [('binary_sensor.motion_bed', 0), ('binary_sensor.motion_mirror', 1), ('binary_sensor.motion_bed', 0)]
tupel = hmm_model.predict_next_obs(obs_seq)
print(tupel)
def test_pred_next_obs_multi(self):
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('#'*100)
obs_seq = [('binary_sensor.motion_bed', 0), ('binary_sensor.motion_mirror', 1), ('binary_sensor.motion_bed', 0)]
#arr = hmm_model.predict_next_obs_arr(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
res_dict = hmm_model.predict_prob_xnp1(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
res_dict = hmm_model.predict_prob_xnp1(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
print('#'*100)
print(res_dict)
def test_encode_loc_data(self):
loc_data = [ {
"name" : "loc1",
"activities" : ['cooking'],
"devices" : ['binary_sensor.motion_hallway', 'binary_sensor.motion_mirror'],
},
{"name" : "loc2",
"activities" : ['cooking', 'eating'],
"devices" : [],
},
{"name" : "loc3",
"activities" : ['sleeping'],
"devices" : ['binary_sensor.motion_bed'],
},
]
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
#print('state_hm: ', hmm_model._state_lbl_hashmap)
#print('obs_hm: ', hmm_model._obs_lbl_hashmap)
#print('raw_loc_data: \t' + str(loc_data))
enc_loc_data = hmm_model._encode_location_data(loc_data)
#print('#'*100)
#print('enc_loc_data: \t' + str(enc_loc_data))
def test_encode_act_data(self):
act_data = [
{"name" : "cooking",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("06:15:00"),
"end" : datetime.time.fromisoformat("08:45:00")
},
{"name" : "eating",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("06:15:00"),
"end" : datetime.time.fromisoformat("08:45:00")
},
{"name" : "eating",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("08:46:00"),
"end" : datetime.time.fromisoformat("10:00:00")
},
]
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
print('raw_act_data: \t' + str(act_data))
print('state_hm: ', hmm_model._state_lbl_hashmap)
print('obs_hm: ', hmm_model._obs_lbl_hashmap)
print('#'*100)
enc_act_data = hmm_model._encode_act_data(act_data)
print('enc_act_data: \t' + str(enc_act_data))
def test_init(self):
loc_data = [ {
"name" : "loc1",
"activities" : ['cooking'],
"devices" : ['binary_sensor.motion_hallway', 'binary_sensor.motion_mirror'],
},
{"name" : "loc2",
"activities" : ['cooking', 'eating'],
"devices" : [],
},
{"name" : "loc3",
"activities" : ['sleeping'],
"devices" : ['binary_sensor.motion_bed'],
},
]
act_data = [
{"name" : "sleeping",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("04:00:00"),
"end" : datetime.time.fromisoformat("06:15:00")
},
{"name" : "cooking",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("06:15:00"),
"end" : datetime.time.fromisoformat("08:45:00")
},
{"name" : "eating",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("08:46:00"),
"end" : datetime.time.fromisoformat("10:00:00")
},
{"name" : "sleeping",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("12:00:00"),
"end" : datetime.time.fromisoformat("13:00:00")
},
{"name" : "sleeping",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("02:00:00"),
"end" : datetime.time.fromisoformat("06:30:00")
},
{"name" : "cooking",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("12:00:00"),
"end" : datetime.time.fromisoformat("13:00:00")
},
{"name" : "cooking",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("19:00:00"),
"end" : datetime.time.fromisoformat("00:00:00")
},
{"name" : "cooking",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("23:00:00"),
"end" : datetime.time.fromisoformat("00:00:00")
},
{"name" : "sleeping",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("00:00:00"),
"end" : datetime.time.fromisoformat("03:00:00")
},
]
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.register_location_info(loc_data)
print('raw_act_data: \t' + str(act_data))
print('state_hm: ', hmm_model._state_lbl_hashmap)
#print('obs_hm: ', hmm_model._obs_lbl_hashmap)
self.ctrl.register_activity_info(act_data)
#print('#'*100)
#enc_act_data = hmm_model._encode_act_data(act_data)
#print('enc_act_data: \t' + str(enc_act_data))
self.ctrl.init_model_on_dataset()
hmm = hmm_model._hmm
hmm.set_format_full(True)
self.assertAlmostEqual(1.0, Probs.prob_to_norm(hmm._pi.sum()), 6)
self.assertTrue(hmm.verify_emission_matrix())
self.assertTrue(hmm.verify_transition_matrix())