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 = ModelHMM_log_scaled(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_bench_train_loss(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)
self.ctrl.register_benchmark()
#self.ctrl.register_loss_file_path('/home/cmeier/code/tmp/train_loss.log')
self.ctrl.register_loss_file_path(
'/home/cmeier/code/data/hassbrain/media/tmp/trainloss.csv')
self.ctrl.train_model()
self.ctrl.show_plot()
def test_bench_train_loss_and_graph(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)
self.ctrl.register_benchmark()
self.ctrl.register_loss_file_path(
'/home/cmeier/code/tmp/train_loss.csv')
self.ctrl.train_model()
self.ctrl.save_loss_plot_to_file(
'/home/cmeier/code/tmp/train_loss_graph.png')
#self.ctrl.save_visualization_to_file('/home/cmeier/code/data/tmp/train_loss_graph.img')
def test_generate_visualization(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)
self.ctrl.save_visualization_to_file(
'/home/cmeier/code/tmp/visualization.png')
def test_bench_train_loss_and_graph(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)
self.ctrl.register_benchmark()
self.ctrl.register_loss_file_path(
'/home/cmeier/code/tmp/train_loss.csv')
self.ctrl.train_model()
self.ctrl.save_loss_plot_to_file(
'/home/cmeier/code/tmp/train_loss_graph.png')
self.ctrl.save_visualization_to_file(
'/home/cmeier/code/tmp/visualization.png')
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)