Python UMAFALL_Model.load_training_data_by_peopleの例

コード例 #1

from scripts.save_workspace import save

#===INITIALIZATION===#
Debug.DEBUG = 0
umafall = UMAFALL_Model()
processing = Processing_DB_Files()
project = Project()
s = save()
#window = 10 # Janela Fixa
window = 30  # Melhor Janela
persons = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
#persons = [7,8,9,10,11,12,13,14,15]

for p in persons:

    data = umafall.load_training_data_by_people(
        p, additional_where="and body=2 and sensor=2")  #MAGNETOMETER in WAIST
    print("Slicing Window....")
    data_tsfresh, y = umafall.slice_by_window_tsfresh(data, window)
    if data_tsfresh is None:
        continue

    y.index += 1
    del data_tsfresh["activity"]

    classes_counts = y.value_counts()
    if len(classes_counts) > 1:
        relevant_features = extract_relevant_features(data_tsfresh,
                                                      y,
                                                      column_id='id',
                                                      column_sort='time')
        s.save_var(

コード例 #2

ファイル: umafall_get_all_relevant_features_each_person_all_sensors.py プロジェクト: SensorNet-UFAL/NewActivityRecognition

from scripts.save_workspace import save

#===INITIALIZATION===#
Debug.DEBUG = 0
umafall = UMAFALL_Model()
processing = Processing_DB_Files()
project = Project()
s = save()
#window = 10 # Janela Fixa
window = 30  # Melhor Janela
persons = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
#persons = [7,8,9,10,11,12,13,14,15]

for p in persons:

    data = umafall.load_training_data_by_people(
        p, additional_where="")  #ALL SENSORS
    print("Slicing Window....")
    data_tsfresh, y = umafall.slice_by_window_tsfresh(data, window)
    if data_tsfresh is None:
        continue

    y.index += 1
    del data_tsfresh["activity"]

    classes_counts = y.value_counts()
    if len(classes_counts) > 1:
        relevant_features = extract_relevant_features(data_tsfresh,
                                                      y,
                                                      column_id='id',
                                                      column_sort='time')
        s.save_var(

コード例 #3

ファイル: umafall_select_best_window.py プロジェクト: SensorNet-UFAL/NewActivityRecognition

persons = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
get_accuracy = Get_Accuracy()
balance_data = BalanceData()
threshold_balance_data = 40
#Select de best windows
t = time.time()
best_model = ExtraTreesClassifier(n_estimators=1000, random_state=0)
w_accuracies = pd.DataFrame(columns=["window", "accurary"])
p = 1  # pessoa com mais registros
project.log(
    "=====================UMAFALL_SELECT_BEST_WINDOWS=====================",
    file="umafall_log_best_window.log")
for w in range(10, 110, 10):

    print("Load data with window len = {}".format(w))
    data = umafall.load_training_data_by_people(
        p, additional_where="and sensor=2 and body=2")
    print("Slicing Window....")
    data_tsfresh, y = umafall.slice_by_window_tsfresh(data, w)
    y.index += 1
    del data_tsfresh["activity"]

    classes_counts = y.value_counts()
    if len(classes_counts) > 1:
        relevant_features = extract_relevant_features(data_tsfresh,
                                                      y,
                                                      column_id='id',
                                                      column_sort='time')
        y = pd.DataFrame(y, columns=[umafall.label_tag])

        balanced_data = balance_data.balance_data(relevant_features, y,
                                                  threshold_balance_data)