metric_array = [
metrics.duration_sitting, metrics.duration_walking,
metrics.duration_washing, metrics.duration_eating,
metrics.duration_sleeping, metrics.duration_studying,
metrics.number_of_unique_activities
]
metric_container, date_container = metrics.run_metric_array(
metric_array, csv='./output/accelerometer_metrics.csv')
return metric_container, date_container
if __name__ == '__main__':
ts, X, y = get_raw_ts_X_y()
features_figure(X[0:X.size:50], feature_names=['X', 'Y', 'Z'])
X, y = preprocess_X_y(ts, X, y)
(X_train, y_train), (X_test, y_test) = split_train_test(X, y)
clf_grid = get_classifier_grid()
clf_grid.fit(X_train, y_train)
print_summary(clf_grid, X_test, y_test)
metric_container_daily, date_container_daily = activity_metrics(
y, ts, 'daily')
figures_dict = plot_metrics(metric_container_daily, date_container_daily)
for key, fig in figures_dict.items():
fig.savefig(os.path.join('./output/', key))
if __name__ == '__main__':
houses = ['A']
for house_ in houses:
ts, X, y = get_raw_ts_X_y(house_)
X, y = preprocess_X_y(ts, X, y)
(X_train, y_train), (X_test, y_test) = split_train_test(X, y)
clf_grid = get_classifier_grid()
clf_grid.fit(X_train, y_train)
print_summary(clf_grid, X_test, y_test)
metric_container_daily, date_container_daily = localisation_metrics(
y, ts, 'daily')
figures_dict = plot_metrics(
metric_container_daily,
date_container_daily,
labels_=['foyer', 'bedroom', 'living_room', 'bathroom'])
fig, ax = features_figure(X,
feature_names=[
'AP1', 'AP2', 'AP3', 'AP4', 'AP5', 'AP6',
'AP7', 'AP8'
])
figures_dict['features'] = fig
for key, fig in figures_dict.items():
fig.savefig(os.path.join('./output/', key))