how-to-use.py

from everywhereml.data import Dataset
from everywhereml.data.collect import SerialCollector
from everywhereml.preprocessing import Pipeline, MinMaxScaler, Window, SpectralFeatures
from everywhereml.sklearn.ensemble import RandomForestClassifier


if __name__ == '__main__':
    """
    Create a SerialCollector object.
    Each data line is marked by the 'IMU:' string
    Collect 30 seconds of data for each gesture
    Replace the port with your own!

    If a imu.csv file already exists, skip collection
    """

    try:
        imu_dataset = Dataset.from_csv(
            'imu.csv', 
            name='ContinuousMotion', 
            target_name_column='target_name'
        )
        
    except FileNotFoundError:
        imu_collector = SerialCollector(
            port='/dev/cu.usbmodem141401', 
            baud=115200, 
            start_of_frame='IMU:', 
            feature_names=['ax', 'ay', 'az', 'gx', 'gy', 'gz']
        )
        imu_dataset = imu_collector.collect_many_classes(
            dataset_name='ContinuousMotion', 
            duration=30
        )
        
        # save dataset to file for later use
        imu_dataset.df.to_csv('imu.csv', index=False)    

    # this is the frequency of your sensor
    # change according to your hardware
    sampling_frequency = 104
    mean_gesture_duration_in_millis = 1000
    window_length = sampling_frequency * mean_gesture_duration_in_millis // 1000

    imu_pipeline = Pipeline(name='ContinousMotionPipeline', steps=[
        MinMaxScaler(),
        # shift can be an integer (number of samples) or a float (percent)
        Window(length=window_length, shift=0.3),
        # order can either be 1 (first-order features) or 2 (add second-order features)
        SpectralFeatures(order=2)
    ])
    
    imu_dataset.apply(imu_pipeline)
    
    # classification
    imu_classifier = RandomForestClassifier(n_estimators=20, max_depth=20)
    imu_train, imu_test = imu_dataset.split(test_size=0.3)
    imu_classifier.fit(imu_train)

    print('Score on test set: %.2f' % imu_classifier.score(imu_test))