def prepare_data():
dataset = load_rutgers()
print('Rutgers loaded ...')
dataset = CustomInterpolation(source='rssi',
strategy='constant',
constant=0).fit_transform(dataset)
print('Interpolation applied ...')
dataset = SyntheticFeatures(source='rssi',
window_size=10).fit_transform(dataset)
print('Synthetic features created ...')
dataset = PRR(source='received', window_size=10, ahead=1,
target='prr').fit_transform(dataset)
print('PRR created ...')
for df in dataset:
df['class_overall'] = prr_to_label(df['received'].astype(bool).sum() /
300.)
print('Created special feature `class_overall`')
dataset = CustomMerger().fit_transform(dataset)
print('Datasets merged ...')
dataset['class'] = dataset['prr'].apply(prr_to_label)
print('Classification applied ...')
dataset = dataset.dropna()
dataset = dataset.drop(['noise', 'src', 'dst', 'received', 'prr'], axis=1)
print('Drop useless features, drop lines with NaN')
return dataset
def prepare_data():
dataset = load_rutgers()
print('Rutgers loaded ...')
dataset = CustomInterpolation(source='rssi',
strategy='constant',
constant=0).fit_transform(dataset)
print('Interpolation applied ...')
dataset = SyntheticFeatures(source='rssi',
window_size=10).fit_transform(dataset)
print('Synthetic features created ...')
dataset = PRR(source='received', window_size=10, ahead=1,
target='prr').fit_transform(dataset)
print('PRR created ...')
print('Apply discrete derivation (backward difference)')
for i in range(len(dataset)):
dataset[i]['drssi'] = dataset[i]['rssi'].diff()
dataset = CustomMerger().fit_transform(dataset)
print('Datasets merged ...')
dataset['class'] = dataset['prr'].apply(prr_to_label)
print('Classification applied ...')
dataset = dataset.dropna()
dataset = dataset.drop(['noise', 'src', 'dst', 'received', 'prr'], axis=1)
print('Drop useless features, drop lines with NaN')
dataset = poly_features(dataset,
include=['rssi', 'rssi_avg', 'rssi_std'],
degree=4,
include_bias=True)
print('Polynomials applied ...')
# Special synthetic features
dataset['rssi^-1'] = 1. / dataset['rssi']
dataset['rssi^-2'] = 1. / dataset['rssi^2']
dataset['rssi^-3'] = 1. / dataset['rssi^3']
dataset['rssi^-4'] = 1. / dataset['rssi^4']
dataset['rssi_avg^-1'] = 1. / dataset['rssi_avg']
dataset['rssi_avg^-2'] = 1. / dataset['rssi_avg^2']
dataset['rssi_avg^-3'] = 1. / dataset['rssi_avg^3']
dataset['rssi_avg^-4'] = 1. / dataset['rssi_avg^4']
dataset['rssi_std^-1'] = 1. / dataset['rssi_std']
dataset['rssi_std^-2'] = 1. / dataset['rssi_std^2']
dataset['rssi_std^-3'] = 1. / dataset['rssi_std^3']
dataset['rssi_std^-4'] = 1. / dataset['rssi_std^4']
return dataset