def test_SigPro_nested_pipeline():
"""Test nested sigpro primitive."""
# setup
aggregations = [{
'primitive': 'sigpro.SigPro',
'init_params': {
'keep_columns':
True,
'input_is_dataframe':
False,
'values_column_name':
'amplitude_values',
'transformations': [{
'primitive':
'sigpro.transformations.frequency.band.frequency_band',
'init_params': {
'low': 100,
'high': 200
}
}],
'aggregations': [{
'primitive':
'sigpro.aggregations.amplitude.statistical.mean'
}]
}
}, {
'primitive': 'sigpro.SigPro',
'init_params': {
'input_is_dataframe':
False,
'values_column_name':
'amplitude_values',
'transformations': [{
'primitive':
'sigpro.transformations.frequency.band.frequency_band',
'init_params': {
'low': 3000,
'high': 4000
}
}],
'aggregations': [{
'name':
'band_3k_4k_mean',
'primitive':
'sigpro.aggregations.amplitude.statistical.mean'
}],
}
}, {
'primitive':
'sigpro.aggregations.amplitude.statistical.mean'
}]
pipeline = MLPipeline({
'primitives': ['sigpro.SigPro'],
'init_params': {
'sigpro.SigPro#1': {
'transformations': [{
'primitive':
'sigpro.transformations.frequency.fft.fft_real'
}],
'aggregations':
aggregations
}
}
})
data = pd.DataFrame({
'timestamp': pd.to_datetime(['2020-01-01 00:00:00']),
'values': [[1, 2, 3, 4, 5, 6]],
'sampling_frequency': [10000],
'dummy': [1],
})
# run
output = pipeline.predict(readings=data)
outputs = dict(zip(pipeline.get_output_names(), output))
# assert
expected_features = [
'fft_real.SigPro.frequency_band.mean.mean_value',
'fft_real.SigPro.frequency_band.band_3k_4k_mean.mean_value',
'fft_real.mean.mean_value'
]
assert outputs['feature_columns'] == expected_features
expected_readings = pd.DataFrame({
'fft_real.SigPro.frequency_band.mean.mean_value': [float('nan')],
'fft_real.SigPro.frequency_band.band_3k_4k_mean.mean_value': [-3.0],
'fft_real.mean.mean_value': [1.0],
})
pd.testing.assert_frame_equal(expected_readings, outputs['readings'])
def test_SigPro():
# setup
pipeline = MLPipeline({
'primitives': [
'sigpro.SigPro',
'sigpro.SigPro',
],
'init_params': {
'sigpro.SigPro#1': {
'values_column_name':
'signal_values',
'keep_columns':
True,
'transformations': [
{
'name':
'identity',
'primitive':
'sigpro.transformations.amplitude.identity.identity',
},
{
'name':
'fft',
'primitive':
'sigpro.transformations.frequency.fft.fft_real',
},
],
'aggregations': [
{
'name':
'mean',
'primitive':
'sigpro.aggregations.amplitude.statistical.mean',
},
{
'name':
'rms',
'primitive':
'sigpro.aggregations.amplitude.statistical.rms',
},
]
},
'sigpro.SigPro#2': {
'values_column_name':
'signal_values',
'keep_columns': ['dummy'],
'transformations': [
{
'name':
'identity',
'primitive':
'sigpro.transformations.amplitude.identity.identity',
},
],
'aggregations': [
{
'name':
'std',
'primitive':
'sigpro.aggregations.amplitude.statistical.std',
},
]
}
}
})
data = pd.DataFrame({
'timestamp': pd.to_datetime(['2020-01-01 00:00:00']),
'signal_values': [[1, 2, 3, 4]],
'sampling_frequency': [1000],
'dummy': [1],
})
# run
output = pipeline.predict(readings=data)
outputs = dict(zip(pipeline.get_output_names(), output))
# assert
expected_features = [
'identity.fft.mean.mean_value', 'identity.fft.rms.rms_value',
'identity.std.std_value'
]
assert outputs['feature_columns'] == expected_features
expected_readings = pd.DataFrame({
'dummy': [1],
'identity.fft.mean.mean_value': [1.0],
'identity.fft.rms.rms_value': [5.291503],
'identity.std.std_value': [1.118034],
})
pd.testing.assert_frame_equal(expected_readings, outputs['readings'])
