Outlier Detection
Detect and filter outliers.
Install
pip install sensor_dataset
Z-SCORE Normalization
Normalize data with Z-SCORE
from sensor_dataset.outlier_detection import ZSCORE
Get a normalized Koalas dataframe for the sensor dataset and fig objects by calling:
kdf, figs = ZSCORE()
figs['NORMAL'].write_image("images/zscore_normal.png")
figs['RECOVERING'].write_image("images/zscore_recovering.png")
figs['BROKEN'].write_image("images/zscore_broken.png")
When running on a notebook you may show an interactive plot by using:
fig.show()
IQR
Filter data using IQR
from sensor_dataset.outlier_detection import IQR
kdf, figs = IQR()
figs['NORMAL'].write_image("images/iqr_normal.png")
figs['RECOVERING'].write_image("images/iqr_recovering.png")
figs['BROKEN'].write_image("images/iqr_broken.png")
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