A simple module for outlier detection thanks to Modified Thompson Tau Test
Project description
Installation
mt3
requires Python 3.8+
To install the package run :
pip install mt3
If you are planing to use it with numpy
and/or pandas
, add optionnal dependencies :
pip install mt3[pandas, numpy] # or pip install mt3[numpy] for numpy only
mt3
will then be capable to deal with numpy.ndarray
and pd.Series
.
By default mt3
is provided with a table of Student T critical values. Available confidence levels are [0.9, 0.95, 0.975, 0.99, 0.995, 0.999]
. To be able to use any confidence level, add scipy
optionnal dependency :
pip install mt3[scipy]
Usage
mt3
main function is modified_thompson_tau_test
:
from mt3 import modified_thompson_tau_test
sample = [-4, 3, -5, -2, 0, 1, 1000]
# You can use it with a simple list :
modified_thompson_tau_test(sample, 0.99)
# [False, False, False, False, False, False, True]
# But you can also use it with a numpy.ndarray or a pandas.Series
import numpy as np
import pandas as pd
modified_thompson_tau_test(np.array(sample), 0.99)
# [False False False False False False True] (numpy array)
modified_thompson_tau_test(pd.Series(sample), 0.99)
# [False False False False False False True] (numpy array)
# If you have nan values in your array or Series, you can choose to treat
# them as outliers
sample_with_nan = np.array([-4, np.nan, 3, -5, -2, 0, 1, 1000])
modified_thompson_tau_test(sample_with_nan, 0.99, nan_is_outlier=True)
# [False True False False False False False True] (numpy array)
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