pymof 0.2

Mass ratio variance-based outlier factor (MOF)

pymof

Installation

You can install pymof using pip

pip install pymof           # normal install
pip install --upgrade pymof  # or update if needed

Required Dependencies :

• Python 3.9 or higher
• numpy>=1.23
• numba>=0.56.0
• scipy>=1.8.0
• scikit-learn>=1.2.0
• matplotlib>=3.5

Mass ratio variance-based outlier factor (MOF)

---

the outlier score of each data point is called MOF. It measures the global deviation of density given sample with respect to other data points. it is global in the outlier score depend on how isolated. data point is with respect to all data points in the data set. the variance of mass ratio can identify data points that have a substantially. lower density compared to other data points. These are considered outliers.

MOF()

Initial a model of MOF

Parameters :
Return :
        self : object
                object of MOF model

MOF.fit(Data)

Fit data to MOF model
Note The data size should not exceed 10000 points because MOF uses high memory.

Parameters :
        Data  : numpy array of shape (n_points, d_dimensions)
                The input samples.
Return :
        self  : object
                fitted estimator

MOF.visualize()

Visualize data points with MOF's scores
Note cannot visualize data points with dimension more than 3

Parameters :
Return :
    decision_scores_ : numpy array of shape (n_samples)
                                decision score for each point

MOF attributes

Attributes	Type	Details
MOF.Data	numpy array of shape (n_points, d_dimensions)	input data for model
MOF.MassRatio	numpy array of shape (n_samples, n_points)	MassRatio for each a pair of points
MOF.decision_scores_	numpy array of shape (n_samples)	decision score for each point

Examples

# This example demonstrates  the usage of MOF
from pymof import MOF
import numpy as np
X = [[-2.30258509,  7.01040212,  5.80242044],
    [ 0.09531018,  7.13894636,  5.91106761],
    [ 0.09531018,  7.61928251,  5.80242044],
    [ 0.09531018,  7.29580291,  6.01640103],
    [-2.30258509, 12.43197678,  5.79331844],
    [ 1.13140211,  9.53156118,  7.22336862],
    [-2.30258509,  7.09431783,  5.79939564],
    [ 0.09531018,  7.50444662,  5.82037962],
    [ 0.09531018,  7.8184705,   5.82334171],
    [ 0.09531018,  7.25212482,  5.91106761]]
X = np.array(X)
c = MOF()
c.fit(X)
scores = c.decision_scores_
print(scores)
c.visualize()

Output

[0.34541068 0.11101711 0.07193073 0.07520904 1.51480377 0.94558894 0.27585581 0.06242823 0.2204504  0.02247725]