eltonlaw / impyute

Complete mnar method in the Corruptor class.

Simplified, MNAR (Missing Not at Random) is a type of missingness in which the probability of a value being missing is conditional (in whole or in part) on unobserved data. Missingness may be simultaneously conditional on observed data in addition to unobserved data.

Implementation: Generate a random selection of new features and base missingness on these features. The number of features to generate may be based on some fraction of the existing features, or a random number between 1 - n_features. These features could (should?) be a mix of continuous & categorical; this could be based on the fraction of each respective feature type in the existing features. Once generated, impose missingness based on these new features.

Be sure that functions accept & return matrices.
Be sure to follow the 4 steps outlined in contributing.md

The below labels are for DDFG (Data Days for Good) participant reference:
Priority: High
Difficulty: Medium

impyute/impyute/dataset/corrupt.py

Lines 48 to 50 in 2c25368

    
           def mnar(self): 
        
               """ Overwrites values with MNAR placed NaN's """ 
        
               pass

	def mnar(self):
	""" Overwrites values with MNAR placed NaN's """
	pass

[DDFG] Complete MNAR missingness generation