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Tool to explain Entity Resolution model predictions

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ExplainER

ERExplain is a package to explain any Entity Resolution model, using a Black-Box approach

Key Features

The explanations can be provided into two different ways:

  • Attribute-Level: the system shows to the user a histogram reporting the importance of the attributes with respect to the prediction. The importance is a number in [0,1]
  • Value-level: the system shows to the user, for each chosen attribute set/single attribute A, the most influential values for A, with respect to the prediction

Usage

To use this package you need some simple requirements:

  1. A model M per Entity Resolution (ER), for example Deepmatcher, but also simpler models, for example a decision tree
  2. A Pandas dataframe D that contains labeled record pairs for ER and that as in this example:
label ltable_product_title ltable_manufacturer rtable_product_title rtable_manufacturer
0 Nvidia rtx 2060 Asus Nvidia Rtx 2070 MSI
1 Asus rog strix rtx 2080 super Asus Asus Geoforce rtx 2060 s Asus

The label 0 indicates a non-matching pair, while label 1 indicates matching pairs. Ltable_ and rtable_ are the prefix for the left and the right attributes respectively. You can use any prefix you prefer

  1. A list of Pandas dataframes containing the sources of records, each of one read in the following way:

source1 = pandas.read_csv('source1_path',dtype=str)

  1. A wrapper function that takes in input three parameters in this exact order: the dataframe D, the model M, and the list columns to ignore in D

Tech

ERExplain uses some well-know libraries:

Installation

To install the package simply run

$ pip install erexplain

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Tool to explain Entity Resolution model predictions

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License

Apache-2.0, Unknown licenses found

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Apache-2.0
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LICENSE.txt

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