Macrobase Diff minimal implementation

This is a mnimial implementation of an idea from DIFF: A Relational Interface for Large-Scale Data Explanation F.Abuzaid et al 2018.

In short: Given a table of numerical and categorical data and a query dividing the table into two groups (outliers/inliers) return attributes (categorical values) that are more common among the outliers (so called explanations).

See a simple case study using this prototype.

You can give it a quick try with provided test.csv:

        num_col,cat_col1,cat_col2
   2   │ 99.8,B,A
   3   │ 1.0,A,A
   4   │ 1.1,A,A
   5   │ 1.2,A,A
   6   │ 1.11,B,A
   7   │ 1.12,A,B
   8   │ 1.22,A,A
   9   │ 1.3,A,A
  10   │ 109.0,B,B

Run the following command to get the output for the basic risk-ratio pipeline:

python -m mbdiff --query 'num_col > 20.0' test.csv

The output will contain two sections:

• rows satisfying your query (outliers)
• attribute combinations that are most associated with the selected outlier group (num_col above the value of 20.0) sorted by their risk ratio.

Outliers:
   num_col cat_col1 cat_col2
0     99.8        B        A
8    109.0        B        B
Explanations
      score  cat_col1    cat_col2
--  -------  ----------  ----------
 0      8    B           B
 1      3.5  -           B
 2      3.5  B           A
Attribute combinations below thresholds
    cat_col1
--  ----------
 0  B

Further Work

The original Macrobase Diff provides more contributions:

• Streaming implementation
• SQL-like REPL interface (to showcase how it could be implemented within an SQL client)
• Plenty of optimizations All of the above are worthwhile for follow-up work in this project.