boexplain 0.1.1

BOExplain

BOExplain, Explaining Inference Queries with Bayesian Optimization

BOExplain is a library for explaining inference queries with Bayesian optimization. The corresponding paper can be found at https://arxiv.org/abs/2102.05308.

Installation

pip install boexplain

Documentation

The documentation is available at https://sfu-db.github.io/BOExplain/. (shortcut to fmin, fmax)

Getting Started

Derive an explanation for why the predicted rate of having an income over $50K is higher for men compared to women in the UCI ML Adult dataset.

1. Load the data and prepare it for ML.

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

df = pd.read_csv("adult.data",
                 names=[
                     "Age", "Workclass", "fnlwgt", "Education",
                     "Education-Num", "Marital Status", "Occupation",
                     "Relationship", "Race", "Gender", "Capital Gain",
                     "Capital Loss", "Hours per week", "Country", "Income"
                 ],
                 na_values=" ?")

df['Income'].replace({" <=50K": 0, ' >50K': 1}, inplace=True)
df['Gender'].replace({" Male": 0, ' Female': 1}, inplace=True)
df = pd.get_dummies(df)

train, test = train_test_split(df, test_size=0.2)
test = test.drop(columns='Income')

2. Define the objective function that trains a random forest classifier and queries the ratio of predicted rates of having an income over $50K between men and women.

def obj(train_filtered):
    rf = RandomForestClassifier(n_estimators=13, random_state=0)
    rf.fit(train_filtered.drop(columns='Income'), train_filtered['Income'])
    test["prediction"] = rf.predict(test)
    rates = test.groupby("Gender")["prediction"].sum() / test.groupby("Gender")["prediction"].size()
    test.drop(columns='prediction', inplace=True)
    return rates[0] / rates[1]

3. Use the function fmin to minimize the objective function.

from boexplain import fmin

train_filtered = fmin(
    data=train,
    f=obj,
    columns=["Age", "Education-Num"],
    runtime=30,
)

Reproduce the Experiments

To reproduce the experiments, you can clone the repo and create a poetry environment (install Poetry). Run

poetry install

To setup the poetry environment a for jupyter notebook, run

poetry run ipython kernel install --name=boexplain

An ipython kernel has been created for this environemnt.

Adult Experiment

To reproduce the results of the Adult experiment and recreate Figure 6, follow the instruction in adult.ipynb.

Credit Experiment

To reproduce the results of the Credit experiment and recreate Figure 8, follow the instruction in credit.ipynb.

House Experiment

To reproduce the results of the House experiment and recreate Figure 7, follow the instruction in house.ipynb.

Scorpion Synthetic Data Experiment

To reproduce the results of the experiment with Scorpion's synthetic data and corresponding query, and recreate Figure 4, follow the instruction in scorpion.ipynb.