upload boexplain

LICENSE

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+MIT License
+
+Copyright (c) 2021, Brandon Lockhart
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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-# BOExplain
-Explaining Inference Queries with Bayesian Optimization
+# 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/](https://sfu-db.github.io/BOExplain/). (shortcut to [fmin](https://sfu-db.github.io/BOExplain/api_reference/boexplain.files.search.html#boexplain.files.search.fmin), [fmax](https://sfu-db.github.io/BOExplain/api_reference/boexplain.files.search.html#boexplain.files.search.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](https://archive.ics.uci.edu/ml/datasets/adult).
+
+1. Load the data and prepare it for ML.
+``` python
+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.
+``` python
+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.
+``` python
+from boexplain import fmin
+
+train_filtered = fmin(
+    data=train,
+    f=obj,
+    columns=["Age", "Education-Num"],
+    runtime=30,
+)
+```
+<!-- which returns a predicate 28 <= Age <= 59 and 6 <= Education-Num <= 16. Removing the tuples satisfying the returned predicate reduces the ratio from 3.54 to 2.7. -->
+
+## Reproduce the Experiments
+
+To reproduce the experiments, you can clone the repo and create a poetry environment (install [Poetry](https://python-poetry.org/docs/#installation)). Run
+
+```bash
+poetry install
+```
+
+To setup the poetry environment a for jupyter notebook, run
+
+```bash
+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](https://github.com/sfu-db/BOExplain/blob/main/adult.ipynb).
+
+### Credit Experiment
+
+To reproduce the results of the Credit experiment and recreate Figure 8, follow the instruction in [credit.ipynb](https://github.com/sfu-db/BOExplain/blob/main/credit.ipynb).
+
+### House Experiment
+
+To reproduce the results of the House experiment and recreate Figure 7, follow the instruction in [house.ipynb](https://github.com/sfu-db/BOExplain/blob/main/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](https://github.com/sfu-db/BOExplain/blob/main/scorpion.ipynb). 

boexplain/__init__.py

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+from .files import fmin, fmax
+
+__all__ = ["fmin", "fmax"]

boexplain/files/__init__.py

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+from .search import fmin, fmax
+
+__all__ = ["fmin", "fmax"]

boexplain/files/cat_xform.py

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+import pandas as pd
+import numpy as np
+
+
+def individual_contribution(df, objective, cat_cols, **kwargs):
+    # dictionary of dictionaries, one dictionary for each column
+    # dictinary keys are the categorical values and the values are the individual contribution
+    # for each value in the column, compute the individual contribution of that column
+    # ie, remove tuples satisfying the single-clause predicate 'col=val',
+    # and compute the objective function with this data
+
+    cat_val_to_indiv_cont = {
+        col: {val: objective(df[df[col] != val], **kwargs) for val in df[col].unique()}
+        for col in cat_cols
+    }
+
+    return cat_val_to_indiv_cont