https://github.com/csinva/disentangled-attribution-curves

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
https://github.com/csinva/disentangled-attribution-curves

ai artificial-intelligence boosting ensemble-model explainable-ai feature-engineering feature-importance interpretability machine-learning ml python random-forest random-forests scikit-learn statistics stats

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Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Host: GitHub
URL: https://github.com/csinva/disentangled-attribution-curves
Owner: csinva
License: mit
Created: 2018-09-01T02:41:23.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2021-02-11T21:17:52.000Z (over 5 years ago)
Last Synced: 2025-03-30T22:11:13.435Z (about 1 year ago)
Topics: ai, artificial-intelligence, boosting, ensemble-model, explainable-ai, feature-engineering, feature-importance, interpretability, machine-learning, ml, python, random-forest, random-forests, scikit-learn, statistics, stats
Language: Python
Homepage: https://arxiv.org/abs/1905.07631
Size: 4.63 MB
Stars: 27
Watchers: 6
Forks: 4
Open Issues: 1
Metadata Files:
- Readme: readme.md
- License: LICENSE

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Disentangled attribution curves (DAC) 🔎

Official code for using / reproducing DAC from the paper Disentangled Attribution Curves for Interpreting Random Forests (arXiv 2018 pdf)

Note: this repo is actively maintained. For any questions please file an issue.

![](figs/fig_xor.png)

# documentation

## using DAC on new models
- quick install: `pip install git+https://github.com/csinva/disentangled-attribution-curves`
- the core of the method code lies in the [dac](dac) folder and is compatible with scikit-learn
- the [examples/xor_dac.ipynb](examples/simple_ex.py) folder contains examples of how to use DAC on a new dataset with some simple datasets (e.g. XOR, etc.)
- the basic api consists of two functions: `from dac import dac, dac_plot`
- ```dac(forest, input_space_x, outcome_space_y, assignment, S, continuous_y=True, class_id=1)```
- inputs:

- `forest`: an sklearn ensemble of decision trees
- `input_space_x`: the matrix of training data (feature values), a numpy 2D array
- `outcome_space_y`: the array of training data (labels/regression targets), a numpy 1D array
- `assignment`: a matrix of feature values that will have their DAC importance score evaluated, a numpy 2D array
- `S`: a binary indicator of whether to include each feature in the importance calculation, a numpy 1D array with values 0 and 1 only
- `continuous_y`: a boolean indicator of whether the y targets are regression(true) or classification(false), defaults to true
- `class_id`: if classification, the class value to return proportions for, defaults to 1
- returns

- `dac_curve`
- for regression: a numpy array whose length corresponds to the number of samples in the assignment input. Each entry is a DAC importance score, a
float between min(outcome_space_y) and max(outcome_space_y)
- for classification: a numpy array whose length corresponds to the number of samples in the assignment input. Each entry is a DAC importance score, a
float between 0 and 1
- ```dac_plot(forest, input_space_x, outcome_space_y, S, interval_x, interval_y, di_x, di_y, C, continuous_y, weights```
- inputs
- `forest`: an sklearn ensemble of decision trees (random forest or adaboosted forest)
- `input_space_x`: the matrix of training data (feature values), a numpy 2D array
- `outcome_space_y`: the array of training data (labels/regression targets), a numpy 1D array
- `S`: a binary indicator of whether to include each feature in the importance calculation, a numpy 1D array with values 0 and 1 only
- `interval_x`: an interval for the x axis of the plot, defaults to None. If None, a reasonable interval will be extrapolated from the range
of the first feature specified in S.
- `interval_y`: an interval for the y axis of the plot (only applicable to heat maps), defaults to None.
If None, a reasonable interval will be extrapolated from the range of the second feature specified in S.
- `di_x`: a step length for the x axis of the plot, defaults to None. If None, a reasonable step length will be extrapolated from the range
of the first feature specified in S.
- `di_y`: a step length for the y axis of the plot (only applicable to heat maps), defaults to None. If None, a reasonable step
length will be extrapolated from the range of the second feature specified in S.
- `C`: a hyper-parameter specifying the number of standard deviations samples can be from the mean of the leaf and be counted into the curve. Smaller values
yield a more sensitive curve, larger values yield a smoother curve.
- `continuous_y`: a boolean indicator of whether the y targets are regression(true) or classification(false), defaults to true
- `weights`: weights for the individual estimators contributions to the curve, defaults to None. If None, weights will be extrapolated from the forest type.
- returns
- ```dac_curve``` a numpy array containing values for the DAC curve or heatmap describing the interaction of the variables specified in S

## reproducing results from the paper

- the [examples/bike_sharing_dac.ipynb](examples/bike_sharing_dac.ipynb) folder contains examples of how to use DAC to reproducing the qualitative curves on the bike-sharing dataset in the paper
- the [simulation script](experiments/simulation/run_sim_synthetic.py) replicates the experiments with running simulations
- the [pmlb script](experiments/pmlb/run_dac_feature_engineered.py) replicates the experiments of automatic feature engineering on pmlb datasets

## dac animation

*a gif demonstrating calculating a DAC curve for a simple tree*

![](figs/dac_animated.gif)

# related work

- this work is part of an overarching project on interpretable machine learning, guided by the [PDR framework](https://arxiv.org/abs/1901.04592) for interpretable machine learning
- for related work, see the [github repo](https://github.com/csinva/hierarchical-dnn-interpretations) for disentangled hierarchical dnn interpretations ([ICLR 2019](https://arxiv.org/abs/1806.05337))

# reference

- feel free to use/share this code openly

- citation for this work:

```c
@article{devlin2019disentangled,
title={Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees},
author={Devlin, Summer and Singh, Chandan and Murdoch, W James and Yu, Bin},
journal={arXiv preprint arXiv:1905.07631},
year={2019}
}
```

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Disentangled attribution curves (DAC) 🔎