Awesome

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 folder and is compatible with scikit-learn
• the examples/xor_dac.ipynb 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 folder contains examples of how to use DAC to reproducing the qualitative curves on the bike-sharing dataset in the paper
• the simulation script replicates the experiments with running simulations
• the pmlb script replicates the experiments of automatic feature engineering on pmlb datasets

dac animation

a gif demonstrating calculating a DAC curve for a simple tree

related work

• this work is part of an overarching project on interpretable machine learning, guided by the PDR framework for interpretable machine learning
• for related work, see the github repo for disentangled hierarchical dnn interpretations (ICLR 2019)

reference

• feel free to use/share this code openly

• citation for this work:

  @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}
  }