Add documentation for SHAP

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RunasSudo 2023-02-25 23:46:48 +11:00
parent 88509c71a3
commit 18727cd950
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
4 changed files with 35 additions and 3 deletions

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@ -45,12 +45,13 @@ The mandatory dependencies of this library are:
Optional dependencies are:
* matplotlib and [seaborn](https://seaborn.pydata.org/), for plotting functions
* [mpmath](https://mpmath.org/), for *beta_ratio* and *beta_oddsratio*
* [PyCryptodome](https://www.pycryptodome.org/), for *pickle_write_encrypted* and *pickle_read_encrypted*
* [rpy2](https://rpy2.github.io/), with R packages:
* [BFpack](https://cran.r-project.org/web/packages/BFpack/index.html), for *bayesfactor_afbf* (*RegressionResult.bayesfactor_beta_zero*)
* [logistf](https://cran.r-project.org/web/packages/logistf/index.html), for *PenalisedLogit*
* matplotlib and [seaborn](https://seaborn.pydata.org/), for plotting functions
* [shap](https://shap.readthedocs.io/en/latest/), for *RegressionResult.shap*
## Functions

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@ -30,6 +30,9 @@ Result classes
.. autoclass:: yli.regress.RegressionResult
:members:
.. autoclass:: yli.shap.ShapResult
:members:
Model terms
-----------

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@ -464,7 +464,17 @@ class RegressionResult:
)
def shap(self, **kwargs):
# TODO: Documentation
"""
Compute SHAP values for the model
Uses the Python *shap* library.
:param kwargs: Keyword arguments to pass to *shap.LinearExplainer*
:rtype: :class:`yli.shap.ShapResult`
**Reference:** Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Guyon I, Von Luxburg U, Bengio S, et al., editors. *Advances in Neural Information Processing Systems*; 2017 Dec 49; Long Beach, CA. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
"""
import shap

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@ -4,7 +4,11 @@ import patsy
from .utils import as_numeric, check_nan, cols_for_formula, convert_pandas_nullable
class ShapResult:
# TODO: Documentation
"""
SHAP values for a regression model
See :meth:`yli.regress.RegressionResult.shap`.
"""
def __init__(self, model, shap_values, features):
self.model = model
@ -37,9 +41,23 @@ class ShapResult:
return xdata
def mean(self):
"""
Compute the mean absolute SHAP value for each parameter
:rtype: Series
"""
return pd.Series(abs(self.shap_values).mean(axis=0), index=self.features)
def plot(self, **kwargs):
"""
Generate a scatterplot of the SHAP values
Uses the Python *matplotlib* library.
:rtype: (Figure, Axes)
"""
import matplotlib.pyplot as plt
import shap