79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
import pandas as pd
|
|
import patsy
|
|
|
|
from .utils import as_numeric, check_nan, cols_for_formula, convert_pandas_nullable
|
|
|
|
class ShapResult:
|
|
"""
|
|
SHAP values for a regression model
|
|
|
|
See :meth:`yli.regress.RegressionModel.shap`.
|
|
"""
|
|
|
|
def __init__(self, model, shap_values, features):
|
|
self.model = model
|
|
self.shap_values = shap_values
|
|
self.features = features
|
|
|
|
@staticmethod
|
|
def _get_xdata(model):
|
|
df = model.df()
|
|
if df is None:
|
|
raise Exception('Referenced DataFrame has been dropped')
|
|
dep = model.dep
|
|
|
|
# Check for/clean NaNs
|
|
# NaN warning/error will already have been handled in regress, so here we pass nan_policy='omit'
|
|
# Following this, we pass nan_policy='raise' to assert no NaNs remaining
|
|
df = df[[dep] + cols_for_formula(model.formula, df)]
|
|
df = check_nan(df, 'omit')
|
|
|
|
# Ensure numeric type for dependent variable
|
|
df[dep], dep_categories = as_numeric(df[dep])
|
|
|
|
# Convert pandas nullable types for independent variables as this breaks statsmodels
|
|
df = convert_pandas_nullable(df)
|
|
|
|
# Get xdata for SHAP
|
|
dmatrix = patsy.dmatrix(model.formula, df, return_type='dataframe')
|
|
xdata = dmatrix.iloc[:, 1:] # FIXME: Assumes zeroth term is intercept
|
|
|
|
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
|
|
|
|
model = self.model()
|
|
if model is None:
|
|
raise Exception('Referenced RegressionModel has been dropped')
|
|
|
|
xdata = self._get_xdata(model)
|
|
|
|
shap.summary_plot(self.shap_values, xdata, show=False, axis_color='black', **kwargs) # pass show=False to get gcf/gca
|
|
|
|
# Fix colour bar
|
|
# https://stackoverflow.com/questions/70461753/shap-the-color-bar-is-not-displayed-in-the-summary-plot
|
|
ax_colorbar = plt.gcf().axes[-1]
|
|
ax_colorbar.set_aspect('auto')
|
|
ax_colorbar.set_box_aspect(50)
|
|
|
|
return plt.gcf(), plt.gca()
|