Implement utilities for SHAP values in regression

yli/regress.py

@@ -32,6 +32,7 @@ import weakref
 
 from .bayes_factors import BayesFactor, bayesfactor_afbf
 from .config import config
+from .shap import ShapResult
 from .sig_tests import ChiSquaredResult, FTestResult
 from .utils import Estimate, PValueStyle, as_numeric, check_nan, cols_for_formula, convert_pandas_nullable, fmt_p, formula_factor_ref_category, parse_patsy_term
 
@@ -460,6 +461,26 @@ class RegressionResult:
 			self.exp
 		)
 	
+	def shap(self, **kwargs):
+		# TODO: Documentation
+		
+		import shap
+		
+		xdata = ShapResult._get_xdata(self)
+		
+		# Combine terms into single list
+		params = []
+		for term in self.terms.values():
+			if isinstance(term, SingleTerm):
+				params.append(term.beta.point)
+			else:
+				params.extend(s.beta.point for s in term.categories.values())
+		
+		explainer = shap.LinearExplainer((np.array(params[1:]), params[0]), xdata, **kwargs)  # FIXME: Assumes zeroth term is intercept
+		shap_values = explainer.shap_values(xdata).astype('float')
+		
+		return ShapResult(weakref.ref(self), shap_values, list(xdata.columns))
+	
 	def _header_table(self, html):
 		"""Return the entries for the header table"""
 		

yli/shap.py

@@ -0,0 +1,60 @@
+import pandas as pd
+import patsy
+
+from .utils import as_numeric, check_nan, cols_for_formula, convert_pandas_nullable
+
+class ShapResult:
+	# TODO: Documentation
+	
+	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):
+		return pd.Series(abs(self.shap_values).mean(axis=0), index=self.features)
+	
+	def plot(self, **kwargs):
+		import matplotlib.pyplot as plt
+		import shap
+		
+		model = self.model()
+		if model is None:
+			raise Exception('Referenced RegressionResult 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()