Refactor parsing of Patsy formulas

yli/regress.py

@@ -16,7 +16,6 @@
 
 import numpy as np
 import pandas as pd
-import patsy
 from scipy import stats
 import statsmodels
 import statsmodels.api as sm
@@ -28,7 +27,7 @@ import itertools
 
 from .bayes_factors import BayesFactor, bayesfactor_afbf
 from .sig_tests import FTestResult
-from .utils import Estimate, check_nan, fmt_p
+from .utils import Estimate, check_nan, cols_for_formula, fmt_p, formula_factor_ref_category
 
 def vif(df, formula=None, nan_policy='warn'):
 	"""
@@ -59,22 +58,6 @@ def vif(df, formula=None, nan_policy='warn'):
 	
 	return pd.Series(vifs)
 
-def cols_for_formula(formula):
-	"""Return the columns corresponding to the Patsy formula"""
-	
-	model_desc = patsy.ModelDesc.from_formula(formula)
-	cols = set()
-	for term in model_desc.rhs_termlist:
-		for factor in term.factors:
-			name = factor.name()
-			if '(' in name:
-				# FIXME: Is there a better way of doing this?
-				name = name[name.index('(')+1:name.index(')')]
-			
-			cols.add(name)
-	
-	return list(cols)
-
 # ----------
 # Regression
 
@@ -408,15 +391,13 @@ def regress(
 			term = raw_name[:raw_name.index('[T.')]
 			category = raw_name[raw_name.index('[T.')+3:raw_name.index(']')]
 			
+			patsy_factor = term
 			if term.startswith('C('):
 				term = term[2:-1]
 			
 			# Add a new categorical term if not exists
 			if term not in terms:
-				# Try to guess the ref_category
-				# FIXME: This is a VERY brittle implementation!!
-				ref_category = sorted(df[term].unique())[0]
-				
+				ref_category = formula_factor_ref_category(formula, df, patsy_factor)
 				terms[term] = CategoricalTerm({}, ref_category)
 			
 			terms[term].categories[category] = SingleTerm(raw_name, beta, result.pvalues[raw_name])

yli/utils.py

@@ -16,9 +16,13 @@
 
 import numpy as np
 import pandas as pd
+import patsy
 
 import warnings
 
+# ----------------------------
+# Data cleaning and validation
+
 def check_nan(df, nan_policy):
 	"""Check df against nan_policy and return cleaned input"""
 	
@@ -53,6 +57,9 @@ def as_2groups(df, data, group):
 	
 	return group1, data1, group2, data2
 
+# ----------
+# Formatting
+
 def do_fmt_p(p):
 	"""Return sign and formatted p value"""
 	
@@ -91,6 +98,9 @@ def fmt_p(p, *, html, nospace=False):
 	
 	return pfmt
 
+# ------------------------------
+# General result-related classes
+
 class Estimate:
 	"""A point estimate and surrounding confidence interval"""
 	
@@ -116,3 +126,39 @@ class Estimate:
 	
 	def exp(self):
 		return Estimate(np.exp(self.point), np.exp(self.ci_lower), np.exp(self.ci_upper))
+
+# --------------------------
+# Patsy formula manipulation
+
+def cols_for_formula(formula):
+	"""Return the columns corresponding to the Patsy formula"""
+	
+	# Parse the formula
+	model_desc = patsy.ModelDesc.from_formula(formula)
+	
+	# Get the columns
+	cols = set()
+	for term in model_desc.rhs_termlist:
+		for factor in term.factors:
+			name = factor.name()
+			if '(' in name:
+				# FIXME: Is there a better way of doing this?
+				# FIXME: This does not handle complex expressions, e.g. C(x, Treatment(y))
+				name = name[name.index('(')+1:name.index(')')]
+			
+			cols.add(name)
+	
+	return list(cols)
+
+def formula_factor_ref_category(formula, df, factor):
+	"""Get the reference category for a term in a Patsy formula referring to a categorical factor"""
+	
+	# Parse the formula
+	design_info = patsy.dmatrix(formula, df).design_info
+	
+	# Get the corresponding factor_info
+	factor_info = next(v for k, v in design_info.factor_infos.items() if k.name() == factor)
+	
+	# FIXME: This does not handle complex expressions, e.g. C(x, Treatment(y))
+	categories = factor_info.categories
+	return categories[0]