diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..40eec09
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,23 @@
+OS   := $(shell uname)
+ifeq ($(OS), Darwin)
+  TIME := /usr/bin/time -l
+else
+  TIME := /usr/bin/time -v
+endif
+
+pylint: *.py
+	pylint -f parseable -j 4 *.py
+
+test-shapley:
+	$(TIME) python qii.py -m shapley final.csv --show
+
+test:
+	$(TIME) python qii.py -m average-unary-individual final.csv
+	$(TIME) python qii.py -m unary-individual final.csv
+	$(TIME) python qii.py -m discrim final.csv
+	$(TIME) python qii.py -m banzhaf final.csv
+	$(TIME) python qii.py -m shapley final.csv
+
+clean:
+	rm -Rf *.pyc
+	rm -Rf *~
diff --git a/README.md b/README.md
index 750f546..5b02eae 100644
--- a/README.md
+++ b/README.md
@@ -1,23 +1,21 @@
 # qii
-QII Code from Datta-Sen-Zick Oakland'16 
+QII Code originally from Datta-Sen-Zick Oakland'16  
 
-To try on the adult dataset run:
-python qii.py adult --show-plot
+To try on the adult dataset run:  
+'''python qii.py adult --show-plot'''
 
-To see additional options:
-Run python qii.py -h
+To see additional options:  
+Run '''python qii.py -h'''
 
-Currently supported datasets:
-adult  : UCI Income dataset
-iwpc   : Warfarin dosage
-nlsy97 : Arrest prediction from the NLSY 97
+Currently supported datasets:  
+* adult  : UCI Income dataset  
+* iwpc   : Warfarin dosage  
+* nlsy97 : Arrest prediction from the NLSY 97  
 
 Currently supported measures:
-discrim           : Unary QII on discrimination
-average-unary-individual : Average unary QII
-unary-individual  : Unary QII on individual outcome (use -i k) for kth individual
-general-inf       : Influence on average classification
-shapley           : Shapley QII (use -i k) for kth individual
-banzhaf           : Banzhaf QII (use -i k) for kth individual
-
-
+* discrim           : Unary QII on discrimination
+* average-unary-individual : Average unary QII
+* unary-individual  : Unary QII on individual outcome (use -i k) for kth individual
+* general-inf       : Influence on average classification
+* shapley           : Shapley QII (use -i k) for kth individual
+* banzhaf           : Banzhaf QII (use -i k) for kth individual
diff --git a/final.csv b/final.csv
new file mode 100644
index 0000000..336b70f
--- /dev/null
+++ b/final.csv
@@ -0,0 +1,157 @@
+LOCATION BOOKS_AND_REFERENCE,LOCATION BUSINESS,LOCATION COMMUNICATION,LOCATION EDUCATION,LOCATION ENTERTAINMENT,LOCATION FINANCE,LOCATION GAME_ACTION,LOCATION GAME_ADVENTURE,LOCATION GAME_ARCADE,LOCATION GAME_BOARD,LOCATION GAME_CARD,LOCATION GAME_CASINO,LOCATION GAME_CASUAL,LOCATION GAME_EDUCATIONAL,LOCATION GAME_PUZZLE,LOCATION GAME_ROLE_PLAYING,LOCATION GAME_SIMULATION,LOCATION GAME_SPORTS,LOCATION GAME_STRATEGY,LOCATION GAME_TRIVIA,LOCATION GAME_WORD,LOCATION HEALTH_AND_FITNESS,LOCATION LIBRARIES_AND_DEMO,LOCATION LIFESTYLE,LOCATION MEDIA_AND_VIDEO,LOCATION MEDICAL,LOCATION MUSIC_AND_AUDIO,LOCATION NEWS_AND_MAGAZINES,LOCATION PERSONALIZATION,LOCATION PHOTOGRAPHY,LOCATION PRODUCTIVITY,LOCATION SHOPPING,LOCATION SOCIAL,LOCATION SPORTS,LOCATION TOOLS,LOCATION TRANSPORTATION,LOCATION TRAVEL_AND_LOCAL,LOCATION WEATHER,CONTACTS BOOKS_AND_REFERENCE,CONTACTS BUSINESS,CONTACTS COMMUNICATION,CONTACTS EDUCATION,CONTACTS ENTERTAINMENT,CONTACTS FINANCE,CONTACTS GAME_ACTION,CONTACTS GAME_ADVENTURE,CONTACTS GAME_ARCADE,CONTACTS GAME_BOARD,CONTACTS GAME_CARD,CONTACTS GAME_CASINO,CONTACTS GAME_CASUAL,CONTACTS GAME_EDUCATIONAL,CONTACTS GAME_PUZZLE,CONTACTS GAME_ROLE_PLAYING,CONTACTS GAME_SIMULATION,CONTACTS GAME_SPORTS,CONTACTS GAME_STRATEGY,CONTACTS GAME_TRIVIA,CONTACTS GAME_WORD,CONTACTS HEALTH_AND_FITNESS,CONTACTS LIBRARIES_AND_DEMO,CONTACTS LIFESTYLE,CONTACTS MEDIA_AND_VIDEO,CONTACTS MEDICAL,CONTACTS MUSIC_AND_AUDIO,CONTACTS NEWS_AND_MAGAZINES,CONTACTS PERSONALIZATION,CONTACTS PHOTOGRAPHY,CONTACTS PRODUCTIVITY,CONTACTS SHOPPING,CONTACTS SOCIAL,CONTACTS SPORTS,CONTACTS TOOLS,CONTACTS TRANSPORTATION,CONTACTS TRAVEL_AND_LOCAL,CONTACTS WEATHER,SMS BOOKS_AND_REFERENCE,SMS BUSINESS,SMS COMMUNICATION,SMS EDUCATION,SMS ENTERTAINMENT,SMS FINANCE,SMS GAME_ACTION,SMS GAME_ADVENTURE,SMS GAME_ARCADE,SMS GAME_BOARD,SMS GAME_CARD,SMS GAME_CASINO,SMS GAME_CASUAL,SMS GAME_EDUCATIONAL,SMS GAME_PUZZLE,SMS GAME_ROLE_PLAYING,SMS GAME_SIMULATION,SMS GAME_SPORTS,SMS GAME_STRATEGY,SMS GAME_TRIVIA,SMS GAME_WORD,SMS HEALTH_AND_FITNESS,SMS LIBRARIES_AND_DEMO,SMS LIFESTYLE,SMS MEDIA_AND_VIDEO,SMS MEDICAL,SMS MUSIC_AND_AUDIO,SMS NEWS_AND_MAGAZINES,SMS PERSONALIZATION,SMS PHOTOGRAPHY,SMS PRODUCTIVITY,SMS SHOPPING,SMS SOCIAL,SMS SPORTS,SMS TOOLS,SMS TRANSPORTATION,SMS TRAVEL_AND_LOCAL,SMS WEATHER,PHONE BOOKS_AND_REFERENCE,PHONE BUSINESS,PHONE COMMUNICATION,PHONE EDUCATION,PHONE ENTERTAINMENT,PHONE FINANCE,PHONE GAME_ACTION,PHONE GAME_ADVENTURE,PHONE GAME_ARCADE,PHONE GAME_BOARD,PHONE GAME_CARD,PHONE GAME_CASINO,PHONE GAME_CASUAL,PHONE GAME_EDUCATIONAL,PHONE GAME_PUZZLE,PHONE GAME_ROLE_PLAYING,PHONE GAME_SIMULATION,PHONE GAME_SPORTS,PHONE GAME_STRATEGY,PHONE GAME_TRIVIA,PHONE GAME_WORD,PHONE HEALTH_AND_FITNESS,PHONE LIBRARIES_AND_DEMO,PHONE LIFESTYLE,PHONE MEDIA_AND_VIDEO,PHONE MEDICAL,PHONE MUSIC_AND_AUDIO,PHONE NEWS_AND_MAGAZINES,PHONE PERSONALIZATION,PHONE PHOTOGRAPHY,PHONE PRODUCTIVITY,PHONE SHOPPING,PHONE SOCIAL,PHONE SPORTS,PHONE TOOLS,PHONE TRANSPORTATION,PHONE TRAVEL_AND_LOCAL,PHONE WEATHER,CAMERA BOOKS_AND_REFERENCE,CAMERA BUSINESS,CAMERA COMMUNICATION,CAMERA EDUCATION,CAMERA ENTERTAINMENT,CAMERA FINANCE,CAMERA GAME_ACTION,CAMERA GAME_ADVENTURE,CAMERA GAME_ARCADE,CAMERA GAME_BOARD,CAMERA GAME_CARD,CAMERA GAME_CASINO,CAMERA GAME_CASUAL,CAMERA GAME_EDUCATIONAL,CAMERA GAME_PUZZLE,CAMERA GAME_ROLE_PLAYING,CAMERA GAME_SIMULATION,CAMERA GAME_SPORTS,CAMERA GAME_STRATEGY,CAMERA GAME_TRIVIA,CAMERA GAME_WORD,CAMERA HEALTH_AND_FITNESS,CAMERA LIBRARIES_AND_DEMO,CAMERA LIFESTYLE,CAMERA MEDIA_AND_VIDEO,CAMERA MEDICAL,CAMERA MUSIC_AND_AUDIO,CAMERA NEWS_AND_MAGAZINES,CAMERA PERSONALIZATION,CAMERA PHOTOGRAPHY,CAMERA PRODUCTIVITY,CAMERA SHOPPING,CAMERA SOCIAL,CAMERA SPORTS,CAMERA TOOLS,CAMERA TRANSPORTATION,CAMERA TRAVEL_AND_LOCAL,CAMERA WEATHER,CALENDAR BOOKS_AND_REFERENCE,CALENDAR BUSINESS,CALENDAR COMMUNICATION,CALENDAR EDUCATION,CALENDAR ENTERTAINMENT,CALENDAR FINANCE,CALENDAR GAME_ACTION,CALENDAR GAME_ADVENTURE,CALENDAR GAME_ARCADE,CALENDAR GAME_BOARD,CALENDAR GAME_CARD,CALENDAR GAME_CASINO,CALENDAR GAME_CASUAL,CALENDAR GAME_EDUCATIONAL,CALENDAR GAME_PUZZLE,CALENDAR GAME_ROLE_PLAYING,CALENDAR GAME_SIMULATION,CALENDAR GAME_SPORTS,CALENDAR GAME_STRATEGY,CALENDAR GAME_TRIVIA,CALENDAR GAME_WORD,CALENDAR HEALTH_AND_FITNESS,CALENDAR LIBRARIES_AND_DEMO,CALENDAR LIFESTYLE,CALENDAR MEDIA_AND_VIDEO,CALENDAR MEDICAL,CALENDAR MUSIC_AND_AUDIO,CALENDAR NEWS_AND_MAGAZINES,CALENDAR PERSONALIZATION,CALENDAR PHOTOGRAPHY,CALENDAR PRODUCTIVITY,CALENDAR SHOPPING,CALENDAR SOCIAL,CALENDAR SPORTS,CALENDAR TOOLS,CALENDAR TRANSPORTATION,CALENDAR TRAVEL_AND_LOCAL,CALENDAR WEATHER,clusters
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+0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8
diff --git a/ml_util.py b/ml_util.py
index 96e5f48..6338d4d 100644
--- a/ml_util.py
+++ b/ml_util.py
@@ -3,6 +3,7 @@
 import statsmodels as sm
 import sklearn as skl
 import sklearn.preprocessing as preprocessing
+from sklearn.preprocessing import LabelEncoder
 import sklearn.cross_validation as cross_validation
 import sklearn.metrics as metrics
 import sklearn.tree as tree
@@ -12,26 +13,43 @@
 import numpy
 import numpy.random
 import arff
-
 import numpy.linalg
 import sys
 from matplotlib.backends.backend_pdf import PdfPages
 import argparse
 import time
 
+from os.path import exists
 
 from qii_lib import *
 
-
-#labelfont = {'fontname':'Times New Roman', 'size':15}
+# labelfont = {'fontname':'Times New Roman', 'size':15}
 labelfont = {}
-#hfont = {'fontname':'Helvetica'}
 
 
+# hfont = {'fontname':'Helvetica'}
+
+def get_column_index(data, cname):
+	try:
+		idx = data.columns.get_loc(cname)
+	except Exception as e:
+		raise ValueError("Unknown column %s" % cname)
+
+	return idx
+
+
+def encode_nominal(col):
+	if col.dtype == object:
+		return LabelEncoder().fit_transform(col)
+	else:
+		return col
+
 
 import argparse
+
+
 class Dataset(object):
-    """
+	"""
     Class that holds a dataset.
     Each dataset has its own quirks and needs some special processing
     to get to the point where we need it to.
@@ -52,262 +70,385 @@ class Dataset(object):
         target_ix: Name of target index
         sensitive_ix: Name of sensitive index
         target: Values of classification target
+    Methods:
+        get_sensitive: extract the sensitive value from a row
+            or the sensitive column from a dataset
+        
     """
-    def __init__( self, dataset, sensitive =''):
-        self.name = dataset
-
-        # Warfarin dosage dataset
-        if (dataset == 'iwpc'):
-            self.num_data = pd.DataFrame.from_records(
-                arff.load('data/iwpc/iwpc_train_class.arff'),
-                columns=[
-                    'index', 'race=black', 'race=asian', 'age', 'height', 'weight', 'amiodarone',
-                    'cyp2c9=13', 'cyp2c9=12', 'cyp2c9=23', 'cyp2c9=33', 'cyp2c9=22',
-                    'vkorc1=CT', 'vkorc1=TT', 'decr', 'dose'
-                    ])
-            self.sup_ind = {}
-            self.sup_ind['race'] = ['race=black','race=asian']
-            self.sup_ind['age'] = ['age']
-            self.sup_ind['height'] = ['height']
-            self.sup_ind['weight'] = ['weight']
-            self.sup_ind['amiodarone'] = ['amiodarone']
-            self.sup_ind['cyp2c9'] = ['cyp2c9=13','cyp2c9=12','cyp2c9=23','cyp2c9=33','cyp2c9=22']
-            self.sup_ind['vkorc1'] = ['vkorc1=CT','vkorc1=TT']
-            self.sup_ind['decr'] = ['decr']
-            self.sup_ind['dose'] = ['dose']
-            self.target_ix = 'dose'
-            self.sensitive_ix = 'race=black'
-            if (sensitive == 'Gender'):
-                self.sensitive = (lambda X: X['race=black'])
-
-            self.target = self.num_data['dose']
-            self.num_data = self.num_data.drop(['index'], axis = 1)
-            self.num_data = self.num_data.drop(self.sup_ind[self.target_ix], axis = 1)
-            del self.sup_ind['dose']
-
-
-        #Adult dataset
-        if (dataset == 'adult'):
-            self.original_data = pd.read_csv(
-                "data/adult/adult.data",
-                names=[
-                    "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Marital Status",
-                    "Occupation", "Relationship", "Race", "Gender", "Capital Gain", "Capital Loss",
-                    "Hours per week", "Country", "Target"],
-                sep=r'\s*,\s*',
-                engine='python',
-                na_values="?")
-            del self.original_data['fnlwgt']
-            self.sup_ind = make_super_indices(self.original_data)
-            self.num_data = pd.get_dummies(self.original_data)
-            self.target_ix = 'Target'
-            self.sensitive_ix = sensitive
-
-            #Define and dedup Target
-            self.target = self.num_data['Target_>50K']
-            self.num_data = self.num_data.drop(self.sup_ind[self.target_ix], axis = 1)
-            del self.sup_ind['Target']
-
-            #Dedup Gender
-            self.num_data['Gender'] = self.num_data['Gender_Male']
-            self.num_data = self.num_data.drop(self.sup_ind['Gender'], axis = 1)
-            self.sup_ind['Gender'] = ['Gender']
-
-            if (sensitive == 'Gender'):
-                self.sensitive = (lambda X: X['Gender'])
-            elif (sensitive == ''):
-                self.sensitive = (lambda X: None)
-            else:
-                raise ValueError('Cannot handle sensitive '+sensitive+' in dataset '+dataset)
-
-
-        #National Longitudinal Survey of Youth 97
-        if (dataset == 'nlsy97'):
-            self.original_data = pd.read_csv(
-                "data/nlsy97/20151026/processed_output.csv",
-                names = ["PUBID.1997", "Gender", "Birth Year", "Census Region",
-                    "Race", "Arrests", "Drug History", "Smoking History"],
-                sep=r'\s*,\s*',
-                engine='python',
-                quoting=2,
-                na_values="?")
-            del self.original_data['PUBID.1997']
-            self.target_ix = 'Arrests'
-            self.sensitive_ix = sensitive
-            self.sup_ind = make_super_indices(self.original_data)
-            self.num_data = pd.get_dummies(self.original_data)
-
-            #Define and dedup Target
-            self.target = (self.num_data['Arrests'] > 0)*1.
-            self.num_data = self.num_data.drop(self.sup_ind[self.target_ix], axis = 1)
-            del self.sup_ind[self.target_ix]
-
-            #Dedup Gender
-            self.num_data['Gender'] = self.num_data['Gender_"Male"']
-            self.num_data = self.num_data.drop(self.sup_ind['Gender'], axis = 1)
-            self.sup_ind['Gender'] = ['Gender']
-
-            if (sensitive == 'Gender'):
-                self.sensitive = (lambda X: X['Gender'])
-            elif (sensitive == 'Race'):
-                self.sensitive = (lambda X: X['Race_"Black"'])
-            else:
-                raise ValueError('Cannot handle sensitive '+sensitive+' in dataset '+dataset)
-
-
-        #German Datset (Incomplete)
-        if (dataset == 'german'):
-        #http://programming-r-pro-bro.blogspot.com/2011/09/modelling-with-r-part-1.html
-            original_data = pd.read_csv(
-                "data/german/processed_output.csv",
-                names = ["PUBID.1997", "Gender", "Birth Year", "Census Region",
-                    "Race", "Arrests", "Drug History", "Smoking History"],
-                sep=r'\s*,\s*',
-                engine='python',
-                na_values="?")
-
-
-    def delete_index ( self, index ):
-        self.num_data.drop(self.sup_ind[index], axis = 1)
-        del self.sup_ind[index]
-
-
-#Categorical features are encoded as binary features, one for each category
-#A super index keeps track of the mapping between a feature and its binary representation
-def make_super_indices( dataset ):
-    sup_ind = {}
-    for i in dataset.columns:
-        if dataset[i].dtype != 'O':
-            sup_ind[i] = [i]
-        else:
-            unique = filter(lambda v: v==v, dataset[i].unique())
-            sup_ind[i] = [i + '_' + s for s in unique]
-    return sup_ind
 
+	def __init__(self, dataset, sensitive=None, target=None):
+		self.name = dataset
+
+		# Warfarin dosage dataset
+		if (dataset == 'iwpc'):
+			self.num_data = pd.DataFrame.from_records(
+				arff.load('data/iwpc/iwpc_train_class.arff'),
+				columns=[
+					'index', 'race=black', 'race=asian', 'age', 'height', 'weight', 'amiodarone',
+					'cyp2c9=13', 'cyp2c9=12', 'cyp2c9=23', 'cyp2c9=33', 'cyp2c9=22',
+					'vkorc1=CT', 'vkorc1=TT', 'decr', 'dose'
+				])
+			self.sup_ind = {}
+			self.sup_ind['race'] = ['race=black', 'race=asian']
+			self.sup_ind['age'] = ['age']
+			self.sup_ind['height'] = ['height']
+			self.sup_ind['weight'] = ['weight']
+			self.sup_ind['amiodarone'] = ['amiodarone']
+			self.sup_ind['cyp2c9'] = ['cyp2c9=13', 'cyp2c9=12', 'cyp2c9=23', 'cyp2c9=33', 'cyp2c9=22']
+			self.sup_ind['vkorc1'] = ['vkorc1=CT', 'vkorc1=TT']
+			self.sup_ind['decr'] = ['decr']
+			self.sup_ind['dose'] = ['dose']
+			self.target_ix = 'dose'
+			self.sensitive_ix = 'race=black'
+			if sensitive is None:
+				self.get_sensitive = (lambda X: X['race=black'])
+
+			self.target = self.num_data['dose']
+			self.num_data = self.num_data.drop(['index'], axis=1)
+			self.num_data = self.num_data.drop(self.sup_ind[self.target_ix], axis=1)
+			del self.sup_ind['dose']
+
+
+		# Adult dataset
+		elif (dataset == 'adult'):
+			self.original_data = pd.read_csv(
+				"data/adult/adult.data",
+				names=[
+					"Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Marital Status",
+					"Occupation", "Relationship", "Race", "Gender", "Capital Gain", "Capital Loss",
+					"Hours per week", "Country", "Target"],
+				sep=r'\s*,\s*',
+				engine='python',
+				na_values="?")
+			del self.original_data['fnlwgt']
+			self.sup_ind = make_super_indices(self.original_data)
+			self.num_data = pd.get_dummies(self.original_data)
+			self.target_ix = 'Target'
+			self.sensitive_ix = sensitive
+
+			# Define and dedup Target
+			self.target = self.num_data['Target_>50K']
+			self.num_data = self.num_data.drop(self.sup_ind[self.target_ix], axis=1)
+			del self.sup_ind['Target']
+
+			# Dedup Gender
+			self.num_data['Gender'] = self.num_data['Gender_Male']
+			self.num_data = self.num_data.drop(self.sup_ind['Gender'], axis=1)
+			self.sup_ind['Gender'] = ['Gender']
+
+			if sensitive is None:
+				self.get_sensitive = (lambda X: X['Gender'])
+			elif (sensitive == ''):
+				self.get_sensitive = (lambda X: None)
+			else:
+				raise ValueError('Cannot handle sensitive ' + sensitive + ' in dataset ' + dataset)
+
+
+		# National Longitudinal Survey of Youth 97
+		elif (dataset == 'nlsy97'):
+			self.original_data = pd.read_csv(
+				"data/nlsy97/20151026/processed_output.csv",
+				names=["PUBID.1997", "Gender", "Birth Year", "Census Region",
+				       "Race", "Arrests", "Drug History", "Smoking History"],
+				sep=r'\s*,\s*',
+				engine='python',
+				quoting=2,
+				na_values="?")
+			del self.original_data['PUBID.1997']
+			self.target_ix = 'Arrests'
+			self.sensitive_ix = sensitive
+			self.sup_ind = make_super_indices(self.original_data)
+			self.num_data = pd.get_dummies(self.original_data)
+
+			# Define and dedup Target
+			self.target = (self.num_data['Arrests'] > 0) * 1.
+			self.num_data = self.num_data.drop(self.sup_ind[self.target_ix], axis=1)
+			del self.sup_ind[self.target_ix]
+
+			# Dedup Gender
+			self.num_data['Gender'] = self.num_data['Gender_"Male"']
+			self.num_data = self.num_data.drop(self.sup_ind['Gender'], axis=1)
+			self.sup_ind['Gender'] = ['Gender']
+
+			if sensitive is None or sensitive == 'Gender':
+				self.get_sensitive = (lambda X: X['Gender'])
+			elif (sensitive == 'Race'):
+				self.get_sensitive = (lambda X: X['Race_"Black"'])
+			else:
+				raise ValueError('Cannot handle sensitive ' + sensitive + ' in dataset ' + dataset)
+
+
+		# German Datset (Incomplete)
+		elif (dataset == 'german'):
+			# http://programming-r-pro-bro.blogspot.com/2011/09/modelling-with-r-part-1.html
+			original_data = pd.read_csv(
+				"data/german/processed_output.csv",
+				names=["PUBID.1997", "Gender", "Birth Year", "Census Region",
+				       "Race", "Arrests", "Drug History", "Smoking History"],
+				sep=r'\s*,\s*',
+				engine='python',
+				na_values="?")
+
+		elif exists(dataset):
+			print "loading new dataset %s" % dataset
+
+			self.original_data = pd.read_csv(dataset)
+
+			if target is None:
+				target = self.original_data.columns[-1]
+			self.target_ix = target
+			if self.target_ix not in self.original_data:
+				raise ValueError("unknown target feature %s" % self.target_ix)
+
+			if sensitive is None:
+				sensitive = self.original_data.columns[0]
+			self.sensitive_ix = sensitive
+			if self.sensitive_ix not in self.original_data:
+				raise ValueError("unkown sensitive feature %s" % self.sensitive_ix)
+
+			if self.sensitive_ix == self.target_ix:
+				print "WARNING: target and sensitive attributes are the same (%s), I'm unsure whether this tool handles this case correctly" % target
+
+			nominal_cols = set(self.original_data.select_dtypes(include=['object']).columns)
+
+			self.num_data = pd.get_dummies(
+				self.original_data,
+				prefix_sep='_',
+				columns=nominal_cols - set([target, sensitive]))
+
+			self.num_data = self.num_data.apply(encode_nominal)
+
+			self.sup_ind = make_super_indices(self.original_data)
+
+			if self.target_ix in nominal_cols:
+				targets = len(set(self.original_data[target]))
+				if targets > 2:
+					print "WARNING: target feature %s has more than 2 values (it has %d), I'm unsure whether this tool handles that correctly" % (
+						target, targets)
+			del self.sup_ind[self.target_ix]
+			#    self.target_ix = "%s_%s" % (self.target_ix,self.original_data[self.target_ix][0])
+
+			if self.sensitive_ix in nominal_cols:
+				targets = len(set(self.original_data[sensitive]))
+				if targets > 2:
+					print "WARNING: sensitive feature %s has more than 2 values (it has %d), I'm unsure whether this tool handles that correctly" % (
+						sensitive, targets)
+				self.sup_ind[self.sensitive_ix] = [self.sensitive_ix]
+			#    self.sensitive_ix = "%s_%s" % (self.sensitive_ix,self.original_data[self.sensitive_ix][0])
+
+			self.target = self.num_data[self.target_ix]
+			self.num_data = self.num_data.drop([self.target_ix], axis=1)
+
+			self.get_sensitive = lambda X: X[self.sensitive_ix]
+
+			print "target feature    = %s" % self.target_ix
+			print "sensitive feature = %s" % self.sensitive_ix
+
+		else:
+			raise ValueError("Unknown dataset %s" % dataset)
+
+	def delete_index(self, index):
+		self.num_data.drop(self.sup_ind[index], axis=1)
+		del self.sup_ind[index]
+
+
+# Categorical features are encoded as binary features, one for each category
+# A super index keeps track of the mapping between a feature and its binary representation
+def make_super_indices(dataset):
+	sup_ind = {}
+	for i in dataset.columns:
+		if dataset[i].dtype != 'O':
+			sup_ind[i] = [i]
+		else:
+			unique = filter(lambda v: v == v, dataset[i].unique())
+			sup_ind[i] = [i + '_' + s for s in unique]
+	return sup_ind
 
 
 ## Parse arguments
 def get_arguments():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dataset', help='Name of dataset used')
-    parser.add_argument('-m', '--measure', default='average-local-inf', help='Quantity of interest')
-    parser.add_argument('-s', '--sensitive', default='Gender', help='Sensitive field')
-    parser.add_argument('-e', '--erase-sensitive', action='store_false', help='Erase sensitive field from dataset')
-    parser.add_argument('-p', '--show-plot', action='store_true', help='Output plot as pdf')
-    parser.add_argument('-o', '--output-pdf', action='store_true', help='Output plot as pdf')
-    parser.add_argument('-c', '--classifier', default='logistic', help='Classifier to use',
-            choices=['logistic', 'svm', 'decision-tree', 'decision-forest'])
-    parser.add_argument('-i', '--individual', default=0, type=int, help='Index for Individualized Transparency Report')
-    parser.add_argument('-r', '--record-counterfactuals', action='store_true', help='Store counterfactual pairs for causal analysis')
-    parser.add_argument('-a', '--active-iterations', type=int, default=10, help='Active Learning Iterations')
-    return parser.parse_args()
-
-def split_and_train_classifier(classifier, dataset, scaler=None):
-    ## Split data into training and test data
-    X_train, X_test, y_train, y_test = cross_validation.train_test_split(dataset.num_data, dataset.target, train_size=0.40)
-
-
-    sens_train = dataset.sensitive(X_train)
-    sens_test  = dataset.sensitive(X_test)
-
-    if (scaler == None):
-        #Initialize scaler to normalize training data
-        scaler = preprocessing.StandardScaler()
-        scaler.fit(X_train)
-
-    #Normalize all training and test data
-    X_train = pd.DataFrame(scaler.transform(X_train), columns=(dataset.num_data.columns))
-    X_test = pd.DataFrame(scaler.transform(X_test), columns=(dataset.num_data.columns))
-
-    cls = train_classifier(classifier, X_train, y_train)
-    return (cls, scaler, X_train, X_test, y_train, y_test, sens_train, sens_test)
-
-
-def train_classifier(classifier, X_train, y_train):
-    #Initialize sklearn classifier model
-    if (classifier == 'logistic'):
-        import sklearn.linear_model as linear_model
-        cls = linear_model.LogisticRegression()
-    elif (classifier == 'svm'):
-        from sklearn import svm
-        cls = svm.SVC(kernel='linear', cache_size=7000)
-    elif (classifier == 'decision-tree'):
-        import sklearn.linear_model as linear_model
-        cls = tree.DecisionTreeClassifier()
-    elif (classifier == 'decision-forest'):
-        from sklearn.ensemble import GradientBoostingClassifier
-        cls = GradientBoostingClassifier(n_estimators=20, learning_rate=1.0, max_depth=2, random_state=0)
-
-    #Train sklearn model
-    cls.fit(X_train, y_train)
-    return cls
-
+	parser = argparse.ArgumentParser()
+	parser.add_argument('dataset', help='Name of dataset used')
+	parser.add_argument('-m', '--measure',
+	                    default='average-unary-individual',
+	                    help='Quantity of interest',
+	                    choices=['average-unary-individual', 'unary-individual',
+	                             'discrim', 'banzhaf', 'shapley', 'average-unary-class'])
+	parser.add_argument('-s', '--sensitive', default=None, help='Sensitive field')
+	parser.add_argument('-t', '--target', default=None, help='Target field', type=str)
+
+	parser.add_argument('-e', '--erase-sensitive', action='store_false', help='Erase sensitive field from dataset')
+	parser.add_argument('-p', '--show-plot', action='store_true', help='Output plot as pdf')
+	parser.add_argument('-o', '--output-pdf', action='store_true', help='Output plot as pdf')
+	parser.add_argument('-c', '--classifier', default='logistic', help='Classifier to use',
+	                    choices=['logistic', 'svm', 'decision-tree', 'decision-forest'])
+
+	parser.add_argument('--max_depth', type=int, default=2, help='Max depth for decision trees and forests')
+	parser.add_argument('--n_estimators', type=int, default=20, help='Number of trees for decision forests')
+	parser.add_argument('--seed', default=None, help='Random seed, auto seeded if not specified', type=int)
+
+	parser.add_argument('-i', '--individual', default=0, type=int, help='Index for Individualized Transparency Report')
+	parser.add_argument('-r', '--record-counterfactuals', action='store_true',
+	                    help='Store counterfactual pairs for causal analysis')
+	parser.add_argument('-a', '--active-iterations', type=int, default=10, help='Active Learning Iterations')
+	parser.add_argument('-q', '--class_influence', default=None, type=int,
+	                    help='Index of the the target class for causal analysis')
+
+	args = parser.parse_args()
+	if args.seed is not None:
+		numpy.random.seed([args.seed])
+
+	return args
+
+
+class Setup(argparse.Namespace):
+	def __init__(self, cls, x_test, y_test, x_target_class, sens_test, **kw):
+		self.cls = cls
+		self.x_test = x_test
+		self.y_test = y_test
+		self.x_target_class = x_target_class
+		self.sens_test = sens_test
+		# for k in kw:
+		#    self.__setattr__(k, kw[k])
+		argparse.Namespace.__init__(self, **kw)
+
+
+def split_and_train_classifier(args, dataset, scaler=None, normalize=True):
+	classifier = args.classifier
+	## Split data into training and test data
+	x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+		dataset.num_data, dataset.target,
+		train_size=0.40, random_state=100
+	)
+
+	x_target_class = None
+	if args.class_influence is not None:
+		target_class_type = type(y_test.iloc[0])
+		target_class = target_class_type(args.class_influence)
+		x_target_class = x_test[y_test == target_class]
+
+	sens_train = dataset.get_sensitive(x_train)
+	sens_test = dataset.get_sensitive(x_test)
+	if normalize:
+		if (scaler == None):
+			# Initialize scaler to normalize training data
+			scaler = preprocessing.StandardScaler()
+			scaler.fit(x_train)
+
+		# Normalize all training and test data
+		x_train = pd.DataFrame(scaler.transform(x_train), columns=(dataset.num_data.columns))
+		x_test = pd.DataFrame(scaler.transform(x_test), columns=(dataset.num_data.columns))
+		if x_target_class is not None:
+			x_target_class = pd.DataFrame(scaler.transform(x_target_class), columns=(dataset.num_data.columns))
+
+	cls = train_classifier(args, x_train, y_train)
+
+	return Setup(cls=cls,
+	             scaler=scaler,
+	             x_train=x_train,
+	             x_test=x_test,
+	             y_train=y_train,
+	             y_test=y_test,
+	             x_target_class=x_target_class,
+	             sens_train=sens_train,
+	             sens_test=sens_test)
+
+
+def train_classifier(args, X_train, y_train):
+	classifier = args.classifier
+	# Initialize sklearn classifier model
+	if (classifier == 'logistic'):
+		import sklearn.linear_model as linear_model
+		cls = linear_model.LogisticRegression()
+	elif (classifier == 'svm'):
+		from sklearn import svm
+		cls = svm.SVC(kernel='linear', cache_size=7000,
+		              )
+	elif (classifier == 'decision-tree'):
+		import sklearn.linear_model as linear_model
+		cls = tree.DecisionTreeClassifier(max_depth=args.max_depth,
+		                                  )
+	elif (classifier == 'decision-forest'):
+		from sklearn.ensemble import GradientBoostingClassifier
+		cls = GradientBoostingClassifier(n_estimators=args.n_estimators,
+		                                 learning_rate=1.0,
+		                                 max_depth=args.max_depth,
+		                                 random_state=100
+		                                 )
+
+	# Train sklearn model
+	cls.fit(X_train, y_train)
+	return cls
 
 
 def plot_series(series, args, xlabel, ylabel):
-    plt.figure(figsize=(5,4))
-    series.sort(ascending = False)
-    #average_local_inf_series.plot(kind="bar", facecolor='#ff9999', edgecolor='white')
-    series.plot(kind="bar")
-    plt.xticks(rotation = 45, ha = 'right', size='small')
-    plt.xlabel(xlabel, labelfont)
-    plt.ylabel(ylabel, labelfont)
-    plt.tight_layout()
-    if (args.output_pdf == True):
-        pp = PdfPages('figure-' + measure + '-' + args.dataset + '-' + args.classifier +'.pdf')
-        print ('Writing to figure-' + measure + '-' + args.dataset + '-' + args.classifier + '.pdf')
-        pp.savefig(bbox_inches='tight')
-        pp.close()
-    plt.show()
+	plt.ioff()
+	plt.figure(figsize=(10, 10))
+	series.sort_values(inplace=True, ascending=False)
+	# average_local_inf_series.plot(kind="bar", facecolor='#ff9999', edgecolor='white')
+	series.plot(kind="bar")
+	plt.xticks(rotation=45, ha='right', size='small')
+	plt.xlabel(xlabel, labelfont)
+	plt.ylabel(ylabel, labelfont)
+	plt.tight_layout()
+	if (args.output_pdf == True):
+		class_value = str(args.class_influence) if args.class_influence is not None else ''
+		pp = PdfPages('figure-' + args.measure + '-' + args.dataset + '-' + args.classifier + class_value + '.pdf')
+		print ('Writing to figure-' + args.measure + '-' + args.dataset + '-' + args.classifier + class_value + '.pdf')
+		pp.savefig(bbox_inches='tight')
+		pp.close()
+	if (args.show_plot == True):
+		plt.show()
 
 
 def plot_series_with_baseline(series, args, xlabel, ylabel, baseline):
-    series.sort(ascending = True)
-    plt.figure(figsize=(5,4))
-    #plt.bar(range(series.size), series.as_matrix() - baseline)
-    #(series - baseline).plot(kind="bar", facecolor='#ff9999', edgecolor='white')
-    (series - baseline).plot(kind="bar")
-    #plt.xticks(range(series.size), series.keys(), size='small')
-    x1,x2,y1,y2 = plt.axis()
-    X = range(series.size)
-    for x,y in zip(X,series.as_matrix() - baseline):
-        x_wd = 1. / series.size
-        if(y < 0):
-            plt.text(x+x_wd/2, y-0.01, '%.2f' % (y), ha='center', va= 'bottom', size='small')
-        else:
-            plt.text(x+x_wd/2, y+0.01, '%.2f' % (y), ha='center', va= 'top', size='small')
-    plt.axis((x1,x2,-baseline,y2 + 0.01))
-    plt.xticks(rotation = 45, ha = 'right', size='small')
-    plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x,_: '%1.2f' % (x + baseline)))
-    plt.axhline(linestyle = 'dashed', color = 'black')
-    plt.text(x_wd, 0, 'Original Discrimination', ha = 'left', va = 'bottom')
-    plt.xlabel(xlabel, labelfont)
-    plt.ylabel(ylabel, labelfont)
-    plt.tight_layout()
-    if (args.output_pdf == True):
-        pp = PdfPages('figure-' + measure + '-' + dataset.name + '-' + dataset.sensitive_ix + '-' + args.classifier + '.pdf')
-        print ('Writing to figure-' + measure + '-' + dataset.name + '-' + dataset.sensitive_ix + '-' + args.classifier + '.pdf')
-        pp.savefig()
-        pp.close()
-    plt.show()
+	series.sort(ascending=True)
+	plt.figure(figsize=(5, 4))
+	# plt.bar(range(series.size), series.as_matrix() - baseline)
+	# (series - baseline).plot(kind="bar", facecolor='#ff9999', edgecolor='white')
+	(series - baseline).plot(kind="bar")
+	# plt.xticks(range(series.size), series.keys(), size='small')
+	x1, x2, y1, y2 = plt.axis()
+	X = range(series.size)
+	for x, y in zip(X, series.as_matrix() - baseline):
+		x_wd = 1. / series.size
+		if (y < 0):
+			plt.text(x + x_wd / 2, y - 0.01, '%.2f' % (y), ha='center', va='bottom', size='small')
+		else:
+			plt.text(x + x_wd / 2, y + 0.01, '%.2f' % (y), ha='center', va='top', size='small')
+	plt.axis((x1, x2, -baseline, y2 + 0.01))
+	plt.xticks(rotation=45, ha='right', size='small')
+	plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: '%1.2f' % (x + baseline)))
+	plt.axhline(linestyle='dashed', color='black')
+	plt.text(x_wd, 0, 'Original Discrimination', ha='left', va='bottom')
+	plt.xlabel(xlabel, labelfont)
+	plt.ylabel(ylabel, labelfont)
+	plt.tight_layout()
+	if (args.output_pdf == True):
+		pp = PdfPages(
+			'figure-' + args.measure + '-' + args.dataset.name + '-' + args.dataset.sensitive_ix + '-' + args.classifier + '.pdf')
+		print (
+			'Writing to figure-' + args.measure + '-' + args.dataset.name + '-' + args.dataset.sensitive_ix + '-' + args.classifier + '.pdf')
+		pp.savefig()
+		pp.close()
+	plt.show()
 
 
 def measure_analytics(dataset, cls, X, y, sens=None):
-    y_pred = cls.predict(X)
-
-    error_rate = numpy.mean((y_pred != y)*1.)
-    print('test error rate: %.3f' % error_rate)
+	y_pred = cls.predict(X)
 
-    discrim0 =  qii.discrim(numpy.array(X), cls, numpy.array(sens))
-    print('Initial Discrimination: %.3f' % discrim0)
+	error_rate = numpy.mean((y_pred != y) * 1.)
+	print('test error rate: %.3f' % error_rate)
 
-    from scipy.stats.stats import pearsonr
-    corr0 = pearsonr(sens, y)[0]
-    print('Correlation: %.3f' % corr0)
+	discrim0 = discrim(numpy.array(X), cls, numpy.array(sens))
+	print('Initial Discrimination: %.3f' % discrim0)
 
-    ji =  metrics.jaccard_similarity_score(y, sens)
-    print('JI: %.3f' % ji)
+	from scipy.stats.stats import pearsonr
+	corr0 = pearsonr(sens, y)[0]
+	print('Correlation: %.3f' % corr0)
 
-    mi = metrics.normalized_mutual_info_score(y, sens)
-    print('MI: %.3f' % mi)
+	ji = metrics.jaccard_similarity_score(y, sens)
+	print('JI: %.3f' % ji)
 
+	mi = metrics.normalized_mutual_info_score(y, sens)
+	print('MI: %.3f' % mi)
diff --git a/qii.py b/qii.py
index 51f82a1..70ef1ee 100644
--- a/qii.py
+++ b/qii.py
@@ -1,99 +1,202 @@
+""" QII mesurement script
+
+author: mostly Shayak
+
+"""
+import pdb
+import time
 import pandas as pd
-import numpy as np
-import sklearn as skl
 import numpy
-
+import matplotlib.pyplot as plt
 import numpy.linalg
-import sys
-import time
-
-from ml_util import *
-from qii_lib import *
-
-from sklearn.datasets import load_svmlight_file
-
-
-#def main():
-
-
-args = get_arguments()
-qii.record_counterfactuals = args.record_counterfactuals
-
-#Read dataset
-dataset = Dataset(args.dataset, args.sensitive)
-#if (args.erase_sensitive):
-#  print 'Erasing sensitive'
-#  dataset.delete_index(args.sensitive)
-
-measure = args.measure
-individual = args.individual
-
-#Get column names
-f_columns = dataset.num_data.columns
-sup_ind = dataset.sup_ind
-
-######### Begin Training Classifier ##########
-
-cls, scaler, X_train, X_test, y_train, y_test, sens_train, sens_test = split_and_train_classifier(args.classifier, dataset)
-print('End Training Classifier')
-######### End Training Classifier ##########
+from matplotlib.backends.backend_pdf import PdfPages
 
-measure_analytics(dataset, cls, X_test, y_test, sens_test)
+from ml_util import split_and_train_classifier, get_arguments, \
+	Dataset, measure_analytics, \
+	plot_series_with_baseline, plot_series
 
-t0 = time.time()
+import qii_lib
 
-if measure == 'discrim':
-    baseline = qii.discrim(numpy.array(X_test), cls, numpy.array(sens_test))
-    discrim_inf = qii.discrim_influence(dataset, cls, X_test, sens_test)
-    discrim_inf_series = pd.Series(discrim_inf, index = discrim_inf.keys())
-    if (args.show_plot):
-        plot_series_with_baseline(discrim_inf_series, args, 'Feature', 'QII on Group Disparity', baseline)
 
-if measure == 'average-unary-individual':
-    (average_local_inf, counterfactuals) = qii.average_local_influence(dataset, cls, X_test)
-    average_local_inf_series = pd.Series(average_local_inf, index = average_local_inf.keys())
-    if (args.show_plot):
-        plot_series(average_local_inf_series, args, 'Feature', 'QII on Outcomes')
+def __main__():
+	args = get_arguments()
+	qii_lib.record_counterfactuals = args.record_counterfactuals
 
-if measure == 'unary-individual':
-    print individual
-    x_individual = scaler.transform(dataset.num_data.ix[individual])
+	# Read dataset
+	dataset = Dataset(args.dataset, sensitive=args.sensitive, target=args.target)
+	# Get column names
+	# f_columns = dataset.num_data.columns
+	# sup_ind = dataset.sup_ind
 
-    (average_local_inf, counterfactuals) = qii.unary_individual_influence(dataset, cls, x_individual, X_test)
-    average_local_inf_series = pd.Series(average_local_inf, index = average_local_inf.keys())
-    if (args.show_plot):
-        plot_series(average_local_inf_series, args, 'Feature', 'QII on Outcomes')
+	######### Begin Training Classifier ##########
 
-if measure == 'banzhaf':
-    print individual
-    x_individual = scaler.transform(dataset.num_data.ix[individual])
-    print dataset.num_data.ix[individual]
+	dat = split_and_train_classifier(args, dataset)
 
-    banzhaf = qii.banzhaf_influence(dataset, cls, x_individual, X_test)
-    banzhaf_series = pd.Series(banzhaf, index = banzhaf.keys())
-    if (args.show_plot):
-        plot_series(banzhaf_series, args, 'Feature', 'QII on Outcomes (Banzhaf)')
+	print 'End Training Classifier'
+	######### End Training Classifier ##########
 
-if measure == 'shapley':
-    print individual
-    x_individual = scaler.transform(dataset.num_data.ix[individual])
-    print dataset.num_data.ix[individual]
+	measure_analytics(dataset, dat.cls, dat.x_test, dat.y_test, dat.sens_test)
 
-    shapley, counterfactuals = qii.shapley_influence(dataset, cls, x_individual, X_test)
-    shapley_series = pd.Series(shapley, index = shapley.keys())
-    if (args.show_plot):
-        plot_series(shapley_series, args, 'Feature', 'QII on Outcomes (Shapley)')
+	t_start = time.time()
 
-t1 = time.time()
-print (t1 - t0)
+	measures = {'discrim': eval_discrim,
+	            'average-unary-individual': eval_average_unary_individual,
+	            'unary-individual': eval_unary_individual,
+	            'banzhaf': eval_banzhaf,
+	            'shapley': eval_shapley,
+	            'average-unary-class': eval_class_average_unary}
 
+	if args.measure in measures:
+		measures[args.measure](dataset, args, dat)
+	else:
+		raise ValueError("Unknown measure %s" % args.measure)
 
+	t_end = time.time()
 
+	print t_end - t_start
 
 
+def eval_discrim(dataset, args, dat):
+	""" Discrimination metric """
 
+	baseline = qii_lib.discrim(numpy.array(dat.x_test), dat.cls, numpy.array(dat.sens_test))
+	discrim_inf = qii_lib.discrim_influence(dataset, dat.cls, dat.x_test, dat.sens_test)
+	discrim_inf_series = pd.Series(discrim_inf, index=discrim_inf.keys())
+	if args.show_plot:
+		plot_series_with_baseline(
+			discrim_inf_series, args,
+			'Feature', 'QII on Group Disparity',
+			baseline)
 
-#if __name__ == '__main__':
-#    main()
 
+def eval_average_unary_individual(dataset, args, dat):
+	""" Unary QII averaged over all individuals. """
 
+	average_local_inf, _ = qii_lib.average_local_influence(
+		dataset, dat.cls, dat.x_test)
+	average_local_inf_series = pd.Series(average_local_inf,
+	                                     index=average_local_inf.keys())
+	top_40 = average_local_inf_series.sort_values(ascending=False).head(40)
+	if args.show_plot or args.output_pdf:
+		plot_series(top_40, args,
+		            'Feature', 'QII on Outcomes')
+
+
+def eval_unary_individual(dataset, args, dat):
+	""" Unary QII. """
+
+	x_individual = dat.scaler.transform(dataset.num_data.ix[args.individual].reshape(1, -1))
+	average_local_inf, _ = qii_lib.unary_individual_influence(
+		dataset, dat.cls, x_individual, dat.x_test)
+	average_local_inf_series = pd.Series(
+		average_local_inf, index=average_local_inf.keys())
+	if args.show_plot or args.output_pdf:
+		plot_series(average_local_inf_series, args,
+		            'Feature', 'QII on Outcomes')
+
+
+def eval_banzhaf(dataset, args, dat):
+	""" Banzhaf metric. """
+
+	x_individual = dat.scaler.transform(dataset.num_data.ix[args.individual])
+
+	banzhaf = qii_lib.banzhaf_influence(dataset, dat.cls, x_individual, dat.x_test)
+	banzhaf_series = pd.Series(banzhaf, index=banzhaf.keys())
+	if args.show_plot or args.output_pdf:
+		plot_series(banzhaf_series, args, 'Feature', 'QII on Outcomes (Banzhaf)')
+
+
+def eval_shapley(dataset, args, dat):
+	""" Shapley metric. """
+
+	row_individual = dataset.num_data.ix[args.individual].reshape(1, -1)
+
+	x_individual = dat.scaler.transform(row_individual)
+
+	shapley, _ = qii_lib.shapley_influence(dataset, dat.cls, x_individual, dat.x_test)
+	shapley_series = pd.Series(shapley, index=shapley.keys())
+	if args.show_plot or args.output_pdf:
+		plot_series(shapley_series, args, 'Feature', 'QII on Outcomes (Shapley)')
+
+
+def eval_class_average_unary(dataset, args, dat):
+	""" Unary QII averaged over all individuals for a particular class """
+	average_local_inf, _ = qii_lib.average_local_class_influence(
+		dataset, dat.cls, dat.x_test, dat.x_target_class)
+	average_local_inf_series = pd.Series(average_local_inf,
+	                                     index=average_local_inf.keys())
+	top_40 = average_local_inf_series.sort_values(ascending=False).head(40)
+	if args.show_plot or args.output_pdf:
+		plot_series(top_40, args,
+		            'Feature', 'QII on Outcomes')
+	top_5 = average_local_inf_series.sort_values(ascending=False).head(5)
+	get_feature_variation_plots(top_5, dataset, args, dat)
+
+
+def get_feature_variation_plots(features_list, dataset, args, dat):
+	def plot_histogram(dataframe):
+		data = dataframe.copy()
+		data = data.drop(['feature', 'class'], axis=1)
+		data = data.set_index('bin_edges')
+		data.hist()
+		del data
+
+	x_test = dat.x_test.reset_index(drop=True)
+	y_test = dat.y_test.reset_index(drop=True)
+	temp = x_test.copy()
+	temp['class'] = y_test
+	features = numpy.array(features_list.keys())
+	for feature in features:
+		plt.figure()
+		# bins = numpy.unique(temp[feature])
+		for class_index, class_group in temp.groupby(['class']):
+			# plt.hist(class_group[feature], bins=bins, label=str(class_index))
+			plt.hist(class_group[feature], label=str(class_index))
+		plt.legend(loc='best')
+		plt.title('Combined Histogram ' + str(feature))
+		if args.output_pdf:
+			pp = PdfPages('Combined Histogram-' + str(feature) + '-'+ args.classifier + '.pdf')
+			print ('Writing to Combined Histogram-' + str(feature) + '-'+ args.classifier + '.pdf')
+			pp.savefig(bbox_inches='tight')
+			pp.close()
+		if args.show_plot:
+			plt.show()
+
+
+
+	feature_variations = pd.DataFrame()
+	for cls in dat.y_test.unique():
+		x_target_class = x_test[y_test == cls]
+		feature_variations = feature_variations.append(qii_lib.get_feature_variations(features_list,
+		                                                                              dataset, dat.cls, dat.x_test,
+		                                                                              x_target_class, cls))
+
+		# features = numpy.array(features_list.keys())
+		# for feature in features:
+		# 	plt.figure()
+		# 	x_target_class[feature].hist()
+		# 	plt.title(str(feature) + '-' + 'class_' + str(cls))
+		# 	if args.output_pdf:
+		# 		pp = PdfPages('Histogram-' + str(feature) + '-' + 'class_' + str(cls) + '_' + args.classifier + '.pdf')
+		# 		print ('Writing to Histogram-' + str(feature) + '-' + 'class_' + str(cls) + '_' + args.classifier + '.pdf')
+		# 		pp.savefig(bbox_inches='tight')
+		# 		pp.close()
+		# 	if args.show_plot:
+		# 		plt.show()
+
+	for index, group in feature_variations.groupby(['feature']):
+		plt.figure()
+		for class_index, class_group in group.groupby(['class']):
+			plt.plot(class_group['bin_edges'], class_group['influences'], label=class_index)
+		plt.legend(loc='best')
+		plt.title(index)
+		if args.output_pdf:
+			pp = PdfPages('figure-' + index + '-' + args.classifier + '.pdf')
+			print ('Writing to figure-' + index + '-' + args.classifier + '.pdf')
+			pp.savefig(bbox_inches='tight')
+			pp.close()
+		if args.show_plot:
+			plt.show()
+
+
+__main__()
diff --git a/qii_lib.py b/qii_lib.py
index 7ff7993..4296aa1 100644
--- a/qii_lib.py
+++ b/qii_lib.py
@@ -1,252 +1,338 @@
+""" Various QII related computations. """
+
 import pandas as pd
 import numpy
+from scipy.stats import binned_statistic
+
+RECORD_COUNTERFACTUALS = False
+
+
+def intervene(X, features, x0):
+	""" Constant intervention """
+
+	X = numpy.array(X, copy=True)
+	x0 = x0.T
+	for f in features:
+		X[:, f] = x0[f]
+	return X
 
-class qii:
-    record_counterfactuals = True
-    #Constant intervention
-    @staticmethod
-    def intervene( X, features, x0 ):
-        X = numpy.array(X, copy=True)
-        x0 = x0.T
-        for f in features:
-            X[:,f] = x0[f]
-        return X
-
-    #Causal Measure with a constant intervention
-    @staticmethod
-    def causal_measure ( clf, X, ep_state, f, x0 ):
-        c0 = clf.predict(x0)
-        X1 = intervene( X, ep_state, x0 )
-        p1 = numpy.mean(1.*(clf.predict(X1) == c0))
-
-        X2 = intervene( X, ep_state + [f], x0 )
-        p2 = numpy.mean(1.*(clf.predict(X2) == c0))
-
-        return p2 - p1
-
-    #Randomly intervene on a a set of columns of X
-    @staticmethod
-    def random_intervene( X, cols ):
-        n = X.shape[0]
-        order = numpy.random.permutation(range(n))
-        X_int = numpy.array(X)
-        for c in cols:
-            X_int[:, c] = X_int[order, c]
-        return X_int
-
-    #Randomly intervene on a a set of columns of x from X
-    @staticmethod
-    def random_intervene_point( X, cols, x0 ):
-        n = X.shape[0]
-        order = numpy.random.permutation(range(n))
-        X_int = numpy.tile(x0, (n, 1))
-        for c in cols:
-            X_int[:, c] = X[order, c]
-        return X_int
-
-
-    @staticmethod
-    def discrim (X, cls, sens):
-        not_sens = 1 - sens
-        y_pred = cls.predict(X)
-        discrim = numpy.abs(numpy.dot(y_pred,not_sens)/sum(not_sens)
-                             - numpy.dot(y_pred,sens)/sum(sens))
-        return discrim
-
-    @staticmethod
-    def discrim_ratio (X, cls, sens):
-        not_sens = 1 - sens
-        y_pred = cls.predict(X)
-        sens_rate = numpy.dot(y_pred,sens)/sum(sens)
-        not_sens_rate = numpy.dot(y_pred,not_sens)/sum(not_sens)
-
-        discrim = not_sens_rate/sens_rate
-        return discrim
-
-
-
-    #Measure influence on discrimination
-    @staticmethod
-    def discrim_influence(dataset, cls, X_test, sens_test):
-        discrim_inf = {}
-        f_columns = dataset.num_data.columns
-        sup_ind = dataset.sup_ind
-        for sf in sup_ind:
-            ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
-            X_inter = qii.random_intervene(numpy.array(X_test), ls)
-            discrim_inter = qii.discrim(X_inter, cls, numpy.array(sens_test))
-            discrim_inf[sf] = discrim_inter
-            print('Discrimination %s: %.3f' % (sf, discrim_inf[sf]))
-        return discrim_inf
-
-    @staticmethod
-    def average_local_influence(dataset, cls, X):
-        average_local_inf = {}
-        counterfactuals = {}
-        iters = 10
-        f_columns = dataset.num_data.columns
-        sup_ind = dataset.sup_ind
-        y_pred = cls.predict(X)
-        for sf in sup_ind:
-            local_influence = numpy.zeros(y_pred.shape[0])
-            if qii.record_counterfactuals:
-                counterfactuals[sf] = (numpy.tile(X, (iters,1)), numpy.tile(X, (iters,1)))
-            ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
-            for i in xrange(0, iters):
-                X_inter = qii.random_intervene(numpy.array(X), ls)
-                y_pred_inter = cls.predict(X_inter)
-                local_influence = local_influence + (y_pred == y_pred_inter)*1.
-                if qii.record_counterfactuals:
-                    n = X_inter.shape[0]
-                    counterfactuals[sf][1][i*n:(i+1)*n]=X_inter
-
-            average_local_inf[sf] = 1 - (local_influence/iters).mean()
-            #print('Influence %s: %.3f' % (sf, average_local_inf[sf]))
-        return (average_local_inf, counterfactuals)
-
-    @staticmethod
-    def unary_individual_influence(dataset, cls, x_ind, X):
-        y_pred = cls.predict(x_ind)
-        average_local_inf = {}
-        counterfactuals = {}
-        iters = 1
-        f_columns = dataset.num_data.columns
-        sup_ind = dataset.sup_ind
-        for sf in sup_ind:
-            local_influence = numpy.zeros(y_pred.shape[0])
-            if qii.record_counterfactuals:
-                counterfactuals[sf] = (numpy.tile(X, (iters,1)), numpy.tile(X, (iters,1)))
-            ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
-            for i in xrange(0, iters):
-                X_inter = qii.random_intervene_point(numpy.array(X), ls, x_ind)
-                y_pred_inter = cls.predict(X_inter)
-                local_influence = local_influence + (y_pred == y_pred_inter)*1.
-                if qii.record_counterfactuals:
-                    n = X_inter.shape[0]
-                    counterfactuals[sf][1][i*n:(i+1)*n]=X_inter
-
-            average_local_inf[sf] = 1 - (local_influence/iters).mean()
-            #print('Influence %s: %.3f' % (sf, average_local_inf[sf]))
-        return (average_local_inf, counterfactuals)
-
-
-
-    @staticmethod
-    def shapley_influence(dataset, cls, x_individual, X_test):
-        p_samples = 600
-        s_samples = 600
-
-        def v(S, x, X_inter):
-            x_rep = numpy.tile(x, (p_samples, 1))
-            for f in S:
-                x_rep[:,f] = X_inter[:,f]
-            p = ((cls.predict(x_rep) == y0)*1.).mean()
-            return (p, x_rep)
-
-
-        #min_i = numpy.argmin(sum_local_influence)
-        y0 = cls.predict(x_individual)
-        print y0
-        b = numpy.random.randint(0,X_test.shape[0],p_samples)
-        X_sample = numpy.array(X_test.ix[b])
-        f_columns = dataset.num_data.columns
-        sup_ind = dataset.sup_ind
-        super_indices = dataset.sup_ind.keys()
-
-        shapley = dict.fromkeys(super_indices, 0)
-        if (qii.record_counterfactuals):
-            base = numpy.tile(x_individual, (2*p_samples*s_samples, 1))
-            #counterfactuals = dict([(sf, (base, numpy.zeros(p_samples*s_samples*2, X_test.shape[1])))
-            #    for sf in dataset.sup_ind.keys()])
-            counterfactuals = dict([(sf, (base, numpy.zeros((p_samples*s_samples*2, X_test.shape[1]))))
-                for sf in dataset.sup_ind.keys()])
-        else:
-            counterfactuals = {}
-
-        for sample in xrange(0, s_samples):
-            perm = numpy.random.permutation(len(super_indices))
-            for i in xrange(0, len(super_indices)):
-                # Choose a random subset and get string indices by flattening
-                #  excluding si
-                si = super_indices[perm[i]]
-                S_m_si = sum([sup_ind[super_indices[perm[j]]] for j in xrange(0, i)], [])
-                #translate into intiger indices
-                ls_m_si = [f_columns.get_loc(f) for f in S_m_si]
-                #repeat x_individual_rep
-                (p_S, X_S) = v(ls_m_si, x_individual, X_sample)
-                #also intervene on s_i
-                ls_si = [f_columns.get_loc(f) for f in sup_ind[si]]
-                (p_S_si, X_S_si) = v(ls_m_si + ls_si, x_individual, X_sample)
-                shapley[si] = shapley[si] - (p_S_si - p_S)/s_samples
-
-                if (qii.record_counterfactuals):
-                    start_ind = 2*sample*p_samples
-                    mid_ind   = (2*sample+1)*p_samples
-                    end_ind   = 2*(sample+1)*p_samples
-                    counterfactuals[si][1][start_ind:mid_ind] = X_S
-                    counterfactuals[si][1][mid_ind:end_ind]   = X_S_si
-
-        return (shapley, counterfactuals)
-
-
-
-
-    def banzhaf_influence(dataset, cls, x_individual, X_test):
-        p_samples = 600
-        s_samples = 600
-
-        def v(S, x, X_inter):
-            x_rep = numpy.tile(x, (p_samples, 1))
-            for f in S:
-                x_rep[:,f] = X_inter[:,f]
-            p = ((cls.predict(x_rep) == y0)*1.).mean()
-            return p
-
-        #min_i = numpy.argmin(sum_local_influence)
-        y0 = cls.predict(x_individual)
-        b = numpy.random.randint(0,X_test.shape[0],p_samples)
-        X_sample = numpy.array(X_test.ix[b])
-        f_columns = dataset.num_data.columns
-        sup_ind = dataset.sup_ind
-        super_indices = dataset.sup_ind.keys()
-
-        banzhaf = dict.fromkeys(super_indices, 0)
-
-        for sample in xrange(0, s_samples):
-            r = numpy.random.ranf(len(super_indices))
-            S = [super_indices[i] for i in xrange(0, len(super_indices)) if r[i] > 0.5]
-            for si in super_indices:
-                # Choose a random subset and get string indices by flattening
-                #  excluding si
-                S_m_si = sum([sup_ind[x] for x in S if x != si], [])
-                #translate into intiger indices
-                ls_m_si = [f_columns.get_loc(f) for f in S_m_si]
-                #repeat x_individual_rep
-                p_S = v(ls_m_si, x_individual, X_sample)
-                #also intervene on s_i
-                ls_si = [f_columns.get_loc(f) for f in sup_ind[si]]
-                p_S_si = v(ls_m_si + ls_si, x_individual, X_sample)
-                banzhaf[si] = banzhaf[si] - (p_S - p_S_si)/s_samples
-        return banzhaf
-
-    @staticmethod
-    def analyze_outliers(counterfactuals, out_cls, cls):
-        outlier_fracs = {}
-        new_outlier_fracs = {}
-        qii = {}
-        for sf,pairs in counterfactuals.iteritems():
-            X = pairs[0]
-            X_cf = pairs[1]
-            outs_X = out_cls.predict(X) == -1
-            outs_X_cf = out_cls.predict(X_cf) == -1
-            outlier_fracs[sf] = numpy.mean(outs_X_cf)
-            lnot = numpy.logical_not
-            land = numpy.logical_and
-            old_outlier_frac = numpy.mean(lnot(outs_X))
-            new_outlier_fracs[sf] = numpy.mean(land(lnot(outs_X), outs_X_cf))/old_outlier_frac
-            qii = numpy.mean(cls.predict(X) != cls.predict(X_cf))
-            print('QII %s %.3f' % (sf, qii))
-        return (outlier_fracs, new_outlier_fracs)
 
+def causal_measure(clf, X, ep_state, f, x0):
+	""" Causal Measure with a constant intervention. """
 
+	c0 = clf.predict(x0)
+	X1 = intervene(X, ep_state, x0)
+	p1 = numpy.mean(1. * (clf.predict(X1) == c0))
 
+	X2 = intervene(X, ep_state + [f], x0)
+	p2 = numpy.mean(1. * (clf.predict(X2) == c0))
+
+	return p2 - p1
+
+
+def random_intervene(X, cols):
+	""" Randomly intervene on a a set of columns of X. """
+
+	n = X.shape[0]
+	order = numpy.random.permutation(range(n))
+	X_int = numpy.array(X)
+	for c in cols:
+		X_int[:, c] = X_int[order, c]
+	return X_int
+
+
+def random_intervene_class(X, target_class_X, cols):
+	""" Randomly intervene on a a set of columns of target_class_X. """
+	n = X.shape[0]
+	p = target_class_X.shape[0]
+	order = numpy.random.choice(n, p)
+	target_int = numpy.array(target_class_X)
+	for c in cols:
+		target_int[:, c] = X[order, c]
+	return target_int
+
+
+def get_histogram_bins(X, cols, num_bins=40):
+	col = cols[0]
+	column_values = X[:, col]
+	_, bin_edges, binned_indices = binned_statistic(column_values, numpy.ones(len(column_values)), statistic='sum',
+	                                                bins=num_bins)
+	return binned_indices, bin_edges
+
+
+def yield_increasing_bins(X, target_class_X, binned_indices, cols, bin):
+	""" Randomly intervene on a a set of columns of target_class_X. """
+	p = target_class_X.shape[0]
+	target_int = numpy.array(target_class_X)
+	indices_list = [index for index, value in enumerate(binned_indices) if value == bin]
+	n = len(indices_list)
+	if n == 0:
+		return None
+	else:
+		order = numpy.random.choice(n, p)
+		for c in cols:
+			target_int[:, c] = X[order, c]
+		return target_int
+
+
+def random_intervene_point(X, cols, x0):
+	""" Randomly intervene on a a set of columns of x from X. """
+	n = X.shape[0]
+	order = numpy.random.permutation(range(n))
+	X_int = numpy.tile(x0, (n, 1))
+	for c in cols:
+		X_int[:, c] = X[order, c]
+	return X_int
+
+
+def discrim(X, cls, sens):
+	not_sens = 1 - sens
+	y_pred = cls.predict(X)
+	discrim = numpy.abs(numpy.dot(y_pred, not_sens) / sum(not_sens)
+	                    - numpy.dot(y_pred, sens) / sum(sens))
+	return discrim
+
+
+def discrim_ratio(X, cls, sens):
+	not_sens = 1 - sens
+	y_pred = cls.predict(X)
+	sens_rate = numpy.dot(y_pred, sens) / sum(sens)
+	not_sens_rate = numpy.dot(y_pred, not_sens) / sum(not_sens)
+
+	discrim = not_sens_rate / sens_rate
+	return discrim
+
+
+def discrim_influence(dataset, cls, X_test, sens_test):
+	""" Measure influence on discrimination. """
+
+	discrim_inf = {}
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	for sf in sup_ind:
+		ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
+		X_inter = random_intervene(numpy.array(X_test), ls)
+		discrim_inter = discrim(X_inter, cls, numpy.array(sens_test))
+		discrim_inf[sf] = discrim_inter
+		print 'Discrimination %s: %.3f' % (sf, discrim_inf[sf])
+	return discrim_inf
+
+
+def average_local_influence(dataset, cls, X):
+	average_local_inf = {}
+	counterfactuals = {}
+	iters = 10
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	y_pred = cls.predict(X)
+	for sf in sup_ind:
+		local_influence = numpy.zeros(y_pred.shape[0])
+		if RECORD_COUNTERFACTUALS:
+			counterfactuals[sf] = (numpy.tile(X, (iters, 1)), numpy.tile(X, (iters, 1)))
+		ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
+		for i in xrange(0, iters):
+			X_inter = random_intervene(numpy.array(X), ls)
+			y_pred_inter = cls.predict(X_inter)
+			local_influence += (y_pred == y_pred_inter) * 1.
+			if RECORD_COUNTERFACTUALS:
+				n = X_inter.shape[0]
+				counterfactuals[sf][1][i * n:(i + 1) * n] = X_inter
+
+		average_local_inf[sf] = 1 - (local_influence / iters).mean()
+		print('Influence %s: %.3f' % (sf, average_local_inf[sf]))
+	return (average_local_inf, counterfactuals)
+
+
+def average_local_class_influence(dataset, cls, X, target_class_X):
+	average_local_inf_class = {}
+	counterfactuals = {}
+	iters = 10
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	y_pred = cls.predict(target_class_X)
+	for sf in sup_ind:
+		local_influence = numpy.zeros(y_pred.shape[0])
+		if RECORD_COUNTERFACTUALS:
+			counterfactuals[sf] = (numpy.tile(target_class_X, (iters, 1)), numpy.tile(target_class_X, (iters, 1)))
+		ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
+		for i in xrange(0, iters):
+			X_inter = random_intervene_class(numpy.array(X), numpy.array(target_class_X), ls)
+			y_pred_inter = cls.predict(X_inter)
+			local_influence += (y_pred == y_pred_inter) * 1.
+			if RECORD_COUNTERFACTUALS:
+				n = X_inter.shape[0]
+				counterfactuals[sf][1][i * n:(i + 1) * n] = X_inter
+
+		average_local_inf_class[sf] = 1 - (local_influence / iters).mean()
+		print('Influence %s: %.3f' % (sf, average_local_inf_class[sf]))
+	return (average_local_inf_class, counterfactuals)
+
+
+def get_feature_variations(features_list, dataset, cls, X, target_class_X, class_name):
+	average_local_inf_class = pd.DataFrame()
+	bins = 40
+	iters = 10
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	y_pred = cls.predict(target_class_X)
+	indices = features_list.reset_index()['index']
+	for sf in indices:
+		ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
+		binned_indices, bin_edges = get_histogram_bins(numpy.array(X), ls, num_bins=bins)
+		feature_dataframe = pd.DataFrame({'bin_edges': bin_edges[0:-1]})
+		feature_dataframe['class'] = class_name
+		feature_dataframe['feature'] = sf
+		influences = []
+		for bin in xrange(0, bins):
+			local_influence = numpy.zeros(y_pred.shape[0])
+			for iter in xrange(0, iters):
+				X_inter = yield_increasing_bins(numpy.array(X), numpy.array(target_class_X), binned_indices, ls, bin)
+				if X_inter is not None:
+					y_pred_inter = cls.predict(X_inter)
+					local_influence = local_influence + (y_pred == y_pred_inter) * 1.
+			influences.append((local_influence / iters).mean())
+		feature_dataframe['influences'] = influences
+		average_local_inf_class = average_local_inf_class.append(feature_dataframe)
+		print('Influence %s is done' % (sf))
+	return average_local_inf_class
+
+
+def unary_individual_influence(dataset, cls, x_ind, X):
+	y_pred = cls.predict(x_ind.reshape(1, -1))
+	average_local_inf = {}
+	counterfactuals = {}
+	iters = 1
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	for sf in sup_ind:
+		local_influence = numpy.zeros(y_pred.shape[0])
+		if RECORD_COUNTERFACTUALS:
+			counterfactuals[sf] = (numpy.tile(X, (iters, 1)), numpy.tile(X, (iters, 1)))
+		ls = [f_columns.get_loc(f) for f in sup_ind[sf]]
+		for i in xrange(0, iters):
+			X_inter = random_intervene_point(numpy.array(X), ls, x_ind)
+			y_pred_inter = cls.predict(X_inter)
+			local_influence = local_influence + (y_pred == y_pred_inter) * 1.
+			if RECORD_COUNTERFACTUALS:
+				n = X_inter.shape[0]
+				counterfactuals[sf][1][i * n:(i + 1) * n] = X_inter
+
+		average_local_inf[sf] = 1 - (local_influence / iters).mean()
+	# print('Influence %s: %.3f' % (sf, average_local_inf[sf]))
+	return (average_local_inf, counterfactuals)
+
+
+def shapley_influence(dataset, cls, x_individual, X_test):
+	p_samples = 600
+	s_samples = 600
+
+	def v(S, x, X_inter):
+		x_rep = numpy.tile(x, (p_samples, 1))
+		for f in S:
+			x_rep[:, f] = X_inter[:, f]
+		p = ((cls.predict(x_rep) == y0) * 1.).mean()
+		return (p, x_rep)
+
+	# min_i = numpy.argmin(sum_local_influence)
+	y0 = cls.predict(x_individual)
+	print y0
+	b = numpy.random.randint(0, X_test.shape[0], p_samples)
+	X_sample = numpy.array(X_test.ix[b])
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	super_indices = dataset.sup_ind.keys()
+
+	shapley = dict.fromkeys(super_indices, 0)
+	if RECORD_COUNTERFACTUALS:
+		base = numpy.tile(x_individual, (2 * p_samples * s_samples, 1))
+		# counterfactuals = dict([(sf, (base, numpy.zeros(p_samples*s_samples*2, X_test.shape[1])))
+		#    for sf in dataset.sup_ind.keys()])
+
+		counterfactuals = dict([(sf, (base,
+		                              numpy.zeros((p_samples * s_samples * 2, X_test.shape[1]))))
+		                        for sf in dataset.sup_ind.keys()])
+	else:
+		counterfactuals = {}
+
+	for sample in xrange(0, s_samples):
+		perm = numpy.random.permutation(len(super_indices))
+		for i in xrange(0, len(super_indices)):
+			# Choose a random subset and get string indices by flattening
+			#  excluding si
+			si = super_indices[perm[i]]
+			S_m_si = sum([sup_ind[super_indices[perm[j]]] for j in xrange(0, i)], [])
+			# translate into intiger indices
+			ls_m_si = [f_columns.get_loc(f) for f in S_m_si]
+			# repeat x_individual_rep
+			(p_S, X_S) = v(ls_m_si, x_individual, X_sample)
+			# also intervene on s_i
+			ls_si = [f_columns.get_loc(f) for f in sup_ind[si]]
+			(p_S_si, X_S_si) = v(ls_m_si + ls_si, x_individual, X_sample)
+			shapley[si] = shapley[si] - (p_S_si - p_S) / s_samples
+
+			if RECORD_COUNTERFACTUALS:
+				start_ind = 2 * sample * p_samples
+				mid_ind = (2 * sample + 1) * p_samples
+				end_ind = 2 * (sample + 1) * p_samples
+				counterfactuals[si][1][start_ind:mid_ind] = X_S
+				counterfactuals[si][1][mid_ind:end_ind] = X_S_si
+
+	return (shapley, counterfactuals)
+
+
+def banzhaf_influence(dataset, cls, x_individual, X_test):
+	p_samples = 600
+	s_samples = 600
+
+	def v(S, x, X_inter):
+		x_rep = numpy.tile(x, (p_samples, 1))
+		for f in S:
+			x_rep[:, f] = X_inter[:, f]
+		p = ((cls.predict(x_rep) == y0) * 1.).mean()
+		return p
+
+	# min_i = numpy.argmin(sum_local_influence)
+	y0 = cls.predict(x_individual)
+	b = numpy.random.randint(0, X_test.shape[0], p_samples)
+	X_sample = numpy.array(X_test.ix[b])
+	f_columns = dataset.num_data.columns
+	sup_ind = dataset.sup_ind
+	super_indices = dataset.sup_ind.keys()
+
+	banzhaf = dict.fromkeys(super_indices, 0)
+
+	for sample in xrange(0, s_samples):
+		r = numpy.random.ranf(len(super_indices))
+		S = [super_indices[i] for i in xrange(0, len(super_indices)) if r[i] > 0.5]
+		for si in super_indices:
+			# Choose a random subset and get string indices by flattening
+			#  excluding si
+			S_m_si = sum([sup_ind[x] for x in S if x != si], [])
+			# translate into intiger indices
+			ls_m_si = [f_columns.get_loc(f) for f in S_m_si]
+			# repeat x_individual_rep
+			p_S = v(ls_m_si, x_individual, X_sample)
+			# also intervene on s_i
+			ls_si = [f_columns.get_loc(f) for f in sup_ind[si]]
+			p_S_si = v(ls_m_si + ls_si, x_individual, X_sample)
+			banzhaf[si] = banzhaf[si] - (p_S - p_S_si) / s_samples
+	return banzhaf
+
+
+def analyze_outliers(counterfactuals, out_cls, cls):
+	outlier_fracs = {}
+	new_outlier_fracs = {}
+	qii = {}
+	for sf, pairs in counterfactuals.iteritems():
+		X = pairs[0]
+		X_cf = pairs[1]
+		outs_X = out_cls.predict(X) == -1
+		outs_X_cf = out_cls.predict(X_cf) == -1
+		outlier_fracs[sf] = numpy.mean(outs_X_cf)
+		lnot = numpy.logical_not
+		land = numpy.logical_and
+		old_outlier_frac = numpy.mean(lnot(outs_X))
+		new_outlier_fracs[sf] = numpy.mean(land(lnot(outs_X), outs_X_cf)) / old_outlier_frac
+		qii = numpy.mean(cls.predict(X) != cls.predict(X_cf))
+		print 'QII %s %.3f' % (sf, qii)
+	return (outlier_fracs, new_outlier_fracs)