/
ares.py
984 lines (901 loc) · 47 KB
/
ares.py
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from mlxtend.frequent_patterns import apriori
from tqdm import tqdm
import numpy as np
from sklearn import preprocessing
import pandas as pd
import copy
import matplotlib.pyplot as plt
import itertools
import warnings
def create_feature_values_tree(features_tree, use_values=False):
"""
Converts feature values back to their parent features REFER TO EFFICIENCY
(used in self.compute_V to determine if rules are valid)
Input: features_tree (a dictionary where features are keys and feature values are values)
Output: dictionary where keys are feature values and values are the parent feature e.g.
{... 'Foreign-Worker = A201': Foreign-Worker, 'Foreign-Worker = A202': Foreign-Worker ...}.
"""
feature_values_tree = {}
if use_values:
for feature_value in features_tree:
feature_values_tree[feature_value] = feature_value.split(' = ')[0]
else:
for feature, feature_values in features_tree.items():
for feature_value in feature_values:
feature_values_tree[feature_value] = feature
return feature_values_tree
def create_features_tree(feature_values):
features_tree = {}
for feature_value in feature_values:
feature = feature_value.split(" = ")[0]
if feature not in features_tree:
features_tree[feature] = [feature_value]
else:
features_tree[feature] += [feature_value]
return features_tree
class AReS:
def __init__(self, model, dataset, X, dropped_features=[],
n_bins=10, ordinal_features=[], normalise=False,
constraints=[20, 7, 10], correctness=False):
"""
Normalise is implemented differently to GLOBE_CE
AReS Implementation:
(required arguments)
model : Any black box model with a predict() method that
returns a binary prediction
x_aff : Pandas DataFrame. Full training data of interest
(positive and negative predictions)
dataset : Our custom dataset_loader object including the data
(there is no direct need to pass X as an argument)
and information on categorical/continuous features
(optional arguments)
dropped_features : List of dropped features in the form of just the
feature e.g. 'Foreign-Worker'
add_redundant : If True, evaluate each candidate rule and reject
those which don't provide any recourse for the
affected inputs (speeds up optimisation)
apriori_threshold : The support threshold used by the apriori
algorithm (probability of an itemset, lower
values thus return more possible rules)
constraints : As defined by the paper
e1 = total number of rules
e2 = maximum rule width (number of conditions)
e3 = total number of unique subgroup descriptors
(outer if-then clauses)
lams : hyperparameters for objective function (list of
size 4 for AReS, size 2 for our objective)
feature_costs : optional vector for defined feature costs
(otherwise, we use l1 norm)
ordinal_features : List of categorical features that require ordinal
costs when moving between categories (typically
continuous features which have been one-hot
encoded before model training)
original_objective : If True, use the original AReS objective function
(otherwise just optimise correctness and cost)
n_bins : number of (equal) bins for continuous variables
normalise : If True, normalise the inputs prior to the
self.model.predict() call
then_generation : Apriori threshold value. In progress. If not
None, then generate the "then" condition using
apriori on a set filtered according to each if-if
condition (search for candidate rules in SDxRL).
May find more relevant rules. If None, search for
candidate rules in SDxRLxRL. There's also the
possibility to set "then" to RL, and divide SD
appropriately to match RL. As well as
(alternatively) the possibility to allow "then"
to not match the "inner-if" entirely.
"""
# Set Input Parameters
self.model = model
self.normalise = normalise
if self.normalise:
self.means = X.values.mean(axis=0)
self.stds = X.values.std(axis=0)
self.preds = self.model.predict((X.values-self.means)/self.stds)
else:
self.preds = self.model.predict(X.values) # to determine affected inputs
self.X_original = X # store original inputs
# copy is needed since continuous features are binned for apriori
self.X = self.X_original.copy()
self.dataset = copy.deepcopy(dataset)
self.dropped_features = dropped_features
self.ordinal_features = ordinal_features
self.n_bins = n_bins
self.correctness = correctness
self.e1, self.e2, self.e3 = constraints
# Store Features. Generate l1 feature costs (need to differentiate between categorical/continuous)
# Continuous/categorical/non-dropped features are all computed/stored
self.features_tree = self.dataset.features_tree # dictionary of form 'feature: [feature values]'
self.features_tree_dropped = copy.deepcopy(self.features_tree)
for feature in self.dropped_features:
del self.features_tree_dropped[feature]
self.features = self.dataset.features # list of feature values (includes class label)
# list of categorical features (not values)
# if a continuous feature was binned before model training, then it's treated as categorical (though ordinal)
self.categorical_features = self.dataset.categorical_features[self.dataset.name]
# Bin continuous features and store resulting data (dimensionality of input data increases)
self.X, self.binned_features, self.binned_features_continuous = self.bin_continuous_features(self.X)
self.continuous_features = [] # list of continuous features
self.feature_costs_vector = np.zeros(len(self.features)-1)
self.non_ordinal_categories_idx = np.ones(len(self.features)-1, dtype=bool)
i = 0
for feature in self.features_tree:
if feature not in self.categorical_features:
self.continuous_features.append(feature)
self.feature_costs_vector[i] = 1/self.bin_widths[feature] # includes dropped features
self.non_ordinal_categories_idx[i] = True
i += 1
else:
n = len(self.features_tree[feature])
if feature in self.ordinal_features:
self.feature_costs_vector[i:i+n] = range(n) # if ordinal, default to unit change between bins
self.non_ordinal_categories_idx[i:i+n] = False
else:
self.feature_costs_vector[i:i+n] = 0.5 # categorical features have cost 1 (2 changes of 0.5)
self.non_ordinal_categories_idx[i:i+n] = True
i += n
# either we bin continuous features before model training (ordinal categories),
# or we don't (non-ordinal categories)
# non-continuous features are also included in non-ordinal categories
# (see self.objective_terms r_costs computation)
self.ordinal_categories_idx = ~self.non_ordinal_categories_idx
self.any_non_ordinal = self.non_ordinal_categories_idx.any()
self.any_ordinal = self.ordinal_categories_idx.any()
# Drop features
self.X_drop = self.X.copy() # self.X_drop is used just for apriori itemset generation
for feature in self.dropped_features:
print("Dropping Feature:", feature)
for feature_value in self.features_tree[feature]:
self.X_drop = self.X_drop.drop(feature_value, axis=1)
# Compute affected features
self.X_aff_original = self.X_original.iloc[self.preds == 0].copy()\
.reset_index(drop=True) # original data
self.X_aff = self.X.iloc[self.preds == 0].copy()\
.reset_index(drop=True) # data with continuous variables binned
self.U = self.n_bins * self.e2 # custom objective function
self.U1 = self.X_aff.shape[0] * self.e1 # incorrectrecourse
self.U3 = 0 # featurecost, not implemented
self.U4 = self.n_bins * self.e1 * self.e2 # featurechange
# Assign features to feature values, used when computing if rules are valid
self.feature_values_tree = create_feature_values_tree(self.features_tree, use_values=False)
# The following are updated using
# self.compute_SD_RL and self.compute_V
self.SD, self.RL, self.RL2 = None, None, None
self.SD_copy, self.RL_copy = None, None
self.V, self.V_copy = None, None
self.f, self.V_opt = None, None
self.R = None
def bin_continuous_features(self, data):
"""
Method for binning continuous features. Also computes self.bin_mids and self.bin_mids_tree (dictionary
and dictionary of dictionaries respectively) which store mid point values for each bin range
Input: original data
Outputs: data with continuous features binned (default is 10 equally sized bins)
list of all feature values, with binned feature values included
list of only binned feature values
"""
self.data_binned = data.copy()
data_oh, features, continuous_features = [], [], set()
self.bins = {}
self.bin_mids = {}
self.bin_mids_tree = {}
self.bin_widths = {}
for x in data.columns:
if x.split()[0] in self.categorical_features:
data_oh.append(pd.DataFrame(data[x]))
features.append(x)
else:
self.data_binned[x], self.bins[x] = pd.cut(self.data_binned[x].apply(lambda x: float(x)),
bins=self.n_bins, retbins=True)
one_hot = pd.get_dummies(self.data_binned[x])
one_hot.columns = pd.Index(list(one_hot.columns)) # necessary?
data_oh.append(one_hot)
cols = self.data_binned[x].cat.categories
self.bin_mids_tree[x] = {}
width = cols.length[-1]
self.bin_widths[x] = width
for i, col in enumerate(cols):
feature_value = x + " = " + str(col)
features.append(feature_value)
continuous_features.add(feature_value)
self.features_tree[x].append(feature_value)
mid = cols.mid[1]-width if i==0 else col.mid # adjust for pd.cut extending the first bin
self.bin_mids[feature_value] = mid
self.bin_mids_tree[x][feature_value] = mid
data_oh = pd.concat(data_oh, axis=1)
data_oh.columns = features
return data_oh, features, continuous_features
def generate_itemsets(self, apriori_threshold, max_width=None,
affected_subgroup=None, save_copy=False):
"""
affected_subgroup : The feature value of the subgroup of interest
e.g. 'Foreign-Worker = A201' (see dataset_loader naming)
If None, SD and RL are set to the same set
generated by apriori
"""
# Max width
if max_width is None:
max_width = self.e2 - 1
# Compute SD and RL
print("Computing Candidate Sets of Conjunctions of Predicates SD and RL")
self.SD = Apriori(x=self.X_drop, apriori_threshold=apriori_threshold,
affected_subgroup=affected_subgroup, max_width=max_width,
feature_values_tree=self.feature_values_tree)
if affected_subgroup is None:
self.RL = copy.deepcopy(self.SD)
else:
self.RL = Apriori(x=self.X_drop, apriori_threshold=apriori_threshold,
affected_subgroup=None, max_width=max_width,
feature_values_tree=self.feature_values_tree)
# Update affected inputs
self.X_aff_original = self.X_original.iloc[(self.preds == 0) & self.SD.sub_idx]\
.copy().reset_index(drop=True) # original data
self.X_aff = self.X.iloc[(self.preds == 0) & self.SD.sub_idx]\
.copy().reset_index(drop=True) # data with continuous variables binned
print("SD and RL Computed with Lengths {} and {}".format(self.SD.length, self.RL.length))
if save_copy:
print("Saving Copies of SD and RL as SD_copy and RL_copy")
self.SD_copy, self.RL_copy = copy.deepcopy(self.SD), copy.deepcopy(self.RL)
def generate_groundset(self, max_width=None, RL_reduction=False,
then_generation=None, save_copy=False):
"""
Compute candidate set of rules for self.optimise(). Determines if rules are valid and also applies
maxwidth constraint. User sets self.add_redundant to False (__init__ method) if we ignore any rules
that do not provide any successful recourse (slower, but completely irrelevant rules are not added).
Size of candidate rules, V, seems to be the bottleneck in the submodular maximisation.
Inputs: SD and RL: outer and inner if conditions (as per paper)
SD_lengths and RL_lengths: widths of each SD/RL element
feature_values_tree: as described in self.encode_feature_values
then_gen UPDATE
Output: candidate set of rules after applying constraints
"""
# Max width
if max_width is None:
max_width = self.e2
self.V = TwoLevelRecourseSet()
self.V.generate_triples(self.SD, self.RL, max_width=max_width,
RL_reduction=RL_reduction, then_generation=then_generation)
print("Ground Set Computed with Length", self.V.length)
if save_copy:
print("Saving Copy of Ground Set as V_copy")
self.V_copy = copy.deepcopy(self.V)
def evaluate_groundset(self, lams, r=None, save_mode=0,
disable_tqdm=False, plot_accuracy=True):
self.V.evaluate_triples(self, r=r, save_mode=save_mode,
disable_tqdm=disable_tqdm,
plot_accuracy=plot_accuracy)
# compute objectives for individual triples
if len(lams) == 2:
self.V.objectives = AReS.f_custom(self.V, lams, self.U,
singleton=True)
else:
bounds = [self.U1, self.U3, self.U4]
self.V.objectives = AReS.f_ares(self.V, lams, bounds,
singleton=True)
# add correctness later if you cba
def select_groundset(self, s=0):
self.V.select_triples(s)
# implement updated cumulative plot
# if plot_accuracy:
# self.V.plot_accuracy()
def optimise_groundset(self, lams, factor=1, print_updates=False,
print_terms=False, save_copy=False):
"""
Submodular maximisation. We make 2 major modifications:
1. Don't repeat procedure k times, where k is the number of constraints. This rarely increased
performance yet increases computation time k-fold (mostly pointless despite formal guarantees)
2. Don't permit up to k elements to be exchanged (computationally infeasible- to this day I am clueless
regarding how this is done efficiently). In this case, you might have 20 choose 2 = 190 options for
elements to exchange (instead of just 20) which is just not a worthwhile trade-off.
Output: Final two level recourse set, S
"""
print("Initialising Copy of Ground Set")
if save_copy:
self.V_opt = None
# ensures python doesn't use an already
# stored version during the deepcopy process
self.V_opt = copy.deepcopy(self.V)
print("Ground Set Copied")
else:
self.V_opt = self.V
N = self.V_opt.length
selected_idx = np.argsort(self.V_opt.objectives)[:-(N+1):-1]
self.V_opt.objectives = self.V_opt.objectives[selected_idx]
self.V_opt.triples_array = self.V_opt.triples_array[selected_idx]
self.V_opt.index_terms(selected_idx)
self.V_opt.cost_matrix[np.isnan(self.V_opt.cost_matrix)] = 0
self.f = AReS.f_custom if len(lams) == 2 else AReS.f_ares
R_idx = np.zeros(N, dtype=bool)
f_argmax = np.argmax(self.V_opt.objectives)
f_max = self.V_opt.objectives[f_argmax]
R_idx[f_argmax] = True
f_thresh = factor * f_max
# Compute objectives then select triples
if len(lams) == 2:
bounds = self.U
self.f = AReS.f_custom
else:
bounds = [self.U1, self.U3, self.U4]
self.f = AReS.f_ares
# While there exists a delete/update operation do:
print("While there exists a delete/update operation, loop:")
delete, add, exchange = True, True, True
while True:
# Delete check
print("Checking Delete")
delete = False
for idx in np.arange(N)[R_idx]:
R_idx[idx] = False
f_delete = self.f(self.V_opt, lams, bounds, idx=R_idx)
if f_delete > f_thresh:
if print_updates:
print("Deleting Element ({} >= {})".format(f_delete, f_thresh))
# self.min_costs.append(self.f_custom(Si_delete, return_costs=True))
if print_terms:
self.f(self.V_opt, lams, bounds,
idx=R_idx, print_terms=True)
f_thresh = factor * f_delete
delete = True
break
R_idx[idx] = True
if (not delete) and print_updates:
print("No Delete Operation Found")
if not (delete or add or exchange):
break
# Actual flow should be: always add elements until
# constraint is reached or no element to add
# Then exchange up to k... ?
# Add check
print("Checking Add")
add = False
if R_idx.sum() < self.e1:
for idx in tqdm(np.arange(N)[~R_idx]):
R_idx[idx] = True
if self.constraints(self.V_opt.triples_array[R_idx]):
f_add = self.f(self.V_opt, lams, bounds, idx=R_idx)
if f_add > f_thresh:
if print_updates:
print("Adding Element ({} >= {})"
.format(f_add, f_thresh))
if print_terms:
self.f(self.V_opt, lams, bounds,
idx=R_idx, print_terms=True)
# self.min_costs.append(self.f_custom(Si_add, return_costs=True))
f_thresh = factor * f_add
add = True
continue
R_idx[idx] = False
if (not add) and print_updates:
print("No Add Operation Found")
if not (delete or add or exchange):
break
# Exchange check
print("Checking Exchange")
exchange = False
for add_idx in tqdm(np.arange(N)[~R_idx]):
# Permit only 1 removal (not k, as in algorithm)
for delete_idx in np.arange(N)[R_idx]:
R_idx[add_idx] = True
R_idx[delete_idx] = False
if self.constraints(self.V_opt.triples_array[R_idx]):
f_exchange = self.f(self.V_opt, lams, bounds, idx=R_idx)
if f_exchange > f_thresh:
if print_updates:
print("Exchanging Element ({} >= {})".
format(f_exchange, f_thresh))
if print_terms:
self.f(self.V_opt, lams, bounds,
idx=R_idx, print_terms=True)
# self.min_costs.append(self.f(Si_exchange, print_terms=True, return_costs=True))
# self.min_costs.append(self.f_custom(Si_exchange, return_costs=True))
f_thresh = factor * f_exchange
exchange = True
break
R_idx[delete_idx] = True
R_idx[add_idx] = False
if (not exchange) and print_updates:
print("No Exchange Operation Found")
if not (delete or add or exchange):
break
#break # fix this to loop multiple times but only if necessary. also skip "add" if size constraints met
#self.min_costs = np.array(self.min_costs)
#self.V -= Si
self.R = TwoLevelRecourseSet()
self.R.triples = set(self.V_opt.triples_array[R_idx])
self.R.length = len(self.R.triples)
self.R.evaluate_triples(self)
@staticmethod
def f_ares(tlrs, lams, bounds, idx=None,
singleton=False): #, print_terms=False, plot_f=False):
# tlrs = two level recourse set
if idx is None:
idx = np.ones(tlrs.correct_matrix.shape[0], dtype=bool)
if not idx.any():
return lams[0] * bounds[0]
featurecost = tlrs.featurecost[idx]
featurechange = tlrs.featurechange[idx]
if singleton:
incorrectrecourse = (tlrs.correct_matrix[idx] == 0).sum(axis=1)
cover = tlrs.cover_matrix[idx].sum(axis=1)
else:
incorrectrecourse = (tlrs.correct_matrix[idx] == 0).sum()
cover = tlrs.cover_matrix[idx].max(axis=0).sum()
featurecost = featurecost.sum()
featurechange = featurechange.sum()
return lams[0] * (bounds[0] - incorrectrecourse) + lams[1] * cover\
+ lams[2] * (bounds[1] - featurecost)\
+ lams[3] * (bounds[2] - featurechange)
# In the cost terms, AReS doesn't consider how many points
# are affected by a certain rule (seems unreliable)
# E.g. you could find one high cost rule that applies to
# all points, then the rest all low costs? Our implementation
# considers the actual magnitudes of cost per triple and input
# if plot_f:
# f.append(self.lams[0] * (self.U1 - incorrectrecourse) + self.lams[1] * cover\
# + self.lams[2] * (self.U3 - featurecost) + self.lams[3] * (self.U4 - featurechange))
# plt.plot(range(len(f)), f)
# plt.show()
# if print_terms:
# print("{}/{}".format((self.U1 - incorrectrecourse), self.U1),
# cover, "{}/{}".format((self.U4 - featurechange), self.U4))
# print("Acc: {}, Cost: {}".format(round(cor.mean()*100, 4),
# round(cos[cor == 1].mean(), 4)))
@staticmethod
def f_custom(tlrs, lams, bound, idx=None,
singleton=False, print_terms=False): #, plot_f=False):
# tlrs = two level recourse set
if singleton and print_terms:
raise ValueError('Cannot use parameters singleton and '
'print_terms simultaneously')
if idx is None:
idx = np.ones(tlrs.correct_matrix.shape[0], dtype=bool)
elif not idx.any():
return 0
if singleton:
correct = tlrs.correct_matrix[idx].mean(axis=1)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
cost = np.nanmean(tlrs.cost_matrix[idx], axis=1)
cost[np.isnan(cost)] = 0
else:
correct = tlrs.correct_matrix[idx].max(axis=0).mean()
if correct == 0:
cost = bound
else:
# Suppress numpy warning for all nan slice
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
cost = np.nanmean(np.nanmin(tlrs.cost_matrix[idx], axis=0))
if np.isnan(cost):
cost = bound
if print_terms:
n = tlrs.correct_matrix.shape[1]
print("Accuracy: {}/{} = {}%".format(int(correct*n/100), n,
round(correct, 2)),
"\nAverage Cost: {}".format(round(cost, 4)))
# "{}/{}".format(round(np.average(objectives[objectives!=0]),2), self.lams[1]*self.U))
return lams[0] * correct + lams[1] * (bound - cost)
def constraints(self, triples):
"""
Computes if constraints (e1: total number of rules, e3: total number of unique sub-descriptors) are violated
Input: Two Level Recourse Set, Si
Output: boolean (True if constraints are not violated)
"""
if len(triples) > self.e1:
return False
subgroups = set()
for triple in triples:
subgroups.add(triple[0]) # only adds if the sub-descriptor is unseen
if len(subgroups) > self.e3:
return False
return True
def bin_X_test(self, data):
"""
Combine with first class method?
"""
label_encoder = preprocessing.LabelEncoder()
data_encode = data.copy()
self.data_binned_te = data.copy()
data_oh = []
for x in data.columns:
if x.split()[0] in self.categorical_features:
data_oh.append(pd.DataFrame(data[x]))
else:
self.data_binned_te[x] = pd.cut(self.data_binned_te[x].apply(lambda x: float(x)),
bins=self.bins[x])
one_hot = pd.get_dummies(self.data_binned_te[x])
one_hot.columns = pd.Index(list(one_hot.columns)) # necessary?
data_oh.append(one_hot)
data_oh = pd.concat(data_oh, axis=1)
data_oh.columns = self.binned_features
return data_oh
@staticmethod
def accuracy_cost_bounds(min_costs):
n = min_costs.shape[0]
min_costs = min_costs[~np.isnan(min_costs)]
min_costs = np.sort(min_costs)
costs = np.zeros(min_costs.shape[0]+1)
corrects = np.zeros(min_costs.shape[0]+1)
# First element of each vector is 0
for i in range(min_costs.shape[0]):
costs[i+1] = min_costs[:i+1].mean()
corrects[i+1] = (i+1)/n*100
return costs, corrects
def lower_bounds(self, min_costs):
n = min_costs.shape[0]
min_costs = min_costs[min_costs!=0]
min_costs = np.sort(min_costs)
costs = np.zeros(min_costs.shape[0]+1)
corrects = np.zeros(min_costs.shape[0]+1)
for i in range(min_costs.shape[0]):
corrects[i+1] = (i+1)/n*100
costs[i+1] = min_costs[:i+1].mean()
return costs, corrects
class Apriori:
# Takes x and (thresh OR affected_subgroup)
# Compute features/widths/length/values from conditions
def __init__(self, x, max_width=None, apriori_threshold=None, verbose=1,
affected_subgroup=None, feature_values_tree=None):
self.x = x
self.max_width = max_width
self.apriori_threshold = apriori_threshold
self.affected_subgroup = affected_subgroup
self.feature_values_tree = feature_values_tree
if self.feature_values_tree is None:
self.feature_values_tree = create_feature_values_tree(x.columns.values, use_values=True)
if (self.apriori_threshold is not None) ^ (self.affected_subgroup is None):
raise ValueError('Please specify either an affected subgroup or an apriori threshold (and not both)')
if (self.affected_subgroup is not None) and (self.apriori_threshold is not None):
# Store indices of affected subgroup matches
self.sub_idx = (self.x[affected_subgroup] == 1).values
# Drop affected subgroup's feature so
# apriori doesn't generate invalid rules
self.x_drop = self.x.copy()
for col in self.x.columns:
if col.split()[0] == affected_subgroup.split()[0]:
self.x_drop = self.x_drop.drop(col, axis=1)
self.conditions = pd.DataFrame(np.array([frozenset({affected_subgroup})]),
columns=['itemsets'])
# as per paper, SD and RL are the same set generated by apriori
elif self.apriori_threshold is not None:
self.sub_idx = np.ones(self.x.shape[0], dtype=bool)
# print(self.x, self.thresh, verbose, max_width)
self.conditions = apriori(self.x, min_support=self.apriori_threshold,
use_colnames=True, max_len=max_width,
verbose=verbose)
self.values = self.conditions.itemsets.values
self.features = self.compute_features(self.values)
self.widths = self.conditions.apply(lambda i: len(i.itemsets), axis=1).values
self.length = len(self.values)
self.valid_idx = None
def compute_features(self, values):
features = np.zeros(len(values), dtype=object)
for i in range(len(values)):
features[i] = frozenset(map(self.feature_values_tree.get, values[i]))
return features
def reduce(self, utilise_bug=False, print_output=True):
"""
IRREVERSIBLE
"""
# INVESTIGATE why the broken np.unique implementation gives much better results on HELOC...
# unfortunately np.unique fails with dtype=object, so we will manually implement a dictionary
if print_output:
print("Reducing Itemsets")
n = self.length
if n==0:
print("Size of itemset is 0 (no valid triples can be generated)")
return
if utilise_bug: # useful bug (easter egg)
un, co = np.unique(self.features, return_counts=True)
valid = un[co > 1]
else:
unique_feature_counts = {}
for feature in self.features:
if feature in unique_feature_counts:
unique_feature_counts[feature] += 1
else:
unique_feature_counts[feature] = 1
valid = set()
for feature in unique_feature_counts:
if unique_feature_counts[feature] > 1:
valid.add(feature)
self.valid_idx = np.zeros(self.length, dtype=bool)
for i in range(self.length):
if self.features[i] in valid:
self.valid_idx[i] = True
if self.valid_idx.any():
self.conditions = self.conditions.loc[self.valid_idx]
self.features = self.features[self.valid_idx]
self.widths = self.widths[self.valid_idx]
self.values = self.conditions.itemsets.values
self.max_width = self.widths.max()
self.length = len(self.values)
if print_output:
print("Size of Itemsets Reduced from {} to {}".format(n, self.length))
else:
self.conditions = None
self.features = None
self.widths = None
self.values = None
self.max_width = 0
self.length = 0
if print_output:
print("Size of Itemsets Reduced from {} to {}".format(n, 0))
print("No Valid Triples can be Generated from the Itemsets")
class TwoLevelRecourseSet:
def __init__(self):
# Class Attributes
self.SD, self.RL = None, None
self.triples = set()
self.triples_array = None
self.length = 0
self.cfx_matrix = None # counterfactuals after applying recourses to x_aff
self.correct_matrix = None
self.cost_matrix = None
self.max_idxs = None
self.cumulative_idxs = None
self.correct_vector = None
self.cost_vector = None
self.accuracy = None
self.average_cost = None
self.correct_max = None
self.correct_cumulative = None
self.cover_matrix = None
self.cover = None
self.objectives = None
self.ares = None
self.featurecost = None
self.featurechange = None
def generate_triples(self, SD, RL, max_width, RL_reduction=False, then_generation=None):
"""
Stores SD, RL, triples and length
"""
# Initialise SD and RL
print("Computing Ground Set of Triples V")
self.SD, self.RL = SD, RL
# Apply RL-Reduction if required
if RL_reduction is True:
print("Reducing RL")
n = self.RL.length
self.RL.reduce(utilise_bug=False, print_output=False)
print("RL Reduced from Size {} to {}".format(n, self.RL.length))
if then_generation is not None:
self.RL.features_tree = create_features_tree(self.RL.x.columns)
disable_tqdm = False if self.SD.length > 1 else True
for i in tqdm(range(self.SD.length), disable=disable_tqdm):
for j in tqdm(range(self.RL.length), disable=(not disable_tqdm)):
no_matching_features = self.SD.features[i].isdisjoint(self.RL.features[j])
width_constraint = (self.SD.widths[i] + self.RL.widths[j]) <= max_width
if width_constraint and no_matching_features:
width = self.RL.widths[j]
if then_generation is not None:
if then_generation == np.inf:
feature_values = [] # all feature values for conditions in RL[j]
for feature in self.RL.features[j]:
feature_values.append(self.RL.features_tree[feature])
RL2_values = np.array(list(map(frozenset, itertools.product(*feature_values))))
RL2_features = self.RL.compute_features(RL2_values)
else:
# selects inputs that don't satisfy Outer-If/Inner-If conditions
# True if RL[j] not satisfied (row)
row = (self.RL.x[self.RL.values[j]] != 1).any(axis=1).values
# selects feature values at have features in Outer-If/Inner-If conditions
col = []
for feature in self.RL.features[j]:
# ALL feature values for each feature in each itemset
col += self.RL.features_tree[feature]
RL2 = Apriori(x=self.RL.x[col][row], max_width=width, verbose=0,
apriori_threshold=then_generation,
feature_values_tree=self.RL.feature_values_tree)
RL2_values, RL2_features = RL2.values, RL2.features
else:
equal_width = self.RL.widths == width
RL2_values = self.RL.values[equal_width]
RL2_features = self.RL.features[equal_width]
if len(RL2_values) != 0:
for k in range(len(RL2_values)):
# checking widths is redundant since we check if sets have same features
identical_features = True if then_generation == np.inf else \
self.RL.features[j] == RL2_features[k]
identical_feature_values = self.RL.values[j] == RL2_values[k]
if identical_features and not identical_feature_values:
rule = (self.SD.values[i], self.RL.values[j],
RL2_values[k])
self.triples.add(rule)
self.length = len(self.triples)
def evaluate_triples(self, ares, r=None, save_mode=0,
disable_tqdm=False, plot_accuracy=True):
"""
Implement objective function parameter
Method for evaluation of two level recourse sets. This needs a massive refactor with:
self.evaluate, self.f_custom, self.f_ares, self.objective_terms
(finds best correctness/cost and counterfactuals for each input)
Inputs: R, final two level recourse set
n_rules, maximum number of counterfactuals (that satisfied at least one rule)
update_V: 0 is no rules are saved; 1 is all rules that satisfied at least
one rule are saved; 2 is only rules that provided a lower cost counterfactual,
or a successful counterfactual where one previously did not exist
disable_tqdm/print_outputs: self-explanatory.
correctness: doesn't compute costs.
Outputs: vector of objective function values for each
member of X_aff (affected inputs requiring recourse)
vector of final counterfactuals for each member of X_aff
"""
r = self.length if r is None else int(r) # number of triples to evaluate
if r > self.length:
if len(self.triples_array) >= r:
self.select_triples(r)
else:
raise ValueError(
"Number of elements for evaluation ({}) greater than number "
"of triples in set ({})".format(r, len(self.triples_array)))
self.ares = ares # originally sized input data
n = self.ares.X_aff_original.shape[0] # number of affected inputs
self.correct_matrix = np.zeros((r, n), dtype=int)
self.cost_matrix = np.zeros((r, n))
self.cfx_matrix = np.zeros((r, *self.ares.X_aff_original.shape))
self.triples_array = np.zeros(r, dtype=object)
self.max_idxs = np.zeros(r, dtype=bool)
self.cumulative_idxs = np.zeros(r, dtype=bool)
cor_max, cor_cumulative = 0, np.zeros(n)
self.correct_max = np.zeros(r)
self.correct_cumulative = np.zeros((r, n))
self.cover_matrix = np.zeros((r, n))
self.featurecost = np.zeros(r)
self.featurechange = np.zeros(r, dtype=int)
for i, triple in tqdm(zip(range(r), self.triples), disable=disable_tqdm):
self.correct_matrix[i], self.cost_matrix[i], self.cfx_matrix[i],\
self.cover_matrix[i], self.featurecost[i], self.featurechange[i]\
= self.evaluate_triple(triple, ares)
cor = self.correct_matrix[i].mean()
if cor > cor_max:
cor_max = cor
self.max_idxs[i] = True
self.correct_max[i] = cor_max
self.correct_cumulative[i] = np.maximum(cor_cumulative, self.correct_matrix[i])
if self.correct_cumulative[i].sum() > cor_cumulative.sum():
self.cumulative_idxs[i] = True
cor_cumulative = self.correct_cumulative[i]
self.triples_array[i] = triple
i += 1
if i == r:
break
if plot_accuracy:
self.plot_accuracy()
# Convert matrices to vectors
if save_mode == 2:
self.index_terms(self.cumulative_idxs)
idx = self.correct_matrix == 0
self.cost_matrix[idx] = np.nan
if self.length != 0:
self.correct_vector = self.correct_matrix.max(axis=0)
self.accuracy = self.correct_vector.mean()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
self.cost_vector = np.nanmin(self.cost_matrix, axis=0)
self.average_cost = np.nanmean(self.cost_vector)
self.cover = self.cover_matrix.max(axis=0)
if save_mode != 0:
if save_mode == 1:
self.triples = set(self.triples_array)
elif save_mode == 2:
self.triples = set(self.triples_array[self.cumulative_idxs])
self.length = len(self.triples)
def index_terms(self, idx):
self.correct_matrix = self.correct_matrix[idx]
self.cost_matrix = self.cost_matrix[idx]
self.cover_matrix = self.cover_matrix[idx]
self.featurechange = self.featurechange[idx]
self.featurecost = self.featurecost[idx]
if idx.any():
self.cover = self.cover_matrix.max(axis=0)
self.correct_vector = self.correct_matrix.max(axis=0)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
self.cost_vector = np.nanmin(self.cost_matrix, axis=0)
else:
self.cover = np.zeros(idx.shape[0])
self.correct_vector = np.zeros(idx.shape[0])
self.cost_vector = np.zeros(idx.shape[0])
def evaluate_triple(self, triple, ares):
# Initialise
n = ares.X_aff_original.shape[0]
triple_cfx = ares.X_aff_original.copy()
triple_corrects = np.zeros(n)
triple_costs = np.zeros(n)
# Compute (AReS requires all to be true)
outer_ifs, inner_ifs, thens =\
list(triple[0]), list(triple[1]), list(triple[2])
triple_cover = ares.X_aff[outer_ifs + inner_ifs]\
.all(axis=1).values
featurecost, featurechange, cfx =\
self.triple_cost(triple, ares, triple_cfx)
if triple_cover.any():
# Compute predictions and costs
cfx = cfx[triple_cover]
if ares.normalise:
cfx_norm = (cfx.values - ares.means) / ares.stds
corrects = ares.model.predict(cfx_norm)
else:
corrects = ares.model.predict(cfx.values)
triple_corrects[triple_cover] = corrects * 100
triple_costs[triple_cover] = featurechange
triple_cfx[triple_cover] = cfx
return triple_corrects, triple_costs, triple_cfx,\
triple_cover, featurecost, featurechange
@staticmethod
def triple_cost(triple, ares, x_aff):
outer_ifs, inner_ifs, thens = list(triple[0]), list(triple[1]), list(triple[2])
featurecost, featurechange = 0, int(0)
x_aff = x_aff.copy()
# Generate counterfactuals to calculate predictions and costs
# improve this by pairing directly the order of inner_ifs and thens
# currently the features are not guaranteed to be in the same order
# hence the search through inner_ifs (would also make reading triples easier)
for then in thens:
if then not in triple[1]: # retain r[1] as opposed to inner-ifs for fast set retrieval
# continuous variables
then_feature = ares.feature_values_tree[then]
for inner_if in inner_ifs:
if ares.feature_values_tree[inner_if] == then_feature:
if then in ares.binned_features_continuous:
then_idx = ares.features.index(ares.feature_values_tree[then])
d = ares.bin_mids[then] - ares.bin_mids[inner_if]
featurechange += np.rint(abs(d * ares.feature_costs_vector[then_idx]))
x_aff[then_feature] += d
else:
then_idx = ares.features.index(then)
if then in ares.ordinal_features:
inner_if_idx = ares.features.index(inner_if)
inner_if_cost = ares.feature_costs_vector[inner_if_idx]
then_cost = ares.feature_costs_vector[then_idx]
featurechange += np.rint(abs(then_cost - inner_if_cost))
else: # bulk write features since they all match conditions
featurechange += np.rint(1) # categorical cost
x_aff[inner_if] = int(0)
x_aff[then] = int(1)
break # match found, exit inner-if loop, resume then loop
return featurecost, featurechange, x_aff # featurecost not implemented
def plot_accuracy(self, n_triples=None):
if self.correct_max is not None:
plt.figure(figsize=(4.5, 3), dpi=200)
n_triples = len(self.correct_max) if n_triples is None else n_triples
plt.plot(range(1, n_triples+1),
self.correct_cumulative.mean(axis=1)[:n_triples],
label='All Selected Triples')
plt.plot(range(1, n_triples+1), self.correct_max[:n_triples],
label='Maximum Single Selected Triple')
plt.title('Performance of Triples vs Number of Triples\nSelected '
'in Ground Set of Total Length {}'.format(self.length))
plt.ylabel('Recourse Accuracy (%)')
plt.xlabel('Number of Triples in V Selected')
plt.legend(loc='lower right')
plt.show()
else:
raise ValueError('Ground set must first be evaluated with self.'
'evaluate_triples() before accuracies can be plotted')
def select_triples(self, s=0, sort=True):
"""
Optional method for selecting candidate rules with
the highest individual objective function values
Speeds up optimisation dramatically
To be applied after self.counterfactuals()
To be followed by self.optimise()
Input: size of final candidate set, s
"""
# Check for valid n_prefilter
s = int(s)
if s > len(self.triples_array):
raise ValueError("Number of filtered elements ({}) greater than length "
"of recourse rules set ({})".format(s, len(self.triples_array)))
# Defaults s=0 to whole set
elif s == 0:
s = len(self.triples_array)
# Create set from filtered numpy array
idx = np.argsort(self.objectives)[:-(s+1):-1] if sort else np.arange(s)
self.triples = set()
for i in self.triples_array[idx]:
self.triples.add(i)
self.length = len(self.triples)
print("Candidate Set Filtered with Length:", s)