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import sys
import copy
import time
import numpy as np
import scipy as sp
import scipy.stats
import sklearn
import xgboost
import xgboost.sklearn
import explainers
from load_datasets import *
import glob
import argparse
import collections
from sklearn import ensemble, cross_validation
import pickle
import parzen_windows
def submodular_fn(explanations, feature_value):
"""TODO: Detail this"""
z_words = set()
for exp in explanations:
z_words = z_words.union([x[0] for x in exp])
normalizer = sum([feature_value[w] for w in z_words])
def fnz(x):
all_words = set()
for doc in x:
all_words = all_words.union([x[0] for x in explanations[doc]])
return sum([feature_value[w] for w in all_words]) / normalizer
fnz.num_items = len(explanations)
return fnz
def greedy(submodular_fn, k, chosen=[]):
chosen = copy.deepcopy(chosen)
all_items = range(submodular_fn.num_items)
current_value = 0
while len(chosen) != k:
best_gain = 0
best_item = all_items[0]
for i in all_items:
gain= submodular_fn(chosen + [i]) - current_value
if gain > best_gain:
best_gain = gain
best_item = i
current_value += best_gain
return chosen
# A pick function takes in the whole map, returns two lists of tuples with instance
# ids and weights, one for each classifier. This won't work later if I want it to be interactive.
def submodular_pick(pickled_map, explainer, B, use_explanation_weights=False,
def get_function(exps):
feature_value = collections.defaultdict(float)
for exp in exps:
for f, v in exp:
if not use_explanation_weights:
v = 1
feature_value[f] += np.abs(v)
for f in feature_value:
feature_value[f] = np.sqrt(feature_value[f])
submodular = submodular_fn(exps, feature_value)
return submodular
out = greedy(submodular, B)
return out
if explainer in ['parzen', 'lime']:
exps1 = [x[0] for x in pickled_map['exps1'][explainer]]
exps2 = [x[0] for x in pickled_map['exps2'][explainer]]
exps1 = pickled_map['exps1'][explainer]
exps2 = pickled_map['exps2'][explainer]
fn1 = get_function(exps1)
fn2 = get_function(exps2)
if not alternate:
return greedy(fn1, B), greedy(fn2, B)
ret = []
for i in range(B):
fn = fn1 if i % 2 == 0 else fn2
ret = greedy(fn, i + 1, ret)
return ret
#return get_list(exps1), get_list(exps2)
def all_pick(pickled_map, explainer, B):
list_ = range(len(pickled_map['exps1'][explainer]))
return list_, list_
def random_pick(pickled_map, explainer, B):
list_ = np.random.choice(range(len(pickled_map['exps1'][explainer])), B, replace=False)
return list_, list_
def find_untrustworthy(explainer, exps, instances, untrustworthy):
found = set()
for i in instances:
if explainer in ['lime', 'parzen']:
exp, mean = exps[i]
exp = exps[i]
found = found.union([x[0] for x in exp if x[0] in untrustworthy])
return found
def tally_mistrust(explainer, exps, predict_probas, untrustworthy):
trust_fn = lambda prev, curr: (prev > 0.5 and curr > 0.5) or (prev <= 0.5 and curr <= 0.5)
trust_fn_all = lambda exp, unt: len([x[0] for x in exp if x[0] in unt]) == 0
mistrust = 0
for i in range(len(exps)):
if explainer in ['lime', 'parzen']:
exp, mean = exps[i]
if explainer == 'lime':
prev_tot = sum([x[1] for x in exp]) + mean
elif explainer == 'parzen':
prev_tot = mean
tot = prev_tot - sum([x[1] for x in exp if x[0] in untrustworthy])
if not trust_fn(tot, prev_tot):
mistrust += 1
exp = exps[i]
if not trust_fn_all(exp, untrustworthy):
mistrust += 1
return mistrust
def main():
parser = argparse.ArgumentParser(description='Evaluate some explanations')
parser.add_argument('--dataset', '-d', type=str, required=True,help='dataset name')
parser.add_argument('--output_folder', '-o', type=str, required=True, help='output folder')
parser.add_argument('--num_features', '-k', type=int, required=True, help='num features')
parser.add_argument('--pick', '-p', type=str, required=False, default='all', help='all, submodular, submodular2 or random')
parser.add_argument('--num_instances', '-n', type=int, required=False, default=1, help='number of instances to look at')
parser.add_argument('--num_rounds', '-r', type=int, required=False, default=10, help='num rounds')
#parser.add_argument('--start_id', '-i', type=int, required=True, help='output start id')
args = parser.parse_args()
dataset = args.dataset
got_right = lambda test1, test2, mistrust1, mistrust2: mistrust1 < mistrust2 if test1 > test2 else mistrust1 > mistrust2
names = ['lime', 'parzen', 'random', 'greedy']
num_exps = 0
B = args.num_instances
rounds = 1
if args.pick == 'all':
pick_function = all_pick
elif args.pick == 'submodular':
pick_function = lambda a,b,c : submodular_pick(a,b,c, use_explanation_weights=True)
elif args.pick == 'random':
pick_function = random_pick
rounds =args.num_rounds
accuracy = collections.defaultdict(lambda: [])
right = collections.defaultdict(lambda: [])
for r in range(rounds):
right = collections.defaultdict(lambda: [])
for filez in glob.glob(os.path.join(args.output_folder, 'comparing_%s*' % args.dataset))[:800]:
num_exps += 1
pickled_map = pickle.load(open(filez))
predict_probas = pickled_map['predict_probas1']
predict_probas2 = pickled_map['predict_probas2']
test1 = pickled_map['test_acc1']
test2 = pickled_map['test_acc2']
untrustworthy = pickled_map['untrustworthy']
for explainer in names:
if explainer.startswith('lime'):
pick1, pick2 = pick_function(pickled_map, 'lime', B)
exps1 = pickled_map['exps1']['lime']
exps2 = pickled_map['exps2']['lime']
elif explainer.startswith('parzen'):
pick1, pick2 = pick_function(pickled_map, 'parzen', B)
exps1 = pickled_map['exps1']['parzen']
exps2 = pickled_map['exps2']['parzen']
pick1, pick2 = pick_function(pickled_map, explainer, B)
exps1 = pickled_map['exps1'][explainer]
exps2 = pickled_map['exps2'][explainer]
if args.pick != 'all':
unt1 = find_untrustworthy(explainer, exps1, pick1, untrustworthy)
unt2 = find_untrustworthy(explainer, exps2, pick2, untrustworthy)
unt1 = unt2 = untrustworthy
mistrust1 = tally_mistrust(explainer, exps1, predict_probas, unt1)
mistrust2 = tally_mistrust(explainer, exps2, predict_probas2, unt2)
while mistrust1 == mistrust2:
mistrust1 = np.random.randint(0,10)
mistrust2 = np.random.randint(0,10)
#print explainer, mistrust1, mistrust2
right[explainer].append(int(got_right(test1, test2, mistrust1, mistrust2)))
right['random_choice'].append(int(got_right(test1, test2, np.random.random(), np.random.random())))
#print [(x[0], sum(x[1])) for x in right.iteritems()]
#print filez
for name in right:
print 'Mean accuracy:'
for name in right:
print name, np.mean(accuracy[name])
if __name__ == "__main__":