def question_learning_data(evaluators, q_ids):
x = []
y = []
for q_id in q_ids:
cand = question_candidates(q_id)
x = x + run_evaluators(cand, evaluators)
y = y + map(lambda a: check_answers.check_answer(q_id, a), cand)
return y, x
def question_learning_data(evaluators,q_ids):
x=[]
y=[]
for q_id in q_ids:
cand=question_candidates(q_id)
x=x+run_evaluators(cand,evaluators)
y=y+map(lambda a:check_answers.check_answer(q_id,a),cand)
return y,x
def demo():
candidates_train=[
('blown', 'AP881126-0094', 55, 'VP'), ('farther', 'AP881126-0094', 56, 'NP'), ('away', 'AP881126-0094', 57, 'S'),
('by', 'AP881126-0094', 58, 'PP'), ('Tropical Storm Keith', 'AP881126-0094', 59, 'NP'), (',', 'AP881126-0094', 62, 'S'),
('which', 'AP881126-0094', 63, 'NP'), ('swept through', 'AP881126-0094', 64, 'PP'), ('the Gulf', 'AP881126-0094', 66, 'NP'),
('of', 'AP881126-0094', 68, 'PP'), ('Mexico', 'AP881126-0094', 69, 'NP'), ('early', 'AP881126-0094', 70, 'S'),
('last week', 'AP881126-0094', 71, 'NP'), ('.', 'AP881126-0094', 73, 'S'), ('Brydes whales', 'AP881126-0094', 74, 'NP'),
('are', 'AP881126-0094', 76, 'VP'), ('native', 'AP881126-0094', 77, 'NP'), ('to', 'AP881126-0094', 78, 'PP'),
('offshore', 'AP881126-0094', 79, 'VP'), ('tropical water', 'AP881126-0094', 80, 'NP'), ('.', 'AP881126-0094', 82, 'S'),
('Dr', 'AP881126-0094', 83, 'NP'), ('.', 'AP881126-0094', 84, 'S'), ('Dan Odell', 'AP881126-0094', 85, 'NP'),
(', also', 'AP881126-0094', 87, 'S'), ('the scientific coordinator', 'AP881126-0094', 89, 'NP'), ('for', 'AP881126-0094', 92, 'PP'),
('the Southeast U .S', 'AP881126-0094', 93, 'NP'), ('.', 'AP881126-0094', 97, 'S'), ('Marine Mammal Stranding Network', 'AP881126-0094', 98, 'NP'),
(',', 'AP881126-0094', 102, 'S'), ('gave', 'AP881126-0094', 103, 'VP'), ('a dim prognosis', 'AP881126-0094', 104, 'NP'),
('for', 'AP881126-0094', 107, 'PP'), ("the whale's survival", 'AP881126-0094', 108, 'NP'), (', but', 'AP881126-0094', 111, 'S'),
('said', 'AP881126-0094', 113, 'VP'), ('the 22-foot', 'AP881126-0094', 114, 'NP'), (',', 'AP881126-0094', 116, 'S'),
('9-inch mammal', 'AP881126-0094', 117, 'NP'), ('was resting', 'AP881126-0094', 119, 'VP'), ('and', 'AP881126-0094', 121, 'S'),
('started making', 'AP881126-0094', 122, 'VP'), ('sounds', 'AP881126-0094', 124, 'NP'), ('.', 'AP881126-0094', 125, 'S'),
("``That's", 'AP881126-0094', 126, 'NP'), ('about', 'AP881126-0094', 127, 'PP'), ('as', 'AP881126-0094', 128, 'S'),
('good', 'AP881126-0094', 129, 'NP'), ('as', 'AP881126-0094', 130, 'PP'), ('you', 'AP881126-0094', 131, 'NP'),
('can expect', 'AP881126-0094', 132, 'VP'), ('from', 'AP881126-0094', 134, 'PP'), ('a beached whale', 'AP881126-0094', 135, 'NP'),
('at', 'AP881126-0094', 138, 'PP'), ('this point', 'AP881126-0094', 139, 'NP'), (",''", 'AP881126-0094', 141, 'S'),
('said', 'AP881126-0094', 142, 'VP'), ('Odell', 'AP881126-0094', 143, 'NP'), ('.', 'AP881126-0094', 144, 'S'),
("``It's been", 'AP881126-0094', 145, 'VP'), ('in', 'AP881126-0094', 147, 'PP'), ('a', 'AP881126-0094', 148, 'NP'),
('very', 'AP881126-0094', 149, 'S'), ('stressful set', 'AP881126-0094', 150, 'NP'), ('of', 'AP881126-0094', 152, 'PP'),
('circumstances', 'AP881126-0094', 153, 'NP'), ('probably', 'AP881126-0094', 154, 'S'), ('for', 'AP881126-0094', 155, 'PP'),
('some days', 'AP881126-0094', 156, 'NP'), (".''", 'AP881126-0094', 158, 'S'), ('Squid', 'AP881126-0094', 159, 'NP'),
('were placed', 'AP881126-0094', 160, 'VP'), ('in', 'AP881126-0094', 162, 'PP'), ("the whale's mouth every three", 'AP881126-0094', 163, 'NP'),
('or', 'AP881126-0094', 168, 'S'), ('four hours', 'AP881126-0094', 169, 'NP'), (', but', 'AP881126-0094', 171, 'S'),
('the whale', 'AP881126-0094', 173, 'NP'), ('did', 'AP881126-0094', 175, 'VP'), ('not', 'AP881126-0094', 176, 'S'),
('respond', 'AP881126-0094', 177, 'VP'), ('well', 'AP881126-0094', 178, 'S'), ('to', 'AP881126-0094', 179, 'PP'),
('the food', 'AP881126-0094', 180, 'NP'), ('.', 'AP881126-0094', 182, 'S'), ('Odell', 'AP881126-0094', 183, 'NP'),
('said', 'AP881126-0094', 184, 'VP'), ('they', 'AP881126-0094', 185, 'NP'), ('hoped', 'AP881126-0094', 186, 'VP')
]
candidate_test=[('to', 'AP881126-0094', 187, 'PP')]
x_train=run_evaluators(candidates_train)
q_id=323
y_train=map(lambda a: check_answers.check_answer(q_id,a),candidates_train)
y_train[4]=1
x_test =run_evaluators(candidate_test)[0]
# print x_test
#Run the predictions for just one of these test question candidates
#The predictions fail if all of the correctness values are the same
print ''
print 'Predictions as to whether an answer is correct'
print 'for random data from various models'
print '----------------------------------------------'
print 'Model Result Confidence? ("Real value")'
print '----- ------ ---------------------------------'
print ' SVM '+str(test(train(mlpy.Svm,y_train,x_train),x_test))
# print ' KNN '+str(test(train(mlpy.Knn,y_train,x_train),x_test))
print ' FDA '+str(test(train(mlpy.Fda,y_train,x_train),x_test))
print 'SRDA '+str(test(train(mlpy.Srda,y_train,x_train),x_test))
print ' PDA '+str(test(train(mlpy.Pda,y_train,x_train),x_test))
# print 'DLDA '+str(test(train(mlpy.Dlda,y_train,x_train),x_test))
print '----------------------------------------------'
def demo():
candidates_train = [('blown', 'AP881126-0094', 55, 'VP'),
('farther', 'AP881126-0094', 56, 'NP'),
('away', 'AP881126-0094', 57, 'S'),
('by', 'AP881126-0094', 58, 'PP'),
('Tropical Storm Keith', 'AP881126-0094', 59, 'NP'),
(',', 'AP881126-0094', 62, 'S'),
('which', 'AP881126-0094', 63, 'NP'),
('swept through', 'AP881126-0094', 64, 'PP'),
('the Gulf', 'AP881126-0094', 66, 'NP'),
('of', 'AP881126-0094', 68, 'PP'),
('Mexico', 'AP881126-0094', 69, 'NP'),
('early', 'AP881126-0094', 70, 'S'),
('last week', 'AP881126-0094', 71, 'NP'),
('.', 'AP881126-0094', 73, 'S'),
('Brydes whales', 'AP881126-0094', 74, 'NP'),
('are', 'AP881126-0094', 76, 'VP'),
('native', 'AP881126-0094', 77, 'NP'),
('to', 'AP881126-0094', 78, 'PP'),
('offshore', 'AP881126-0094', 79, 'VP'),
('tropical water', 'AP881126-0094', 80, 'NP'),
('.', 'AP881126-0094', 82, 'S'),
('Dr', 'AP881126-0094', 83, 'NP'),
('.', 'AP881126-0094', 84, 'S'),
('Dan Odell', 'AP881126-0094', 85, 'NP'),
(', also', 'AP881126-0094', 87, 'S'),
('the scientific coordinator', 'AP881126-0094', 89,
'NP'), ('for', 'AP881126-0094', 92, 'PP'),
('the Southeast U .S', 'AP881126-0094', 93, 'NP'),
('.', 'AP881126-0094', 97, 'S'),
('Marine Mammal Stranding Network', 'AP881126-0094',
98, 'NP'), (',', 'AP881126-0094', 102, 'S'),
('gave', 'AP881126-0094', 103, 'VP'),
('a dim prognosis', 'AP881126-0094', 104, 'NP'),
('for', 'AP881126-0094', 107, 'PP'),
("the whale's survival", 'AP881126-0094', 108, 'NP'),
(', but', 'AP881126-0094', 111, 'S'),
('said', 'AP881126-0094', 113, 'VP'),
('the 22-foot', 'AP881126-0094', 114, 'NP'),
(',', 'AP881126-0094', 116, 'S'),
('9-inch mammal', 'AP881126-0094', 117, 'NP'),
('was resting', 'AP881126-0094', 119, 'VP'),
('and', 'AP881126-0094', 121, 'S'),
('started making', 'AP881126-0094', 122, 'VP'),
('sounds', 'AP881126-0094', 124, 'NP'),
('.', 'AP881126-0094', 125, 'S'),
("``That's", 'AP881126-0094', 126, 'NP'),
('about', 'AP881126-0094', 127, 'PP'),
('as', 'AP881126-0094', 128, 'S'),
('good', 'AP881126-0094', 129, 'NP'),
('as', 'AP881126-0094', 130, 'PP'),
('you', 'AP881126-0094', 131, 'NP'),
('can expect', 'AP881126-0094', 132, 'VP'),
('from', 'AP881126-0094', 134, 'PP'),
('a beached whale', 'AP881126-0094', 135, 'NP'),
('at', 'AP881126-0094', 138, 'PP'),
('this point', 'AP881126-0094', 139, 'NP'),
(",''", 'AP881126-0094', 141, 'S'),
('said', 'AP881126-0094', 142, 'VP'),
('Odell', 'AP881126-0094', 143, 'NP'),
('.', 'AP881126-0094', 144, 'S'),
("``It's been", 'AP881126-0094', 145, 'VP'),
('in', 'AP881126-0094', 147, 'PP'),
('a', 'AP881126-0094', 148, 'NP'),
('very', 'AP881126-0094', 149, 'S'),
('stressful set', 'AP881126-0094', 150, 'NP'),
('of', 'AP881126-0094', 152, 'PP'),
('circumstances', 'AP881126-0094', 153, 'NP'),
('probably', 'AP881126-0094', 154, 'S'),
('for', 'AP881126-0094', 155, 'PP'),
('some days', 'AP881126-0094', 156, 'NP'),
(".''", 'AP881126-0094', 158, 'S'),
('Squid', 'AP881126-0094', 159, 'NP'),
('were placed', 'AP881126-0094', 160, 'VP'),
('in', 'AP881126-0094', 162, 'PP'),
("the whale's mouth every three", 'AP881126-0094', 163,
'NP'), ('or', 'AP881126-0094', 168, 'S'),
('four hours', 'AP881126-0094', 169, 'NP'),
(', but', 'AP881126-0094', 171, 'S'),
('the whale', 'AP881126-0094', 173, 'NP'),
('did', 'AP881126-0094', 175, 'VP'),
('not', 'AP881126-0094', 176, 'S'),
('respond', 'AP881126-0094', 177, 'VP'),
('well', 'AP881126-0094', 178, 'S'),
('to', 'AP881126-0094', 179, 'PP'),
('the food', 'AP881126-0094', 180, 'NP'),
('.', 'AP881126-0094', 182, 'S'),
('Odell', 'AP881126-0094', 183, 'NP'),
('said', 'AP881126-0094', 184, 'VP'),
('they', 'AP881126-0094', 185, 'NP'),
('hoped', 'AP881126-0094', 186, 'VP')]
candidate_test = [('to', 'AP881126-0094', 187, 'PP')]
x_train = run_evaluators(candidates_train)
q_id = 323
y_train = map(lambda a: check_answers.check_answer(q_id, a),
candidates_train)
y_train[4] = 1
x_test = run_evaluators(candidate_test)[0]
# print x_test
#Run the predictions for just one of these test question candidates
#The predictions fail if all of the correctness values are the same
print ''
print 'Predictions as to whether an answer is correct'
print 'for random data from various models'
print '----------------------------------------------'
print 'Model Result Confidence? ("Real value")'
print '----- ------ ---------------------------------'
print ' SVM ' + str(test(train(mlpy.Svm, y_train, x_train), x_test))
# print ' KNN '+str(test(train(mlpy.Knn,y_train,x_train),x_test))
print ' FDA ' + str(test(train(mlpy.Fda, y_train, x_train), x_test))
print 'SRDA ' + str(test(train(mlpy.Srda, y_train, x_train), x_test))
print ' PDA ' + str(test(train(mlpy.Pda, y_train, x_train), x_test))
# print 'DLDA '+str(test(train(mlpy.Dlda,y_train,x_train),x_test))
print '----------------------------------------------'
