def MissEvaluate(x, y):
param = {}
param['adaboost'] = 10
param['measure'] = 'info_gain'
param['cv_fold'] = 10
param['bootstrap'] = 10
param['class_num'] = 4
train_y = y
for rate in [0.1, 0.3, 0.5, 0.7, 0.9]:
train_x = ml.GenMissValueArray(x, rate, 3196, 36)
model = ml.Train(train_x, y, param)
pred = model.Predict(train_x)
logger.info('训练集测试准确率:%f', 1.0 * sum(pred == train_y) / len(train_y))
ml.HoldoutMethod(train_x, train_y, param)
ml.CrossValidation(train_x, train_y, param)
ml.Bootstrap(train_x, train_y, param)
ml.Adaboost(train_x, train_y, param)
logger.debug('-------------------')
param = {}
param['adaboost'] = 10
param['measure'] = 'info_gain'
param['cv_fold'] = 10
param['bootstrap'] = 10
param['class_num'] = 3
# param['pre_pruning'] = 0.3
# logger.info('使用prepruning,阈值:%f', param['pre_pruning'])
#pessimistic pruning
# param['pos_pruning'] = 'pessimistic'
# param['factor'] = 1
# logger.info('使用悲观剪枝法,惩罚值:%f', param['factor'])
# x = ml.GenMissValueArray(train_x, 0.2, 625, 4)
# logger.debug(train_x)
# logger.debug(np.sum(np.isnan(x)))
MissEvaluate(train_x, train_y)
exit()
model = ml.Train(train_x, train_y, param)
pred = model.Predict(train_x)
# logger.debug('tree:%s', model.root)
# logger.debug('pred:%s', pred)
# logger.debug(' y:%s', train_y)
logger.info('训练集测试准确率:%f', 1.0 * sum(pred == train_y) / len(train_y))
ml.HoldoutMethod(train_x, train_y, param)
ml.CrossValidation(train_x, train_y, param)
ml.Bootstrap(train_x, train_y, param)
ml.Adaboost(train_x, train_y, param)
logger.debug('-------------------')