xgboost python分類_Xgboost分類

Kaggle裡Xgboost是比較流行的一個模型，跟GBM，Adaboost類似，可以用于二分類或多分類，通過将多個弱分類器組合為強分類器提高精度，支援多線程。做完基本的特征工程後，使用Xgboost訓練模型來預測，暫時還沒有參數調優：

# coding=gbk

'''

Created on 2016年8月23日

@author: oul

'''

import datetime

import pandas as pd

import numpy as np

from sklearn.cross_validation import

train_test_split

import xgboost as xgb

import random

from operator import itemgetter

import zipfile

from sklearn.metrics import roc_auc_score

import time

random.seed(3)​

training_filename =

'C:\\Users\\ganda\\Desktop\\train.csv'

testing_filename =

'C:\\Users\\ganda\\Desktop\\test.csv'

submission_dir = 'C:\\Users\\ganda\\Desktop\\'

def load_train():

print("Loading

training data csv: " + training_filename)

training_data =

pd.read_csv(training_filename)

return

training_data

def load_test():

#print("Loading

training data csv: " + training_filename)

test =

pd.read_csv(testing_filename)

# TODO

return

test

def create_feature_map(features):

outfile =

open('xgb.fmap', 'w')

for i, feat in

enumerate(features):

outfile.write('{0}\t{1}\tq\n'.format(i,

feat))

outfile.close()

def get_importance(gbm, features):

create_feature_map(features)

importance =

gbm.get_fscore(fmap='xgb.fmap')

importance =

sorted(importance.items(), key=itemgetter(1),

reverse=True)

return

importance

def print_features_importance(imp):

for i in

range(len(imp)):

print("# " + str(imp[i][1]))

print('output.remove(\'' + imp[i][0] +

'\')')

def run_XGBoost(train, test,

features,target,random_state=0):

eta = 0.1

max_depth =

5

subsample =

0.8

colsample_bytree =

0.8

start_time =

time.time()

print('XGBoost

params. ETA: {}, MAX_DEPTH: {}, SUBSAMPLE: {}, COLSAMPLE_BY_TREE:

{}'.format(eta, max_depth, subsample, colsample_bytree))

params =

{

"objective": "binary:logistic",

"booster" : "gbtree",

"eval_metric": "auc",

"eta": eta,

"max_depth": max_depth,

"subsample": subsample,

"colsample_bytree": colsample_bytree,

"silent": 1,

"seed": random_state

}

num_boost_round =

260

early_stopping_rounds = 20

test_size =

0.1

X_train, X_valid =

train_test_split(train, test_size=test_size,

random_state=random_state)

y_train =

X_train[target]

y_valid =

X_valid[target]

#添加缺失資料

dtrain =

xgb.DMatrix(X_train[features],y_train,missing = -9999)

dvalid =

xgb.DMatrix(X_valid[features],y_valid,missing = -9999)

watchlist =

[(dtrain, 'train'), (dvalid, 'eval')]

gbm =

xgb.train(params, dtrain, num_boost_round, evals=watchlist,

early_stopping_rounds=early_stopping_rounds,

verbose_eval=True)

print("Validating...")

check =

gbm.predict(xgb.DMatrix(X_valid[features]),

ntree_limit=gbm.best_ntree_limit)

score =

roc_auc_score(y_valid.values,check)

print('Check error

value: {:.6f}'.format(score))

imp =

get_importance(gbm, features)

print('Importance

array: ', imp)

print("Predict test

set...")

test_prediction =

gbm.predict(xgb.DMatrix(test[features]),

ntree_limit=gbm.best_ntree_limit)

print('Training

time: {} minutes'.format(round((time.time() - start_time)/60,

2)))

return

test_prediction.tolist(), score

def create_submission(score, test, prediction):

# Make

Submission

now =

datetime.datetime.now()

sub_file =

submission_dir + 'submission_' + str(score) + '_' +

str(now.strftime("%Y-%m-%d-%H-%M")) + '.csv'

print('Writing

submission: ', sub_file)

f = open(sub_file,

'w')

f.write('id,probability\n')

total = 0

for id in

test['id']:

str1 = str(id) + ',' +

str(prediction[total])

str1 += '\n'

total += 1

f.write(str1)

f.close()

train = load_train()

test = load_test()

features = []

# TODO!

#添加特征​

features.extend([''])

print('Length of train: ', len(train))

print('Length of test: ', len(test))

print('Features [{}]: {}'.format(len(features),

sorted(features)))

#label是目标字段​

test_prediction, score = run_XGBoost(train, test,

features,'label')

print('Score = {}'.format(score))

create_submission(score, test, test_prediction)

​