def test(**kwargs):
# ---------------------- 更新参数 ----------------------
opt = DefaultConfig()
opt.update(**kwargs)
opt.printf()
# ---------------------- 数据处理 ----------------------
# 获取数据
train, test = get_test_data(opt)
gc.collect()
# # 获取样本
# test_sample = get_sample(train, test, load=True)
# gc.collect()
# # 获取特征
# test_feat = get_feat(train, test_sample)
# gc.collect()
# 保存特征至文件
# test_feat.to_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_{}.hdf'.format(test.shape[0]), 'w', complib='blosc', complevel=5)
test_feat = pd.read_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_{}.hdf'.format(test.shape[0]))
test_feat = get_feat(train, test_feat)
gc.collect()
test_feat.to_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_{}_filter.hdf'.format(test.shape[0]), 'w', complib='blosc', complevel=5)
# ---------------------- 载入模型 ----------------------
# opt['model_name'] = 'lgb_1_90_all.pkl'
# gbm0, use_feat0 = load_model(opt)
opt['model_name'] = 'lgb_2017-09-23#20:14:52_0.58893.pkl'
gbm1, use_feat1 = load_model(opt)
# opt['model_name'] = 'lgb_2_300_top15.pkl'
# gbm2, use_feat2 = load_model(opt)
# opt['model_name'] = 'lgb_3_300_top10.pkl'
# gbm3, use_feat3 = load_model(opt)
# opt['model_name'] = 'lgb_4_300_top5.pkl'
# gbm4, use_feat4 = load_model(opt)
# ---------------------- 保存预测结果 -------------------
# test_feat.loc[:, 'pred'] = gbm0.predict(test_feat[use_feat0])
# gc.collect()
# res = test_feat[['orderid', 'geohashed_end_loc', 'pred']].sort_values(by=['orderid', 'pred'], ascending=False).groupby('orderid').head(25)
# res[['orderid', 'geohashed_end_loc']].to_hdf('/home/xuwenchao/dyj-storage/sample_25_{}_filter_leak_sample.hdf'.format(test.shape[0]), 'w', complib='blosc', complevel=5)
# gc.collect()
# test_feat.loc[:, 'pred'] = gbm1.predict(test_feat[use_feat1])
# test_feat[['orderid', 'geohashed_end_loc', 'pred']].to_hdf('/home/xuwenchao/dyj-storage/pred/pred_{}_0.58820.hdf'.format(test.shape[0]), 'w', complib='blosc', complevel=5)
res = predict(test_feat, use_feat1, gbm1)
test_feat[['orderid', 'geohashed_end_loc', 'pred']].to_hdf('/home/xuwenchao/dyj-storage/pred/pred_{}_0.58893.hdf'.format(test.shape[0]), 'w', complib='blosc', complevel=5)
gc.collect()
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
res_path = '{}/day{}_{}_wc_sample_0.58893.csv'.format(opt['result_dir'], opt['test_startday'], cur_time)
res.to_csv(res_path, index=False)
print('保存测试结果至：', res_path)
def val(**kwargs):
# ---------------------- 更新参数 ----------------------
opt = DefaultConfig()
opt.update(**kwargs)
opt.printf()
# ---------------------- 数据处理 ----------------------
# 获取数据
# train1, train2, train_test = get_train_data(opt)
# 获取样本
# train_sample = get_sample(train1, train2, load=True)
# 获取特征
# train_feat = get_feat(train_test, train_sample)
# gc.collect()
# train_feat.to_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_{}.hdf'.format(opt['startday']), 'w', complib='blosc', complevel=5)
train_feat = pd.read_hdf(
'/home/xuwenchao/dyj-storage/all-feat/feat_23_24_label.hdf')
# ---------------------- 载入模型 ----------------------
# opt['model_name'] = 'lgb_1_90_all.pkl'
# gbm0, use_feat0 = load_model(opt)
opt['model_name'] = 'lgb_1_2017-09-15#19:50:48_0.58820.pkl'
gbm, use_feat = load_model(opt)
opt['model_name'] = 'lgb_2017-09-23#20:14:52_0.58893.pkl'
gbm1, use_feat1 = load_model(opt)
# gbm2, use_feat2 = load_model(opt)
# opt['model_name'] = 'lgb_2017-09-03#23:24:26_0.57836.pkl'
# gbm3, use_feat3 = load_model(opt)
# opt['model_name'] = ''
# gbm4, use_feat4 = load_model(opt)
# ---------------------- 评估 -------------------------
train_feat.loc[:, 'pred'] = gbm.predict(train_feat[use_feat])
gc.collect()
train_feat[['orderid', 'geohashed_end_loc', 'pred']].to_csv(
'/home/xuwenchao/dyj-storage/pred/pred_23_24_0.58820.csv', index=None)
train_feat.loc[:, 'pred'] = gbm1.predict(train_feat[use_feat1])
gc.collect()
train_feat[['orderid', 'geohashed_end_loc', 'pred']].to_csv(
'/home/xuwenchao/dyj-storage/pred/pred_23_24_0.58893.csv', index=None)
def val(**kwargs):
# ---------------------- 更新参数 ----------------------
opt = DefaultConfig()
opt.update(**kwargs)
opt.printf()
# ---------------------- 数据处理 ----------------------
# 获取数据
# train1, train2, train_test = get_train_data(opt)
# 获取样本
# train_sample = get_sample(train1, train2, load=True)
# 获取特征
# train_feat = get_feat(train_test, train_sample)
# gc.collect()
# train_feat.to_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_{}.hdf'.format(opt['startday']), 'w', complib='blosc', complevel=5)
train_feat = pd.read_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_23_24_label.hdf')
# ---------------------- 载入模型 ----------------------
# opt['model_name'] = 'lgb_1_90_all.pkl'
# gbm0, use_feat0 = load_model(opt)
opt['model_name'] = 'lgb_1_2017-09-15#19:50:48_0.58820.pkl'
gbm, use_feat = load_model(opt)
opt['model_name'] = 'lgb_2017-09-23#20:14:52_0.58893.pkl'
gbm1, use_feat1 = load_model(opt)
# gbm2, use_feat2 = load_model(opt)
# opt['model_name'] = 'lgb_2017-09-03#23:24:26_0.57836.pkl'
# gbm3, use_feat3 = load_model(opt)
# opt['model_name'] = ''
# gbm4, use_feat4 = load_model(opt)
# ---------------------- 评估 -------------------------
train_feat.loc[:, 'pred'] = gbm.predict(train_feat[use_feat])
gc.collect()
train_feat[['orderid', 'geohashed_end_loc', 'pred']].to_csv('/home/xuwenchao/dyj-storage/pred/pred_23_24_0.58820.csv', index=None)
train_feat.loc[:, 'pred'] = gbm1.predict(train_feat[use_feat1])
gc.collect()
train_feat[['orderid', 'geohashed_end_loc', 'pred']].to_csv('/home/xuwenchao/dyj-storage/pred/pred_23_24_0.58893.csv', index=None)
def train(**kwargs):
# ---------------------- 更新参数 ----------------------
opt = DefaultConfig()
opt.update(**kwargs)
opt.printf()
# ---------------------- 数据处理 ----------------------
# 获取数据
train1, train2 = get_train_data(opt)
# 获取样本
# train_sample = get_sample(train1, train2, load=True)
# 获取特征
# train_feat = get_feat(train1, train_sample)
# 获取标签
# train_all = get_label(train_feat, opt)
# gc.collect()
# train_all.to_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_23_24_label.hdf', 'w', complib='blosc', complevel=5)
train_all = pd.read_hdf(
'/home/xuwenchao/dyj-storage/all-feat/feat_23_24_label.hdf')
print(train_all.shape)
# 取出需要用的特征
# opt['model_name'] = 'lgb_1_2017-09-15#19:50:48_0.58820.pkl'
# gbm, use_feat = load_model(opt)
# predictors_100 = pd.DataFrame(data={'feature_name': gbm.feature_name(), 'feature_importance': gbm.feature_importance()})
# predictors_100 = predictors_100.sort_values(by=['feature_importance'], ascending=False)['feature_name'].values[:100]
# use_feat = list(predictors_100) + ['orderid', 'geohashed_end_loc', 'label'] + ['sloc_eloc_common_eloc_count', 'sloc_eloc_common_sloc_count', 'sloc_eloc_common_conn1_count', 'sloc_eloc_common_conn2_count', 'sloc_eloc_common_eloc_rate', 'sloc_eloc_common_sloc_rate', 'sloc_eloc_common_conn1_rate', 'sloc_eloc_common_conn2_rate', 'user_sloc_eloc_common_eloc_count', 'user_sloc_eloc_common_sloc_count', 'user_sloc_eloc_common_conn1_count', 'user_sloc_eloc_common_conn2_count', 'user_sloc_eloc_common_eloc_rate', 'user_sloc_eloc_common_sloc_rate', 'user_sloc_eloc_common_conn1_rate', 'user_sloc_eloc_common_conn2_rate']
# train_all = train_all[use_feat]
# gc.collect()
# -------------------- 训练第一层 ------------------------
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义使用哪些特征
# opt['model_name'] = 'lgb_1_2017-09-15#19:50:48_0.58820.pkl'
# gbm, use_feat = load_model(opt)
filters = set([
'orderid', 'userid', 'biketype', 'geohashed_start_loc', 'bikeid',
'starttime', 'geohashed_end_loc', 'label'
])
predictors = list(
filter(lambda x: x not in filters, train_all.columns.tolist()))
# predictors = pd.DataFrame(data={'feature_name': gbm.feature_name(), 'feature_importance': gbm.feature_importance()})
# predictors = predictors.sort_values(by=['feature_importance'], ascending=False)['feature_name'].values[:100]
# use_feat = list(predictors) + ['orderid', 'geohashed_end_loc'] + ['sloc_eloc_common_eloc_count', 'sloc_eloc_common_sloc_count', 'sloc_eloc_common_conn1_count', 'sloc_eloc_common_conn2_count', 'sloc_eloc_common_eloc_rate', 'sloc_eloc_common_sloc_rate', 'sloc_eloc_common_conn1_rate', 'sloc_eloc_common_conn2_rate', 'user_sloc_eloc_common_eloc_count', 'user_sloc_eloc_common_sloc_count', 'user_sloc_eloc_common_conn1_count', 'user_sloc_eloc_common_conn2_count', 'user_sloc_eloc_common_eloc_rate', 'user_sloc_eloc_common_sloc_rate', 'user_sloc_eloc_common_conn1_rate', 'user_sloc_eloc_common_conn2_rate']
# predictors = list(predictors_100) + ['sloc_eloc_common_eloc_count', 'sloc_eloc_common_sloc_count', 'sloc_eloc_common_conn1_count', 'sloc_eloc_common_conn2_count', 'sloc_eloc_common_eloc_rate', 'sloc_eloc_common_sloc_rate', 'sloc_eloc_common_conn1_rate', 'sloc_eloc_common_conn2_rate', 'user_sloc_eloc_common_eloc_count', 'user_sloc_eloc_common_sloc_count', 'user_sloc_eloc_common_conn1_count', 'user_sloc_eloc_common_conn2_count', 'user_sloc_eloc_common_eloc_rate', 'user_sloc_eloc_common_sloc_rate', 'user_sloc_eloc_common_conn1_rate', 'user_sloc_eloc_common_conn2_rate']
print('使用的特征：{}维\n'.format(len(predictors)), predictors)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# ********* LightGBM *********
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# 配置
params = {
'objective': 'binary',
'metric': {'auc', 'binary_logloss'},
'is_unbalance': True,
'num_leaves': opt['lgb_leaves'],
'learning_rate': opt['lgb_lr'],
'feature_fraction': 0.886,
'bagging_fraction': 0.886,
'bagging_freq': 5
}
gc.collect()
# ********** 开始训练 *********
gbm1 = lgb.train(params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5)
gc.collect()
# # ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
# save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb', cur_time, score[0])
save_path = '{}/{}_{}.pkl'.format(opt['model_dir'], 'lgb', cur_time)
with open(save_path, 'wb') as fout:
pickle.dump(gbm1, fout)
print('保存模型：', save_path)
gc.collect()
# # ********* 评估 *********
# # 在训练集上看效果
del X_train, y_train, X_val, y_val
gc.collect()
score = get_score(train_all, predictors, gbm1, opt)
print('训练集分数：{}'.format(score))
import sys
sys.exit(0)
# save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_1_300_top25')
# with open(save_path, 'wb') as fout:
# pickle.dump(gbm1, fout)
# print('保存模型(第一层)：', save_path)
# ********* save predict *****
# train_all[['orderid', 'geohashed_end_loc', 'pred']].to_hdf('/home/xuwenchao/dyj-storage/train2324_80_pred_res.hdf', 'w', complib='blosc', complevel=5)
# print('Save train_pred_res.hdf successful!!!')
# import sys
# sys.exit(0)
# -------------------- 训练第二层 ------------------------
# opt['model_name'] = 'lgb_1_300_top25.pkl'
# gbm1, use_feat1 = load_model(opt)
# train_all.loc[:, 'pred'] = gbm1.predict(train_all[use_feat1])
# 去掉重要性较低的特征，筛选出排名前十的候选样本，重新训练模型（后期可以载入模型finetune，
# 尤其是对于样本量较少的情况，甚至可以选前5，
# 但15可以覆盖99.5%的原始label，10可以覆盖98%的原始label，
# 这两者可能会好一些，备选方案：5(+finetune)，10(+finetune)，15(+finetune)）
predictors = pd.DataFrame(
data={
'feature_name': gbm1.feature_name(),
'feature_importance': gbm1.feature_importance()
})
predictors = predictors[
predictors['feature_importance'] > 0]['feature_name'].values
print('第二层使用的特征：{}维\n'.format(len(predictors)), predictors)
train_all = train_all.sort_values(
by=['orderid', 'pred'], ascending=False).groupby('orderid').head(15)
# train_all = rank(train_all, 'orderid', 'pred', ascending=False)
del train_all['pred']
print('第二层数据：', train_all.shape)
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# ********** 开始训练 *********
gbm2 = lgb.train(params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
# init_model=gbm1 # finetune
)
# ********* 评估 *********
# 在训练集上看效果
score = get_score(train_all, predictors, gbm2, opt)
print('训练集分数(第二层)：{}'.format(score))
# ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb_2',
cur_time, score[0])
with open(save_path, 'wb') as fout:
pickle.dump(gbm2, fout)
print('保存模型(第二层)：', save_path)
# save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_2_300_top15')
# with open(save_path, 'wb') as fout:
# pickle.dump(gbm2, fout)
# print('保存模型(第二层)：', save_path)
import sys
sys.exit(0)
# -------------------- 训练第三层 ------------------------
# 筛选出排名前五的候选样本
predictors = pd.DataFrame(
data={
'feature_name': gbm2.feature_name(),
'feature_importance': gbm2.feature_importance()
})
predictors = predictors[
predictors['feature_importance'] > 0]['feature_name'].values
print('第三层使用的特征：{}维\n'.format(len(predictors)), predictors)
train_all = train_all.sort_values(
by=['orderid', 'pred'], ascending=False).groupby('orderid').head(10)
# train_all = rank(train_all, 'orderid', 'pred', ascending=False)
del train_all['pred']
print('第三层数据：', train_all.shape)
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# ********** 开始训练 *********
gbm3 = lgb.train(params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
# init_model=gbm2 # finetune
)
# ********* 评估 *********
# 在训练集上看效果
score = get_score(train_all, predictors, gbm3, opt)
print('训练集分数(第三层)：{}'.format(score))
# ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb_3',
cur_time, score[0])
with open(save_path, 'wb') as fout:
pickle.dump(gbm3, fout)
print('保存模型(第三层)：', save_path)
save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_3_300_top10')
with open(save_path, 'wb') as fout:
pickle.dump(gbm3, fout)
print('保存模型(第三层)：', save_path)
# -------------------- 训练第四层 ------------------------
# 筛选出排名前三的候选样本
predictors = pd.DataFrame(
data={
'feature_name': gbm3.feature_name(),
'feature_importance': gbm3.feature_importance()
})
predictors = predictors[
predictors['feature_importance'] > 0]['feature_name'].values
print('第四层使用的特征：{}维\n'.format(len(predictors)), predictors)
train_all = train_all.sort_values(
by=['orderid', 'pred'], ascending=False).groupby('orderid').head(5)
# train_all = rank(train_all, 'orderid', 'pred', ascending=False)
del train_all['pred']
print('第四层数据：', train_all.shape)
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# ********** 开始训练 *********
gbm4 = lgb.train(params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
# init_model=gbm3 # finetune
)
# ********* 评估 *********
# 在训练集上看效果
score = get_score(train_all, predictors, gbm4, opt)
print('训练集分数(第四层)：{}'.format(score))
# ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb_4',
cur_time, score[0])
with open(save_path, 'wb') as fout:
pickle.dump(gbm4, fout)
print('保存模型(第四层)：', save_path)
save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_4_300_top5')
with open(save_path, 'wb') as fout:
pickle.dump(gbm4, fout)
print('保存模型(第四层)：', save_path)
def train(**kwargs):
# ---------------------- 更新参数 ----------------------
opt = DefaultConfig()
opt.update(**kwargs)
opt.printf()
# ---------------------- 数据处理 ----------------------
# 获取数据
# train1, train2 = get_train_data(opt)
# 获取样本
# train_sample = get_sample(train1, train2, load=True)
# 获取特征
# train_feat = get_feat(train1, train_sample)
# 获取标签
# train_all = get_label(train_feat, opt)
# gc.collect()
# train_all.to_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_23_24_label.hdf', 'w', complib='blosc', complevel=5)
train_all = pd.read_hdf('/home/xuwenchao/dyj-storage/all-feat/feat_23_24_label.hdf')
print(train_all.shape)
# 取出需要用的特征
# opt['model_name'] = 'lgb_1_2017-09-15#19:50:48_0.58820.pkl'
# gbm, use_feat = load_model(opt)
# predictors_100 = pd.DataFrame(data={'feature_name': gbm.feature_name(), 'feature_importance': gbm.feature_importance()})
# predictors_100 = predictors_100.sort_values(by=['feature_importance'], ascending=False)['feature_name'].values[:100]
# use_feat = list(predictors_100) + ['orderid', 'geohashed_end_loc', 'label'] + ['sloc_eloc_common_eloc_count', 'sloc_eloc_common_sloc_count', 'sloc_eloc_common_conn1_count', 'sloc_eloc_common_conn2_count', 'sloc_eloc_common_eloc_rate', 'sloc_eloc_common_sloc_rate', 'sloc_eloc_common_conn1_rate', 'sloc_eloc_common_conn2_rate', 'user_sloc_eloc_common_eloc_count', 'user_sloc_eloc_common_sloc_count', 'user_sloc_eloc_common_conn1_count', 'user_sloc_eloc_common_conn2_count', 'user_sloc_eloc_common_eloc_rate', 'user_sloc_eloc_common_sloc_rate', 'user_sloc_eloc_common_conn1_rate', 'user_sloc_eloc_common_conn2_rate']
# train_all = train_all[use_feat]
# gc.collect()
# -------------------- 训练第一层 ------------------------
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义使用哪些特征
# opt['model_name'] = 'lgb_1_2017-09-15#19:50:48_0.58820.pkl'
# gbm, use_feat = load_model(opt)
filters = set(['orderid', 'userid', 'biketype', 'geohashed_start_loc', 'bikeid', 'starttime', 'geohashed_end_loc', 'label'])
predictors = list(filter(lambda x: x not in filters, train_all.columns.tolist()))
# predictors = pd.DataFrame(data={'feature_name': gbm.feature_name(), 'feature_importance': gbm.feature_importance()})
# predictors = predictors.sort_values(by=['feature_importance'], ascending=False)['feature_name'].values[:100]
# use_feat = list(predictors) + ['orderid', 'geohashed_end_loc'] + ['sloc_eloc_common_eloc_count', 'sloc_eloc_common_sloc_count', 'sloc_eloc_common_conn1_count', 'sloc_eloc_common_conn2_count', 'sloc_eloc_common_eloc_rate', 'sloc_eloc_common_sloc_rate', 'sloc_eloc_common_conn1_rate', 'sloc_eloc_common_conn2_rate', 'user_sloc_eloc_common_eloc_count', 'user_sloc_eloc_common_sloc_count', 'user_sloc_eloc_common_conn1_count', 'user_sloc_eloc_common_conn2_count', 'user_sloc_eloc_common_eloc_rate', 'user_sloc_eloc_common_sloc_rate', 'user_sloc_eloc_common_conn1_rate', 'user_sloc_eloc_common_conn2_rate']
# predictors = list(predictors_100) + ['sloc_eloc_common_eloc_count', 'sloc_eloc_common_sloc_count', 'sloc_eloc_common_conn1_count', 'sloc_eloc_common_conn2_count', 'sloc_eloc_common_eloc_rate', 'sloc_eloc_common_sloc_rate', 'sloc_eloc_common_conn1_rate', 'sloc_eloc_common_conn2_rate', 'user_sloc_eloc_common_eloc_count', 'user_sloc_eloc_common_sloc_count', 'user_sloc_eloc_common_conn1_count', 'user_sloc_eloc_common_conn2_count', 'user_sloc_eloc_common_eloc_rate', 'user_sloc_eloc_common_sloc_rate', 'user_sloc_eloc_common_conn1_rate', 'user_sloc_eloc_common_conn2_rate']
print('使用的特征：{}维\n'.format(len(predictors)), predictors)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# ********* LightGBM *********
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# 配置
params = {
'objective': 'binary',
'metric': {'auc', 'binary_logloss'},
'is_unbalance': True,
'num_leaves': opt['lgb_leaves'],
'learning_rate': opt['lgb_lr'],
'feature_fraction': 0.886,
'bagging_fraction': 0.886,
'bagging_freq': 5
}
gc.collect()
# ********** 开始训练 *********
gbm1 = lgb.train(
params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
)
gc.collect()
# # ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
# save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb', cur_time, score[0])
save_path = '{}/{}_{}.pkl'.format(opt['model_dir'], 'lgb', cur_time)
with open(save_path, 'wb') as fout:
pickle.dump(gbm1, fout)
print('保存模型：', save_path)
gc.collect()
# # ********* 评估 *********
# # 在训练集上看效果
del X_train, y_train, X_val, y_val
gc.collect()
score = get_score(train_all, predictors, gbm1, opt)
print('训练集分数：{}'.format(score))
import sys
sys.exit(0)
# save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_1_300_top25')
# with open(save_path, 'wb') as fout:
# pickle.dump(gbm1, fout)
# print('保存模型(第一层)：', save_path)
# ********* save predict *****
# train_all[['orderid', 'geohashed_end_loc', 'pred']].to_hdf('/home/xuwenchao/dyj-storage/train2324_80_pred_res.hdf', 'w', complib='blosc', complevel=5)
# print('Save train_pred_res.hdf successful!!!')
# import sys
# sys.exit(0)
# -------------------- 训练第二层 ------------------------
# opt['model_name'] = 'lgb_1_300_top25.pkl'
# gbm1, use_feat1 = load_model(opt)
# train_all.loc[:, 'pred'] = gbm1.predict(train_all[use_feat1])
# 去掉重要性较低的特征，筛选出排名前十的候选样本，重新训练模型（后期可以载入模型finetune，尤其是对于样本量较少的情况，甚至可以选前5，但15可以覆盖99.5%的原始label，10可以覆盖98%的原始label，这两者可能会好一些，备选方案：5(+finetune)，10(+finetune)，15(+finetune)）
predictors = pd.DataFrame(data={'feature_name': gbm1.feature_name(), 'feature_importance': gbm1.feature_importance()})
predictors = predictors[predictors['feature_importance']>0]['feature_name'].values
print('第二层使用的特征：{}维\n'.format(len(predictors)), predictors)
train_all = train_all.sort_values(by=['orderid', 'pred'], ascending=False).groupby('orderid').head(15)
# train_all = rank(train_all, 'orderid', 'pred', ascending=False)
del train_all['pred']
print('第二层数据：', train_all.shape)
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# ********** 开始训练 *********
gbm2 = lgb.train(
params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
# init_model=gbm1 # finetune
)
# ********* 评估 *********
# 在训练集上看效果
score = get_score(train_all, predictors, gbm2, opt)
print('训练集分数(第二层)：{}'.format(score))
# ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb_2', cur_time, score[0])
with open(save_path, 'wb') as fout:
pickle.dump(gbm2, fout)
print('保存模型(第二层)：', save_path)
# save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_2_300_top15')
# with open(save_path, 'wb') as fout:
# pickle.dump(gbm2, fout)
# print('保存模型(第二层)：', save_path)
import sys
sys.exit(0)
# -------------------- 训练第三层 ------------------------
# 筛选出排名前五的候选样本
predictors = pd.DataFrame(data={'feature_name': gbm2.feature_name(), 'feature_importance': gbm2.feature_importance()})
predictors = predictors[predictors['feature_importance']>0]['feature_name'].values
print('第三层使用的特征：{}维\n'.format(len(predictors)), predictors)
train_all = train_all.sort_values(by=['orderid', 'pred'], ascending=False).groupby('orderid').head(10)
# train_all = rank(train_all, 'orderid', 'pred', ascending=False)
del train_all['pred']
print('第三层数据：', train_all.shape)
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# ********** 开始训练 *********
gbm3 = lgb.train(
params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
# init_model=gbm2 # finetune
)
# ********* 评估 *********
# 在训练集上看效果
score = get_score(train_all, predictors, gbm3, opt)
print('训练集分数(第三层)：{}'.format(score))
# ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb_3', cur_time, score[0])
with open(save_path, 'wb') as fout:
pickle.dump(gbm3, fout)
print('保存模型(第三层)：', save_path)
save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_3_300_top10')
with open(save_path, 'wb') as fout:
pickle.dump(gbm3, fout)
print('保存模型(第三层)：', save_path)
# -------------------- 训练第四层 ------------------------
# 筛选出排名前三的候选样本
predictors = pd.DataFrame(data={'feature_name': gbm3.feature_name(), 'feature_importance': gbm3.feature_importance()})
predictors = predictors[predictors['feature_importance']>0]['feature_name'].values
print('第四层使用的特征：{}维\n'.format(len(predictors)), predictors)
train_all = train_all.sort_values(by=['orderid', 'pred'], ascending=False).groupby('orderid').head(5)
# train_all = rank(train_all, 'orderid', 'pred', ascending=False)
del train_all['pred']
print('第四层数据：', train_all.shape)
# ********* 准备数据 **********
# 划分验证集
train, val = train_test_split(train_all, test_size=0.1)
# 定义数据集
X_train = train[predictors]
y_train = train['label']
X_val = val[predictors]
y_val = val['label']
del train, val
gc.collect()
# 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)
# ********** 开始训练 *********
gbm4 = lgb.train(
params,
lgb_train,
num_boost_round=1200,
valid_sets=[lgb_train, lgb_val],
early_stopping_rounds=5
# init_model=gbm3 # finetune
)
# ********* 评估 *********
# 在训练集上看效果
score = get_score(train_all, predictors, gbm4, opt)
print('训练集分数(第四层)：{}'.format(score))
# ********* 保存模型 *********
cur_time = datetime.datetime.now().strftime('%Y-%m-%d#%H:%M:%S')
save_path = '{}/{}_{}_{:.5f}.pkl'.format(opt['model_dir'], 'lgb_4', cur_time, score[0])
with open(save_path, 'wb') as fout:
pickle.dump(gbm4, fout)
print('保存模型(第四层)：', save_path)
save_path = '{}/{}.pkl'.format(opt['model_dir'], 'lgb_4_300_top5')
with open(save_path, 'wb') as fout:
pickle.dump(gbm4, fout)
print('保存模型(第四层)：', save_path)
