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base.py
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# coding=utf-8
# @author:bryan
import lightgbm as lgb
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from scipy import sparse
from sklearn.model_selection import train_test_split
def LR_test(train_x,train_y,test_x,test_y):
from sklearn.metrics import log_loss
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=0.06, random_state=None,
solver='liblinear', max_iter=100,
verbose=1)
lr.fit(train_x,train_y)
predict = lr.predict_proba(test_x)[:, 1]
print(log_loss(test_y,predict))
def LGB_test(train_x,train_y,test_x,test_y,cate_col=None):
if cate_col:
data = pd.concat([train_x, test_x])
for fea in cate_col:
data[fea]=data[fea].fillna('-1')
data[fea] = LabelEncoder().fit_transform(data[fea].apply(str))
train_x=data[:len(train_x)]
test_x=data[len(train_x):]
print("LGB test")
clf = lgb.LGBMClassifier(
boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,
max_depth=-1, n_estimators=3000, objective='binary',
subsample=0.7, colsample_bytree=0.7, subsample_freq=1, # colsample_bylevel=0.7,
learning_rate=0.01, min_child_weight=25,random_state=2018,n_jobs=50
)
clf.fit(train_x, train_y,eval_set=[(train_x,train_y),(test_x,test_y)],early_stopping_rounds=100)
feature_importances=sorted(zip(train_x.columns,clf.feature_importances_),key=lambda x:x[1])
return clf.best_score_[ 'valid_1']['binary_logloss'],feature_importances
def LGB_predict(data,file):
import math
data=data.drop(['hour48','hour', 'user_id', 'shop_id','query1','query',
'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property'], axis=1)
data['item_category_list'] = LabelEncoder().fit_transform(data['item_category_list'])
train=data[data['is_trade']>-1]
predict=data[data['is_trade']==-2]
res=predict[['instance_id']]
train_y=train.pop('is_trade')
train_x=train.drop(['day','instance_id'], axis=1)
test_x = predict.drop(['day', 'instance_id','is_trade'], axis=1)
clf = lgb.LGBMClassifier(
boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,
max_depth=-1, n_estimators=3000, objective='binary',
subsample=0.7, colsample_bytree=0.7, subsample_freq=1, # colsample_bylevel=0.7,
learning_rate=0.01, min_child_weight=25, random_state=2018, n_jobs=50
)
clf.fit(train_x, train_y, eval_set=[(train_x, train_y)])
res['predicted_score']=clf.predict_proba(test_x)[:,1]
testb = pd.read_csv('../data/round2_ijcai_18_test_b_20180510.txt', sep=' ')[['instance_id']]
res=pd.merge(testb,res,on='instance_id',how='left')
res.to_csv('../submit/' + file + '.txt', sep=' ', index=False)
"""随机划分15%作为测试"""
def off_test_split(org,cate_col=None):
data = org[org.is_trade >-1]
data = data.drop(
['hour48', 'hour', 'user_id','query1','query',
'instance_id', 'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property'], axis=1)
data['item_category_list'] = LabelEncoder().fit_transform(data['item_category_list'])
y = data.pop('is_trade')
train_x, test_x, train_y, test_y = train_test_split(data, y, test_size=0.15, random_state=2018)
train_x.drop('day', axis=1, inplace=True)
test_x.drop('day', axis=1, inplace=True)
score = LGB_test(train_x, train_y, test_x, test_y,cate_col)
return score
"""取最后2个小时作为测试"""
def off_test_2hour(org,cate_col=None):
data = org[org.is_trade >-1]
data = data.drop(
['hour48', 'user_id','query1','query',
'instance_id', 'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property'], axis=1)
# data = data.drop(
# ['hour48', 'shop_score_delivery', 'shop_star_level', 'user_id', 'shop_review_num_level', 'shop_id',
# 'instance_id', 'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property'], axis=1)
data['item_category_list'] = LabelEncoder().fit_transform(data['item_category_list'])
train=data[data.hour<10]
test=data[data.hour>=10]
train=train.drop(['hour','day'],axis=1)
test = test.drop(['hour','day'], axis=1)
train_y=train.pop('is_trade')
test_y=test.pop('is_trade')
train_x=train
test_x=test
score = LGB_test(train_x, train_y, test_x, test_y,cate_col)
return score
"""合并多部分特征，f1为train,f2为其他特征的集合"""
def add(f1,f2):
for i in f2:
f1=pd.merge(f1,i,on='instance_id',how='left')
return f1
"""测试两种划分方式"""
"""返回验证分数，和base提升分数，特征重要性"""
def test(org,cate_col=None):
s1=off_test_split(org,cate_col)
s2=off_test_2hour(org,cate_col)
def f(x):
return float('%.6f' % x)
print(f(s1[0]),f(s2[0]))
print(f(0.175994-s1[0]),f(0.16987-s2[0]))
print(s1[1])
print(s2[1])