kaggle PredictingRedHatBusinessValue 简单的xgboost的交叉验证

PredictingRedHatBusinessValue 这个超级简单的比赛

随手在一个kernels上面随便改了改，交叉验证的xgboost：

感觉还是稍微有一点借鉴意义的（x

注释的部分是OneHot+线性模型的结果，非注释的就是随机森林。

线性模型跑的比随即森林的结果好很多，至于为什么，我也不知道。

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import OneHotEncoderdef reduce_dimen(dataset,column,toreplace):
for index,i in dataset[column].duplicated(keep=False).iteritems():
if i==False:
dataset.set_value(index,column,toreplace)
return datasetdef act_data_treatment(dsname):
dataset = dsname for col in list(dataset.columns):
if col not in ['people_id', 'activity_id', 'date', 'char_38', 'outcome']:
if dataset[col].dtype == 'object':
dataset[col].fillna('type 0', inplace=True)
dataset[col] = dataset[col].apply(lambda x: x.split(' ')[1]).astype(np.int32)
elif dataset[col].dtype == 'bool':
dataset[col] = dataset[col].astype(np.int8) dataset['year'] = dataset['date'].dt.year
dataset['month'] = dataset['date'].dt.month
dataset['day'] = dataset['date'].dt.day
dataset['isweekend'] = (dataset['date'].dt.weekday >= 5).astype(int)
dataset = dataset.drop('date', axis = 1) return datasetact_train_data = pd.read_csv("D://kaggle//PredictingRedHatBusinessValue//data//act_train.csv",dtype={'people_id': np.str, 'activity_id': np.str, 'outcome': np.int8}, parse_dates=['date'])
act_test_data = pd.read_csv("D://kaggle//PredictingRedHatBusinessValue//data//act_test.csv", dtype={'people_id': np.str, 'activity_id': np.str}, parse_dates=['date'])
people_data = pd.read_csv("D://kaggle//PredictingRedHatBusinessValue//data//people.csv", dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])act_train_data=act_train_data.drop('char_10',axis=1)
act_test_data=act_test_data.drop('char_10',axis=1)print("Train data shape: " + format(act_train_data.shape))
print("Test data shape: " + format(act_test_data.shape))
print("People data shape: " + format(people_data.shape))act_train_data = act_data_treatment(act_train_data)
act_test_data = act_data_treatment(act_test_data)
people_data = act_data_treatment(people_data)train = act_train_data.merge(people_data, on='people_id', how='left', left_index=True)
test = act_test_data.merge(people_data, on='people_id', how='left', left_index=True)del act_train_data
del act_test_data
del people_datatrain=train.sort_values(['people_id'], ascending=[1])
test=test.sort_values(['people_id'], ascending=[1])train_columns = train.columns.values
test_columns = test.columns.values
features = list(set(train_columns) & set(test_columns))train.fillna('NA', inplace=True)
test.fillna('NA', inplace=True)y = train.outcome
train=train.drop('outcome',axis=1)whole=pd.concat([train,test],ignore_index=True)
categorical=['group_1','activity_category','char_1_x','char_2_x','char_3_x','char_4_x','char_5_x','char_6_x','char_7_x','char_8_x','char_9_x','char_2_y','char_3_y','char_4_y','char_5_y','char_6_y','char_7_y','char_8_y','char_9_y']
for category in categorical:
whole=reduce_dimen(whole,category,9999999)Len = int(0.3*len(train))
X_train=whole[:Len]
Y_train=y[:Len]
X=whole[:len(train)]
Y=y[:len(train)]
X_test=whole[len(train):]del train
del wholeX=X.sort_values(['people_id'], ascending=[1])
X_train = X_train.sort_values(['people_id'], ascending=[1])X_train = X_train[features].drop(['people_id', 'activity_id'], axis = 1)
X = X[features].drop(['people_id', 'activity_id'], axis = 1)
X_test = X_test[features].drop(['people_id', 'activity_id'], axis = 1)categorical=['group_1','activity_category','char_1_x','char_2_x','char_3_x','char_4_x','char_5_x','char_6_x','char_7_x','char_8_x','char_9_x','char_2_y','char_3_y','char_4_y','char_5_y','char_6_y','char_7_y','char_8_y','char_9_y']
not_categorical=[]
for category in X.columns:
if category not in categorical:
not_categorical.append(category)# enc = OneHotEncoder(handle_unknown='ignore')
# enc=enc.fit(pd.concat([X[categorical],X_test[categorical]]))
# X_cat_sparse=enc.transform(X[categorical])
# X_test_cat_sparse=enc.transform(X_test[categorical])# from scipy.sparse import hstack
# X_sparse=hstack((X[not_categorical], X_cat_sparse))
# X_test_sparse=hstack((X_test[not_categorical], X_test_cat_sparse))# print("Training data: " + format(X_sparse.shape))
# print("Test data: " + format(X_test_sparse.shape))
# print("###########")
# print("One Hot enconded Test Dataset Script")# dtrain = xgb.DMatrix(X_sparse,label=y)
# dtest = xgb.DMatrix(X_test_sparse)# param = {'max_depth':10, 'eta':0.02, 'silent':1, 'objective':'binary:logistic' }
# param['nthread'] = 4
# param['eval_metric'] = 'auc'
# param['subsample'] = 0.7
# param['colsample_bytree']= 0.7
# param['min_child_weight'] = 0
# param['booster'] = "gblinear"# watchlist = [(dtrain,'train')]
# num_round = 300
# early_stopping_rounds=10
# bst = xgb.train(param, dtrain, num_round, watchlist,early_stopping_rounds=early_stopping_rounds)dtrain2 = xgb.DMatrix(X_train,label=Y_train)
dtrain = xgb.DMatrix(X,label=Y)
dtest = xgb.DMatrix(X_test)eta = 0.9
max_depth = 5
subsample = 0.8
colsample_bytree = 0.8print('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": 19960429
}watchlist = [(dtrain,'train'),(dtrain2,'val')]
num_round = 300
early_stopping_rounds=10
bst = xgb.train(params, dtrain, num_round, watchlist,early_stopping_rounds=early_stopping_rounds)ypred = bst.predict(dtest)
output = pd.DataFrame({ 'activity_id' : test['activity_id'], 'outcome': ypred })
output.head()
output.to_csv('D://kaggle//PredictingRedHatBusinessValue//data//without_leak.csv', index = False)