该内容展示了一个模型从基线版本V1到V5的迭代过程，分数从86.746提升至89.0787。过程中进行了数据探索，处理缺失值和object类型字段，优化特征工程（如构造面积、时间差等特征），尝试LightGBM、XGBoost等模型，采用交叉验证，最终得到分数89.1093的最佳版本。
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#best版本-89.1093分数import pandas as pdimport warnings
warnings.filterwarnings('ignore')# 数据加载和去除Unnameed字段train = pd.read_csv('./train.csv')
test = pd.read_csv('./test1.csv')
train = train.iloc[:, 1:]
test = test.iloc[:,1:]
res = pd.DataFrame(test['sid'])# 去除数据探索发现问题的字段col = train.columns.tolist()
remove_list = ['lan', 'os','label', 'sid']for i in remove_list:
col.remove(i)
features = train[col]
test_features = test[col]# 对osv进行数据清洗def osv_trans(x):
x = str(x).replace('Android_', '').replace('Android ', '').replace('W', '') if str(x).find('.')>0:
temp_index1 = x.find('.') if x.find(' ')>0:
temp_index2 = x.find(' ') else:
temp_index2 = len(x)
if x.find('-')>0:
temp_index2 = x.find('-')
result = x[0:temp_index1] + '.' + x[temp_index1+1:temp_index2].replace('.', '') try: return float(result) except: print('有错误: '+x) return 0
try: return float(x) except: print('有错误: '+x) return 0features['osv'].fillna('8.1.0', inplace=True)
features['osv'] = features['osv'].apply(osv_trans)
test_features['osv'].fillna('8.1.0', inplace=True)
test_features['osv'] = test_features['osv'].apply(osv_trans)# 对timestamp进行数据清洗与特征变换,from datetime import datetime
features['timestamp'] = features['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))
test_features['timestamp'] = test_features['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))
temp = pd.DatetimeIndex(features['timestamp'])
features['year'] = temp.year
features['month'] = temp.month
features['day'] = temp.day
features['hour'] = temp.hour
features['minute'] = temp.minute
features['week_day'] = temp.weekday #星期几start_time = features['timestamp'].min()
features['time_diff'] = features['timestamp'] - start_time
features['time_diff'] = features['time_diff'].dt.days + features['time_diff'].dt.seconds/3600/24temp = pd.DatetimeIndex(test_features['timestamp'])
test_features['year'] = temp.year
test_features['month'] = temp.month
test_features['day'] = temp.day
test_features['hour'] = temp.hour
test_features['minute'] = temp.minute
test_features['week_day'] = temp.weekday #星期几 test_features['time_diff'] = test_features['timestamp'] - start_time
test_features['time_diff'] = test_features['time_diff'].dt.days + test_features['time_diff'].dt.seconds/3600/24features = features.drop(['timestamp'],axis = 1)
test_features = test_features.drop(['timestamp'],axis = 1)# 对version进行数据清洗与特征变换def version_trans(x):
if x=='V3': return 3
if x=='v1': return 1
if x=='P_Final_6': return 6
if x=='V6': return 6
if x=='GA3': return 3
if x=='GA2': return 2
if x=='V2': return 2
if x=='50': return 5
return int(x)
features['version'] = features['version'].apply(version_trans)
test_features['version'] = test_features['version'].apply(version_trans)
features['version'] = features['version'].astype('int')
test_features['version'] = test_features['version'].astype('int')# 对lan进行数据清洗与特征变换 对于有缺失的lan 设置为22 lan_map = {'zh-CN': 1, 'zh_CN':2, 'Zh-CN': 3, 'zh-cn': 4, 'zh_CN_#Hans':5, 'zh': 6, 'ZH': 7, 'cn':8, 'CN':9, 'zh-HK': 10, 'tw': 11, 'TW': 12, 'zh-TW': 13, 'zh-MO':14, 'en':15, 'en-GB': 16, 'en-US': 17, 'ko': 18, 'ja': 19, 'it': 20, 'mi':21}
train['lan'] = train['lan'].map(lan_map)
test['lan'] = test['lan'].map(lan_map)
train['lan'].fillna(22, inplace=True)
test['lan'].fillna(22, inplace=True)# 构造面积特征和构造相除特征features['dev_area'] = features['dev_height'] * features['dev_width']
test_features['dev_area'] = test_features['dev_height'] * test_features['dev_width']
features['dev_rato'] = features['dev_height'] / features['dev_width']
test_features['dev_rato'] = test_features['dev_height'] / test_features['dev_width']# APP版本与操作系统版本差features['version_osv'] = features['osv'] - features['version']
test_features['version_osv'] = test_features['osv'] - test_features['version']# 对fea_hash与fea1_hash特征变换features['fea_hash_len'] = features['fea_hash'].map(lambda x: len(str(x)))
features['fea1_hash_len'] = features['fea1_hash'].map(lambda x: len(str(x)))
features['fea_hash'] = features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
features['fea1_hash'] = features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
test_features['fea_hash_len'] = test_features['fea_hash'].map(lambda x: len(str(x)))
test_features['fea1_hash_len'] = test_features['fea1_hash'].map(lambda x: len(str(x)))
test_features['fea_hash'] = test_features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
test_features['fea1_hash'] = test_features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))#通过特征比，寻找关键特征，构造新特征，新特征字段 = 原始特征字段 + 1def find_key_feature(train, selected):
temp = pd.DataFrame(columns = [0,1])
temp0 = train[train['label'] == 0]
temp1 = train[train['label'] == 1]
temp[0] = temp0[selected].value_counts() / len(temp0) * 100
temp[1] = temp1[selected].value_counts() / len(temp1) * 100
temp[2] = temp[1] / temp[0] #选出大于10倍的特征
result = temp[temp[2] > 10].sort_values(2, ascending = False).index return result
selected_cols = ['osv','apptype', 'carrier', 'dev_height', 'dev_ppi','dev_width', 'media_id',
'package', 'version', 'fea_hash', 'location', 'fea1_hash','cus_type']
key_feature = {}for selected in selected_cols:
key_feature[selected] = find_key_feature(train, selected)def f(x, selected):
if x in key_feature[selected]: return 1
else: return 0for selected in selected_cols: if len(key_feature[selected]) > 0:
features[selected+'1'] = features[selected].apply(f, args = (selected,))
test_features[selected+'1'] = test_features[selected].apply(f, args = (selected,)) print(selected+'1 created')#CatBoost模型from catboost import CatBoostClassifierfrom sklearn.model_selection import StratifiedKFoldfrom sklearn.metrics import roc_auc_score
model=CatBoostClassifier(
loss_function="Logloss",
eval_metric="AUC",
task_type="GPU",
learning_rate=0.1,
iterations=1000,
random_seed=2021,
od_type="Iter",
depth=7)
n_folds =10 #十折交叉校验answers = []
mean_score = 0data_x=features
data_y=train['label']
sk = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=2021)
all_test = test_features.copy()for train, test in sk.split(data_x, data_y):
x_train = data_x.iloc[train]
y_train = data_y.iloc[train]
x_test = data_x.iloc[test]
y_test = data_y.iloc[test]
clf = model.fit(x_train,y_train, eval_set=(x_test,y_test),verbose=500) # 500条打印一条日志
yy_pred_valid=clf.predict(x_test,prediction_type='Probability')[:,-1] print('cat验证的auc:{}'.format(roc_auc_score(y_test, yy_pred_valid)))
mean_score += roc_auc_score(y_test, yy_pred_valid) / n_folds
y_pred_valid = clf.predict(all_test,prediction_type='Probability')[:,-1]
answers.append(y_pred_valid)
print('mean valAuc:{}'.format(mean_score))
cat_pre=sum(answers)/n_folds
cat_pre
res['label']=[1 if x>=0.5 else 0 for x in cat_pre]
res.to_csv('./baselinev6.csv',index=False)
有错误: f073b_changxiang_v01_b1b8_20180915 有错误: %E6%B1%9F%E7%81%B5OS+5.0 有错误: GIONEE_YNGA
项目思考的过程与baseline迭代版本
BaseLine V1_lgb--分数: 86.746
切换盘符：
jupyter notebook D:\
一、数据探索
1、去除Unnameed字段
train = train.iloc[:, 1:] test = test.iloc[:,1:]
2、查看字段类型
写法1:
train.info()
写法2:
或者直接查看类型为object的列
train.select_dtypes(include='object').columns
发现以下字段为object类型需要进行数值变换
7 lan 316720 non-null object 10 os 500000 non-null object 11 osv 493439 non-null object 15 version 500000 non-null object 16 fea_hash 500000 non-null object
以lan为例查看里面数据情况
train['lan'].value_counts()
3、查看缺失值的个数
写法1：
train.isnull().sum()
写法2：
t = train.isnull().sum() t[t>0]
发现以下字段缺少比较多
lan 183280osv 6561
4、唯一值的个数
查看唯一值的个数
features = train.columns.tolist()for feature in features: if train[feature].nunique() ==1: print(feature,train[feature].nunique())
发现os字段的唯一值个数太少
os 2
查看os
train['os'].value_counts()
发现os数据都为android
android 303175Android 196825Name: os, dtype: int64
5、数据探索的结论
object类型字段有：lan、osv 、osv、version、fea_hash
缺失值较多的字段有：lan、osv
唯一值个数较少且意义不大：os
没有意义的字段：sid
BaselineV1中也先去除timestamp
6、特征的相关性分析(补充)
# 对特征列进行相关性分析import matplotlib.pyplot as plt %matplotlib inlineimport seaborn as sns plt.figure(figsize=(10,10)) sns.heatmap(train.corr(),cbar=True,annot=True,cmap='Blues')
二、数据预处理
最终去掉：【lan】【os】【osv】【version】【label】【sid】【timestamp】
remove_list = ['lan', 'os', 'osv', 'version', 'label', 'sid','timestamp']
col = features #字段名for i in remove_list:
col.remove(i)
features = train[col]
三、特征工程
1、fea_hash特征变换
#查看数据值train['fea_hash'].value_counts()#查看统计信息train['fea_hash'].describe()#查看映射的长度特征情况train['fea_hash'].map(lambda x:len(str(x))).value_counts()
fea_hash进行特征变换
# fea_hash的长度为新特征features['fea_hash_len'] = features['fea_hash'].map(lambda x: len(str(x))) features['fea1_hash_len'] = features['fea1_hash'].map(lambda x: len(str(x)))# 如果fea_hash很长，都归为0，否则为自己的本身features['fea_hash'] = features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x)) features['fea1_hash'] = features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
四、模型建立
test 做和train同样处理，利用lightgbm进行训练与预测，并保存，上诉过程全部合并代码如下：
#BaselineV1import pandas as pdimport warningsimport lightgbm as lgb
warnings.filterwarnings('ignore')# 数据加载train = pd.read_csv('./train.csv')
test = pd.read_csv('./test1.csv')# 去除Unnameed字段train = train.iloc[:, 1:]
test = test.iloc[:,1:]# 去除数据探索发现问题的字段col = train.columns.tolist()
remove_list = ['lan', 'os', 'osv', 'version', 'label', 'sid','timestamp']for i in remove_list:
col.remove(i)
features = train[col]
test_features = test[col]# fea_hash特征变换features['fea_hash_len'] = features['fea_hash'].map(lambda x: len(str(x)))
features['fea1_hash_len'] = features['fea1_hash'].map(lambda x: len(str(x)))
features['fea_hash'] = features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
features['fea1_hash'] = features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
test_features['fea_hash_len'] = test_features['fea_hash'].map(lambda x: len(str(x)))
test_features['fea1_hash_len'] = test_features['fea1_hash'].map(lambda x: len(str(x)))
test_features['fea_hash'] = test_features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
test_features['fea1_hash'] = test_features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))#lightgbm进行训练与预测model = lgb.LGBMClassifier()
model.fit(features,train['label'])
result = model.predict(test_features)#res包括sid字段与label字段res = pd.DataFrame(test['sid'])
res['label'] = result#保存在csv中res.to_csv('./baselineV1.csv',index=False)
BaseLine V2_lgb--分数: 88.2007
一、特征工程优化
1、利用osv特征
# 对osv进行数据清洗def osv_trans(x):
x = str(x).replace('Android_', '').replace('Android ', '').replace('W', '') if str(x).find('.')>0:
temp_index1 = x.find('.') if x.find(' ')>0:
temp_index2 = x.find(' ') else:
temp_index2 = len(x)
if x.find('-')>0:
temp_index2 = x.find('-')
result = x[0:temp_index1] + '.' + x[temp_index1+1:temp_index2].replace('.', '') try: return float(result) except: print('有错误: '+x) return 0
try: return float(x) except: print('有错误: '+x) return 0features['osv'].fillna('8.1.0', inplace=True)
features['osv'] = features['osv'].apply(osv_trans)
test_features['osv'].fillna('8.1.0', inplace=True)
test_features['osv'] = test_features['osv'].apply(osv_trans)
2、利用TimeStamp特征
提取时间多尺度并计算时间diff(时间差)
# 对timestamp进行数据清洗与特征变换from datetime import datetime
features['timestamp'] = features['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))
test_features['timestamp'] = test_features['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))
temp = pd.DatetimeIndex(features['timestamp'])
features['year'] = temp.year
features['month'] = temp.month
features['day'] = temp.day
features['hour'] = temp.hour
features['minute'] = temp.minute
features['week_day'] = temp.weekday #星期几start_time = features['timestamp'].min()
features['time_diff'] = features['timestamp'] - start_time
features['time_diff'] = features['time_diff'].dt.days + features['time_diff'].dt.seconds/3600/24temp = pd.DatetimeIndex(test_features['timestamp'])
test_features['year'] = temp.year
test_features['month'] = temp.month
test_features['day'] = temp.day
test_features['hour'] = temp.hour
test_features['minute'] = temp.minute
test_features['week_day'] = temp.weekday #星期几 test_features['time_diff'] = test_features['timestamp'] - start_time
test_features['time_diff'] = test_features['time_diff'].dt.days + test_features['time_diff'].dt.seconds/3600/24col = features.columns.tolist()
col.remove('timestamp')
features = features[col]
test_features = test_features[col]
3、利用Version特征
# 对version进行数据清洗与特征变换def version_trans(x):
if x=='V3': return 3
if x=='v1': return 1
if x=='P_Final_6': return 6
if x=='V6': return 6
if x=='GA3': return 3
if x=='GA2': return 2
if x=='V2': return 2
if x=='50': return 5
return int(x)
features['version'] = features['version'].apply(version_trans)
test_features['version'] = test_features['version'].apply(version_trans)
features['version'] = features['version'].astype('int')
test_features['version'] = test_features['version'].astype('int')
二、模型建立
上诉过程合并代码如下：
import pandas as pdimport warningsimport lightgbm as lgb
warnings.filterwarnings('ignore')# 数据加载和去除Unnameed字段train = pd.read_csv('./train.csv')
test = pd.read_csv('./test1.csv')
train = train.iloc[:, 1:]
test = test.iloc[:,1:]# 去除数据探索发现问题的字段col = train.columns.tolist()
remove_list = ['lan', 'os','label', 'sid']for i in remove_list:
col.remove(i)
features = train[col]
test_features = test[col]# 对osv进行数据清洗def osv_trans(x):
x = str(x).replace('Android_', '').replace('Android ', '').replace('W', '') if str(x).find('.')>0:
temp_index1 = x.find('.') if x.find(' ')>0:
temp_index2 = x.find(' ') else:
temp_index2 = len(x)
if x.find('-')>0:
temp_index2 = x.find('-')
result = x[0:temp_index1] + '.' + x[temp_index1+1:temp_index2].replace('.', '') try: return float(result) except: print('有错误: '+x) return 0
try: return float(x) except: print('有错误: '+x) return 0features['osv'].fillna('8.1.0', inplace=True)
features['osv'] = features['osv'].apply(osv_trans)
test_features['osv'].fillna('8.1.0', inplace=True)
test_features['osv'] = test_features['osv'].apply(osv_trans)# 对timestamp进行数据清洗与特征变换,from datetime import datetime
features['timestamp'] = features['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))
test_features['timestamp'] = test_features['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))
temp = pd.DatetimeIndex(features['timestamp'])
features['year'] = temp.year
features['month'] = temp.month
features['day'] = temp.day
features['hour'] = temp.hour
features['minute'] = temp.minute
features['week_day'] = temp.weekday #星期几start_time = features['timestamp'].min()
features['time_diff'] = features['timestamp'] - start_time
features['time_diff'] = features['time_diff'].dt.days + features['time_diff'].dt.seconds/3600/24temp = pd.DatetimeIndex(test_features['timestamp'])
test_features['year'] = temp.year
test_features['month'] = temp.month
test_features['day'] = temp.day
test_features['hour'] = temp.hour
test_features['minute'] = temp.minute
test_features['week_day'] = temp.weekday #星期几 test_features['time_diff'] = test_features['timestamp'] - start_time
test_features['time_diff'] = test_features['time_diff'].dt.days + test_features['time_diff'].dt.seconds/3600/24features = features.drop(['timestamp'],axis = 1)
test_features = test_features.drop(['timestamp'],axis = 1)# 对version进行数据清洗与特征变换def version_trans(x):
if x=='V3': return 3
if x=='v1': return 1
if x=='P_Final_6': return 6
if x=='V6': return 6
if x=='GA3': return 3
if x=='GA2': return 2
if x=='V2': return 2
if x=='50': return 5
return int(x)
features['version'] = features['version'].apply(version_trans)
test_features['version'] = test_features['version'].apply(version_trans)
features['version'] = features['version'].astype('int')
test_features['version'] = test_features['version'].astype('int')# 对fea_hash与fea1_hash特征变换features['fea_hash_len'] = features['fea_hash'].map(lambda x: len(str(x)))
features['fea1_hash_len'] = features['fea1_hash'].map(lambda x: len(str(x)))
features['fea_hash'] = features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
features['fea1_hash'] = features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
test_features['fea_hash_len'] = test_features['fea_hash'].map(lambda x: len(str(x)))
test_features['fea1_hash_len'] = test_features['fea1_hash'].map(lambda x: len(str(x)))
test_features['fea_hash'] = test_features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))
test_features['fea1_hash'] = test_features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))#lightgbm进行训练与预测model = lgb.LGBMClassifier()
model.fit(features,train['label'])
result = model.predict(test_features)#res包括sid字段与label字段res = pd.DataFrame(test['sid'])
res['label'] = result#保存在csv中res.to_csv('./baselineV2.csv',index=False)print("已完成")
BaseLine V3_xgb--分数: 88.5073
一、特征工程优化
1、构造面积特征和相除特征
features['dev_area'] = features['dev_height'] * features['dev_width'] test_features['dev_area'] = test_features['dev_height'] * test_features['dev_width'] features['dev_rato'] = features['dev_height'] / features['dev_width'] test_features['dev_rato'] = test_features['dev_height'] / test_features['dev_width']
2、APP版本与操作系统版本差
features['version_osv'] = features['osv'] - features['version'] test_features['version_osv'] = test_features['osv'] - test_features['version']
二、xgboost模型
1、LightGBM 祖传参数
clf = lgb.LGBMClassifier(
num_leaves=2**5-1, reg_alpha=0.25, reg_lambda=0.25, objective='multiclass',
max_depth=-1, learning_rate=0.005, min_child_samples=3, random_state=2021,
n_estimators=2000, subsample=1, colsample_bytree=1)
device = gpu
gpu_platform_id = 0gpu_device_id = 0
2、XGBoost祖传参数
model_xgb = xgb.XGBClassifier(
max_depth=9, learning_rate=0.005, n_estimators=2000,
objective='multi:softprob', tree_method='gpu_hist',
subsample=0.8, colsample_bytree=0.8,
min_child_samples=3, eval_metric='logloss', reg_lambda=0.5)
3、使用xgboost并使用祖传参数
%%time#lightgbm进行训练与预测import xgboost as xgb
model_xgb = xgb.XGBClassifier(
max_depth=15, learning_rate=0.05, n_estimators=5000,
objective='binary:logistic', tree_method='gpu_hist',
subsample=0.8, colsample_bytree=0.8,
min_child_samples=3, eval_metric='auc', reg_lambda=0.5
)
model_xgb.fit(features,train['label'])
result_xgb = model.predict(test_features)
res = pd.DataFrame(test['sid'])
res['label'] = result_xgb
res.to_csv('./baselineV3.csv',index=False)print("已完成")
使用了xgboost的祖传参数
| 参数 | 含义 |
|---|---|
| max_depth | 含义：树的最大深度，用来避免过拟合的。max_depth越大，模型会学到更具体更局部的样本，需要使用CV函数来进行调优。 默认值：6，典型值：3-10。 调参：值越大，越容易过拟合；值越小，越容易欠拟合。 |
| learning_rate | 含义：学习率，控制每次迭代更新权重时的步长 默认值：0.3，典型值：0.01-0.2。 调参：值越小，训练越慢。 |
| n_estimators | 总共迭代的次数，即决策树的个数，相当于训练的轮数 |
| objective | 回归任务：reg:linear (默认) reg: logistic 二分类 binary:logistic (概率) binary：logitraw (类别) 多分类 multi：softmax num_class=n (返回类别) multi：softprob num_class=n(返回概率) |
| tree_method | 可调用gpu：gpu_hist。使用功能的树的构建方法，hist代表使用直方图优化的近似贪婪的算法 |
| subsample | 含义：训练样本采样率（行采样），训练每棵树时，使用的数据占全部训练集的比例。这个参数控制对于每棵树，随机采样的比例。 减小这个参数的值，算法会更加保守，避免过拟合。但是，如果这个值设置得过小，它可能会导致欠拟合。 默认值：1，典型值：0.5-1。 调参：防止过拟合。 |
| colsample_bytree | 含义：训练每棵树时，使用的数据占全部训练集的比例。默认值为1，典型值为0.5-1。和GBM中的subsample参数一模一样。这个参数控制对于每棵树，随机采样的比例。 减小这个参数的值，算法会更加保守，避免过拟合。但是，如果这个值设置得过小，它可能会导致欠拟合。 典型值：0.5-1 调参：防止过拟合。 |
| min_child_samples | |
| eval_metric | 用户可以添加多种评价指标，对于Python用户要以list传递参数对给程序 可供的选择如下: 回归任务(默认rmse) ：rmse--均方根误差 mae--平均绝对误差 分类任务(默认error) ： auc--roc曲线下面积 error--错误率（二分类） merror--错误率（多分类） logloss--负对数似然函数（二分类） mlogloss--负对数似然函数（多分类） |
| reg_lambda | L2正则化系数 |
4、可视化的方式查看特征的重要程度
from xgboost import plot_importanceimport matplotlib.pyplot as plt plot_importance(model_xgb)
BaseLine V4_xgb--分数: 88.946
一、使用十折交叉验证优化
%%time# 定义10折子模型from sklearn.model_selection import StratifiedKFoldfrom sklearn.metrics import accuracy_scoredef xgb_model(clf,train_x,train_y,test):
sk=StratifiedKFold(n_splits=10,random_state=2021,shuffle = True)
prob=[]
mean_acc=0
for k,(train_index,val_index) in enumerate(sk.split(train_x,train_y)):
train_x_real=train_x.iloc[train_index]
train_y_real=train_y.iloc[train_index]
val_x=train_x.iloc[val_index]
val_y=train_y.iloc[val_index] #模型训练及验证集测试
clf=clf.fit(train_x_real,train_y_real)
val_y_pred=clf.predict(val_x)
acc_val=accuracy_score(val_y,val_y_pred) print('第{}个子模型 accuracy{}'.format(k+1,acc_val))
mean_acc+=mean_acc/10
#预测测试集
test_y_pred=clf.predict_proba(test)
prob.append(test_y_pred) print(mean_acc)
mean_prob=sum(prob)/10
return mean_prob
import xgboost as xgb
model_xgb2 = xgb.XGBClassifier(
max_depth=15, learning_rate=0.005, n_estimators=5300,
objective='binary:logistic', tree_method='gpu_hist',
subsample=0.7, colsample_bytree=0.7,
min_child_samples=3, eval_metric='auc', reg_lambda=0.5
)
result_xgb=xgb_model(model_xgb2,features,train['label'],test_features)
result_xgb2=[x[1] for x in result_xgb]
result_xgb2=[1 if x>=0.5 else 0 for x in result_xgb2]
res = pd.DataFrame(test['sid'])
res['label'] = result_xgb2
res.to_csv('./baselineV4.csv', index=False)print('已完成')
BaseLine V5_xgb--分数: 89.0787
一、特征工程优化
通过特征比，寻找关键特征，构造新特征，新特征字段 = 原始特征字段 + 1
#通过特征比，寻找关键特征，构造新特征，新特征字段 = 原始特征字段 + 1def find_key_feature(train, selected):
temp = pd.DataFrame(columns = [0,1])
temp0 = train[train['label'] == 0]
temp1 = train[train['label'] == 1]
temp[0] = temp0[selected].value_counts() / len(temp0) * 100
temp[1] = temp1[selected].value_counts() / len(temp1) * 100
temp[2] = temp[1] / temp[0] #选出大于10倍的特征
result = temp[temp[2] > 10].sort_values(2, ascending = False).index return result
selected_cols = ['osv','apptype', 'carrier', 'dev_height', 'dev_ppi','dev_width', 'media_id',
'package', 'version', 'fea_hash', 'location', 'fea1_hash','cus_type']
key_feature = {}for selected in selected_cols:
key_feature[selected] = find_key_feature(train, selected)
key_featuredef f(x, selected):
if x in key_feature[selected]: return 1
else: return 0for selected in selected_cols: if len(key_feature[selected]) > 0:
features[selected+'1'] = features[selected].apply(f, args = (selected,))
test_features[selected+'1'] = test_features[selected].apply(f, args = (selected,)) print(selected+'1 created')
