“梧桐杯”中国移动大数据应用创新大赛-智慧城市赛道baseline0.827（纯代码+详细注释）
公众号：ChallengeHub
原创 枵央
01 前言
感谢作者「枵央」开源，为国内的开源精神点赞，本次赛题开源的特征较少，采用xgb、rf模型融合，具体开源内容见开源方案（附有详细代码注释）。
02 赛题链接
https://js.dclab.run/v2/cmptDetail.html?id=464
03 竞赛奖项
-
复赛：
所有获奖选手颁发证书与奖金
1）第一名：￥29,999，1个
2）第二名：￥9,999，2个
3）第三名：￥2,999，9个
-
决赛：
1）一等奖：￥100,000，1个
2）二等奖：￥50,000，2个
3）三等奖：￥10,000，9个
04 时间安排
-
2021年2月1日开放报名，3月14日报名截止
-
预热阶段：2021年1月18日-1月31日
-
初赛：2021年2月1日-4月8日，分为三个赛道；算法赛，客观评分+主观评分，每个赛道的综合评分前12名晋级复赛。主办方导师将为参赛队伍提供技术辅导。
-
复赛：2021年4月9日-4月30日，延续初赛的三个赛道；创意赛，线上评审+线下路演，每个赛道的综合评分前3名直接晋级决赛。
-
复活赛：2021年5月1日-5月16日，延续初赛的三个赛道；复赛第4-10名有机会复活，选取人气投票票数最高的队伍晋级决赛。在官宣平台公布各赛道4-10名的作品信息，通过网上投票的方式每个赛道复活1支队伍参与决赛。
-
决赛：2021年5月17日-6月30日，创意赛，12支队伍在复赛的基础上再做完善，将采用现场路演的方式展示PPT与数据模型DEMO，根据现场评委打分评选出一、二、三等奖。
-
*如遇到疫情突发等不可抗力事件时间节点可调整，届时可在官网或官微公告变动时间，或将线下活动改为线上进行。
05 任务描述
基于每月用户更换5G套餐数据，分析4G用户更换5G套餐的行为特征，从更换5G套餐的4G用户的基础信息、消费行为、超套信息、宽带信息、其他信息等维度，构建5G套餐潜客识别模型，识别出目前4G用户具有更换5G套餐的需求群体，进行5G潜客营销，作为5G智慧城市打造的先头军。
06 开源方案
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# ## 读取数据
# In[2]:
train_set = pd.read_csv('./train_set.csv')
train_set.head()
# ### 正样本占比0.2
# In[3]:
train_label = pd.read_csv('./train_label.csv')
train_label[train_label['label'] == 1].shape[0] / train_label.shape[0]
# In[4]:
# 测试集读入
test = pd.read_csv('result_predict_A.csv')
test['label'] = -1
# test.info()
# In[5]:
# 全样本构建,flag判断是训练集还是测试集
train = pd.merge(train_set, train_label)
all_data = train.append(test).reset_index(drop='True')
all_data['flag'] = all_data['label'].map(lambda x: 'test' if x == -1 else 'train')
# all_data['X5'].mode()
all_data['X5'] = all_data['X5'].fillna('大众用户')
all_data[all_data['X6'].isnull() & (all_data['label'] == -1)]
all_data.head()
# In[91]:
all_data.info()
# ### 测试集中X6到17缺失的人直接赋值0
# ### X5用众数即大众用户填充
# ### X6,X7,X8具有强相关性,X4和userid有强相关性，X3，32，33和6，7，8有关系
# In[6]:
train.corr() #3,6,7,8,32,33,24
# In[7]:
corr_dict = dict(train.corr()[train.corr() > 0.1].iloc[:, -1].dropna())
columns = list(corr_dict.keys())[:-1] # 强相关的列
# ### 进一步分析相关性
# In[8]:
train['X5'] = train['X5'].fillna('大众用户')
set(train['X5'].to_list())
# ## 特征工程
# ### 考虑6-14要不要删除
# ### 24,28极强相关
# In[10]:
# columns = ['X3', 'X5', 'X15', 'X16', 'X17', 'X24', 'X29', 'X32', 'X34', 'X37', 'X39']
# columns = ['X' + str(i) for i in [3,5,6,7,8,9,10,11,12,13,14,15,16,17,24,29,32,33,34,37,39]]
columns = ['X' + str(i) for i in [3,5,6,7,8,9,12,15,16,17,24,29,32,34,37,38,39,41,42,43]]
columns.append('user_id')
# ### 尝试加入其他特征
# In[11]:
all_data = all_data[[i for i in columns] + ['label', 'flag']]
all_data['X38'] = all_data['X38'].fillna(0)
all_data.head()
# In[12]:
all_data = all_data.dropna(axis=0, subset=['X16'])
all_data.info()
# 改变数据分布
# In[13]:
all_data['X8'] = np.log(all_data['X8'].values+1)
sns.kdeplot(all_data['X8'], color="Red", shade = True)
# In[14]:
all_data['X7'] = np.log(all_data['X7'].values+1)
sns.kdeplot(all_data['X7'], color="Red", shade = True)
# In[15]:
all_data['X6'] = np.log(all_data['X6'].values+1)
sns.kdeplot(all_data['X6'], color="Red", shade = True)
# In[16]:
all_data['X9'] = np.log(all_data['X9'].values+1)
sns.kdeplot(all_data['X9'], color="Red", shade = True)
# In[17]:
all_data['X15'] = np.log(all_data['X15'].values+1)
sns.kdeplot(all_data['X15'], color="Red", shade = True)
# In[18]:
all_data['X16'] = np.log(all_data['X16'].values+1)
sns.kdeplot(all_data['X16'], color="Red", shade = True)
# 新增特征
# In[19]:
def trans(x):
if x <= 1:
return 0
elif x > 1 and x < 6:
return 1
else:
return 2
# In[20]:
all_data['X16_range'] = all_data['X16'].apply(trans)
# In[21]:
all_data['X17'] = np.log(all_data['X17'].values+1)
sns.kdeplot(all_data['X17'], color="Red", shade = True)
# In[22]:
sns.kdeplot(all_data['X24'], color="Red", shade = True)
# In[23]:
sns.kdeplot(all_data['X29'], color="Red", shade = True)
# In[24]:
all_data.head()
# ### 用数字特征填补缺失值
# In[25]:
sns.kdeplot(all_data['X3'], color="Red", shade = True)
all_data['X3'] = all_data['X3'].fillna(3)
# In[26]:
sns.kdeplot(all_data['X29'], color="Red", shade = True) # 24,32，33
all_data['X29'] = all_data['X29'].fillna(0)
# In[35]:
sns.kdeplot(all_data['X34'], color="Red", shade = True) # 24,32，33
all_data['X34'] = all_data['X34'].fillna(0)
# In[27]:
all_data = pd.concat([pd.get_dummies(all_data['X5']), all_data], axis=1).drop('X5', axis=1)
all_data.head()
# 处理X32
# In[31]:
all_data['X32'] = np.log(all_data['X32'].values+1)
sns.kdeplot(all_data['X32'], color="Red", shade = True)
# In[ ]:
# 填充缺失值
from sklearn.ensemble import RandomForestRegressor
temp = all_data
# X32
known = temp[temp['X32'].notnull()]
unknown = temp[temp['X32'].isnull()]
X = known.drop(['user_id', 'X32', 'label', 'flag'], axis=1).values
y = known['X32'].values
rfr = RandomForestRegressor(random_state=0, n_estimators=100)
rfr.fit(X, y)
predict_X32 = rfr.predict(unknown.drop(['user_id', 'X32', 'label', 'flag'], axis=1).values)
all_data.loc[all_data['X32'].isnull(), 'X32'] = predict_X32
# 新增特征
# In[124]:
def transX32(x):
if x < 2.7:
return 0
elif 2.7 <= x < 3.15:
return 1
elif 3.15 <= x < 3.92:
return 2
elif 3.92 <= x < 4.4:
return 3
elif 4.4 <= x <4.9:
return 3
else:
return 4
# In[125]:
all_data['X32_range'] = all_data['X32'].apply(transX32)
# In[128]:
del all_data['X38']
# ### 分割训练集，验证集，测试集
# In[137]:
train = all_data[all_data['flag'] == 'train'].drop(['flag', 'user_id'], axis=1)
test = all_data[all_data['flag'] == 'test'].drop(['label', 'flag', 'user_id'], axis=1).reset_index(drop=True)
# In[140]:
import xgboost as xgb
from tqdm import tqdm
from xgboost.sklearn import XGBClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, f1_score # 均方误差
import catboost as cb
X_train, X_cv, y_train, y_cv = train_test_split(train.drop(['label'], axis=1), train['label'], test_size=0.2)
# ### 网格调参
# In[758]:
param_grid = [
{'n_estimators': list(range(100, 501, 100)), 'max_depth':list(range(2,21,5))}
]
rf = RandomForestClassifier()
grid_search_rf = GridSearchCV(rf, param_grid, cv=5,
scoring='f1')
grid_search_rf.fit(low_consume_train.drop(['label'], axis=1), low_consume_train['label'])
print(grid_search_rf.best_estimator_) # max_depth=17,n_estimators=500
# In[760]:
param_dist = {
'n_estimators':list(range(20, 141, 20)), # 120
'max_depth':list(range(2,15,5)), # 7
'learning_rate':list(np.linspace(0.01,2,5)), # 0.01
# 'subsample':list(np.linspace(0.7,0.9,5)),
# 'colsample_bytree':list(np.linspace(0.5,0.98,3)),
# 'min_child_weight':list(range(1,9,3)) # 6
}
xgb = XGBClassifier()
grid_search_xgb = GridSearchCV(xgb, param_dist,cv = 3,n_jobs = -1, scoring='f1')
grid_search_xgb.fit(train.iloc[:, :-1], train['label'])
print(grid_search_xgb.best_estimator_)
# ### 验证集效果
# In[141]:
train = all_data[all_data['flag'] == 'train'].drop(['flag', 'user_id'], axis=1)
test = all_data[all_data['flag'] == 'test'].drop(['label', 'flag', 'user_id'], axis=1).reset_index(drop=True)
X_train, X_cv, y_train, y_cv = train_test_split(train.drop(['label'], axis=1), train['label'], test_size=0.2)
# In[142]:
rf = RandomForestClassifier(n_estimators=500, max_depth=17).fit(X_train, y_train)
print('rf F1: {}' .format(f1_score(rf.predict(X_cv), y_cv)))
# In[143]:
xgb = XGBClassifier().fit(X_train, y_train)
print('xgb F1: {}' .format(f1_score(xgb.predict(X_cv), y_cv)))
# In[168]:
from sklearn.linear_model import LogisticRegression
for x in np.linspace(500, 1500, 10):
clf3 = LogisticRegression(penalty='l2', C=0.1, max_iter=x, tol=1e-4, solver='lbfgs').fit(X_train, y_train)
print(x)
print('lr F1: {}' .format(f1_score(clf3.predict(X_cv), y_cv)))
# In[203]:
clf4 = cb.CatBoostClassifier(n_estimators=7000).fit(X_train, y_train)
print('catboost F1: {}' .format(f1_score(clf4.predict(X_cv), y_cv)))
# In[206]:
y_pred_1 = rf.predict_proba(X_cv)[:, 0]
y_pred_2 = clf4.predict_proba(X_cv)[:, 0]
y_pred = (y_pred_1 + y_pred_2 ) / 2
y_pred = list(map(lambda x: 1 if x<0.62 else 0, y_pred))
print(f1_score(y_pred, y_cv))
# 遍历找到阈值
score_lst = []
for i in list(np.linspace(0.45,0.75,100)):
i = round(i, 4)
y_pred = (y_pred_1 + y_pred_2 ) / 2
y_pred = list(map(lambda x: 1 if x<i else 0, y_pred))
score = f1_score(y_pred, y_cv)
score_lst.append([i, score])
print('i={}, total F1: {}' .format(i, score))
score_lst = np.array(score_lst)
plt.plot(score_lst[:, 0], score_lst[:, 1])
# ### 预测
# In[1]:
clf1 = RandomForestClassifier(n_estimators=500, max_depth=17)
clf2 = cb.CatBoostClassifier(n_estimators=5000)
clf1.fit(train.drop(['label'], axis=1), train['label'])
print('训练完了')
clf2.fit(train.drop(['label'], axis=1), train['label'])
# In[232]:
y_pred_1 = clf1.predict_proba(test)[:, 0]
y_pred_2 = clf2.predict_proba(test)[:, 0]
y_pred = (y_pred_1 + y_pred_2 ) / 2
y_pred = list(map(lambda x: 1 if x<0.75 else 0, y_pred))
# 添加特殊用户
# In[233]:
temp = pd.read_csv('result_predict_A.csv')
temp[temp['X16'].isnull()]
# In[190]:
extra = pd.DataFrame([['2697592699877', 0], ['2697527496793', 0], ['2697624945417', 0]], columns=['user_id', 'label'])
extra.head()
# In[234]:
result = pd.read_csv('result_predict_A.csv')
result = result.dropna(axis=0, subset=['X16'])
result['label'] = y_pred
result = result[['user_id', 'label']]
# 加入X16为NAN的三个样本
result = result.append(extra)
result.head()
# In[235]:
result.shape
# In[236]:
result.to_csv('./submit.csv', index=False)
写在最后
作者「枵央」优快云博客：
https://blog.youkuaiyun.com/m0_46162954/article/details/114270982
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