Feature Engineering 特征工程 1. Baseline Model人工智能Michael是个半路程序员-

learn from https://www.kaggle.com/learn/feature-engineering

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1. 读取数据

预测任务：用户是否会下载APP，当其点击广告以后
数据集：ks-projects-201801.csv

• 读取数据，指定两个特征'deadline','launched'，parse_dates解析为时间

ks = pd.read_csv('ks-projects-201801.csv',parse_dates=['deadline','launched']) 

预测Kickstarter项目是否会成功。state作为结果label
可以使用类别category，货币currency，资金目标funding goal，国家country以及启动时间launched等特征

2. 处理label

• 准备标签列，看看有哪些值，转换成可用的数字格式

pd.unique(ks.state) 

有6种数值

array(['failed', 'canceled', 'successful', 'live', 'undefined', 'suspended'], dtype=object) 

每种多少个？按state分组，每组中ID行数有多少

ks.groupby('state')['ID'].count() 

state canceled       38779 failed        197719 live            2799 successful    133956 suspended       1846 undefined       3562 Name: ID, dtype: int64 

• 简单处理下标签列，正在进行的项目live丢弃，successful的标记为1，其余的为0

ks = ks.query('state != "live"') # live行不要 ks = ks.assign(outcome=(ks['state']=='successful').astype(int)) # label 转成1,0，int型 

3. 添加特征

• 把launched时间拆分成，年月日小时，作为新的特征

ks = ks.assign(hour=ks.launched.dt.hour,                day=ks.launched.dt.day,                month=ks.launched.dt.month,                year=ks.launched.dt.year) ks.head() 

• 转换文字特征category, currency, country为数字

from sklearn.preprocessing import LabelEncoder  cat_features = ['category','currency','country'] encoder = LabelEncoder()  encoded = ks[cat_features].apply(encoder.fit_transform) encoded.head(10) 

• 将选择使用的特征合并在一个数据里

X = ks[['goal', 'hour', 'day', 'month', 'year', 'outcome']].join(encoded) X.head() 

4. 数据集切片

• 数据切片，按比例分成训练集、验证集、测试集（0.8，0.1，0.1）
• 更高级的简单做法sklearn.model_selection.StratifiedShuffleSplit

valid_ratio = 0.1 valid_size = int(len(X)*valid_ratio) train = X[ : -2*valid_size] valid = X[-2*valid_size : -valid_size] test = X[-valid_size : ] 

需要关注下，label 在每个数据集中的占比是否接近

for each in [train, valid, test]: print("Outcome fraction = {:.4f}".format(each.outcome.mean())) 

Outcome fraction = 0.3570 Outcome fraction = 0.3539 Outcome fraction = 0.3542 

5. 训练

• 使用LightGBM模型进行训练

机器学习算法之LightGBM

feature_cols = train.columns.drop('outcome')  dtrain = lgb.Dataset(train[feature_cols], label=train['outcome']) dvalid = lgb.Dataset(valid[feature_cols], label=valid['outcome'])  param = {'num_leaves': 64, 'objective': 'binary'} param['metric'] = 'auc' num_round = 1000 bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid],     early_stopping_rounds=10, verbose_eval=False) 

6. 预测

• 对测试集进行预测

from sklearn import metrics ypred = bst.predict(test[feature_cols]) score = metrics.roc_auc_score(test['outcome'], ypred) print(f"Test AUC score: {score}") 

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