python - 如果我对分类数据进行 LabelEncode，在创建 LightGBM 数据集时是否还需要使用 categorical_feature？

Question

我正在尝试使用两个特征在 lightgbm 中创建一个简单模型，一个是分类特征，另一个是距离特征。我正在关注一个教程 ( https://sefiks.com/2018/10/13/a-gentle-introduction-to-lightgbm-for-applied-machine-learning/ )，其中指出即使在 LabelEncoding 之后，我仍然需要告诉 lightgbm 我的编码特征本质上是分类的。但是，当我尝试这样做时，我收到了这些系列的警告消息:

UserWarning: Using categorical_feature in Dataset.
  warnings.warn('Using categorical_feature in Dataset.')
UserWarning: categorical_feature in Dataset is overridden.
New categorical_feature is ['type']
 'New categorical_feature is 
    {}'.format(sorted(list(categorical_feature))))
 categorical_feature in param dict is overridden.
  warnings.warn('categorical_feature in param dict is overridden.')

我想知道 lightgbm 是否确实理解该列本质上是绝对的。看起来确实如此，但我不确定为什么本教程明确指出它没有。下面是我的代码:

trainDataProc = pd.read_csv('trainDataPrepared.csv', header=0)

le=prep.LabelEncoder()

num_columns=trainDataProc.shape[1]

for i in range(0, num_columns):
    column_name=trainDataProc.columns[i]
    column_type=trainDataProc[column_name].dtypes
    if column_type == 'object':
        le.fit(trainDataProc[column_name])
        encoded_feature=le.transform(trainDataProc[column_name])
        trainDataProc[column_name]=pd.DataFrame(encoded_feature)

# Prepare train X and Y column names.
trainColumnsX = ['type', 'dist']
cat_feat=['type']
trainColumnsY = ['scalar']

# Perform K-fold split.
kfold = mls.KFold(n_splits=5, shuffle=True, random_state=0)
result = next(kfold.split(trainDataProc), None)
train = trainDataProc.iloc[result[0]]
test = trainDataProc.iloc[result[1]]

# Train model via lightGBM.
lgbTrain = lgb.Dataset(train[trainColumnsX], label=train[trainColumnsY], 
                       categorical_feature=cat_feat)
lgbEval = lgb.Dataset(test[trainColumnsX], label=test[trainColumnsY])

# Model parameters.
params = {
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': {'mae'},
    'num_leaves': 25,
    'learning_rate': 0.0001,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': 0
}

# Set up training.
gbm = lgb.train(params,
                lgbTrain,
                num_boost_round=200,
                valid_sets=lgbEval,
                early_stopping_rounds=50)

Answer 1

我也遇到了类似的警告信息并查看了 scikit-learn 文档。

如果您使用具有分类特征(和编码为整数的标签)的 pandas Dataframe，则不需要单独定义。

LGBM 的参数“categorical_feature”的默认值为“Auto”，这确保自动使用 pandas 分类列。