LightGBM on Numerical+Categorical+Text Features >> TypeError: 参数类型未知：boosting_type，得到：dict

Question

我正在尝试在由数值、分类和文本数据组成的数据集上训练 lightGBM 模型。但是，在训练阶段，我收到以下错误：

params = {
'num_class':5,
'max_depth':8,
'num_leaves':200,
'learning_rate': 0.05,
'n_estimators':500
}

clf = LGBMClassifier(params)
data_processor = ColumnTransformer([
    ('numerical_processing', numerical_processor, numerical_features),
    ('categorical_processing', categorical_processor, categorical_features),
    ('text_processing_0', text_processor_1, text_features[0]),
    ('text_processing_1', text_processor_1, text_features[1])
                                    ]) 
pipeline = Pipeline([
    ('data_processing', data_processor),
    ('lgbm', clf)
                    ])
pipeline.fit(X_train, y_train)

错误是：

TypeError: Unknown type of parameter:boosting_type, got:dict

这是我的管道：

我基本上有两个文本特征，都是我主要执行词干提取的某种形式的名称。

任何指示将不胜感激。

Answer 1

您错误地设置了分类器，这给您带来了错误，您可以在进入管道之前轻松尝试此操作：

params = {
'num_class':5,
'max_depth':8,
'num_leaves':200,
'learning_rate': 0.05,
'n_estimators':500
}

clf = LGBMClassifier(params)
clf.fit(np.random.uniform(0,1,(50,2)),np.random.randint(0,5,50))

给你同样的错误：

TypeError: Unknown type of parameter:boosting_type, got:dict

您可以像这样设置分类器：

clf = LGBMClassifier(**params)

然后使用示例，您可以看到它运行：

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer

numerical_processor = StandardScaler()
categorical_processor = OneHotEncoder()
numerical_features = ['A']
categorical_features = ['B']

data_processor = ColumnTransformer([('numerical_processing', numerical_processor, numerical_features),
('categorical_processing', categorical_processor, categorical_features)])

X_train = pd.DataFrame({'A':np.random.uniform(100),
'B':np.random.choice(['j','k'],100)})

y_train = np.random.randint(0,5,100)

pipeline = Pipeline([('data_processing', data_processor),('lgbm', clf)])

pipeline.fit(X_train, y_train)