LightGBM on Numerical+Categorical+Text Features >> TypeError: Unknown type of parameter:boosting_type, got:dict

Question

Im trying to train a lightGBM model on a dataset consisting of numerical, Categorical and Textual data. However, during the training phase, i get the following error:

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)

and the error is:

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

Here's my pipeline:

I basically have two textual features, both are some form of names on which im performing stemming mainly .

Any pointers would be highly appreciated.

Answer 1

You are setting up the classifier wrongly, this is giving you the error and you can easily try this before going to the pipeline:

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))

Gives you the same error:

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

You can set up the classifier like this:

clf = LGBMClassifier(**params)

Then using an example, you can see it runs:

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)