TypeError: booster must be Booster or LGBMModel

Question

I have a LightGBMclassifier and want to plot its features importance, for that, I used the line bellow :

import lightgbm as lgb

lgb.plot_importance(model, figsize=(8,6))

but I get this error :

TypeError: booster must be Booster or LGBMModel.

can someone help please ? I do not get this error.

and here's how I built the model :

   lgbmc_classifier = LGBMClassifier(
        boosting_type='goss',
      colsample_bytree=0.17,
      lambda_l1=69.12,
      lambda_l2=0.0001,
      learning_rate=0.09,
      max_bin=512,
      min_child_samples=3,
      n_estimators=7782,
      num_leaves=10,
      subsample=0.26,
      random_state=356,
      importance_type='gain',
    )

model = Pipeline([
    ("preprocessor", preprocessor),
    ("standardizer", standardizer),
    ("classifier", lgbmc_classifier),
])

#model



model.fit(X_train, y_train)

##################################################

for data processing, on the model pipeline I also use :

for col in ["Time_Taken_hours", "Date"]:
    timestamp_transformer = TimestampTransformer()
    ohe_transformer = ColumnTransformer(
        [("ohe", OneHotEncoder(sparse=False, handle_unknown="ignore"), [timestamp_transformer.HOUR_COLUMN_INDEX])],
        remainder="passthrough")
    timestamp_preprocessor = Pipeline([
        ("extractor", timestamp_transformer),
        ("onehot_encoder", ohe_transformer)
    ])
    transformers.append((f"timestamp_{col}", timestamp_preprocessor, [col]))


from sklearn.compose import ColumnTransformer

preprocessor = ColumnTransformer(transformers, remainder="passthrough", sparse_threshold=0)

###################################################

standardizer = StandardScaler()

Answer 1

According to your error, model is not of the type that lgb.plot_importance expects, namely Booster or LGBMModel.

This is kind of clear, since you define model by

model = Pipeline([
    ("preprocessor", preprocessor),
    ("standardizer", standardizer),
    ("classifier", lgbmc_classifier),
])

The Pipeline function returns a class Pipeline instead of Booster or LGBMModel Pipeline.

But you need LGBMModel or Booster. So how do you get that? Well, you gave it the name classifier, hence you can access it using model["classifier"]. This is what you can use:

import lightgbm as lgb
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris

X_train, y_train = load_iris(return_X_y=True)

lgbmc_classifier = lgb.LGBMClassifier(
    boosting_type='goss',
    learning_rate=0.01,
    min_child_samples=10,  
    n_estimators=100,
    num_leaves=10,
    random_state=1,
    importance_type='gain',
)
lgbmc_classifier.fit(X_train, y_train)

model = Pipeline([
    ("classifier", lgbmc_classifier)
])

lgb.plot_importance(model['classifier'], figsize=(4,2))

returns

Note that I have tweaked your regularization parameters a bit in order to be able to gain some results on the iris dataset. Also, I removed the two other Pipeline elements as I don't know how you use, them, but for the result that doesn't matter in this case.

You could also just get the data, and match the data with the feature columns, e.g. using plotly:

import plotly.express as px
importances = pd.DataFrame({"feature_names": X_train.columns, "values": lgbmc_classifier.feature_importances_})
fig = px.bar(importances, x="feature_names", y="values" , width=400, height=400)
fig.show()

Or maybe you wanted a pie chart instead

fig = px.pie(importances, names="feature_names", values="values" , width=400, height=400)
fig.show()