lkakpm.blogg.se

# load packages and data import dalex as dx from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.impute import SimpleImputer from pose import ColumnTransformer from lightgbm import LGBMClassifier data = dx.datasets.load_titanic() X = data.drop(columns = 'survived') y = data.survived # split the data X_train, X_test, y_train, y_test = train_test_split(X, y) # fit a pipeline model numerical_features = numerical_transformer = Pipeline( steps = ) categorical_features = categorical_transformer = Pipeline( steps = ) preprocessor = ColumnTransformer( transformers = ) classifier = LGBMClassifier(n_estimators = 300) model = Pipeline( steps = ) model.fit(X_train, y_train) # create an explainer for the model explainer = dx.Explainer(model, data =X_test, y =y_test, label = 'lightgbm') # pack the explainer into a pickle file explainer.dump( open( 'explainer_lightgbm.pickle', 'wb'))