clearml/examples/frameworks/lightgbm/lightgbm_example.py

# ClearML - Example of LightGBM integration
#
import lightgbm as lgb
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import mean_squared_error

from clearml import Task


def main():
    # Connecting ClearML with the current process,
    # from here on everything is logged automatically
    task = Task.init(project_name="examples", task_name="LightGBM")

    print('Loading data...')

    # Load or create your dataset

    df_train = pd.read_csv(
        'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.train',
        header=None, sep='\t'
    )
    df_test = pd.read_csv(
        'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.test',
        header=None, sep='\t'
    )

    y_train = df_train[0]
    y_test = df_test[0]
    X_train = df_train.drop(0, axis=1)
    X_test = df_test.drop(0, axis=1)

    # Create dataset for lightgbm
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

    # Specify your configurations as a dict
    params = {
        'boosting_type': 'gbdt',
        'objective': 'regression',
        'metric': {'l2', 'l1'},
        'num_leaves': 200,
        'max_depth': 0,
        'learning_rate': 0.05,
        'feature_fraction': 0.9,
        'bagging_fraction': 0.8,
        'bagging_freq': 5,
        'verbose': 0,
        'force_col_wise': True,
        'deterministic': True,
    }

    evals_result = {}  # to record eval results for plotting

    print('Starting training...')

    # Train
    gbm = lgb.train(
        params,
        lgb_train,
        num_boost_round=500,
        valid_sets=[lgb_train, lgb_eval],
        feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],
        categorical_feature=[21],
        callbacks=[
            lgb.record_evaluation(evals_result),
        ],
    )

    print('Saving model...')

    # Save model to file
    gbm.save_model('model.txt')

    print('Plotting metrics recorded during training...')

    ax = lgb.plot_metric(evals_result, metric='l1')
    plt.show()

    print('Plotting feature importances...')

    ax = lgb.plot_importance(gbm, max_num_features=10)
    plt.show()

    print('Plotting split value histogram...')

    ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
    plt.show()

    print('Loading model to predict...')

    # Load model to predict
    bst = lgb.Booster(model_file='model.txt')

    # Can only predict with the best iteration (or the saving iteration)
    y_pred = bst.predict(X_test)

    # Eval with loaded model
    print("The rmse of loaded model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)


if __name__ == '__main__':
    main()