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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def experiment(objective, label_type, data):
 """Measure performance of an objective. Parameters objective : string 'binary' or 'xentropy' Objective function. label_type : string 'binary' or 'probability' Type of the label. data : dict Data for training. Returns ------- result : dict Experiment summary stats. """

    np.random.seed(0)
    nrounds = 5
    lgb_data = data['lgb_with_' + label_type + '_labels']
    params = {
        'objective': objective,
        'feature_fraction': 1,
        'bagging_fraction': 1,
        'verbose': -1
    }
    time_zero = time.time()
    gbm = lgb.train(params, lgb_data, num_boost_round=nrounds)
    y_fitted = gbm.predict(data['X'])
    y_true = data[label_type + '_labels']
    duration = time.time() - time_zero
    return {
        'time': duration,
        'correlation': np.corrcoef(y_fitted, y_true)[0, 1],
        'logloss': log_loss(y_fitted, y_true)
    }