How to use the fklearn.training.classification.logistic_classification_learner function in fklearn

def train_fn():
    return logistic_classification_learner(target="target",
                                           prediction_column="prediction",
                                           weight_column="w",
                                           params={"random_state": 52})

def train_fn():
    return logistic_classification_learner(target="target",
                                           prediction_column="prediction",
                                           weight_column="w",
                                           params={"random_state": 52})

'x1': [10.0, 13.0, 10.0, 13.0, 20, 13],
        "x2": [0, 1, 1, 0, 1, 1],
        "w": [2, 1, 2, 0.5, 0.5, 3],
        'y': [0, 1, 2, 0, 1, 2]
    })

    df_test_multinomial = pd.DataFrame({
        'id': ["id4", "id4", "id5", "id6", "id6", "id7"],
        'x1': [12.0, 1000.0, -4.0, 0.0, 0.0, 1],
        "x2": [1, 1, 0, 1, 1, 0],
        "w": [1, 2, 0, 0.5, 0.1, 2],
        'y': [2, 0, 1, 1, 0, 2]
    })

    # test binomial case
    learner_binary = logistic_classification_learner(features=["x1", "x2"],
                                                     target="y",
                                                     params={"max_iter": 2000})

    predict_fn, pred_train, log = learner_binary(df_train_binary)

    pred_test = predict_fn(df_test_binary)

    expected_col_train = df_train_binary.columns.tolist() + ["prediction"]
    expected_col_test = df_test_binary.columns.tolist() + ["prediction"]

    assert Counter(expected_col_train) == Counter(pred_train.columns.tolist())
    assert Counter(expected_col_test) == Counter(pred_test.columns.tolist())
    assert pred_test.prediction.max() < 1
    assert pred_test.prediction.min() > 0
    assert (pred_test.columns == pred_train.columns).all()

def train_fn():
    return logistic_classification_learner(target="target",
                                           prediction_column="prediction",
                                           weight_column="w",
                                           params={"random_state": 52})

predict_fn, pred_train, log = learner_binary(df_train_binary)

    pred_test = predict_fn(df_test_binary)

    expected_col_train = df_train_binary.columns.tolist() + ["prediction"]
    expected_col_test = df_test_binary.columns.tolist() + ["prediction"]

    assert Counter(expected_col_train) == Counter(pred_train.columns.tolist())
    assert Counter(expected_col_test) == Counter(pred_test.columns.tolist())
    assert pred_test.prediction.max() < 1
    assert pred_test.prediction.min() > 0
    assert (pred_test.columns == pred_train.columns).all()

    # test multinomial case
    learner_multinomial = logistic_classification_learner(features=["x1", "x2"],
                                                          target="y",
                                                          params={"multi_class": "multinomial",
                                                                  "solver": "sag",
                                                                  "max_iter": 2000},
                                                          weight_column="w")

    predict_fn, pred_train, log = learner_multinomial(df_train_multinomial)

    pred_test = predict_fn(df_test_multinomial)

    expected_col_train = df_train_binary.columns.tolist() + ["prediction_0", "prediction_1", "prediction_2",
                                                             "prediction"]
    expected_col_test = df_test_binary.columns.tolist() + ["prediction_0", "prediction_1", "prediction_2",
                                                           "prediction"]

    assert Counter(expected_col_train) == Counter(pred_train.columns.tolist())