Function score_columns

scalg.py:106–131

Analyse data file using a range based percentual proximity algorithm and calculate the linear maximum likelihood estimation. Args: source_data (list): Data set to process. columns (list): Indexes of the source_data columns to be scored. weights (list): Weights corresp

(source_data: list, columns: list, weights: list)

def score_columns(source_data: list, columns: list, weights: list) -> list:
    """Analyse data file using a range based percentual proximity
    algorithm and calculate the linear maximum likelihood estimation.
    Args:
        source_data (list): Data set to process.
        columns (list): Indexes of the source_data columns to be scored.
        weights (list): Weights corresponding to each column from the data set.
            0 if lower values have higher weight in the data set,
            1 if higher values have higher weight in the data set
    Raises:
        ValueError: Weights can only be either 0 or 1 (int)
    Returns:
        list: Source data with the score of the set appended at as the last element.
    """

    temp_data = []
    for item in source_data:
        temp_data.append([item[c] for c in columns])

    if len(weights) > len(columns):
        weights = [weights[item] for item in columns]

    for i, sc in enumerate(score(temp_data, weights, "scores")):
        source_data[i].append(sc)

    return source_data