python - 熵计算的奇怪结果

Question

我正在尝试编写一个函数来正确计算给定数据集的熵。但是，我得到了非常奇怪的熵值。

我遵循的理解是所有 熵计算必须介于 0 和 1 之间，但我始终得到高于 2 的值。

注意:我必须为此使用log base 2

有人可以解释为什么我会产生不正确的熵结果吗？ 我正在测试的数据集是 ecoli dataset from the UCI Machine Learning Repository

import numpy
import math


#################### DATA HANDLING LIBRARY ####################
def csv_to_array(file):
    # Open the file, and load it in delimiting on the ',' for a comma separated value file
    data = open(file, 'r')
    data = numpy.loadtxt(data, delimiter=',')

    # Loop through the data in the array
    for index in range(len(data)):
        # Utilize a try catch to try and convert to float, if it can't convert to float, converts to 0
        try:
            data[index] = [float(x) for x in data[index]]
        except Exception:
            data[index] = 0
        except ValueError:
            data[index] = 0

    # Return the now type-formatted data
    return data


# Function that utilizes the numpy library to randomize the dataset.
def randomize_data(csv):
    csv = numpy.random.shuffle(csv)
    return csv


# Function to split the data into test, training set, and validation sets
def split_data(csv):
    # Call the randomize data function
    randomize_data(csv)
    # Grab the number of rows and calculate where to split
    num_rows = csv.shape[0]
    validation_split = int(num_rows * 0.10)
    training_split = int(num_rows * 0.72)
    testing_split = int(num_rows * 0.18)

    # Validation set as the first 10% of the data
    validation_set = csv[:validation_split]
    # Training set as the next 72
    training_set = csv[validation_split:training_split + validation_split]
    # Testing set as the last 18
    testing_set = csv[training_split + validation_split:]
    # Split the data into classes vs actual data
    training_cols = training_set.shape[1]
    testing_cols = testing_set.shape[1]
    validation_cols = validation_set.shape[1]
    training_classes = training_set[:, training_cols - 1]
    testing_classes = testing_set[:, testing_cols - 1]
    validation_classes = validation_set[:, validation_cols - 1]

    # Take the sets and remove the last (classification) column
    training_set = training_set[:-1]
    testing_set = testing_set[:-1]
    validation_set = validation_set[:-1]

    # Return the datasets
    return testing_set, testing_classes, training_set, training_classes, validation_set, validation_classes


#################### DATA HANDLING LIBRARY ####################

# This function returns the list of classes, and their associated weights (i.e. distributions)
# for a given dataset
def class_distribution(dataset):
    # Ensure the dataset is a numpy array
    dataset = numpy.asarray(dataset)
    # Collect # of total rows and columns, using numpy
    num_total_rows = dataset.shape[0]
    num_columns = dataset.shape[1]
    # Create a numpy array of just the classes
    classes = dataset[:, num_columns - 1]
    # Use numpy.unique to remove duplicates
    classes = numpy.unique(classes)
    # Create an empty array for the class weights
    class_weights = []

    # Loop through the classes one by one
    for aclass in classes:
        # Create storage variables
        total = 0
        weight = 0
        # Now loop through the dataset
        for row in dataset:
            # If the class of the dataset is equal to the current class you are evaluating, increase the total
            if numpy.array_equal(aclass, row[-1]):
                total = total + 1
            # If not, continue
            else:
                continue
        # Divide the # of occurences by total rows
        weight = float((total / num_total_rows))
        # Add that weight to the list of class weights
        class_weights.append(weight)

    # Turn the weights into a numpy array
    class_weights = numpy.asarray(class_weights)
    # Return the array
    return classes, class_weights

# This function returns the entropy for a given dataset
# Can be used across an entire csv, or just for a column of data (feature)
def get_entropy(dataset):
    # Set initial entropy
    entropy = 0.0
    # Determine the classes and their frequencies (weights) of the dataset
    classes, class_freq = class_distribution(dataset)
    # Utilize numpy's quicksort to test the most occurring class first
    numpy.sort(class_freq)
    # Determine the max entropy for the dataset
    max_entropy = math.log(len(classes), 2)
    print("MAX ENTROPY FOR THIS DATASET: ", max_entropy)
    # Loop through the frequencies and use given formula to calculate entropy
    # For...Each simulates the sequence operator
    for freq in class_freq:
        entropy += float(-freq * math.log(freq, 2))

    # Return the entropy value
    return entropy



def main():
    ecol = csv_to_array('ecoli.csv')
    testing_set, testing_classes, training_set, training_classes, validation_set, validation_classes = split_data(ecol)

    entropy = get_entropy(ecol)
    print(entropy)

main()

Answer 1

以下函数用于计算熵:

# Function to return Shannon's Entropy
def entropy(attributes, dataset, targetAttr):
    freq = {}
    entropy = 0.0
    index = 0
    for item in attributes:
        if (targetAttr == item):
            break
        else:
            index = index + 1
    index = index - 1
    for item in dataset:
        if ((item[index]) in freq):
            # Increase the index
            freq[item[index]] += 1.0
        else:
            # Initialize it by setting it to 0
            freq[item[index]] = 1.0

    for freq in freq.values():
        entropy = entropy + (-freq / len(dataset)) * math.log(freq / len(dataset), 2)
    return entropy

正如@MattTimmermans 所指出的，熵的值实际上取决于类的数量。对于严格的 2 类，它包含在 0 到 1(含) 范围内。然而，对于超过 2 个类(正在测试的类)，熵是用不同的公式计算的(转换为上面的 Pythonic 代码)。 This post here更详细地解释了这些数学和计算。