Python 机器学习朴素贝叶斯分类

Question

我有很少的数据集，我想使用 python 测试朴素贝叶斯分类，我尝试了很多不同的方法，但代码无法运行，我没有得到预期的输出，所以最后我想这里有人会弄清楚我的问题

我有庞大的数据集样本数据集用于测试我想在 Python 机器学习中测试朴素贝叶斯分类。

我的数据集链接 http://www.wikisend.com/download/663276/

Answer 1

这是一个功能齐全的代码，来自机器学习精通的Jason Brownlee，我通过删除csv文件并将数据作为矩阵嵌入到文件中来适应您的需求。

我买了他的几本书，并在日常工作中实施了他的一些时间序列预测技术。

以下代码生成以下输出，您可以按如下方式构建流程：

/

usr/local/bin/python3.8/Users/fisheyjay/PycharmProjects/naive_bayes_iris/naive_bayes_iris.py 成绩： [93.33333333333333， 96.66666666666667， 100.0，

93.333333333333333， 93.33333333333333] 平均准确率：95.333%进程已完成，退出代码为 0

这是代码：

# Naive Bayes On The Iris iris_data_set
from random import seed
from random import randrange
from math import sqrt
from math import exp
from math import pi

# Convert string column to float
def str_column_to_float(iris_data_set, column):
for row in iris_data_set:
row[column] = float(row[column].strip())

# Convert string column to integer
def str_column_to_int(iris_data_set, column):
class_values = [row[column] for row in iris_data_set]
unique = set(class_values)
lookup = dict()
for i, value in enumerate(unique):
lookup[value] = i
for row in iris_data_set:
row[column] = lookup[row[column]]
return lookup

# Split a iris_data_set into k folds
def cross_validation_split(iris_data_set, n_folds):
iris_data_set_split = list()
iris_data_set_copy = list(iris_data_set)
fold_size = int(len(iris_data_set) / n_folds)
for _ in range(n_folds):
fold = list()
while len(fold) < fold_size:
index = randrange(len(iris_data_set_copy))
fold.append(iris_data_set_copy.pop(index))
iris_data_set_split.append(fold)
return iris_data_set_split

# Calculate accuracy percentage
def accuracy_metric(actual, predicted):
correct = 0
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / float(len(actual)) * 100.0

# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(iris_data_set, algorithm, n_folds, *args):
folds = cross_validation_split(iris_data_set, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
test_set = list()
for row in fold:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores

# Split the iris_data_set by class values, returns a dictionary
def separate_by_class(iris_data_set):
separated = dict()
for i in range(len(iris_data_set)):
vector = iris_data_set[i]
class_value = vector[-1]
if (class_value not in separated):
separated[class_value] = list()
separated[class_value].append(vector)
return separated

# Calculate the mean of a list of numbers
def mean(numbers):
return sum(numbers) / float(len(numbers))

# Calculate the standard deviation of a list of numbers
def standard_deviation(numbers):
avg = mean(numbers)
variance = sum([(x - avg) ** 2 for x in numbers]) / float(len(numbers) - 1)
return sqrt(variance)

# Calculate the mean, standard_deviation and count for each column in a iris_data_set
def summarize_iris_data_set(iris_data_set):
summaries = [(mean(column), standard_deviation(column), len(column)) for column in zip(*iris_data_set)]
del (summaries[-1])
return summaries

# Split iris_data_set by class then calculate statistics for each row
def summarize_by_class(iris_data_set):
separated = separate_by_class(iris_data_set)
summaries = dict()
for class_value, rows in separated.items():
summaries[class_value] = summarize_iris_data_set(rows)
return summaries

# Calculate the Gaussian probability distribution function for x
def calculate_probability(x, mean, standard_deviation):
exponent = exp(-((x - mean) ** 2 / (2 * standard_deviation ** 2)))
return (1 / (sqrt(2 * pi) * standard_deviation)) * exponent

# Calculate the probabilities of predicting each class for a given row
def calculate_class_probabilities(summaries, row):
total_rows = sum([summaries[label][0][2] for label in summaries])
probabilities = dict()
for class_value, class_summaries in summaries.items():
probabilities[class_value] = summaries[class_value][0][2] / float(total_rows)
for i in range(len(class_summaries)):
mean, standard_deviation, _ = class_summaries[i]
probabilities[class_value] *= calculate_probability(row[i], mean, standard_deviation)
return probabilities

# Predict the class for a given row
def predict(summaries, row):
probabilities = calculate_class_probabilities(summaries, row)
best_label, best_prob = None, -1
for class_value, probability in probabilities.items():
if best_label is None or probability > best_prob:
best_prob = probability
best_label = class_value
return best_label

# Naive Bayes Algorithm
def naive_bayes(train, test):
summarize = summarize_by_class(train)
predictions = list()
for row in test:
output = predict(summarize, row)
predictions.append(output)
return (predictions)

# Test Naive Bayes on Iris iris_data_set
seed(1)
iris_data_set = [['5.1', '3.5', '1.4', '0.2', 'Iris-setosa'], ['4.9', '3.0', '1.4', '0.2', 'Iris-setosa'],
['4.7', '3.2', '1.3', '0.2', 'Iris-setosa'], ['4.6', '3.1', '1.5', '0.2', 'Iris-setosa'],
['5.0', '3.6', '1.4', '0.2', 'Iris-setosa'], ['5.4', '3.9', '1.7', '0.4', 'Iris-setosa'],
['4.6', '3.4', '1.4', '0.3', 'Iris-setosa'], ['5.0', '3.4', '1.5', '0.2', 'Iris-setosa'],
['4.4', '2.9', '1.4', '0.2', 'Iris-setosa'], ['4.9', '3.1', '1.5', '0.1', 'Iris-setosa'],
['5.4', '3.7', '1.5', '0.2', 'Iris-setosa'], ['4.8', '3.4', '1.6', '0.2', 'Iris-setosa'],
['4.8', '3.0', '1.4', '0.1', 'Iris-setosa'], ['4.3', '3.0', '1.1', '0.1', 'Iris-setosa'],
['5.8', '4.0', '1.2', '0.2', 'Iris-setosa'], ['5.7', '4.4', '1.5', '0.4', 'Iris-setosa'],
['5.4', '3.9', '1.3', '0.4', 'Iris-setosa'], ['5.1', '3.5', '1.4', '0.3', 'Iris-setosa'],
['5.7', '3.8', '1.7', '0.3', 'Iris-setosa'], ['5.1', '3.8', '1.5', '0.3', 'Iris-setosa'],
['5.4', '3.4', '1.7', '0.2', 'Iris-setosa'], ['5.1', '3.7', '1.5', '0.4', 'Iris-setosa'],
['4.6', '3.6', '1.0', '0.2', 'Iris-setosa'], ['5.1', '3.3', '1.7', '0.5', 'Iris-setosa'],
['4.8', '3.4', '1.9', '0.2', 'Iris-setosa'], ['5.0', '3.0', '1.6', '0.2', 'Iris-setosa'],
['5.0', '3.4', '1.6', '0.4', 'Iris-setosa'], ['5.2', '3.5', '1.5', '0.2', 'Iris-setosa'],
['5.2', '3.4', '1.4', '0.2', 'Iris-setosa'], ['4.7', '3.2', '1.6', '0.2', 'Iris-setosa'],
['4.8', '3.1', '1.6', '0.2', 'Iris-setosa'], ['5.4', '3.4', '1.5', '0.4', 'Iris-setosa'],
['5.2', '4.1', '1.5', '0.1', 'Iris-setosa'], ['5.5', '4.2', '1.4', '0.2', 'Iris-setosa'],
['4.9', '3.1', '1.5', '0.1', 'Iris-setosa'], ['5.0', '3.2', '1.2', '0.2', 'Iris-setosa'],
['5.5', '3.5', '1.3', '0.2', 'Iris-setosa'], ['4.9', '3.1', '1.5', '0.1', 'Iris-setosa'],
['4.4', '3.0', '1.3', '0.2', 'Iris-setosa'], ['5.1', '3.4', '1.5', '0.2', 'Iris-setosa'],
['5.0', '3.5', '1.3', '0.3', 'Iris-setosa'], ['4.5', '2.3', '1.3', '0.3', 'Iris-setosa'],
['4.4', '3.2', '1.3', '0.2', 'Iris-setosa'], ['5.0', '3.5', '1.6', '0.6', 'Iris-setosa'],
['5.1', '3.8', '1.9', '0.4', 'Iris-setosa'], ['4.8', '3.0', '1.4', '0.3', 'Iris-setosa'],
['5.1', '3.8', '1.6', '0.2', 'Iris-setosa'], ['4.6', '3.2', '1.4', '0.2', 'Iris-setosa'],
['5.3', '3.7', '1.5', '0.2', 'Iris-setosa'], ['5.0', '3.3', '1.4', '0.2', 'Iris-setosa'],
['7.0', '3.2', '4.7', '1.4', 'Iris-versicolor'], ['6.4', '3.2', '4.5', '1.5', 'Iris-versicolor'],
['6.9', '3.1', '4.9', '1.5', 'Iris-versicolor'], ['5.5', '2.3', '4.0', '1.3', 'Iris-versicolor'],
['6.5', '2.8', '4.6', '1.5', 'Iris-versicolor'], ['5.7', '2.8', '4.5', '1.3', 'Iris-versicolor'],
['6.3', '3.3', '4.7', '1.6', 'Iris-versicolor'], ['4.9', '2.4', '3.3', '1.0', 'Iris-versicolor'],
['6.6', '2.9', '4.6', '1.3', 'Iris-versicolor'], ['5.2', '2.7', '3.9', '1.4', 'Iris-versicolor'],
['5.0', '2.0', '3.5', '1.0', 'Iris-versicolor'], ['5.9', '3.0', '4.2', '1.5', 'Iris-versicolor'],
['6.0', '2.2', '4.0', '1.0', 'Iris-versicolor'], ['6.1', '2.9', '4.7', '1.4', 'Iris-versicolor'],
['5.6', '2.9', '3.6', '1.3', 'Iris-versicolor'], ['6.7', '3.1', '4.4', '1.4', 'Iris-versicolor'],
['5.6', '3.0', '4.5', '1.5', 'Iris-versicolor'], ['5.8', '2.7', '4.1', '1.0', 'Iris-versicolor'],
['6.2', '2.2', '4.5', '1.5', 'Iris-versicolor'], ['5.6', '2.5', '3.9', '1.1', 'Iris-versicolor'],
['5.9', '3.2', '4.8', '1.8', 'Iris-versicolor'], ['6.1', '2.8', '4.0', '1.3', 'Iris-versicolor'],
['6.3', '2.5', '4.9', '1.5', 'Iris-versicolor'], ['6.1', '2.8', '4.7', '1.2', 'Iris-versicolor'],
['6.4', '2.9', '4.3', '1.3', 'Iris-versicolor'], ['6.6', '3.0', '4.4', '1.4', 'Iris-versicolor'],
['6.8', '2.8', '4.8', '1.4', 'Iris-versicolor'], ['6.7', '3.0', '5.0', '1.7', 'Iris-versicolor'],
['6.0', '2.9', '4.5', '1.5', 'Iris-versicolor'], ['5.7', '2.6', '3.5', '1.0', 'Iris-versicolor'],
['5.5', '2.4', '3.8', '1.1', 'Iris-versicolor'], ['5.5', '2.4', '3.7', '1.0', 'Iris-versicolor'],
['5.8', '2.7', '3.9', '1.2', 'Iris-versicolor'], ['6.0', '2.7', '5.1', '1.6', 'Iris-versicolor'],
['5.4', '3.0', '4.5', '1.5', 'Iris-versicolor'], ['6.0', '3.4', '4.5', '1.6', 'Iris-versicolor'],
['6.7', '3.1', '4.7', '1.5', 'Iris-versicolor'], ['6.3', '2.3', '4.4', '1.3', 'Iris-versicolor'],
['5.6', '3.0', '4.1', '1.3', 'Iris-versicolor'], ['5.5', '2.5', '4.0', '1.3', 'Iris-versicolor'],
['5.5', '2.6', '4.4', '1.2', 'Iris-versicolor'], ['6.1', '3.0', '4.6', '1.4', 'Iris-versicolor'],
['5.8', '2.6', '4.0', '1.2', 'Iris-versicolor'], ['5.0', '2.3', '3.3', '1.0', 'Iris-versicolor'],
['5.6', '2.7', '4.2', '1.3', 'Iris-versicolor'], ['5.7', '3.0', '4.2', '1.2', 'Iris-versicolor'],
['5.7', '2.9', '4.2', '1.3', 'Iris-versicolor'], ['6.2', '2.9', '4.3', '1.3', 'Iris-versicolor'],
['5.1', '2.5', '3.0', '1.1', 'Iris-versicolor'], ['5.7', '2.8', '4.1', '1.3', 'Iris-versicolor'],
['6.3', '3.3', '6.0', '2.5', 'Iris-virginica'], ['5.8', '2.7', '5.1', '1.9', 'Iris-virginica'],
['7.1', '3.0', '5.9', '2.1', 'Iris-virginica'], ['6.3', '2.9', '5.6', '1.8', 'Iris-virginica'],
['6.5', '3.0', '5.8', '2.2', 'Iris-virginica'], ['7.6', '3.0', '6.6', '2.1', 'Iris-virginica'],
['4.9', '2.5', '4.5', '1.7', 'Iris-virginica'], ['7.3', '2.9', '6.3', '1.8', 'Iris-virginica'],
['6.7', '2.5', '5.8', '1.8', 'Iris-virginica'], ['7.2', '3.6', '6.1', '2.5', 'Iris-virginica'],
['6.5', '3.2', '5.1', '2.0', 'Iris-virginica'], ['6.4', '2.7', '5.3', '1.9', 'Iris-virginica'],
['6.8', '3.0', '5.5', '2.1', 'Iris-virginica'], ['5.7', '2.5', '5.0', '2.0', 'Iris-virginica'],
['5.8', '2.8', '5.1', '2.4', 'Iris-virginica'], ['6.4', '3.2', '5.3', '2.3', 'Iris-virginica'],
['6.5', '3.0', '5.5', '1.8', 'Iris-virginica'], ['7.7', '3.8', '6.7', '2.2', 'Iris-virginica'],
['7.7', '2.6', '6.9', '2.3', 'Iris-virginica'], ['6.0', '2.2', '5.0', '1.5', 'Iris-virginica'],
['6.9', '3.2', '5.7', '2.3', 'Iris-virginica'], ['5.6', '2.8', '4.9', '2.0', 'Iris-virginica'],
['7.7', '2.8', '6.7', '2.0', 'Iris-virginica'], ['6.3', '2.7', '4.9', '1.8', 'Iris-virginica'],
['6.7', '3.3', '5.7', '2.1', 'Iris-virginica'], ['7.2', '3.2', '6.0', '1.8', 'Iris-virginica'],
['6.2', '2.8', '4.8', '1.8', 'Iris-virginica'], ['6.1', '3.0', '4.9', '1.8', 'Iris-virginica'],
['6.4', '2.8', '5.6', '2.1', 'Iris-virginica'], ['7.2', '3.0', '5.8', '1.6', 'Iris-virginica'],
['7.4', '2.8', '6.1', '1.9', 'Iris-virginica'], ['7.9', '3.8', '6.4', '2.0', 'Iris-virginica'],
['6.4', '2.8', '5.6', '2.2', 'Iris-virginica'], ['6.3', '2.8', '5.1', '1.5', 'Iris-virginica'],
['6.1', '2.6', '5.6', '1.4', 'Iris-virginica'], ['7.7', '3.0', '6.1', '2.3', 'Iris-virginica'],
['6.3', '3.4', '5.6', '2.4', 'Iris-virginica'], ['6.4', '3.1', '5.5', '1.8', 'Iris-virginica'],
['6.0', '3.0', '4.8', '1.8', 'Iris-virginica'], ['6.9', '3.1', '5.4', '2.1', 'Iris-virginica'],
['6.7', '3.1', '5.6', '2.4', 'Iris-virginica'], ['6.9', '3.1', '5.1', '2.3', 'Iris-virginica'],
['5.8', '2.7', '5.1', '1.9', 'Iris-virginica'], ['6.8', '3.2', '5.9', '2.3', 'Iris-virginica'],
['6.7', '3.3', '5.7', '2.5', 'Iris-virginica'], ['6.7', '3.0', '5.2', '2.3', 'Iris-virginica'],
['6.3', '2.5', '5.0', '1.9', 'Iris-virginica'], ['6.5', '3.0', '5.2', '2.0', 'Iris-virginica'],
['6.2', '3.4', '5.4', '2.3', 'Iris-virginica'], ['5.9', '3.0', '5.1', '1.8', 'Iris-virginica']]
for i in range(len(iris_data_set[0]) - 1):
str_column_to_float(iris_data_set, i)
# convert class column to integers
str_column_to_int(iris_data_set, len(iris_data_set[0]) - 1)
# evaluate algorithm
n_folds = 5
scores = evaluate_algorithm(iris_data_set, naive_bayes, n_folds)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores) / float(len(scores))))