我有一个数据集，我想在上面实现Naïve Bayes算法，但它在第107行触发了一个错误；str_column_to_float(数据集，i(如下"无法将字符串转换为浮点值："；我以为这是因为各个列的标题，但即使在我删除它们并运行代码后，它仍然会给我同样的错误。任何帮助都将不胜感激。数据集的链接如下所示；[数据集][1]代码低于
# Make Predictions with Naive Bayes On The Accident Project Dataset
from csv import reader
from math import sqrt
from math import exp
from math import pi
# Load a CSV file
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset
# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
# Convert string column to integer
def str_column_to_int(dataset, column):
class_values = [row[column] for row in dataset]
unique = set(class_values)
lookup = dict()
for i, value in enumerate(unique):
lookup[value] = i
print('[%s] => %d' % (value, i))
for row in dataset:
row[column] = lookup[row[column]]
return lookup
# Split the dataset by class values, returns a dictionary
def separate_by_class(dataset):
separated = dict()
for i in range(len(dataset)):
vector = dataset[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 stdev(numbers):
avg = mean(numbers)
variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1)
return sqrt(variance)
# Calculate the mean, stdev and count for each column in a dataset
def summarize_dataset(dataset):
summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)]
del(summaries[-1])
return summaries
# Split dataset by class then calculate statistics for each row
def summarize_by_class(dataset):
separated = separate_by_class(dataset)
summaries = dict()
for class_value, rows in separated.items():
summaries[class_value] = summarize_dataset(rows)
return summaries
# Calculate the Gaussian probability distribution function for x
def calculate_probability(x, mean, stdev):
exponent = exp(-((x-mean)**2 / (2 * stdev**2 )))
return (1 / (sqrt(2 * pi) * stdev)) * 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, stdev, _ = class_summaries[i]
probabilities[class_value] *= calculate_probability(row[i], mean, stdev)
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
# Make a prediction with Naive Bayes on Accident Dataset
filename = 'C:/Users/Vince/Desktop/University of Wyoming PHD/Year 2/Machine Learning/Term
Project/Accident Project dataset.csv'
dataset = load_csv(filename)
for i in range(len(dataset[1])-1):
str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# fit model
model = summarize_by_class(dataset)
# define a new record
row = [1,0,1,0,1,0,1,0,1,0,1,0,1]
# predict the label
label = predict(model, row)
print('Data=%s, Predicted: %s' % (row, label))
[1]: https://docs.google.com/spreadsheets/d/1aFJLSYqo59QUYJ6es09ZHY0UBqwH6cbgV4JjxY1HXZo/edit?
usp=sharing
由于float((试图将单词强制转换为字符串，因此引发ValueError。
# Raises the ValueError
float("one")
# Does not raise the ValueError
float("1")
你需要找到非数字字符串，然后手动转换。你可以更改代码来帮助你找到它，比如：
def str_column_to_float(dataset, column):
i =0
try:
for row in dataset:
row[column] = float(row[column].strip())
except ValueError:
print(f'Change value: {row[column]} on row {i} column {column} to numeric.')
finally:
i+=1