尝试使用随机梯度下降实现线性回归

Question

[Dataset]1我正在尝试使用 python 实现随机梯度下降的线性回归。我有使我能够执行此操作的代码，但出于某种原因，它在“row[column] = float(row[column].strip())”处触发错误 - 无法将字符串转换为 float：'C' "。如果有人愿意帮助我解决此错误，我们将不胜感激。

  # Linear Regression With Stochastic Gradient Descent for Pima- Indians-Diabetes

  from random import seed
  from random import randrange
  from csv import reader
  from math import sqrt
  filename = 'C:/Users/Vince/Desktop/University of Wyoming PHD/Year 2/Machine 
  Learning/Homeworks/Solutions/HW4/pima-indians-diabetes-training.csv'
  # Load a CSV file
  def load_csv(filename):
  dataset = list()
  with open(filename, 'r') as file:
    csv_reader = reader(filename)
    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())

  # Find the min and max values for each column
  def dataset_minmax(dataset):
  minmax = list()
  for i in range(len(dataset[0])):
    col_values = [row[i] for row in dataset]
    value_min = min(col_values)
    value_max = max(col_values)
    minmax.append([value_min, value_max])
  return minmax

  # Rescale dataset columns to the range 0-1
  def normalize_dataset(dataset, minmax):
    for row in dataset:
    for i in range(len(row)):
        row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])

  # Split a dataset into k folds
  def cross_validation_split(dataset, n_folds):
  dataset_split = list()
  dataset_copy = list(dataset)
  fold_size = int(len(dataset) / n_folds)
  for i in range(n_folds):
    fold = list()
    while len(fold) < fold_size:
        index = randrange(len(dataset_copy))
        fold.append(dataset_copy.pop(index))
    dataset_split.append(fold)
 return dataset_split

# Calculate root mean squared error
def rmse_metric(actual, predicted):
sum_error = 0.0
for i in range(len(actual)):
    prediction_error = predicted[i] - actual[i]
    sum_error += (prediction_error ** 2)
mean_error = sum_error / float(len(actual))
return sqrt(mean_error)

# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
folds = cross_validation_split(dataset, 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]
    rmse = rmse_metric(actual, predicted)
    scores.append(rmse)
return scores

# Make a prediction with coefficients
def predict(row, coefficients):
yhat = coefficients[0]
for i in range(len(row)-1):
    yhat += coefficients[i + 1] * row[i]
return yhat

# Estimate linear regression coefficients using stochastic gradient descent
def coefficients_sgd(train, l_rate, n_epoch):
coef = [0.0 for i in range(len(train[0]))]
for epoch in range(n_epoch):
    for row in train:
        yhat = predict(row, coef)
        error = yhat - row[-1]
        coef[0] = coef[0] - l_rate * error
        for i in range(len(row)-1):
            coef[i + 1] = coef[i + 1] - l_rate * error * row[i]
        # print(l_rate, n_epoch, error)
   return coef

   # Linear Regression Algorithm With Stochastic Gradient Descent
  def linear_regression_sgd(train, test, l_rate, n_epoch):
  predictions = list()
  coef = coefficients_sgd(train, l_rate, n_epoch)
  for row in test:
    yhat = predict(row, coef)
    predictions.append(yhat)
 return(predictions)

 # Linear Regression on Indians Pima Database
  seed(1)
# load and prepare data
filename = 'C:/Users/Vince/Desktop/University of Wyoming PHD/Year 2/Machine 
Learning/Homeworks/Solutions/HW4/pima-indians-diabetes-training.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])):
str_column_to_float(dataset, i)
# normalize
minmax = dataset_minmax(dataset)
normalize_dataset(dataset, minmax)
# evaluate algorithm
n_folds = 5
l_rate = 0.01
n_epoch = 5 0
scores = evaluate_algorithm(dataset, linear_regression_sgd, n_folds, l_rate, n_epoch)
print('Scores: %s' % scores)
print('Mean RMSE: %.3f' % (sum(scores)/float(len(scores))))

Answer 1

在函数 load_csv 中，您正在使用 with open(filename, 'r') as file: 但是在下一行中，不是传递文件对象 file，而是传递 reader 字符串 filename，并且您的数据集是使用此 filename 字符串创建的，因此当您结束时尝试将字母 'C' 转换为浮点数。只需将行 reader(filename) 更改为 reader(file) 那应该可以解决您的问题。

Answer 2

添加@Agni 的回答

您正在阅读的 CSV 文件有 header 行

num_preg PlGlcConc BloodP tricept insulin BMI ped_func Age HasDiabetes

当您使用 reader(file) 读取文件然后遍历它时，第一行也会添加到 dataset 中。因此，dataset 列表中的第一个元素是：

>>> dataset
[['num_preg', 'PlGlcConc', 'BloodP', 'tricept', 'insulin', 'BMI', 'ped_func', 'Age', 'HasDiabetes'], ...]

因此，当您尝试将其转换为浮点数时，它会抛出错误， Could not convert string to float): numpreg

这是最终编辑的代码：

def load_csv(filename):
    dataset = list()
    with open(filename, 'r') as file:
        csv_reader = reader(file)
        fieldnames = next(csv_reader) # Skip the first row and store in case you need it
        dataset = list(csv_reader) # You can convert an iterator to list directly
    return dataset

尝试使用随机梯度下降实现线性回归

Trying to Implement Linear Regression with Stochastic Gradient Descent

python

machine-learning

linear-regression

stochastic

logistics