Trying to Implement Linear Regression with Stochastic Gradient Descent

Question

[Dataset]1I'm attempting to implement linear regression for stochastic gradient descent using python. I have the code to enable me do this but for some reason, its triggering an error at "row[column] = float(row[column].strip())"-could not convert string to float: 'C'". Anyone who will assist me troubleshoot this error will be greatly appreciated.

  # 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

Adding on to the answer from @Agni

The CSV file that you are reading has a header line

num_preg PlGlcConc BloodP tricept insulin BMI ped_func Age HasDiabetes

When you use reader(file) to read the file and then iterate over it, the first line also gets added in dataset. Hence, the first element in dataset list is:

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

So when you try to convert it into float it throws the error, Could not convert string to float): numpreg

Here is the final edited code:

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

Answer 2

In function load_csv, you are using with open(filename, 'r') as file: But in the next line instead of passing the file object file you pass the reader the string filename, and your dataset is created using this filename string, therefore when you end up trying to convert the letter 'C' to float. Just change the line reader(filename) to reader(file) That should solve your issue.