training-testing.py

training-testing.py

def createRegression(samples,variavel_numbers, n_noise):
  from sklearn.datasets import make_regression
  x, y = make_regression(n_samples=samples, n_features=variavel_numbers, noise=n_noise)
  return x, y

if __name__ =='__main__':

  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from matplotlib import pyplot as plt

  reg = createRegression(200, 1, 30)
  model = LinearRegression()

  # Divide the data into Training and Testing - 30% for testing.
  x_train, x_test, y_train, y_test = train_test_split(reg[0], reg[1], test_size=0.30)

  # Just the training data is transferred to the fit() function (which finds the best values ​​for m and b).
  model.fit(x_train, y_train)

  a_coeff = model.coef_ # Take Angular Coefficient - m
  l_coeff = model.intercept_ # Take Linear Coefficient - b

  print('Angular Coefficient: {0}\nTake Linear Coefficient: {1}'.format(a_coeff, l_coeff))

  plt.figure(figsize=(10, 7))
  plt.subplot(211)
  plt.scatter(reg[0], reg[1])
  plt.title('Complete Sample')
  plt.plot(x_train, a_coeff*x_train + l_coeff,color='red')

  plt.subplot(223)
  plt.scatter(x_train, y_train)
  plt.title('Training Set (70%)')
  plt.plot(x_train, a_coeff*x_train + l_coeff,color='red')

  plt.subplot(224)
  plt.scatter(x_test, y_test)
  plt.title('Testing set (30%)')
  plt.plot(x_train, a_coeff*x_train + l_coeff,color='red')

  plt.savefig('../images/plot-01.png', format='png')
  plt.show()