DRY și „Fă un singur lucru"

Scrierea funcțiilor în Python

Shayne Miel

Software Architect @ Duo Security

Nu vă repetați (DRY)

train = pd.read_csv('train.csv')
train_y = train['labels'].values
train_X = train[col for col in train.columns if col != 'labels'].values
train_pca = PCA(n_components=2).fit_transform(train_X)
plt.scatter(train_pca[:,0], train_pca[:,1])

val = pd.read_csv('validation.csv')
val_y = val['labels'].values
val_X = val[col for col in val.columns if col != 'labels'].values
val_pca = PCA(n_components=2).fit_transform(val_X)
plt.scatter(val_pca[:,0], val_pca[:,1])

test = pd.read_csv('test.csv')
test_y = test['labels'].values
test_X = test[col for col in test.columns if col != 'labels'].values
test_pca = PCA(n_components=2).fit_transform(train_X)
plt.scatter(test_pca[:,0], test_pca[:,1])

Problema repetiției în cod

train = pd.read_csv('train.csv')
train_y = train['labels'].values
train_X = train[col for col in train.columns if col != 'labels'].values
train_pca = PCA(n_components=2).fit_transform(train_X)
plt.scatter(train_pca[:,0], train_pca[:,1])

val = pd.read_csv('validation.csv')
val_y = val['labels'].values
val_X = val[col for col in val.columns if col != 'labels'].values
val_pca = PCA(n_components=2).fit_transform(val_X)
plt.scatter(val_pca[:,0], val_pca[:,1])

test = pd.read_csv('test.csv')
test_y = test['labels'].values
test_X = test[col for col in test.columns if col != 'labels'].values
test_pca = PCA(n_components=2).fit_transform(train_X)  ### yikes! ###
plt.scatter(test_pca[:,0], test_pca[:,1])

O altă problemă a repetiției în cod

train = pd.read_csv('train.csv')
train_y = train['labels'].values  ### <- there and there --v ### 
train_X = train[col for col in train.columns if col != 'labels'].values
train_pca = PCA(n_components=2).fit_transform(train_X)
plt.scatter(train_pca[:,0], train_pca[:,1])

val = pd.read_csv('validation.csv')
val_y = val['labels'].values  ### <- there and there --v ### 
val_X = val[col for col in val.columns if col != 'labels'].values
val_pca = PCA(n_components=2).fit_transform(val_X)
plt.scatter(val_pca[:,0], val_pca[:,1])

test = pd.read_csv('test.csv')
test_y = test['labels'].values  ### <- there and there --v ### 
test_X = test[col for col in test.columns if col != 'labels'].values
test_pca = PCA(n_components=2).fit_transform(test_X)
plt.scatter(test_pca[:,0], test_pca[:,1])

Folosiți funcții pentru a evita repetiția

def load_and_plot(path):
  """Load a dataset and plot the first two principal components.

  Args:
    path (str): The location of a CSV file.

  Returns:
    tuple of ndarray: (features, labels)
  """
  data = pd.read_csv(path)
  y = data['label'].values
  X = data[col for col in data.columns if col != 'label'].values
  pca = PCA(n_components=2).fit_transform(X)
  plt.scatter(pca[:,0], pca[:,1])
  return X, y

train_X, train_y = load_and_plot('train.csv')

val_X, val_y = load_and_plot('validation.csv')

test_X, test_y = load_and_plot('test.csv')

def load_and_plot(path):
  """Load a dataset and plot the first two principal components.

  Args:
    path (str): The location of a CSV file.

  Returns:
    tuple of ndarray: (features, labels)
  """
  data = pd.read_csv(path)
  y = data['label'].values
  X = data[col for col in data.columns if col != 'label'].values

  pca = PCA(n_components=2).fit_transform(X)
  plt.scatter(pca[:,0], pca[:,1])

  return X, y

def load_and_plot(path):
  """Load a dataset and plot the first two principal components.

  Args:
    path (str): The location of a CSV file.

  Returns:
    tuple of ndarray: (features, labels)
  """
  # load the data
  data = pd.read_csv(path)
  y = data['label'].values
  X = data[col for col in data.columns if col != 'label'].values

  pca = PCA(n_components=2).fit_transform(X)
  plt.scatter(pca[:,0], pca[:,1])

  return X, y

def load_and_plot(path):
  """Load a dataset and plot the first two principal components.

  Args:
    path (str): The location of a CSV file.

  Returns:
    tuple of ndarray: (features, labels)
  """
  # load the data
  data = pd.read_csv(path)
  y = data['label'].values
  X = data[col for col in data.columns if col != 'label'].values

  # plot the first two principal components
  pca = PCA(n_components=2).fit_transform(X)
  plt.scatter(pca[:,0], pca[:,1])

  return X, y

def load_and_plot(path):
  """Load a dataset and plot the first two principal components.

  Args:
    path (str): The location of a CSV file.

  Returns:
    tuple of ndarray: (features, labels)
  """
  # load the data
  data = pd.read_csv(path)
  y = data['label'].values
  X = data[col for col in data.columns if col != 'label'].values

  # plot the first two principle components
  pca = PCA(n_components=2).fit_transform(X)
  plt.scatter(pca[:,0], pca[:,1])

  # return loaded data
  return X, y

Fă un singur lucru

def load_data(path):
  """Load a dataset.

  Args:
    path (str): The location of a CSV file.

  Returns:
    tuple of ndarray: (features, labels)
  """
  data = pd.read_csv(path)
  y = data['labels'].values
  X = data[col for col in data.columns 
           if col != 'labels'].values
  return X, y

def plot_data(X):
  """Plot the first two principal components of a matrix.

  Args:
    X (numpy.ndarray): The data to plot.
  """
  pca = PCA(n_components=2).fit_transform(X)
  plt.scatter(pca[:,0], pca[:,1])

Avantajele principiului „un singur lucru"

Codul devine:

Mai flexibil
Mai ușor de înțeles
Mai simplu de testat
Mai simplu de depanat
Mai ușor de modificat

Code smells și refactorizare

„Orice programator poate scrie cod pe care îl înțelege calculatorul. Programatorii buni scriu cod pe care îl înțeleg oamenii." - Martin Fowler (1999)

„Refactoring" de Martin Fowler

Să exersăm!

Scrierea funcțiilor în Python