cluster.py 文件源码

def analyze(clf,labels=None):
  def _do(matrix, test_ratio=0.0):
    if labels:  # Learning mode

      # Split train & test folds
      shuffle = ShuffleSplit(len(matrix), test_size=test_ratio)
      trainlist, testlist = [(a,b) for (a,b) in shuffle][-1]
      X_train = [x for x in map(lambda i: matrix[i], trainlist)]
      Y_train = [y for y in map(lambda i: labels[i], trainlist)]
      X_valid = [x for x in map(lambda i: matrix[i], testlist)]
      Y_valid = [y for y in map(lambda i: labels[i], testlist)]

      # Display what the underlying classifier is
      print(colored(clf[-1],'yellow'))

      # Display the dimension of the training elements
      print(colored('Trainset:','cyan'))
      print(colored('X: {0}'.format(np.shape(X_train)),'yellow'))
      print(colored('y: {0}'.format(np.shape(Y_train)),'yellow'))

      # Process trainset
      for opr in clf[:-1]:
        print(colored(opr,'yellow'))
        X_train = opr.fit_transform(X_train,Y_train)
      # NOTE: The last operation of the CLF is always a clustering algo
      clf[-1].fit(X_train,Y_train)

      # Display the dimension of the training elements
      print(colored('Validation set:','cyan'))
      print(colored('X: {0}'.format(np.shape(X_valid)),'yellow'))
      print(colored('y: {0}'.format(np.shape(Y_valid)),'yellow'))

      # Process validation set
      for opr in clf[:-1]:
        print(colored(opr,'yellow'))
        X_valid = opr.transform(X_valid)

      # Return tuple of [actual], [prediction] 
      # on the validation set
      return (Y_valid, clf[-1].predict(X_valid))

    else: # Classification mode
      X = matrix

      # Feature transformations
      for opr in clf[:-1]:
        X = opr.transform(X)

      # NOTE: Predict the clusters with the last operation
      y = clf[-1].predict(X)
      return iter(y)

  return _do