CDF Transformer (WIP)

  """Acts like a Cumulative Distribution Function (CDF)."""

  def __init__(self, transform_X=False,

               transform_y=False,

               bins=2,

               dataset=None):

    self.dataset = dataset

               bins=2):

    self.transform_X = transform_X

    self.transform_y = transform_y

    self.bins = bins

  def transform(self, dataset):

  def transform(self, dataset, bins):

    """Performs CDF transform on data."""

    X, y, w, ids = dataset.to_numpy()

    w_t = w

    ids_t = ids

    if self.transform_X:

      X_t = self.get_cdf_values(X)

      X_t = get_cdf_values(X,self.bins)

      y_t = y

    if self.transform_y:

      y_t = self.get_cdf_values(y)

      y_t = get_cdf_values(y,self.bins)

      X_t = X

    # TODO (rbharath): Find a more elegant solution to saving the data?

    # shutil.rmtree(dataset.data_dir)

    return X_t, y_t, w_t, ids_t

  def get_cdf_values(self, array): 

    cols = len(array[0,:])

    rows = len(array[:,0])

    array_t = np.zeros((rows,cols))

    parts = rows/self.bins

    hist_values = np.zeros((rows,1))

    sorted_hist_values = np.zeros((rows,1))

    for row in xrange(rows):

      if np.remainder(self.bins,2)==1:

        hist_values[row,0] = np.floor(np.divide(row,parts))*1/(self.bins-1)

      else:

        hist_values[row,0] = np.floor(np.divide(row,parts))*1/self.bins

    order = np.argsort(array, axis=0)

    for col in xrange(cols):

      sorted_hist_values[:,0] = hist_values[order[:,col],0]

      array_t[:,col] = sorted_hist_values[:,0]

    return array_t

  def untransform(self, z):

    print("Cannot undo CDF Transformer.")

    # Need this for transform_y

def get_cdf_values(array, bins): 

  n_cols = array.shape[1]

  n_rows = array.shape[0]

  array_t = np.zeros((n_rows,n_cols))

  parts = n_rows/bins

  hist_values = np.zeros(n_rows)

  sorted_hist_values = np.zeros(n_rows)

  for row in xrange(n_rows):

    if np.remainder(bins,2)==1:

      hist_values[row] = np.floor(np.divide(row,parts))/(bins-1)

    else:

      hist_values[row] = np.floor(np.divide(row,parts))/bins

  for col in xrange(n_cols):

    order = np.argsort(array[:,col], axis=0)

    sorted_hist_values = hist_values[order]

    array_t[:,col] = sorted_hist_values

  return array_t

 No newline at end of file

    target = np.array(np.transpose(np.linspace(0.,1.,1001)))

    target = np.array([target])

    gaussian_dataset = self.load_gaussian_cdf_data()

    bins=1001

    cdf_transformer = CDFTransformer(

        transform_X=True, bins=1001, dataset=gaussian_dataset)

        transform_X=True, bins=bins)

    #cdf_transformer = CDFTransformer(

    #    transform_X=True, bins=1001, dataset=gaussian_dataset)

    # cdf_transformer.transform(gaussian_dataset)

    # X_t, y_t, w_t, ids_t = gaussian_dataset.to_numpy()

    X_t, y_t, w_t, ids_t = cdf_transformer.transform(gaussian_dataset)

    X_t, y_t, w_t, ids_t = cdf_transformer.transform(gaussian_dataset,bins=bins)

    X, y, w, ids = gaussian_dataset.to_numpy()

    # Check ids are unchanged.