Merge pull request #2033 from peastman/load

  n_samples = df.shape[0]

  n_tasks = len(tasks)

  time1 = time.time()

  y = np.hstack(

      [np.reshape(np.array(df[task].values), (n_samples, 1)) for task in tasks])

  time2 = time.time()

  w = np.ones((n_samples, n_tasks))

  missing = np.zeros_like(y).astype(int)

  feature_shape = None

  for ind in range(n_samples):

    for task in range(n_tasks):

      if y[ind, task] == "":

        missing[ind, task] = 1

  # ids = df[id_field].values

  # Set missing data to have weight zero

  for ind in range(n_samples):

    for task in range(n_tasks):

      if missing[ind, task]:

        y[ind, task] = 0.

        w[ind, task] = 0.

  if y.dtype.kind in ['O', 'U']:

    missing = (y == '')

    y[missing] = 0

    w[missing] = 0

  return y.astype(float), w.astype(float)

    rval = []

    for conf in mol.GetConformers():

      d = self.get_interatomic_distances(conf)

      m = np.zeros((n_atoms, n_atoms))

      for i in range(mol.GetNumAtoms()):

        for j in range(mol.GetNumAtoms()):

          if i == j:

            m[i, j] = 0.5 * z[i]**2.4

          elif i < j:

            m[i, j] = (z[i] * z[j]) / d[i, j]

            m[j, i] = m[i, j]

          else:

            continue

      m = np.outer(z, z) / d

      m[range(n_atoms), range(n_atoms)] = 0.5 * np.array(z)**2.4

      if self.randomize:

        for random_m in self.randomize_coulomb_matrix(m):

          random_m = pad_array(random_m, self.max_atoms)

    ]  # Convert AtomPositions from Angstrom to bohr (atomic units)

    d = np.zeros((n_atoms, n_atoms), dtype=float)

    for i in range(n_atoms):

      for j in range(n_atoms):

        if i < j:

      for j in range(i):

        d[i, j] = coords[i].Distance(coords[j])

        d[j, i] = d[i, j]

        else:

          continue

    return d