1533d1db4d1c0ebf278e2e963c05ef8ffd92cd52,deepchem/models/tensorgraph/models/graph_models.py,GraphConvModel,default_generator,#GraphConvModel#Any#Any#Any#Any#Any#,731

Before Change

              self.batch_size,
              pad_batches=pad_batches,
              deterministic=deterministic)):
        d = {}
        if self.mode == "classification":
          d[self.labels[0]] = to_one_hot(y_b.flatten(), self.n_classes).reshape(
              -1, self.n_tasks, self.n_classes)
        else:
          d[self.labels[0]] = y_b
        d[self.task_weights[0]] = w_b
        multiConvMol = ConvMol.agglomerate_mols(X_b)
        d[self.atom_features] = multiConvMol.get_atom_features()
        d[self.degree_slice] = multiConvMol.deg_slice
        d[self.membership] = multiConvMol.membership
        for i in range(1, len(multiConvMol.get_deg_adjacency_lists())):
          d[self.deg_adjs[i - 1]] = multiConvMol.get_deg_adjacency_lists()[i]
        yield d

  def predict_on_smiles(self, smiles, transformers=[], untransform=False):
    Generates predictions on a numpy array of smile strings

After Change

          y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(
              -1, self.n_tasks, self.n_classes)
        multiConvMol = ConvMol.agglomerate_mols(X_b)
        n_samples = np.array(X_b.shape[0])
        inputs = [
            multiConvMol.get_atom_features(), multiConvMol.deg_slice,
            np.array(multiConvMol.membership), n_samples
        ]
        for i in range(1, len(multiConvMol.get_deg_adjacency_lists())):
          inputs.append(multiConvMol.get_deg_adjacency_lists()[i])
        yield (inputs, [y_b], [w_b])


class MPNNModel(TensorGraph):
   Message Passing Neural Network,

Instances