Model.Predict
===========================

`Click here </en/latest/document/Model/Predict.html>`_ to go back to the reference.


.. code-block:: python

    def _Eval(model,
              data_loader,
              save_path_prediction: str):
        """"""
        if save_path_prediction is not None:
            assert save_path_prediction[-4:] == '.csv'
        cell_encoder, drug_encoder, fusion_module = None, None, None
        if type(model) == tuple:
            cell_encoder, drug_encoder, fusion_module = model
            cell_encoder = cell_encoder.to(device)
            drug_encoder = drug_encoder.to(device)
            fusion_module = fusion_module.to(device)
        else:
            model = model.to(device)
        loss_func = nn.MSELoss()
        real_pre_dict = dict()
        print('Start prediction!')
        start = time.time()
        if type(model) == tuple:
            epoch_loss, real, pre, cell_ls, drug_ls = _InferenceTP(cell_encoder, drug_encoder, fusion_module, data_loader, loss_func)
        else:
            epoch_loss, real, pre, cell_ls, drug_ls = _Inference(model, data_loader, loss_func)
        if save_path_prediction is not None:
            real_pre_dict['cell'] = cell_ls
            real_pre_dict['drug'] = drug_ls
            real_pre_dict['pre'] = pre
            dataframe = pd.DataFrame(real_pre_dict)
            dataframe.to_csv(save_path_prediction, index=False, sep=',')
        end = time.time()
        print('Time consumed {:.6f}'.format(end - start))
        print('Prediction completed!')
        return pre, cell_ls, drug_ls


    def Predict(model,
                data,
                save_path_prediction: str = None):
        """"""
        for i in range(len(data)):
            data.pair_ls[i] += [0.0] if len(data.pair_ls[i]) == 2 else []
        data_loader = DrDataLoader(DrDataset(data), batch_size=64, shuffle=False)
        return _Eval(model, data_loader, save_path_prediction)