prmodel.py 文件源码

def save_grid_to_db(self, model=None):
        if model is not None:
            self.model = model
        f_instance = Formulation.query.get(self.f_id)
        f_instance.formulation_data_grid.delete()
        # prepare data lines to plot
        X, y, data_traces = self.get_formulation_training_data()
        # train model to fit data lines
        scaler = StandardScaler().fit(X)
        # prepare mesh grid to plot
        max_t, max_f = np.amax(X, axis=0)
        min_t, min_f = np.amin(X, axis=0)
        xv, yv = np.meshgrid(np.arange(floor(min_t), ceil(max_t)),
                             np.arange(floor(min_f), ceil(max_f)),
                             indexing='ij')
        xv = xv.reshape((xv.shape[0], xv.shape[1], -1))
        yv = yv.reshape((yv.shape[0], yv.shape[1], -1))
        grid_xys = np.concatenate((xv, yv), axis=2).reshape((-1, 2))
        # predict z for grid
        grid_zs = self.model.predict(scaler.transform(grid_xys)).reshape((-1))
        for x, y, z in zip(grid_xys[:, 0], grid_xys[:, 1], grid_zs):
            f_instance.formulation_data_grid.append(FormulationDataGrid(x_value=x, y_value=y, z_value=z))
        db.session.commit()