How to graph grid scores from GridSearchCV?

Question

I am looking for a way to graph grid_scores_ from GridSearchCV in sklearn. In this example I am trying to grid search for best gamma and C parameters for an SVR algorithm. My c

Answer 1

For plotting the results when tuning several hyperparameters, what I did was fixed all parameters to their best value except for one and plotted the mean score for the other parameter for each of its values.

def plot_search_results(grid):
    """
    Params: 
        grid: A trained GridSearchCV object.
    """
    ## Results from grid search
    results = grid.cv_results_
    means_test = results['mean_test_score']
    stds_test = results['std_test_score']
    means_train = results['mean_train_score']
    stds_train = results['std_train_score']

    ## Getting indexes of values per hyper-parameter
    masks=[]
    masks_names= list(grid.best_params_.keys())
    for p_k, p_v in grid.best_params_.items():
        masks.append(list(results['param_'+p_k].data==p_v))

    params=grid.param_grid

    ## Ploting results
    fig, ax = plt.subplots(1,len(params),sharex='none', sharey='all',figsize=(20,5))
    fig.suptitle('Score per parameter')
    fig.text(0.04, 0.5, 'MEAN SCORE', va='center', rotation='vertical')
    pram_preformace_in_best = {}
    for i, p in enumerate(masks_names):
        m = np.stack(masks[:i] + masks[i+1:])
        pram_preformace_in_best
        best_parms_mask = m.all(axis=0)
        best_index = np.where(best_parms_mask)[0]
        x = np.array(params[p])
        y_1 = np.array(means_test[best_index])
        e_1 = np.array(stds_test[best_index])
        y_2 = np.array(means_train[best_index])
        e_2 = np.array(stds_train[best_index])
        ax[i].errorbar(x, y_1, e_1, linestyle='--', marker='o', label='test')
        ax[i].errorbar(x, y_2, e_2, linestyle='-', marker='^',label='train' )
        ax[i].set_xlabel(p.upper())

    plt.legend()
    plt.show()

Result