Python seaborn 模块，light_palette() 实例源码

def predictions_vs_actual_classification(model_results, model_name, n_bins, figsize=(7, 3)):
    holdout = model_results.holdout_data
    target = model_results.target
    bins = np.arange(0, 1.001, 1 / n_bins)
    bin_mids = (bins[:-1] + bins[1:]) / 2
    binned = pd.cut(holdout['prediction'], bins=bins)
    bin_counts = holdout.groupby(binned)[target].count()
    bin_means = holdout.groupby(binned)[target].mean()

    fig = plt.figure(figsize=figsize)
    plt.suptitle('{0}: Predictions vs Actual'.format(model_name), fontsize=14)
    ax1 = plt.gca()
    ax1.grid(False)
    ax1.bar(bin_mids, bin_counts, width=1/n_bins, color=sns.light_palette('green')[1],
            label='row count', edgecolor='black')
    ax1.set_xlabel('predicted probability')
    ax1.set_ylabel('row count')

    ax2 = ax1.twinx()
    ax2.plot(bin_mids, bin_means, linewidth=3,
             marker='.', markersize=16, label='actual rate')
    ax2.plot(bins, bins, color=sns.color_palette()[2], label='main diagonal')

    ax2.set_ylabel('actual rate')

    handles, labels = ax1.get_legend_handles_labels()
    handles2, labels2 = ax2.get_legend_handles_labels()
    legend = plt.legend(handles + handles2, labels + labels2,
                        loc='best',
                        frameon=True,
                        framealpha=0.7)
    frame = legend.get_frame()
    frame.set_facecolor('white')
    return fig

def plot_experiment(self, path=""):
        color = self.color
        data = self.experiment_data
        cmap = sns.light_palette(color, as_cmap=True)

        fig, ax = plt.subplots()
        occupants, readings = (np.array(array) for array in zip(*data))

        # ax_left, im_left = plot_linear_fit(
        # ax_left, occupants, readings, self.model, self.model_sigma, color,
        # cmap)

        ax, im = plot_linear_fit(
            ax, readings, occupants,
            self.predictor, self.predictor_sigma,
            color, cmap
        )

        ax.set_xlabel("{} sensor readout ({})".format(self.name, self.units))
        ax.set_ylabel("Number of train car occupants")

        # cax, kw = mpl.colorbar.make_axes(
        # [ax_left, ax_right], location="bottom"
        # )

        # norm = mpl.colors.Normalize(vmin=0, vmax=1)
        # cbar = mpl.colorbar.ColorbarBase(
        #     ax, cmap=cmap, norm=norm, alpha=0.5)

        cbar = plt.colorbar(im, alpha=0.5, extend='neither', ticks=[
            gaussian(3 * self.predictor_sigma, 0, self.predictor_sigma),
            gaussian(2 * self.predictor_sigma, 0, self.predictor_sigma),
            gaussian(self.predictor_sigma, 0, self.predictor_sigma),
            gaussian(0, 0, self.predictor_sigma),
        ])
        # cbar.solids.set_edgecolor("face")

        cbar.set_ticklabels(
            ['$3 \sigma$', '$2 \sigma$', '$\sigma$', '{:.2%}'.format(
                gaussian(0, 0, self.predictor_sigma))],
            update_ticks=True
        )

        fig.savefig(os.path.join(path, self.name+".svg"))