unsupervised.py 文件源码

def plotScalabilityResultsForTest(dfs, test, colName, title, xlabel, savePath,
    ax=None, logxscale=False, logyscale=True):

    dfs = filter(lambda df: df['test'][0] == test, dfs)

    if ax is None:
        plt.figure()
        ax = plt.gca()

    lines = []
    labels = []
    for df in dfs:
        x = df[colName]
        y = df[TIME_COL] / 60. # convert to minutes
        algo = df[ALGORITHM_COL][0]
        p = ALGO_2_LINE_PARAMS[algo]
        line, = ax.plot(x, y, label=algo, color=p.color, lw=p.width, ls=p.style)
        ax.set_xlim([np.min(x), np.max(x)])

        lines.append(line)
        labels.append(algo)

    # seaborn tsplot version; confidence intervals like invisibly thin
    # around the lines, so not much point (although verify this once
    # all experiments run TODO)
    # df = pd.concat(dfs, ignore_index=True)
    # colors = dict([algo, p.color] for algo, p in ALGO_2_LINE_PARAMS.items()])
    # sb.tsplot(time=colName, value=TIME_COL, unit=SEED_COL,
    #   condition=ALGORITHM_COL, data=df, ax=ax)
    # lines, labels = ax.get_legend_handles_labels()

    if logxscale:
        ax.set_xscale('log')
    if logyscale:
        ax.set_yscale('log')
    # x = df[colName]
    # ax.set_xlim([np.min(x), np.max(x)])

    ax.set_title(title)
    ax.set_xlabel(xlabel)
    ax.set_ylabel("Runtime (min)")

    if savePath:
        plt.savefig(savePath)

    return lines, labels