mcs-cluster.py 文件源码

def categorizeCI2(inputDF, subsampleFactor=10, title=None):
    #inputDF = normalize(inputDF)
    binLabels = ['Low', 'Medium', 'High']
    indices = range(0, inputDF.shape[0], subsampleFactor)
    plotDF = inputDF.iloc[indices].copy()
    plotDF['bin'] = pd.qcut(inputDF['ci'], len(binLabels), labels=binLabels)
    plotDF.drop(['ci'], axis=1, inplace=True)
    alpha = 0.3
    g = parallel_coordinates(plotDF, 'bin',
                             color=[[0.8,0.0,0.1,alpha],
                                    [0.0,0.8,0.1,alpha],
                                    [0.1,0.1,0.8,alpha],
                                   ])
    plt.xticks(rotation=270)
    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
    if title:
        title += ' (factor=%d)' % subsampleFactor
        g.set_title(title)
    return g