recipe_clustering.py 文件源码

def plot_bokeh(df,sublist,filename):
    lenlist=[0]
    df_sub = df[df['cuisine']==sublist[0]]
    lenlist.append(df_sub.shape[0])
    for cuisine in sublist[1:]:
        temp = df[df['cuisine']==cuisine]
        df_sub = pd.concat([df_sub, temp],axis=0,ignore_index=True)
        lenlist.append(df_sub.shape[0])
    df_X = df_sub.drop(['cuisine','recipeName'],axis=1)
    print df_X.shape, lenlist

    dist = squareform(pdist(df_X, metric='cosine'))
    tsne = TSNE(metric='precomputed').fit_transform(dist)
    #cannot use seaborn palette for bokeh
    palette =['red','green','blue','yellow']
    colors =[]
    for i in range(len(sublist)):
        for j in range(lenlist[i+1]-lenlist[i]):
            colors.append(palette[i])
    #plot with boken
    output_file(filename)
    source = ColumnDataSource(
            data=dict(x=tsne[:,0],y=tsne[:,1],
                cuisine = df_sub['cuisine'],
                recipe = df_sub['recipeName']))

    hover = HoverTool(tooltips=[
                ("cuisine", "@cuisine"),
                ("recipe", "@recipe")])

    p = figure(plot_width=1000, plot_height=1000, tools=[hover],
               title="flavor clustering")

    p.circle('x', 'y', size=10, source=source,fill_color=colors)

    show(p)