python - 使用 seaborn 绘制 QQ 图的 FacetGrid

Question

我无法绘制 FacetGrid的 QQ-plots与 seaborn .

我有一个 m 行(观察值)和 n 列(特征)的矩阵，我想为每个特征(列)绘制一个 QQ 图以将其与正态分布进行比较。

到目前为止，我的代码是这样的:

import scipy.stats as ss

def qqplots(fpath, expr, title):

    def quantile_plot(x, **kwargs):
        x = ss.zscore(x)
        qntls, xr = ss.probplot(x, dist="norm")
        plt.scatter(xr, qntls, **kwargs)

    expr_m = pd.melt(expr)
    expr_m.columns = ["Feature", "Value"]
    n_feat = len(expr_m["Feature"].value_counts().index)

    n_cols = int(np.sqrt(n_feat)) + 1

    g = sns.FacetGrid(expr_m, col="Feature", col_wrap=n_cols)
    g.map(quantile_plot, "Value");
    plt.savefig(fpath + ".pdf", bbox_inches="tight")
    plt.savefig(fpath + ".png", bbox_inches="tight")
    plt.close()

qqplots("lognorm_qqplot", np.log2(expr), "Log-normal qqplot")

expr 变量是一个具有 m 行(观察值)和 n 列(特征)的 pandas DataFrame。

我得到的异常如下:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-52-f9333a55702e> in <module>()
     39     plt.close()
     40 
---> 41 qqplots("lognorm_qqplot", np.log2(expr), "Log-normal qqplot")

<ipython-input-52-f9333a55702e> in qqplots(fpath, expr, title)
     34 
     35     g = sns.FacetGrid(expr_m, col="Feature", col_wrap=n_cols)
---> 36     g.map(quantile_plot, "Value");
     37     plt.savefig(fpath + ".pdf", bbox_inches="tight")
     38     plt.savefig(fpath + ".png", bbox_inches="tight")

/usr/local/lib/python3.5/site-packages/seaborn/axisgrid.py in map(self, func, *args, **kwargs)
    726 
    727             # Draw the plot
--> 728             self._facet_plot(func, ax, plot_args, kwargs)
    729 
    730         # Finalize the annotations and layout

/usr/local/lib/python3.5/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
    810 
    811         # Draw the plot
--> 812         func(*plot_args, **plot_kwargs)
    813 
    814         # Sort out the supporting information

<ipython-input-52-f9333a55702e> in quantile_plot(y, **kwargs)
     25         y = ss.zscore(y)
     26         qntls, xr = ss.probplot(y, dist="norm")
---> 27         plt.scatter(xr, qntls, **kwargs)
     28 
     29     expr_m = pd.melt(expr)

/usr/local/lib/python3.5/site-packages/matplotlib/pyplot.py in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)
   3249                          vmin=vmin, vmax=vmax, alpha=alpha,
   3250                          linewidths=linewidths, verts=verts,
-> 3251                          edgecolors=edgecolors, data=data, **kwargs)
   3252     finally:
   3253         ax.hold(washold)

/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
   1810                     warnings.warn(msg % (label_namer, func.__name__),
   1811                                   RuntimeWarning, stacklevel=2)
-> 1812             return func(ax, *args, **kwargs)
   1813         pre_doc = inner.__doc__
   1814         if pre_doc is None:

/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
   3838         y = np.ma.ravel(y)
   3839         if x.size != y.size:
-> 3840             raise ValueError("x and y must be the same size")
   3841 
   3842         s = np.ma.ravel(s)  # This doesn't have to match x, y in size.

ValueError: x and y must be the same size

Answer 1

我做到了这一点，并且还使用以下代码更改了颜色以使用 Seaborn 调色板:

def qqplots(fpath, expr, title):

    def quantile_plot(x, **kwargs):
        x = ss.zscore(x)
        ss.probplot(x, plot=plt)

    expr_m = pd.melt(expr)
    expr_m.columns = ["Feature", "Value"]
    n_feat = len(expr_m["Feature"].value_counts().index)

    n_cols = int(np.sqrt(n_feat)) + 1

    g = sns.FacetGrid(expr_m, col="Feature", col_wrap=n_cols)
    g.map(quantile_plot, "Value");
    for ax in g.axes:
        ax.get_lines()[0].set_markerfacecolor(sns.color_palette()[0])
        ax.get_lines()[1].set_color(sns.color_palette()[3])
    plt.savefig(fpath + ".pdf", bbox_inches="tight")
    plt.savefig(fpath + ".png", bbox_inches="tight")
    plt.close()

qqplots("lognorm_qqplot", np.log2(expr), "Log-normal qqplot")