斯科特是谁？ - Seaborn pairplot 中的 ValueError：无法将字符串转换为浮点数：'scott'

Posted 2023-03-12

【中文标题】斯科特是谁？ - Seaborn pairplot 中的 ValueError：无法将字符串转换为浮点数：\'scott\'

【英文标题】：Who is Scott? - ValueError in Seaborn pairplot: Could not convert string to float: 'scott'斯科特是谁？ - Seaborn pairplot 中的 ValueError：无法将字符串转换为浮点数：'scott'

【发布时间】：2020-08-09 21:42:26

【问题描述】：

斯科特是谁？

问题

尝试使用 seaborn 将贷款预测数据集中的 Education 属性添加到 pairplot 时出现以下错误：

ValueError Traceback（最近一次调用最后一次） ~/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py in kdensityfft(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid) 450 尝试： --> 451 体重 = 浮动（体重） 452 除了：

ValueError: 无法将字符串转换为浮点数：'scott'

我查看了原始数据，但在任何地方都找不到“scott”，所以我的问题是这是从哪里来的，我该如何解决？

我还收到一个运行时错误“RuntimeError: Selected KDE bandwidth is 0. Cannot estiamte density.”。我不确定这是由第一个错误引起的，还是完全是一个单独的问题。如果有人能对此有所了解，我将不胜感激。

数据集

我正在使用here 找到的贷款预测数据集。属性如下：

    Loan_ID     Gender  Married     Dependents  Education     Self_Employed     ApplicantIncome     CoapplicantIncome   LoanAmount  Loan_Amount_Term    Credit_History  Property_Area   Loan_Status
0   LP001002    Male    No          0           Graduate      No                5849                0.0                 NaN         360.0               1.0             Urban           Y
1   LP001003    Male    Yes         1           Graduate      No                4583                1508.0              128.0       360.0               1.0             Rural           N
2   LP001005    Male    Yes         0           Graduate      Yes               3000                0.0                 66.0        360.0               1.0             Urban           Y
3   LP001006    Male    Yes         0           Not Graduate  No                2583                2358.0              120.0       360.0               1.0             Urban           Y
4   LP001008    Male    No          0           Graduate      No                6000                0.0                 141.0       360.0               1.0             Urban           Y

代码

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline # I'm using ipython notebook

train_data = pd.read_csv("train_ctrUa4K.csv")

bad_credit = train_data[train_data["Credit_History"] == 0]
bad_credit["Education"] = bad_credit["Education"].map("Graduate":1,"Not Graduate":0)
sns.pairplot(bad_credit,vars=["ApplicantIncome","Education","LoanAmount"],hue="Loan_Status")

错误

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py in kdensityfft(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)
    450     try:
--> 451         bw = float(bw)
    452     except:

ValueError: could not convert string to float: 'scott'

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-25-0cd48ab0d803> in <module>
      2 bad_credit = train_data[train_data["Credit_History"] == 0]
      3 bad_credit["Education"] = bad_credit["Education"].map("Graduate":1,"Not Graduate":0)
----> 4 sns.pairplot(bad_credit,vars=["ApplicantIncome","Education","LoanAmount"],hue="Loan_Status")

~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
   2119             diag_kws.setdefault("shade", True)
   2120             diag_kws["legend"] = False
-> 2121             grid.map_diag(kdeplot, **diag_kws)
   2122 
   2123     # Maybe plot on the off-diagonals

~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py in map_diag(self, func, **kwargs)
   1488                     data_k = utils.remove_na(data_k)
   1489 
-> 1490                 func(data_k, label=label_k, color=color, **kwargs)
   1491 
   1492             self._clean_axis(ax)

~/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py in kdeplot(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)
    703         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,
    704                                  gridsize, cut, clip, legend, ax,
--> 705                                  cumulative=cumulative, **kwargs)
    706 
    707     return ax

~/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py in _univariate_kdeplot(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)
    293         x, y = _statsmodels_univariate_kde(data, kernel, bw,
    294                                            gridsize, cut, clip,
--> 295                                            cumulative=cumulative)
    296     else:
    297         # Fall back to scipy if missing statsmodels

~/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py in _statsmodels_univariate_kde(data, kernel, bw, gridsize, cut, clip, cumulative)
    365     fft = kernel == "gau"
    366     kde = smnp.KDEUnivariate(data)
--> 367     kde.fit(kernel, bw, fft, gridsize=gridsize, cut=cut, clip=clip)
    368     if cumulative:
    369         grid, y = kde.support, kde.cdf

~/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py in fit(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)
    138             density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,
    139                     adjust=adjust, weights=weights, gridsize=gridsize,
--> 140                     clip=clip, cut=cut)
    141         else:
    142             density, grid, bw = kdensity(endog, kernel=kernel, bw=bw,

~/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py in kdensityfft(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)
    451         bw = float(bw)
    452     except:
--> 453         bw = bandwidths.select_bandwidth(X, bw, kern) # will cross-val fit this pattern?
    454     bw *= adjust
    455 

~/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/bandwidths.py in select_bandwidth(x, bw, kernel)
    172         # eventually this can fall back on another selection criterion.
    173         err = "Selected KDE bandwidth is 0. Cannot estiamte density."
--> 174         raise RuntimeError(err)
    175     else:
    176         return bandwidth

RuntimeError: Selected KDE bandwidth is 0. Cannot estiamte density.

【问题讨论】：

大卫斯科特的homepage 告诉你更多。

@JohanC 谢谢，不认识他，但他的简历很不错

【参考方案1】：

scott 是在绘制核密度估计 (KDE) 时选择带宽的方法的名称。它以 DW Scott (1) 的名字命名。

我无法查看您的数据，但我的猜测是，对于某个色相级别的一对变量中的一个很奇怪，这会阻止 seaborn 计算正确的带宽。

您可以使用diag_kws 将参数传递给sns.kdeplot()，pairplot 使用它来绘制对角线上的单变量分布。

例如：

sns.pairplot(..., diag_kws='bw':'silverman')

会强制sns.kdeplot() 使用“silverman”方法来选择带宽，在您的情况下这可能比 Scott 方法更好吗？

(1) Scott，“多元密度估计：理论、实践和可视化”，John Wiley & Sons，纽约，奇斯特，1992 年。

编辑

要尝试找出罪魁祸首，您必须使用PairGrid 而不是pairplot()。 PairGrid 允许您使用自定义函数来绘制对角线。如果您在该函数中包含打印语句，您可以看到将传递给 sns.kdeplot() 的数据是什么。执行应该在数据“不正确”的地方停止，您也许可以弄清楚如何处理。

例如：

def test_func(*data, **kwargs):
    print("data received:", data)
    print("hue name + other params:", kwargs)
    sns.kdeplot(*data, **kwargs)

iris = sns.load_dataset('iris')
g = sns.PairGrid(iris, hue="species")
g = g.map_diag(test_func)

对于每个变量（列）和每个级别，您都会得到如下所示的输出：

data received: (array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
       4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
       5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
       5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. ]),)
hue name + other params: 'label': 'setosa', 'color': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765)
data received: (array([7. , 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. ,
       6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6,
       6.8, 6.7, 6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6,
       5.5, 5.5, 6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7]),)
hue name + other params: 'label': 'versicolor', 'color': (1.0, 0.4980392156862745, 0.054901960784313725)
(...)

【讨论】：

感谢您的解释！我尝试按照您的建议传递“silverman”，但得到了相同的 ValueError，但现在用“silverman”代替了 scott。我现在用“1.0”替换了它，令我惊讶的是，这确实产生了一些有利的结果，但我不确定为什么。有没有办法可以追踪/找到导致无法计算带宽的对？

我添加了一种方法来尝试追踪问题发生的位置