谁是斯科特？
问题
尝试使用seaborn将“贷款预测”数据集中的“教育”属性添加到对图中时，出现以下错误:
我已经查看了原始数据，但是在任何地方都找不到“scott”，所以我的问题是它来自何处，如何解决？
我也收到运行时错误“RuntimeError:选定的KDE带宽为0。无法估计密度。”。我不确定这是由第一个错误引起的，还是完全是另一个问题。如果有人可以对此发出任何光芒，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
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.
最佳答案
scott是在绘制内核密度估计(KDE)时选择带宽的方法的名称。它以DW Scott(1)的名字命名。
我看不到您的数据，但我的猜测是，对于某个色调级别，一对变量之一对某些变量很奇怪，从而阻止seaborn计算正确的带宽。
您可以使用diag_kws将参数传递给 sns.kdeplot() ，pairplot使用它来在对角线上绘制单变量分布。
例如:
sns.pairplot(..., diag_kws={'bw':'silverman'})
会迫使
sns.kdeplot()使用“silverman”方法选择带宽，在您的情况下，该方法可能比Scott方法更好？(1)D.W.斯科特，“多元密度估计:理论，实践和可视化”，约翰·威利父子出版社，纽约，切斯特，1992年。
编辑
要尝试查明罪魁祸首，您必须使用
PairGrid而不是pairplot()。 PairGrid允许您使用自定义函数绘制对角线。如果在该函数中包含打印语句，则可以看到将传递到sns.kdeplot()的数据是什么。执行应该在数据“不正确”的地方停止，您也许可以弄清楚该怎么做。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)和每个水平，您将获得如下所示的输出:
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)}
(...)