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

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 (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'

我已经查看了原始数据，但我在任何地方都找不到“scott”，所以我的问题是这是从哪里来的，我如何修复它

我还得到一个运行时错误“RuntimeError:Selected 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，还是完全是一个单独的问题。如果有人能对这件事有所了解，我将不胜感激

数据集

我正在使用找到的贷款预测数据集here。这些属性如下所示：

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.

1条回答

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1楼 · 发布于 2024-05-29 05:49:13

scott是绘制内核密度估计（KDE）时选择带宽的方法的名称。它以DW Scott（1）命名

我无法查看您的数据，但我的猜测是，对于某个色调级别，其中一对变量有点奇怪，它阻止seaborn计算正确的波段

您可以使用diag_kws将参数传递给^{}，pairplot使用该参数在对角线上绘制单变量分布

例如：

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

是否会强制sns.kdeplot()使用“silverman”方法来选择bandwith，在您的案例中，它可能比Scott方法更有效

（1）D.W.Scott，“多元密度估计：理论、实践和可视化”，John Wiley和；儿子，纽约，奇切斯特，1992年

编辑

要想找出罪魁祸首，你必须使用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print语句，则可以查看将传递给sns.kdeplot（）的数据。执行应该在数据“不正确”的地方停止，您可能会知道如何处理这些数据

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，您将获得如下输出：

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)}
(...)