我正在使用shap来确定mlp_classifier.
（注意 - 我使用的是虚拟数据和模型，因为我的正常数据和模型是专有的）。
import shap, pandas as pd, numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn import datasets
from sklearn.model_selection import train_test_split
iris = datasets.load_iris()
X = iris.data
y = iris.target
data = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
columns= iris['feature_names'] + ['target'])
label = data['target']
data.drop('target', axis=1, inplace=True)
X_train, X_test, y_train, y_test = train_test_split(data, label,random_state=np.random.randint(1,10), test_size=0.3)
mlp = MLPClassifier(max_iter=150).fit(X_train, y_train)
mlp.score(X_test, y_test)
explainer = shap.KernelExplainer(mlp.predict_proba, shap.kmeans(X_train, 5))
shap_values = explainer.shap_values(X_test)
# First plot
shap.summary_plot(shap_values[1], feature_names = X_test.columns, plot_type='bar')
# Second, error, empty plot
import matplotlib.pyplot as plt; plt.rcdefaults()
y_pos = np.arange(len(X_test.columns))
plt.bar(y_pos, shap_values[1], align='center', alpha=0.5)
plt.xticks(y_pos, X_test.columns)
plt.ylabel('SHAP Importance')
plt.title('MLP Feature Importances')
plt.show()
shap.summary_plot按照本指南使用，我得到如下图：
在我的实际数据集中，我有关于10,000特征。我要做的是从该列表中获取最重要的功能，并按照本指南n使用 绘制它们。但是，我收到一个错误和一个空白图表：matplotlib
完整追溯：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-42-ba03083152ca> in <module>
1 import matplotlib.pyplot as plt; plt.rcdefaults()
2 y_pos = np.arange(len(X_test.columns))
----> 3 plt.bar(y_pos, shap_values[1], align='center', alpha=0.5)
4 plt.xticks(y_pos, X_test.columns)
5 plt.ylabel('SHAP Importance')
c:\python367-64\lib\site-packages\matplotlib\pyplot.py in bar(x, height, width, bottom, align, data, **kwargs)
2407 return gca().bar(
2408 x, height, width=width, bottom=bottom, align=align,
-> 2409 **({"data": data} if data is not None else {}), **kwargs)
2410
2411
c:\python367-64\lib\site-packages\matplotlib\__init__.py in inner(ax, data, *args, **kwargs)
1563 def inner(ax, *args, data=None, **kwargs):
1564 if data is None:
-> 1565 return func(ax, *map(sanitize_sequence, args), **kwargs)
1566
1567 bound = new_sig.bind(ax, *args, **kwargs)
c:\python367-64\lib\site-packages\matplotlib\axes\_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
2393 edgecolor=e,
2394 linewidth=lw,
-> 2395 label='_nolegend_',
2396 )
2397 r.update(kwargs)
c:\python367-64\lib\site-packages\matplotlib\patches.py in __init__(self, xy, width, height, angle, **kwargs)
725 """
726
--> 727 Patch.__init__(self, **kwargs)
728
729 self._x0 = xy[0]
c:\python367-64\lib\site-packages\matplotlib\patches.py in __init__(self, edgecolor, facecolor, color, linewidth, linestyle, antialiased, hatch, fill, capstyle, joinstyle, **kwargs)
87 self.set_fill(fill)
88 self.set_linestyle(linestyle)
---> 89 self.set_linewidth(linewidth)
90 self.set_antialiased(antialiased)
91 self.set_hatch(hatch)
c:\python367-64\lib\site-packages\matplotlib\patches.py in set_linewidth(self, w)
393 w = mpl.rcParams['axes.linewidth']
394
--> 395 self._linewidth = float(w)
396 # scale the dash pattern by the linewidth
397 offset, ls = self._us_dashes
TypeError: only size-1 arrays can be converted to Python scalars
我无法将shap_valuein映射shap_values[1]到X_test.columns. 我怎样才能正确地占据顶部n并绘制matplotlib？
