资源=缓存.get（item）TypeError：不易损坏的类型（我得到的错误是在运行leme解释器之后）

Question

代码如下：

# coding=utf-8 from __future__ import print_function

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import sklearn
import sklearn.datasets
import sklearn.ensemble
import numpy as np
import lime
import lime.lime_tabular

class Randomforest:

    def __init__(self):
        self.trained_model = None
        self.clf = None
        pass

    def split_dataset(self, dataset, train_percentage, feature_headers, target_header):
        # Split dataset into train and test dataset
        self.train_x, self.test_x, self.train_y, self.test_y = train_test_split(dataset[feature_headers], dataset[target_header],
                                                            train_size=train_percentage)
        return self.train_x, self.test_x, self.train_y, self.test_y

    def random_forest_classifier(self, features, target):
        self.clf = RandomForestClassifier()
        self.clf.fit(features, target)
        return self.clf

    def predictProba(self, input):
        return self.clf.predict_proba(input)

    def predict_proba(self, input):
        return self.predictProba(input)

    rf = Randomforest()

    Headers = ["vectorName", "abmessungen_Lange", "starrflugler", "tragflachen", "triebwerke", "rumpf", "leitwerk", "drehflugler",
                "drehflugler_Rumpf_Cockpit", "doppeldecker", "tragflachen_Stellung_Gerade","hochDecker","triebwerke_triebwerksart",
                "rumpf_Rumpfformen","drehflugler_Rotor","drehflugler_Triebwerk","drehflugler_Rumpf","drehflugler_Heckausleger",
                "drehflugler_Triebwerk_Lufteinlass","drehflugler_Triebwerk_Luftauslass", "result"]

    dataset = pd.read_csv("filename.csv")

    train_x, test_x, train_y, test_y = rf.split_dataset(dataset, 0.7, Headers[1:-1], Headers[-1])

    trained_model = rf.random_forest_classifier(train_x, train_y)

    predictions = trained_model.predict(test_x)

    feature_names = Headers[1:-1]

    class_names = ['1', '0']

    explainer = lime.lime_tabular.LimeTabularExplainer(train_x, feature_names= feature_names, class_names=class_names,
                                                   categorical_features= None, categorical_names=None, discretize_continuous=True,
                                                   kernel_width=3)

误差如下：

^{pr2}$

我在运行如上面代码所示的leme解释器之后遇到这个错误。我不确定这是熊猫包错误还是石灰包错误。我尝试了很多方法来调试它，比如把一个数据帧而不是一个列表发送到leme explainer函数中等等，但是没有一个有效。如果有人能尽早告诉我一个问题，那就太好了。谢谢。在

Answer 1

问题是因为你正在传递一个pandas数据帧（我想）我也有同样的问题。在

最简单的方法是用 train_x.values

希望这次比赛
干杯