ModuleNotFoundError:没有名为“sklearn.tree.tree”的模块

Question

我正在尝试学习如何使用本教程创建机器学习API，但是，在下面的python app.py中，当我键入命令python app.py时会出现以下错误

Traceback (most recent call last):   
File "C:\Users\Breno\Desktop\flask-api\app.py", line 24, in <module>
        model = p.load(open(modelfile, 'rb'))
ModuleNotFoundError: No module named 'sklearn.tree.tree'

我的代码：

from flask import Flask, request, redirect, url_for, flash, jsonify
import numpy as np
import pickle as p
import pandas as pd
import json
#from sklearn.tree import DecisionTreeClassifier

app = Flask(__name__)


@app.route('/api/', methods=['POST'])
def makecalc():
    j_data = request.get_json()

    prediction = np.array2string(model.predict(j_data))

    return jsonify(prediction)


if __name__ == '__main__':

    modelfile = 'models/final_prediction.pickle'    

    model = p.load(open(modelfile, 'rb'))

    app.run(debug=True,host='0.0.0.0')

有人能帮帮我吗？

Answer 1

Pickles不一定兼容所有的scikit-学习版本，因此这种行为是预期的(并且不支持用例)。有关更多细节，请参见持久化。用果酱代替泡菜。举个例子：

>>> from sklearn import svm
>>> from sklearn import datasets
>>> clf = svm.SVC()
>>> X, y= datasets.load_iris(return_X_y=True)
>>> clf.fit(X, y)
SVC()

>>> from joblib import dump, load
>>> dump(clf, open('filename.joblib','wb'))
>>> clf2 = load(open('filename.joblib','rb'))
>>> clf2.predict(X[0:1])
array([0])
>>> y[0]
0

Answer 2

对于任何遇到这个问题的人(可能是很久以前编写的代码)，sklearn.tree.tree现在处于sklearn.tree之下(与v0.24一样)。从导入错误警告中可以看出这一点：

from sklearn.tree.tree import BaseDecisionTree
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.tree.tree module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.tree. Anything that cannot be imported from sklearn.tree is now part of the private API.
  warnings.warn(message, FutureWarning)

相反，请使用：

from sklearn.tree import BaseDecisionTree

Answer 3

问题在于sklearn的版本。模块sklearn.tree.tree将从0.24版本中删除。很可能，您的模型是用旧版本生成的。试着安装一个旧版本的sklearn：

pip uninstall scikit-learn
pip install scikit-learn==0.20.4