ModuleNotFoundError:没有名为“sklearn.tree.tree"的模块 [英] ModuleNotFoundError: No module named 'sklearn.tree.tree'

问题描述

我正在尝试学习如何使用 Flask 创建机器学习 API，但是，遵循 本教程，输入命令python app.py时出现如下错误:

回溯(最近一次调用最后一次):文件C:\Users\Breno\Desktop\flask-api\app.py"，第 24 行，在 <module> 中.模型 = p.load(open(modelfile, 'rb'))ModuleNotFoundError:没有名为sklearn.tree.tree"的模块

我的代码:

from flask import Flask, request, redirect, url_for, flash, jsonify将 numpy 导入为 np将泡菜作为 p 导入将熊猫导入为 pd导入json#from sklearn.tree import DecisionTreeClassifierapp = Flask(__name__)@app.route('/api/', methods=['POST'])def makecalc():j_data = request.get_json()预测 = np.array2string(model.predict(j_data))返回 jsonify(预测)如果 __name__ == '__main__':模型文件 = '模型/final_prediction.pickle'模型 = p.load(open(modelfile, 'rb'))app.run(debug=True,host='0.0.0.0')

有人可以帮我吗?

解决方案

Pickles 不一定跨 scikit-learn 版本兼容，因此这种行为是预期的(并且不支持用例).有关更多详细信息，请参阅 https://scikit-learn.org/dev/modules/model_persistence.html#model-persistence.用 joblib 替换 pickle.举例:

<预><代码>>>>从 sklearn 导入 svm>>>从 sklearn 导入数据集>>>clf = svm.SVC()>>>X, y= datasets.load_iris(return_X_y=True)>>>clf.fit(X, y)SVC()>>>从 joblib 导入转储，加载>>>转储(clf，打开('文件名.joblib'，'wb'))>>>clf2 = load(open('filename.joblib','rb'))>>>clf2.predict(X[0:1])数组([0])>>>y[0]0

I'm trying to learn how to create a machine learning API with Flask, however, following this tutorial, the following error appears when I type the command python app.py:

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'

My code:

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')

Could someone help me please?

解决方案

Pickles are not necessarily compatible across scikit-learn versions so this behavior is expected (and the use case is not supported). For more details, see https://scikit-learn.org/dev/modules/model_persistence.html#model-persistence. Replace pickle by joblib. by example :

>>> 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

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