ModuleNotFoundError：沒有名為“sklearn.neighbors._dist_metrics”的模塊

我訓練了一個核密度模型，然后使用 joblib 轉儲模型。然后我在呼叫相同的 .pkl 檔案時創建了一個函式。它在我的本地機器上運行良好，但是當我將它部署在云機器上并用它創建一個 docker 映像時，我得到以下錯誤之一：

ModuleNotFoundError: No module named 'sklearn.neighbors._dist_metrics' 

要么

ModuleNotFoundError: No module named 'sklearn.neighbors._kde'

什么可能導致此問題以及如何解決？

初始培訓的代碼是：

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os
%matplotlib inline 
import seaborn as sns
import csv 
from sklearn.neighbors import KernelDensity
import joblib


arr = df_trim.values
kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(arr)
joblib.dump(kde, 'kde.pkl')

 # This is the array that is used for training 
 # array([[3.5, 3.5, 3.5, 3.5],
       [4. , 4. , 3.5, 4. ],
       [3.5, 3. , 2.5, 3. ],
       ...,
       [2.5, 2.5, 2. , 2. ],
       [1.5, 1.5, 2. , 2.5],
       [3. , 3. , 2.5, 3. ]])

以下代碼用于呼叫此已保存模型的函式：

from itertools import combinations
import joblib


filename = 'kde.pkl'  # filename for the model's pickle file.
model = joblib.load(filename) # loading the pre trained model using joblib.



def rSubset(arr, r):
  
    # return list of all subsets of length r
    # to deal with duplicate subsets use 
    # set(list(combinations(arr, r)))

    return list(combinations(arr, r))



def datapred(*args):
    
    no_args = len(args)
    args = list(args)
        
    pred_data = []
    model_score = []
    arr = [3.5 , 4 ,  3,  2.5,  1.5,  2,   1,   0.5,  0.25]
    n = (4 - no_args)
    comb_arr = (rSubset(arr, n))

    if(no_args==1):

        gpa1 = args[0]

        for i in range(1,len(comb_arr)):
                                      
            var = comb_arr[i]
            var = list(var)
            var = [gpa1] var
            output = model.score_samples([var])
            model_score.append(output)
            pred_data.append(var)
            position = model_score.index(max(model_score))
            result = pred_data[position]
            return(result)

    elif(no_args==2):

        gpa1 = args[0]
        gpa2 = args[1] 

        for i in range(1,len(comb_arr)):
                                      
            var = comb_arr[i]
            var = list(var)
            var = [gpa1] [gpa2] var

            output = model.score_samples([var])
            model_score.append(output)
            pred_data.append(var)
            position = model_score.index(max(model_score))
            result = pred_data[position]
            return(result)

    elif(no_args==3):

        gpa1 = args[0]
        gpa2 = args[1]
        gpa3 = args[2] 

        for i in range(1,len(comb_arr)):
                                      
            var = comb_arr[i]
            var = list(var)
            var = [gpa1] [gpa2] [gpa3] var

            output = model.score_samples([var])
            model_score.append(output)
            pred_data.append(var)
            position = model_score.index(max(model_score))
            result = pred_data[position]
            return(result)       



            

以下是 docker 鏡像的 requirements.txt 檔案：

logger
Flask==1.1.2
Flask-RESTful==0.3.8
joblib==0.15.1
MarkupSafe==1.1.1
pandas==1.0.3
scikit-learn==0.19
sklearn >= 0.0
threadpoolctl==2.0.0
gunicorn==20.0.4
xgboost ==1.5.2
scipy >= 0.0

uj5u.com熱心網友回復：

該scikit-learn庫是您的云計算機上的不同版本。

具體來說，sklearn.neighbors._dist_metrics在 version 周圍洗掉了1.0.2。也許您的 docker 容器實際上并沒有正確使用您的 requirements.txt。

以下是不同版本的示例：

這個不會報錯

>>> import sklearn
>>> sklearn.__version__
'0.24.2'
>>> from sklearn.neighbors import _dist_metrics

這個拋出一個錯誤

>>> import sklearn
>>> sklearn.__version__
'1.0.2'
>>> from sklearn.neighbors import _dist_metrics
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name '_dist_metrics' from 'sklearn.neighbors'

標籤：Python 码头工人 机器学习 scikit-学习

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