模块未找到错误：没有名为'sklearn.neighbors._dist_metrics'的模块

Question

我训练了一个内核密度模型，然后使用joblib转储该模型。然后，我在调用相同的.pkl文件时生成了一个函数。它在我的本地机器上运行良好，但当我将它部署在云机器上并用它创建docker映像时，我会出现以下错误之一：

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

或

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

是什么原因导致了这个问题，以及如何解决它？

初始培训的代码是：

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. ]])

以下代码用于调用此保存模型的函数：

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映像的requirements.txt文件：

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

Answer 1

scikit-learn库是云计算机上的另一个版本。

具体地说，sklearn.neighbors._dist_metrics是在版本1.0.2周围删除的。也许您的docker容器实际上没有正确使用requirements.txt。

以下是不同版本的示例：

这个没有抛出错误

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

这个抛出错误

>>> 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'