mac OS 中 anaconda 环境中 juypter 笔记本中的 pycaret 库的导入问题

问：

在 Mac OS 10.13.6 python 3.8 环境中的 Anaconda Navigator 2.5.1 中，在本地安装的 jupyter notebook 中导入 pycaret 库时，遇到了以下部分中显示的问题。导入时收到错误堆栈，其他类也会遇到相同的错误（即 和 ） 尽管 pycaret 的安装是成功的。from pycaret.classification import *from pycaret.classification import *from pycaret.regression import validate_params

怀疑 pycaret 库问题，尝试 ，但没有锁定解决问题。此外，尝试降级到 pycaret 2.3.3，就像在 pycaret 版本 2.3.3 中一样，prefer_skip_nested_validation 参数是可选的。不幸的是，由于许多依赖失败，降级无法成功，这可能是因为 27 个月前发布的版本，并且在依赖包中进行了许多更新。任何解决进口问题的建议将不胜感激。!pip install pycaret --upgrade!pip uninstall pycaret!pip install pycaret

> --------------------------------------------------------------------------- TypeError                                 Traceback (most recent call
> last) Cell In[47], line 1
> ----> 1 from pycaret.classification import *
> 
> File
> //anaconda3/lib/python3.8/site-packages/pycaret/classification/__init__.py:1
> ----> 1 from pycaret.classification.functional import (
>       2     add_metric,
>       3     automl,
>       4     blend_models,
>       5     calibrate_model,
>       6     check_drift,
>       7     check_fairness,
>       8     compare_models,
>       9     convert_model,
>      10     create_api,
>      11     create_app,
>      12     create_docker,
>      13     create_model,
>      14     dashboard,
>      15     deploy_model,
>      16     ensemble_model,
>      17     evaluate_model,
>      18     finalize_model,
>      19     get_allowed_engines,
>      20     get_config,
>      21     get_current_experiment,
>      22     get_engine,
>      23     get_leaderboard,
>      24     get_logs,
>      25     get_metrics,
>      26     interpret_model,
>      27     load_experiment,
>      28     load_model,
>      29     models,
>      30     optimize_threshold,
>      31     plot_model,
>      32     predict_model,
>      33     pull,
>      34     remove_metric,
>      35     save_experiment,
>      36     save_model,
>      37     set_config,
>      38     set_current_experiment,
>      39     setup,
>      40     stack_models,
>      41     tune_model,
>      42 )
>      43 from pycaret.classification.oop import ClassificationExperiment
>      45 __all__ = [
>      46     "ClassificationExperiment",
>      47     "setup",    (...)
>      86     "check_drift",
>      87 ]
> 
> File
> //anaconda3/lib/python3.8/site-packages/pycaret/classification/functional.py:8
>       5 import pandas as pd
>       6 from joblib.memory import Memory
> ----> 8 from pycaret.classification.oop import ClassificationExperiment
>       9 from pycaret.internal.parallel.parallel_backend import ParallelBackend
>      10 from pycaret.loggers.base_logger import BaseLogger
> 
> File
> //anaconda3/lib/python3.8/site-packages/pycaret/classification/oop.py:16
>      13 from joblib.memory import Memory
>      14 from scipy.optimize import shgo
> ---> 16 from pycaret.containers.metrics.classification import get_all_metric_containers
>      17 from pycaret.containers.models.classification import (
>      18     ALL_ALLOWED_ENGINES,
>      19     get_all_model_containers,
>      20     get_container_default_engines,
>      21 )
>      22 from pycaret.internal.display import CommonDisplay
> 
> File
> //anaconda3/lib/python3.8/site-packages/pycaret/containers/metrics/classification.py:13
>       1 # Module: containers.metrics.classification
>       2 # Author: Antoni Baum (Yard1) <[email protected]>
>       3 # License: MIT    (...)
>       8 # `ClassificationMetricContainer` as a base, set all of the required parameters in the `__init__` and then call `super().__init__`
>       9 # to complete the process. Refer to the existing classes for examples.
>      11 from typing import Any, Dict, Optional, Union
> ---> 13 from sklearn import metrics
>      14 from sklearn.metrics._scorer import _BaseScorer
>      16 import pycaret.containers.base_container
> 
> File
> //anaconda3/lib/python3.8/site-packages/sklearn/metrics/__init__.py:20
>      17 from ._ranking import roc_curve
>      18 from ._ranking import top_k_accuracy_score
> ---> 20 from ._classification import accuracy_score
>      21 from ._classification import balanced_accuracy_score
>      22 from ._classification import class_likelihood_ratios
> 
> File
> //anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:146
>     142     else:
>     143         return sample_score.sum()
> --> 146 @validate_params(
>     147     {
>     148         "y_true": ["array-like", "sparse matrix"],
>     149         "y_pred": ["array-like", "sparse matrix"],
>     150         "normalize": ["boolean"],
>     151         "sample_weight": ["array-like", None],
>     152     }
>     153 )
>     154 def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
>     155     """Accuracy classification score.
>     156 
>     157     In multilabel classification, this function computes subset accuracy:    (...)
>     217     0.5
>     218     """
>     220     # Compute accuracy for each possible representation
> 
> TypeError: validate_params() missing 1 required keyword-only argument:
> 'prefer_skip_nested_validation'