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我正在尝试为分类 categorical_features=['sex','cp','fbs','retecg','exang','thal']创建一个模型 我在 pandas 数据框中有列，例如

df.columns

Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],
      dtype='object')

当我在 ( from pycaret.classification import * )

predict_model(best_model, data=df.drop('age',axis=1).tail())#not working

我收到类似的错误

KeyError：“['age'] 不在索引中”

但是正如您在 df.column 的索引中看到的那样，我们有“年龄”，我不知道为什么除了“目标”列之外的所有列都显示错误，我可以在轴 = 1 中删除该列

predict_model(best_model, data=df.drop('target',axis=1).tail())#working

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_63/493668063.py in <module>
----> 1 predict_model(best_model, data=df.drop('age',axis=1).tail())  #while creating predict_model

/opt/conda/lib/python3.7/site-packages/pycaret/classification.py in predict_model(estimator, data, probability_threshold, encoded_labels, raw_score, round, verbose)
   1952         round=round,
   1953         verbose=verbose,
-> 1954         ml_usecase=MLUsecase.CLASSIFICATION,
   1955     )
   1956 

/opt/conda/lib/python3.7/site-packages/pycaret/internal/tabular.py in predict_model(estimator, data, probability_threshold, encoded_labels, raw_score, round, verbose, ml_usecase, display)
   8665     # prediction starts here
   8666 
-> 8667     pred = np.nan_to_num(estimator.predict(X_test_))
   8668 
   8669     try:

/opt/conda/lib/python3.7/site-packages/sklearn/utils/metaestimators.py in <lambda>(*args, **kwargs)
    117 
    118         # lambda, but not partial, allows help() to work with update_wrapper
--> 119         out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
    120         # update the docstring of the returned function
    121         update_wrapper(out, self.fn)

/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in predict(self, X, **predict_params)
    405         Xt = X
    406         for _, name, transform in self._iter(with_final=False):
--> 407             Xt = transform.transform(Xt)
    408         return self.steps[-1][-1].predict(Xt, **predict_params)
    409 

/opt/conda/lib/python3.7/site-packages/pycaret/internal/preprocess.py in transform(self, dataset, y)
    362         # drop any columns that were asked to drop
    363         data.drop(columns=self.features_todrop, errors="ignore", inplace=True)
--> 364         data = data[self.final_training_columns]
    365 
    366         # also make sure that all the column names are string

/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
   3459             if is_iterator(key):
   3460                 key = list(key)
-> 3461             indexer = self.loc._get_listlike_indexer(key, axis=1)[1]
   3462 
   3463         # take() does not accept boolean indexers

/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis)
   1312             keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
   1313 
-> 1314         self._validate_read_indexer(keyarr, indexer, axis)
   1315 
   1316         if needs_i8_conversion(ax.dtype) or isinstance(

/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis)
   1375 
   1376             not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
-> 1377             raise KeyError(f"{not_found} not in index")
   1378 
   1379 

KeyError: "['age'] not in index"
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0 回答 0