Use more `assert` for checking the variable properties. #31920

lionelkusch · 2025-08-11T08:53:39Z

lionelkusch
Aug 11, 2025

By looking at the code, I see that most of the checks of the variable are based on the next schema:

 if .....
         raise ValueError( ... )

For example,

scikit-learn/sklearn/neighbors/_nca.py

Lines 248 to 292 in 24844a0

    
           if self.n_components is not None and self.n_components > X.shape[1]: 
        
               raise ValueError( 
        
                   "The preferred dimensionality of the " 
        
                   f"projected space `n_components` ({self.n_components}) cannot " 
        
                   "be greater than the given data " 
        
                   f"dimensionality ({X.shape[1]})!" 
        
               ) 
        
           # If warm_start is enabled, check that the inputs are consistent 
        
           if ( 
        
               self.warm_start 
        
               and hasattr(self, "components_") 
        
               and self.components_.shape[1] != X.shape[1] 
        
           ): 
        
               raise ValueError( 
        
                   f"The new inputs dimensionality ({X.shape[1]}) does not " 
        
                   "match the input dimensionality of the " 
        
                   f"previously learned transformation ({self.components_.shape[1]})." 
        
               ) 
        
           # Check how the linear transformation should be initialized 
        
           init = self.init 
        
           if isinstance(init, np.ndarray): 
        
               init = check_array(init) 
        
               # Assert that init.shape[1] = X.shape[1] 
        
               if init.shape[1] != X.shape[1]: 
        
                   raise ValueError( 
        
                       f"The input dimensionality ({init.shape[1]}) of the given " 
        
                       "linear transformation `init` must match the " 
        
                       f"dimensionality of the given inputs `X` ({X.shape[1]})." 
        
                   ) 
        
               # Assert that init.shape[0] <= init.shape[1] 
        
               if init.shape[0] > init.shape[1]: 
        
                   raise ValueError( 
        
                       f"The output dimensionality ({init.shape[0]}) of the given " 
        
                       "linear transformation `init` cannot be " 
        
                       f"greater than its input dimensionality ({init.shape[1]})." 
        
                   ) 
        
               # Assert that self.n_components = init.shape[0] 
        
               if self.n_components is not None and self.n_components != init.shape[0]: 
        
                   raise ValueError( 
        
                       "The preferred dimensionality of the " 
        
                       f"projected space `n_components` ({self.n_components}) does" 
        
                       " not match the output dimensionality of " 
        
                       "the given linear transformation " 
        
                       f"`init` ({init.shape[0]})!" 
        
                   )

I am wondering why the assertion is not used in this case.
The advantage of assertion is that there can be deactivate it by option "-O" of python and it can increase the speed of execution of a program, especially in the case of a pipeline.

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Use more `assert` for checking the variable properties. #31920

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Use more assert for checking the variable properties. #31920

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lionelkusch Aug 11, 2025

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Use more `assert` for checking the variable properties. #31920

lionelkusch
Aug 11, 2025