Error Handling

Custom Exception Classes

binlearn provides detailed error messages with actionable suggestions to help users quickly identify and resolve issues.

Error Categories

BinningError: Base exception for all binning-related errors
ConfigurationError: Raised when parameters are invalid or incompatible
FittingError: Raised when the fitting process encounters problems
InvalidDataError: Raised when input data doesn’t meet requirements
DataQualityWarning: Warning for data quality issues that don’t prevent operation

Error Message Features

All binlearn errors include:

Clear Description: What went wrong and why
Actionable Suggestions: Specific steps to fix the problem
Context Information: Relevant data about the error situation
Examples: When helpful, examples of correct usage

Example Error Messages

# ConfigurationError example
try:
    binner = EqualWidthBinning(n_bins=0)
except ConfigurationError as e:
    print(e)
    # Output:
    # n_bins must be a positive integer, got 0
    # Suggestions:
    # - Set n_bins to a positive integer (e.g., n_bins=5)
    # - Consider using 'auto' for automatic bin number selection

# InvalidDataError example
try:
    binner.fit(data_with_all_nans)
except InvalidDataError as e:
    print(e)
    # Output:
    # All values are NaN in column 'feature1'
    # Suggestions:
    # - Remove columns with all NaN values
    # - Impute missing values before binning
    # - Check data loading process

Best Practices

When handling binlearn errors:

Read the full error message - Contains specific guidance
Check the suggestions - Usually provide the exact fix needed
Validate your data first - Many errors stem from data quality issues
Use appropriate parameters - Error messages guide parameter selection
Test with small datasets - Easier to debug issues with smaller data