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change_model

This tool allows you to modify components within an existing MedModel without the need to retrain the entire model. It's an efficient way to make targeted adjustments to a model's pipeline, especially for production, testing, or debugging purposes. This tool, is part of the AllTools compilation

Common use cases include:

  • Modifying Production Settings: nable special flags in components like RepBasicOutlierCleaner to store outlier information. This process is typically slower and more memory-intensive, so you might only want to activate it in production.
  • Controlling Pre-processing: Temporarily disable or adjust pre-processing steps like normalization or imputation for specific testing or data matrix creation.
  • Adjusting ButWhy Explanations: Change arguments for the explainability component, such as the number of contributors to output, without re-learning the model.

change_modelsupports both interactive and non-interactive command-line interfaces for controlling and modifying model objects. This documentation focuses on the non-interactive use case, which is most common for scripting and automation.

Arguments

There are two primary methods for using change_model in non-interactive mode: a simple one-liner for a single change or a JSON file for multiple, more complex changes.

Method 1: Single Change via Command Line

For a single, quick modification, use the --search_object_name and --object_command flags.

Example: Disabling Outlier Attribute Storage

This command finds all RepBasicOutlierCleaner objects and clears the attributes used for storing outlier information, effectively disabling that feature to improve performance.

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change_model --model_path <model_path> --output_path <output_model_path> --interactive_change 0 --search_object_name RepBasicOutlierCleaner --object_command "nrem_attr=;nrem_suff=;ntrim_attr=;ntrim_suff="
  • --model_path: Path to your existing model file.
  • --output_path: Path where the modified model will be saved.
  • --interactive_change: 0 Activates non-interactive mode. Default is 1
  • --search_object_name: The name of the object to find and modify. In this case, RepBasicOutlierCleaner.
  • --object_command: A string of semicolon-separated key=value pairs to set new parameters. To delete an object, use "DELETE".

Method 2: Multiple Changes via JSON File

For complex or multiple modifications, you can provide a JSON file containing all your change instructions using the --change_model_file argument. This file will be used to initiate a ChangeModelInfo object, which then executes the requested changes.

JSON Change Block Structure

The JSON file contains an array of "change blocks," where each block defines a specific modification.

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{ 
  "changes": [
       // Define your change blocks here
  ]
}

Each block has the following parameters:

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{
    "change_name": "<name_for_logging>",
    "object_type_name": "<The object we seek to change>",
    "json_query_whitelist": [ "<string list of regex items>" ],
    "json_query_blacklist": [ "<string list of regex items>" ],
    "change_command": "<command string>",
    "verbose_level": "<verbosity level for this action (optional)>"
}
  • json_query_whitelist: A list of regular expressions. A component will only be selected if its string representation matches all regex strings in this array (AND condition).
  • json_query_blacklist: A list of regular expressions. A component will be filtered out if its string representation matches any regex string in this array.

Warning!: Be extremely cautious when modifying a model. Only change things that do not require re-training. For instance, do not remove features that are essential for the classifier to function correctly.

JSON Examples

Example 1: Remove Normalizers and Imputers

This example shows how to selectively remove specific normalizers and all imputers. The first block uses json_query_whitelist to target only the FeatureNormalizer objects for Age and Gender while leaving others untouched. The second block removes all FeatureImputer objects.

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{ 
  "changes": [
       {
            "change_name":"Remove Normalizers",
            "object_type_name":"FeatureNormalizer",
            "json_query_whitelist": [ "Age|Gender" ],
            "json_query_blacklist": [],
            "change_command": "DELETE",
            "verbose_level":"2"
       },
       {
            "change_name":"Remove Imputers",
            "object_type_name":"FeatureImputer",
            "json_query_whitelist": [],
            "json_query_blacklist": [],
            "change_command": "DELETE",
            "verbose_level":"2"
       }
  ]
}

Example 2: Multiple Modifications in One File

This example demonstrates how to perform several changes in a single operation:

  • Removing outlier attributes - that reports on outliers. makes think slower and consume more memory.
  • Deleting RepCheckReq objects - that checks eligibility criteria. makes think slower and consume more memory.
  • adjusting the max_data_in_mem parameter for the MedModel object itself, to adjusts the model's memory limit to process larger prediction batches.
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{ 
  "changes": [
       {
            "change_name":"Remove attributes from cleaners",
            "object_type_name":"RepBasicOutlierCleaner",
            "json_query_whitelist": [],
            "json_query_blacklist": [],
            "change_command": "nrem_attr=;ntrim_attr=;nrem_suff=;ntrim_suff=",
            "verbose_level":"1"
       },
       {
            "change_name":"Remove attributes Req",
            "object_type_name":"RepCheckReq",
            "json_query_whitelist": [],
            "json_query_blacklist": [],
            "change_command": "DELETE",
            "verbose_level":"1"
       },
       {
            "change_name":"MedeModel Memory",
            "object_type_name":"MedModel",
            "json_query_whitelist": [],
            "json_query_blacklist": [],
            "change_command": "max_data_in_mem=0",
            "verbose_level":"1"
       }
  ]
}