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official/legacy/bert/model_training_utils.py

Summary

Maintainability
F
3 days
Test Coverage

Function run_customized_training_loop has a Cognitive Complexity of 99 (exceeds 5 allowed). Consider refactoring.
Open

def run_customized_training_loop(
    # pylint: disable=invalid-name
    _sentinel=None,
    # pylint: enable=invalid-name
    strategy=None,
Severity: Minor
Found in official/legacy/bert/model_training_utils.py - About 1 day to fix

Cognitive Complexity

Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

A method's cognitive complexity is based on a few simple rules:

  • Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
  • Code is considered more complex for each "break in the linear flow of the code"
  • Code is considered more complex when "flow breaking structures are nested"

Further reading

File model_training_utils.py has 442 lines of code (exceeds 250 allowed). Consider refactoring.
Open

# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
Severity: Minor
Found in official/legacy/bert/model_training_utils.py - About 6 hrs to fix

    Function run_customized_training_loop has 25 arguments (exceeds 4 allowed). Consider refactoring.
    Open

    def run_customized_training_loop(
    Severity: Major
    Found in official/legacy/bert/model_training_utils.py - About 3 hrs to fix

      Avoid deeply nested control flow statements.
      Open

                if eval_input_fn:
            # Re-initialize evaluation metric.
            eval_loss_metric.reset_states()
            for metric in eval_metrics + model.metrics:
              metric.reset_states()
      Severity: Major
      Found in official/legacy/bert/model_training_utils.py - About 45 mins to fix

        Similar blocks of code found in 2 locations. Consider refactoring.
        Open

            if eval_metrics:
      training_summary['last_train_metrics'] = _float_metric_value(
          train_metrics[0])
      training_summary['eval_metrics'] = _float_metric_value(eval_metrics[0])
        Severity: Major
        Found in official/legacy/bert/model_training_utils.py and 1 other location - About 1 hr to fix
        official/legacy/image_classification/resnet/common.py on lines 166..168

        Duplicated Code

        Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

        Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

        When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

        Tuning

        This issue has a mass of 46.

        We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

        The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

        If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

        See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

        Refactorings

        Further Reading

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