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Merge pull request ahmedfgad#307 from ahmedfgad/master
Add OpenSSF Action
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.github/workflows/scorecard.yml

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# This workflow uses actions that are not certified by GitHub. They are provided
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# by a third-party and are governed by separate terms of service, privacy
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# policy, and support documentation.
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name: Scorecard supply-chain security
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on:
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# For Branch-Protection check. Only the default branch is supported. See
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# https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection
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branch_protection_rule:
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# To guarantee Maintained check is occasionally updated. See
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# https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained
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schedule:
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- cron: '42 5 * * 2'
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push:
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branches: [ "master" ]
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# Declare default permissions as read only.
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permissions: read-all
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jobs:
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analysis:
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name: Scorecard analysis
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runs-on: ubuntu-latest
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permissions:
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# Needed to upload the results to code-scanning dashboard.
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security-events: write
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# Needed to publish results and get a badge (see publish_results below).
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id-token: write
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# Uncomment the permissions below if installing in a private repository.
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# contents: read
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# actions: read
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steps:
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- name: "Checkout code"
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uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
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with:
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persist-credentials: false
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- name: "Run analysis"
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uses: ossf/scorecard-action@0864cf19026789058feabb7e87baa5f140aac736 # v2.3.1
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with:
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results_file: results.sarif
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results_format: sarif
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# (Optional) "write" PAT token. Uncomment the `repo_token` line below if:
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# - you want to enable the Branch-Protection check on a *public* repository, or
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# - you are installing Scorecard on a *private* repository
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# To create the PAT, follow the steps in https://github.com/ossf/scorecard-action?tab=readme-ov-file#authentication-with-fine-grained-pat-optional.
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# repo_token: ${{ secrets.SCORECARD_TOKEN }}
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# Public repositories:
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# - Publish results to OpenSSF REST API for easy access by consumers
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# - Allows the repository to include the Scorecard badge.
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# - See https://github.com/ossf/scorecard-action#publishing-results.
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# For private repositories:
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# - `publish_results` will always be set to `false`, regardless
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# of the value entered here.
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publish_results: true
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# Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF
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# format to the repository Actions tab.
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- name: "Upload artifact"
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uses: actions/upload-artifact@97a0fba1372883ab732affbe8f94b823f91727db # v3.pre.node20
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with:
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name: SARIF file
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path: results.sarif
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retention-days: 5
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# Upload the results to GitHub's code scanning dashboard (optional).
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# Commenting out will disable upload of results to your repo's Code Scanning dashboard
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- name: "Upload to code-scanning"
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uses: github/codeql-action/upload-sarif@v3
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with:
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sarif_file: results.sarif

pygad/pygad.py

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# Validate delay_after_gen
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if type(delay_after_gen) in GA.supported_int_float_types:
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if delay_after_gen != 0.0:
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if not self.suppress_warnings:
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warnings.warn("The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.")
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if not self.suppress_warnings:
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warnings.warn("The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.")
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if delay_after_gen >= 0.0:
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self.delay_after_gen = delay_after_gen
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else:
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# Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute.
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self.last_generation_fitness = self.cal_pop_fitness()
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# Know whether the problem is SOO or MOO.
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if type(self.last_generation_fitness[0]) in GA.supported_int_float_types:
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# Single-objective problem.
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# If the problem is SOO, the parent selection type cannot be nsga2 or tournament_nsga2.
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if self.parent_selection_type in ['nsga2', 'tournament_nsga2']:
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raise TypeError(f"Incorrect parent selection type. The fitness function returned a single numeric fitness value which means the problem is single-objective. But the parent selection type {self.parent_selection_type} is used which only works for multi-objective optimization problems.")
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elif type(self.last_generation_fitness[0]) in [list, tuple, numpy.ndarray]:
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# Multi-objective problem.
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pass
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best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness)
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# Appending the best solution in the initial population to the best_solutions list.

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