pytest-ml-tester

Testing & Quality

ML-specific testing skill using pytest with fixtures for data, models, and predictions.

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pytest-ml-tester

Overview

ML-specific testing skill using pytest with specialized fixtures for data validation, model loading, prediction testing, and ML pipeline verification.

Capabilities

Data validation fixtures
Model loading fixtures
Prediction testing utilities
Performance regression tests
Integration test helpers
Coverage reporting for ML code
Property-based testing with Hypothesis
Parameterized test generation

Target Processes

ML System Integration Testing
Model Evaluation and Validation Framework
Data Collection and Validation Pipeline

Tools and Libraries

pytest
pytest-cov
hypothesis
great-expectations (optional)

Input Schema

{
  "type": "object",
  "required": ["action"],
  "properties": {
    "action": {
      "type": "string",
      "enum": ["run", "generate", "coverage", "fixtures"],
      "description": "Testing action to perform"
    },
    "testConfig": {
      "type": "object",
      "properties": {
        "testPath": { "type": "string" },
        "markers": { "type": "array", "items": { "type": "string" } },
        "verbose": { "type": "boolean" },
        "failFast": { "type": "boolean" },
        "parallel": { "type": "integer" }
      }
    },
    "coverageConfig": {
      "type": "object",
      "properties": {
        "sourcePath": { "type": "string" },
        "minCoverage": { "type": "number" },
        "reportFormat": { "type": "string", "enum": ["html", "xml", "term"] }
      }
    },
    "generateConfig": {
      "type": "object",
      "properties": {
        "testType": {
          "type": "string",
          "enum": ["data_validation", "model_inference", "performance", "integration"]
        },
        "targetPath": { "type": "string" },
        "modelPath": { "type": "string" },
        "dataPath": { "type": "string" }
      }
    },
    "fixtureConfig": {
      "type": "object",
      "properties": {
        "dataFixtures": { "type": "array" },
        "modelFixtures": { "type": "array" },
        "scope": { "type": "string", "enum": ["function", "class", "module", "session"] }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "results"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["passed", "failed", "error"]
    },
    "results": {
      "type": "object",
      "properties": {
        "totalTests": { "type": "integer" },
        "passed": { "type": "integer" },
        "failed": { "type": "integer" },
        "skipped": { "type": "integer" },
        "duration": { "type": "number" }
      }
    },
    "failures": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "testName": { "type": "string" },
          "error": { "type": "string" },
          "traceback": { "type": "string" }
        }
      }
    },
    "coverage": {
      "type": "object",
      "properties": {
        "percentage": { "type": "number" },
        "reportPath": { "type": "string" },
        "uncoveredLines": { "type": "object" }
      }
    },
    "generatedTests": {
      "type": "array",
      "items": { "type": "string" }
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Run ML integration tests',
  skill: {
    name: 'pytest-ml-tester',
    context: {
      action: 'run',
      testConfig: {
        testPath: 'tests/integration/',
        markers: ['integration', 'model'],
        verbose: true,
        parallel: 4
      },
      coverageConfig: {
        sourcePath: 'src/',
        minCoverage: 80,
        reportFormat: 'html'
      }
    }
  }
}