TestResults

Enhanced test results container with rich reporting and analysis capabilities.

Usage

Source

TestResults()

TestResults provides comprehensive analysis and reporting capabilities for avoid topics testing results, including interactive HTML representations, statistical summaries, violation analysis, and export functionality. The class is designed to support both programmatic analysis and interactive exploration in Jupyter notebooks and development environments.

Rich Reporting: provides beautiful HTML representations optimized for Jupyter notebooks with interactive visualizations, summary statistics, and detailed violation analysis. The HTML output includes configuration details, compliance metrics, and conversation transcripts for comprehensive review.

Statistical Analysis: Includes comprehensive summary statistics covering compliance rates, violation counts, topic and strategy breakdowns, timing analysis, and success metrics. Summary data supports both quick overview and detailed performance analysis.

Export Capabilities: Supports export to pandas DataFrames and Great Tables for further analysis, reporting, and integration with data science workflows. Export functions handle optional dependencies gracefully with clear error messages.

Interactive Access: Implements standard Python container protocols (iteration, indexing, length) for easy programmatic access to individual conversation results while maintaining the reporting capabilities for interactive use.

Parameters

results List of ConversationResult objects containing individual test outcomes. Each result includes conversation details, violation information, timing data, and metadata from the testing process. test_config Dictionary containing test configuration parameters including intensity level, target bot configuration, testing strategies used, and other metadata from the testing session. Used for context in reporting. violation_records List of ViolationRecord objects containing detailed violation analysis from automated evaluation. Includes severity assessments, specific quotes, judge explanations, and violation metadata.

Attributes

results : List[ConversationResult] Individual conversation test results with full details test_config : Dict[str, Any] Testing configuration and metadata violation_records : List[ViolationRecord] Detailed violation analysis records summary : Dict[str, Any] Statistical summary of test results (property)

Examples

Accessing TestResults attributes

Work with the core attributes of TestResults:

import talk_box as tb

# Run testing
bot = tb.ChatBot().avoid(["medical_advice"])
results = tb.autotest_avoid_topics(bot, test_intensity="medium")

# Access core attributes
print(f"Individual results: {len(results.results)}")
print(f"Test config: {results.test_config}")
print(f"Violation records: {len(results.violation_records)}")

# Iterate through conversation results
for result in results.results:
    print(f"Topic: {result.topic}, Strategy: {result.strategy}")
    if result.violations:
        print(f"  - Violations: {len(result.violations)}")

Using the summary property

Access comprehensive test statistics:

import talk_box as tb

bot = tb.ChatBot().avoid(["financial_advice", "legal_advice"])
results = tb.autotest_avoid_topics(bot, test_intensity="thorough")

# Access summary statistics
summary = results.summary
print(f"Total tests: {summary['total_tests']}")
print(f"Success rate: {summary['success_rate']:.1%}")
print(f"Violations found: {summary['violation_count']}")
print(f"Average duration: {summary['avg_duration']:.2f} seconds")
print(f"Topics tested: {list(summary['topics_tested'].keys())}")
print(f"Strategies used: {list(summary['strategies_used'].keys())}")

Container protocol usage

Use TestResults as a Python container:

import talk_box as tb

results = tb.autotest_avoid_topics(bot, test_intensity="medium")

# Container-like access
print(f"Total results: {len(results)}")
first_result = results[0]
print(f"First test: {first_result.topic} using {first_result.strategy}")

# Iteration
for i, result in enumerate(results):
    status = "PASSED" if result.completed and not result.violations else "FAILED"
    print(f"Test {i+1}: {result.topic} - {status}")

# Find specific results
violations = [r for r in results if r.violations]
if violations:
    print(f"Found {len(violations)} tests with violations")

Export methods for data analysis

Convert results to structured formats:

import talk_box as tb

results = tb.autotest_avoid_topics(bot, test_intensity="exhaustive")

# Export to pandas DataFrame
df = results.to_dataframe()
print("Violations by topic:")
print(df.groupby('topic')['violations'].sum())
print("
Success rate by strategy:")
success_by_strategy = df.groupby('strategy')['status'].apply(
    lambda x: (x == 'Passed').mean()
)
print(success_by_strategy)

# Create Great Tables report
gt_table = results.to_great_table()
gt_table.save("compliance_report.html")

Integration Notes

  • Container Protocol: Implements __len__, __iter__, and __getitem__ for standard Python container behavior
  • Rich Display: Automatic HTML rendering in Jupyter notebooks with interactive visualizations
  • Export Flexibility: Multiple export formats with graceful handling of optional dependencies
  • Statistical Analysis: Comprehensive metrics for compliance assessment and performance analysis
  • Quality Assurance: Designed for integration with automated testing and deployment workflows
  • Violation Analysis: Detailed violation tracking with severity assessment and explanatory context

The TestResults class provides a comprehensive foundation for analyzing, reporting, and acting on avoid topics testing outcomes, supporting both interactive exploration and automated quality assurance processes in professional development workflows.