---

name: ds-reproducible-research compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Implements reproducible research practices including code organization environment management, documentation, and experiment tracking"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-data-versioning, ds-explainability, ds-model-robustness, ds-privacy-ml role: implementation scope: implementation triggers: reproducible research, reproducibility, code organization, environment notebooks, how do I reproduce archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

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Reproducible Research

Comprehensive guide to reproducible research in machine learning and data science workflows.

When to Use This Skill

• Solving real-world reproducibility & responsible ai problems
• Building machine learning pipelines with reproducible research
• Implementing best practices for reproducible research
• Optimizing model performance using reproducible research techniques
• Learning industry-standard approaches to reproducible research

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require reproducible research rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Reproducible Research is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Reproducible Research

import os
import random
import logging
import numpy as np
import pandas as pd
from typing import Dict, Any

logger = logging.getLogger(__name__)

def setup_reproducible_environment(seed: int = 42) -> Dict[str, Any]:
    """
    Configures deterministic behavior for Python, NumPy, and random modules.
    Follows DRY principles by centralizing seed configuration.
    Returns configuration dictionary for tracking and audit trails.
    """
    os.environ['PYTHONHASHSEED'] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    
    config = {
        'seed': seed
        'environment': 'reproducible'
        'timestamp': 'setup_complete'
        'libraries': {'numpy': np.__version__, 'pandas': pd.__version__}
    }
    logger.info(f"Reproducible environment configured with seed: {seed}")
    return config

Pattern 2: Production-Ready Reproducible Research

import os
import json
import logging
import pandas as pd
import numpy as np
from typing import Any, Dict, List
from pathlib import Path

logger = logging.getLogger(__name__)

class ExperimentManager:
    """Manages reproducible experiment workflows with artifact tracking."""
    
    def __init__(self, project_root: str = "./experiments", seed: int = 42):
        self.project_root = Path(project_root)
        self.project_root.mkdir(parents=True, exist_ok=True)
        self.seed = seed
        self.runs: List[Dict[str, Any]] = []
        self._setup_environment()
        
    def _setup_environment(self) -> None:
        os.environ['PYTHONHASHSEED'] = str(self.seed)
        np.random.seed(self.seed)
        
    def execute(self, data: pd.DataFrame, model_params: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a reproducible experiment run with validation and logging."""
        if data.empty:
            raise ValueError("Input data cannot be empty")
            
        run_id = f"run_{len(self.runs) + 1:03d}"
        run_dir = self.project_root / run_id
        run_dir.mkdir(exist_ok=True)
        
        # Simulate model training & evaluation
        np.random.shuffle(data.values)
        metrics = {'accuracy': 0.85, 'f1': 0.82, 'seed': self.seed}
        
        # Save artifacts
        metadata = {
            'run_id': run_id
            'params': model_params
            'metrics': metrics
            'data_shape': list(data.shape)
        }
        with open(run_dir / "metadata.json", "w") as f:
            json.dump(metadata, f, indent=2)
            
        self.runs.append(metadata)
        logger.info(f"Experiment {run_id} completed successfully.")
        return metadata

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

BAD vs GOOD Practices

# BAD: Hardcoded seeds, no validation, missing logging
def bad_pipeline(data):
    np.random.seed(123)
    return data.mean()

# GOOD: Configurable seeds, input validation, structured logging
def good_pipeline(data: pd.DataFrame, seed: int = 42) -> float:
    if data.empty:
        raise ValueError("Data cannot be empty")
    np.random.seed(seed)
    logger.info(f"Processing {len(data)} rows with seed {seed}")
    return float(data.mean().mean())

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Reproducible Research — Wikipedia
• Jupyter Documentation
• DVC (Data Version Control)
• MLflow Reproducible Workflows
• Computational Reproducibility — Nature Publishing Group