Hypogenic

# HypoGeniC Configuration Template
# Complete example configuration for hypothesis generation and testing

# Dataset paths
data:
  train: "data/train.json"
  validation: "data/val.json"
  test: "data/test.json"

  # Dataset should contain:
  # - text_features_1, text_features_2, ... text_features_n (lists of strings)
  # - label (list of strings)

# Model configuration
model:
  name: "gpt-4"  # or "gpt-3.5-turbo", "claude-3", etc.
  api_key_env: "OPENAI_API_KEY"  # Environment variable for API key
  temperature: 0.7
  max_tokens: 2048

# Redis caching (optional - reduces API costs)
cache:
  enabled: true
  host: "localhost"
  port: 6832

# Hypothesis generation parameters
generation:
  method: "hypogenic"  # Options: "hypogenic", "hyporefine", "union"
  num_hypotheses: 20
  batch_size: 5
  max_iterations: 10

  # For HypoRefine method
  literature:
    papers_directory: "papers/"  # Directory containing PDF files
    num_papers: 10

  # For Union methods
  union:
    literature_hypotheses: "literature_hypotheses.json"
    deduplicate: true

# Prompt templates
prompts:
  # Observations prompt - generates initial observations from data
  observations: |
    Analyze the following data samples and identify patterns:

    {data_samples}

    Generate 5 distinct observations about patterns that distinguish between the two classes.
    Focus on specific, testable characteristics.

  # Batched generation prompt - creates hypotheses from observations
  batched_generation: |
    Based on these observations about the data:

    {observations}

    Generate {num_hypotheses} distinct, testable hypotheses that could explain the differences between classes.
    Each hypothesis should:
    1. Be specific and measurable
    2. Focus on a single characteristic or pattern
    3. Be falsifiable through empirical testing

    Format each hypothesis as: "Hypothesis X: [clear statement]"

  # Inference prompt - tests hypotheses against data
  inference: |
    Hypothesis: {hypothesis}

    Data sample:
    {sample_text}

    Does this sample support or contradict the hypothesis?
    Respond with: SUPPORT, CONTRADICT, or NEUTRAL

    Explanation: [brief reasoning]

  # Relevance checking prompt - filters hypotheses
  relevance_check: |
    Hypothesis: {hypothesis}
    Task: {task_description}

    Is this hypothesis relevant and testable for the given task?
    Respond with: RELEVANT or NOT_RELEVANT

    Reasoning: [brief explanation]

  # Adaptive refinement prompt - for HypoRefine
  adaptive_refinement: |
    Current hypothesis: {hypothesis}

    This hypothesis performed poorly on these challenging examples:
    {challenging_examples}

    Generate an improved hypothesis that addresses these failures while maintaining the core insight.

    Improved hypothesis: [statement]

# Inference configuration
inference:
  method: "voting"  # Options: "voting", "weighted", "ensemble"
  confidence_threshold: 0.7
  max_samples: 1000  # Limit for large test sets

# Output configuration
output:
  directory: "output/"
  save_intermediate: true  # Save hypotheses after each iteration
  format: "json"  # Options: "json", "csv"
  verbose: true

# Custom label extraction (optional)
# Define a custom function in your code to parse specific output formats
label_extraction:
  pattern: "PREDICTION: {label}"  # Regex pattern for extracting predictions
  valid_labels: ["0", "1"]  # Expected label values

# Task-specific settings
task:
  name: "example_task"
  description: "Binary classification task for [describe your specific domain]"
  features:
    - name: "text_features_1"
      description: "Primary text content"
    - name: "text_features_2"
      description: "Additional contextual information"
  labels:
    - name: "0"
      description: "Negative class"
    - name: "1"
      description: "Positive class"

# Evaluation metrics
evaluation:
  metrics:
    - "accuracy"
    - "precision"
    - "recall"
    - "f1"
  cross_validation: false
  num_folds: 5

# Logging
loggi