hypogenic

What this skill does

# Hypogenic

## Overview

Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses).

## Quick Start

Get started with Hypogenic in minutes:

```bash
# Install the package
uv pip install hypogenic

# Clone example datasets
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# Run basic hypothesis generation
hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20

# Run inference on generated hypotheses
hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json
```

**Or use Python API:**

```python
from hypogenic import BaseTask

# Create task with your configuration
task = BaseTask(config_path="./data/your_task/config.yaml")

# Generate hypotheses
task.generate_hypotheses(method="hypogenic", num_hypotheses=20)

# Run inference
results = task.inference(hypothesis_bank="./output/hypotheses.json")
```

## When to Use This Skill

Use this skill when working on:
- Generating scientific hypotheses from observational datasets
- Testing multiple competing hypotheses systematically
- Combining literature insights with empirical patterns
- Accelerating research discovery through automated hypothesis ideation
- Domains requiring hypothesis-driven analysis: deception detection, AI-generated content identification, mental health indicators, predictive modeling, or other empirical research

## Key Features

**Automated Hypothesis Generation**
- Generate 10-20+ testable hypotheses from data in minutes
- Iterative refinement based on validation performance
- Support for both API-based (OpenAI, Anthropic) and local LLMs

**Literature Integration**
- Extract insights from research papers via PDF processing
- Combine theoretical foundations with empirical patterns
- Systematic literature-to-hypothesis pipeline with GROBID

**Performance Optimization**
- Redis caching reduces API costs for repeated experiments
- Parallel processing for large-scale hypothesis testing
- Adaptive refinement focuses on challenging examples

**Flexible Configuration**
- Template-based prompt engineering with variable injection
- Custom label extraction for domain-specific tasks
- Modular architecture for easy extension

**Proven Results**
- 8.97% improvement over few-shot baselines
- 15.75% improvement over literature-only approaches
- 80-84% hypothesis diversity (non-redundant insights)
- Human evaluators report significant decision-making improvements

## Core Capabilities

### 1. HypoGeniC: Data-Driven Hypothesis Generation

Generate hypotheses solely from observational data through iterative refinement.

**Process:**
1. Initialize with a small data subset to generate candidate hypotheses
2. Iteratively refine hypotheses based on performance
3. Replace poorly-performing hypotheses with new ones from challenging examples

**Best for:** Exploratory research without existing literature, pattern discovery in novel datasets

### 2. HypoRefine: Literature and Data Integration

Synergistically combine existing literature with empirical data through an agentic framework.

**Process:**
1. Extract insights from relevant research papers (typically 10 papers)
2. Generate theory-grounded hypotheses from literature
3. Generate data-driven hypotheses from observational patterns
4. Refine both hypothesis banks through iterative improvement

**Best for:** Research with established theoretical foundations, validating or extending existing theories

### 3. Union Methods

Mechanistically combine literature-only hypotheses with framework outputs.

**Variants:**
- **Literature ∪ HypoGeniC**: Combines literature hypotheses with data-driven generation
- **Literature ∪ HypoRefine**: Combines literature hypotheses with integrated approach

**Best for:** Comprehensive hypothesis coverage, eliminating redundancy while maintaining diverse perspectives

## Installation

Install via pip:
```bash
uv pip install hypogenic
```

**Optional dependencies:**
- **Redis server** (port 6832): Enables caching of LLM responses to significantly reduce API costs during iterative hypothesis generation
- **s2orc-doc2json**: Required for processing literature PDFs in HypoRefine workflows
- **GROBID**: Required for PDF preprocessing (see Literature Processing section)

**Clone example datasets:**
```bash
# For HypoGeniC examples
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# For HypoRefine/Union examples
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data
```

## Dataset Format

Datasets must follow HuggingFace datasets format with specific naming conventions:

**Required files:**
- `<TASK>_train.json`: Training data
- `<TASK>_val.json`: Validation data  
- `<TASK>_test.json`: Test data

**Required keys in JSON:**
- `text_features_1` through `text_features_n`: Lists of strings containing feature values
- `label`: List of strings containing ground truth labels

**Example (headline click prediction):**
```json
{
  "headline_1": [
    "What Up, Comet? You Just Got *PROBED*",
    "Scientists Made a Breakthrough in Quantum Computing"
  ],
  "headline_2": [
    "Scientists Everywhere Were Holding Their Breath Today. Here's Why.",
    "New Quantum Computer Achieves Milestone"
  ],
  "label": [
    "Headline 2 has more clicks than Headline 1",
    "Headline 1 has more clicks than Headline 2"
  ]
}
```

**Important notes:**
- All lists must have the same length
- Label format must match your `extract_label()` function output format
- Feature keys can be customized to match your domain (e.g., `review_text`, `post_content`, etc.)

## Configuration

Each task requires a `config.yaml` file specifying:

**Required elements:**
- Dataset paths (train/val/test)
- Prompt templates for:
  - Observations generation
  - Batched hypothesis generation
  - Hypothesis inference
  - Relevance checking
  - Adaptive methods (for HypoRefine)

**Template capabilities:**
- Dataset placeholders for dynamic variable injection (e.g., `${text_features_1}`, `${num_hypotheses}`)
- Custom label extraction functions for domain-specific parsing
- Role-based prompt structure (system, user, assistant roles)

**Configuration structure:**
```yaml
task_name: your_task_name

train_data_path: ./your_task_train.json
val_data_path: ./your_task_val.json
test_data_path: ./your_task_test.json

prompt_templates:
  # Extra keys for reusable prompt components
  observations: |
    Feature 1: ${text_features_1}
    Feature 2: ${text_features_2}
    Observation: ${label}
  
  # Required templates
  batched_generation:
    system: "Your system prompt here"
    user: "Your user prompt with ${num_hypotheses} placeholder"
  
  inference:
    system: "Your inference system prompt"
    user: "Your inference user prompt"
  
  # Optional templates for advanced features
  few_shot_baseline: {...}
  is_relevant: {...}
  adaptive_inference: {...}
  adaptive_selection: {...}
```

Refer to `references/config_template.yaml` for a complete example configuration.

## Literature Processing (HypoRefine/Union Methods)

To use literature-based hypothesis generation, you must preprocess PDF papers:

**Step 1: Setup GROBID** (first time only)
```bash
bash ./modules/setup_grobid.sh
```

**Step 2: Add PDF files**
Place research papers in `literature/YOUR_TASK_NAME/raw/`

**Step 3: Process PDFs**
```bash
# Start GROBID service
bash ./modules/run_grobid.sh

# Process PDFs for your task
cd examples
python pdf_preprocess.py --task_name YOUR_TASK_NAME
```

This converts PDFs to structured format for hypothesis extraction. Automated literature search will be supported in future releases.

## CLI Usage

### Hypothesis Generation

```bash
hypogenic_generation --help
```

**Key para