grandjury 1.0.0

Python client for GrandJury server API - collective intelligence for model evaluation

grandjury

Python client for the GrandJury ML evaluation and verdict analysis API.

This package provides comprehensive access to the GrandJury server for ML model evaluation and voting analysis, supporting:

• Model scoring with decay-adjusted algorithms
• Vote analysis across multiple dimensions (time, completeness, confidence)
• Multiple data formats (pandas, polars, CSV, parquet, dict/list)
• Performance optimizations with optional dependencies
• Backward compatibility with existing code

Patent Pending.

Installation

pip install grandjury

Optional performance dependencies:

pip install grandjury[performance]  # Installs msgspec, pyarrow, polars

Quick Start

Basic Model Evaluation

from grandjury import GrandJuryClient

# Initialize client
client = GrandJuryClient(api_key="your-api-key")

# Evaluate model performance
result = client.evaluate_model(
    previous_score=0.7,
    votes=[0.9, 0.8, 0.6],
    reputations=[1.0, 1.0, 0.8]
)
print(f"Score: {result['score']:.4f}")

Vote Analysis with Multiple Data Formats

import pandas as pd
import polars as pl

# Your vote data
vote_data = [
    {
        "inference_id": 1,
        "vote": True,
        "voter_id": 101,
        "vote_time": "2024-07-07T19:22:30",
        # ... other fields
    }
    # ... more votes
]

# No authentication needed for analysis endpoints  
client = GrandJuryClient()

# Use with different data formats
histogram = client.vote_histogram(vote_data)  # dict/list
histogram = client.vote_histogram(pd.DataFrame(vote_data))  # pandas
histogram = client.vote_histogram(pl.DataFrame(vote_data))  # polars
histogram = client.vote_histogram("votes.csv")  # CSV file
histogram = client.vote_histogram("votes.parquet")  # Parquet file

# Vote completeness analysis
completeness = client.vote_completeness(
    data=vote_data,
    voter_list=[101, 102, 103]
)

# Population confidence
confidence = client.population_confidence(
    data=vote_data,
    voter_list=[101, 102, 103]
)

# Majority vote analysis
majority = client.majority_good_votes(
    data=vote_data,
    good_vote=True,
    threshold=0.5
)

# Vote distribution per inference
distribution = client.votes_distribution(vote_data)

Backward Compatibility

# Original function still works
from grandjury import evaluate_model

result = evaluate_model(
    predictions=["Model output 1", "Model output 2"],
    references=["Expected 1", "Expected 2"],
    api_key="your-api-key"
)

API Endpoints

Method	Description	Authentication
evaluate_model()	Model scoring with decay algorithms	Required
vote_histogram()	Vote time distribution analysis	Optional
vote_completeness()	Voting completeness metrics	Optional
population_confidence()	Population confidence analysis	Optional
majority_good_votes()	Majority vote counting	Optional
votes_distribution()	Vote distribution per inference	Optional

Performance Features

The client automatically uses performance optimizations when available:

• msgspec: Faster JSON serialization
• PyArrow: Efficient Parquet file reading
• Polars: Native DataFrame support

Install with: pip install msgspec pyarrow polars

Error Handling

try:
    result = client.vote_histogram(invalid_data)
except Exception as e:
    print(f"API Error: {e}")

Server URL Configuration

# Default: https://grandjury-server.onrender.com/api/v1
client = GrandJuryClient()

# Custom server
client = GrandJuryClient(base_url="https://your-server.com")
# Automatically appends /api/v1 if missing

pip install grandjury

Optional performance dependencies:

pip install grandjury[performance]  # Installs msgspec, pyarrow, polars

Quick Start

Basic Model Evaluation

from grandjury import GrandJuryClient

# Initialize client
client = GrandJuryClient(api_key="your-api-key")

# Evaluate model performance
result = client.evaluate_model(
    previous_score=0.7,
    votes=[0.9, 0.8, 0.6],
    reputations=[1.0, 1.0, 0.8]
)
print(f"Score: {result['score']:.4f}")

Vote Analysis with Multiple Data Formats

import pandas as pd
import polars as pl

# Your vote data
vote_data = [
    {
        "inference_id": 1,
        "vote": True,
        "voter_id": 101,
        "vote_time": "2024-07-07T19:22:30",
        # ... other fields
    }
    # ... more votes
]

# No authentication needed for analysis endpoints  
client = GrandJuryClient()

# Use with different data formats
histogram = client.vote_histogram(vote_data)  # dict/list
histogram = client.vote_histogram(pd.DataFrame(vote_data))  # pandas
histogram = client.vote_histogram(pl.DataFrame(vote_data))  # polars
histogram = client.vote_histogram("votes.csv")  # CSV file
histogram = client.vote_histogram("votes.parquet")  # Parquet file

# Vote completeness analysis
completeness = client.vote_completeness(
    data=vote_data,
    voter_list=[101, 102, 103]
)

# Population confidence
confidence = client.population_confidence(
    data=vote_data,
    voter_list=[101, 102, 103]
)

# Majority vote analysis
majority = client.majority_good_votes(
    data=vote_data,
    good_vote=True,
    threshold=0.5
)

# Vote distribution per inference
distribution = client.votes_distribution(vote_data)

Backward Compatibility

# Original function still works
from grandjury import evaluate_model

result = evaluate_model(
    predictions=["Model output 1", "Model output 2"],
    references=["Expected 1", "Expected 2"],
    api_key="your-api-key"
)

API Endpoints

Method	Description	Authentication
evaluate_model()	Model scoring with decay algorithms	Required
vote_histogram()	Vote time distribution analysis	Optional
vote_completeness()	Voting completeness metrics	Optional
population_confidence()	Population confidence analysis	Optional
majority_good_votes()	Majority vote counting	Optional
votes_distribution()	Vote distribution per inference	Optional

Performance Features

The client automatically uses performance optimizations when available:

• msgspec: Faster JSON serialization
• PyArrow: Efficient Parquet file reading
• Polars: Native DataFrame support

Install with: pip install msgspec pyarrow polars

Error Handling

try:
    result = client.vote_histogram(invalid_data)
except Exception as e:
    print(f"API Error: {e}")

Server URL Configuration

# Default: https://grandjury-server.onrender.com/api/v1
client = GrandJuryClient()

# Custom server
client = GrandJuryClient(base_url="https://your-server.com")
# Automatically appends /api/v1 if missing