pikaia

Welcome to the documentation for pikaia, a Python package for data analysis using evolutionary simulation (Genetic AI).

pikaia enables you to analyze tabular data using evolutionary strategies inspired by genetics and game theory. It is designed for researchers, data scientists, and engineers who want to explore, rank, and understand data without the need for supervised learning or labeled datasets.

Key Features:

• No training data required — fully unsupervised
• Flexible evolutionary strategies (dominant, altruistic, selfish, balanced, etc.)
• Works on any tabular data (normalized to [0, 1])
• Visualizes gene and organism fitness over time
• Extensible and modular design

New to pikaia? Start with the Tutorial. For a conceptual introduction, see the Overview. For the full API, see the Reference. To add new strategies, see the Contributor Guide.

Quick Start

import numpy as np
from pikaia.data import PikaiaPopulation
from pikaia.models import PikaiaModel
from pikaia.plotting import PikaiaPlotter, PlotType
from pikaia.schemas import GeneStrategyEnum, OrgStrategyEnum, MixStrategyEnum
from pikaia.strategies import GeneStrategyFactory, OrgStrategyFactory, MixStrategyFactory

data = np.array([[0.1, 0.5, 0.9], [0.2, 0.3, 0.7], [0.8, 0.2, 0.4]])
population = PikaiaPopulation(data)
gene_strategies = [GeneStrategyFactory.get_strategy(GeneStrategyEnum.DOMINANT)]
org_strategies = [OrgStrategyFactory.get_strategy(OrgStrategyEnum.BALANCED)]
gene_mix_strategy = org_mix_strategy = MixStrategyFactory.get_strategy(MixStrategyEnum.FIXED)
model = PikaiaModel(
    population=population,
    gene_strategies=gene_strategies,
    org_strategies=org_strategies,
    gene_mix_strategy=gene_mix_strategy,
    org_mix_strategy=org_mix_strategy,
    max_iter=16,
)
model.fit()
plotter = PikaiaPlotter(model)
plotter.plot(plot_type=PlotType.GENE_FITNESS_HISTORY, show=True)