mikoshi-curiosity 0.1.1

Domain-agnostic curiosity engine — explore any state space and find interesting things

Mikoshi Curiosity

Explore any state space. Find what you didn't know you were looking for.

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Recommendation engines predict what you'll like. Curiosity engines find what you don't know you'd like.

Mikoshi Curiosity is a domain-agnostic exploration engine that uses intrinsic motivation, state memory, and diversity pressure to discover interesting things in any state space — datasets, text corpora, graphs, parameter spaces, or APIs.

Inspired by Go-Explore and intrinsic motivation research.

Quick Start

pip install mikoshi-curiosity
# With extras:
pip install mikoshi-curiosity[data]  # pandas support
pip install mikoshi-curiosity[all]   # pandas + matplotlib

Examples

🔍 Find Anomalies in a Dataset

import pandas as pd
from mikoshi_curiosity import CuriosityEngine
from mikoshi_curiosity.contexts.dataset import DatasetSpace

df = pd.read_csv("sales_data.csv")
space = DatasetSpace(df)
engine = CuriosityEngine(space, strategy="novelty")

seed = space.get_state("0")
result = engine.explore(seed, budget=200)

print(result.summary())
for d in result.top(5):
    print(f"  [{d.score:.2f}] Row {d.state.id}: {d.reason}")
    print(f"         {d.state.features}")

📚 Explore a Text Corpus

from mikoshi_curiosity import CuriosityEngine
from mikoshi_curiosity.contexts.text import TextSpace

docs = [
    {"id": "paper1", "text": "Deep learning for image recognition..."},
    {"id": "paper2", "text": "Quantum entanglement in biological systems..."},
    # ... hundreds of papers
]

space = TextSpace(docs)
engine = CuriosityEngine(space, strategy="diversity")
result = engine.explore(space.get_state("paper1"), budget=100)

# Find bridging documents that connect different topics
for d in result.top(10):
    print(f"  {d.state.id}: {d.reason}")

🎛️ Explore a Parameter Space

from mikoshi_curiosity import CuriosityEngine
from mikoshi_curiosity.contexts.numeric import NumericSpace

def simulate(params):
    """Your simulation / evaluation function."""
    return complex_score(params["gravity"], params["friction"], params["elasticity"])

space = NumericSpace(
    dimensions={"gravity": (0, 20), "friction": (0, 1), "elasticity": (0.1, 5)},
    eval_fn=simulate,
)
engine = CuriosityEngine(space, strategy="balanced")
result = engine.explore(space.get_random(5), budget=500)

# Find interesting parameter combinations, phase transitions, sweet spots
for d in result.top(10):
    print(f"  Score: {d.score:.3f} | Params: {d.state.features}")

🕸️ Explore a Network

from mikoshi_curiosity import CuriosityEngine
from mikoshi_curiosity.contexts.graph import GraphSpace

space = GraphSpace(
    nodes=["alice", "bob", "carol", ...],
    edges=[("alice", "bob"), ("bob", "carol"), ...],
)
engine = CuriosityEngine(space, strategy="novelty")
result = engine.explore(space.get_state("alice"), budget=100)

# Discover bridge nodes, structural holes, unexpected clusters

Strategies

Strategy	What it optimises	Best for
novelty	Distance from seen states	Finding outliers, anomalies
surprise	Prediction error	Finding rule-breakers
diversity	Distance from current discoveries	Broad coverage
serendipity	Novelty × relevance to profile	Personalised exploration
balanced	Weighted combination of all	General-purpose

API Reference

Core

• State — A point in exploration space (id, features, embedding, metadata)
• StateSpace — Abstract space to explore (subclass for your domain)
• CuriosityEngine — The exploration engine
• ExplorationMemory — Go-Explore style state archive
• PredictionModel — Online model for surprise detection
• Discovery — A single interesting finding with score and reason
• ExplorationResult — Container with discoveries, stats, and memory

Built-in Contexts

Context	Module	Input
Tabular data	contexts.dataset.DatasetSpace	DataFrame or CSV path
Text corpus	contexts.text.TextSpace	List of {id, text} dicts
Graph/network	contexts.graph.GraphSpace	Nodes + edges
Numeric/params	contexts.numeric.NumericSpace	Dimension bounds + eval function
External API	contexts.api.APISpace	Fetch function

Custom State Space

from mikoshi_curiosity import StateSpace, State

class MySpace(StateSpace):
    def get_neighbors(self, state, n=10):
        ...  # Return nearby states

    def get_random(self, n=10):
        ...  # Return random states

    def get_state(self, id):
        ...  # Lookup by id

    def embed(self, state):
        ...  # Return numpy vector

    def size(self):
        ...  # Approximate space size

Visualization

from mikoshi_curiosity.viz import plot_exploration, plot_discovery_scores

fig = plot_exploration(result)   # 2D PCA projection with discoveries highlighted
fig = plot_discovery_scores(result)  # Score breakdown bar chart

Requires pip install mikoshi-curiosity[viz].

Design Philosophy

• Zero external API dependencies — core uses only NumPy
• Domain-agnostic — works with any state space you define
• Go-Explore inspired — maintains an archive of interesting states and explores from frontiers
• Multiple signals — novelty, surprise, diversity, serendipity, diminishing returns
• Online learning — prediction model updates as you explore
• Resumable — ExplorationResult contains full memory state

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Built by Mikoshi Ltd