Reproducibility

Genetic algorithms are stochastic — results vary between runs unless every random source is seeded. This page shows a complete reproducibility setup.

Prerequisites

• Completed Basic Usage

Seed All Random Sources

import random
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold, train_test_split

from sklearn_genetic import EvolutionConfig, GASearchCV, PopulationConfig, RuntimeConfig
from sklearn_genetic.space import Categorical, Continuous, Integer

# 1. Seed Python's built-in random module
random.seed(42)

# 2. Seed NumPy
np.random.seed(42)

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y, random_state=42  # 3. Seed the split
)

search = GASearchCV(
    estimator=RandomForestClassifier(random_state=42),  # 4. Seed the estimator
    param_grid={
        "n_estimators": Integer(50, 250),
        "max_depth": Integer(2, 14),
        "max_features": Categorical(["sqrt", "log2"]),
        "ccp_alpha": Continuous(0.0, 0.03),
    },
    cv=StratifiedKFold(n_splits=3, shuffle=True, random_state=42),  # 5. Seed CV
    scoring="roc_auc",
    evolution_config=EvolutionConfig(population_size=20, generations=12),
    population_config=PopulationConfig(initializer="smart"),
    runtime_config=RuntimeConfig(n_jobs=1),  # 6. n_jobs=1 for full determinism
)

search.fit(X_train, y_train)

Why n_jobs=1?

Parallel execution with n_jobs > 1 uses joblib, which may introduce non-determinism when multiple workers race to write results. For fully reproducible results, use n_jobs=1. For production runs where speed matters, accept that results may vary slightly between runs.

Checkpointing

For long runs, save the logbook periodically so you can inspect progress even if the search is interrupted:

from sklearn_genetic.callbacks import LogbookSaver

search.fit(X_train, y_train, callbacks=[
    LogbookSaver(checkpoint_path="./logbook.pkl")
])

# Load later:
import pickle
with open("./logbook.pkl", "rb") as f:
    logbook = pickle.load(f)

Tips & Gotchas

• Seed random, np.random, the CV splitter, and every estimator that accepts random_state.
• If using PopulationConfig(warm_start_configs=[...]), the warm-start configs are deterministic by definition.
• DEAP's internal RNG is seeded through Python's random module — seeding random before fit is sufficient.

Next Steps

• Troubleshooting — debugging non-deterministic results.
• Callbacks — use LogbookSaver for checkpointing.