Meta-Optimization

Complexity

Total Complexity

1

Size/Duplication

Total Lines	56
Duplicated Lines	58.93 %

Importance

Changes

0

1 Function

Rating	Name	Duplication	Size	Complexity
A	meta_opt()	33	33	1

import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer
from hyperactive import Hyperactive

data = load_breast_cancer()
X, y = data.data, data.target


def meta_opt(para, X, y):

This code seems to be duplicated in your project.

    def model(para, X, y):
        model = DecisionTreeClassifier(
            max_depth=para["max_depth"],
            min_samples_split=para["min_samples_split"],
            min_samples_leaf=para["min_samples_leaf"],
        )
        scores = cross_val_score(model, X, y, cv=3)

        return scores.mean()

    search_config = {
        model: {
            "max_depth": range(2, 50),
            "min_samples_split": range(2, 50),
            "min_samples_leaf": range(1, 50),
        }
    }

    opt = Hyperactive(
        search_config,
        optimizer={
            "ParticleSwarm": {
                "inertia": para["inertia"],
                "cognitive_weight": para["cognitive_weight"],
                "social_weight": para["social_weight"],
            }
        },
        verbosity=None,
    )
    opt.search(X, y)

    return opt.score_best


search_config = {
    meta_opt: {
        "inertia": np.arange(0, 1, 0.01),
        "cognitive_weight": np.arange(0, 1, 0.01),
        "social_weight": np.arange(0, 1, 0.01),
    }
}

opt = Hyperactive(X, y)
opt.search(search_config, optimizer="Bayesian", n_iter=50)