tests.test_memory

Complexity

Total Complexity

7

Size/Duplication

Total Lines	183
Duplicated Lines	48.09 %

Importance

Changes

0

7 Functions

Rating	Name	Duplication	Size	Complexity
A	test_long_term_memory_search_space_expansion()	26	26	1
A	test_long_term_memory_obj_storage()	0	29	1
A	test_long_term_memory_with_data()	18	18	1
A	test_short_term_memory()	0	13	1
A	test_long_term_memory_without_data()	18	18	1
A	test_long_term_memory_best_model()	0	22	1
A	test_long_term_memory_search_space_reduction()	26	26	1

# Author: Simon Blanke
# Email: simon.blanke@yahoo.com
# License: MIT License

import numpy as np

from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from hyperactive import Hyperactive

data = load_iris()
X = data.data
y = data.target

memory = False


def test_short_term_memory():
    def model1(para, X_train, y_train):
        model = DecisionTreeClassifier(criterion=para["criterion"])
        scores = cross_val_score(model, X_train, y_train, cv=5)

        return scores.mean()

    search_config = {model1: {"criterion": ["gini"]}}

    opt = Hyperactive(X, y, memory="short")
    opt.search(search_config, n_iter=1000)

    assert np.array(opt.eval_times[model1]).mean() < 1


def test_long_term_memory_with_data():

This code seems to be duplicated in your project.

    def model2(para, X_train, y_train):
        model = DecisionTreeClassifier(
            criterion=para["criterion"], max_depth=para["max_depth"]
        )
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {
        model2: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}
    }

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)


def test_long_term_memory_without_data():

This code seems to be duplicated in your project.

    def model3(para, X_train, y_train):
        model = DecisionTreeClassifier(
            criterion=para["criterion"], max_depth=para["max_depth"]
        )
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {
        model3: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}
    }

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config, n_iter=0)

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)


def test_long_term_memory_best_model():
    def model4(para, X_train, y_train):
        model = DecisionTreeClassifier(
            criterion=para["criterion"], max_depth=para["max_depth"]
        )
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {
        model4: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}
    }

    opt1 = Hyperactive(X, y, memory="long")
    opt1.search(search_config)

    best_para = opt1.results[model4]

    opt2 = Hyperactive(X, y, memory="long")
    opt2.search(search_config, n_iter=0, init_config=opt1.results)

    assert best_para == opt2.results[model4]


def test_long_term_memory_obj_storage():
    from sklearn.gaussian_process import GaussianProcessClassifier

    from sklearn.gaussian_process.kernels import RBF, Matern, ConstantKernel

    def model(para, X_train, y_train):
        gpc = GaussianProcessClassifier(
            kernel=para["kernel"], max_iter_predict=para["max_iter_predict"]
        )
        scores = cross_val_score(gpc, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {
        model: {
            "kernel": [RBF(), Matern(), ConstantKernel()],
            "max_iter_predict": range(90, 100),
        }
    }

    opt1 = Hyperactive(X, y, memory="long")
    opt1.search(search_config)

    best_para = opt1.results[model]

    opt2 = Hyperactive(X, y, memory="long")
    opt2.search(search_config, n_iter=0, init_config=opt1.results)

    assert best_para == opt2.results[model]


def test_long_term_memory_search_space_expansion():

This code seems to be duplicated in your project.

    def model5(para, X_train, y_train):
        model = DecisionTreeClassifier(criterion=para["criterion"])
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {model5: {"criterion": ["gini", "entropy"]}}

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)

    def model5(para, X_train, y_train):
        model = DecisionTreeClassifier(
            criterion=para["criterion"], max_depth=para["max_depth"]
        )
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {
        model5: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}
    }

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)


def test_long_term_memory_search_space_reduction():

This code seems to be duplicated in your project.

    def model6(para, X_train, y_train):
        model = DecisionTreeClassifier(
            criterion=para["criterion"], max_depth=para["max_depth"]
        )
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {
        model6: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}
    }

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)

    def model6(para, X_train, y_train):
        model = DecisionTreeClassifier(criterion=para["criterion"])
        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {model6: {"criterion": ["gini", "entropy"]}}

    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config)