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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import numpy as np |
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from sklearn.datasets import load_iris |
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from sklearn.model_selection import cross_val_score |
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from sklearn.tree import DecisionTreeClassifier |
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from hyperactive import Optimizer |
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data = load_iris() |
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X, y = data.data, data.target |
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def objective_function(para): |
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dtc = DecisionTreeClassifier( |
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max_depth=para["max_depth"], |
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min_samples_split=para["min_samples_split"], |
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min_samples_leaf=para["min_samples_leaf"], |
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) |
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scores = cross_val_score(dtc, para["features"], para["target"], cv=2) |
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return scores.mean() |
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search_space = { |
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"max_depth": range(1, 21), |
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"min_samples_split": range(2, 21), |
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"min_samples_leaf": range(1, 21), |
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} |
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def _base_test(search, opt_args={}): |
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opt = Optimizer(**opt_args) |
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opt.add_search(**search) |
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opt.run() |
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def test_init_para(): |
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search = { |
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"objective_function": objective_function, |
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"function_parameter": {"features": X, "target": y}, |
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"search_space": search_space, |
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} |
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init_para1 = { |
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"max_depth": 3, |
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"min_samples_split": 3, |
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"min_samples_leaf": 3, |
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} |
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init_para_list = [[init_para1]] |
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for init_para in init_para_list: |
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search["init_para"] = init_para |
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_base_test(search) |
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test_init_para() |
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def test_verbosity(): |
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search = { |
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"objective_function": objective_function, |
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"function_parameter": {"features": X, "target": y}, |
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"search_space": search_space, |
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} |
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verbosity_list = [0, 1, 2, 3] |
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for verbosity in verbosity_list: |
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_base_test(search, opt_args={"verbosity": verbosity}) |
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def test_n_jobs(): |
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search = { |
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"objective_function": objective_function, |
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"function_parameter": {"features": X, "target": y}, |
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"search_space": search_space, |
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} |
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n_jobs_list = [1, 2, 4, 10, -1] |
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for n_jobs in n_jobs_list: |
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search["n_jobs"] = n_jobs |
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_base_test(search) |
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def test_positional_args(): |
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search = { |
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"objective_function": objective_function, |
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"function_parameter": {"features": X, "target": y}, |
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"search_space": search_space, |
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} |
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_base_test(search) |
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def test_n_iter(): |
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search = { |
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"objective_function": objective_function, |
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"function_parameter": {"features": X, "target": y}, |
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"search_space": search_space, |
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} |
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n_iter_list = [0, 1, 2, 4, 10, 100] |
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for n_iter in n_iter_list: |
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search["n_iter"] = n_iter |
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_base_test(search) |
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def test_optimizer(): |
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search = { |
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"objective_function": objective_function, |
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"function_parameter": {"features": X, "target": y}, |
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"search_space": search_space, |
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"n_iter": 33, |
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} |
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optimizer_list = [ |
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"HillClimbing", |
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"StochasticHillClimbing", |
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"TabuSearch", |
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"RandomSearch", |
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"RandomRestartHillClimbing", |
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"RandomAnnealing", |
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"SimulatedAnnealing", |
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"StochasticTunneling", |
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"ParallelTempering", |
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"ParticleSwarm", |
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"EvolutionStrategy", |
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"Bayesian", |
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"TPE", |
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"DecisionTree", |
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] |
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for optimizer in optimizer_list: |
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search["optimizer"] = optimizer |
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_base_test(search) |
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SimonBlanke /
Hyperactive
