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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 sklearn.ensemble import GradientBoostingClassifier |
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from hyperactive import Hyperactive |
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data = load_iris() |
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X = data.data |
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y = data.target |
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memory = False |
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def model(para, X, y): |
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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, X, y, cv=2) |
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return scores.mean() |
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search_config = { |
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model: { |
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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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} |
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def model0(para, X_train, y_train): |
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model = DecisionTreeClassifier(criterion=para["criterion"]) |
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scores = cross_val_score(model, X_train, y_train, cv=2) |
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return scores.mean() |
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def model1(para, X_train, y_train): |
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model = GradientBoostingClassifier(n_estimators=para["n_estimators"]) |
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scores = cross_val_score(model, X_train, y_train, cv=2) |
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return scores.mean() |
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search_config_2 = { |
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model0: {"criterion": ["gini"]}, |
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model1: {"n_estimators": range(10, 100)}, |
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} |
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def test_n_jobs_2(): |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, n_jobs=2) |
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def test_n_jobs_4(): |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, n_jobs=4) |
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def test_positional_args(): |
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opt0 = Hyperactive(X, y, random_state=False, memory=memory) |
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opt0.search(search_config) |
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opt1 = Hyperactive(X, y, random_state=1, memory=memory) |
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opt1.search(search_config) |
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opt2 = Hyperactive(X, y, random_state=1, memory=memory) |
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opt2.search(search_config) |
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def test_random_state(): |
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opt0 = Hyperactive(X, y, random_state=False, memory=memory) |
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opt0.search(search_config) |
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opt1 = Hyperactive(X, y, random_state=0, memory=memory) |
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opt1.search(search_config) |
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opt2 = Hyperactive(X, y, random_state=1, memory=memory) |
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opt2.search(search_config) |
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def test_max_time(): |
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opt0 = Hyperactive(X, y, memory=memory) |
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opt0.search(search_config, max_time=0.00001) |
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def test_memory(): |
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opt0 = Hyperactive(X, y, memory=True) |
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opt0.search(search_config) |
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opt1 = Hyperactive(X, y, memory=False) |
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opt1.search(search_config) |
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opt2 = Hyperactive(X, y, memory="short") |
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opt2.search(search_config) |
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opt3 = Hyperactive(X, y, memory="long") |
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opt3.search(search_config) |
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opt4 = Hyperactive(X, y, memory="long") |
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opt4.search(search_config) |
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opt = Hyperactive(X, y, memory=memory, verbosity=0) |
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opt.search(search_config) |
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""" |
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def test_dill(): |
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from sklearn.gaussian_process import GaussianProcessClassifier |
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from sklearn.gaussian_process.kernels import RBF, Matern |
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from hypermemory import reset_memory |
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reset_memory( |
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meta_path="/home/simon/git_workspace/Hyperactive/hyperactive/meta_data/", |
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force_true=True, |
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) |
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def model(para, X, y): |
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gpc = GaussianProcessClassifier(kernel=para["kernel"]) |
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scores = cross_val_score(gpc, X, y, cv=2) |
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return scores.mean() |
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search_config = {model: {"kernel": [RBF(), Matern()]}} |
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opt0 = Hyperactive(X, y, memory="long") |
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opt0.search(search_config) |
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print("\n\n ------------------------------------------------------- \n\n") |
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opt1 = Hyperactive(X, y, memory="long") |
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opt1.search(search_config) |
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""" |
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def test_verbosity0(): |
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opt = Hyperactive(X, y, verbosity=0, memory=memory) |
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opt.search(search_config) |
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def test_verbosity1(): |
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opt = Hyperactive(X, y, verbosity=0, memory=memory) |
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opt.search(search_config, n_jobs=2) |
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def test_verbosity2(): |
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opt = Hyperactive(X, y, verbosity=1, memory=memory) |
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opt.search(search_config, n_jobs=2) |
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def test_verbosity3(): |
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opt = Hyperactive(X, y, verbosity=1, memory=memory) |
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opt.search(search_config) |
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def test_verbosity4(): |
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opt = Hyperactive(X, y, verbosity=2, memory=memory) |
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opt.search(search_config) |
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def test_verbosity5(): |
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opt = Hyperactive(X, y, verbosity=2, memory=memory) |
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opt.search(search_config, n_jobs=2) |
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def test_scatter_init(): |
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init_config = {model: {"scatter_init": 10}} |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, init_config=init_config) |
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opt = Hyperactive(X, y, memory=memory, verbosity=0) |
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opt.search(search_config, init_config=init_config) |
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def test_warm_start(): |
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init_config = { |
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model: {"max_depth": 10, "min_samples_split": 2, "min_samples_leaf": 5} |
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} |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, n_iter=0, init_config=init_config) |
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assert opt.results[model] == init_config[model] |
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def test_warm_start_multiple(): |
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opt = Hyperactive(X, y, memory="short") |
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opt.search(search_config, n_iter=10, n_jobs=2) |
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def test_partial_warm_start(): |
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init_config = {model: {"min_samples_split": 2, "min_samples_leaf": 5}} |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, n_iter=0, init_config=init_config) |
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opt = Hyperactive(X, y, memory=memory, verbosity=0) |
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opt.search(search_config, n_iter=0, init_config=init_config) |
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def test_optimizer_args(): |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, optimizer={"HillClimbing": {"epsilon": 0.1}}) |
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""" |
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def test_ray_1(): |
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ray.init() |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, n_jobs=1) |
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def test_ray_2(): |
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ray.init() |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search(search_config, n_jobs=2) |
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""" |
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SimonBlanke /
Hyperactive
