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import time |
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import numpy as np |
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import pandas as pd |
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from sklearn.datasets import load_breast_cancer |
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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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def objective_function(opt): |
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score = -opt["x1"] * opt["x1"] |
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return score |
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search_space = { |
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"x1": list(np.arange(0, 10, 1)), |
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} |
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def test_memory_timeSave_0(): |
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data = load_breast_cancer() |
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X, y = data.data, data.target |
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def objective_function(opt): |
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dtc = DecisionTreeClassifier( |
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min_samples_split=opt["min_samples_split"] |
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) |
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scores = cross_val_score(dtc, X, y, cv=5) |
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return scores.mean() |
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search_space = { |
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"min_samples_split": np.arange(2, 20), |
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} |
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c_time1 = time.time() |
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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=100) |
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hyper.run() |
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diff_time1 = time.time() - c_time1 |
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c_time2 = time.time() |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=100, memory=False |
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) |
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hyper.run() |
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diff_time2 = time.time() - c_time2 |
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assert diff_time1 < diff_time2 * 0.8 |
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def test_memory_timeSave_1(): |
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data = load_breast_cancer() |
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X, y = data.data, data.target |
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def objective_function(opt): |
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dtc = DecisionTreeClassifier(max_depth=opt["max_depth"]) |
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scores = cross_val_score(dtc, X, y, cv=5) |
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return scores.mean() |
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search_space = { |
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"max_depth": list(np.arange(1, 101)), |
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} |
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results = pd.DataFrame(np.arange(1, 101), columns=["max_depth"]) |
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results["score"] = 0 |
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c_time1 = time.time() |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, search_space, n_iter=300, memory_warm_start=results |
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) |
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hyper.run() |
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diff_time1 = time.time() - c_time1 |
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assert diff_time1 < 1 |
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def test_memory_warm_start(): |
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data = load_breast_cancer() |
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X, y = data.data, data.target |
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def objective_function(opt): |
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dtc = DecisionTreeClassifier( |
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max_depth=opt["max_depth"], |
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min_samples_split=opt["min_samples_split"], |
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) |
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scores = cross_val_score(dtc, X, y, cv=5) |
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return scores.mean() |
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search_space = { |
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"max_depth": list(np.arange(1, 10)), |
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"min_samples_split": list(np.arange(2, 20)), |
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} |
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c_time1 = time.time() |
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hyper0 = Hyperactive() |
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hyper0.add_search(objective_function, search_space, n_iter=300) |
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hyper0.run() |
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diff_time1 = time.time() - c_time1 |
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c_time2 = time.time() |
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results0 = hyper0.results(objective_function) |
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hyper1 = Hyperactive() |
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hyper1.add_search( |
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objective_function, |
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search_space, |
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n_iter=300, |
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memory_warm_start=results0, |
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) |
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hyper1.run() |
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diff_time2 = time.time() - c_time2 |
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assert diff_time2 < diff_time1 * 0.5 |
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def test_memory_warm_start_manual(): |
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data = load_breast_cancer() |
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X, y = data.data, data.target |
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def objective_function(opt): |
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dtc = GradientBoostingClassifier(n_estimators=opt["n_estimators"],) |
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scores = cross_val_score(dtc, X, y, cv=5) |
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return scores.mean() |
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search_space = { |
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"n_estimators": list(np.arange(500, 502)), |
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} |
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c_time_1 = time.time() |
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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=1) |
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hyper.run() |
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diff_time_1 = time.time() - c_time_1 |
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memory_warm_start = pd.DataFrame( |
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[[500, 0.9], [501, 0.91]], columns=["n_estimators", "score"] |
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) |
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150
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c_time = time.time() |
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hyper0 = Hyperactive() |
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hyper0.add_search( |
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objective_function, |
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search_space, |
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n_iter=10, |
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memory_warm_start=memory_warm_start, |
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) |
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hyper0.run() |
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diff_time = time.time() - c_time |
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assert diff_time_1 > diff_time * 0.3 |
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SimonBlanke /
Hyperactive
