1 | import random |
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2 | import numpy as pd |
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3 | import pandas as pd |
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4 | |||
5 | from sklearn.datasets import load_iris |
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6 | from sklearn.datasets import make_classification |
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7 | from sklearn.neighbors import KNeighborsClassifier |
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8 | from sklearn.ensemble import GradientBoostingRegressor |
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9 | from sklearn.model_selection import cross_val_score |
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10 | |||
11 | from hyperactive import Hyperactive |
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12 | |||
13 | |||
14 | def model(opt): |
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15 | knr = KNeighborsClassifier(n_neighbors=opt["n_neighbors"]) |
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16 | scores = cross_val_score(knr, X, y, cv=5) |
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17 | score = scores.mean() |
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18 | |||
19 | return score |
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20 | |||
21 | |||
22 | search_space = { |
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23 | "n_neighbors": list(range(1, 80)), |
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24 | } |
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25 | |||
26 | |||
27 | search_data_list = [] |
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28 | |||
29 | for i in range(25): |
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30 | n_samples = random.randint(100, 1000) |
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31 | n_features = random.randint(3, 20) |
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32 | n_informative = n_features - random.randint(0, n_features - 2) |
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33 | |||
34 | X, y = make_classification( |
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35 | n_samples=n_samples, |
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36 | n_classes=2, |
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37 | n_features=n_features, |
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38 | n_informative=n_informative, |
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39 | n_redundant=0, |
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40 | random_state=i, |
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41 | ) |
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42 | |||
43 | hyper = Hyperactive(verbosity=False) |
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44 | hyper.add_search(model, search_space, n_iter=10) |
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45 | hyper.run() |
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46 | |||
47 | search_data = hyper.search_data(model) |
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48 | |||
49 | search_data["size_X"] = X.size |
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50 | search_data["itemsize_X"] = X.itemsize |
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51 | search_data["ndim_X"] = X.ndim |
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52 | |||
53 | search_data["size_y"] = y.size |
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54 | search_data["itemsize_y"] = y.itemsize |
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55 | search_data["ndim_y"] = y.ndim |
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56 | |||
57 | search_data_list.append(search_data) |
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58 | |||
59 | |||
60 | meta_data = pd.concat(search_data_list) |
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61 | |||
62 | X_meta = meta_data.drop(["score"], axis=1) |
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63 | y_meta = meta_data["score"] |
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64 | |||
65 | |||
66 | gbr = GradientBoostingRegressor() |
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67 | gbr.fit(X_meta, y_meta) |
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68 | |||
69 | data = load_iris() |
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70 | X_new, y_new = data.data, data.target |
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71 | |||
72 | X_meta_test = pd.DataFrame(range(1, 100), columns=["n_neighbors"]) |
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73 | |||
74 | X_meta_test["size_X"] = X_new.size |
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75 | X_meta_test["itemsize_X"] = X_new.itemsize |
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76 | X_meta_test["ndim_X"] = X_new.ndim |
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77 | |||
78 | X_meta_test["size_y"] = y_new.size |
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79 | X_meta_test["itemsize_y"] = y_new.itemsize |
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80 | X_meta_test["ndim_y"] = y_new.ndim |
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81 | |||
82 | |||
83 | y_meta_pred = gbr.predict(X_meta_test) |
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84 | |||
85 | y_meta_pred_max_idx = y_meta_pred.argmax() |
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86 | n_neighbors_best = search_space["n_neighbors"][y_meta_pred_max_idx] |
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87 | |||
88 | hyper = Hyperactive() |
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89 | hyper.add_search(model, search_space, n_iter=200) |
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90 | hyper.run() |
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91 |