SimonBlanke /
Hyperactive
| @@ 9-59 (lines=51) @@ | ||
| 6 | ||
| 7 | from hyperactive.base import BaseExperiment |
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| 8 | ||
| 9 | class SklearnCvExperiment(BaseExperiment): |
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| 10 | ||
| 11 | def __init__(self, estimator, scoring, cv, X, y): |
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| 12 | self.estimator = estimator |
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| 13 | self.X = X |
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| 14 | self.y = y |
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| 15 | self.scoring = scoring |
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| 16 | self.cv = cv |
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| 17 | ||
| 18 | def _paramnames(self): |
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| 19 | """Return the parameter names of the search. |
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| 20 | ||
| 21 | Returns |
|
| 22 | ------- |
|
| 23 | list of str |
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| 24 | The parameter names of the search parameters. |
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| 25 | """ |
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| 26 | return list(self.estimator.get_params().keys()) |
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| 27 | ||
| 28 | def _score(self, **params): |
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| 29 | """Score the parameters. |
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| 30 | ||
| 31 | Parameters |
|
| 32 | ---------- |
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| 33 | params : dict with string keys |
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| 34 | Parameters to score. |
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| 35 | ||
| 36 | Returns |
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| 37 | ------- |
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| 38 | float |
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| 39 | The score of the parameters. |
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| 40 | dict |
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| 41 | Additional metadata about the search. |
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| 42 | """ |
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| 43 | estimator = clone(self.estimator) |
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| 44 | estimator.set_params(**params) |
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| 45 | ||
| 46 | cv_results = cross_validate( |
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| 47 | self.estimator, |
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| 48 | self.X, |
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| 49 | self.y, |
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| 50 | cv=self.cv, |
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| 51 | ) |
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| 52 | ||
| 53 | add_info_d = { |
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| 54 | "score_time": cv_results["score_time"], |
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| 55 | "fit_time": cv_results["fit_time"], |
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| 56 | "n_test_samples": _num_samples(self.X), |
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| 57 | } |
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| 58 | ||
| 59 | return cv_results["test_score"].mean(), add_info_d |
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| 60 | ||
| @@ 9-59 (lines=51) @@ | ||
| 6 | ||
| 7 | from hyperactive.base import BaseExperiment |
|
| 8 | ||
| 9 | class SklearnCvExperiment(BaseExperiment): |
|
| 10 | ||
| 11 | def __init__(self, estimator, scoring, cv, X, y): |
|
| 12 | self.estimator = estimator |
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| 13 | self.X = X |
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| 14 | self.y = y |
|
| 15 | self.scoring = scoring |
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| 16 | self.cv = cv |
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| 17 | ||
| 18 | def _paramnames(self): |
|
| 19 | """Return the parameter names of the search. |
|
| 20 | ||
| 21 | Returns |
|
| 22 | ------- |
|
| 23 | list of str |
|
| 24 | The parameter names of the search parameters. |
|
| 25 | """ |
|
| 26 | return list(self.estimator.get_params().keys()) |
|
| 27 | ||
| 28 | def _score(self, **params): |
|
| 29 | """Score the parameters. |
|
| 30 | ||
| 31 | Parameters |
|
| 32 | ---------- |
|
| 33 | params : dict with string keys |
|
| 34 | Parameters to score. |
|
| 35 | ||
| 36 | Returns |
|
| 37 | ------- |
|
| 38 | float |
|
| 39 | The score of the parameters. |
|
| 40 | dict |
|
| 41 | Additional metadata about the search. |
|
| 42 | """ |
|
| 43 | estimator = clone(self.estimator) |
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| 44 | estimator.set_params(**params) |
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| 45 | ||
| 46 | cv_results = cross_validate( |
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| 47 | self.estimator, |
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| 48 | self.X, |
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| 49 | self.y, |
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| 50 | cv=self.cv, |
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| 51 | ) |
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| 52 | ||
| 53 | add_info_d = { |
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| 54 | "score_time": cv_results["score_time"], |
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| 55 | "fit_time": cv_results["fit_time"], |
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| 56 | "n_test_samples": _num_samples(self.X), |
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| 57 | } |
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| 58 | ||
| 59 | return cv_results["test_score"].mean(), add_info_d |
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| 60 | ||
