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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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class Memory: |
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def __init__(self): |
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current_path = os.path.realpath(__file__) |
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meta_learn_path, _ = current_path.rsplit("/", 1) |
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self.meta_data_path = meta_learn_path + "/meta_data/" |
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def _get_opt_meta_data(self, _cand_, X, y): |
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results_dict = {} |
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para_list = [] |
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score_list = [] |
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for key in _cand_._space_.memory.keys(): |
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pos = np.fromstring(key, dtype=int) |
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para = _cand_._space_.pos2para(pos) |
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score = _cand_._space_.memory[key] |
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if score != 0: |
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para_list.append(para) |
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score_list.append(score) |
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results_dict["params"] = para_list |
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results_dict["mean_test_score"] = score_list |
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return results_dict |
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def collect(self, X, y, _cand_): |
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results_dict = self._get_opt_meta_data(_cand_, X, y) |
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para_pd = pd.DataFrame(results_dict["params"]) |
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md_model = para_pd.reindex(sorted(para_pd.columns), axis=1) |
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metric_pd = pd.DataFrame( |
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results_dict["mean_test_score"], columns=["mean_test_score"] |
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) |
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md_model = pd.concat([para_pd, metric_pd], axis=1, ignore_index=False) |
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return md_model |
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def _get_hash(self, object): |
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return hashlib.sha1(object).hexdigest() |
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def _get_func_str(self, func): |
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return inspect.getsource(func) |
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def _get_file_path(self, X_train, y_train, model_func): |
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func_str = self._get_func_str(model_func) |
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feature_hash = self._get_hash(X_train) |
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label_hash = self._get_hash(y_train) |
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self.func_path = self._get_hash(func_str.encode("utf-8")) + "/" |
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directory = self.meta_data_path + self.func_path |
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if not os.path.exists(directory): |
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os.makedirs(directory) |
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return directory + ( |
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"metadata" |
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+ "__feature_hash=" |
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+ feature_hash |
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+ "__label_hash=" |
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+ label_hash |
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+ "__.csv" |
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) |
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SimonBlanke /
Hyperactive
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