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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 os |
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import glob |
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import hashlib |
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import inspect |
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import numpy as np |
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import pandas as pd |
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class Memory: |
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def __init__(self, _space_, _main_args_): |
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self._space_ = _space_ |
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self._main_args_ = _main_args_ |
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self.pos_best = None |
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self.score_best = -np.inf |
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self.memory_type = _main_args_.memory |
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self.memory_dict = {} |
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self.meta_data_found = False |
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def load_memory(self, model_func): |
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pass |
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def save_memory(self, _main_args_, _cand_): |
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pass |
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class ShortTermMemory(Memory): |
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def __init__(self, _space_, _main_args_): |
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super().__init__(_space_, _main_args_) |
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class LongTermMemory(Memory): |
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def __init__(self, _space_, _main_args_): |
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super().__init__(_space_, _main_args_) |
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self.score_col_name = "mean_test_score" |
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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 load_memory(self, model_func): |
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para, score = self._read_func_metadata(model_func) |
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if para is None or score is None: |
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return |
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self._load_data_into_memory(para, score) |
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def save_memory(self, _main_args_, _cand_): |
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meta_data = self._collect() |
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path = self._get_file_path(_cand_.func_) |
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self._save_toCSV(meta_data, path) |
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def _save_toCSV(self, meta_data_new, path): |
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if os.path.exists(path): |
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meta_data_old = pd.read_csv(path) |
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meta_data = meta_data_old.append(meta_data_new) |
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columns = list(meta_data.columns) |
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noScore = ["mean_test_score", "cv_default_score"] |
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columns_noScore = [c for c in columns if c not in noScore] |
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meta_data = meta_data.drop_duplicates(subset=columns_noScore) |
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else: |
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meta_data = meta_data_new |
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meta_data.to_csv(path, index=False) |
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def _read_func_metadata(self, model_func): |
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paths = glob.glob(self._get_func_file_paths(model_func)) |
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meta_data_list = [] |
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for path in paths: |
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meta_data = pd.read_csv(path) |
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meta_data_list.append(meta_data) |
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self.meta_data_found = True |
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if len(meta_data_list) > 0: |
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meta_data = pd.concat(meta_data_list, ignore_index=True) |
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column_names = meta_data.columns |
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score_name = [name for name in column_names if self.score_col_name in name] |
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para = meta_data.drop(score_name, axis=1) |
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score = meta_data[score_name] |
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return para, score |
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else: |
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print("Warning: No meta data found for following function:", model_func) |
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return None, None |
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def _get_opt_meta_data(self): |
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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 self.memory_dict.keys(): |
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pos = np.fromstring(key, dtype=float) |
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para = self._space_.pos2para(pos) |
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score = self.memory_dict[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 _load_data_into_memory(self, paras, scores): |
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for idx in range(paras.shape[0]): |
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pos = self._space_.para2pos(paras.iloc[[idx]]) |
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pos_str = pos.tostring() |
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score = float(scores.values[idx]) |
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self.memory_dict[pos_str] = score |
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if score > self.score_best: |
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self.score_best = score |
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self.pos_best = pos |
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def _get_para(self): |
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results_dict = self._get_opt_meta_data() |
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return pd.DataFrame(results_dict["params"]) |
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def _get_score(self): |
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results_dict = self._get_opt_meta_data() |
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return pd.DataFrame( |
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results_dict["mean_test_score"], columns=["mean_test_score"] |
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) |
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def _collect(self): |
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para_pd = self._get_para() |
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md_model = para_pd.reindex(sorted(para_pd.columns), axis=1) |
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metric_pd = self._get_score() |
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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_func_file_paths(self, model_func): |
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func_str = self._get_func_str(model_func) |
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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, exist_ok=True) |
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return directory + ("metadata" + "*" + "__.csv") |
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def _get_file_path(self, model_func): |
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func_str = self._get_func_str(model_func) |
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feature_hash = self._get_hash(self._main_args_.X) |
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label_hash = self._get_hash(self._main_args_.y) |
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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
