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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 sys |
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import glob |
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import json |
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import dill |
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import pickle |
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import datetime |
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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_, _cand_): |
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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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class ShortTermMemory(Memory): |
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def __init__(self, _space_, _main_args_, _cand_): |
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super().__init__(_space_, _main_args_, _cand_) |
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class LongTermMemory(Memory): |
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def __init__(self, _space_, _main_args_, _cand_): |
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super().__init__(_space_, _main_args_, _cand_) |
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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.datetime = datetime.datetime.now().strftime("%d.%m.%Y - %H:%M:%S") |
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func_str = self._get_func_str(_cand_.func_) |
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self.func_path_ = self._get_hash(func_str.encode("utf-8")) + "/" |
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self.meta_path = meta_learn_path + "/meta_data/" |
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self.func_path = self.meta_path + self.func_path_ |
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self.date_path = self.meta_path + self.func_path_ + self.datetime + "/" |
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if not os.path.exists(self.date_path): |
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os.makedirs(self.date_path, exist_ok=True) |
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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_, _opt_args_, _cand_): |
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path = self._get_file_path(_cand_.func_) |
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meta_data = self._collect(_cand_) |
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meta_data["run"] = self.datetime |
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self._save_toCSV(meta_data, path) |
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obj_func_path = self.func_path + "objective_function.py" |
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if not os.path.exists(obj_func_path): |
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file = open(obj_func_path, "w") |
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file.write(self._get_func_str(_cand_.func_)) |
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file.close() |
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search_config_path = self.date_path + "search_config.py" |
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search_config_temp = dict(self._main_args_.search_config) |
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for key in search_config_temp.keys(): |
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if isinstance(key, str): |
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continue |
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search_config_temp[key.__name__] = search_config_temp[key] |
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del search_config_temp[key] |
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search_config_str = "search_config = " + str(search_config_temp) |
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if not os.path.exists(search_config_path): |
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file = open(search_config_path, "w") |
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file.write(search_config_str) |
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file.close() |
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""" |
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os.chdir(self.date_path) |
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os.system("black search_config.py") |
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os.getcwd() |
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""" |
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run_data = { |
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"random_state": self._main_args_.random_state, |
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"max_time": self._main_args_.random_state, |
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"n_iter": self._main_args_.n_iter, |
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"optimizer": self._main_args_.optimizer, |
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"n_jobs": self._main_args_.n_jobs, |
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"eval_time": np.array(_cand_.eval_time).sum(), |
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"total_time": _cand_.total_time, |
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} |
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with open("run_data.json", "w") as f: |
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json.dump(run_data, f, indent=4) |
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""" |
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print("_opt_args_.kwargs_opt", _opt_args_.kwargs_opt) |
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opt_para = pd.DataFrame.from_dict(_opt_args_.kwargs_opt, dtype=object) |
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print("opt_para", opt_para) |
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opt_para.to_csv( |
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self.meta_data_path + self.func_path + self.datetime + "opt_para", |
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index=False, |
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) |
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""" |
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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", "eval_time", "run"] |
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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 = self._get_func_data_names() |
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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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print("\rLoading meta data successful ") |
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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=int) |
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para = self._space_.pos2para(pos) |
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score = self.memory_dict[key] |
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for key in para.keys(): |
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if ( |
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not isinstance(para[key], int) |
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and not isinstance(para[key], float) |
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and not isinstance(para[key], str) |
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): |
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para_dill = dill.dumps(para[key]) |
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para_hash = self._get_hash(para_dill) |
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with open( |
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self.func_path + str(para_hash) + ".pkl", "wb" |
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) as pickle_file: |
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dill.dump(para_dill, pickle_file) |
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para[key] = para_hash |
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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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para = paras.iloc[[idx]] |
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pos = self._space_.para2pos(paras.iloc[[idx]], self._get_pkl_hash) |
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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 _collect(self, _cand_): |
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results_dict = self._get_opt_meta_data() |
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para_pd = pd.DataFrame(results_dict["params"]) |
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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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eval_time = pd.DataFrame(_cand_.eval_time, columns=["eval_time"]) |
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md_model = pd.concat( |
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[para_pd, metric_pd, eval_time], axis=1, ignore_index=False |
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) |
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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_subdirs(self): |
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subdirs = glob.glob(self.func_path + "*/") |
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return subdirs |
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def _get_func_data_names1(self): |
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subdirs = self._get_subdirs() |
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path_list = [] |
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for subdir in subdirs: |
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paths = glob.glob(subdir + "*.csv") |
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path_list = path_list + paths |
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return path_list |
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def _get_func_data_names(self): |
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paths = glob.glob(self.func_path + "*_.csv") |
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return paths |
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def _get_pkl_hash(self, hash): |
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paths = glob.glob(self.func_path + hash + "*.pkl") |
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return paths |
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def _get_file_path(self, 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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if not os.path.exists(self.date_path): |
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os.makedirs(self.date_path) |
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return self.func_path + (feature_hash + "_" + label_hash + "_.csv") |
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SimonBlanke /
Hyperactive
