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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 json |
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import glob |
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import numpy as np |
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import pandas as pd |
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from functools import partial |
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from .memory_io import MemoryIO |
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from .utils import model_id, model_path |
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class MemoryLoad(MemoryIO): |
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def __init__(self, X, y, model, search_space): |
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super().__init__(X, y, model, search_space) |
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self.pos_best = None |
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self.score_best = -np.inf |
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self.meta_data_found = False |
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self.con_ids = [] |
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if not os.path.exists(self.meta_path + "model_connections.json"): |
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with open(self.meta_path + "model_connections.json", "w") as f: |
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json.dump({}, f, indent=4) |
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with open(self.meta_path + "model_connections.json") as f: |
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self.model_con = json.load(f) |
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model_id_ = model_id(self.model) |
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if model_id_ in self.model_con: |
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self._get_id_list(self.model_con[model_id_]) |
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else: |
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self.con_ids = [model_id_] |
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self.con_ids = set(self.con_ids) |
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def _get_id_list(self, id_list): |
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self.con_ids = self.con_ids + id_list |
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for id in id_list: |
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id_list_new = self.model_con[id] |
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if set(id_list_new).issubset(self.con_ids): |
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continue |
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self._get_id_list(id_list_new) |
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def hyperactive_memory_load(self): |
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para, score = self._read_func_metadata(self.model) |
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if para is None or score is None: |
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print("No meta data found") |
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return {} |
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# print(len(para), "samples found") |
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# _verb_.load_samples(para) |
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memory_dict = self._load_data_into_memory(para, score) |
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self.n_dims = len(para.columns) |
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return memory_dict |
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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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para = meta_data[self.para_names] |
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score = meta_data[self.score_col_name] |
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# _verb_.load_meta_data() |
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return para, score |
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else: |
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# _verb_.no_meta_data(model_func) |
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return None, None |
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def _get_func_data_names(self): |
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paths = [] |
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for id in self.con_ids: |
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paths = paths + glob.glob( |
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self.meta_path + model_path(id) + self.meta_data_name |
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) |
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return paths |
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def idx_closest_values(self, X, Y): |
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dist = np.absolute(X - Y[:, np.newaxis]) |
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return dist.argmin(axis=1) |
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def apply_index(self, pos_key, df): |
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return ( |
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self.search_space[pos_key].index(df) |
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if df in self.search_space[pos_key] |
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else None |
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) |
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def para2pos(self, paras): |
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paras = paras[self.para_names] |
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pos = paras.copy() |
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for pos_key in self.search_space: |
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is_float = isinstance(self.search_space[pos_key][0], float) |
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if is_float: |
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pos[pos_key] = self.idx_closest_values( |
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self.search_space[pos_key], paras[pos_key] |
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) |
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else: |
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apply_index = partial(self.apply_index, pos_key) |
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pos[pos_key] = paras[pos_key].apply(apply_index) |
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# print("\n pos \n", pos, type(pos)) |
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pos.dropna(how="any", inplace=True) |
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pos = pos.astype("int64") |
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return pos |
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def _load_data_into_memory(self, paras, scores): |
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# print("\n paras \n", paras, type(paras)) |
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paras = paras.replace(self.hash2obj) |
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pos = self.para2pos(paras) |
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scores_np = np.array(scores)[:, 0] |
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paras_np = np.array(paras) |
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print("scores_np", scores_np) |
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idx = np.argmax(scores_np) |
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self.score_best = scores_np[idx] |
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self.pos_best = paras_np[idx] |
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print("self.score_best", self.score_best) |
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scores = scores.to_dict("records") |
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tuple_list = list(map(tuple, pos.values)) |
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memory_dict = dict(zip(tuple_list, scores)) |
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print("Meta data successfully loaded") |
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return memory_dict |
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SimonBlanke /
Optimization-Metadata
