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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 dill |
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import datetime |
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import numpy as np |
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import pandas as pd |
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import hashlib |
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import inspect |
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from .paths import HypermemoryPaths |
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from .io_helpers import ( |
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save_object, |
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load_object, |
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save_json, |
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load_json, |
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save_dataframe, |
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load_dataframes, |
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) |
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from .verbosity import VerbosityLVL0, VerbosityLVL1 |
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from .dataset_features import dataset_features |
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from .memory_conv import memory_dict2dataframe, convert_dataframe, dataframe2memory_dict |
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def get_datetime(): |
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return datetime.datetime.now().strftime("%d.%m.%Y - %H:%M:%S:%f") |
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class HyperactiveWrapper: |
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def __init__(self, main_path, X, y, model, search_space, verbosity=1): |
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self.paths = HypermemoryPaths(main_path) |
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self.paths.add_directory(name="X", prefix="X_ID:", id_type="array", object_=X) |
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self.paths.add_directory(name="y", prefix="y_ID:", id_type="array", object_=y) |
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self.paths.add_directory( |
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name="model", prefix="model_ID:", id_type="function", object_=model |
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) |
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self.paths.add_directory( |
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name="search_space", |
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prefix="search_space_ID:", |
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id_type="dictionary", |
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object_=search_space, |
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) |
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self.X = X |
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self.y = y |
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self.model = model |
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self.search_space = search_space |
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if verbosity == 0: |
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self.verb = VerbosityLVL0() |
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else: |
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self.verb = VerbosityLVL1() |
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def _drop_duplicates(self, dataframe): |
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columns_drop = list(self.search_space.keys()) |
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return dataframe.drop_duplicates(subset=columns_drop, keep="last") |
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def _load_dataframes(self): |
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subdirs = self.paths.subdirs("model") |
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dataframes_all = [] |
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for subdir in subdirs: |
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search_space = load_object(path=subdir, name="search_space") |
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dataframes = load_dataframes(subdir) |
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for dataframe in dataframes: |
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dataframe = convert_dataframe( |
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dataframe, search_space, self.search_space |
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) |
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dataframes_all.append(dataframe) |
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return dataframes_all |
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def load(self): |
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self.verb.load_search_data(self.model) |
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dataframes_all = self._load_dataframes() |
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if len(dataframes_all) == 0: |
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return {} |
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dataframe = pd.concat(dataframes_all, axis=0) |
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dataframe = self._drop_duplicates(dataframe) |
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memory_dict = dataframe2memory_dict(dataframe, self.search_space) |
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self.verb.load_search_data_success(self.model, dataframe) |
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return memory_dict |
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def save(self, memory_dict): |
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dataframe = memory_dict2dataframe(memory_dict, self.search_space) |
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if len(dataframe) == 0: |
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self.verb.save_search_data_canceled(self.model) |
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else: |
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self.verb.save_search_data(self.model) |
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X_info = dataset_features(self.X) |
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y_info = dataset_features(self.y) |
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io_X_path = self.paths.path_dict["X"] |
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io_y_path = self.paths.path_dict["y"] |
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io_model_path = self.paths.path_dict["model"] |
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io_search_space_path = self.paths.path_dict["search_space"] |
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save_json(io_X_path, "X_meta_data", X_info) |
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save_json(io_y_path, "y_meta_data", y_info) |
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save_object(io_model_path, "model", self.model) |
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save_object(io_search_space_path, "search_space", self.search_space) |
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save_dataframe( |
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io_search_space_path, "search_data_" + str(get_datetime()), dataframe |
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) |
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self.verb.save_search_data_success(self.model, dataframe) |
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SimonBlanke /
Optimization-Metadata
