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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 numpy as np |
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import pandas as pd |
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from functools import reduce |
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from typing import Optional |
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from ..._result import Result |
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def check_numpy_array(search_space): |
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for para_name, dim_values in search_space.items(): |
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def error_message(wrong_type): |
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return "\n Value in '{}' of search space dictionary must be of type array but is '{}' \n".format( |
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para_name, wrong_type |
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) |
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if not isinstance(dim_values, np.ndarray): |
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raise ValueError(error_message(type(dim_values))) |
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class Converter: |
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def __init__(self, search_space: dict, constraints: list = None) -> None: |
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check_numpy_array(search_space) |
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self.n_dimensions = len(search_space) |
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self.search_space = search_space |
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if constraints is None: |
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self.constraints = [] |
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else: |
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self.constraints = constraints |
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self.para_names = list(search_space.keys()) |
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dim_sizes_list = [len(array) for array in search_space.values()] |
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self.dim_sizes = np.array(dim_sizes_list) |
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# product of list |
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self.search_space_size = reduce((lambda x, y: x * y), dim_sizes_list) |
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self.max_dim = np.amax(self.dim_sizes) |
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self.search_space_positions = [ |
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list(range(len(array))) for array in search_space.values() |
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] |
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self.pos_space = dict( |
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zip( |
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self.para_names, |
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[np.arange(len(array)) for array in search_space.values()], |
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) |
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) |
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self.max_positions = self.dim_sizes - 1 |
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self.search_space_values = list(search_space.values()) |
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def not_in_constraint(self, position): |
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para = self.value2para(self.position2value(position)) |
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for constraint in self.constraints: |
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if not constraint(para): |
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return False |
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return True |
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def returnNoneIfArgNone(func_): |
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def wrapper(self, *args): |
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for arg in [*args]: |
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if arg is None: |
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return None |
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else: |
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return func_(self, *args) |
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return wrapper |
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@returnNoneIfArgNone |
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def position2value(self, position: Optional[list]) -> Optional[list]: |
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value = [] |
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for n, space_dim in enumerate(self.search_space_values): |
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value.append(space_dim[position[n]]) |
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return value |
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@returnNoneIfArgNone |
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def value2position(self, value: Optional[list]) -> Optional[list]: |
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position = [] |
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for n, space_dim in enumerate(self.search_space_values): |
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pos = np.abs(value[n] - np.array(space_dim)).argmin() |
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position.append(int(pos)) |
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return np.array(position) |
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@returnNoneIfArgNone |
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def value2para(self, value: Optional[list]) -> Optional[dict]: |
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para = {} |
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for key, p_ in zip(self.para_names, value): |
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para[key] = p_ |
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return para |
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@returnNoneIfArgNone |
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def para2value(self, para: Optional[dict]) -> Optional[list]: |
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value = [] |
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for para_name in self.para_names: |
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value.append(para[para_name]) |
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return value |
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@returnNoneIfArgNone |
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def values2positions(self, values: Optional[list]) -> Optional[list]: |
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positions_temp = [] |
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values_np = np.array(values) |
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for n, space_dim in enumerate(self.search_space_values): |
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values_1d = values_np[:, n] |
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# m_conv = np.abs(values_1d - space_dim[:, np.newaxis]) |
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# pos_list = m_conv.argmin(0) |
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pos_list = space_dim.searchsorted(values_1d) |
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positions_temp.append(pos_list) |
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positions = list(np.array(positions_temp).T.astype(int)) |
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return positions |
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@returnNoneIfArgNone |
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def positions2values(self, positions: Optional[list]) -> Optional[list]: |
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values = [] |
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positions_np = np.array(positions) |
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for n, space_dim in enumerate(self.search_space_values): |
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pos_1d = positions_np[:, n] |
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value_ = np.take(space_dim, pos_1d, axis=0) |
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values.append(value_) |
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values = [list(t) for t in zip(*values)] |
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return values |
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@returnNoneIfArgNone |
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def values2paras(self, values: list) -> list: |
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paras = [] |
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for value in values: |
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paras.append(self.value2para(value)) |
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return paras |
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@returnNoneIfArgNone |
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def positions_scores2memory_dict( |
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self, positions: Optional[list], scores: Optional[list] |
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) -> Optional[dict]: |
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value_tuple_list = list(map(tuple, positions)) |
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# Convert scores to Result objects |
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result_objects = [Result(float(score), {}) for score in scores] |
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memory_dict = dict(zip(value_tuple_list, result_objects)) |
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return memory_dict |
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@returnNoneIfArgNone |
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def memory_dict2positions_scores(self, memory_dict: Optional[dict]): |
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positions = [np.array(pos).astype(int) for pos in list(memory_dict.keys())] |
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# Extract scores from Result objects |
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scores = [result.score if isinstance(result, Result) else result |
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for result in memory_dict.values()] |
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return positions, scores |
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@returnNoneIfArgNone |
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def dataframe2memory_dict( |
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self, dataframe: Optional[pd.DataFrame] |
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) -> Optional[dict]: |
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parameter = set(self.search_space.keys()) |
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memory_para = set(dataframe.columns) |
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if parameter <= memory_para: |
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values = list(dataframe[self.para_names].values) |
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positions = self.values2positions(values) |
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scores = dataframe["score"] |
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memory_dict = self.positions_scores2memory_dict(positions, scores) |
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return memory_dict |
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else: |
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missing = parameter - memory_para |
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print( |
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"\nWarning:", |
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'"{}"'.format(*missing), |
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"is in search_space but not in memory dataframe", |
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) |
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print("Optimization run will continue without memory warm start\n") |
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return {} |
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@returnNoneIfArgNone |
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def memory_dict2dataframe( |
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self, memory_dict: Optional[dict] |
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) -> Optional[pd.DataFrame]: |
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positions, score = self.memory_dict2positions_scores(memory_dict) |
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values = self.positions2values(positions) |
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dataframe = pd.DataFrame(values, columns=self.para_names) |
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dataframe["score"] = score |
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return dataframe |
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SimonBlanke /
Gradient-Free-Optimizers
