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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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View Code Duplication |
class DictClass: |
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def __init__(self, search_space): |
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self.search_space = search_space |
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def __getitem__(self, key): |
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return self.search_space[key] |
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def keys(self): |
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return self.search_space.keys() |
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def values(self): |
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return self.search_space.values() |
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View Code Duplication |
class SearchSpace(DictClass): |
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def __init__(self, search_space): |
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super().__init__(search_space) |
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self.search_space = search_space |
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self.dim_keys = list(search_space.keys()) |
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self.values_l = list(self.search_space.values()) |
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positions = {} |
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for key in search_space.keys(): |
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positions[key] = np.array(range(len(search_space[key]))) |
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self.positions = positions |
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self.check_list() |
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self.check_non_num_values() |
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self.data_types = self.dim_types() |
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self.func2str = self._create_num_str_ss() |
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def __call__(self): |
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return self.search_space |
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def dim_types(self): |
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data_types = {} |
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for dim_key in self.dim_keys: |
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dim_values = np.array(list(self.search_space[dim_key])) |
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try: |
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np.subtract(dim_values, dim_values) |
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np.array(dim_values).searchsorted(dim_values) |
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except: |
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_type_ = "object" |
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else: |
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_type_ = "number" |
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data_types[dim_key] = _type_ |
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return data_types |
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def _create_num_str_ss(self): |
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func2str = {} |
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for dim_key in self.dim_keys: |
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if self.data_types[dim_key] == "number": |
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func2str[dim_key] = self.search_space[dim_key] |
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else: |
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func2str[dim_key] = [] |
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dim_values = self.search_space[dim_key] |
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for value in dim_values: |
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try: |
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func_name = value.__name__ |
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except: |
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func_name = value |
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func2str[dim_key].append(func_name) |
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return func2str |
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def check_list(self): |
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for dim_key in self.dim_keys: |
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search_dim = self.search_space[dim_key] |
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err_msg = "\n Value in '{}' of search space dictionary must be of type list \n".format( |
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dim_key |
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) |
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if not isinstance(search_dim, list): |
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raise ValueError(err_msg) |
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@staticmethod |
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def is_function(value): |
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try: |
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value.__name__ |
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except: |
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return False |
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else: |
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return True |
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@staticmethod |
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def is_number(value): |
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try: |
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float(value) |
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value * 0.1 |
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value - 0.1 |
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value / 0.1 |
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except: |
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return False |
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else: |
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return True |
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def _string_or_object(self, dim_key, dim_values): |
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for dim_value in dim_values: |
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is_str = isinstance(dim_value, str) |
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is_func = self.is_function(dim_value) |
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is_number = self.is_number(dim_value) |
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if not is_str and not is_func and not is_number: |
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msg = "\n The value '{}' of type '{}' in the search space dimension '{}' must be number, string or function \n".format( |
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dim_value, type(dim_value), dim_key |
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) |
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raise ValueError(msg) |
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def check_non_num_values(self): |
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for dim_key in self.dim_keys: |
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dim_values = np.array(list(self.search_space[dim_key])) |
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try: |
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np.subtract(dim_values, dim_values) |
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np.array(dim_values).searchsorted(dim_values) |
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except: |
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self._string_or_object(dim_key, dim_values) |
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else: |
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if dim_values.ndim != 1: |
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msg = "Array-like object in '{}' must be one dimensional".format( |
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dim_key |
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) |
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raise ValueError(msg) |
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SimonBlanke /
Hyperactive
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