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Ignas BaranauskasandGitHub
af0f32db70
* Chnage the isort profile to black, and add pkg dir for black and flake8 Signed-off-by: Ignas Baranauskas <[email protected]> * Fix the formating Signed-off-by: Ignas Baranauskas <[email protected]> * Fix flake8 lint issues Signed-off-by: Ignas Baranauskas <[email protected]> --------- Signed-off-by: Ignas Baranauskas <[email protected]>
78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
# Copyright 2022 The Kubeflow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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class ParameterConfig:
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"""
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Class to hold the parameter configuration for an experiment.
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Attributes:
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name_ids (dict): Mapping from a parameter name to the index of that
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parameter in the other fields.
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dim (int): Dimension of the vectors created when parameter assignments
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are mapped to a vector. Each int, double, or discrete parameter
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adds one to the dimension, and each categorical parameter adds
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the number of feasible values for that parameter due to one-hot
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encoding.
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lower_bounds (ndarray): The lower bounds for each parameter in the
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search space.
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upper_bounds (ndarray): The lower bounds for each parameter in the
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search space.
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parameter_types (list): The type of each parameter.
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names (list): The name of each parameter.
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discrete_info (list): A list of dicts where each dict contains the
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information for a single discrete parameter. An example of a dict
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is {"name": "discrete_parameter, "values": [2, 3, 5]}]
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categorical_info (list): A list of dicts where each dict contains the
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information for a single categorical parameter. An example dict is
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{"name": "cat_param", "values": ["true", "false"], "number": 2}.
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"""
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def __init__(
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self,
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name_ids,
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dim,
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lower_bounds,
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upper_bounds,
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parameter_types,
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names,
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discrete_info,
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categorical_info,
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):
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self.name_ids = name_ids
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self.dim = dim
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self.lower_bounds = np.array(lower_bounds).reshape((1, dim))
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self.upper_bounds = np.array(upper_bounds).reshape((1, dim))
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self.parameter_types = parameter_types
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self.names = names
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self.discrete_info = discrete_info
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self.categorical_info = categorical_info
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if len(self.names) != len(set(self.names)):
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raise Exception("Parameter names are not unique.")
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def create_scaler(self):
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search_space = np.append(self.lower_bounds, self.upper_bounds, axis=0)
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scaler = MinMaxScaler()
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scaler.fit(search_space)
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return scaler
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def random_sample(self):
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new_sample = np.random.uniform(
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self.lower_bounds, self.upper_bounds, size=(1, self.dim)
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)
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return new_sample
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