Adding random suggestion algorithm implementation and manifests #540
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/lgtm |
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| self.manager_addr = "katib-manager" | ||
| self.manager_port = 6789 | ||
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| def _get_experiment(self, name): |
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Should this be part of a common lib?
| return y_array | ||
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| def parse_x_next_vector(x_next, param_types, param_names, discrete_info, categorical_info): |
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Nit - The names of these methods (and the class) feel somewhat weird. "Parse*" seems to suggest taking in a string and extracting the contents from it.
| if len(self.names) != len(set(self.names)): | ||
| raise Exception("Parameter names are not unique.") | ||
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| def create_scaler(self): |
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Do these methods need to be in this class? They seem to be specific to Bayestian optimization and random search respectively, so maybe they should just be in the specific algorithms.
Also the file name should be parameter_config.py.
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| def parse_metric(y_train, goal): | ||
| """ | ||
| Parse the metric to the dictionary |
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This doesn't return a dictionary?
| return feasible_values[int(index)] | ||
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| def parse_parameter_configs(parameter_configs): |
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Seems like this would belong in the ParameterConfig class.
This PR implements the random suggestion algorithm. I have tested it independently using API manager. Controller changes will be added separately.
Related: #407
Fixes are made into the base changes from #435
This change is