Skip to content

Random Generator

RandomGenerator

Bases: Generator

Random number generator.

Source code in xopt/generators/random.py 
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
class RandomGenerator(Generator):
    """
    Random number generator.
    """

    name = "random"
    supports_batch_generation: bool = True
    supports_multi_objective: bool = True
    supports_single_objective: bool = True
    supports_constraints: bool = True

    def generate(self, n_candidates) -> list[dict]:
        """generate uniform random data points"""
        return random_inputs(self.vocs, n_candidates, include_constants=False)

__init__(**kwargs)

Initialize the generator.

Source code in xopt/generator.py 
119
120
121
122
123
124
def __init__(self, **kwargs):
    """
    Initialize the generator.
    """
    super().__init__(**kwargs)
    logger.info(f"Initialized generator {self.name}")

add_data(new_data)

update dataframe with results from new evaluations.

This is intended for generators that maintain their own data.

Source code in xopt/generator.py 
140
141
142
143
144
145
146
147
148
149
150
def add_data(self, new_data: pd.DataFrame):
    """
    update dataframe with results from new evaluations.

    This is intended for generators that maintain their own data.

    """
    if self.data is not None:
        self.data = pd.concat([self.data, new_data], axis=0, ignore_index=True)
    else:
        self.data = new_data

generate(n_candidates)

generate uniform random data points

Source code in xopt/generators/random.py 
16
17
18
def generate(self, n_candidates) -> list[dict]:
    """generate uniform random data points"""
    return random_inputs(self.vocs, n_candidates, include_constants=False)

model_dump(*args, **kwargs)

overwrite model dump to remove faux class attrs

Source code in xopt/generator.py 
152
153
154
155
156
157
158
159
160
def model_dump(self, *args: Any, **kwargs: Any) -> dict[str, Any]:
    """overwrite model dump to remove faux class attrs"""

    res = super().model_dump(*args, **kwargs)

    res.pop("supports_batch_generation", None)
    res.pop("supports_multi_objective", None)

    return res

yaml(**kwargs)

serialize first then dump to yaml string

Source code in xopt/pydantic.py 
231
232
233
234
235
236
237
238
def yaml(self, **kwargs):
    """serialize first then dump to yaml string"""
    output = json.loads(
        self.to_json(
            **kwargs,
        )
    )
    return yaml.dump(output)