Skip to content

Base generator class

Generator

Bases: XoptBaseModel, ABC

Source code in xopt/generator.py 
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
class Generator(XoptBaseModel, ABC):
    name: ClassVar[str] = Field(description="generator name")

    supports_batch_generation: bool = Field(
        default=False,
        description="flag that describes if this "
        "generator can generate "
        "batches of points",
        frozen=True,
        exclude=True,
    )
    supports_multi_objective: bool = Field(
        default=False,
        description="flag that describes if this generator can solve multi-objective "
        "problems",
        frozen=True,
        exclude=True,
    )

    vocs: VOCS = Field(description="generator VOCS", exclude=True)
    data: Optional[pd.DataFrame] = Field(
        None, description="generator data", exclude=True
    )

    model_config = ConfigDict(validate_assignment=True)

    _is_done = False

    @field_validator("vocs", mode="after")
    def validate_vocs(cls, v, info: ValidationInfo):
        if v.n_objectives != 1 and not info.data["supports_multi_objective"]:
            raise ValueError("this generator only supports single objective")
        return v

    @field_validator("data", mode="before")
    def validate_data(cls, v):
        if isinstance(v, dict):
            try:
                v = pd.DataFrame(v)
            except IndexError:
                v = pd.DataFrame(v, index=[0])
        return v

    def __init__(self, **kwargs):
        """
        Initialize the generator.

        """
        super().__init__(**kwargs)
        logger.info(f"Initialized generator {self.name}")

    @property
    def is_done(self):
        return self._is_done

    @abstractmethod
    def generate(self, n_candidates) -> list[dict]:
        pass

    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)
        else:
            self.data = new_data

    def model_dump(self, *args, **kwargs) -> 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

__init__(**kwargs)

Initialize the generator.

Source code in xopt/generator.py 
58
59
60
61
62
63
64
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 
74
75
76
77
78
79
80
81
82
83
84
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)
    else:
        self.data = new_data

model_dump(*args, **kwargs)

overwrite model dump to remove faux class attrs

Source code in xopt/generator.py 
86
87
88
89
90
91
92
93
94
def model_dump(self, *args, **kwargs) -> 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