Source code for arize._generated.api_client.models.experiment

# coding: utf-8

"""
    Arize REST API

    API specification for the backend data server. The API is hosted globally at https://api.arize.com/v2 or in your own environment. 

    The version of the OpenAPI document: 2.0.0
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from datetime import datetime
from pydantic import BaseModel, ConfigDict, Field, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from typing import Optional, Set
from typing_extensions import Self



class Experiment(BaseModel):
    """
    Experiments combine a dataset (example inputs/expected outputs), a task (the function that produces model outputs), and one or more evaluators (code or LLM judges) to measure performance. Each run is stored independently so you can compare runs, track progress, and validate improvements over time. See the full definition on the Experiments page.  Use an experiment to run tasks on a dataset, attach evaluators to score outputs, and compare runs to confirm improvements. 
    """ # noqa: E501
    id: StrictStr = Field(description="Unique identifier for the experiment")
    name: StrictStr = Field(description="Name of the experiment")
    dataset_id: StrictStr = Field(description="Unique identifier for the dataset this experiment belongs to")
    dataset_version_id: StrictStr = Field(description="Unique identifier for the dataset version this experiment belongs to")
    created_at: datetime = Field(description="Timestamp for when the experiment was created")
    updated_at: datetime = Field(description="Timestamp for the last update of the experiment")
    experiment_traces_project_id: Optional[StrictStr] = Field(default=None, description="Unique identifier for the experiment traces project this experiment belongs to (if it exists)")
    __properties: ClassVar[List[str]] = ["id", "name", "dataset_id", "dataset_version_id", "created_at", "updated_at", "experiment_traces_project_id"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
    )




    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))




    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())




    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of Experiment from a JSON string"""
        return cls.from_dict(json.loads(json_str))




    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        excluded_fields: Set[str] = set([
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        return _dict




    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of Experiment from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        # raise errors for additional fields in the input
        for _key in obj.keys():
            if _key not in cls.__properties:
                raise ValueError("Error due to additional fields (not defined in Experiment) in the input: " + _key)

        _obj = cls.model_validate({
            "id": obj.get("id"),
            "name": obj.get("name"),
            "dataset_id": obj.get("dataset_id"),
            "dataset_version_id": obj.get("dataset_version_id"),
            "created_at": obj.get("created_at"),
            "updated_at": obj.get("updated_at"),
            "experiment_traces_project_id": obj.get("experiment_traces_project_id")
        })
        return _obj