# coding: utf-8

"""
    Mixpeek API

    This is the Mixpeek API, providing access to various endpoints for data processing and retrieval.

    The version of the OpenAPI document: 1.3.0
    Contact: info@mixpeek.com
    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 pydantic import BaseModel, ConfigDict, Field, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from typing_extensions import Annotated
from typing import Optional, Set
from typing_extensions import Self

class PipelineComparison(BaseModel):
    """
    Statistical comparison between a candidate and baseline pipeline.
    """ # noqa: E501
    candidate_retriever_id: StrictStr = Field(description="ID of the candidate pipeline.")
    ndcg_delta: Dict[str, Union[StrictFloat, StrictInt]] = Field(description="Change in NDCG at each K (positive = candidate better).")
    recall_delta: Dict[str, Union[StrictFloat, StrictInt]] = Field(description="Change in recall at each K (positive = candidate better).")
    latency_delta_ms: Union[StrictFloat, StrictInt] = Field(description="Change in mean latency (positive = candidate slower).")
    p_value: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Statistical significance of the difference (paired t-test).")
    confidence_interval: Optional[Annotated[List[Any], Field(min_length=2, max_length=2)]] = Field(default=None, description="95% confidence interval for NDCG@10 delta.")
    taxonomy_wins: Optional[List[StrictStr]] = Field(default=None, description="Taxonomy nodes where candidate significantly outperforms.")
    taxonomy_losses: Optional[List[StrictStr]] = Field(default=None, description="Taxonomy nodes where candidate significantly underperforms.")
    __properties: ClassVar[List[str]] = ["candidate_retriever_id", "ndcg_delta", "recall_delta", "latency_delta_ms", "p_value", "confidence_interval", "taxonomy_wins", "taxonomy_losses"]

    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 PipelineComparison 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 PipelineComparison from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "candidate_retriever_id": obj.get("candidate_retriever_id"),
            "ndcg_delta": obj.get("ndcg_delta"),
            "recall_delta": obj.get("recall_delta"),
            "latency_delta_ms": obj.get("latency_delta_ms"),
            "p_value": obj.get("p_value"),
            "confidence_interval": obj.get("confidence_interval"),
            "taxonomy_wins": obj.get("taxonomy_wins"),
            "taxonomy_losses": obj.get("taxonomy_losses")
        })
        return _obj


