agents.common.models.grade.base¶

Base classes for grade models.

This module defines the fundamental abstractions for all grading models including the grade type enumeration and abstract base class.

Classes¶

`Grade`	Abstract base class for all grade models.
`GradeType`	Types of grade models available.

Module Contents¶

class agents.common.models.grade.base.Grade(/, **data)¶

Bases: pydantic.BaseModel, abc.ABC

Abstract base class for all grade models.

This class provides the common interface and functionality that all grade models must implement. It includes metadata, validation, and utility methods.

Parameters:: data (Any)

grade_type¶: The type of grade model

justification¶: Explanation for the grade assigned

confidence¶: Confidence level in the grade (0.0 to 1.0)

metadata¶: Additional metadata about the grading

grader_id¶: Identifier of the grader (agent, human, etc.)

timestamp¶: When the grade was assigned

Example

# This is an abstract class, use concrete implementations
grade = BinaryGrade(
value=True,
justification="Response meets all criteria",
confidence=0.95
)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

compare_to(other)¶

Compare this grade to another grade.

Parameters:: other (Grade) – Another Grade instance to compare against
Returns:: Dictionary containing comparison information
Raises:: TypeError – If other is not a Grade instance
Return type:: dict[str, float | str]

get_grade_summary()¶

Get a summary of the grade information.

Returns:: Dictionary containing key grade information for display
Return type:: dict[str, Any]

abstractmethod get_normalized_score()¶

Get the grade as a normalized score between 0.0 and 1.0.

This method must be implemented by all concrete grade classes to provide a common way to compare grades across different types.

Returns:: A float between 0.0 and 1.0 representing the normalized grade
Return type:: float

abstractmethod is_passing(threshold=None)¶

Determine if the grade represents a passing score.

Parameters:: threshold (float | None) – Optional custom threshold for passing. If None, uses grade type default.
Returns:: True if the grade is considered passing, False otherwise
Return type:: bool

to_display_string()¶

Convert grade to a human-readable display string.

Returns:: Formatted string representation of the grade
Return type:: str

validate_grade_value(value)¶

Validate that a grade value is appropriate for this grade type.

This method should be overridden by concrete classes to provide type-specific validation.

Parameters:: value (Any) – The value to validate
Returns:: True if valid, False otherwise
Return type:: bool

classmethod validate_justification(v)¶

Validate that justification is meaningful.

Parameters:: v (str) – The justification string to validate
Returns:: The validated justification string
Raises:: ValueError – If justification is empty, too short, or meaningless
Return type:: str

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class agents.common.models.grade.base.GradeType¶

Bases: str, enum.Enum

Types of grade models available.

BINARY¶: Simple pass/fail or yes/no grading

NUMERIC¶: Numeric scoring with configurable ranges

PERCENTAGE¶: Percentage-based scoring (0-100%)

LETTER¶: Traditional letter-based grading (A-F)

RUBRIC¶: Multi-criteria rubric-based evaluation

QUALITATIVE¶: Text-based qualitative assessment

SCALE¶: Likert-scale or custom scale grading

COMPOSITE¶: Combination of multiple grade types

Initialize self. See help(type(self)) for accurate signature.