Checkpoint React frontend migration
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Dwindi Ramadhana
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backend/app/models/tryout.py
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backend/app/models/tryout.py
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"""
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Tryout model with configuration for assessment sessions.
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Represents tryout exams with configurable scoring, selection, and normalization modes.
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"""
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from datetime import datetime
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from typing import Literal, Union
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from sqlalchemy import (
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Boolean,
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CheckConstraint,
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DateTime,
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Float,
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ForeignKey,
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Integer,
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String,
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UniqueConstraint,
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func,
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)
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.database import Base
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class Tryout(Base):
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"""
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Tryout model with configuration for assessment sessions.
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Supports multiple scoring modes (CTT, IRT, hybrid), selection strategies
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(fixed, adaptive, hybrid), and normalization modes (static, dynamic, hybrid).
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Attributes:
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id: Primary key
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website_id: Website identifier
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tryout_id: Tryout identifier (unique per website)
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name: Tryout name
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description: Tryout description
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scoring_mode: Scoring algorithm (ctt, irt, hybrid)
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selection_mode: Item selection strategy (fixed, adaptive, hybrid)
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normalization_mode: Normalization method (static, dynamic, hybrid)
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min_sample_for_dynamic: Minimum sample size for dynamic normalization
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static_rataan: Static mean value for manual normalization
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static_sb: Static standard deviation for manual normalization
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AI_generation_enabled: Enable/disable AI question generation
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hybrid_transition_slot: Slot number to transition from fixed to adaptive
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min_calibration_sample: Minimum responses needed for IRT calibration
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theta_estimation_method: Method for estimating theta (mle, map, eap)
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fallback_to_ctt_on_error: Fallback to CTT if IRT fails
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created_at: Record creation timestamp
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updated_at: Record update timestamp
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website: Website relationship
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items: Items in this tryout
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sessions: Sessions for this tryout
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stats: Tryout statistics
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"""
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__tablename__ = "tryouts"
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# Primary key
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id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True)
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# Foreign keys
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website_id: Mapped[int] = mapped_column(
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ForeignKey("websites.id", ondelete="CASCADE", onupdate="CASCADE"),
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nullable=False,
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index=True,
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comment="Website identifier",
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)
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# Tryout identifier (unique per website)
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tryout_id: Mapped[str] = mapped_column(
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String(255),
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nullable=False,
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index=True,
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comment="Tryout identifier (unique per website)",
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)
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# Basic information
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name: Mapped[str] = mapped_column(
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String(255), nullable=False, comment="Tryout name"
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)
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description: Mapped[Union[str, None]] = mapped_column(
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String(1000), nullable=True, comment="Tryout description"
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)
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# Scoring mode: ctt (Classical Test Theory), irt (Item Response Theory), hybrid
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scoring_mode: Mapped[Literal["ctt", "irt", "hybrid"]] = mapped_column(
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String(50), nullable=False, default="ctt", comment="Scoring mode"
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)
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# Selection mode: fixed (slot order), adaptive (CAT), hybrid (mixed)
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selection_mode: Mapped[Literal["fixed", "adaptive", "hybrid"]] = mapped_column(
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String(50), nullable=False, default="fixed", comment="Item selection mode"
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)
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# Normalization mode: static (hardcoded), dynamic (real-time), hybrid
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normalization_mode: Mapped[Literal["static", "dynamic", "hybrid"]] = mapped_column(
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String(50), nullable=False, default="static", comment="Normalization mode"
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)
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# Normalization settings
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min_sample_for_dynamic: Mapped[int] = mapped_column(
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Integer,
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nullable=False,
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default=100,
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comment="Minimum sample size for dynamic normalization",
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)
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static_rataan: Mapped[float] = mapped_column(
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Float,
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nullable=False,
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default=500.0,
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comment="Static mean value for manual normalization",
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)
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static_sb: Mapped[float] = mapped_column(
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Float,
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nullable=False,
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default=100.0,
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comment="Static standard deviation for manual normalization",
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)
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# AI generation settings
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ai_generation_enabled: Mapped[bool] = mapped_column(
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Boolean,
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nullable=False,
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default=False,
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comment="Enable/disable AI question generation",
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)
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# Hybrid mode settings
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hybrid_transition_slot: Mapped[Union[int, None]] = mapped_column(
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Integer,
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nullable=True,
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comment="Slot number to transition from fixed to adaptive (hybrid mode)",
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)
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# IRT settings
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min_calibration_sample: Mapped[int] = mapped_column(
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Integer,
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nullable=False,
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default=100,
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comment="Minimum responses needed for IRT calibration",
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)
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theta_estimation_method: Mapped[Literal["mle", "map", "eap"]] = mapped_column(
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String(50),
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nullable=False,
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default="mle",
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comment="Method for estimating theta",
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)
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fallback_to_ctt_on_error: Mapped[bool] = mapped_column(
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Boolean,
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nullable=False,
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default=True,
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comment="Fallback to CTT if IRT fails",
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)
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# Timestamps
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, server_default=func.now()
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True),
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nullable=False,
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server_default=func.now(),
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onupdate=func.now(),
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)
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# Relationships
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website: Mapped["Website"] = relationship(
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"Website", back_populates="tryouts", lazy="selectin"
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)
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items: Mapped[list["Item"]] = relationship(
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"Item", back_populates="tryout", lazy="selectin", cascade="all, delete-orphan"
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)
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sessions: Mapped[list["Session"]] = relationship(
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"Session",
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back_populates="tryout",
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lazy="selectin",
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cascade="all, delete-orphan",
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overlaps="user",
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)
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stats: Mapped["TryoutStats"] = relationship(
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"TryoutStats", back_populates="tryout", lazy="selectin", uselist=False
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)
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# Constraints and indexes
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__table_args__ = (
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UniqueConstraint(
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"website_id",
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"tryout_id",
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name="uq_tryouts_website_id_tryout_id",
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),
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CheckConstraint("min_sample_for_dynamic > 0", "ck_min_sample_positive"),
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CheckConstraint("static_rataan > 0", "ck_static_rataan_positive"),
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CheckConstraint("static_sb > 0", "ck_static_sb_positive"),
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CheckConstraint("min_calibration_sample > 0", "ck_min_calibration_positive"),
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)
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def __repr__(self) -> str:
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return f"<Tryout(id={self.id}, tryout_id={self.tryout_id}, website_id={self.website_id})>"
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