progress-measures-simulation/config.py at main · adaptive-learning/progress-measures-simulation · GitHub

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"""Simulation configuration, parameters.
All specific parameter values should be collected here."""

from models import Scenario
from progress_measures import *

LOW_SKILL, AVG_SKILL, HIGH_SKILL = -3, 0, 3
SKILLS = list(range(LOW_SKILL, HIGH_SKILL+1))

EVAL_SPECS_TIME = {
    'L_under_0': 600,
    'L_under_1': 540,
    'A_over_1': 350,
    'A_over_0': 200,
    'A_under_0': 140,
    'A_under_1': 50,
    'H_over_1': 280,
    'H_over_0': 80,
    'poor_std': 220
}

def get_eval_specs(scenario, as_time=False):
    """For a specific scenario, either in number of attempts or time."""
    if as_time:
        return EVAL_SPECS_TIME
    t = scenario.time_intensity
    return {atr: EVAL_SPECS_TIME[atr] / t for atr in EVAL_SPECS_TIME}


def get_attempts_limit(scenario):
    t = scenario.time_intensity
    return int(EVAL_SPECS_TIME["L_under_0"] / t)


scenarios = {
    "acq": Scenario(
        name="ACQ",
        guess_chance=0.5,
        time_intensity=3,
        desc="alternate choice question, fast answer",
        difficulty_mean=-0.25,
        difficulty_std=1,
        student_std=1
    ),
    "mcq": Scenario(
        name="MCQ",
        guess_chance=0.25,
        time_intensity=5,
        desc="multiple choice question, 4 options, vocabulary practice",
        difficulty_mean=0.25,
        difficulty_std=1,
        student_std=1
    ),
    "wa": Scenario(
        name="WA",
        guess_chance=0.05,
        time_intensity=15,
        desc="constructed response (written answer), math equations, slow answers",
        difficulty_mean=0,
        difficulty_std=1,
        student_std=1
    )
}


pm_lists = {
    "acq-opt":[
        ProgressMeasureTC(36),
        ProgressMeasureNCC(11),
        ProgressMeasureTOW(bonus=0.037, pen=0.055),
        ProgressMeasureEMA(decay=0.942, threshold=0.805),
        ProgressMeasureVSLinear(base_step=0.029, slope=0.619, guess_chance=0.5, decay=0.536),
        ProgressMeasureVSHMM(base_step=0.031, guess_chance=0.5, slip_chance=0.017, skill_change=0.002, low_speed=0.525, high_speed=1.282)
    ],
    "mcq-opt":[
        ProgressMeasureTC(36),
        ProgressMeasureNCC(6),
        ProgressMeasureTOW(bonus=0.068, pen=0.032),
        ProgressMeasureEMA(decay=0.818, threshold=0.801),
        ProgressMeasureVSLinear(base_step=0.063, slope=0.341, guess_chance=0.25, decay=0.68),
        ProgressMeasureVSHMM(base_step=0.065, guess_chance=0.25, slip_chance=0.015, skill_change=0.001, low_speed=0.076, high_speed=1.029)
    ],
    "wa-opt":[
        ProgressMeasureTC(7),
        ProgressMeasureNCC(3),
        ProgressMeasureTOW(bonus=0.199, pen=0.036),
        ProgressMeasureEMA(decay=0.578, threshold=0.809),
        ProgressMeasureVSLinear(base_step=0.189, slope=1.276, guess_chance=0.05, decay=0.954),
        ProgressMeasureVSHMM(base_step=0.191, guess_chance=0.05, slip_chance=0.006, skill_change=0.004, low_speed=0.09, high_speed=1.035)
    ]
}