issue35.py

import ebisu
import numpy
import random

exp = numpy.exp

model = (3, 3, 1)

m2pd = ebisu.modelToPercentileDecay
ur = ebisu.updateRecall


def update_growth(growth_model, successful, attempts, test_time):
  original_model = growth_model[:3]
  original_updated_model = ur(original_model, successful, attempts, test_time)

  adjustment = m2pd(original_updated_model, 0.5) / m2pd(original_model, 0.5)

  new_a = original_updated_model[0]
  new_b = original_updated_model[1]
  new_growth_coefficient = growth_model[3] * adjustment
  new_t = new_growth_coefficient * growth_model[2]

  new_growth_model = (new_a, new_b, new_t, new_growth_coefficient)

  return new_growth_model


import numpy as np
from scipy.special import betaln


def _meanVarToBeta(mean, var):
  "This function was stolen from ebisu.py"
  tmp = mean * (1 - mean) / var - 1
  alpha = mean * tmp
  beta = (1 - mean) * tmp
  return alpha, beta


def setHalflife(prior, newHalflife):
  "Given an Ebisu model, safely time-shift it to any new halflife"
  # Step 1: time-shift `prior` to its halflife via GB1 and Beta-fit there
  (alpha, beta, t) = prior
  oldHalflife = ebisu.modelToPercentileDecay(prior)
  dt = oldHalflife / t

  logDenominator = betaln(alpha, beta)
  logMean = betaln(alpha + dt, beta) - logDenominator
  logm2 = betaln(alpha + 2 * dt, beta) - logDenominator
  mean = np.exp(logMean)
  var = np.exp(logm2) - np.exp(2 * logMean)

  newAlpha, newBeta = _meanVarToBeta(mean, var)

  # Step 2: <indiana-jones.gif> just swap the true old halflife for new
  return (newAlpha, newBeta, newHalflife)


def update_growth2(growth_model, successful, attempts, test_time):
  original_model = growth_model[:3]
  original_updated_model = ur(original_model, successful, attempts, test_time)

  adjustment = m2pd(original_updated_model, 0.5) / m2pd(original_model, 0.5)

  new_growth_coefficient = growth_model[3] * adjustment
  new_t = new_growth_coefficient * growth_model[2]

  return setHalflife(original_updated_model, new_t) + (new_growth_coefficient,)


new_model = model + (2.1,)
last_test_time = 0

rng = random.Random(123)

for i in range(30):
  test_time = m2pd(new_model[:3], 0.85)
  success = (rng.random() < 0.85)
  new_model = update_growth2(new_model, success, 1, test_time)
  if True or i > 2:
    print(f"{i}: {success} at {round(test_time,3)} :: {new_model}")
  last_test_time = test_time