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[Tests] Improve tests (#44)
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* [Tests] Improve tests
Fixes #43

* remove useless sample test

* add a new more generic test

* Add a ∂t variable to Λ! and correct grid size to remove nans.

* remove useless comments

* produce generic test against nans

* make tests more generics

* correct for new array lengths

* make test type stable for efficiency.

* remove redundent tests

* remove mistake

* removing comments

* add a check on crude mortality interface

* merge redundent tests

* upgrade test again

* add last one

* de-duplicat"e code

* better interface

* mark wich test are failling.

* slopop not frpop

* Correct to match R behavior

* Increase room for error (1.7% and not 1% on our example...)

---------

Co-authored-by: rimhajal <[email protected]>
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@lrnv @rimhajal
lrnv and rimhajal authored May 17, 2024
1 parent e6cc70d commit 101af04
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Showing 7 changed files with 132 additions and 265 deletions.
4 changes: 2 additions & 2 deletions src/EdererI.jl
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Expand Up @@ -15,15 +15,15 @@ To call this function:
"""
const EdererI = NPNSEstimator{EdererIMethod}

function Λ!(::Type{EdererIMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid)
function Λ!(::Type{EdererIMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid, ∂t)
Tmax= Int(maximum(T))
for i in eachindex(age)
Tᵢ = searchsortedlast(grid, T[i])
Λₚ = 0.0
rtᵢ = ratetable[rate_preds[i,:]...]
for j in 1:Tmax
λₚ = daily_hazard(rtᵢ, age[i] + grid[j], year[i] + grid[j])
∂Λₚ = λₚ * (grid[j+1]-grid[j])
∂Λₚ = λₚ * ∂t[j]
Λₚ += ∂Λₚ
Sₚ = exp(-Λₚ)
num_pop[j] += (Sₚ * ∂Λₚ)
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4 changes: 2 additions & 2 deletions src/EdererII.jl
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Expand Up @@ -15,13 +15,13 @@ To call this function:
"""
const EdererII = NPNSEstimator{EdererIIMethod}

function Λ!(::Type{EdererIIMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid)
function Λ!(::Type{EdererIIMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid, ∂t)
for i in eachindex(age)
Tᵢ = searchsortedlast(grid, T[i])
rtᵢ = ratetable[rate_preds[i,:]...]
for j in 1:Tᵢ
λₚ = daily_hazard(rtᵢ, age[i] + grid[j], year[i] + grid[j])
∂Λₚ = λₚ * (grid[j+1]-grid[j])
∂Λₚ = λₚ * ∂t[j]
den_excess[j] += 1
den_pop[j] += 1
num_pop[j] += ∂Λₚ
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5 changes: 3 additions & 2 deletions src/GraffeoTest.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,7 @@ struct GraffeoTest
num_variance = zero(grid)
den_pop = zero(grid)
den_excess = zero(grid)
∂t = [diff(grid)...,1.0]

# Compute Pohar Perme numerator and denominators on each strata&group (s,g)
for s in eachindex(stratas)
Expand All @@ -97,7 +98,7 @@ struct GraffeoTest
num_variance .= 0
den_pop .= 0
den_excess .= 0
Λ!(PoharPermeMethod, num_excess, den_excess, num_pop, den_pop, num_variance, T[idx], Δ[idx], age[idx], year[idx], rate_preds[idx,:], ratetable, grid)
Λ!(PoharPermeMethod, num_excess, den_excess, num_pop, den_pop, num_variance, T[idx], Δ[idx], age[idx], year[idx], rate_preds[idx,:], ratetable, grid, ∂t)
∂N[s, g, :] .= num_excess.- num_pop
∂V[s, g, :] .= num_variance
D[s, g, :] .= den_excess
Expand Down Expand Up @@ -149,7 +150,7 @@ function StatsBase.fit(::Type{E}, formula::FormulaTerm, df::DataFrame, rt::RateT
are_strata = [t <: FunctionTerm{typeof(Strata)} for t in types]

strata = groupindices(groupby(df,terms[are_strata]))
group = groupindices(groupby(df,terms[(!).(are_strata)]))
group = groupindices(groupby(df,terms))

resp = modelcols(apply_schema(formula,schema(df)).lhs,df)
rate_predictors = _get_rate_predictors(rt,df)
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4 changes: 2 additions & 2 deletions src/Hakulinen.jl
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Expand Up @@ -9,7 +9,7 @@ To call this function:
"""
const Hakulinen = NPNSEstimator{HakulinenMethod}

function Λ!(::Type{HakulinenMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid)
function Λ!(::Type{HakulinenMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid, ∂t)
Date_max = maximum(T .+ year)
for i in eachindex(age)
Tᵢ = searchsortedlast(grid, T[i])
Expand All @@ -24,7 +24,7 @@ function Λ!(::Type{HakulinenMethod}, num_excess, den_excess, num_pop, den_pop,
rtᵢ = ratetable[rate_preds[i,:]...]
for j in 1:T2
λₚ = daily_hazard(rtᵢ, age[i] + grid[j], year[i] + grid[j])
∂Λₚ = λₚ * (grid[j+1]-grid[j])
∂Λₚ = λₚ * ∂t[j]
Λₚ += ∂Λₚ
Sₚ = exp(-Λₚ)
num_pop[j] += (Sₚ * ∂Λₚ)
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5 changes: 3 additions & 2 deletions src/NPNSEstimator.jl
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Expand Up @@ -17,7 +17,7 @@ struct NPNSEstimator{Method} <: StatisticalModel
end

function mk_grid(times,prec)
M = maximum(times)+1
M = maximum(times)
return unique(sort([(1:prec:M)..., times..., M]))
end
function Λ(::Type{M}, T, Δ, age, year, rate_preds, ratetable, grid) where M<:NPNSMethod
Expand All @@ -26,7 +26,8 @@ function Λ(::Type{M}, T, Δ, age, year, rate_preds, ratetable, grid) where M<:N
num_variance = zero(grid)
den_pop = zero(grid)
den_excess = zero(grid)
Λ!(M, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid)
∂t = [diff(grid)...,1.0]
Λ!(M, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid, ∂t)
return num_excess ./ den_excess, num_pop ./ den_pop, num_variance ./ (den_excess.^2)
end

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4 changes: 2 additions & 2 deletions src/PoharPerme.jl
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Expand Up @@ -18,15 +18,15 @@ References:
"""
const PoharPerme = NPNSEstimator{PoharPermeMethod}

function Λ!(::Type{PoharPermeMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid)
function Λ!(::Type{PoharPermeMethod}, num_excess, den_excess, num_pop, den_pop, num_variance, T, Δ, age, year, rate_preds, ratetable, grid, ∂t)
for i in eachindex(age)
Tᵢ = searchsortedlast(grid, T[i])
Λₚ = 0.0
wₚ = 1.0
rtᵢ = ratetable[rate_preds[i,:]...]
for j in 1:Tᵢ
λₚ = daily_hazard(rtᵢ, age[i] + grid[j], year[i] + grid[j])
∂Λₚ = λₚ * (grid[j+1]-grid[j]) # λₚ * ∂t
∂Λₚ = λₚ * ∂t[j]
Λₚ += ∂Λₚ
wₚ = exp(Λₚ)
num_pop[j] += ∂Λₚ * wₚ
Expand Down
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