move glue code function to the end
This commit is contained in:
parent
212f72f596
commit
cbda7b1e5c
562
main.go
562
main.go
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@ -34,9 +34,283 @@ type Poll struct {
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/* Globals */
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var r = rand.New(rand.NewPCG(uint64(100), uint64(2224)))
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var dev = false
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/* Sampling helper functions */
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func getNormalCDF(x float64, mean float64, std float64) float64 {
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erf_term := (x - mean) / (std * math.Sqrt2)
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return (1 + math.Erf(erf_term)) / 2
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}
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func getProbabilityAboveX(x float64, mean float64, std float64) float64 {
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return 1 - getNormalCDF(x, mean, std)
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}
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func getChanceCandidateWinsFromPollShare(candidate_p float64, poll_sample_size float64) float64 {
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std := math.Sqrt(candidate_p * (1 - candidate_p) / poll_sample_size) // https://stats.stackexchange.com/questions/258879/how-to-interpret-margin-of-error-in-a-non-binary-poll
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return getProbabilityAboveX(0.5, candidate_p, std)
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}
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func getChanceRepublicanWinFromPoll(poll Poll, pretty_print bool) float64 {
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biden_percentage, biden_exists := poll.PollResults["Biden"]
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trump_percentage, trump_exists := poll.PollResults["Trump"]
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if !biden_exists || !trump_exists {
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panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
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}
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biden_share := biden_percentage / 100.0 // will panic if the item is not found, but we've previously filtered for it
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trump_share := trump_percentage / 100.0
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normalized_trump_share := trump_share / (trump_share + biden_share)
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normalized_biden_share := biden_share / (trump_share + biden_share)
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joint_trump_biden_sample_size := (biden_share + trump_share) * float64(poll.SampleSize)
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std_error_poll_mean := math.Sqrt((normalized_trump_share * normalized_biden_share) / joint_trump_biden_sample_size)
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p_republican_win := getProbabilityAboveX(0.5, normalized_trump_share, std_error_poll_mean)
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if pretty_print {
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fmt.Printf("\n\t\tSample size: %f", joint_trump_biden_sample_size)
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fmt.Printf("\n\t\tMean R: %f", 100.0*normalized_trump_share)
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fmt.Printf("\n\t\tStd of mean R: %f", 100*std_error_poll_mean)
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fmt.Printf("\n\t\tPoll says chance of R win: %f", p_republican_win)
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}
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return p_republican_win
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}
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func getChanceRepublicanWinFromPollPlusUncertainty(poll Poll, state State, pretty_print bool) float64 {
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// Uncertainty from the state
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n_republican_win := 0
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for _, party := range state.PresidentialElectoralHistory {
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if party == "R" {
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n_republican_win++
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}
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}
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// Get the uncertainty from the poll
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biden_percentage, biden_exists := poll.PollResults["Biden"]
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trump_percentage, trump_exists := poll.PollResults["Trump"]
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if !biden_exists || !trump_exists {
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panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
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}
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biden_share := biden_percentage / 100.0 // will panic if the item is not found, but we've previously filtered for it
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trump_share := trump_percentage / 100.0
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normalized_trump_share := trump_share / (trump_share + biden_share)
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normalized_biden_share := biden_share / (trump_share + biden_share)
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joint_trump_biden_sample_size := (biden_share + trump_share) * float64(poll.SampleSize)
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std_error_poll_mean := math.Sqrt((normalized_trump_share * normalized_biden_share) / joint_trump_biden_sample_size)
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/* Inject additional uncertainty */
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/*
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Possible factors:
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- National drift between now and the election (biggest one)
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- States more uncertain than the national average
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- Idiosyncratic factors
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- Polls not being as good as gallup
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- Increased polarization
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Also note that the polls already have some error already
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*/
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std_additional_uncertainty := 5.0 / 100.0
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if n_republican_win == 0 || n_republican_win == 6 {
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// if solid states for the last 6 elections
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std_additional_uncertainty = std_additional_uncertainty / 3.0
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if pretty_print {
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fmt.Printf("\n\t\tN republican wins: %d", n_republican_win)
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fmt.Printf("\n\t\t=> Reducing additional uncertainty")
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}
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}
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std_error := std_error_poll_mean + std_additional_uncertainty
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// std_error := std_error_poll_mean + 0.065
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p_republican_win := getProbabilityAboveX(0.5, normalized_trump_share, std_error)
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if pretty_print {
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fmt.Printf("\n\t\tStd with std_additional_uncertainty R: %f", 100*std_error)
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fmt.Printf("\n\t\tPoll plus uncertainty says chance of R win: %f", p_republican_win)
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}
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return p_republican_win
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}
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/* Print state by state data */
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func printStates(states []State) {
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for _, state := range states {
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fmt.Printf("\n\nState: %s", state.Name)
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fmt.Printf("\n\tVotes: %d", state.Votes)
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fmt.Printf("\n\tHistory: %s", state.PresidentialElectoralHistory)
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p_baserate_republican_win := 0.0
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for _, party := range state.PresidentialElectoralHistory {
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if party == "R" {
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p_baserate_republican_win++
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}
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}
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fmt.Printf("\n\tHistorical base rate of R win: %f", p_baserate_republican_win/float64(len(state.PresidentialElectoralHistory)))
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// Individual poll
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for _, poll := range state.Polls {
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fmt.Printf("\n\tPoll: %+v", poll)
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_ = getChanceRepublicanWinFromPoll(poll, true)
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_ = getChanceRepublicanWinFromPollPlusUncertainty(poll, state, true)
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}
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// Aggregate poll
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num_biden_votes := 0.0
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num_trump_votes := 0.0
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for _, poll := range state.Polls {
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biden_percentage, biden_exists := poll.PollResults["Biden"]
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trump_percentage, trump_exists := poll.PollResults["Trump"]
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if !biden_exists || !trump_exists {
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panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
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}
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num_biden_votes += (biden_percentage / 100.0) * float64(poll.SampleSize)
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num_trump_votes += (trump_percentage / 100.0) * float64(poll.SampleSize)
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}
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aggregate_sample_size := num_biden_votes + num_trump_votes
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if aggregate_sample_size != 0.0 {
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var aggregate_poll = Poll{SampleSize: int(aggregate_sample_size), PollResults: make(map[string]float64)}
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aggregate_poll.PollResults["Biden"] = 100.0 * num_biden_votes / aggregate_sample_size
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aggregate_poll.PollResults["Trump"] = 100.0 * num_trump_votes / aggregate_sample_size
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fmt.Printf("\n\tAggregate poll: %+v", aggregate_poll)
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_ = getChanceRepublicanWinFromPoll(aggregate_poll, true)
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_ = getChanceRepublicanWinFromPollPlusUncertainty(aggregate_poll, state, true)
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}
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}
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}
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/* Sample state by state */
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func sampleFromState(state State) VotesForEachParty {
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switch state.Name {
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case "Nebraska":
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/*
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2000: R
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2004: R
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2008: Split, 1 D, 4 R
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2012: R
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2016: R
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2020: Split, 1 D, 4 R
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*/
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p_split := 2.0 / 6.0
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if r.Float64() < p_split {
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return VotesForEachParty{Democrats: 1, Republicans: 4}
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} else {
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return VotesForEachParty{Democrats: 0, Republicans: 5}
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}
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case "Maine":
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/*
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2000: D
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2004: D
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2008: D
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2012: D
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2016: Split: 3 D, 1 R
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2020: Split, 3 D, 1 R
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*/
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p_split := 2.0 / 6.0
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if r.Float64() < p_split {
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return VotesForEachParty{Democrats: 3, Republicans: 1}
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} else {
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return VotesForEachParty{Democrats: 1, Republicans: 0}
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}
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default:
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{
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/* Consider the base rate for the state */
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p_baserate_republican_win := 0.0
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for _, party := range state.PresidentialElectoralHistory {
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if party == "R" {
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p_baserate_republican_win++
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}
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}
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p_baserate_republican_win = p_baserate_republican_win / float64(len(state.PresidentialElectoralHistory))
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p_republican_win := p_baserate_republican_win // if no polls
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/* Consider polls */
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num_biden_votes := 0.0
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num_trump_votes := 0.0
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for _, poll := range state.Polls {
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biden_percentage, biden_exists := poll.PollResults["Biden"]
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trump_percentage, trump_exists := poll.PollResults["Trump"]
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if !biden_exists || !trump_exists {
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panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
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}
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num_biden_votes += (biden_percentage / 100.0) * float64(poll.SampleSize)
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num_trump_votes += (trump_percentage / 100.0) * float64(poll.SampleSize)
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}
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aggregate_sample_size := num_biden_votes + num_trump_votes
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if aggregate_sample_size != 0.0 {
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var aggregate_poll = Poll{SampleSize: int(aggregate_sample_size), PollResults: make(map[string]float64)}
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aggregate_poll.PollResults["Biden"] = 100.0 * num_biden_votes / aggregate_sample_size
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aggregate_poll.PollResults["Trump"] = 100.0 * num_trump_votes / aggregate_sample_size
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p_republican_win_aggregate_polls := getChanceRepublicanWinFromPollPlusUncertainty(aggregate_poll, state, false)
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// p_republican_win_aggregate_polls = getChanceRepublicanWinFromPoll(aggregate_poll, false)
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// weight_polls := 0.75
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// p_republican = weight_polls*p_republican_win_aggregate_polls + (1.0-weight_polls)*p_baserate_republican_win
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p_republican_win = p_republican_win_aggregate_polls
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}
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if r.Float64() < p_republican_win {
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return VotesForEachParty{Democrats: 0, Republicans: state.Votes}
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} else {
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return VotesForEachParty{Democrats: state.Votes, Republicans: 0}
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}
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}
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}
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}
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/* Simulate election */
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func simulateElection(states []State) int {
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republican_seats := 0
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for _, state := range states {
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election_sample := sampleFromState(state)
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republican_seats += election_sample.Republicans
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}
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return republican_seats
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}
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/* Histogram */
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func barString(n int) string {
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str := ""
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for i := 0; i < n; i++ {
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str += "█"
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}
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return str
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}
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func printElectoralCollegeHistogram(samples []int) {
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histogram := [538]int{}
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for _, sample := range samples {
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histogram[sample]++
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}
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max_count := 0
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for _, count := range histogram {
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if count > max_count {
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max_count = count
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}
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}
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cp := 0.0
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for i, count := range histogram {
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bar_length := (count * 75) / max_count // Assuming max_count bar length is 50 characters
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p := float64(count) / float64(len(samples)) * 100
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cp += p
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if i > 130 && i < 400 {
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fmt.Printf("[ %2d, %4d): %s %.2f%% (%.0f%%)\n", i, i+1, barString(bar_length), p, cp)
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} else if p >= 0.01 {
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fmt.Printf(">0.01 probability outside of domain, you might want to change histogram parameters\n")
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}
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}
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}
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/* Load data from csvs */
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// Glue code
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func readStates() ([]State, error) {
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var states map[string]State = make(map[string]State)
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@ -205,294 +479,9 @@ func readStates() ([]State, error) {
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for _, state := range states {
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states_slice = append(states_slice, state)
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}
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return states_slice, nil
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}
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/* Sampling helper functions */
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func getNormalCDF(x float64, mean float64, std float64) float64 {
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erf_term := (x - mean) / (std * math.Sqrt2)
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return (1 + math.Erf(erf_term)) / 2
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}
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func getProbabilityAboveX(x float64, mean float64, std float64) float64 {
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return 1 - getNormalCDF(x, mean, std)
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}
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func getChanceCandidateWinsFromPollShare(candidate_p float64, poll_sample_size float64) float64 {
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std := math.Sqrt(candidate_p * (1 - candidate_p) / poll_sample_size) // https://stats.stackexchange.com/questions/258879/how-to-interpret-margin-of-error-in-a-non-binary-poll
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return getProbabilityAboveX(0.5, candidate_p, std)
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}
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func getChanceRepublicanWinFromPoll(poll Poll, pretty_print bool) float64 {
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biden_percentage, biden_exists := poll.PollResults["Biden"]
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trump_percentage, trump_exists := poll.PollResults["Trump"]
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if !biden_exists || !trump_exists {
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panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
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}
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biden_share := biden_percentage / 100.0 // will panic if the item is not found, but we've previously filtered for it
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trump_share := trump_percentage / 100.0
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normalized_trump_share := trump_share / (trump_share + biden_share)
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normalized_biden_share := biden_share / (trump_share + biden_share)
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joint_trump_biden_sample_size := (biden_share + trump_share) * float64(poll.SampleSize)
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std_error_poll_mean := math.Sqrt((normalized_trump_share * normalized_biden_share) / joint_trump_biden_sample_size)
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p_republican_win := getProbabilityAboveX(0.5, normalized_trump_share, std_error_poll_mean)
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if pretty_print {
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fmt.Printf("\n\t\tSample size: %f", joint_trump_biden_sample_size)
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fmt.Printf("\n\t\tMean R: %f", 100.0*normalized_trump_share)
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fmt.Printf("\n\t\tStd of mean R: %f", 100*std_error_poll_mean)
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fmt.Printf("\n\t\tPoll says chance of R win: %f", p_republican_win)
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}
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return p_republican_win
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}
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func getChanceRepublicanWinFromPollPlusUncertainty(poll Poll, state State, pretty_print bool) float64 {
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// Uncertainty from the state
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n_republican_win := 0
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for _, party := range state.PresidentialElectoralHistory {
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if party == "R" {
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n_republican_win++
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}
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}
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// Get the uncertainty from the poll
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biden_percentage, biden_exists := poll.PollResults["Biden"]
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trump_percentage, trump_exists := poll.PollResults["Trump"]
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if !biden_exists || !trump_exists {
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panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
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}
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biden_share := biden_percentage / 100.0 // will panic if the item is not found, but we've previously filtered for it
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trump_share := trump_percentage / 100.0
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normalized_trump_share := trump_share / (trump_share + biden_share)
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normalized_biden_share := biden_share / (trump_share + biden_share)
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joint_trump_biden_sample_size := (biden_share + trump_share) * float64(poll.SampleSize)
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std_error_poll_mean := math.Sqrt((normalized_trump_share * normalized_biden_share) / joint_trump_biden_sample_size)
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/* Inject additional uncertainty */
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/*
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Possible factors:
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- National drift between now and the election (biggest one)
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- States more uncertain than the national average
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- Idiosyncratic factors
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- Polls not being as good as gallup
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- Increased polarization
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Also note that the polls already have some error already
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*/
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std_additional_uncertainty := 5.0 / 100.0
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if n_republican_win == 0 || n_republican_win == 6 {
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// if solid states for the last 6 elections
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std_additional_uncertainty = std_additional_uncertainty / 3.0
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if pretty_print {
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fmt.Printf("\n\t\tN republican wins: %d", n_republican_win)
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fmt.Printf("\n\t\t=> Reducing additional uncertainty")
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}
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}
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std_error := std_error_poll_mean + std_additional_uncertainty
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// std_error := std_error_poll_mean + 0.065
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p_republican_win := getProbabilityAboveX(0.5, normalized_trump_share, std_error)
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if pretty_print {
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fmt.Printf("\n\t\tStd with std_additional_uncertainty R: %f", 100*std_error)
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fmt.Printf("\n\t\tPoll plus uncertainty says chance of R win: %f", p_republican_win)
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}
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return p_republican_win
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}
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/* Print state by state data */
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func printStates(states []State) {
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for _, state := range states {
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fmt.Printf("\n\nState: %s", state.Name)
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fmt.Printf("\n\tVotes: %d", state.Votes)
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fmt.Printf("\n\tHistory: %s", state.PresidentialElectoralHistory)
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p_baserate_republican_win := 0.0
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for _, party := range state.PresidentialElectoralHistory {
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if party == "R" {
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p_baserate_republican_win++
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}
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}
|
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fmt.Printf("\n\tHistorical base rate of R win: %f", p_baserate_republican_win/float64(len(state.PresidentialElectoralHistory)))
|
||||
|
||||
// Individual poll
|
||||
for _, poll := range state.Polls {
|
||||
fmt.Printf("\n\tPoll: %+v", poll)
|
||||
_ = getChanceRepublicanWinFromPoll(poll, true)
|
||||
_ = getChanceRepublicanWinFromPollPlusUncertainty(poll, state, true)
|
||||
}
|
||||
|
||||
// Aggregate poll
|
||||
num_biden_votes := 0.0
|
||||
num_trump_votes := 0.0
|
||||
for _, poll := range state.Polls {
|
||||
biden_percentage, biden_exists := poll.PollResults["Biden"]
|
||||
trump_percentage, trump_exists := poll.PollResults["Trump"]
|
||||
if !biden_exists || !trump_exists {
|
||||
panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
|
||||
}
|
||||
num_biden_votes += (biden_percentage / 100.0) * float64(poll.SampleSize)
|
||||
num_trump_votes += (trump_percentage / 100.0) * float64(poll.SampleSize)
|
||||
}
|
||||
aggregate_sample_size := num_biden_votes + num_trump_votes
|
||||
if aggregate_sample_size != 0.0 {
|
||||
var aggregate_poll = Poll{SampleSize: int(aggregate_sample_size), PollResults: make(map[string]float64)}
|
||||
aggregate_poll.PollResults["Biden"] = 100.0 * num_biden_votes / aggregate_sample_size
|
||||
aggregate_poll.PollResults["Trump"] = 100.0 * num_trump_votes / aggregate_sample_size
|
||||
|
||||
fmt.Printf("\n\tAggregate poll: %+v", aggregate_poll)
|
||||
_ = getChanceRepublicanWinFromPoll(aggregate_poll, true)
|
||||
_ = getChanceRepublicanWinFromPollPlusUncertainty(aggregate_poll, state, true)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/* Sample state by state */
|
||||
func sampleFromState(state State) VotesForEachParty {
|
||||
switch state.Name {
|
||||
case "Nebraska":
|
||||
/*
|
||||
2000: R
|
||||
2004: R
|
||||
2008: Split, 1 D, 4 R
|
||||
2012: R
|
||||
2016: R
|
||||
2020: Split, 1 D, 4 R
|
||||
*/
|
||||
p_split := 2.0 / 6.0
|
||||
if r.Float64() < p_split {
|
||||
return VotesForEachParty{Democrats: 1, Republicans: 4}
|
||||
} else {
|
||||
return VotesForEachParty{Democrats: 0, Republicans: 5}
|
||||
}
|
||||
case "Maine":
|
||||
/*
|
||||
2000: D
|
||||
2004: D
|
||||
2008: D
|
||||
2012: D
|
||||
2016: Split: 3 D, 1 R
|
||||
2020: Split, 3 D, 1 R
|
||||
*/
|
||||
p_split := 2.0 / 6.0
|
||||
if r.Float64() < p_split {
|
||||
return VotesForEachParty{Democrats: 3, Republicans: 1}
|
||||
} else {
|
||||
return VotesForEachParty{Democrats: 1, Republicans: 0}
|
||||
}
|
||||
default:
|
||||
{
|
||||
/* Consider the base rate for the state */
|
||||
p_baserate_republican_win := 0.0
|
||||
for _, party := range state.PresidentialElectoralHistory {
|
||||
if party == "R" {
|
||||
p_baserate_republican_win++
|
||||
}
|
||||
}
|
||||
p_baserate_republican_win = p_baserate_republican_win / float64(len(state.PresidentialElectoralHistory))
|
||||
p_republican_win := p_baserate_republican_win // if no polls
|
||||
|
||||
/* Consider polls */
|
||||
num_biden_votes := 0.0
|
||||
num_trump_votes := 0.0
|
||||
for _, poll := range state.Polls {
|
||||
biden_percentage, biden_exists := poll.PollResults["Biden"]
|
||||
trump_percentage, trump_exists := poll.PollResults["Trump"]
|
||||
if !biden_exists || !trump_exists {
|
||||
panic("PollResults of poll filtered to have Biden/Trump doesn't have Biden/Trump")
|
||||
}
|
||||
num_biden_votes += (biden_percentage / 100.0) * float64(poll.SampleSize)
|
||||
num_trump_votes += (trump_percentage / 100.0) * float64(poll.SampleSize)
|
||||
}
|
||||
|
||||
aggregate_sample_size := num_biden_votes + num_trump_votes
|
||||
if aggregate_sample_size != 0.0 {
|
||||
var aggregate_poll = Poll{SampleSize: int(aggregate_sample_size), PollResults: make(map[string]float64)}
|
||||
aggregate_poll.PollResults["Biden"] = 100.0 * num_biden_votes / aggregate_sample_size
|
||||
aggregate_poll.PollResults["Trump"] = 100.0 * num_trump_votes / aggregate_sample_size
|
||||
|
||||
p_republican_win_aggregate_polls := getChanceRepublicanWinFromPollPlusUncertainty(aggregate_poll, state, false)
|
||||
// p_republican_win_aggregate_polls = getChanceRepublicanWinFromPoll(aggregate_poll, false)
|
||||
|
||||
// weight_polls := 0.75
|
||||
// p_republican = weight_polls*p_republican_win_aggregate_polls + (1.0-weight_polls)*p_baserate_republican_win
|
||||
p_republican_win = p_republican_win_aggregate_polls
|
||||
}
|
||||
|
||||
if r.Float64() < p_republican_win {
|
||||
return VotesForEachParty{Democrats: 0, Republicans: state.Votes}
|
||||
} else {
|
||||
return VotesForEachParty{Democrats: state.Votes, Republicans: 0}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/* Simulate election */
|
||||
func simulateElection(states []State) int {
|
||||
|
||||
republican_seats := 0
|
||||
for _, state := range states {
|
||||
election_sample := sampleFromState(state)
|
||||
republican_seats += election_sample.Republicans
|
||||
}
|
||||
return republican_seats
|
||||
}
|
||||
|
||||
/* Histogram */
|
||||
func barString(n int) string {
|
||||
str := ""
|
||||
for i := 0; i < n; i++ {
|
||||
str += "█"
|
||||
}
|
||||
return str
|
||||
}
|
||||
|
||||
func printElectoralCollegeHistogram(samples []int) {
|
||||
|
||||
histogram := [538]int{}
|
||||
for _, sample := range samples {
|
||||
histogram[sample]++
|
||||
}
|
||||
|
||||
max_count := 0
|
||||
for _, count := range histogram {
|
||||
if count > max_count {
|
||||
max_count = count
|
||||
}
|
||||
}
|
||||
|
||||
cp := 0.0
|
||||
for i, count := range histogram {
|
||||
bar_length := (count * 75) / max_count // Assuming max_count bar length is 50 characters
|
||||
p := float64(count) / float64(len(samples)) * 100
|
||||
cp += p
|
||||
|
||||
if i > 130 && i < 400 {
|
||||
fmt.Printf("[ %2d, %4d): %s %.2f%% (%.0f%%)\n", i, i+1, barString(bar_length), p, cp)
|
||||
} else if p >= 0.01 {
|
||||
fmt.Printf(">0.01 probability outside of domain, you might want to change histogram parameters\n")
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
func main() {
|
||||
states, err := readStates()
|
||||
if err != nil {
|
||||
|
|
@ -518,5 +507,4 @@ func main() {
|
|||
|
||||
p_republicans = p_republicans / float64(n_sims)
|
||||
fmt.Printf("\n%% republicans: %f\n", p_republicans)
|
||||
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user