Cheat notebook

Created 14 Nov 2015 • Last modified 15 Aug 2017

Birthdates in voter data

  • CA
    • 1931-01-01: 17,195
    • 1900-01-02 to 1901-01-01: each has 50 or more, compared to 1 to 1901-01-03
  • FL
    • 1942-10-10: 674, compared to 573 at most in the rest of the month
  • KS
    • 1901-01-01: 32, compared to 1s and 0s in the surrounding months
  • NY
    • 1901-01-01, 1950-01-01: 3,024 and 1,695, compared to less than 1,000 elsewhere
    • 1921-01-01: 529, compared to at most 204 in the surrounding months
  • OK
    • There are 44 unique impossible dates: 19080000 19100000 19110000 19130000 19140000 19160000 19170000 19180000 19190000 19200000 19210000 19220000 19230000 19240000 19250000 19260000 19270000 19280000 19290000 19300000 19310000 19320000 19330000 19340000 19340931 19350000 19360000 19370000 19380000 19390000 19400000 19410000 19420000 19430000 19450000 19460100 19470000 19480000 19480014 19490000 19510000 19530000 19550000 19640000

AM and voter data

Descriptive

["Total voters" (format (.sum ($ counts voters)) ",")]
Total voters 48,852,975

Match rate:

(setv statecounts (.sum (.groupby
  (getl counts : (qw state voters am_users)) "state")))
(setv state-summary (apply cbind (rmap [state (filt (in it am-users-in-states) states)]
  (setv matched (int (getl statecounts state "am_users")))
  (setv total-am (get am-users-in-states state))
  (setv voters (int (getl statecounts state "voters")))
  (setv pop (get state-pop-2012 state))
  (setv party-counts (do
    (setv d (ss counts (= $state state)))
    (setv [pv vv] (map second (.iteritems (getl d : (qw party voters)))))
    (setv pv (.set-categories pv.cat (+ keep-parties ["rest"])))
    (setv pv (.fillna pv "rest"))
    (setv d (.sum (.groupby (cbind pv vv) 0)))
    (list (.iteritems (geti d : 0)))))
  (pds-from-pairs :name (get state-names state) (+ party-counts (pairs
      "Total registered voters" voters
      "Population" pop
      "Percent covered" (round (* 100 (/ voters pop)))
      "AM users matched with voters" matched
      "Total AM users" total-am
      "Percent matched" (round (* 100 (/ matched total-am)))))))))
(setv state-summary.index.name "State")
(setv state-summary (.fillna state-summary 0))
(setv state-summary (.applymap state-summary (λ (format (int it) ","))))
(.rename state-summary :inplace T :index {
  "np" "Unaffiliated voters"
  "DEM" "Democrats"
  "REP" "Republicans"
  "GRE" "Greens"
  "LBT" "Libertarians"
  "rest" "Voters in other party"})
state-summary
State California Florida Kansas New York Oklahoma
Unaffiliated voters 2,710,719 2,700,397 522,094 3,016,645 229,494
Democrats 7,936,325 4,988,433 432,858 7,134,846 879,343
Republicans 5,297,443 4,378,861 773,346 3,484,717 844,305
Greens 114,126 6,143 0 32,811 0
Libertarians 110,808 12,016 11,690 4,710 0
Voters in other party 2,004,915 429,399 0 796,528 3
Total registered voters 18,174,336 12,515,249 1,739,988 14,470,257 1,953,145
Population 38,041,430 19,317,568 2,885,905 19,570,261 3,814,820
Percent covered 48 65 60 74 51
AM users matched with voters 32,457 17,928 2,642 21,776 2,072
Total AM users 92,058 43,987 6,282 54,203 6,603
Percent matched 35 41 42 40 31

Match rates range from 31% to 42%.

Here's a breakdown of gender and gender imputation.

(setv d (.reset-index (.applymap
  (.dropna (.sum (.groupby
    (.replace (getl counts : (qw state gender_known gender voters)) np.nan "Unknown")
    (qw state gender_known gender))))
  (λ (format (int it) ",")))))
(.sort-values d (qw state gender_known gender)
  :ascending [True False True])
I state gender_known gender voters
3 CA True Female 6,279,902
4 CA True Male 5,453,470
0 CA False Female 2,991,313
1 CA False Male 2,617,152
2 CA False Unknown 832,499
8 FL True Female 6,641,276
9 FL True Male 5,645,059
5 FL False Female 97,195
6 FL False Male 94,095
7 FL False Unknown 37,624
13 KS True Female 921,923
14 KS True Male 817,735
10 KS False Female 132
11 KS False Male 122
12 KS False Unknown 76
18 NY True Female 7,909,124
19 NY True Male 6,560,341
15 NY False Female 298
16 NY False Male 285
17 NY False Unknown 209
20 OK False Female 938,158
21 OK False Male 777,237
22 OK False Unknown 237,750

The OK voter data has no gender information, so we had to impute every case there.

(setv n-total (.sum ($ counts voters)))
(setv n-not-given (.sum ($ (ss counts (~ $gender_known)) voters)))
(setv n-imputed (.sum ($
  (ss counts (& (.notnull $gender) (~ $gender_known)))
  voters)))
(rd [
  ["Proportion of genders missing" (/ n-not-given n-total)]
  ["Proprtion of missing genders imputed" (/ n-imputed n-not-given)]
  ["Proportion of genders still missing" (/ (- n-not-given n-imputed) n-total)]])
Proportion of genders missing 0.177
Proprtion of missing genders imputed 0.872
Proportion of genders still missing 0.023
(setv n-bad-age (.sum ($ (ss counts (.isnull $age)) voters)))
["Proportion of ages invalid" (format (/ n-bad-age n-total) ".03f")]
Proportion of ages invalid 0.010

Here is the number of registered voters per party and state (ignoring the AM data).

(setv x (.sort-values
  (.reset-index
    (.sum ($ (.set-index counts ["state" "party"]) voters)
      :level ["state" "party"]))
  ["state" "voters"]
  :ascending [True False]))
(setv ($ x voters) (amap (.format "{:,}" it) ($ x voters)))
(setv x (.reset-index :drop T x))
x
I state party voters
0 CA DEM 7,936,325
1 CA REP 5,297,443
2 CA np 2,710,719
3 CA DS 1,114,635
4 CA AI 479,378
5 CA OTH 271,806
6 CA GRE 114,126
7 CA LBT 110,808
8 CA PF 63,014
9 CA MIS 61,684
10 CA REF 7,786
11 CA AME 3,379
12 CA NAT 2,271
13 CA WWP 313
14 CA JP 146
15 CA CTP 113
16 CA CPC 96
17 CA WP 50
18 CA PIR 31
19 CA CP 31
20 CA CMP 31
21 CA HUM 27
22 CA HPC 14
23 CA MMW 11
24 CA OP 9
25 CA PPC 9
26 CA NRP 8
27 CA TVP 7
28 CA ACP 7
29 CA UCA 6
30 CA MCP 4
31 CA FED 4
32 CA NMB 4
33 CA DP 4
34 CA AMC 4
35 CA SEU 4
36 CA WFP 4
37 CA SAP 3
38 CA LRU 3
39 CA CPP 3
40 CA EJP 3
41 CA POT 2
42 CA GSP 2
43 CA EGA 2
44 CA UMP 2
45 CA APP 1
46 CA UCB 1
47 CA NSP 1
48 CA U08 1
49 CA ATP 1
50 FL DEM 4,988,433
51 FL REP 4,378,861
52 FL np 2,700,397
53 FL INT 280,485
54 FL UNK 59,740
55 FL IDP 58,517
56 FL NRS 25,928
57 FL LBT 12,016
58 FL GRE 6,143
59 FL REF 2,037
60 FL CPF 1,119
61 FL AIP 452
62 FL TPF 391
63 FL FPP 336
64 FL ECO 166
65 FL PSL 122
66 FL PFP 45
67 FL FSW 29
68 FL JPF 12
69 FL OBJ 12
70 FL AEL 4
71 FL FWP 3
72 FL SOC 1
73 KS REP 773,346
74 KS np 522,094
75 KS DEM 432,858
76 KS LBT 11,690
77 NY DEM 7,134,846
78 NY REP 3,484,717
79 NY np 3,016,645
80 NY IND 557,885
81 NY CON 183,041
82 NY WOR 55,420
83 NY GRE 32,811
84 NY LBT 4,710
85 NY FDM 110
86 NY RTH 58
87 NY TXP 6
88 NY APP 5
89 NY SWP 3
90 OK DEM 879,343
91 OK REP 844,305
92 OK np 229,494
93 OK AE 3

Florida and California have quite a few voters with undocumented party codes.

The web page for New York's Independence Party states that "The Party's leadership recognizes that individuals do sometimes unwittingly register as members of the Independence Party when their intent was to register to vote as a 'blank'." That may explain why CA, FL, and NY all have so many people in an "Independence Party" or "Independent Party" when I've never even heard of such a party before. Here's the number of people in each such party:

(ss x (.isin $party (qw IND IDP INT AI)))
I state party voters
4 CA AI 479,378
53 FL INT 280,485
55 FL IDP 58,517
80 NY IND 557,885

Below, voters shows the number of registered voters for each state and party, and AMr shows the reciprocal of the Ashley Madison match rate (e.g., 500 means that 1 in 500 such voters were matched to an Ashley Madison user).

(setv x (.dropna (.sum (.groupby
  (getl (drop-unused-cats (ss counts (.isin $party keep-parties)))
    : (qw state party voters am_users))
  (qw state party)))))
(setv ($ x AMr) (wc x (/ $voters $am_users)))
(setv x (.drop x "am_users" 1))
(setv xR (.reset-index x))
(setv ($ xR state) (.map ($ xR state) state-names))
(.to-csv xR "/tmp/amr.csv")
(.applymap x (λ (if (numeric? it) (format (int (round it)) ",") it)))
state party voters AMr
CA np 2,710,719 527
CA DEM 7,936,325 703
CA REP 5,297,443 476
CA GRE 114,126 399
CA LBT 110,808 260
FL np 2,700,397 629
FL DEM 4,988,433 1,057
FL REP 4,378,861 542
FL GRE 6,143 410
FL LBT 12,016 300
KS np 522,094 587
KS DEM 432,858 1,007
KS REP 773,346 604
KS LBT 11,690 285
NY np 3,016,645 627
NY DEM 7,134,846 901
NY REP 3,484,717 485
NY GRE 32,811 566
NY LBT 4,710 236
OK np 229,494 702
OK DEM 879,343 1,573
OK REP 844,305 712

Here's a plot. (The y-axis is upside-down so that higher points mean more Ashley Madison users.)

library(ggplot2)

d = read.csv("/tmp/amr.csv")
d = transform(d, party = factor(party, levels =
    c("np", "DEM", "REP", "GRE", "LBT")))
breaks = c(1, seq(250, 1750, 250))
amr.plot = ggplot(d) +
    geom_point(aes(state, AMr)) +
    geom_text(aes(
        x = as.integer(state) + ifelse(party == "REP", -.05, .05),
        y = AMr,
        label = c(
            np = "none", DEM = "Dem", REP = "Rep",
            GRE = "Green", LBT = "Lib")[as.character(party)],
        hjust = ifelse(party == "REP", 1, 0))) +
    xlab("State") +
    scale_y_reverse(name = "Ashley Madison users",
      expand = c(0, 0),
      breaks = breaks,
      labels = paste("1 in", formatC(breaks, big.mark = ","))) +
    coord_cartesian(ylim = range(breaks)) +
    theme(
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        axis.text.x = element_text(color = "black"),
        axis.text.y = element_text(color = "black"),
        axis.line = element_line(color = "black"))
amr.plot

amr.png

In all five states, Dems cheat less than other party categories (including the more liberal Greens). In all four states with a libertarian party, libertarians cheat more than other party categories.

Here are the AM rates by state alone:

(setv x (getl (.sum (.groupby counts "state")) : (qw voters am_users)))
(setv ($ x AMr) (wc x (/ $voters $am_users)))
;(setv x (.drop x "am_users" 1))
;(setv xR (.reset-index x))
;(setv ($ xR state) (.map ($ xR state) state-names))
;(.to-csv xR "/tmp/amr.csv")
(ordf (.applymap x (λ (if (numeric? it) (format (int (round it)) ",") it)))
  $AMr)
state voters am_users AMr
CA 18,174,336 32,457 560
KS 1,739,988 2,642 659
NY 14,470,257 21,776 665
FL 12,515,249 17,928 698
OK 1,953,145 2,072 943

Party membership differs by gender and age. If we consider only males, and we control for age, do we still see these AM usage differences by party? Below is a crude graph where we group men by year of birth.

(setv cs (getl
  (ss counts
    (& (= $gender "Male") (.notnull $born)
      (.isin $party ["np" "DEM" "REP"])))
  :
  (qw state born party am n)))
(setv out (.dropna (.apply
  (.groupby cs ["state" (// ($ cs born) 10000) "party"])
  (λ (if (.any ($ it am))
    (/ (.sum ($ (ss it [[var:a]][[var:m]]) [[var:n]])) (.[[var:s]][[var:u]][[var:m]] ( it n)))
    0)))))
(setv out (kwc pd.DataFrame out :columns ["am_proportion"]))
;(pd.Series [(.sum ($ it n)) (.sum ($ (ss it $am) n))] ["n" "a"])))))
(.to-csv out "/tmp/am-year-party.csv")
d = read.csv("/tmp/am-year-party.csv")
library(ggplot2)
ggplot(subset(d, born >= 1915)) +
   geom_line(aes(born, am_proportion, color = party)) +
   scale_color_manual(values = c("blue", "darkgreen", "red")) +
   facet_grid(state ~ ., scales = "free")

am-year-party.png

It looks like it, pretty much.

Modeling

Basic information about the data we'll use for modeling:

(setv d (am-usage-filter counts))
(setv base-rate (/ (.sum ($ d am_users)) (.sum ($ d voters))))
[
  ["Sample size" (format (.sum ($ d voters)) ",")]
  ["AM users"    (format (.sum ($ d am_users)) ",")]
  ["Base rate" (+ "1 in " (str (int (round (/ 1 base-rate)))))]
  ["Base MSE" (round (* base-rate (- 1 base-rate)) 8)]]
Sample size 44,172,769
AM users 69,023
Base rate 1 in 640
Base MSE 0.00156013
(setv folds (do
  (setv old-rng-state (np.random.get-state))
  (np.random.seed 55)
  (setv out (gen-counts-cv-folds-ts ($ d voters) ($ d am_users) 10))
  (np.random.set-state old-rng-state)
  out))

(import [collections [OrderedDict]])

(setv models (OrderedDict (,
  [:Trivial {:desc "No predictors" :formula
    None}]
  [:Parties {:desc "Predictors: party only" :formula
    "party"}]
  [:Demo {:desc "Predictors: state, gender, age" :formula
    "state + female + age + age2"}]
  [:DemoParties {:desc "Predictors: state, gender, age, party" :formula
    "state + female + age + age2 + party"}]
  [:IntDemo {:desc "Demo, plus all first-order interactions" :formula (.join " " [
    "state + female + age + age2 +"
    "female:(state + age + age2) +"
    "state:(age + age2)"])}]
  [:IntDemoParties {:desc "DemoParties, plus all first-order interactions" :formula (.join " " [
    "state + female + age + age2 + party +"
    "female:(state + age + age2 + party) +"
    "state:(age + age2 + party) +"
    "party:(age + age2)"])}])))
(for [[k v] (.items models)]
  (setv (get v :name) (keyword->str k)))

(for [model (.values models)]
  (.update model (cached-eval
    (+ "am-usage-cv " (get model :name))
    :cache-dir cache-dir
    (fn []
      (if (= (get model :name) "Trivial")
        (am-usage "cv" counts "state" folds :trivial-model T)
        (am-usage "cv" counts (get model :formula) folds))))))

(setv cv-results (df-from-pairs (rmap [model (.values models)] (pairs
  "Model" (get model :name)
  "Description" (get model :desc)
  "Terms" (if (= (get model :name) "Trivial") 1
    (+ 1 (len (get model :x-column-names))))
  "MSE" (round (get model :cv-mse) 9) 
  "[[var:p]]_{0}" (.format "^{}" (int (round (/ 1 (get model :cv-p-cond 0)))))
  "[[var:p]]_{1}" (.format "^{}" (int (round (/ 1 (get model :cv-p-cond 1)))))))))
(setv cv-results (.set-index cv-results "Model"))
cv-results
Model Description Terms MSE p0 p1
Trivial No predictors 1 0.001560127 ^640 ^640
Parties Predictors: party only 5 0.001559982 ^640 ^604
Demo Predictors: state, gender, age 8 0.001555791 ^642 ^232
DemoParties Predictors: state, gender, age, party 12 0.001555600 ^642 ^226
IntDemo Demo, plus all first-order interactions 22 0.001555783 ^642 ^231
IntDemoParties DemoParties, plus all first-order interactions 51 0.001555565 ^642 ^224

Here p0 is the reciprocal of the mean predicted probability among all voters who didn't actually use AM, and p1 is the same thing for voters who actually did use AM.

None of the models can improve much on the base rate for p0. parties_only somewhat improves p1, while all the other nontrivial models substantially improve p1. Parties help, and interaction terms help a tiny bit more.

Below are the coefficients of the parties and ints&parties models.

(setv noint-coefs (cache
  (am-usage "fit_all" counts :formula
    (get models :DemoParties :formula))))
(setv int-coefs (cache
  (am-usage "fit_all" counts :formula
    (get models :IntDemoParties :formula))))

(defn pretty-term-name [s] (cond
  [(in ":" s) (do
    (setv [s1 s2] (.split s ":"))
    (+ (pretty-term-name s1) " × " (pretty-term-name s2)))]
  [(= s "age")
    "Age"]
  [(= s "age2")
    "Age²"]
  [(= s "female[T.True]")
    "Female"]
  [(.startswith s "state[")
    (.format "State: {}" (get state-names (cut (.rstrip s "]") -2)))]
  [(.startswith s "party[")
    (.format "Party: {}" (get long-party-names (cut (.rstrip s "]") -3)))]
  [True
    s]))

(setv pretty-coefs (.applymap
  (cbind :DemoParties noint-coefs :IntDemoParties int-coefs)
  (λ (if (np.isnan it) "" (.replace (format it ".02f") "-" "−")))))
(setv pretty-coefs.index (amap (pretty-term-name it) pretty-coefs.index))
(setv pretty-coefs.index.name "Term")
pretty-coefs
Term DemoParties IntDemoParties
Intercept −6.20 −6.22
Age −1.07 −1.10
Age² −2.23 −2.14
Female −3.31 −3.13
Female × Age   0.52
Female × Age²   0.43
Female × Party: Democratic   0.18
Female × Party: Green   −0.08
Female × Party: Libertarian   0.03
Female × Party: Republican   −0.01
Female × State: California   −0.14
Female × State: Florida   −0.14
Female × State: Kansas   −0.23
Female × State: Oklahoma   −0.23
Party: Democratic −0.18 −0.17
Party: Democratic × Age   −0.13
Party: Democratic × Age²   0.08
Party: Green 0.02 −0.19
Party: Green × Age   0.22
Party: Green × Age²   −0.29
Party: Libertarian 0.46 0.47
Party: Libertarian × Age   −0.34
Party: Libertarian × Age²   −0.34
Party: Republican 0.19 0.28
Party: Republican × Age   −0.21
Party: Republican × Age²   0.05
State: California 0.14 0.16
State: California × Age   0.22
State: California × Age²   −0.19
State: California × Party: Democratic   0.06
State: California × Party: Green   0.23
State: California × Party: Libertarian   −0.15
State: California × Party: Republican   −0.16
State: Florida −0.04 0.01
State: Florida × Age   0.19
State: Florida × Age²   −0.22
State: Florida × Party: Democratic   −0.13
State: Florida × Party: Green   0.42
State: Florida × Party: Libertarian   −0.25
State: Florida × Party: Republican   −0.10
State: Kansas −0.10 −0.16
State: Kansas × Age   −0.33
State: Kansas × Age²   −0.59
State: Kansas × Party: Democratic   −0.07
State: Kansas × Party: Libertarian   −0.08
State: Kansas × Party: Republican   −0.25
State: Oklahoma −0.31 −0.15
State: Oklahoma × Age   0.23
State: Oklahoma × Age²   −0.14
State: Oklahoma × Party: Democratic   −0.26
State: Oklahoma × Party: Republican   −0.20

Let's make predictions for a 40-year-old male New Yorker.

Using the parties model:

(setv [age-t age2-t] (am-usage-transform-ages
  (am-usage-filter counts) 40))
(defn f [coefs]
  (defn c [k] (.get coefs k 0))
  (setv l (lc [[name v] (+ [["party[T.np]" 0]] (list (.iteritems coefs)))]
    (re.match r"party\[T\.[A-Za-z]+\]\Z" name)
    (, (.group (re.search "T.([A-Za-z]+)" name) 1) (int (round (/ 1 (ilogit (+
      (get noint-coefs "Intercept")
      (* age-t (c "age"))
      (* age2-t (c "age2"))
      (* age-t (c (+ name ":age")))
      (* age2-t (c (+ name ":age2")))
      v))))))))
  (geti (.set-index (pd.DataFrame l) 0) : 0))
(.sort-values (f noint-coefs))
I 1
LBT 117
REP 152
GRE 180
np 184
DEM 219

Using the ints_and_parties model:

(setv predcompare (cbind :DemoParties (f noint-coefs) :IntDemoParties (f int-coefs)))
(setv predcompare (.applymap (.sort-values predcompare "DemoParties")
  (λ (.format "^{}" it))))
(setv predcompare.index (amap (get long-party-names it) predcompare.index))
(setv predcompare.index.name "Party")
predcompare
Party DemoParties IntDemoParties
Libertarian ^117 ^98
Republican ^152 ^138
Green ^180 ^219
unaffiliated ^184 ^189
Democratic ^219 ^223

Old modeling (with SGD)

Would it make things substantially easier if all IVs were discrete? Potentially, yes, since when all IVs of a logistic-regression model are discrete, and all interaction terms are included (and there's no regularization or anything else special), the best-fitting logistic-regression model just reproduces the contingency table. However, the cells of the IV contingency table here vary widely in size. Some are 0, because, e.g., I don't have data for Greens in Kansas.

SGDClassifier can do probabilistic prediction not only with logistic loss (which is what I've done) but also with modified Huber loss. However, I bet it would be less effective.

(setv [n-modeling-subjects n-modeling-cheated] (cache (get-modeling-ns)))
(setv base-rate (/ n-modeling-cheated n-modeling-subjects))
[
  ["Sample size" (format n-modeling-subjects ",")]
  ["Cheated" (format n-modeling-cheated ",")]
  ["Base rate" (+ "1 in " (str (int (/ 1 base-rate))))]
  ["Base MSE" (round (* base-rate (- 1 base-rate)) 8)]]
Sample size 45,201,799
Cheated 68,602
Base rate 1 in 658
Base MSE 0.00151538
(setv cv-results (dict (cache (lc [ii [False True] ipp [False True]]
  (, (, ii ipp) (kwc try-model "cv" n-modeling-subjects n-modeling-cheated
    :include-interactions ii
    :include-party-params ipp))))))
(kwc .set-index :keys "model" (kwc pd.DataFrame :columns ["model" "MSE" "P when 0" "P when 1"] (+
  [["trivial" (round (* base-rate (- 1 base-rate)) 8)
    (int (/ 1 base-rate)) (int (/ 1 base-rate))]]
  (lc [ii [False True] ipp [False True]] [
      (.format "{}, {}"
        (if ii "interacts" "no interacts")
        (if ipp "parties" "no parties"))
      (round (get cv-results (, ii ipp) "mse") 8)
      (int (/ 1 (get cv-results (, ii ipp) "mean pred prob when 0")))
      (int (/ 1 (get cv-results (, ii ipp) "mean pred prob when 1")))]))))
model MSE P when 0 P when 1
trivial 0.00151538 658 658
no interacts, no parties 0.00139625 709 333
no interacts, parties 0.00139611 724 330
interacts, no parties 0.00139643 706 375
interacts, parties 0.00139615 719 351

Here we compared the fivefold cross-validate performance of four models, and also include base rates for comparison. The rightmost two columns give the mean predicted probability of cheating (expressed as a reciprocal) among subjects who were not (0) or were (1) actually cheating. We see that all models improve substantially upon the base rate, and adding party terms helps, but interactions don't help. So let's use the model with parties but no interactions.

Here are its parameters with fit to the whole dataset:

(setv params (OrderedDict (cache (kwc try-model "fit" n-modeling-subjects n-modeling-cheated
  :!include-interactions :+include-party-params))))
(lc [[name value] (.items params)] [name (rd value)])
Intercept -5.106
CA 0.064
FL -0.196
KS -0.247
OK -0.430
known_female -3.226
unknown_gender -0.934
age -1.059
age_squared -0.978
DEM -0.198
REP 0.191
GRE -0.044
LBT 0.085

We can use them to create a little table of the predicted probability of a 25-year-old male New Yorker cheating:

(defn p [x] (get params x))
(kwc sorted :key second (lc [party keep-parties]
  (, party (int (/ 1 (ilogit (+
    (p "Intercept")
    ;(p "NY")
    (* (p "age") (first (transformed-age 25)))
    (* (p "age_squared") (second (transformed-age 25)))
    (if (= party "np") 0 (p party)))))))))
REP 182
LBT 202
np 220
GRE 230
DEM 268

We see that Democrats have the lowest probability of cheating, but differently from the simple graph above, Greens and libertarians are between Republicans and Democrats rather than cheating more than Republicans. The differences here are generally much smaller than those in the graph; the biggest difference is between Republicans and Democrats, which has Democrats cheat about a third less than Republicans.

Matching voter records across years

First attempt

As a test run, we consider NY 2010 and 2012. I compute how many records each dataset has that the other doesn't have by just subtracting the number of shared records.

  • Matching by rn
    • 2010: 13,026,768 records
    • 2012: 14,500,804 records
    • Shared: 13,017,759
    • 2010-only records: 9,009
    • 2012-only records: 1,483,045
  • Matching by name, zip, addrnum1, addrnum2
    • 2010: 12,852,502 distinct records
    • 2012: 14,283,937 distinct records
    • Shared: 10,854,796
    • 2010-only records: 1,997,706
    • 2012-only records: 3,429,141

Matching by rn, 1,385,648 of the pairs of records have a different addrnum1 or addrnum2.

Matching by rn, 12,999,937 of the 13,017,473 (99.87%) pairs of records have matching birthdates.

2010 voters matched to AM: 20,191

  • By rn, 20,171 of these are also present in 2012.
  • And 1,803 have a new addrnum1 or addrnum2.
    • sqlite3 voter.sqlite 'attach "voter-2010.sqlite" as V10; select count(*) from (select rn, addrnum1, addrnum2 from V10.T where am_user notnull) as T10 join T as T12 using (rn) where T10.addrnum1 != T12.addrnum1 or T10.addrnum2 != T12.addrnum2
  • 1,803 / 20,171 = .09

2010 voters not matched to AM:

  • By rn, 12,997,588 are present in 2012.
  • And 1,383,845 of those have a new addrnum1 or addrnum2.
  • 1,383,845 / 12,997,588 = .11

Notice that this difference is in the wrong direction: 2010 New York voters were slightly less likely to have a different address in 2012 if they were on AM.

We might do this better by handling time more explicitly on both sides, using transaction dates to date AM usage and record modification dates (when they exist) to date voter records.

Using transaction dates

I hoped to use registration dates as an indicator of when each voter record was last updated. However, this doesn't seem to be correct, at least for New York, because sqlite3 voter.sqlite 'attach "voter-2010.sqlite" as V10; select V10.T.registered < V12T.registered as updated, V10.T.addrnum1 != V12T.addrnum1 as moved, count(*) from (T as V12T inner join V10.T using (rn)) group by updated, moved returns:

0 0 11639406
0 1 1076701
1 0 11582
1 1 289784

which shows us that quite a few seemingly non-updated 2012 records have new addresses. So instead, let's just use January 1st, 2012, as the threshold date. (Or rather, January 3rd, to provide some wiggle room for time-zone shenanigans.)

attach "voter-2010.sqlite" as V10;
attach "amcc.sqlite" as AMCC;
select
  V12T.am_user notnull and early_enough as am_before,
  V12T.addrnum1 != V10.T.addrnum1 as moved,
  count(*)
  from (T as V12T inner join V10.T using (rn))
      left outer join (select
         usernum,
         min(time) < cast(strftime("%s", "20120103") as integer)
             as early_enough
--         cast(strftime("%Y%m%d", min(time) - 2*24*60*60, "unixepoch", "utc") as integer)
--             as first_am
         from AMCC.T
         group by usernum)
      on V12T.am_user = usernum
  where V12T.am_user isnull or V12T.am_user != -1
  group by am_before, moved;
0 0 11632713
0 1 1363900
1 0 16329
1 1 2337
  • Among moved, 2337 / 1363900 = 1 / 584 used AM before 2012
  • Among non-moved, 16329 / 11632713 = 1 / 712 used AM before 2012
  • Among AM users before 2012, 2337 / 16329 = 14% moved
  • Among non-AM users before 2012, 1363900 / 11632713 = 12% moved

That's in the right direction, at least.

I'm still matching up AM users with voters using 2012 voter addresses. Should I be using the 2010 voter addresses instead? Should I use both somehow?

am_am

AMLIB_SelectOptions.class.php

From srcdump/ashleymadison, repository ashley.git, revision HEAD, file common/pinflib/amlib/AMLIB/AMLIB_SelectOptions.class.php.

This defines the numeric codes for "preferences" (opento in am_am), "tastes" (turnsmeon), and "desires" (lookingfor); gender; the various seeking types; etc.

For each code type, the codes are pretty much equivalent between the four gender-seeking classes, although not every class has access to every code, and there are some minor differences in wording. am_am has some codes that aren't listed, but these generally don't appear in profiles created before 2009, suggesting that the ability to indicate them on the website was removed.

Frequency tables

(thousep (pd.read-sql :con amam
  "select female, count(*) as n from Users group by female"))
I female n
0 nan 12
1 0.0 720,634
2 1.0 18,022
(user-var-freq "ethnicity")
ethnicity n
Caucasian (white) 596,751
Hispanic 45,340
African American (black) 40,826
Other 23,026
Rather Not Say 12,747
Asian 12,020
East Indian 4,666
Middle Eastern 2,144
First Nations 1,134
N/A 14
(user-var-freq "seeking")
seeking n
attached male seeking females 536,882
single male seeking females 182,460
attached female seeking males 10,861
female seeking females 4,528
single female seeking males 2,633
male seeking males 1,292
N/A 12
(user-var-freq "smoking")
smoking n
Not specified 403,585
Never 298,460
Occasionally 27,106
Regularly 9,026
N/A 491
(user-var-freq "drinking")
drinking n
N/A 737,081
Never 1,485
Socially 62
Occasionally 36
Regularly 4
(msf-prefcode-freq "OpenTo")
code n p
Conventional Sex 408,457 0.568
Likes to Give Oral Sex 405,127 0.563
Likes to Receive Oral Sex 386,918 0.538
Kissing 339,375 0.472
Good With Your Hands 307,319 0.427
Sensual Massage 302,026 0.420
Light Kinky Fun 301,648 0.419
Sex Talk 299,921 0.417
Extended Foreplay/Teasing 292,603 0.407
Open to Experimentation 276,365 0.384
Sharing Fantasies 243,226 0.338
Cuddling & Hugging 241,644 0.336
One-Night Stands 233,780 0.325
Experimenting with Sex Toys 224,530 0.312
Gentleness 211,383 0.294
Lots of Stamina 207,447 0.288
Erotic Movies 191,189 0.266
Role Playing 182,222 0.253
Bubble Bath for 2 179,206 0.249
Threesome 178,851 0.249
Likes to Go Slow 164,568 0.229
Someone Who Can Teach Me 161,172 0.224
Someone I Can Teach 157,231 0.219
Aggressiveness 128,866 0.179
Blindfolding 115,807 0.161
Experimenting with Tantric Sex 113,165 0.157
Spanking 112,192 0.156
Erotic Tickling 92,255 0.128
Fetishes 91,510 0.127
Likes to be Watched/Exhibitionism 81,645 0.113
Dressing Up/Lingerie 80,545 0.112
Being Dominant/Master 78,000 0.108
N/A 72,520 0.101
Curious - Domination 62,970 0.088
Curious - Submission 59,080 0.082
Bondage 49,549 0.069
Being Submissive/Slave 41,613 0.058
Nothing Kinky 10,672 0.015
I Like to Cross Dress 1,668 0.002
You Like to Cross Dress 1,486 0.002
Transvestitism 153 0.000
(msf-prefcode-freq "TurnsMeOn")
code n p
N/A 525,202 0.730
Sense of Humor 360,543 0.501
Good Personal Hygiene 333,933 0.464
Discretion/Secrecy 326,886 0.454
High Sex Drive 309,451 0.430
Naughty Girl 293,261 0.408
Relaxed and Easy Going 285,479 0.397
Casual Jeans/T-shirt Type 283,472 0.394
Imagination 282,552 0.393
Creative and Adventurous 279,220 0.388
Girl Next Door 267,147 0.371
Stylish/Classy 261,757 0.364
A Professional/Well Groomed 261,029 0.363
Confidence 255,735 0.356
Drug Free 231,210 0.321
Not Possessive 212,026 0.295
Good Communicator 206,881 0.288
Casual/Social Drinker 199,718 0.278
Petite Figure 198,221 0.276
Long Hair 195,490 0.272
Muscular/Fit Body 162,050 0.225
Short Height 136,838 0.190
A Good Listener 132,376 0.184
Short Hair 129,734 0.180
Aggressive/Take Charge Nature 126,987 0.177
Has a Secret Love Nest 121,362 0.169
Tall Height 117,615 0.164
Tattoos 117,150 0.163
Dislikes Routine 108,025 0.150
Natural Breasts 105,325 0.146
Hopeless Romantic 102,383 0.142
Body Piercing 93,689 0.130
Average Sex Drive 85,712 0.119
BBW 38,142 0.053
Seeking a Sugar Baby 24,032 0.033
Likes Routine 22,985 0.032
A Don Juan 122 0.000
Slim to Average Body 70 0.000
Full Size Body 52 0.000
Bad Boy 33 0.000
Boy Next Door 21 0.000
Tall, Dark and Handsome 2 0.000
Seeking a Sugar Daddy 1 0.000
(msf-prefcode-freq "LookingFor")
code n p
N/A 324,141 0.451
I Am a Social Drinker 251,773 0.350
Music Lover 224,666 0.312
Travel 213,776 0.297
The Outdoors/Nature 212,929 0.296
Daring Rendezvous 198,557 0.276
Physical Fitness 195,492 0.272
Fine Dining/Candle Lit Dinners 188,344 0.262
Cooking/Barbequing 169,270 0.235
Romantic Walks 165,913 0.231
Watching Sports 165,029 0.229
Long Drives 155,997 0.217
Skinny Dipping 155,110 0.216
Playing Sports 152,602 0.212
Wine Tasting 150,099 0.209
Strip Poker/Adult Games 115,963 0.161
Dancing 105,301 0.146
Boating 104,142 0.145
Picnics 100,305 0.139
Theatre 98,428 0.137
Photography 96,024 0.133
Shopping for Sexy Clothes/Lingerie 95,847 0.133
Erotic Literature 79,711 0.111
Motorcycles 71,445 0.099
Politics 62,349 0.087
Cards 52,595 0.073
Board Games 49,910 0.069
Cottage Country 46,598 0.065
Visiting Adult Swing Clubs 41,912 0.058
On-line Games 28,016 0.039
Karaoke 26,561 0.037
Opera 22,451 0.031
I Do Not Drink 17,088 0.024