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13/05/2013 Gijs Dekkers Gaëtan de Menten Raphaël Desmet

LIAM2 Introduction and demo model. Présentation pour le pôle Prévoyance de la Caisse de Dépôt et de Gestion Rabat, Maroc. 13/05/2013 Gijs Dekkers Gaëtan de Menten Raphaël Desmet. Introduction to Liam2.

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13/05/2013 Gijs Dekkers Gaëtan de Menten Raphaël Desmet

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  1. LIAM2 • Introduction and demo model • Présentation pour le pôle Prévoyance de la Caisse de Dépôt et de Gestion • Rabat, Maroc. 13/05/2013 Gijs Dekkers Gaëtan de Menten Raphaël Desmet

  2. Introduction to Liam2 • Tool for the development of dynamic microsimulation models with dynamic cross-sectional ageing. • ≠ a microsimulation model (<> Midas) • Simulation framework that allows for comprehensive modelling and various simulation techniques • Prospective / Retrospective simulation • Work in progress … • Immigration • Weights • More sophisticated regressions and simulation techniques • Speed optimisation • You get it for free!

  3. How to get it. • Check http://liam2.plan.be • This website contains • The LIAM 2 executable. • A synthetic dataset of 20,200 individuals grouped in 14,700 households in HDF5 format. • A small model containing • Fertility and mortality (aligned) • Educational attainment level • Some labour market characteristics • Documentation • A LIAM 2 user guide • A ready-to-use “bundle” of notepad integrated with LIAM 2 and the synthetic dataset.

  4. Overview • Written in Python • High level open source language • Efficient libraries mostly C • Input • Model description: text file (YAML) • Alignment: CSV files • Internal data engine: HDF5 file format and library for storing scientific data (meteorology, astronomy, …) • Output • HDF5 file, CSV file on demand • Interactive console

  5. Model definition: the simulation file • Declare entities (=data) • What is modelled? (person, household, enterprise, …) • Entity characteristics • fields: • what do we know about an individual? • what do we want to know? • How can we store the data? • Flag: boolean (eg. alive/dead, male/female) • discrete/category: integer (eg. single/married/divorced/…) • Continue/value: float (eg. Income) • links: interaction between entities • same kind: who is the mother? • different kinds: in what household does the person live? • Globals (=data) • External time series • Eg. macroeconomic context

  6. Model definition: the simulation file • Simulation (=model) • Processes: • What happens to the entities in their lives? • In what order? • input: Which input file to use? • output: Where is the output? • start period: • periods: How many periods do we want to simulate?

  7. Simple simulation file entities: person: fields: # period and id are implicit - age: int - gender: bool processes: age: age + 1 isfemale: gender = True simulation: processes: - person: [age, isfemale] input: file: base.h5 output: file: output.h5 start_period: 2002 # first simulated period periods: 20

  8. Liam2 bundled with Notepad++ model/YAML Interactive console

  9. Liam2 bundled with Notepad++ • Simulation file (YAML-format, yml extension, highlighting) • indentation (grouping, levels) • colon, dash, brackets, double quotes, quotes, ... • comment (#) • Console • run: F6 • import: F5 • output • interactive (history)

  10. LIAM 2 – demo model • First simulation • simple entity • simple functions • first run • some output

  11. basic simulation setup demo01.yml

  12. Basic setup • Description of the data : entities • fields: • name • type = bool (boolean), int (integer) or float • initialdata (data from input or new data) • The model definition: processes • model definition (transformation, regressions, alignment, ...) • Order of the processes: simulation • database (input, output) • what processes and when? (model order) • start_period, # periods

  13. Basic simulation file entities: person: fields: # period and id are implicit - age: int - gender: bool processes: age: age + 1 simulation: processes: - person: [age] input: file: base.h5 output: file: output.h5 start_period: 2002 # first simulated period periods: 20

  14. Simple simulation (to run the file, press F6) entities: person: fields: # period and id are implicit - age: int - gender: bool # fields not present in input - agegroup: {type: int, initialdata: false} processes: age: age + 1 agegroup: 5 * trunc(age / 5) simulation: processes: - person: [age, agegroup] input: file: simple2001.h5 output: file: simulation.h5 start_period: 2002 periods: 2

  15. Console output Using simulation file: 'C:\usr\Liam2Suite\Synthetic\demo00.yml' reading data from C:\usr\Liam2Suite\Synthetic\simple2001.h5 ... person ... … period 2002 - loading input data * person ... done (0 ms elapsed). -> 20200 individuals - 1/2 age ... done (2 ms elapsed). - 2/2 agegroup ... done (3 ms elapsed). - storing period data * person ... done (2 ms elapsed). -> 20200 individuals period 2002 done (0.01 second elapsed). … period 2003 … top 10 processes: - agegroup: 0.01 second - age: 3 ms total for top 10 processes: 0.01 second

  16. Output • Internal format = HDF5 file • Write to the console • show(expr1[, expr2, … ]): evaluates the expressions and shows the result • dump(expr1[, expr2, …, filter, missing, header): produces a table with the expressions given as argument evaluated over many (possibly all) individuals of the dataset. • Write to CSV-files • csv(expr1[, expr2, …, suffix, fname, mode]): function writes values to a csv-file • Pivot tables: • groupby(expr1[, expr2, …, filter=None, percent=False])

  17. Some functions • Expressions • Arithmetic operators: +, -, *, /, ** (exponent), % (modulo) • Comparison operators: <, <=, ==, !=, >=, > • Boolean operators: and, or, not • Conditional expressions: if(condition, expression_if_true, expression_if_false) • Mathematical functions • abs, log, exp, round, trunc, ... • Aggregate functions • grpcount, grpsum, grpavg, grpstd, grpmax, grpmin • Temporal functions • lag, value_for_period, duration, tavg, tsum • Random functions • Uniform, normal, randint

  18. Simple simulation (to run the file, press F6) entities: person: fields: # period and id are implicit - age: int - gender: bool # fields not present in input - agegroup: {type: int, initialdata: false} processes: age: age + 1 agegroup: if(age < 50, 5 * trunc(age / 5), 10 * trunc(age / 10)) # produces 2 csv files (one per period): "person_20xx.csv“ # default name for csv-file = {entity}_{period}.csv dump_info: csv(dump(id, age, gender)) show_demography: show(groupby(agegroup, gender))

  19. … simulation: processes: - person: [age, agegroup, dump_info, show_demography] input: file: simple2001.h5 output: file: simulation.h5 # first simulated period start_period: 2002 periods: 2

  20. Interactive console Welcome to LIAM interactive console. help: print this help q[uit] or exit: quit the program entity [name]: set the current entity (this is required before any query) period [period]: set the current period (if not set, uses the last period simulated) fields [entity]: list the fields of that entity (or the current entity) show is implicit on all commands >>> period 2002 current period set to 2002 >>> entity person current entity set to person >>> grpcount(gender) 10100 >>> grpcount(not gender) 10100

  21. Remarks • All output functions can be used both during the simulation and in the interactive console • Some examples - show • show(groupby(age, gender, filter=age<=10)) • show(grpcount(age >= 18)) • show(grpcount(not dead), grpavg(age, filter=not dead)) • show("Count:", grpcount(), "\nAverage age:", grpavg(age), "\nAgestddev:", grpstd(age)) • Some examples – csv • csv(grpavg(age)) • csv(period, grpavg(age), fname=‘avg_income.csv’, mode=‘a’) • Some examples – groupby • groupby(trunc(age/10),gender) • groupby(trunc(age/10),gender, percent=True)

  22. links, init, procedures, choice demo02.yml

  23. Links: model interaction • second entity (eg household) • links: interaction between entities (eg. persons, households) • one2many (one household has many persons) person: fields: # period and id are implicit - age:int - gender:bool ... - hh_id:int household: fields: # period and id are implicit - nb_persons: int - nb_children: int links: persons: {type: one2many, target: person, field: hh_id}

  24. Use the links: aggregate functions entities: household: fields: # period and id are implicit - nb_persons: {type: int, initialdata: false} - nb_children: {type: int, initialdata: false} links: persons: {type: one2many, target: person, field: hh_id} processes: household_composition: - nb_persons: countlink(persons) - nb_children: countlink(persons, age < 18) To use information stored in the linked entities you have to use aggregate functions countlink (eg. countlink(persons) gives the numbers of persons in the household) sumlink (eg. sumlink(persons, income) sums up all incomes from the members in a household) avglink (eg. avglink(persons, age) gives the average age of the members in a household) minlink, maxlink (eg. minlink(persons, age) gives the age of the youngest member of the household)

  25. many2one and the “.”-function entities: person: fields: - age: int - gender: bool # link fields - hh_id: int links: household: {type: many2one, target: household, field: hh_id} many2one : link the item of the entity to one other item in the same (eg. a person to its mother) or another entity (eg. a person to its household). To access a the value field of a linked item, you use: link_name.field_name processes: # produces "person_20xx_info.csv" dump_info: csv(dump(id, age, gender, household.nb_persons), suffix='info')

  26. many2one and the “.”-function person: fields: # period and id are implicit - age: int - gender: bool # link fields - mother_id: int - partner_id: int - hh_id: int links: mother: {type: many2one, target: person, field: mother_id} partner: {type: many2one, target: person, field: partner_id} household: {type: many2one, target: household, field: hh_id} children: {type: one2many, target: person, field: mother_id} Some examples: mother.age mother.mother.age age - partner.age

  27. Simulation: init - processes simulation: init: - household: [init_region_id, household_composition] processes: - household: [household_composition] - person: [ageing, dump_info] input: file: simple2001.h5 output: file: simulation.h5 # first simulated period start_period: 2002 periods: 2 init: executes the processes in start_period - 1 (here 2001) to initialise the household variables processes: executes in 2002, 2003

  28. Simulation: procedures – local variables processes: ageing: - age: age + 1 - juniors: 5 * trunc(age / 5) - plus50: 10 * trunc(age / 10) - agegroup: if(age < 50, juniors, plus50) dump_info: csv(dump(id, age, gender, hh_id, household.nb_persons, mother.age, partner.age), suffix='info') show_demography: show(groupby(agegroup, gender)) procedures • single process (ex. dump_info) • multi process (ex. ageing) • local variables • temporary: only available in the ageing procedure • not stored (ex. juniors, plus50 in the ageing procedure)

  29. Stochastic changes I: probabilistic simulation entities: household: fields: # period and id are implicit - nb_persons: {type: int, initialdata: false} - nb_children: {type: int, initialdata: false} - region_id: {type: int, initialdata: false} links: persons: {type: one2many, target: person, field: hh_id} processes: init_region_id: - region_id: choice([0, 1, 2, 3], [0.1, 0.2, 0.3, 0.4]) choice • region_id: 10% chance to get 0, 20% for 1, 30% for 2 and 40% for 3 • beware: sum of prob. = 100%

  30. regressions, macros, new, remove demo03.yml

  31. Stochastic changes II: behavioural equations • Logit: • logit_regr(expr, filter=None, align=percentage) • logit_regr(expr, filter=None, align='filename.csv') • Alignment : • align(expr, [take=take_filter,] [leave=leave_filter,] fname=’filename.csv’) • Continuous (expr + normal(0, 1) * mult + error_var): • cont_regr(expr, filter, mult, error_var) • Clipped continuous (always positive): • clip_regr(expr, filter, mult, error_var) • Log continuous (exponential of continuous): • log_regr(expr, filter, mult, error_var)

  32. logit + alignexample processes: ageing: - age: age + 1 birth: - to_give_birth: logit_regr(0.0, filter=not gender and (age >= 15) and (age <= 50), align='al_p_birth.csv') logit_regr(expr, filter, align) • Expr • filter: select individuals from entity • apply alignment using al_p_birth.csv

  33. macros: easier to read, maintain processes: ageing: - age: age + 1 birth: - to_give_birth: logit_regr(0.0, filter=not gender and (age >= 15) and (age <= 50), align='al_p_birth.csv') person: fields: - age: int . . .  macros: MALE: True FEMALE: False ISMALE: gender ISFEMALE: not gender processes: ageing: - age: age + 1 birth: - to_give_birth: logit_regr(0.0, filter=ISFEMALE and (age >= 15) and (age <= 50), align='al_p_birth.csv') • macros • defined on entity level • re-evaluated on each execution

  34. Life cycle functions – new – create new entities birth: - to_give_birth: logit_regr(0.0, filter=ISFEMALE and (age >= 15) and (age <= 50), align='al_p_birth.csv') - new('person', filter=to_give_birth, mother_id = id, hh_id = hh_id, age = 0, partner_id = UNSET, civilstate = SINGLE, gender = choice([MALE, FEMALE], [0.51, 0.49]) ) new • entity name: what (same or other eg. household on marriage) • filter: who • set initial values to a selection of variables

  35. Life cycle functions – remove – remove entities death: - dead: if(ISMALE, logit_regr(0.0, align='al_p_dead_m.csv'), logit_regr(0.0, align='al_p_dead_f.csv')) - civilstate: if(partner.dead, WIDOW, civilstate) - partner_id: if(partner.dead, UNSET, partner_id) - show('Avg age of dead men', grpavg(age, filter=dead and ISMALE)) - show('Avg age of dead women', grpavg(age, filter=dead and ISFEMALE)) - show('Widows', grpsum(ISWIDOW)) - remove(dead) remove • filter: who has to removed? • Item is removed form the entity set • No data is available for that period and later • Historical data is still accessible • Links must be cleaned manually if necessary

  36. Remove empty households entities: household: fields: - nb_persons: {type: int, initialdata: false} links: persons: {type: one2many, target: person, field: hh_id} processes: household_composition: - nb_persons: countlink(persons) - nb_children: countlink(persons, age < 18) clean_empty: remove(nb_persons == 0) . . . simulation:  processes: - person: [list of processes] - household: [household_composition, clean_empty]

  37. Debugging possibilities • show and dump functions • skip_shows: if set to True, annuls all show() functions • interactive console • period • entity • output: aggregate, groupby functions • breakpoint • breakpoint () • breakpoint(2021) • step (or s) • resume (or r) • random_seed • fix random seed: if you want to have several runs of a simulation use the same random numbers.

  38. matching, change links demo04.yml

  39. Matching - aka Marriage market • matches individuals from subset 1 with individuals from subset 2 • Give each individual in subset 1 a particular order (orderby) • Compute the score of all (unmatched) individuals in subset 2 • take the best score matching( set1filter=boolean_expr, set2filter=boolean_expr, orderby=difficult_match, score=coef1 * field1 + coef2 * other.field2 + ...)

  40. Marriage marriage: - in_couple: ISMARRIED - to_couple: if((age >= 18) and (age <= 90) and not in_couple, if(ISMALE, logit_regr(0.0, align='al_p_mmkt_m.csv'), logit_regr(0.0, align='al_p_mmkt_f.csv')), False) - difficult_match: if(to_couple and ISFEMALE, abs(age - grpavg(age, filter=to_couple and ISMALE)), nan) - partner_id: if(to_couple, matching(set1filter=ISFEMALE, set2filter=ISMALE, score=- 0.4893 * other.age + 0.0131 * other.age ** 2 ... orderby=difficult_match), partner_id) - justcoupled: to_couple and (partner_id != UNSET)  - civilstate: if(justcoupled, MARRIED, civilstate)

  41. New links, change links marriage: - in_couple: ISMARRIED ...  - civilstate: if(justcoupled, MARRIED, civilstate) - newhousehold: new('household', filter=justcoupled and ISFEMALE, region_id=choice([0, 1, 2, 3], [0.1, 0.2, 0.3, 0.4])) - hh_id: if(justcoupled, if(ISMALE, partner.newhousehold, newhousehold), hh_id) - csv(dump(id, age, gender, partner.id, partner.age, partner.gender, hh_id, filter=justcoupled), suffix='new_couples') new link • change the value of the linked field

  42. break links, lag demo05.yml

  43. Remove links divorce: - agediff: if(ISFEMALE and ISMARRIED, age - partner.age, 0) # select females to divorce - divorce: logit_regr(0.6713593 * household.nb_children - 0.0785202 * dur_in_couple + 0.1429621 * agediff - 0.0088308 * agediff **2 - 4.546278, filter = ISFEMALE and ISMARRIED and (dur_in_couple > 0), align = 'al_p_divorce.csv') # break link to partner - to_divorce: divorce or partner.divorce - partner_id: if(to_divorce, UNSET, partner_id) - civilstate: if(to_divorce, DIVORCED, civilstate) - dur_in_couple: if(to_divorce, 0, dur_in_couple) # move out males - hh_id: if(ISMALE and to_divorce, new('household', region_id=household.region_id), hh_id)

  44. globals, regr + align demo06.yml

  45. 1. Graduate people ineducation: # unemployed if graduated - workstate: if(ISSTUDENT and (((age >= 16) and IS_LOWER_SECONDARY_EDU) or ((age >= 19) and IS_UPPER_SECONDARY_EDU) or ((age >= 24) and IS_TERTIARY_EDU)), UNEMPLOYED, workstate) - show('num students', grpsum(ISSTUDENT))

  46. 2. Retire people globals: periodic: - WEMRA: float • # retire • - workstate: if(ISMALE, • if((age >= 65), RETIRED, workstate), • if((age >= WEMRA), RETIRED, workstate)) • globals • variables that do not relate to any particular entity • periodic globals can have a different value for each period

  47. 3. Pick people … to work in 2002 inwork: - work_score: UNSET # men - work_score: if(ISMALE and (age > 15) and (age < 65) and ISINWORK, logit_score(-0.196599 * age + 0.0086552 * age **2 - 0.000988 * age **3 + 0.1892796 * ISMARRIED + 3.554612), work_score) - work_score: if(ISMALE and (age > 15) and (age < 50) and ISUNEMPLOYED, logit_score(0.9780908 * age - 0.0261765 * age **2 + 0.000199 * age **3 - 12.39108), work_score) # women … # align on Number of Workers / Population by age class - work: if((age > 15) and (age < 65), if(ISMALE, align(work_score, leave=ISSTUDENT or ISRETIRED, fname='al_p_inwork_m.csv'), align(work_score, leave=ISSTUDENT or ISRETIRED, fname='al_p_inwork_f.csv')), False) - workstate: if(work, INWORK, workstate) - workstate: if(not work and lag(ISINWORK), -1, workstate)

  48. 4. Pick people … to be unemployed in 2002 + 5. Remain … unemp_process: - unemp_score: -1 - unemp_condition: (age > 15) and (age < 65) and not ISINWORK # Probability of being unemployed from being unemployed previously - unemp_score: if(unemp_condition and lag(ISUNEMPLOYED), logit_score(- 0.1988979 * age + 0.0026222 * age **2 + ...), unemp_score) # Probability of being unemployed from being inwork previously - unemp_score: if(unemp_condition_m and lag(ISINWORK), logit_score(0.1396404 * age - 0.0024024 * age **2 + ...), unemp_score) # Alignment of unemployment based on those not selected by inwork # [Number of new unemployed / (Population - Number of Workers)] by age # The here below condition must correspond to the here above denumerator - unemp: if((age > 15) and (age < 65) and not ISINWORK, align(unemp_score, leave=ISSTUDENT or ISRETIRED, fname='al_p_unemployed.csv'), False) - workstate: if(unemp, UNEMPLOYED, workstate) - workstate: if((workstate==-1) and not unemp, OTHERINACTIVE, workstate)

  49. import data demo_import.yml

  50. Import data (to run the file, press F5) # this is an "import" file. To use it press F5 in liam2 environment, or run # the following command in a console: # INSTALL_PATH\liam2 import demo_import.yml output: simple2001.h5 entities: person: path: input\person.csv fields: # period and id are implicit - age: int - gender: bool - ... household: path: input\household.csv # if fields are not specified, they are all imported

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