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Description

implemented a C++ based incremental decision tree. incremental learning of machine learning is frequently requested in real-world industries, but in practice still lacks a clear implementation.

gaenari runs AI over the long term with clear APIs for insert, update and rebuild. `rebuild` presents a solution to the problem of concept drift as data trends change. it is implemented in C++, so it is lightweight and advantageous for porting to other platforms.

Programming language: C++
License: Apache License 2.0

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README

gaenari

gaenari is the Korean name for spring-blooming forsythia in East Asia. it is a plant with small yellow flowers.\ [forsythia](./doc/img/forsythia.jpg)

Here, gaenari means:

  • full project name with C++17 header-only libraries.
  • a single decision tree like ID3 and C4.5.

when machine learning works with real world data, its accuracy decreases over time. it is quite different from the AI industry news or the success stories of academic journals. what is the cause of the problem?

concept drift

concept drift is one of the biggest obstacles of machine learning in the real world AI.

can data before the COVID-19 pandemic predict future information? not easy. because data trends are always changing, so current models cannot easily predict the future one step ahead.

we live in an incomplete real world rather than a toy world that separates dog and cat images.

solutions

  • assume that perfect modeling is impossible.\ it relies on heuristic technique instead of mathemetical and statistical algorithms.
  • update the model through incremental learning.
  • accumulate data and provide insights.
  • minimize the use of complex hyper-parameters.

this is an example of resolving the decrease in accuracy due to data trend change by calling rebuild().

supul

supul means forest in Korean, and it is a metaphor for multiple decision trees. supul is the another library in gaenari.

  • support for incremental learning through inserts, updates, and rebuilds.
  • combine multiple decision trees.
  • manage a database.

generation

the supul expands by generation. rebuild trains a single decision tree on the weak parts and then combine it.

[legend](./doc/img/legend.png)\ [generation](./doc/img/generation.gif)

the goal is to increase(or keep) accuracy through rule segmentation. similar to the effect of sharpening a photo as shown in the picture below.

[apples](./doc/img/apple.gif)

i learned that overfitting is bad.

as above, the tree seems to overfit over time. so it is true that negative thoughts arise.

in traditional machine learning, the training data is a sampled subset of the population. so there is a sampling error(the training data are not representative of all cases in the population), overfitting is the target of avoidance.

however, the goal of the supul is to learn the whole data, not the samples. therefore, in this case, it adaps to new data through continous incremental learning. this reduces the risk of overfitting.

library design

[design](./doc/img/design.gif)

a single decision tree and dataset are implemented in gaenari. supul implements a public supul methods that can be called externally. database and model processing for incremental learning are key.

dataframe repository is implemented as an interface and can be easily appended to. the same goes for databases. databases other than sqlite are also possible.

build

gaenari is a header-only library, so only include is needed. but some external libraries, such as sqlite, require c/c++ compilation for link.

build gaenari

$ cd /path/to/gaenari
$ mkdir build
$ cd build
$ cmake ..
$ cmake --build . --config release

test

$ ctest --verbose

find executables in build/tests/* and just execute it, display in color.

[log](./doc/img/log.gif)

build with gaenari

wrapper/wrapper.cpp
#include "gaenari/gaenari.hpp"
int main(void) {
    gaenari::logger::init1("/temp/_log.txt");
    using supul_t = supul::supul::supul_t;
    supul_t::api::project::create("/temp/supul_dir");
    supul_t::api::project::add_field("/temp/supul_dir", "x1", "REAL");
    supul_t::api::project::add_field("/temp/supul_dir", "x2", "INTEGER");
    supul_t::api::project::add_field("/temp/supul_dir", "x3", "TEXT_ID");
    supul_t::api::project::add_field("/temp/supul_dir", "y0", "TEXT_ID");
    supul_t::api::project::x("/temp/supul_dir", {"x1", "x2", "x3"});
    supul_t::api::project::y("/temp/supul_dir", "y0");
    supul_t::api::project::set_property("/temp/supul_dir", "db.type", "sqlite");
    supul_t supul;
    supul.api.lifetime.open("/temp/supul_dir");
    supul.api.model.insert_chunk_csv("/temp/dataset.csv");
    supul.api.model.update();
    // ...
    supul.api.model.rebuild();
    supul.api.lifetime.close();
    return 0;
}
wrapper/CMakeLists.txt
cmake_minimum_required(VERSION 3.6)
project(wrapper)

# call order is important.

add_subdirectory(</path/to/gaenari>)
check_cpp17_gaenari()

add_executable(wrapper wrapper.cpp)
add_gaenari(wrapper)
build
wrapper/build$ cmake ..
wrapper/build$ cmake --build . --config release

walkthrough

you can call supul.api.<category>.<function>(...). it is implemented as noexcept, so checks for errors by checking the return false(or std::nullopt). for convenience, the return value check is omitted. see comments for more details.

walkthrough :: ready

since gaenari has only headers, a single include is required.

#include "gaenari/gaenari.hpp"

initialize log:

gaenari::logger::init1("/temp/log_gaenari.log");

walkthrough :: project

supul runs as a project in a directory unit. the project directory contains configuration, and sqlite database files. the project creation is as follows.

supul::supul::supul_t::api::project::create("/temp/my_project");

all functions in project category are static, so they can be called directly without an object. the main files in the project directory are:

file name note
property.txt project configuration
attributes.json schema definition
*.db sqlite database file
/temp/my_project/property.txt
# supul configuration.
ver = 1.0.0
# supported db type : sqlite.
db.type = sqlite
# set default database name.
db.dbname = supul
# set table name prefix.
db.tablename.prefix = 
# if the treenode is less accurate(<=) than this value, it is weak. the higher value, the more aggresive rebuild, and the more complex the tree.
model.weak_treenode_condition.accuracy = 0.8
# it is weak when the number of treenode's instances is greater(>=) than this. the lower value, the more aggresive rebuild, and the more complex the tree.
model.weak_treenode_condition.total_count = 5

you must choose db.type after project_create(). for example, select sqlite. you can edit manually or fix it using following function.

supul::supul::supul_t::api::project::set_property("/temp/my_project",
                                                  "db.type",
                                                  "sqlite");
attributes.json
{
    "revision": 0,
    "fields": {
        "salary": "REAL",
        "commission": "REAL",
        "age": "INTEGER",
        "elevel": "TEXT_ID",
        "car": "TEXT_ID",
        "zipcode": "TEXT_ID",
        "hvalue": "REAL",
        "hyears": "INTEGER",
        "loan": "REAL",
        "group": "TEXT_ID"
    },
    "x": [
        "salary",
        "commission",
        "age",
        "elevel",
        "car",
        "zipcode",
        "hvalue",
        "hyears",
        "loan"
    ],
    "y": "group"
}

the above json is an example of agrawal dataset. it is created with the dataset generator provided by weka and divided into two groups. there are 9 functions and the same function have the same data trend. used for concept drift experiments.

INTEGER, REAL, and TEXT_ID are supported as data types. TEXT_ID use index stored in a string table. it is nominal data.

these fields should be included in the header of the csv where the instances are stored. not all fields need to be included in x(e.g. internal id values needed for tracking). explicitly determines the x items in the fields. and choose one y item as well.

you can edit the json manually or use the function below, too.

using supul_t = supul::supul::supul_t;
std::string base_dir = "/temp/my_project";

supul_t::api::project::add_field(base_dir, "salary",     "REAL");
supul_t::api::project::add_field(base_dir, "commission", "REAL");
...
supul_t::api::project::add_field(base_dir, "group",      "TEXT_ID");

supul_t::api::project::x(base_dir, {"salary", "commission", ..., "loan"});
supul_t::api::project::y(base_dir, "group");

walkthrough :: create a supul object

after project creation, create a supul object.

supul::supul::supul_t supul;

if you want to use a supul object as a function return, you can use unique_ptr.

auto supul = std::make_unique<supul::supul::supul_t>();
...
return supul;

we can get supul api hints from ide tools (ex, visual studio).

[intellisense](./doc/img/intellisense.gif)

you can use the lifetime api to open and close your project.

supul.api.lifetime.open("/temp/my_project");

walkthrough :: insert a csv file

supul supports incremental learning. train a continuous dataset, and one dataset is called a chunk.

prepare the csv in the same format as the definition in attributes.json.

salary commision age elevel car zipcode hvalue hyears loan group
111811.9025 0 50 L2 C16 Z2 135000 9 374566.1561 G1
62308.5782 33338.59959 52 L3 C3 Z0 135000 6 64557.41339 G1
... ... ... ... ... ... ... ... ... ...

when creating a csv with weka, elevel, car, zipcode, and group are expressed only as numbers(actually nominal).

we can create agrawal dataset.csv as below.

$ java -classpath weka.jar weka.datagenerators.classifiers.classification.Agrawal -r temp -S 0 -n 100 -F 0 -P 0.005 > dataset.arff
$ java -classpath weka.jar weka.core.converters.CSVSaver -i data.arff -o dataset.csv

(see create_agrawal_dataset() function.)

insert an instances in csv into the database.

supul.api.model.insert_chunk_csv("/temp/dataset.csv");

supul inserts all new in-comming data into database. therefore, the database size is continuously increasing. it requres techniques to keep it on a limited scale. it is in TO-DO.

walkthrough :: update

insert stores only instance data, so we need to call update() for the next step. the update information includes things like the evaluation results for the current model. so, unlike insert, update requires extra time.

supul.api.model.update();

when update is called, the first model training will automatically proceed if the model has not yet been built.

update also stores statistical data(accuracy, etc.) for each chunk. this allows you to see how well the currently trained model reflects the new chunk.

walkthrough :: rebuild

when the trend in the data changes, the accuracy of the chunks decreases. rebuild() finds weak instances, re-trains only those parts, and combines them with the existing tree to overcome the loss of accuracy.

supul.api.model.rebuild();

if the rebuild results in somewhat less accurate, rollback to the previous state.

rebuild increases the size of the model because it is a continous method of combining models. the way to maintain a limited scale is included in TO-DO.

reubild is not yet automatically invoked by trigger. the call to rebuild under certain conditions is not yet implemented.

walkthrough :: predict

predict the y value of the x parameters that is input to the current model. the previous model is used by database transactions when changes (insert, update, rebuild, etc.) are currently in progress. a map of (key, value) is used for the x parameter, where key and value are strings. value is automatically converted by attributes.json.

std::unordered_map<std::string, std::string> x;
x = {{"salary",    "1000.0"},
     {"commision", "0.0"},
     {"age",       "25"},
     {"elevel",    "3"},
     {"car",       "1"},
     {"zipcode",   "1"},
     {"hvalue",    "132000"},
     {"hyears",    "3"}};
auto ret = supul.api.model.predict(x);

returned information of predict:

// predict result.
struct predict_result {
    bool        error = false;
    std::string errormsg;
    int64_t     label_index = 0;
    std::string label;
    int64_t     correct_count = 0;
    int64_t     total_count = 0;
    double      accuracy = 0.0;
};

the label value is the predicted y value. label_index is the string table index of the label. correct_count, total_count, and accuracy are information of the leaf tree node classified in the decision tree.

these three values can be used as confidence information for prediction.

walkthrough :: report

current status can be output as json and gnuplot charts.

install gnuplot and add to path.

to get report as json:

auto ret = supul.api.report.json("");
if (not ret) {/* error */}
auto& json = ret.value();

to get report as gnuplot (png):

supul.api.report.gnuplot(json, {
    {"terminal",        "pngcairo"},
    {"terminal_option", "font `Times-New-Roman,10` size 800,800"},
    {"output_filepath", "/tmp/chart.png"},
    {"plt_filepath",    "/tmp/gnuplot_script.plt"},
});

see configuring gnuplot for terminal and terminal option. if the terminal is dumb, it will output an ascii chart.

pretty processed json:

{
  "doc_ver": 1,
  "error": false,
  "category": {
    "global": {
      "schema_version": 1,
      "instance_count": 5000,
      "updated_instance_count": 5000,
      "instance_correct_count": 3766,
      "instance_accuracy": 0.7532,
      "acc_weak_instance_count": 4029
    },
    "confusion_matrix": {
      "label_name": [
        "1",
        "0"
      ],
      ...

chart.png:

chunk_history allows you to see the current accuracy trend of the model and call rebuild if necessary for better accuracy.

the chunk_history above is the result of _develop.hpp::report(). it processed in the following order(agrawal dataset).

  1. insert and update 10 chunks (func=1)
  2. insert and update 10 chunks (func=2)
  3. rebuild
  4. insert and update 10 chunks (func=2)
  5. rebuild
  6. insert and update 10 chunks (func=2)
  7. insert and update 10 chunks (func=1)

gnuplot_script.plt

# ${SET_TERMINAL}

# common

# data block
$data_block_chunk_history << EOD
0 0.99 100
...
# multiplot: chunk_history
set origin 0, 0.67
set size 1, 0.34
...
reset
unset key
unset multiplot

gnuplot's script leaves the terminal configuration blank to use the system defaults. set it yourself if necessary.

walkthrough :: wrap-up

stage 1: create a project that calls only once at the start.
#include "gaenari/gaenari.hpp"
...
gaenari::logger::init1("/temp/log_gaenari.log");
std::string base_dir = "/temp/my_project";
supul::supul::supul_t::api::project::create(base_dir);
supul::supul::supul_t::api::project::set_property(base_dir, "db.type", "sqlite");
supul::supul::supul_t::api::project::add_field(base_dir, "salary",     "REAL");
supul::supul::supul_t::api::project::add_field(base_dir, "commission", "REAL");
supul::supul::supul_t::api::project::add_field(base_dir, "group",      "TEXT_ID");
// ... omit ...
supul::supul::supul_t::api::project::x(base_dir, {"salary", "commission", ..., "loan"});
supul::supul::supul_t::api::project::y(base_dir, "group");
stage 2: continuous insert of new data(chunks).
#include "gaenari/gaenari.hpp"
...
gaenari::logger::init1("/temp/log_gaenari.log");
supul::supul::supul_t supul;
supul.api.lifetime.open("/temp/my_project");
supul.api.model.insert_chunk_csv("/temp/dataset1.csv");
supul.api.model.update();
supul.api.model.insert_chunk_csv("/temp/dataset2.csv");
supul.api.model.update();
supul.api.model.insert_chunk_csv("/temp/dataset3.csv");
supul.api.model.update();
stage 3: predict a instance.
#include "gaenari/gaenari.hpp"
...
std::unordered_map<std::string, std::string> instance = {{"salary":"3"}, ...};
...
gaenari::logger::init1("/temp/log_gaenari.log");
supul::supul::supul_t supul;
supul.api.lifetime.open("/temp/my_project");
auto ret = supul.api.model.predict(instance);
auto& predicted = ret.label;
stage 4: rebuild due to data trend change.
#include "gaenari/gaenari.hpp"
...
gaenari::logger::init1("/temp/log_gaenari.log");
supul::supul::supul_t supul;
supul.api.lifetime.open("/temp/my_project");
supul.api.model.rebuild();
stage 5: analyze the report.
#include "gaenari/gaenari.hpp"
...
gaenari::logger::init1("/temp/log_gaenari.log");
supul::supul::supul_t supul;
supul.api.lifetime.open("/temp/my_project");
auto ret = supul.api.report.json("");
if (not ret) {/* error */}
auto& json = ret.value();
supul.api.report.gnuplot(json, {
    {"terminal",        "pngcairo"},
    {"terminal_option", "font `Times-New-Roman,10` size 800,800"},
    {"output_filepath", "/tmp/chart.png"},
    {"plt_filepath",    "/tmp/gnuplot_script.plt"},
});

database

database is at the heart of supul. so, it is helpful to understand the database structure.

er-diagram:\

  • the primary key for all tables is id.
  • fields in the instance table are dynamically determined by attributes.json.
  • fields with *ref_* are references to other table id.
  • implementations that rely on specific database are prohibited.
  • fields used in the where clause are added to the index.
  • numerous treenode queries are required while running predict. so the cache is used for performance.
  • use prepared statements for security and performance.

a tool like DB Browser for SQLite makes it easier to understand the structure.

[sqlite_browser](./doc/img/sqlite_browser0.jpg)\ the sqlite database file with extension .db is located under the project directory. the agrwal instances are in the instance table.

let's look for misclassified instances.

execute this query:

select instance.*
  from instance
  join instance_info on instance.id = instance_info.ref_instance_id
where  instance_info.correct = 0

[sqlite_browser](./doc/img/sqlite_browser1.jpg)\ 5629 instances were found (id: 1, 3, 7, ...).

[sqlite_browser](./doc/img/sqlite_browser2.png)\ 30000 - 24371 = 5629, matches the calculation result of the global value.

error handling

the supul api in api category is a noexcept function, so no exceptions are thrown. check for errors with the return value.

return type error
bool false
std::optional std::nullopt
struct some bool member variable

if an error occurs, check the reason by:

auto msg = supul.api.misc.errmsg();

static functions are not supported.

example

if x in the predict call does not have the required value:

std::unordered_map<std::string, std::string> x = {{"foobar", "1"}};
auto result = supul.api.model.predict(x);
if (result.error) {
    std::cout << "* supul.api.misc.errmsg(): " << supul.api.misc.errmsg() << std::endl;
}

[error_msg](./doc/img/err0.png)

the error occurred because x does not have a required age value. the code location and reason are printed. it is also returned by calling errmsg().

api list

here is the list of supported apis.\ see the comments in the code for detail.

category static name
project O create
O set_property
O add_field
O x
O y
lifetime open
close
model insert_chunk_csv
update
rebuild
predict
report json
O gnuplot
misc O version
errmsg
property set_property
get_property
save
reload
test verify

property

the property.txt file in the project directory is the configuration file.
call set_property() or modify it yourself. see the comments in property.txt for detail.

name change possible type default desc
ver str library version
db.type str none support sqlite
db.tablename.prefix str set prefix table name
model.weak_treenode_condition.accuracy O double 0.8 see comment
model.weak_treenode_condition.total_count O int 5 see comment
limit.chunk.use O bool true see comment
limit.chunk.instance_lower_bound O int 1000000 see comment
limit.chunk.instance_upper_bound O int 2000000 see comment