RemixAutoML (version 0.5.4)

AutoLightGBMRegression: AutoLightGBMRegression

Description

AutoLightGBMRegression is an automated lightgbm modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting.

Usage

AutoLightGBMRegression(
  data = NULL,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = NULL,
  FeatureColNames = NULL,
  PrimaryDateColumn = NULL,
  WeightsColumnName = NULL,
  IDcols = NULL,
  OutputSelection = c("Importances", "EvalPlots", "EvalMetrics", "Score_TrainData"),
  model_path = NULL,
  metadata_path = NULL,
  DebugMode = FALSE,
  SaveInfoToPDF = FALSE,
  ModelID = "TestModel",
  ReturnFactorLevels = TRUE,
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  EncodingMethod = "credibility",
  TransformNumericColumns = NULL,
  Methods = c("Asinh", "Log", "LogPlus1", "Sqrt", "Asin", "Logit"),
  Verbose = 0L,
  NumOfParDepPlots = 3L,
  GridTune = FALSE,
  grid_eval_metric = "r2",
  BaselineComparison = "default",
  MaxModelsInGrid = 10L,
  MaxRunsWithoutNewWinner = 20L,
  MaxRunMinutes = 24L * 60L,
  PassInGrid = NULL,
  input_model = NULL,
  task = "train",
  device_type = "CPU",
  NThreads = parallel::detectCores()/2,
  objective = "regression",
  metric = "rmse",
  boosting = "gbdt",
  LinearTree = FALSE,
  Trees = 50L,
  eta = NULL,
  num_leaves = 31,
  deterministic = TRUE,
  force_col_wise = FALSE,
  force_row_wise = FALSE,
  max_depth = NULL,
  min_data_in_leaf = 20,
  min_sum_hessian_in_leaf = 0.001,
  bagging_freq = 0,
  bagging_fraction = 1,
  feature_fraction = 1,
  feature_fraction_bynode = 1,
  extra_trees = FALSE,
  early_stopping_round = 10,
  first_metric_only = TRUE,
  max_delta_step = 0,
  lambda_l1 = 0,
  lambda_l2 = 0,
  linear_lambda = 0,
  min_gain_to_split = 0,
  drop_rate_dart = 0.1,
  max_drop_dart = 50,
  skip_drop_dart = 0.5,
  uniform_drop_dart = FALSE,
  top_rate_goss = FALSE,
  other_rate_goss = FALSE,
  monotone_constraints = NULL,
  monotone_constraints_method = "advanced",
  monotone_penalty = 0,
  forcedsplits_filename = NULL,
  refit_decay_rate = 0.9,
  path_smooth = 0,
  max_bin = 255,
  min_data_in_bin = 3,
  data_random_seed = 1,
  is_enable_sparse = TRUE,
  enable_bundle = TRUE,
  use_missing = TRUE,
  zero_as_missing = FALSE,
  two_round = FALSE,
  convert_model = NULL,
  convert_model_language = "cpp",
  boost_from_average = TRUE,
  alpha = 0.9,
  fair_c = 1,
  poisson_max_delta_step = 0.7,
  tweedie_variance_power = 1.5,
  lambdarank_truncation_level = 30,
  is_provide_training_metric = TRUE,
  eval_at = c(1, 2, 3, 4, 5),
  num_machines = 1,
  gpu_platform_id = -1,
  gpu_device_id = -1,
  gpu_use_dp = TRUE,
  num_gpu = 1
)

Arguments

data

This is your data set for training and testing your model

TrainOnFull

Set to TRUE to train on full data

ValidationData

This is your holdout data set used in modeling either refine your hyperparameters.

TestData

This is your holdout data set.

TargetColumnName

Either supply the target column name OR the column number where the target is located (but not mixed types).

FeatureColNames

Either supply the feature column names OR the column number where the target is located (but not mixed types)

PrimaryDateColumn

Supply a date or datetime column for catboost to utilize time as its basis for handling categorical features, instead of random shuffling

WeightsColumnName

Supply a column name for your weights column. Leave NULL otherwise

IDcols

A vector of column names or column numbers to keep in your data but not include in the modeling.

OutputSelection

You can select what type of output you want returned. Choose from c('Importances', 'EvalPlots', 'EvalMetrics', 'PDFs', 'Score_TrainData')

model_path

A character string of your path file to where you want your output saved

metadata_path

A character string of your path file to where you want your model evaluation output saved. If left NULL, all output will be saved to model_path.

DebugMode

Set to TRUE to get a print out of the steps taken throughout the function

SaveInfoToPDF

Set to TRUE to save model insights to pdf

ModelID

A character string to name your model and output

ReturnFactorLevels

Set to TRUE to have the factor levels returned with the other model objects

ReturnModelObjects

Set to TRUE to output all modeling objects (E.g. plots and evaluation metrics)

SaveModelObjects

Set to TRUE to return all modeling objects to your environment

EncodingMethod

Choose from 'binary', 'm_estimator', 'credibility', 'woe', 'target_encoding', 'poly_encode', 'backward_difference', 'helmert'

TransformNumericColumns

Set to NULL to do nothing; otherwise supply the column names of numeric variables you want transformed

Methods

Choose from 'BoxCox', 'Asinh', 'Asin', 'Log', 'LogPlus1', 'Sqrt', 'Logit', 'YeoJohnson'. Function will determine if one cannot be used because of the underlying data.

Verbose

Set to 0 if you want to suppress model evaluation updates in training

NumOfParDepPlots

Tell the function the number of partial dependence calibration plots you want to create.

GridTune

Set to TRUE to run a grid tuning procedure. Set a number in MaxModelsInGrid to tell the procedure how many models you want to test.

grid_eval_metric

'mae', 'mape', 'rmse', 'r2'. Case sensitive

BaselineComparison

Set to either 'default' or 'best'. Default is to compare each successive model build to the baseline model using max trees (from function args). Best makes the comparison to the current best model.

MaxModelsInGrid

Number of models to test from grid options (243 total possible options)

MaxRunsWithoutNewWinner

Runs without new winner to end procedure

MaxRunMinutes

In minutes

PassInGrid

Default is NULL. Provide a data.table of grid options from a previous run.

input_model

= NULL, # continue training a model that is stored to fil

# Core parameters https://lightgbm.readthedocs.io/en/latest/Parameters.html#core-parameter

task

'train' or 'refit'

device_type

'cpu' or 'gpu'

NThreads

only list up to number of cores, not threads. parallel::detectCores() / 2

objective

'regression'

metric

'rmse', 'l1', 'l2', 'quantile', 'mape', 'huber', 'fair', 'poisson', 'gamma', 'gamma_deviance', 'tweedie', 'ndcg'

boosting

'gbdt', 'rf', 'dart', 'goss'

LinearTree

FALSE

Trees

50L

eta

NULL

num_leaves

31

deterministic

TRUE

# Learning Parameters https://lightgbm.readthedocs.io/en/latest/Parameters.html#learning-control-parameter

force_col_wise

FALSE

force_row_wise

FALSE

max_depth

NULL

min_data_in_leaf

20

min_sum_hessian_in_leaf

0.001

bagging_freq

0

bagging_fraction

1.0

feature_fraction

1.0

feature_fraction_bynode

1.0

extra_trees

FALSE

early_stopping_round

10

first_metric_only

TRUE

max_delta_step

0.0

lambda_l1

0.0

lambda_l2

0.0

linear_lambda

0.0

min_gain_to_split

0

drop_rate_dart

0.10

max_drop_dart

50

skip_drop_dart

0.50

uniform_drop_dart

FALSE

top_rate_goss

FALSE

other_rate_goss

FALSE

monotone_constraints

NULL, 'gbdt_prediction.cpp'

monotone_constraints_method

'advanced'

monotone_penalty

0.0

forcedsplits_filename

NULL # use for AutoStack option; .json fil

refit_decay_rate

0.90

path_smooth

0.0

# IO Dataset Parameters https://lightgbm.readthedocs.io/en/latest/Parameters.html#io-parameters

max_bin

255

min_data_in_bin

3

data_random_seed

1

is_enable_sparse

TRUE

enable_bundle

TRUE

use_missing

TRUE

zero_as_missing

FALSE

two_round

FALSE

# Convert Parameters # https://lightgbm.readthedocs.io/en/latest/Parameters.html#convert-parameters

convert_model

'gbdt_prediction.cpp'

convert_model_language

'cpp'

# Objective Parameters https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective-parameters

boost_from_average

TRUE

alpha

0.90

fair_c

1.0

poisson_max_delta_step

0.70

tweedie_variance_power

1.5

lambdarank_truncation_level

30

# Metric Parameters (metric is in Core)

is_provide_training_metric

TRUE

eval_at

c(1,2,3,4,5)

# Network Parameter

num_machines

1

# GPU Parameter

gpu_platform_id

-1

gpu_device_id

-1

gpu_use_dp

TRUE

num_gpu

1

Value

Saves to file and returned in list: VariableImportance.csv, Model, ValidationData.csv, EvalutionPlot.png, EvalutionBoxPlot.png, EvaluationMetrics.csv, ParDepPlots.R a named list of features with partial dependence calibration plots, ParDepBoxPlots.R, GridCollect, and GridList

See Also

Other Automated Supervised Learning - Regression: AutoCatBoostRegression(), AutoH2oDRFRegression(), AutoH2oGAMRegression(), AutoH2oGBMRegression(), AutoH2oGLMRegression(), AutoH2oMLRegression(), AutoNLS(), AutoXGBoostRegression()

Examples

# NOT RUN {
# Create some dummy correlated data
data <- RemixAutoML::FakeDataGenerator(
  Correlation = 0.85,
  N = 1000,
  ID = 2,
  ZIP = 0,
  AddDate = FALSE,
  Classification = FALSE,
  MultiClass = FALSE)

# Run function
TestModel <- RemixAutoML::AutoLightGBMRegression(

  # Metadata args
  OutputSelection = c('Importances','EvalPlots','EvalMetrics','Score_TrainData'),
  model_path = normalizePath('./'),
  metadata_path = NULL,
  ModelID = 'Test_Model_1',
  NumOfParDepPlots = 3L,
  EncodingMethod = 'credibility',
  ReturnFactorLevels = TRUE,
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  SaveInfoToPDF = FALSE,
  DebugMode = FALSE,

  # Data args
  data = data,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = 'Adrian',
  FeatureColNames = names(data)[!names(data) %in% c('IDcol_1', 'IDcol_2','Adrian')],
  PrimaryDateColumn = NULL,
  WeightsColumnName = NULL,
  IDcols = c('IDcol_1','IDcol_2'),
  TransformNumericColumns = NULL,
  Methods = c('Asinh','Asin','Log','LogPlus1','Sqrt','Logit'),

  # Grid parameters
  GridTune = FALSE,
  grid_eval_metric = 'r2',
  BaselineComparison = 'default',
  MaxModelsInGrid = 10L,
  MaxRunsWithoutNewWinner = 20L,
  MaxRunMinutes = 24L*60L,
  PassInGrid = NULL,

  # Core parameters
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#core-parameters
  input_model = NULL, # continue training a model that is stored to file
  task = 'train',
  device_type = 'CPU',
  NThreads = parallel::detectCores() / 2,
  objective = 'regression',
  metric = 'rmse',
  boosting = 'gbdt',
  LinearTree = FALSE,
  Trees = 50L,
  eta = NULL,
  num_leaves = 31,
  deterministic = TRUE,

  # Learning Parameters
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#learning-control-parameters
  force_col_wise = FALSE,
  force_row_wise = FALSE,
  max_depth = NULL,
  min_data_in_leaf = 20,
  min_sum_hessian_in_leaf = 0.001,
  bagging_freq = 0,
  bagging_fraction = 1.0,
  feature_fraction = 1.0,
  feature_fraction_bynode = 1.0,
  extra_trees = FALSE,
  early_stopping_round = 10,
  first_metric_only = TRUE,
  max_delta_step = 0.0,
  lambda_l1 = 0.0,
  lambda_l2 = 0.0,
  linear_lambda = 0.0,
  min_gain_to_split = 0,
  drop_rate_dart = 0.10,
  max_drop_dart = 50,
  skip_drop_dart = 0.50,
  uniform_drop_dart = FALSE,
  top_rate_goss = FALSE,
  other_rate_goss = FALSE,
  monotone_constraints = NULL,
  monotone_constraints_method = 'advanced',
  monotone_penalty = 0.0,
  forcedsplits_filename = NULL, # use for AutoStack option; .json file
  refit_decay_rate = 0.90,
  path_smooth = 0.0,

  # IO Dataset Parameters
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#io-parameters
  max_bin = 255,
  min_data_in_bin = 3,
  data_random_seed = 1,
  is_enable_sparse = TRUE,
  enable_bundle = TRUE,
  use_missing = TRUE,
  zero_as_missing = FALSE,
  two_round = FALSE,

  # Convert Parameters
  convert_model = NULL,
  convert_model_language = 'cpp',

  # Objective Parameters
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective-parameters
  boost_from_average = TRUE,
  alpha = 0.90,
  fair_c = 1.0,
  poisson_max_delta_step = 0.70,
  tweedie_variance_power = 1.5,
  lambdarank_truncation_level = 30,

  # Metric Parameters (metric is in Core)
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters
  is_provide_training_metric = TRUE,
  eval_at = c(1,2,3,4,5),

  # Network Parameters
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#network-parameters
  num_machines = 1,

  # GPU Parameters
  # https://lightgbm.readthedocs.io/en/latest/Parameters.html#gpu-parameters
  gpu_platform_id = -1,
  gpu_device_id = -1,
  gpu_use_dp = TRUE,
  num_gpu = 1)
# }