R: Construct validation data

lgb.Dataset.create.valid {lightgbm} | R Documentation

## Construct validation data

### Description

Construct validation data according to training data

### Usage

    lgb.Dataset.create.valid(
      dataset,
      data,
      info = list(),
      label = NULL,
      weight = NULL,
      group = NULL,
      init_score = NULL,
      params = list(),
      ...
    )

### Arguments

`dataset` | `lgb.Dataset` object, training data
`data` | a `matrix` object, a `dgCMatrix` object, a character representing a path to a text file (CSV, TSV, or LibSVM), or a character representing a path to a binary `Dataset` file
`info` | a list of information of the `lgb.Dataset` object. NOTE: use of `info` is deprecated as of v3.3.0. Use keyword arguments (e.g. `init_score = init_score`) directly.
`label` | vector of labels to use as the target variable
`weight` | numeric vector of sample weights
`group` | used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with `group = c(10, 20, 40, 10, 10, 10)`, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.
`init_score` | initial score is the base prediction lightgbm will boost from
`params` | a list of parameters. See The "Dataset Parameters" section of the documentation for a list of parameters and valid values. If this is an empty list (the default), the validation Dataset will have the same parameters as the Dataset passed to argument `dataset`.
`...` | additional `lgb.Dataset` parameters. NOTE: As of v3.3.0, use of `...` is deprecated. Add parameters to `params` directly.

### Value

constructed dataset

### Examples

    data(agaricus.train, package = "lightgbm")
    train <- agaricus.train
    dtrain <- lgb.Dataset(train$data, label = train$label)
    data(agaricus.test, package = "lightgbm")
    test <- agaricus.test
    dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)

    # parameters can be changed between the training data and validation set,
    # for example to account for training data in a text file with a header row
    # and validation data in a text file without it
    train_file <- tempfile(pattern = "train_", fileext = ".csv")
    write.table(
      data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))
      , file = train_file
      , sep = ","
      , col.names = TRUE
      , row.names = FALSE
      , quote = FALSE
    )

    valid_file <- tempfile(pattern = "valid_", fileext = ".csv")
    write.table(
      data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))
      , file = valid_file
      , sep = ","
      , col.names = FALSE
      , row.names = FALSE
      , quote = FALSE
    )

    dtrain <- lgb.Dataset(
      data = train_file
      , params = list(has_header = TRUE)
    )
    dtrain$construct()

    dvalid <- lgb.Dataset(
      data = valid_file
      , params = list(has_header = FALSE)
    )
    dvalid$construct()

[Package lightgbm version 3.3.2 Index]