dot-finish_data_preparation: Internal function for finalising generic data processing
In familiar: End-to-End Automated Machine Learning and Model Evaluation

.finish_data_preparation

R Documentation

Internal function for finalising generic data processing

Description

Internal function for finalising generic data processing

Usage

.finish_data_preparation(
  data,
  sample_id_column,
  batch_id_column,
  series_id_column,
  outcome_column,
  outcome_type,
  include_features,
  class_levels,
  censoring_indicator,
  event_indicator,
  competing_risk_indicator,
  check_stringency = "strict",
  reference_method = "auto"
)

Arguments

`data`	data.table with feature data
`sample_id_column`	(recommended) Name of the column containing sample or subject identifiers. See `batch_id_column` above for more details. If unset, every row will be identified as a single sample.
`batch_id_column`	(recommended) Name of the column containing batch or cohort identifiers. This parameter is required if more than one dataset is provided, or if external validation is performed. In familiar any row of data is organised by four identifiers: The batch identifier `batch_id_column`: This denotes the group to which a set of samples belongs, e.g. patients from a single study, samples measured in a batch, etc. The batch identifier is used for batch normalisation, as well as selection of development and validation datasets. The sample identifier `sample_id_column`: This denotes the sample level, e.g. data from a single individual. Subsets of data, e.g. bootstraps or cross-validation folds, are created at this level. The series identifier `series_id_column`: Indicates measurements on a single sample that may not share the same outcome value, e.g. a time series, or the number of cells in a view. The repetition identifier: Indicates repeated measurements in a single series where any feature values may differ, but the outcome does not. Repetition identifiers are always implicitly set when multiple entries for the same series of the same sample in the same batch that share the same outcome are encountered.
`series_id_column`	(optional) Name of the column containing series identifiers, which distinguish between measurements that are part of a series for a single sample. See `batch_id_column` above for more details. If unset, rows which share the same batch and sample identifiers but have a different outcome are assigned unique series identifiers.
`outcome_column`	(recommended) Name of the column containing the outcome of interest. May be identified from a formula, if a formula is provided as an argument. Otherwise an error is raised. Note that `survival` and `competing_risk` outcome type outcomes require two columns that indicate the time-to-event or the time of last follow-up and the event status.
`outcome_type`	(recommended) Type of outcome found in the outcome column. The outcome type determines many aspects of the overall process, e.g. the available feature selection methods and learners, but also the type of assessments that can be conducted to evaluate the resulting models. Implemented outcome types are: `binomial`: categorical outcome with 2 levels. `multinomial`: categorical outcome with 2 or more levels. `count`: Poisson-distributed numeric outcomes. `continuous`: general continuous numeric outcomes. `survival`: survival outcome for time-to-event data. If not provided, the algorithm will attempt to obtain outcome_type from contents of the outcome column. This may lead to unexpected results, and we therefore advise to provide this information manually. Note that `competing_risk` survival analysis are not fully supported, and is currently not a valid choice for `outcome_type`.
`include_features`	(optional) Feature columns that are specifically included in the data set. By default all features are included. Cannot overlap with `exclude_features`, but may overlap `signature`. Features in `signature` and `novelty_features` are always included. If both `exclude_features` and `include_features` are provided, `include_features` takes precedence, provided that there is no overlap between the two.
`class_levels`	(optional) Class levels for `binomial` or `multinomial` outcomes. This argument can be used to specify the ordering of levels for categorical outcomes. These class levels must exactly match the levels present in the outcome column.
`censoring_indicator`	(recommended) Indicator for right-censoring in `survival` and `competing_risk` analyses. `familiar` will automatically recognise `0`, `false`, `f`, `n`, `no` as censoring indicators, including different capitalisations. If this parameter is set, it replaces the default values.
`event_indicator`	(recommended) Indicator for events in `survival` and `competing_risk` analyses. `familiar` will automatically recognise `1`, `true`, `t`, `y` and `yes` as event indicators, including different capitalisations. If this parameter is set, it replaces the default values.
`competing_risk_indicator`	(recommended) Indicator for competing risks in `competing_risk` analyses. There are no default values, and if unset, all values other than those specified by the `event_indicator` and `censoring_indicator` parameters are considered to indicate competing risks.
`check_stringency`	Specifies stringency of various checks. This is mostly: `strict`: default value used for `summon_familiar`. Thoroughly checks input data. Used internally for checking development data. `external_warn`: value used for `extract_data` and related methods. Less stringent checks, but will warn for possible issues. Used internally for checking data for evaluation and explanation. `external`: value used for external methods such as `predict`. Less stringent checks, particularly for identifier and outcome columns, which may be completely absent. Used internally for `predict`.
`reference_method`	(optional) Method used to set reference levels for categorical features. There are several options: `auto` (default): Categorical features that are not explicitly set by the user, i.e. columns containing boolean values or characters, use the most frequent level as reference. Categorical features that are explicitly set, i.e. as factors, are used as is. `always`: Both automatically detected and user-specified categorical features have the reference level set to the most frequent level. Ordinal features are not altered, but are used as is. `never`: User-specified categorical features are used as is. Automatically detected categorical features are simply sorted, and the first level is then used as the reference level. This was the behaviour prior to familiar version 1.3.0.

Details

This function is used to update data.table provided by loading the data. When part of the main familiar workflow, this function is used after .parse_initial_settings –> .load_data –> .update_initial_settings.

When used to parse external data (e.g. in conjunction with familiarModel) it follows after .load_data. Hence the function contains several checks which are otherwise part of .update_initial_settings.