as_familiar_data-methods: Conversion to familiarData object.
In familiar: End-to-End Automated Machine Learning and Model Evaluation

as_familiar_data

R Documentation

Conversion to familiarData object.

Description

Creates familiarData a object from familiarEnsemble or familiarModel objects.

Usage

as_familiar_data(object, ...)

## S4 method for signature 'familiarData'
as_familiar_data(object, ...)

## S4 method for signature 'familiarEnsemble'
as_familiar_data(object, name = NULL, ...)

## S4 method for signature 'familiarModel'
as_familiar_data(object, ...)

## S4 method for signature 'list'
as_familiar_data(object, ...)

## S4 method for signature 'character'
as_familiar_data(object, ...)

## S4 method for signature 'ANY'
as_familiar_data(object, ...)

Arguments

object

A familiarData object, or a familiarEnsemble or familiarModel objects that will be internally converted to a familiarData object. Paths to such objects can also be provided.

...

Arguments passed on to extract_data

data

A dataObject object, data.table or data.frame that constitutes the data that are assessed.

is_pre_processed

Flag that indicates whether the data was already pre-processed externally, e.g. normalised and clustered. Only used if the data argument is a data.table or data.frame.

cl

Cluster created using the parallel package. This cluster is then used to speed up computation through parallellisation.

time_max

Time point which is used as the benchmark for e.g. cumulative risks generated by random forest, or the cut-off value for Uno's concordance index. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects. Only used for survival outcomes.

evaluation_times

One or more time points that are used for in analysis of survival problems when data has to be assessed at a set time, e.g. calibration. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects. Only used for survival outcomes.

aggregation_method

Method for aggregating variable importances for the purpose of evaluation. Variable importances are determined during feature selection steps and after training the model. Both types are evaluated, but feature selection variable importance is only evaluated at run-time.

See the documentation for the vimp_aggregation_method argument in summon_familiar for information concerning the different available methods.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

rank_threshold

The threshold used to define the subset of highly important features during evaluation.

See the documentation for the vimp_aggregation_rank_threshold argument in summon_familiar for more information.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

ensemble_method

Method for ensembling predictions from models for the same sample. Available methods are:

median (default): Use the median of the predicted values as the ensemble value for a sample.
mean: Use the mean of the predicted values as the ensemble value for a sample.

metric

One or more metrics for assessing model performance. See the vignette on performance metrics for the available metrics. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

feature_cluster_method

The method used to perform clustering. These are the same methods as for the cluster_method configuration parameter: none, hclust, agnes, diana and pam.

none cannot be used when extracting data regarding mutual correlation or feature expressions.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

feature_linkage_method

The method used for agglomerative clustering in hclust and agnes. These are the same methods as for the cluster_linkage_method configuration parameter: average, single, complete, weighted, and ward.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

feature_cluster_cut_method

The method used to divide features into separate clusters. The available methods are the same as for the cluster_cut_method configuration parameter: silhouette, fixed_cut and dynamic_cut.

silhouette is available for all cluster methods, but fixed_cut only applies to methods that create hierarchical trees (hclust, agnes and diana). dynamic_cut requires the dynamicTreeCut package and can only be used with agnes and hclust.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

feature_similarity_threshold

The threshold level for pair-wise similarity that is required to form feature clusters with the fixed_cut method.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

feature_similarity_metric

Metric to determine pairwise similarity between features. Similarity is computed in the same manner as for clustering, and feature_similarity_metric therefore has the same options as cluster_similarity_metric: mcfadden_r2, cox_snell_r2, nagelkerke_r2, spearman, kendall and pearson.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

sample_cluster_method

The method used to perform clustering based on distance between samples. These are the same methods as for the cluster_method configuration parameter: hclust, agnes, diana and pam.

none cannot be used when extracting data for feature expressions.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

sample_linkage_method

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

sample_similarity_metric

Metric to determine pairwise similarity between samples. Similarity is computed in the same manner as for clustering, but sample_similarity_metric has different options that are better suited to computing distance between samples instead of between features: gower, euclidean.

The underlying feature data is scaled to the [0, 1] range (for numerical features) using the feature values across the samples. The normalisation parameters required can optionally be computed from feature data with the outer 5% (on both sides) of feature values trimmed or winsorised. To do so append ⁠_trim⁠ (trimming) or ⁠_winsor⁠ (winsorising) to the metric name. This reduces the effect of outliers somewhat.

If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

icc_type

String indicating the type of intraclass correlation coefficient (1, 2 or 3) that should be used to compute robustness for features in repeated measurements during the evaluation of univariate importance. These types correspond to the types in Shrout and Fleiss (1979). If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.

verbose

Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.

message_indent

Number of indentation steps for messages shown during computation and extraction of various data elements.

data_element

String indicating which data elements are to be extracted. Default is all, but specific elements can be specified to speed up computations if not all elements are to be computed. This is an internal parameter that is set by, e.g. the export_model_vimp method.

sample_limit

(optional) Set the upper limit of the number of samples that are used during evaluation steps. Cannot be less than 20.

This setting can be specified per data element by providing a parameter value in a named list with data elements, e.g. list("sample_similarity"=100, "permutation_vimp"=1000).

This parameter can be set for the following data elements: sample_similarity and ice_data.

detail_level

(optional) Sets the level at which results are computed and aggregated.

ensemble: Results are computed at the ensemble level, i.e. over all models in the ensemble. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the model performance of the ensemble model for each bootstrap.
hybrid (default): Results are computed at the level of models in an ensemble. This means that, for example, bias-corrected estimates of model performance are directly computed using the models in the ensemble. If there are at least 20 trained models in the ensemble, performance is computed for each model, in contrast to ensemble where performance is computed for the ensemble of models. If there are less than 20 trained models in the ensemble, bootstraps are created so that at least 20 point estimates can be made.
model: Results are computed at the model level. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the performance of the model for each bootstrap.

Note that each level of detail has a different interpretation for bootstrap confidence intervals. For ensemble and model these are the confidence intervals for the ensemble and an individual model, respectively. That is, the confidence interval describes the range where an estimate produced by a respective ensemble or model trained on a repeat of the experiment may be found with the probability of the confidence level. For hybrid, it represents the range where any single model trained on a repeat of the experiment may be found with the probability of the confidence level. By definition, confidence intervals obtained using hybrid are at least as wide as those for ensemble. hybrid offers the correct interpretation if the goal of the analysis is to assess the result of a single, unspecified, model.

hybrid is generally computationally less expensive then ensemble, which in turn is somewhat less expensive than model.

A non-default detail_level parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"="ensemble", "model_performance"="hybrid"). This parameter can be set for the following data elements: auc_data, decision_curve_analyis, model_performance, permutation_vimp, ice_data, prediction_data and confusion_matrix.

estimation_type

(optional) Sets the type of estimation that should be possible. This has the following options:

point: Point estimates.
bias_correction or bc: Bias-corrected estimates. A bias-corrected estimate is computed from (at least) 20 point estimates, and familiar may bootstrap the data to create them.
bootstrap_confidence_interval or bci (default): Bias-corrected estimates with bootstrap confidence intervals (Efron and Hastie, 2016). The number of point estimates required depends on the confidence_level parameter, and familiar may bootstrap the data to create them.

As with detail_level, a non-default estimation_type parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"="bci", "model_performance"="point"). This parameter can be set for the following data elements: auc_data, decision_curve_analyis, model_performance, permutation_vimp, ice_data, and prediction_data.

aggregate_results

(optional) Flag that signifies whether results should be aggregated during evaluation. If estimation_type is bias_correction or bc, aggregation leads to a single bias-corrected estimate. If estimation_type is bootstrap_confidence_interval or bci, aggregation leads to a single bias-corrected estimate with lower and upper boundaries of the confidence interval. This has no effect if estimation_type is point.

The default value is equal to TRUE except when assessing metrics to assess model performance, as the default violin plot requires underlying data.

As with detail_level and estimation_type, a non-default aggregate_results parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"=TRUE, , "model_performance"=FALSE). This parameter exists for the same elements as estimation_type.

confidence_level

(optional) Numeric value for the level at which confidence intervals are determined. In the case bootstraps are used to determine the confidence intervals bootstrap estimation, familiar uses the rule of thumb n = 20 / ci.level to determine the number of required bootstraps.

The default value is 0.95.

bootstrap_ci_method

(optional) Method used to determine bootstrap confidence intervals (Efron and Hastie, 2016). The following methods are implemented:

percentile (default): Confidence intervals obtained using the percentile method.
bc: Bias-corrected confidence intervals.

Note that the standard method is not implemented because this method is often not suitable due to non-normal distributions. The bias-corrected and accelerated (BCa) method is not implemented yet.

stratification_method

(optional) Method for determining the stratification threshold for creating survival groups. The actual, model-dependent, threshold value is obtained from the development data, and can afterwards be used to perform stratification on validation data.

The following stratification methods are available:

median (default): The median predicted value in the development cohort is used to stratify the samples into two risk groups. For predicted outcome values that build a continuous spectrum, the two risk groups in the development cohort will be roughly equal in size.
mean: The mean predicted value in the development cohort is used to stratify the samples into two risk groups.
mean_trim: As mean, but based on the set of predicted values where the 5% lowest and 5% highest values are discarded. This reduces the effect of outliers.
mean_winsor: As mean, but based on the set of predicted values where the 5% lowest and 5% highest values are winsorised. This reduces the effect of outliers.
fixed: Samples are stratified based on the sample quantiles of the predicted values. These quantiles are defined using the stratification_threshold parameter.
optimised: Use maximally selected rank statistics to determine the optimal threshold (Lausen and Schumacher, 1992; Hothorn et al., 2003) to stratify samples into two optimally separated risk groups.

One or more stratification methods can be selected simultaneously.

This parameter is only relevant for survival outcomes.

dynamic_model_loading

(optional) Enables dynamic loading of models during the evaluation process, if TRUE. Defaults to FALSE. Dynamic loading of models may reduce the overall memory footprint, at the cost of increased disk or network IO. Models can only be dynamically loaded if they are found at an accessible disk or network location. Setting this parameter to TRUE may help if parallel processing causes out-of-memory issues during evaluation.

name

Name of the familiarData object. If not set, a name is automatically generated.