--- a
+++ b/man/evaluate_model.Rd
@@ -0,0 +1,148 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/evaluation.R
+\name{evaluate_model}
+\alias{evaluate_model}
+\title{Evaluates a trained model on fasta, fastq or rds files}
+\usage{
+evaluate_model(
+  path_input,
+  model = NULL,
+  batch_size = 100,
+  step = 1,
+  padding = FALSE,
+  vocabulary = c("a", "c", "g", "t"),
+  vocabulary_label = list(c("a", "c", "g", "t")),
+  number_batches = 10,
+  format = "fasta",
+  target_middle = FALSE,
+  mode = "lm",
+  output_format = "target_right",
+  ambiguous_nuc = "zero",
+  evaluate_all_files = FALSE,
+  verbose = TRUE,
+  max_iter = 20000,
+  target_from_csv = NULL,
+  max_samples = NULL,
+  proportion_per_seq = NULL,
+  concat_seq = NULL,
+  seed = 1234,
+  auc = FALSE,
+  auprc = FALSE,
+  path_pred_list = NULL,
+  exact_num_samples = NULL,
+  activations = NULL,
+  shuffle_file_order = FALSE,
+  include_seq = FALSE,
+  ...
+)
+}
+\arguments{
+\item{path_input}{Input directory where fasta, fastq or rds files are located.}
+
+\item{model}{A keras model.}
+
+\item{batch_size}{Number of samples per batch.}
+
+\item{step}{How often to take a sample.}
+
+\item{padding}{Whether to pad sequences too short for one sample with zeros.}
+
+\item{vocabulary}{Vector of allowed characters. Character outside vocabulary get encoded as specified in ambiguous_nuc.}
+
+\item{vocabulary_label}{List of labels for targets of each output layer.}
+
+\item{number_batches}{How many batches to evaluate.}
+
+\item{format}{File format, \code{"fasta"}, \code{"fastq"} or \code{"rds"}.}
+
+\item{target_middle}{Whether model is language model with separate input layers.}
+
+\item{mode}{Either \code{"lm"} for language model or \code{"label_header"}, \code{"label_csv"} or \code{"label_folder"} for label classification.}
+
+\item{output_format}{Determines shape of output tensor for language model.
+Either \code{"target_right"}, \code{"target_middle_lstm"}, \code{"target_middle_cnn"} or \code{"wavenet"}.
+Assume a sequence \code{"AACCGTA"}. Output correspond as follows
+\itemize{
+\item \verb{"target_right": X = "AACCGT", Y = "A"}
+\item \verb{"target_middle_lstm": X = (X_1 = "AAC", X_2 = "ATG"), Y = "C"} (note reversed order of X_2)
+\item \verb{"target_middle_cnn": X = "AACGTA", Y = "C"}
+\item \verb{"wavenet": X = "AACCGT", Y = "ACCGTA"}
+}}
+
+\item{ambiguous_nuc}{How to handle nucleotides outside vocabulary, either \code{"zero"}, \code{"discard"}, \code{"empirical"} or \code{"equal"}.
+\itemize{
+\item If \code{"zero"}, input gets encoded as zero vector.
+\item If \code{"equal"}, input is repetition of \code{1/length(vocabulary)}.
+\item If \code{"discard"}, samples containing nucleotides outside vocabulary get discarded.
+\item If \code{"empirical"}, use nucleotide distribution of current file.
+}}
+
+\item{evaluate_all_files}{Boolean, if \code{TRUE} will iterate over all files in \code{path_input} once. \code{number_batches} will be overwritten.}
+
+\item{verbose}{Boolean.}
+
+\item{max_iter}{Stop after \code{max_iter} number of iterations failed to produce a new batch.}
+
+\item{target_from_csv}{Path to csv file with target mapping. One column should be called "file" and other entries in row are the targets.}
+
+\item{max_samples}{Maximum number of samples to use from one file. If not \code{NULL} and file has more than \code{max_samples} samples, will randomly choose a
+subset of \code{max_samples} samples.}
+
+\item{proportion_per_seq}{Numerical value between 0 and 1. Proportion of sequence to take samples from (use random subsequence).}
+
+\item{concat_seq}{Character string or \code{NULL}. If not \code{NULL} all entries from file get concatenated to one sequence with \code{concat_seq} string between them.
+Example: If 1.entry AACC, 2. entry TTTG and \code{concat_seq = "ZZZ"} this becomes AACCZZZTTTG.}
+
+\item{seed}{Sets seed for \code{set.seed} function for reproducible results.}
+
+\item{auc}{Whether to include AUC metric. If output layer activation is \code{"softmax"}, only possible for 2 targets. Computes the average if output layer has sigmoid
+activation and multiple targets.}
+
+\item{auprc}{Whether to include AUPRC metric. If output layer activation is \code{"softmax"}, only possible for 2 targets. Computes the average if output layer has sigmoid
+activation and multiple targets.}
+
+\item{path_pred_list}{Path to store list of predictions (output of output layers) and corresponding true labels as rds file.}
+
+\item{exact_num_samples}{Exact number of samples to evaluate. If you want to evaluate a number of samples not divisible by batch_size. Useful if you want
+to evaluate a data set exactly ones and know the number of samples already. Should be a vector if \code{mode = "label_folder"} (with same length as \code{vocabulary_label})
+and else an integer.}
+
+\item{activations}{List containing output formats for output layers (\verb{softmax, sigmoid} or \code{linear}). If \code{NULL}, will be estimated from model.}
+
+\item{shuffle_file_order}{Logical, whether to go through files randomly or sequentially.}
+
+\item{include_seq}{Whether to store input. Only applies if \code{path_pred_list} is not \code{NULL}.}
+
+\item{...}{Further generator options. See \code{\link{get_generator}}.}
+}
+\value{
+A list of evaluation results. Each list element corresponds to an output layer of the model.
+}
+\description{
+Returns evaluation metric like confusion matrix, loss, AUC, AUPRC, MAE, MSE (depending on output layer).
+}
+\examples{
+\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+# create dummy data
+path_input <- tempfile()
+dir.create(path_input)
+create_dummy_data(file_path = path_input,
+                  num_files = 3,
+                  seq_length = 11, 
+                  num_seq = 5,
+                  vocabulary = c("a", "c", "g", "t"))
+# create model
+model <- create_model_lstm_cnn(layer_lstm = 8, layer_dense = 4, maxlen = 10, verbose = FALSE)
+# evaluate
+evaluate_model(path_input = path_input,
+  model = model,
+  step = 11,
+  vocabulary = c("a", "c", "g", "t"),
+  vocabulary_label = list(c("a", "c", "g", "t")),
+  mode = "lm",
+  output_format = "target_right",
+  evaluate_all_files = TRUE,
+  verbose = FALSE)
+  
+\dontshow{\}) # examplesIf}
+}