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Count Cells from TreeBagger Classifier Output

Source: R/ifcb_summarize_class_counts.R
ifcb_summarize_class_counts.Rd

This function summarizes class results for a series of classifier output files and returns a summary data list.

Usage

ifcb_summarize_class_counts(
  classpath_generic,
  hdr_folder,
  year_range,
  use_python = FALSE
)

Arguments

classpath_generic

Character string specifying the location of the classifier output files. The path should include 'xxxx' in place of the 4-digit year (e.g., 'classxxxx_v1/').

hdr_folder

Character string specifying the directory where the data (hdr files) are located. This can be a URL for web services or a full path for local files.

year_range

Numeric vector specifying the range of years (e.g., 2013:2014) to process.

use_python

Logical. If TRUE, attempts to read the .mat file using a Python-based method. Default is FALSE.

Value

A list containing the following elements:

class2useTB

Classes used in the TreeBagger classifier.

classcountTB

Counts of each class considering each target placed in the winning class.

classcountTB_above_optthresh

Counts of each class considering only classifications above the optimal threshold for maximum accuracy.

ml_analyzedTB

Volume analyzed for each file.

mdateTB

Dates associated with each file.

filelistTB

List of files processed.

classpath_generic

The generic classpath provided as input.

classcountTB_above_adhocthresh (optional)

Counts of each class considering only classifications above the adhoc threshold.

adhocthresh (optional)

The adhoc threshold used for classification.

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Examples

if (FALSE) { # \dontrun{
ifcb_summarize_class_counts('path/to/class/classxxxx_v1/',
                            'path/to/data/', 2014)
} # }