confusion

README

Confusion implements a confusion matrix: records output responses for discrete categories / classes.

• Rows (outer dimension) are for each class as the ground truth, correct answer.

• Columns (inner dimension) are the response generated for each ground-truth class.

The main result is in the Prob field, computed from the Sum and N values added incrementally.

Documentation

Index

• Variables
• type Matrix
  • func (cm *Matrix) Incr(class, resp int)
  • func (cm *Matrix) Init(n int)
  • func (cm *Matrix) OpenCSV(filename gi.FileName)
  • func (cm *Matrix) Probs()
  • func (cm *Matrix) SaveCSV(filename gi.FileName)
  • func (cm *Matrix) SetLabels(lbls []string)

Variables

var KiT_Matrix = kit.Types.AddType(&Matrix{}, MatrixProps)

var MatrixProps = ki.Props{
	"ToolBar": ki.PropSlice{
		{"SaveCSV", ki.Props{
			"label": "Save CSV...",
			"icon":  "file-save",
			"desc":  "Save CSV-formatted confusion probabilities (Probs)",
			"Args": ki.PropSlice{
				{"CSV File Name", ki.Props{
					"ext": ".csv",
				}},
			},
		}},
		{"OpenCSV", ki.Props{
			"label": "Open CSV...",
			"icon":  "file-open",
			"desc":  "Open CSV-formatted confusion probabilities (Probs)",
			"Args": ki.PropSlice{
				{"Weights File Name", ki.Props{
					"ext": ".csv",
				}},
			},
		}},
	},
}

Types

type Matrix

type Matrix struct {
	Prob etensor.Float64 `view:"no-inline" desc:"normalized probability of confusion: Row = ground truth class, Col = actual response for that class."`
	Sum  etensor.Float64 `view:"no-inline" desc:"incremental sums"`
	N    etensor.Float64 `view:"no-inline" desc:"counts per ground truth (rows)"`
	Vis  simat.SimMat    `view:"no-inline" desc:"visualization using SimMat"`
}

Matrix computes the confusion matrix, with rows representing the ground truth correct class, and columns representing the actual answer produced. Correct answers are along the diagonal.

func (*Matrix) Incr

func (cm *Matrix) Incr(class, resp int)

Incr increments the data for given class ground truth and response.

func (*Matrix) Init

func (cm *Matrix) Init(n int)

Init initializes the Matrix for given number of classes, and resets the data to zero.

func (*Matrix) OpenCSV

func (cm *Matrix) OpenCSV(filename gi.FileName)

OpenCSV opens Prob result from a CSV file, comma separated

func (*Matrix) Probs

func (cm *Matrix) Probs()

Probs computes the probabilities based on accumulated data

func (*Matrix) SaveCSV

func (cm *Matrix) SaveCSV(filename gi.FileName)

SaveCSV saves Prob result to a CSV file, comma separated

func (*Matrix) SetLabels

func (cm *Matrix) SetLabels(lbls []string)

SetLabels sets the class labels, for visualization in Vis