NDCG

NDCG (Normalized Discounted Cumulative Gain) is a ranking quality metric that handles graded relevance — items can be highly relevant, somewhat relevant, or not relevant at all, rather than just relevant or not. It also applies a logarithmic discount so that items ranked higher contribute more to the score than items ranked lower.

Constants

const (
    AbsTol = 1e-08
    RelTol = 1e-05
)

These tolerances are used by IsClose and IsZero for floating-point comparisons throughout the library.

Constant	Value	Description
`AbsTol`	`1e-08`	Absolute tolerance for float comparison
`RelTol`	`1e-05`	Relative tolerance for float comparison

`NDCG`

func NDCG(predicted []string, relevance map[string]int, k int) float64

Returns the Normalized Discounted Cumulative Gain at cutoff K. NDCG compares the DCG of the actual ranked list against the ideal DCG (the best possible ordering of the same items). A score of 1.0 indicates a perfect ranking; 0.0 means no relevant items were retrieved. Returns 0.0 when the ideal DCG is zero.

[]string

required

The ranked list of item identifiers returned by the system, ordered from most to least relevant.

map[string]int

required

A map of item identifier to integer relevance grade. Higher grades indicate greater relevance. Items not present in the map are treated as grade 0.

int

required

The cutoff depth. Only the first k positions are evaluated.

Returns float64 — DCG of the predicted ranking divided by the ideal DCG, in the range [0, 1].

Example

func ExampleNDCG() {
	predicted := []string{"A", "B", "C", "D"}
	relevance := map[string]int{
		"A": 3,
		"B": 2,
		"C": 1,
		"D": 0,
		"E": 3,
	}

	s := reval.NDCG(predicted, relevance, 3)
	fmt.Println("NDCG@3:", s)

	// Output:
	// NDCG@3: 0.7271926019583822
}

The ideal top-3 ranking would be “A” (grade 3), “E” (grade 3), “B” (grade 2). The actual top-3 is “A” (3), “B” (2), “C” (1). The actual DCG is lower because “E” was not retrieved at rank 2, giving NDCG ≈ 0.727.

`DCG`

func DCG(relevance []int) float64

Computes the Discounted Cumulative Gain for a sequence of relevance grades. Grades are discounted by the logarithm of their rank position so that higher-ranked items contribute more to the total. The formula applied at each position i (0-indexed) is:

DCG = Σ (2^rel_i - 1) / log2(i + 2)

[]int

required

A slice of integer relevance grades in rank order. The first element is rank 1, the second is rank 2, and so on.

Returns float64 — the summed discounted gain across all positions.

`IsClose`

func IsClose(a, b float64) bool

Reports whether two float64 values are approximately equal, using both absolute and relative tolerances defined by AbsTol and RelTol. The comparison uses the formula:

|a - b| ≤ AbsTol + RelTol × max(|a|, |b|)

float64

required

The first value to compare.

float64

required

The second value to compare.

Returns bool — true if a and b are within tolerance of each other.

`IsZero`

func IsZero(a float64) bool

Reports whether a float64 value is approximately zero. Equivalent to calling IsClose(a, 0.0).

float64

required

The value to test.

Returns bool — true if a is within tolerance of 0.0.

Get Started

Metrics Reference

Guides

Constants

`NDCG`

Example

`DCG`

`IsClose`

`IsZero`

​Constants

​NDCG

​Example

​DCG

​IsClose

​IsZero

Constants

`NDCG`

Example

`DCG`

`IsClose`

`IsZero`