new RankingMetrics(predictionAndLabels)
Parameters:
| Name | Type | Description |
|---|---|---|
predictionAndLabels |
module:eclairjs.RDD |
Methods
(static) of(predictionAndLabels) → {module:eclairjs/mllib/evaluation.RankingMetrics}
Creates a module:eclairjs/mllib/evaluation.RankingMetrics instance
Parameters:
| Name | Type | Description |
|---|---|---|
predictionAndLabels |
module:eclairjs.RDD | a JavaRDD of (predicted ranking, ground truth set) pairs |
Returns:
ndcgAt(k) → {number}
Compute the average NDCG value of all the queries, truncated at ranking position k.
The discounted cumulative gain at position k is computed as:
sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1),
and the NDCG is obtained by dividing the DCG value on the ground truth set. In the current
implementation, the relevance value is binary.
If a query has an empty ground truth set, zero will be used as ndcg together with
a log warning.
See the following paper for detail:
IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
Parameters:
| Name | Type | Description |
|---|---|---|
k |
number | the position to compute the truncated ndcg, must be positive |
Returns:
the average ndcg at the first k ranking positions
- Type
- number
precisionAt(k) → {number}
Compute the average precision of all the queries, truncated at ranking position k.
If for a query, the ranking algorithm returns n (n < k) results, the precision value will be
computed as #(relevant items retrieved) / k. This formula also applies when the size of the
ground truth set is less than k.
If a query has an empty ground truth set, zero will be used as precision together with
a log warning.
See the following paper for detail:
IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
Parameters:
| Name | Type | Description |
|---|---|---|
k |
number | the position to compute the truncated precision, must be positive |
Returns:
the average precision at the first k ranking positions
- Type
- number