View Source Scholar.Metrics.Ranking (Scholar v0.3.1)
Provides metrics and calculations related to ranking quality.
Ranking metrics evaluate the quality of ordered lists of items, often used in information retrieval and recommendation systems.
This module currently supports the following ranking metrics:
- Discounted Cumulative Gain (DCG)
Summary
Functions
Options
:k- Truncation parameter to consider only the top-k elements.
Computes the DCG based on true relevance scores (y_true) and their respective predicted scores (y_score).
Computes the normalized discounted cumulative gain (NDCG) based on true relevance scores y_true and their respective predicted scores y_score.
Functions
Options
:k- Truncation parameter to consider only the top-k elements.
Computes the DCG based on true relevance scores (y_true) and their respective predicted scores (y_score).
Computes the normalized discounted cumulative gain (NDCG) based on true relevance scores y_true and their respective predicted scores y_score.
Options
:k- Truncation parameter to consider only the top-k elements.
Examples
iex> true_relevance = Nx.tensor([10, 0, 0, 1, 5])
iex> scores = Nx.tensor([0.1, 0.2, 0.3, 4, 70])
iex> Scholar.Metrics.Ranking.ndcg_n(true_relevance, scores)
#Nx.Tensor<
f32
0.6956940293312073
>
iex> scores = Nx.tensor([0.05, 1.1, 1.0, 0.5, 0.0])
iex> Scholar.Metrics.Ranking.ndcg_n(true_relevance, scores)
#Nx.Tensor<
f32
0.4936802089214325
>
iex> scores = Nx.tensor([0.05, 1.1, 1.0, 0.5, 0.0])
iex> Scholar.Metrics.Ranking.ndcg_n(true_relevance, scores, k: 4)
#Nx.Tensor<
f32
0.352024108171463
>
iex> Scholar.Metrics.Ranking.ndcg_n(true_relevance, true_relevance, k: 4)
#Nx.Tensor<
f32
1.0
>