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Ranking

How to log your model schema for ranking models

Ranking Model Overview

There are four key ranking model use cases to consider:
  • Search Ranking
  • Collaborative Filtering Recommender Systems
  • Content Filtering Recommender Systems
  • Classification-Based Ranking Models
Different metrics are used for ranking model evaluation based on your model use case, score, and label availability. The case determines the available performance metrics. Click here for all valid model types and metric combinations.
Ranking Cases
Example Use Case
Expected Fields
Performance Metrics
Model predicts score used to rank
rank, relevance score
​NDCG​
Model predicts binary actions a user can take which is used to rank
rank, relevance_labels
​NDCG, GroupAUC, MAP, MRR​
Model predicts multiple actions a user can take which is used to rank
rank, relevance_labels (list of strings)
Model can also be evaluated using AUC + LogLoss
Ranking Case 2 or 3, prediction score
​NDCG, GroupAUC, MAP, MRR, AUC, PR-AUC, Log Loss​

General Ranking Model Schema

Ranking models have a few unique model schema fields that help Arize effectively monitor, trace, and visualize your ranking model data.
  • Prediction Group ID: A subgroup of prediction data. Max 100 ranked items within each group
  • Rank: Unique value within each prediction group (1-100)
  • Relevancy Score/Label: Ground truth score/label associated with the model

Case 1: Ranking with Relevance Score

In the ranking model context, a relevance score is the numerical score used to rank items in a list. For example, the higher the relevance_score, the more important the item is. Relevancy scores often represent probabilities of an engagement or action such as probability of a click or purchase.
rank and relevance_score are required to compute rank-aware evaluation metrics on your model.
Ranking Model Fields
Data Type
Example
rank
int from 1-100
1
relevance_score
numeric (float | int)
0.5
prediction_group_id
string limited to 128 characters
148

Code Example

Python Pandas
Python Single Record
Data Connector

Example Row

state
price
search_id
rank
relevance_score
prediction_ts
ca
98
148
1
0.5
1618590882
schema = Schema(
prediction_id_column_name="prediction_id",
timestamp_column_name="prediction_ts",
prediction_group_id_column_name = "search_id",
rank_column_name = "rank",
relevance_score_column_name = "relevance_score",
feature_column_names=["state", "price"]
)
​
response = arize_client.log(
dataframe=df,
model_id="ranking-relevance-score-batch-ingestion-tutorial",
model_version="1.0",
model_type=ModelTypes.RANKING,
metrics_validation=[Metrics.RANKING],
environment=Environments.PRODUCTION,
schema=schema,
)
For more details on Python Batch API Reference, visit here:
# import extra dependencies
from arize.utils.types import Environments, ModelTypes, Schema, RankingPredictionLabel, RankingActualLabel
​
# define prediction label arguments
pred_label = RankingPredictionLabel(
group_id="148",
rank=1,
score=0.155441
)
​
# define actual label argument
act_label = RankingActualLabel(
relevance_score=0
)
​
# log data to Arize
response = arize_client.log(
model_id="demo-ranking-with-relevance-score",
model_version="v1",
environment=Environments.PRODUCTION,
model_type=ModelTypes.RANKING,
prediction_id="311103e3-a493-40ea-a21a-e457d617c956",
prediction_label=pred_label,
actual_label=act_label,
features=features
)
Download an example Parquet file: Open parquet reader here.
Learn how to upload files via various Data Connectors:

Case 2: Ranking with Single Label

Since relevance_score is required to compute rank-aware evaluation metrics, Arize uses an attribution model to create a relevance_score based on your positive class and relevance_labels. Learn more about our attribution model here.
Ranking Model Fields
Data Type
Example
rank
int from 1-100
1
relevance_labels
string
“click”
prediction_group_id
string limited to 128 characters
148

Code Example

Python Pandas
Python Single Record
Data Connectors

Example Row

state
price
search_id
rank
actual_relevancy
prediction_ts
ca
98
148
1
"not relevant"
1618590882
schema = Schema(
prediction_id_column_name="prediction_id",
timestamp_column_name="prediction_ts",
prediction_group_id_column_name = "search_id",
rank_column_name = "rank",
relevance_labels_column_name = "actual_relevancy",
feature_column_names=["state", "price"]
)
​
response = arize_client.log(
dataframe=df,
model_id="ranking-single-label-batch-ingestion-tutorial",
model_version="1.0",
model_type=ModelTypes.RANKING,
metrics_validation=[Metrics.RANKING, Metrics.RANKING_LABEL],
environment=Environments.PRODUCTION,
schema=schema,
)
For more details on Python Batch API Reference, visit here:
# import extra dependencies
from arize.utils.types import Environments, ModelTypes, Schema, RankingPredictionLabel, RankingActualLabel
​
# define prediction label arguments
pred_label = RankingPredictionLabel(
group_id="148",
rank=1,
label="relevant"
)
​
# define actual label argument
act_label = RankingActualLabel(
relevance_labels=["Not relevant"]
)
​
# log data to Arize
response = arize_client.log(
model_id="demo-ranking-with-single-label",
model_version="v1",
environment=Environments.PRODUCTION,
model_type=ModelTypes.RANKING,
prediction_id="311103e3-a493-40ea-a21a-e457d617c956",
prediction_label=pred_label,
actual_label=act_label,
features=features
)
Download an example Parquet file: Open parquet reader here.
Learn how to upload files via various Data Connectors:

Case 3: Ranking with Multiple Labels

In this case, each prediction on an item within a list is sent as an individual event while the possible relevance_labels can be multi-label (list) as ground truth can contain multiple events for an individual group or list.
Since relevance_score is required to compute rank-aware evaluation metrics, Arize uses an attribution model to create a relevance_score based on your positive class and relevance_labels. Learn more about our attribution model here.
Ranking Model Fields
Data Type
Example
rank
int from 1-100
1
relevance_labels
list of strings
[“click”, “favorite”, “buy”]
prediction_group_id
string limited to 128 characters
148

Code Example

Python Pandas
Python Single Record
Data Connector

Example Row

state
price
search_id
rank
attributions
prediction_ts
ca
98
148
1
"click, favorite, buy"
1618590882
schema = Schema(
prediction_id_column_name="prediction_id",
timestamp_column_name="prediction_ts",
prediction_group_id_column_name = "search_id",
rank_column_name = "rank",
relevance_labels_column_name = "attributions"
feature_column_names=["state", "price"]
)
​
response = arize_client.log(
dataframe=df,
model_id="ranking-multiple-labels-batch-ingestion-tutorial",
model_version="1.0",
model_type=ModelTypes.RANKING,
metrics_validation=[Metrics.RANKING, Metrics.RANKING_LABEL],
environment=Environments.PRODUCTION,
schema=schema,
)
For more details on Python Batch API Reference, visit here:
# import extra dependencies
from arize.utils.types import Environments, ModelTypes, Schema, RankingPredictionLabel, RankingActualLabel
​
# define prediction label arguments
pred_label = RankingPredictionLabel(
group_id="148",
rank=2,
label="click"
)
​
# define actual label argument
act_label = RankingActualLabel(
relevance_labels=["book", "click"],
relevance_score=0
)
​
# log data to Arize
response = arize_client.log(
model_id="demo-ranking-with-multiple-labels",
model_version="v1",
environment=Environments.PRODUCTION,
model_type=ModelTypes.RANKING,
prediction_id="dd19bee3-e7f4-4207-aef9-3abdad2a9be0",
prediction_label=pred_label,
actual_label=act_label,
features=features
)
Download an example Parquet file: Open parquet reader here.
Learn how to upload files via various Data Connectors:

Ranking Single or Multi-Label + AUC and LogLoss

For ranked lists based on a prediction of the action a user can take across single or multiple possible actions. AUC and LogLoss are computed based on prediction_score and relevance_labels (or default relevance_labels in the case of multi-label).
Ranking Model Fields
Data Type
Example
rank
int from 1-100
1
prediction_score
float
0.5
prediction_group_id
string limited to 128 characters
148

Ranking Performance Metrics

Rank-aware evaluation metrics: NDCG @k (MAP @K & MRR coming soon)
Evaluation metrics: AUC, PR-AUC, LogLoss

NDCG @k

Normalized discounted cumulative gain (NDCG) is a rank-aware evaluation metric that measures a model's ability to rank query results in the order of the highest relevance (graded relevance). You can read more about how NDCG is computed here.

What is @k?

The k value determines the metric computation up to position k in a list.

Selecting Relevance Score or Label - Attribution Model

A relevance score is required to calculate rank-aware evaluation metrics. If your relevance_score is unavailable, the Arize platform will calculate a relevance_score using a simple attribution model with a prediction label and a relevance label. Arize computes a binary relevance value (0/1) based on the default positive class.
  • Positive class "buy" and relevance label is "buy" --> relevance will be attributed to 1.
  • Positive class "buy" and relevance label is else --> relevance will be attributed to 0.
  • Positive class "buy" and relevance labels are ["buy", "click", "scroll"] --> relevance will be attributed to sum([1,0,0])

Ranking Quick Definitions

Ranking model: Assigns a rank to each item in a prediction group (also known as a batch or query), across many possible groups.
Arize supports pointwise, pairwise, and listwise ranking models
Prediction Group: A group of predictions within which items are ranked.
Example: A user of a hotel booking site types in a search term (“skiing”) and is presented with a list of results representing a single query
Rank: The predicted rank of an item in a prediction group (Integer between 1-100).
Example: Each item in the search prediction group has a rank determined by the model (i.e. Aspen is assigned rank=1, Tahoe is assigned rank=2, etc. based on input features and query features to the model)
Relevance Score (i.e. Actual Scores): The ground truth relevance score (numeric). Higher scores denote higher relevance.
Example: Each item in the search prediction group has a score determined by the action a user took on the item (i.e. “clicking” on an item indicates relevance score = 0.5, purchasing an item indicates relevance score = 1)
Rank-Aware Evaluation Metric: A rank-aware evaluation metric is an evaluation metric that gauges rank order and relevancy of predictions.
Rank-aware evaluation metrics include NDCG, MRR, and MAP. Note that MRR and MAP also require relevance_labels to be provided to be computed.
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Collaborative Filtering
Last modified 14d ago