Publishing Ranking Events

Publish Inference Events for Ranking Models

A ranking machine learning model orders items based on their relevance to a specific query or context. Unlike classification or regression models that predict absolute values, ranking models focus on relative ordering between items.

Grouping Format for Ranking Model Inference Events

Before publishing ranking model events to Fiddler, format them in a grouped format. This means items returned for the same query ID (specified by the group_by parameter in TaskParams) appear in a single row with values stored as lists.

Example of correctly grouped format:

srch_id	price_usd	review_score	...	prediction	target
101	[134.77,180.74,159.80]	[5.0,2.5,4.5]	[...]	[1.97, 0.84,-0.69]	[1,0,0]
...	...		...	...	...
112	[26.00,51.00,205.11,73.2]	[3.0,4.5,2.0,1.0]	[...]	[10.75,8.41,-0.23,-3.2]	[0,1,0,0]

In this example, srch_id is the group_by column, and all other columns contain lists corresponding to each group.

How to Convert a Flat CSV File Into Grouped Format

If your data is in a flat CSV file (one item per row), use Fiddler's utility function to convert it to the required grouped format:

from fiddler.utils.helpers import group_by
import pandas as pd

# Read the flat CSV file
df = pd.read_csv('path/to/ranking_events.csv')

# Convert to grouped format
df_grouped = group_by(df=df, group_by_col='srch_id')

The group_by_col argument should match the column specified in the group_by parameter of your Model object.

Publish Grouped Ranking Events

After preparing your grouped data, publish it to Fiddler:

project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

# df_grouped DataFrame from above example
publish_job = model.publish(df_grouped)

# The publish() method is asynchronous. Use the publish job's wait() method 
# if synchronous behavior is desired.
publish_job.wait() 

Update Ranking Events

You can update existing ranking events while maintaining the same grouped format.

Prepare the Updating DataFrame

To update the ground truth labels (called 'target values' in Fiddler), create a dataframe with the required updates while keeping the grouped format.

Call model.publish() with update set to true:

project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

# Publish updates to existing events
event_id_list = model.publish(source=modified_df_grouped, update=True)

print(f'Updated events: {event_id_list}')

Fiddler will update only the columns included in your modified dataframe while preserving all other data. Event update behavior is described in more detail in the Event Update guide.

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