Principle:Snorkel team Snorkel Labeling Function Application

Knowledge Sources	Data Programming: Creating Large Training Sets Quickly Training Complex Models with Multi-Task Weak Supervision
Domains	Weak_Supervision, Data_Programming, Distributed_Computing
Last Updated	2026-02-14 20:00 GMT

Overview

A process for systematically applying a set of labeling functions to a dataset to produce a label matrix encoding all noisy votes.

Description

Labeling Function Application is the step where defined labeling functions are executed across an entire dataset to produce a label matrix $L\in {\mathbb{\mathbb{Z}}}^{n\times m}$. Each entry $L_{i,j}$ represents the vote of the $j$-th labeling function on the $i$-th data point, with $-1$ indicating abstention.

This step bridges the gap between individual LF definitions and the statistical model that will combine their votes. The label matrix is a sparse structure (most LFs abstain on most data points) that captures the full voting pattern of all labeling functions.

The application process must handle:

• Fault tolerance: Gracefully handling LFs that throw exceptions on certain data points
• Scalability: Supporting Pandas, Dask, and Spark backends for different dataset sizes
• Progress tracking: Reporting application progress for large datasets

Usage

Use this principle after defining labeling functions and before training a label model. Apply LFs whenever you need to generate the label matrix that will serve as input to LF analysis and the generative label model. Choose the appropriate backend (Pandas for small/medium data, Dask/Spark for large distributed datasets).

Theoretical Basis

Given $m$ labeling functions $\{{\lambda }_1,\dots ,{\lambda }_m\}$ and $n$ data points $\{x_1,\dots ,x_n\}$, the application step constructs:

$L_{i,j}={\lambda }_j(x_i)\in \{-1,0,1,\dots ,k-1\}$

The resulting label matrix $L$ is typically sparse because each LF only labels a subset of data points (its coverage). The sparsity pattern encodes valuable information about LF agreement and disagreement that the label model exploits.

Pseudo-code:

# Abstract label matrix construction
L = empty_matrix(n_examples, n_lfs)
for i, data_point in enumerate(dataset):
    for j, lf in enumerate(labeling_functions):
        L[i, j] = lf(data_point)  # Returns label or ABSTAIN (-1)

Related Pages

Implemented By

• Implementation:Snorkel_team_Snorkel_PandasLFApplier_Apply