onlinerake: Streaming Survey Raking Via MWU and SGD

Modern online surveys and passive data collection streams generate responses one record at a time. Classic weighting methods such as iterative proportional fitting (IPF, or “raking”) and calibration weighting are inherently batch procedures: they reprocess the entire dataset whenever a new case arrives. The onlinerake package provides incremental, per‑observation updates to survey weights so that weighted margins track known population totals in real time.

The package implements two complementary algorithms:

• SGD raking – an additive update that performs stochastic gradient descent on a squared–error loss over the margins. It produces smooth weight trajectories and maintains high effective sample size (ESS).
• MWU raking – a multiplicative update inspired by the multiplicative‑weights update rule. It corresponds to mirror descent under the Kullback–Leibler divergence and yields weight distributions reminiscent of classic IPF. However, it can produce heavier tails when the learning rate is large.

Both methods share the same API: call .partial_fit(obs) for each incoming observation and inspect properties such as .margins, .loss and .effective_sample_size to monitor progress.

Installation

Install from PyPI:

pip install onlinerake

For development, clone the repository and install in editable mode:

git clone https://github.com/finite-sample/onlinerake.git
cd onlinerake
pip install -e .

No external dependencies are required beyond numpy and pandas.

Usage

from onlinerake import OnlineRakingSGD, OnlineRakingMWU, Targets

# define target population margins (proportion of the population with indicator = 1)
targets = Targets(age=0.5, gender=0.5, education=0.4, region=0.3)

# instantiate a raker
raker = OnlineRakingSGD(targets, learning_rate=5.0)

# stream demographic observations
for obs in stream_of_dicts:
    raker.partial_fit(obs)
    print(raker.margins)  # current weighted margins

print("final effective sample size", raker.effective_sample_size)

To use the multiplicative‑weights version, replace OnlineRakingSGD with OnlineRakingMWU and adjust the learning_rate (a typical default is 1.0). See the docstrings for full parameter descriptions.

Simulation results

To understand the behavior of the two update rules we simulated three typical non‑stationary bias patterns: a linear drift in demographic composition, a sudden shift halfway through the stream, and an oscillation around the target frame. For each scenario we generated 300 observations per seed and averaged results over five random seeds. SGD used a learning rate of 5.0 and MWU used a learning rate of 1.0 with three update steps per observation. The table below summarizes the mean improvement in absolute margin error relative to the unweighted baseline (positive values indicate an improvement), the final effective sample size (ESS) and the mean final loss (squared‑error on margins). Higher ESS and larger improvements are better.

Scenario	Method	Age Imp (%)	Gender Imp (%)	Education Imp (%)	Region Imp (%)	Overall Imp (%)	Final ESS	Final Loss
linear	SGD	82.8	78.6	76.8	67.5	77.0	251.8	0.00147
linear	MWU	57.2	53.6	46.9	34.6	48.8	240.9	0.00676
sudden	SGD	82.9	82.3	79.6	63.5	79.5	225.5	0.00102
sudden	MWU	52.6	51.2	46.3	26.3	47.3	175.9	0.01235
oscillating	SGD	69.7	78.5	65.6	72.0	72.2	278.7	0.00023
oscillating	MWU	49.6	57.3	48.3	50.1	52.0	276.0	0.00048

Interpretation

• In all scenarios the online rakers dramatically reduce the margin errors relative to the unweighted baseline. For example, in the sudden‑shift scenario the SGD raker reduces the average age error from 0.20 to about 0.03 (a 83% improvement).
• The SGD update consistently yields higher improvements and lower final loss than the MWU update, albeit at the cost of choosing a more aggressive learning rate.
• The MWU update, while less accurate in these settings, maintains comparable effective sample sizes and might be preferable when multiplicative adjustments are desired (e.g., when starting from unequal base weights).

You can reproduce these results and explore interactive examples by running the Jupyter notebooks:

# Install with documentation dependencies
pip install onlinerake[docs]

# Launch interactive notebooks
jupyter notebook docs/notebooks/

See the 02_performance_comparison.ipynb notebook for detailed algorithm analysis and benchmarking.

Interactive Examples

Comprehensive examples with visualizations are provided as Jupyter notebooks in docs/notebooks/:

• 01_getting_started.ipynb - Introduction with visual demonstrations
• 02_performance_comparison.ipynb - Algorithm benchmarking and analysis
• 03_advanced_diagnostics.ipynb - Convergence monitoring and diagnostics

The notebooks include:

• Correcting feature bias in online surveys
• Real-time polling with demographic shifts
• Performance comparison between SGD and MWU algorithms
• Visual validation that the algorithms work correctly

Run the interactive examples:

# Install with documentation dependencies
pip install onlinerake[docs]

# Launch Jupyter notebooks
jupyter notebook docs/notebooks/

Testing

Run the comprehensive test suite:

pytest tests/test_onlinerake.py -v

Contributing

Pull requests are welcome! Feel free to open issues if you find bugs or have suggestions for new features, such as support for multi‑level controls or adaptive learning‑rate schedules.