Wise Pizza: Automatically finding unusual segments in your data

WisePizza is a library to find and visualise the most interesting slices in multidimensional data, which provides different functions to find segments whose average is most different from the global one or find segments most useful in explaining the difference between two datasets.

The approach

WisePizza assumes you have a dataset with a number of discrete dimensions (could be currency, region, etc). For each combination of dimensions, the dataset must have a total value (total of the metric over that segment, for example the total volume in that region and currency), and an optional size value (set to 1 if not specified), this could for example be the total number of customers for that region and currency. The average value of the outcome for the segment is defined as total divided by size, in this example it would be the average volume per customer.

explain_levels takes such a dataset and looks for a small number of 'simple' segments (each only constraining a small number of dimensions) that between them explain most of the variation in the averages; you could also think of them as the segments whose size-weighted deviation from the overall dataset average is the largest. This trades off unusual averages (which will naturally occur more for smaller segments) against segment size.

Yet another way of looking at it is that we look for segments which, if their average was reset to the overall dataset average, would move overall total the most.

explain_changes_in_totals and explain_changes_in_average take two datasets of the kind described above, with the same column names, and apply the same kind of logic to find the segments that contribute most to the difference (in total or average, respectively) between the two datasets, optionally splitting that into contributions from changes in segment size and changes in segment total.

Sometimes, rather than explaining the change in totals from one period to the next, one wishes to explain a change in averages. The analytics of this are a little different - for example, while (as long as all weights and totals are positive) increasing a segment size (other things remaining equal) always increases the overall total, it can increase or decrease the pverall average, depending on whether the average value of that segment is below or above the overall average.

Table of Contents

• What can this do for you?
  • Find interesting slices
  • Comparison between two datasets
• Installation
• Quick Start
• Streamlit app
  • Docker container
• For Developers
• Tests

What can this do for you?

The automated search for interesting segments can give you the following:

1. Better information about segments and subsegments in your data

By using WisePizza and defining initial segments, you can find a segment which maximizes a specific outcome, such as adoption rates.

2. Understanding differences in two time periods or two dataframes

If you have two time periods or two datasets, you can find segments that experience the largest change in the totals from previous period/dataset.

Installation

You can always get the newest wise_pizza release using pip: https://pypi.org/project/wise-pizza/

pip install wise-pizza

From the command line (another way):

pip install git+https://github.com/transferwise/wise-pizza.git

From Jupyter notebook (another way):

!pip install git+https://github.com/transferwise/wise-pizza.git

Or you can clone and run from source, in which case you should pip -r requirements.txt before running.

Quick Start

The wisepizza package can be used for finding segments with unusual average:

sf = explain_levels(
    df=data,
    dims=dims,
    total_name=totals,
    size_name=size,
    max_depth=2,
    min_segments=20,
    solver="lasso"
)

Or for finding changes between two datasets in totals:

sf1 = explain_changes_in_totals(
    df1=pre_data,
    df2=data,
    dims=dims,
    total_name=totals,
    size_name=size,
    max_depth=2,
    min_segments=20,
    how="totals",
    solver="lasso"
)

Or for finding changes between two datasets in average:

sf1 = explain_changes_in_average(
    df1=pre_data,
    df2=data,
    dims=dims,
    total_name=totals,
    size_name=size,
    max_depth=2,
    min_segments=20,
    how="totals",
    solver="lasso"
)

In addition to single-value slices, consider slices that consist of a group of segments from the same dimension with similar naive averages For that goal you can use cluster_values=True parameter.

And then you can visualize differences:

sf.plot()

And check segments:

sf.segments

if you use cluster values, you can also check relevant cluster names:

sf.relevant_cluster_names

Please see the full example here

Streamlit app

In the root directory of this repository, there is a Streamlit app. This is an interface that allows you to upload your own files and run analyses, as you saw in the Jupyter Notebook provided as an example.

To run it after "pip install wise-pizza" (available in wise-pizza==0.2.3+) you should just do this in the terminal:

run_wise_pizza_streamlit

It will open the streamlit app.

To run this from the github version, you need to:

1. Create a virtual environment (e.g. using pyenv)
2. Activate the virtual environment
3. Run pip -r requirements.txt before running, to install necessary dependencies
4. Run streamlit run wise_pizza/streamlit_app.py to run Streamlit

Docker container

We created a Docker container that makes it easier to deploy this solution elsewhere

You need to first: Create the Docker image Create the Docker image

docker build -t streamlit .      

And then simply run the image

docker run -p 8501:8501 streamlit    

For Developers

Testing

We use PyTest for testing. If you want to contribute code, make sure that the tests in tests/ run without errors.

Wise-pizza is open sourced and maintained by Wise Plc. Copyright 2023 Wise Plc.