Awesome Open Source
Awesome Open Source

Population Shift Monitoring

Build status Package docs status Latest GitHub release GitHub Release Date PyPi downloads

POPMON logo

popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets.

popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two features. popmon can automatically flag and alert on changes observed over time, such as trends, shifts, peaks, outliers, anomalies, changing correlations, etc, using monitoring business rules.

Traffic Light Overview

Announcements

Spark 3.0

With Spark 3.0, based on Scala 2.12, make sure to pick up the correct histogrammar jar files:

spark = SparkSession.builder.config(
    "spark.jars.packages",
    "io.github.histogrammar:histogrammar_2.12:1.0.20,io.github.histogrammar:histogrammar-sparksql_2.12:1.0.20",
).getOrCreate()

For Spark 2.X compiled against scala 2.11, in the string above simply replace 2.12 with 2.11.

Examples

Documentation

The entire popmon documentation including tutorials can be found at read-the-docs.

Notebooks

Tutorial Colab link
Basic tutorial Open in Colab
Detailed example (featuring configuration, Apache Spark and more) Open in Colab
Incremental datasets (online analysis) Open in Colab
Report interpretation (step-by-step guide) Open in Colab

Check it out

The popmon library requires Python 3.6+ and is pip friendly. To get started, simply do:

$ pip install popmon

or check out the code from our GitHub repository:

$ git clone https://github.com/ing-bank/popmon.git
$ pip install -e popmon

where in this example the code is installed in edit mode (option -e).

You can now use the package in Python with:

import popmon

Congratulations, you are now ready to use the popmon library!

Quick run

As a quick example, you can do:

import pandas as pd
import popmon
from popmon import resources

# open synthetic data
df = pd.read_csv(resources.data("test.csv.gz"), parse_dates=["date"])
df.head()

# generate stability report using automatic binning of all encountered features
# (importing popmon automatically adds this functionality to a dataframe)
report = df.pm_stability_report(time_axis="date", features=["date:age", "date:gender"])

# to show the output of the report in a Jupyter notebook you can simply run:
report

# or save the report to file
report.to_file("monitoring_report.html")

To specify your own binning specifications and features you want to report on, you do:

# time-axis specifications alone; all other features are auto-binned.
report = df.pm_stability_report(
    time_axis="date", time_width="1w", time_offset="2020-1-6"
)

# histogram selections. Here 'date' is the first axis of each histogram.
features = [
    "date:isActive",
    "date:age",
    "date:eyeColor",
    "date:gender",
    "date:latitude",
    "date:longitude",
    "date:isActive:age",
]

# Specify your own binning specifications for individual features or combinations thereof.
# This bin specification uses open-ended ("sparse") histograms; unspecified features get
# auto-binned. The time-axis binning, when specified here, needs to be in nanoseconds.
bin_specs = {
    "longitude": {"bin_width": 5.0, "bin_offset": 0.0},
    "latitude": {"bin_width": 5.0, "bin_offset": 0.0},
    "age": {"bin_width": 10.0, "bin_offset": 0.0},
    "date": {
        "bin_width": pd.Timedelta("4w").value,
        "bin_offset": pd.Timestamp("2015-1-1").value,
    },
}

# generate stability report
report = df.pm_stability_report(features=features, bin_specs=bin_specs, time_axis=True)

These examples also work with spark dataframes. You can see the output of such example notebook code here. For all available examples, please see the tutorials at read-the-docs.

Pipelines for monitoring dataset shift

Advanced users can leverage popmon's modular data pipeline to customize their workflow. Visualization of the pipeline can be useful when debugging, or for didactic purposes. There is a script included with the package that you can use. The plotting is configurable, and depending on the options you will obtain a result that can be used for understanding the data flow, the high-level components and the (re)use of datasets.

Pipeline Visualization

Example pipeline visualization (click to enlarge)

Resources

Presentations

Title Host Date Speaker
Popmon - population monitoring made easy Big Data Technology Warsaw Summit 2021 February 25, 2021 Simon Brugman
Popmon - population monitoring made easy Data Lunch @ Eneco October 29, 2020 Max Baak, Simon Brugman
Popmon - population monitoring made easy Data Science Summit 2020 October 16, 2020 Max Baak
Population Shift Monitoring Made Easy: the popmon package Online Data Science Meetup @ ING WBAA July 8 2020 Tomas Sostak
Popmon: Population Shift Monitoring Made Easy PyData Fest Amsterdam 2020 June 16, 2020 Tomas Sostak
Popmon: Population Shift Monitoring Made Easy Amundsen Community Meetup June 4, 2020 Max Baak

Articles

Project contributors

This package was authored by ING Wholesale Banking Advanced Analytics. Special thanks to the following people who have contributed to the development of this package: Ahmet Erdem, Fabian Jansen, Nanne Aben, Mathieu Grimal.

Contact and support

Please note that ING WBAA provides support only on a best-effort basis.

License

Copyright ING WBAA. popmon is completely free, open-source and licensed under the MIT license.


Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
Python (1,143,903
Hacktoberfest (37,971
Data Science (9,208
Statistics (4,340
Monitoring (4,213
Pandas (3,944
Data Analysis (3,788
Spark (2,840
Jupyter (1,798
Tracking (1,206
Mlops (341
Ipython (231
Statistical Tests (65
Data Profiling (32
Data Distributions (8
Statistical Process Control (5
Related Projects