Skip to main content

Python client to manage PrimedIO

Project description

pyprimed: a python library to manage PrimedIO

Create a personalized web application that is unique and relevant for each and every user with


pip install pyprimed


Import the SDK and initiate the connection

from pyprimed.pio import Pio

pio = Pio(uri='http://<user>:<password>@<api_url>:<port>')

Create a Universe, and attach a few Targets

# create a new universe and attach a single target
  .upsert([{'key':'ARTICLE-1', 'value':{'url': ''}}])

# retrieving the newly created universe
u = pio.universes.filter(name='myfirstuniverse').first

# list all targets currently associated with this universe
for target in u.targets.all():
  print(target.key, target.created_at)

# prepare a list of new targets
new_targets = [
  {'key': 'ARTICLE-2', 'value': {'url': ''}}, 
  {'key': 'ARTICLE-3', 'value': {'url': ''}}

# upsert the new targets

# targets are upserted, which means that for a given key there
# can be only one instance in the database. Trying to create an
# instance with the same key will update the value of the record
# in the database
u.targets.upsert([{'key':'ARTICLE-1', 'value':{'url': 'THIS IS NEW!'}}])

Create a Model, and attach a few Signals

# create a new model and attach a single signal

# retrieving the created model
m = pio.models.filter(name='myfirstmodel').first

# list all signals currently associated with this model
for signal in m.signals.all():
  print(signal.key, signal.created_at)

# prepare a list of new signals
new_signals = [
  {'key': 'BOB'}, 
  {'key': 'CHRIS'}

# create the new signals

# prepare a set of predictions (sk stand for signal.key, and tk for target.key)
# WARNING: `sk` and `tk` should always be a string!
predictions = [
  {'sk': 'ALICE', 'tk': 'ARTICLE-1', 'score': 0.35},
  {'sk': 'BOB', 'tk': 'ARTICLE-1', 'score': 0.75}, 
  {'sk': 'CHRIS', 'tk': 'ARTICLE-1', 'score': 0.15}

# create the new predictions 
u = pio.universes.filter(name='myfirstuniverse').first

    .on(model=m, universe=u)\

Create a Campaign, Experiment and set up an AB test to start using the Predictions

from pyprimed.models.abvariant import CustomAbvariant, RandomControl, NullControl

# we create a custom abvariant that blends models m1 and m2 using a 60%/40% weight ratio
ab0 = CustomAbvariant(label='A', models={m1: 0.6, m2: 0.4})
ab1 = RandomControlAbvariant(label='B')
ab2 = NullControlAbvariant(label='C')

# we attach the abvariants to the experiment
# `ab0` will receive 80% of traffic, `ab1` and `ab2`
# receive 10% each
e.abvariants.create({ab0: 0.8, ab1: 0.1, ab2: 0.1})

res = c.personalise(
  signals={'userid': 'BOB'},
)  # abvariant with label 'A' will be returned

Update Experiment

from pyprimed.models.abvariant import CustomAbvariant, RandomControl, NullControl

# obtain existing experiment
e = c.experiments.filter(name="myexperiment").first()

# change property
e.salt = "new_salt"

# update abvariants
ab1 = CustomAbvariant(label="NEWLABEL", {m1: 1.0})
ab2 = CustomAbvariant(label="ANOTHERLABEL", {m1: 0.33, m2: 0.77})

e.abvariants.update({ab1: 0.5, ab2: 0.5})


Build the documentation:

cd docs && pydocmd build

Preview documentation on http://localhost:8000

cd docs && pydocmd serve

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pyprimed, version 2.1.9
Filename, size File type Python version Upload date Hashes
Filename, size pyprimed-2.1.9.tar.gz (29.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page