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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)\
    .upsert(predictions, asynchronous=False)
# the `asynchronous=False` settings waits for this operation to end before 
# continuing

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

from pyprimed.models.abvariant import CustomAbvariant, RandomControlAbvariant, NullControlAbvariant

# 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, RandomControlAbvariant, NullControlAbvariant

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

# change property
e.salt = "new_salt"

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

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

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