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 Primed.io.

Installation

pip install pyprimed

Quickstart

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
pio\
  .universes\
  .create(name='myfirstuniverse')\
  .targets\
  .upsert([{'key':'ARTICLE-1', 'value':{'url': 'www.example.com/article-1'}}])

# 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': 'www.example.com/article-2'}}, 
  {'key': 'ARTICLE-3', 'value': {'url': 'www.example.com/article-3'}}
]

# upsert the new targets
u.targets.upsert(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!'}}])
u.targets.filter(key='ARTICLE-1').first.value 

Create a Model, and attach a few Signals

# create a new model and attach a single signal
pio\
  .models\
  .create(name='myfirstmodel')\
  .signals\
  .upsert([{'key':'ALICE'}])

# 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
m.signals.upsert(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

pio\
    .predictions\
    .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(
  pubkey='mypubkey',
  secretkey='mysecretkey',
  signals={'userid': 'BOB'},
  abvariant='A'
)  # 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"
e.update()

# 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})

Developer

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.

Source Distribution

pyprimed-2.2.2.tar.gz (25.2 kB view details)

Uploaded Source

File details

Details for the file pyprimed-2.2.2.tar.gz.

File metadata

  • Download URL: pyprimed-2.2.2.tar.gz
  • Upload date:
  • Size: 25.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/40.5.0 requests-toolbelt/0.8.0 tqdm/4.27.0 CPython/3.6.3

File hashes

Hashes for pyprimed-2.2.2.tar.gz
Algorithm Hash digest
SHA256 6367b68ddbf571ba18a4c567c879183dd71724e3d22fc3e18b0f63e2e47a537e
MD5 439455888e0af366da92695a6eb7ac44
BLAKE2b-256 aa7e44da542246b51334ea8b475ddb8bacca0c1d58a0979eabe53cf20ce96471

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page