A/B Analysis for a Recommendation Model

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In this tutorial, you will learn how to retrospectively compare the behavior of two different models.

By the end of this tutorial you will know how to:

  • Set up an A/B application

  • Analyze production data


Set Up an A/B Application

Prepare a model for uploading


Install the dependencies in your local environment.

pip install -r requirements.txt
import sys

import joblib
from lightfm import LightFM
from lightfm.datasets import fetch_movielens

if __name__ == "__main__":
    no_components = int(sys.argv[1])
    print(f"Number of components is set to {no_components}")

    # Load the MovieLens 100k dataset. Only five
    # star ratings are treated as positive.
    data = fetch_movielens(min_rating=5.0)

    # Instantiate and train the model
    model = LightFM(no_components=no_components, loss='warp')
    model.fit(data['train'], epochs=30, num_threads=2)

    # Save the model
    joblib.dump(model, "model.joblib")
import joblib
import numpy as np
from lightfm import LightFM

# Load model once
model: LightFM = joblib.load("/model/files/model.joblib")

# Get all item ids
item_ids = np.arange(0, 1682)

def get_top_rank_item(user_id):
    # Calculate scores per item id
    y = model.predict(user_ids=[user_id], item_ids=item_ids)

    # Pick top 3
    top_3 = y.argsort()[:-4:-1]

    # Return {'top_1': ..., 'top_2': ..., 'top_3': ...}
    return dict([(f"top_{i + 1}", item_id) for i, item_id in enumerate(top_3)])
kind: Model
name: movie_rec
runtime: hydrosphere/serving-runtime-python-3.7:3.0.0-alpha.2
install-command: sudo apt install --yes gcc && pip install -r requirements.txt
  - src/
  - requirements.txt
  - model.joblib
  name: get_top_rank_item
      shape: scalar
      type: int64
      profile: numerical
      shape: scalar
      type: int64
      profile: numerical
      shape: scalar
      type: int64
      profile: numerical
      shape: scalar
      type: int64
      profile: numerical

Upload Model A

We train and upload our model with 5 components as movie_rec:v1

python train_model.py 5
hs apply -f serving.yaml

Upload Model B

Next, we train and upload a new version of our original model with 20 components as movie_rec:v2

python train_model.py 20
hs apply -f serving.yaml

We can check that we have multiple versions of our model by running:

hs model list

Create an Application

To create an A/B deployment we need to create an Application with a single execution stage consisting of two model variants. These model variants are our Model A and Model B correspondingly.

The following code will create such an application:

from hydrosdk import ModelVersion, Cluster
from hydrosdk.application import ApplicationBuilder, ExecutionStageBuilder

cluster = Cluster('http://localhost')

model_a = ModelVersion.find(cluster, "movie_rec", 1)
model_b = ModelVersion.find(cluster, "movie_rec", 2)

stage_builder = ExecutionStageBuilder()
stage = stage_builder.with_model_variant(model_version=model_a, weight=50). \
    with_model_variant(model_version=model_b, weight=50). \

app = ApplicationBuilder("movie-ab-app").with_stage(stage).build(cluster)

Invoking movie-ab-app

We'll simulate production data flow by repeatedly asking our model for recommendations.

import numpy as np
from hydrosdk import Cluster, Application
from tqdm.auto import tqdm

cluster = Cluster("http://localhost", grpc_address="localhost:9090")

app = Application.find(cluster, "movie-ab-app")
predictor = app.predictor()

user_ids = np.arange(0, 943)

for uid in tqdm(np.random.choice(user_ids, 2000, replace=True)):
    result = predictor.predict({"user_id": uid})

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