Simulator

Simulator is framework for training and evaluating recommendation algorithms on real or synthetic data. Framework is based on pyspark library to work with big data. As a part of simulation process the framework incorporates data generators, response functions and other tools, that can provide flexible usage of simulator.

Table of contents

• Installation
• Quickstart
• Examples
• Build from sources
• Building documentation
• Running tests

Installation

pip install sim4rec

If the installation takes too long, try

pip install sim4rec --use-deprecated=legacy-resolver

To install dependencies with poetry run

pip install --upgrade pip wheel poetry
poetry install

Quickstart

The following example shows how to use simulator to train model iteratively by refitting recommendation algorithm on the new upcoming history log

import numpy as np
import pandas as pd

import pyspark.sql.types as st
from pyspark.ml import PipelineModel
from sim4rec.utils import pandas_to_spark
from sim4rec.modules import RealDataGenerator, Simulator
from sim4rec.response import NoiseResponse, BernoulliResponse

from ucb import UCB
from replay.metrics import NDCG

LOG_SCHEMA = st.StructType([
    st.StructField('user_idx', st.LongType(), True),
    st.StructField('item_idx', st.LongType(), True),
    st.StructField('relevance', st.DoubleType(), False),
    st.StructField('response', st.IntegerType(), False)
])

users_df = pd.DataFrame(
    data=np.random.normal(0, 1, size=(100, 15)),
    columns=[f'user_attr_{i}' for i in range(15)]
)
items_df = pd.DataFrame(
    data=np.random.normal(1, 1, size=(30, 10)),
    columns=[f'item_attr_{i}' for i in range(10)]
)
history_df = pandas_to_spark(pd.DataFrame({
    'user_idx' : [1, 10, 10, 50],
    'item_idx' : [4, 25, 26, 25],
    'relevance' : [1.0, 0.0, 1.0, 1.0],
    'response' : [1, 0, 1, 1]
}), schema=LOG_SCHEMA)

users_df['user_idx'] = np.arange(len(users_df))
items_df['item_idx'] = np.arange(len(items_df))

users_df = pandas_to_spark(users_df)
items_df = pandas_to_spark(items_df)

user_gen = RealDataGenerator(label='users_real')
item_gen = RealDataGenerator(label='items_real')
user_gen.fit(users_df)
item_gen.fit(items_df)
_ = user_gen.generate(100)
_ = item_gen.generate(30)

sim = Simulator(
    user_gen=user_gen,
    item_gen=item_gen,
    data_dir='test_simulator',
    user_key_col='user_idx',
    item_key_col='item_idx',
    log_df=history_df
)

noise_resp = NoiseResponse(mu=0.5, sigma=0.2, outputCol='__noise')
br = BernoulliResponse(inputCol='__noise', outputCol='response')
pipeline = PipelineModel(stages=[noise_resp, br])

model = UCB()
model.fit(log=history_df)

ndcg = NDCG()

train_ndcg = []
for i in range(10):
    users = sim.sample_users(0.1).cache()

    recs = model.predict(log=sim.log, k=5, users=users, items=items_df, filter_seen_items=True).cache()

    true_resp = sim.sample_responses(
        recs_df=recs,
        user_features=users,
        item_features=items_df,
        action_models=pipeline
    ).select('user_idx', 'item_idx', 'relevance', 'response').cache()

    sim.update_log(true_resp, iteration=i)

    train_ndcg.append(ndcg(recs, true_resp.filter(true_resp['response'] >= 1), 5))

    model.fit(sim.log.drop('relevance').withColumnRenamed('response', 'relevance'))

    users.unpersist()
    recs.unpersist()
    true_resp.unpersist()

print(train_ndcg)

Examples

You can find useful examples in notebooks folder, which demonstrates how to use synthetic data generators, composite generators, evaluate scores of the generators, iteratively refit recommendation algorithm, use response functions and more.

Experiments with different datasets and tutorial how to write custom response functions can be found in 'experiments' folder.

Build from sources

poetry build
pip install ./dist/sim4rec-0.0.1-py3-none-any.whl

Compile documentation

cd docs
make clean && make html

Tests

For tests the pytest python library is used and to run tests for all modules you can run the following command from repository root directory

pytest

Licence

Sim4Rec is distributed under the Apache License Version 2.0, nevertheless the SDV package, imported by the Sim4Rec for synthetic data generation, is distributed under Business Source License (BSL) 1.1.

Synthetic tabular data generation not a purpose of the Sit4Rec framework. The Sim4Rec offers an API and wrappers to run simulation with synthetic data, but the method of synthetic data generation is determined by the user. SDV package is imported for illustration purposes and may be replaced by another synthetic data generation solution.

Thus, synthetic data generation functional and quality evaluation with SDV library, namely the SDVDataGenerator from generator.py and evaluate_synthetic from evaluation.py should be used for non-production purposes only according to the SDV License.