darkseed / Alpenglow

• Alpenglow is a free and open source C++ framework with easy-to-use Python API.
• Alpenglow is capable of training and evaluating industry standard recommendation algorithms including variants of popularity, nearest neighbor, and factorization models.
• Besides batch training and evaluation, Alpenglow supports online training of recommendation models capable of adapting to concept drift in non-stationary environments.

Requirements

• Anaconda + Python 3.5 or 3.6 environment

Installation from conda repositories

• conda install -c conda-forge alpenglow

Installation from source (linux only at the moment)

• cd Alpenglow
• conda install libgcc
• pip install .

Getting Started

• First download the sample dataset

from alpenglow.experiments import FactorExperiment
from alpenglow.evaluation import DcgScore
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt

data = pd.read_csv("/path/to/sample_dataset")

factor_model_experiment = FactorExperiment(
    top_k=100,
    seed=254938879,
    dimension=10,
    learning_rate=0.14,
    negative_rate=100
)

rankings = factor_model_experiment.run(data, verbose=True)
rankings['dcg'] = DcgScore(rankings)
day = 86400
averages = rankings['dcg'].groupby((rankings['time']-rankings['time'].min())//day).mean()
plt.plot(averages)

Development

• For faster recompilation, use export CC="ccache cc"
• E.g. to enable compilation on 4 threads, use echo 4 > .parallel
• Reinstall modified version using pip install --upgrade --force-reinstall --no-deps .
• To build and use in the current folder, use pip install --upgrade --force-reinstall --no-deps -e . and export PYTHONPATH="$(pwd)/python:$PYTHONPATH"