Tools for recommendation systems development
Project description
ml-recsys-tools
This is an updated version of the stale ml-recsys-tools source repo
Open source repo for various tools for recommender systems development work.
Main purpose is to provide a single wrapper for various recommender packages to train, tune, evaluate and get data in and recommendations / similarities out.
Installation:
Pip:
- PyPi:
pip install ml-recsys-tools
- Github
master
:pip install git+https://github.com/artdgn/ml-recsys-tools@master#egg=ml_recsys_tools
Basic usage:
# dataset: download and prepare dataframes
from ml_recsys_tools.datasets.prep_movielense_data import get_and_prep_data
rating_csv_path, users_csv_path, movies_csv_path = get_and_prep_data()
# read the interactions dataframe and create a data handler object and split to train and test
import pandas as pd
ratings_df = pd.read_csv(rating_csv_path)
from ml_recsys_tools.data_handlers.interaction_handlers_base import ObservationsDF
obs = ObservationsDF(ratings_df, uid_col='userid', iid_col='itemid')
train_obs, test_obs = obs.split_train_test(ratio=0.2)
# train and test LightFM recommender
from ml_recsys_tools.recommenders.lightfm_recommender import LightFMRecommender
lfm_rec = LightFMRecommender()
lfm_rec.fit(train_obs, epochs=10)
# print summary evaluation report:
print(lfm_rec.eval_on_test_by_ranking(test_obs.df_obs, prefix='lfm ', n_rec=100))
# get all recommendations and print a sample (training interactions are filtered out by default)
recs = lfm_rec.get_recommendations(lfm_rec.all_users, n_rec=5)
print(recs.sample(5))
# get all similarities and print a sample
simils = lfm_rec.get_similar_items(lfm_rec.all_items, n_simil=5)
print(simils.sample(10))
Additional examples in the examples/
folder:
- Cosine similarity
- Ensembles
- Hybrid features for LightFM
- Additional recommenders
- Using multiple testsets and Evaluation
Recommender models and tools:
-
LightFM package based recommender.
-
Implicit package based ALS recommender.
-
Evaluation features added for most recommenders:
- Dataframes for all inputs and outputs
- adding external features (for LightFM hybrid mode)
- fast batched methods for:
- user recommendation sampling
- similar items samplilng with different similarity measures
- similar users sampling
- evaluation by sampling and ranking
- dense user x item recommendation and item x item similarity
- Dataframes for all inputs and outputs
-
Additional recommender models:
-
Similarity based:
- cooccurence (items, users)
- generic similarity based (can be used with external features)
-
-
Ensembles:
- subdivision based (multiple recommenders each on subset of data - e.g. geographical region):
- geo based: simple grid, equidense grid, geo clustering
- LightFM and cooccurrence based
- combination based - combining recommendations from multiple recommenders
- similarity combination based - similarity based recommender on similarities from multiple recommenders
- cascade ensemble
- subdivision based (multiple recommenders each on subset of data - e.g. geographical region):
-
Interaction dataframe and sparse matrix handlers / builders:
- sampling, data splitting,
- external features matrix creation (additional item features), with feature engineering: binning / one*hot encoding (via pandas_sklearn)
- evaluation and ranking helpers
- handlers for observations coupled with external features and features with geo coordinates
-
Evaluation utils:
- score reports on lightfm metrics (AUC, precision, recall, reciprocal)
- n-DCG, and n-MRR metrics, n-precision / recall
- references: best possible ranking and chance ranking
-
Utilities:
- similarity calculation helpers (similarities, dot, top N, top N on sparse)
- parallelism utils
- sklearn transformer extenstions (for feature engineering)
- logging, debug printouts decorators and other instrumentation and inspection tools
- pandas utils
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