Develop (#22)

* GHA action changes:

* Run wheel build only when pushed to master or release created.
* use pre-commit instead of mypy alone.

* Rename workflows

* Separage optimization and optuna study creation.

* Fixing for pre-commit check.

* Add some docs.

* Abolish BaseRecommenderWithThreadingSupport.

Author: tohtsky

.github/workflows/mypy-check.yml

@@ -0,0 +1,11 @@
+name: pre-commit
+on:
+  pull_request:
+  push:
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+    - uses: pre-commit/[email protected]

.github/workflows/pre-commit.yml

@@ -1,5 +1,5 @@
 name: Full Test & Upload coverage
-on: [push]
+on: [push, pull_request]
 jobs:
   run_pytest_upload_coverage:
     runs-on: ubuntu-latest

.github/workflows/main.yml renamed to .github/workflows/run-test.yml

@@ -1,5 +1,11 @@
 name: Build
-on: [push, pull_request]
+on:
+  push:
+    branches:
+      - main
+  release:
+    types:
+      - created
 jobs:
   build_sdist:
     name: Build source distribution

.github/workflows/wheels.yml

@@ -12,7 +12,8 @@ set(CPACK_PROJECT_VERSION ${PROJECT_VERSION})
 include(CPack)
 
 add_subdirectory(pybind11)
-#pybind11_add_module(_ials cpp_source/als/wrapper.cpp)
-pybind11_add_module(_knn cpp_source/knn/wrapper.cpp)
+pybind11_add_module(irspack.recommenders._ials cpp_source/als/wrapper.cpp)
+pybind11_add_module(irspack.recommenders._knn cpp_source/knn/wrapper.cpp)
 pybind11_add_module(irspack.utils._util_cpp cpp_source/util.cpp)
-
+pybind11_add_module(irspack._evapuator cpp_source/evaluator.cpp)
+pybind11_add_module(irspack._rwr cpp_source/rws.cpp)

CMakeLists.txt

@@ -3,22 +3,23 @@
 [![Python](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org)
 [![pypi](https://img.shields.io/pypi/v/irspack.svg)](https://pypi.python.org/pypi/irspack)
 [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/tohtsky/irspack)
+[![Build](https://github.com/tohtsky/irspack/workflows/Build/badge.svg?branch=main)](https://github.com/tohtsky/irspack)
 [![Read the Docs](https://readthedocs.org/projects/irspack/badge/?version=stable)](https://irspack.readthedocs.io/en/stable/)
 [![codecov](https://codecov.io/gh/tohtsky/irspack/branch/main/graph/badge.svg?token=kLgOKTQqcV)](https://codecov.io/gh/tohtsky/irspack)
 
-**irspack** is a Python package to train, evaluate, and optimize recommender systems based on implicit feedback.
+**irspack** is a Python package for train, evaluate, and optimize recommender systems based on implicit feedback.
 
-While there are already many other great packages for this purpose, like
+There are already great packages for this purpose like
 
 - [implicit](https://github.com/benfred/implicit)
 - [daisyRec](https://github.com/AmazingDD/daisyRec)
 - [RecSys2019_DeepLearning_Evaluation](https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation) (which has influenced this project the most)
 
-I have decided to implement my own one to
+However, I decided to implement my own one to
 
 - Use [optuna](https://github.com/optuna/optuna) for more efficient parameter search. In particular, if an early stopping scheme is available, optuna can prune unpromising trial based on the intermediate validation score, which drastically reduces overall running time for tuning.
 - Use multi-threaded implementations of the number of algorithms (KNN and IALS) in C++.
-- Deal with user cold-start scenarios using [CB2CF strategy](https://dl.acm.org/doi/10.1145/3298689.3347038), which I found very convenient in practice.
+- Deal with user cold-start scenarios using ["CB2CF" strategy](https://dl.acm.org/doi/10.1145/3298689.3347038), which I found very convenient in practice.
 
 # Installation & Optional Dependencies
 
@@ -38,7 +39,7 @@ In that case, you must have a decent version of C++ compiler (with C++11 support
 
 ## Optional Dependencies
 
-I have also prepared a wrapper class (`BPRFMRecommender`) to train and optimize BPR/warp loss Matrix factorization implemented in [lightfm](https://github.com/lyst/lightfm). To use it you have to install `lightfm` separately by e.g.,
+I have also prepared a wrapper class (`BPRFMRecommender`) to train and optimize BPR/warp loss Matrix factorization implemented in [lightfm](https://github.com/lyst/lightfm). To use it you have to install `lightfm` separately, e.g. by
 
 ```sh
 pip install lightfm

Dockerfile

@@ -23,4 +23,4 @@ PYBIND11_MODULE(_util_cpp, m) {
         py::arg("X"), py::arg("k1") = 1.2, py::arg("b") = 0.75);
   m.def("tf_idf_weight", &sparse_util::tf_idf_weight<double>, py::arg("X"),
         py::arg("smooth") = true);
-}
+}

Readme.md

@@ -77,8 +77,8 @@ Optimizers
 
 .. currentmodule:: irspack.split
 
-SplitFunctions
---------------
+Split Functions
+---------------
 .. autosummary::
     :toctree: api_reference
     :nosignatures: