add files

.codecov.yml

@@ -0,0 +1,25 @@
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        # Commits pushed to main should not make the overall
+        # project coverage decrease by more than 1%:
+        target: auto
+        threshold: 1%
+    patch:
+      default:
+        # Be tolerant on slight code coverage diff on PRs to limit
+        # noisy red coverage status on github PRs.
+        # Note: The coverage stats are still uploaded
+        # to codecov so that PR reviewers can see uncovered lines
+        target: auto
+        threshold: 1%
+
+codecov:
+  notify:
+    # Prevent coverage status to upload multiple times for parallel and long
+    # running CI pipelines. This configuration is particularly useful on PRs
+    # to avoid confusion.
+    after_n_builds: 6

.isort.cfg

@@ -0,0 +1,5 @@
+[settings]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = True
+skip = [".gitignore", ".rst", "examples."]

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/pycqa/isort
+    rev: 5.11.2
+    hooks:
+      - id: isort
+        name: isort
+        entry: isort
+        description: Order imports
+        language: system
+        types: [python]
+  - repo: https://github.com/pycqa/flake8
+    rev: 6.0.0
+    hooks:
+      - id: flake8
+        name: flake8
+        entry: flake8
+        language: system
+        types: [python]
+  - repo: https://github.com/psf/black
+    rev: 23.3.0
+    hooks:
+      - id: black
+        name: black
+        entry: black
+        description: Code reformatting
+        language: system
+        types: [python]

.readthedocs.yml

@@ -5,15 +5,26 @@
 # Required
 version: 2
 
+build:
+  os: "ubuntu-20.04"
+  tools:
+    python: "miniconda3-4.7"
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
+  builder: html
   configuration: docs/conf.py
 
 # Optionally build your docs in additional formats such as PDF and ePub
-formats: all
+formats:
+  - pdf
 
 # ReadTheDocs.io need to install the dependencies to be able
 # to build the documentation automatically.
 python:
   install:
-    - requirements: requirements.txt
+    - requirements: requirements.txt
+
+submodules:
+  include: all
+  recursive: true

.travis.yml

@@ -0,0 +1,37 @@
+language: python
+os: linux
+dist: xenial
+
+env:
+  global:
+    PIP_PREFER_BINARY=true
+
+python:
+  - "3.5"
+  - "3.6"
+  - "3.7"
+  - "3.8"
+  - "3.9"
+  - "3.10"
+  - "3.11"
+
+install:
+  - pip install --upgrade pip
+  - pip install -r requirements_ci.txt
+  - pip install pytest
+  - pip install pytest-cov
+  - pip install coveralls
+
+# command to run tests
+script:
+   pytest --cov=pyod/
+
+after_success:
+  - coveralls
+
+notifications:
+  email:
+    recipients:
+      - kq441morita@gmail.com
+  on_success: never # default: change
+  on_failure: always # default: always

README.rst

@@ -7,14 +7,38 @@ FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
    :target: https://pypi.org/project/focus-cfe/
    :alt: PyPI version
 
-.. image:: https://coveralls.io/repos/github/kyosek/focus-cfe/badge.svg
-   :target: https://coveralls.io/github/kyosek/focus-cfe
+.. image:: https://readthedocs.org/projects/focus-cfe/badge/?version=latest
+   :target: https://focus-cfe.readthedocs.io/en/latest/?badge=latest
+   :alt: Documentation status
+
+.. image:: https://img.shields.io/github/stars/kyosek/focus.svg
+   :target: https://github.com/kyosek/focus/stargazers
+   :alt: GitHub stars
+
+.. image:: https://img.shields.io/github/forks/kyosek/focus.svg?color=blue
+   :target: https://github.com/kyosek/focus/network
+   :alt: GitHub forks
+
+.. image:: https://pepy.tech/badge/focus-cfe
+   :target: https://pepy.tech/project/focus-cfe
+   :alt: Downloads
+
+.. image:: https://coveralls.io/repos/github/kyosek/focus/badge.svg
+   :target: https://coveralls.io/github/kyosek/focus
    :alt: Coverage Status
 
 .. image:: https://api.codeclimate.com/v1/badges/93840d29606abb212051/maintainability
    :target: https://codeclimate.com/github/kyosek/focus-cfe/maintainability
    :alt: Maintainability
 
+.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit
+   :target: https://github.com/kyosek/focus-cfe
+   :alt: pre-commit
+
+.. image:: https://img.shields.io/github/license/kyosek/focus.svg
+   :target: https://github.com/kyosek/focus/blob/master/LICENSE
+   :alt: License
+
 -----
 
 This library is an implementation of `FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles <https://arxiv.org/abs/1911.12199>`_.

circleci.yml

@@ -0,0 +1,31 @@
+version: 2.1
+
+orbs:
+  node: circleci/node@1.1.6
+
+jobs:
+  test:
+    executor:
+      name: node/default
+
+    steps:
+      - checkout
+
+      - node/with-cache:
+          steps:
+            - run: npm install
+            - run: make test-coverage
+
+      # More compact variant without hashsum check
+      # - run: curl -sL https://coveralls.io/coveralls-linux.tar.gz | tar -xz && ./coveralls
+      - run: |
+          curl -sLO https://github.com/coverallsapp/coverage-reporter/releases/latest/download/coveralls-linux.tar.gz
+          curl -sLO https://github.com/coverallsapp/coverage-reporter/releases/latest/download/coveralls-checksums.txt
+          cat coveralls-checksums.txt | grep coveralls-linux.tar.gz | sha256sum --check
+          tar -xzf coveralls-linux.tar.gz
+          ./coveralls
+
+workflows:
+  test:
+    jobs:
+      - test

examples/focus_example.py

@@ -13,13 +13,19 @@
 
 def run_example():
     """
-    Runs an example workflow to showcase the usage of different functions and modules.
+    Runs an example workflow to showcase the
+    usage of different functions and modules.
 
-    This function generates example data, standardizes the features, trains a decision tree model,
-    performs perturbation using the FOCUS algorithm, and visualizes the results using PCA plots.
+    This function
+        - generates example data
+        - standardizes the features
+        - trains a decision tree model
+        - performs perturbation using the FOCUS algorithm
+        - visualizes the results using PCA plots
 
     Returns:
-        None: This function displays the plots but does not return any value.
+        None: This function displays the plots
+        but does not return any value.
     """
     start_time = time.time()
 

examples/hyperparameter_tuning_example.py

@@ -1,27 +1,37 @@
 import optuna
 import tensorflow as tf
-from utils import generate_example_data, standardize_features, train_decision_tree_model
+from utils import (
+    generate_example_data,
+    standardize_features,
+    train_decision_tree_model,
+)
 
 from focus import Focus
 
 
 def objective(trial):
     """
-    This function is an objective function for hyperparameter tuning using optuna.
-    It explores the hyperparameter sets and evaluates the result on a given model and dataset
-    Mean distance and number of unchanged instances are used for the evaluation.
+    This function is an objective function for
+    hyperparameter tuning using optuna.
+    It explores the hyperparameter sets and evaluates the result on a
+    given model and dataset
+
+    Mean distance and number of unchanged instances are
+    used for the evaluation.
 
     Args:
-    trial (optuna.Trial): Object that contains information about the current trial,
-        including hyperparameters.
+    trial (optuna.Trial):
+    Object that contains information about the current trial,
+    including hyperparameters.
 
     Returns:
     Mean CFE distance + number of unchanged instances squared -
     This is the objective function for hyperparameter optimization
 
     * Note: typically we want to minimise a number of unchanged first,
         so penalising the score by having squared number.
-    Also, to not distort this objective, having the mean distance divided by 100.
+    Also, to not distort this objective,
+    having the mean distance divided by 100.
     """
     X_train, X_test, y_train, y_test = generate_example_data(1000)
     X_train, X_test = standardize_features(X_train, X_test)