Merge pull request #25 from TNanukem/develop

Develop

README.md

@@ -2,14 +2,35 @@
 
 ![Codecov branch](https://img.shields.io/codecov/c/github/tnanukem/sknet/develop?token=PIQ338YNK1)
 
-The sknet project is a scikit-learn compatible framework for machine learning in complex networks. It provides learning algorithms for complex networks, as well as transforming methods to turn tabular data into complex networks.
+The sknet project is a scikit-learn and NetworkX compatible framework for machine learning in complex networks. It provides learning algorithms for complex networks, as well as transforming methods to turn tabular data into complex networks.
 
 It started in 2021 as a project from volunteers to help to improve the development of research on the interface between complex networks and machine learning.
 
 ## :computer: Installation
 
+The sknet installation is available via PiPy:
+
+    pip install scikit-net
+
 ## :high_brightness: Quickstart
 
+The following code snippet shows how one can transform tabular data into a complex network and then use it to create a classifier:
+
+    from sklearn.datasets import load_iris
+    from sknet.network_construction import KNNConstructor
+    from sknet.supervised import EaseOfAccessClassifier
+
+    X, y = load_iris(return_X_y = True)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
+
+    # The constructor responsible for transforming the tabular data into a complex network
+    knn_c = KNNConstructor(k=5)
+
+    classifier = EaseOfAccessClassifier()
+    classifier.fit(X_train, y_train, constructor=knn_c)
+    y_pred = classifier.predict(X_test)
+    accuracy_score(y_test, y_pred)
+
 ## :pencil: Documentation
 
-## :wave: Contributing
+We provide an extensive API documentation as well with some user guides. The documentation is available on https://tnanukem.github.io/sknet/

docs/source/getting_started/installation.rst

@@ -1,4 +1,7 @@
 Installation
 ============
 
-In order to install the sknet...
+In order to install the sknet you can use pip using the ``scikit-net`` name.
+
+.. code-block:: python
+    pip install scikit-net

setup.py

@@ -0,0 +1,31 @@
+import pathlib
+from setuptools import setup, find_packages
+
+HERE = pathlib.Path(__file__).parent
+
+README = (HERE / "README.md").read_text()
+
+with open(HERE / 'requirements.txt') as f:
+    required = f.read().splitlines()
+
+setup(
+    name="scikit-net",
+    version="0.0.1",
+    description="Machine Learning in Complex Networks",
+    long_description=README,
+    long_description_content_type="text/markdown",
+    url="https://github.com/TNanukem/sknet",
+    download_url='https://github.com/TNanukem/sknet/archive/v-0.0.1.tar.gz',
+    keywords=['Machine Learning', 'Complex Networks'],
+    author="Tiago Toledo Jr",
+    author_email="tiago.nanu@gmailcom",
+    license="MIT",
+    classifiers=[
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+    ],
+    packages=find_packages(exclude=("tests",)),
+    include_package_data=True,
+    install_requires=required,
+)