Update version, readme, added changelog

CHANGES.md

@@ -0,0 +1,10 @@
+# Changes
+
+## 2.0.0
+
+- Added a Makefile
+- Substantially refactored FFX to modernize the codebase. Broke up `core.py` into a module.
+- Replaced custom CLI tooling with Click
+- Adopted pytest for tests; improved test coverage
+- Updated Travis CI to use a modern Python build matrix
+- Added pylint, isort, black. See the `make validate` command

Makefile

@@ -10,7 +10,7 @@ isort:
 validate: pylint isort black
 
 pypi:
-	rm dist/*
+	rm -rf dist/*
 	python setup.py sdist bdist_egg bdist_wheel
 	twine upload dist/*
 	#twine upload --repository-url https://test.pypi.org/legacy/ dist/* # testpypi

README.md

@@ -1,4 +1,4 @@
-#FFX: Fast Function Extraction
+# FFX: Fast Function Extraction
 
 [![Build Status at Travis CI](https://travis-ci.org/natekupp/ffx.svg?branch=master)](https://travis-ci.org/natekupp/ffx)
 [![Coverage Status](https://coveralls.io/repos/github/natekupp/ffx/badge.svg?branch=master)](https://coveralls.io/github/natekupp/ffx?branch=master)
@@ -13,54 +13,62 @@ FFX is a technique for symbolic regression. It is:
 
 To install from PyPI, simply run:
 
-    pip install ffx
+```shell
+pip install ffx
+```
 
 ## Usage
 
-FFX can either be run in stand-alone mode, or within your existing Python code using its own API or a Scikit-learn style API. It installs both a command-line utility `runffx` and the Python module `ffx`.
+FFX can either be run in stand-alone mode, or within your existing Python code using its own API or a Scikit-learn style API. It installs both a command-line utility `ffx` and the Python module `ffx`.
 
 **Standalone**
 
-    runffx test train_X.csv train_y.csv test_X.csv test_y.csv
+```shell
+ffx test train_X.csv train_y.csv test_X.csv test_y.csv
+```
 
-Use `runffx help` for more information on using the command-line utility.
+Use `ffx help` for more information on using the command-line utility.
 
 **Python Module (run interface)**
 
 The FFX Python module exposes a function, `ffx.run()`. The following snippet is a simple example of how to use FFX this way. Note that all arguments are expected to be of type `numpy.ndarray` or `pandas.DataFrame`.
 
-    import numpy as np
-    import ffx
+```python
+import numpy as np
+import ffx
 
-    train_X = np.array( [ (1.5,2,3), (4,5,6) ] ).T
-    train_y = np.array( [1,2,3])
+train_X = np.array( [ (1.5,2,3), (4,5,6) ] ).T
+train_y = np.array( [1,2,3])
 
-    test_X = np.array( [ (5.241,1.23, 3.125), (1.1,0.124,0.391) ] ).T
-    test_y = np.array( [3.03,0.9113,1.823])
+test_X = np.array( [ (5.241,1.23, 3.125), (1.1,0.124,0.391) ] ).T
+test_y = np.array( [3.03,0.9113,1.823])
 
-    models = ffx.run(train_X, train_y, test_X, test_y, ["predictor_a", "predictor_b"])
-    for model in models:
-        yhat = model.simulate(test_X)
-        print(model)
+models = ffx.run(train_X, train_y, test_X, test_y, ["predictor_a", "predictor_b"])
+for model in models:
+    yhat = model.simulate(test_X)
+    print(model)
+```
 
 **Scikit-Learn interface**
 
 The FFX Python module also exposes a class, `ffx.FFXRegressor` which provides a Scikit-learn API, in particular `fit(X, y)`, `predict(X)`, and `score(X, y)` methods. In this API, all of the models produced by FFX (the whole Pareto front) are accessible after `fit()`ing as `_models`, but `predict()` and `score()` will use only the model of highest accuracy and highest complexity. Here is an example of usage.
 
-    import numpy as np
-    import ffx
+```python
+import numpy as np
+import ffx
 
-    # This creates a dataset of 2 predictors
-    X = np.random.random((20, 2))
-    y = 0.1 * X[:, 0] + 0.5 * X[:, 1]
+# This creates a dataset of 2 predictors
+X = np.random.random((20, 2))
+y = 0.1 * X[:, 0] + 0.5 * X[:, 1]
 
-    train_X, test_X = X[:10], X[10:]
-    train_y, test_y = y[:10], y[10:]
+train_X, test_X = X[:10], X[10:]
+train_y, test_y = y[:10], y[10:]
 
-    FFX = ffx.FFXRegressor()
-    FFX.fit(train_X, train_y)
-    print("Prediction:", FFX.predict(test_X))
-    print("Score:", FFX.score(test_X, test_y))
+FFX = ffx.FFXRegressor()
+FFX.fit(train_X, train_y)
+print("Prediction:", FFX.predict(test_X))
+print("Score:", FFX.score(test_X, test_y))
+```
 
 ## Technical details
 

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='ffx',
-    version='1.4.0',
+    version='2.0.0',
     author='Trent McConaghy',
     author_email='gtrent@gmail.com',
     maintainer='Nate Kupp',