Stata package designed to quickly export feature and target matrices as CSV for use with SciKit Learn. Designed for a State-Python side-by-side workflows.
This package produces two csv files. The first with the suffix _X is intended to be used as a feature matrix with SciKit Learn. While the second with the suffix _y is intended to be used as a target matrix. More inforamtion about the feature and target matricies is over at http://scikit-learn.org/stable/documentation.html.
This Jupyter notebook demonstrates importing skbuddy output for use with SciKit Learn.
At present, not planning to send to SSC for distribution. Available for install via:
net install skbuddy, from(https://raw.githubusercontent.com/adamrossnelson/skbuddy/master)
The alternative to skbuddy would be to manually convert Stata dta files to csv or another format readily accessible in Python. A more direct option would be to use pd.read_stata(). For example:
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', 8)
# Load example dta provided by Stata
exfile = pd.read_stata('http://www.stata-press.com/data/r15/auto2.dta')
# Define features and targets
X = exfile[['price','mpg','length']]
y = exfile[['foreign']]
# Use Scikit-Learn to fit a model
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(max_depth=4, criterion='entropy')
clf.fit(X, y)