To start with now, we must create the framework for scoring our model in a test. It will look like the following:
import pandas
import sklearn.metrics
import statsmodels.formula.api as smf
import numpy as np
def logistic_regression_test():
df = pandas.DataFrame.from_csv('./generated_logistic_data.csv')
generated_model = smf.logit('y ~ variable_d', df)
generated_fit = generated_model.fit()
roc_data = sklearn.metrics.roc_curve(df['y'], generated_fit.predict(df))
auc = sklearn.metrics.auc(roc_data[0], roc_data[1])
print generated_fit.summary()
print "AUC score: {0}".format(auc)
assert auc > .6, 'AUC should be significantly above random'The previous code also includes a first stab at a model. Because we generated the data, we know that variable_d is completely unhelpful, but it makes this a bit more of an interesting exploration.
When we run the previous code, the test fails, as expected. I have the test set up to give the full statistical summary, as...