Test-Driven Machine Learning

Test driving our model

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 ...