def build_and_evaluate_user_model(X, y):
with open("categories.pickle", 'rb') as f:
category_model = pickle.load(f)
kf = KFold(n_splits=5)
aucs = []
for train, test in kf.split(X):
X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
features = category_model.predict_proba(X_train)
user_model = LogisticRegression()
user_model.fit(features, y_train)
y_pred = user_model.predict_proba(category_model.predict_proba(X_test))[:,1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
aucs.append(metrics.auc(fpr,tpr))
user_model.auc = sum(aucs)/len(aucs)
features = category_model.predict_proba(X)
user_model.fit(features, y)
print(metrics.classification_report(y, user_model.predict(features)))
with open(BASEDIR + "user_models/" + username + ".pickle", 'wb') as f:
pickle.dump(user_model, f)
print "Model written out to " + BASEDIR + "user_models/" + username + ".pickle"
return user_model
