def makeCV(kfolds,X,Labels,User,Meta,clf,opts):
users = np.unique(User)
toPredData=[]
Gauc = []
for train_users,test_users in kfolds[1]:
allProb = 0
test_index = np.array([True if u in set(users[test_users]) else False for u in User])
if opts.has_key('bagging'):
bagging = baggingIterator(opts,[users[i] for i in train_users])
else:
bagging = [[-1]]
for bag in bagging:
bagUsers = np.array([True if u in set(bag) else False for u in User])
train_index = np.logical_xor(np.negative(test_index), bagUsers)
try:
# train
updateMeta(clf,Meta[train_index])
clf.fit(X[train_index,:,:],Labels[train_index])
# predict
prob = []
for ut in np.unique(users[test_users]):
updateMeta(clf,Meta[User==ut,...])
prob.extend(clf.predict(X[User==ut,...]))
prob = np.array(prob)
allProb += prob/len(bagging)
except:
print kfolds[0]
print [users[i] for i in train_users]
print bag
continue
def BaggingFunc(bag, Labels, X, Meta, User, X_test, Meta_test, User_test):
bagUsers = np.array([True if u in set(bag) else False for u in User])
train_index = np.negative(bagUsers)
updateMeta(clf, Meta[train_index])
clf.fit(X[train_index, :, :], Labels[train_index])
### predicting
prob = []
for ut in users_test:
updateMeta(clf, Meta_test[User_test == ut, ...])
prob.extend(clf.predict(X_test[User_test == ut, ...]))
prob = np.array(prob)
return prob
def BaggingFunc(bag,Labels,X,Meta,User,X_test,Meta_test,User_test):
bagUsers = np.array([True if u in set(bag) else False for u in User])
train_index = np.negative(bagUsers)
updateMeta(clf,Meta[train_index])
clf.fit(X[train_index,:,:],Labels[train_index])
### predicting
prob = []
for ut in users_test:
updateMeta(clf,Meta_test[User_test==ut,...])
prob.extend(clf.predict(X_test[User_test==ut,...]))
prob = np.array(prob)
return prob
def makeCV(kfolds, X, Labels, User, Meta, clf, opts):
users = np.unique(User)
toPredData = []
Gauc = []
for train_users, test_users in kfolds[1]:
allProb = 0
test_index = np.array(
[True if u in set(users[test_users]) else False for u in User])
if opts.has_key('bagging'):
bagging = baggingIterator(opts, [users[i] for i in train_users])
else:
bagging = [[-1]]
for bag in bagging:
bagUsers = np.array(
[True if u in set(bag) else False for u in User])
train_index = np.logical_xor(np.negative(test_index), bagUsers)
try:
# train
updateMeta(clf, Meta[train_index])
clf.fit(X[train_index, :, :], Labels[train_index])
# predict
prob = []
for ut in np.unique(users[test_users]):
updateMeta(clf, Meta[User == ut, ...])
prob.extend(clf.predict(X[User == ut, ...]))
prob = np.array(prob)
allProb += prob / len(bagging)
except:
print kfolds[0]
print[users[i] for i in train_users]
print bag
continue
# save & return
predictions = OrderedDict()
predictions['user'] = User[test_index]
predictions['label'] = Labels[test_index]
predictions['prediction'] = allProb
if opts.has_key('leak'):
predictions['prediction'] += opts['leak']['coeff'] * (
1 - Meta[test_index, -1])
predictions = pd.DataFrame(predictions)
Gauc.append(roc_auc_score(predictions.label, predictions.prediction))
toPredData.append(predictions)
predData = pd.concat(toPredData)
Sauc = [
roc_auc_score(predData.loc[predData.user == i].label,
predData.loc[predData.user == i].prediction)
for i in np.unique(predData.user)
]
print 'Rep %d: gAUC (mean of folds) %0.5f, sAUC %0.5f (%0.5f)' % (
kfolds[0], np.mean(Gauc), np.mean(Sauc), np.std(Sauc))
return [Gauc, Sauc]