def cluster_nn(X_train, y_train, X_test, y_test, savedir, ds, cluster_type):
logging.info(
'Running neural net with {} clusters as features'.format(cluster_type))
pipe = A3.baseline_ann(X_train, y_train, ds)
ypred = pipe.predict(X_test)
util.confusionMatrix('{}-{}-ann'.format(ds, cluster_type), y_test, ypred,
savedir)
return f1_score(y_test, ypred)
def dr_ann(X_train,
y_train,
X_test,
y_test,
dr_steps,
savedir,
ds,
cluster=None):
if cluster is not None:
c = ' with clustering from {}'.format(cluster)
else:
c = ''
logging.info('ANN: Running baseline neural net' + c)
baseline = A3.baseline_ann(X_train, y_train, ds)
ypred = baseline.predict(X_test)
util.confusionMatrix('{}{}-baseline'.format(ds, cluster), y_test, ypred,
savedir)
scores = [f1_score(y_test, ypred)]
score_names = ['baseline']
for dr_step in dr_steps:
drname = dr_step.__class__.__name__.lower()
score_names.append(drname)
logging.info('ANN: Running neural net with {} dimension reduction'.
format(drname) + c)
# Get trained ann with dr
ann = A3.dr_ann(X_train, y_train, dr_step, ds)
ypred = ann.predict(X_test)
util.confusionMatrix('{}-{}{}'.format(ds, drname, cluster), y_test,
ypred, savedir)
scores.append(f1_score(y_test, ypred))
logging.info('ANN {} F1 Scores: {}'.format(c, scores))
util.plotBarScores(scores,
score_names,
ds,
savedir,
phaseName='{}-{}'.format(ds, cluster))
plt.close('all')