def test_kfold_cv(self):
trainer = TrainModelCV([LogisticRegression, classification_error, None, (), {'lam':0.5}],
X=self.X, y=self.y, folds=self.folds)
errors = trainer.run()
self.assertTrue(np.abs(errors[errors.keys()[0]]['train'] - 0.063340259665816398) < 1e-12)
self.assertTrue(np.abs(errors[errors.keys()[0]]['test'] - 0.049633305762338022)< 1e-12)
def test_aggregate_error(self):
# test an aggregate error function (that returns more than one value)
trainer = TrainModelCV([LogisticRegression, TestTrainModelCV.agg_err, None, (), {'lam':0.5}],
X=self.X, y=self.y, Xtest=self.X[:50, :], ytest=self.y[:50])
errors = trainer.run()
self.assertTrue(np.abs(errors[errors.keys()[0]]['train']['accuracy'] - 0.06) < 1e-8)
self.assertTrue(np.abs(errors[errors.keys()[0]]['train']['auc'] - 0.99310661764705888) < 1e-8)
def test_map_train_model(self):
trainer = TrainModelCV([LogisticRegression, classification_error, '/tmp/logistic.json', (), {'lam':0.5}], X=self.X, y=self.y)
errors = trainer.run()
# load model
trained_model = LogisticRegression.load_model('/tmp/logistic.json')
loaded_model_error = classification_error(self.y, trained_model.predict(self.X))
# check the errors
self.assertTrue(np.abs(errors[errors.keys()[0]]['train'] - 0.06) < 1e-12)
self.assertTrue(np.abs(errors[errors.keys()[0]]['train'] - loaded_model_error) < 1e-12)
def test_kfold_cv(self):
trainer = TrainModelCV(
[LogisticRegression, classification_error, None, (), {
'lam': 0.5
}],
X=self.X,
y=self.y,
folds=self.folds)
errors = trainer.run()
self.assertTrue(
np.abs(errors[errors.keys()[0]]['train'] -
0.063340259665816398) < 1e-12)
self.assertTrue(
np.abs(errors[errors.keys()[0]]['test'] -
0.049633305762338022) < 1e-12)
def test_aggregate_error(self):
# test an aggregate error function (that returns more than one value)
trainer = TrainModelCV([
LogisticRegression, TestTrainModelCV.agg_err, None, (), {
'lam': 0.5
}
],
X=self.X,
y=self.y,
Xtest=self.X[:50, :],
ytest=self.y[:50])
errors = trainer.run()
self.assertTrue(
np.abs(errors[errors.keys()[0]]['train']['accuracy'] -
0.06) < 1e-8)
self.assertTrue(
np.abs(errors[errors.keys()[0]]['train']['auc'] -
0.99310661764705888) < 1e-8)
def test_map_train_model(self):
trainer = TrainModelCV([
LogisticRegression, classification_error, '/tmp/logistic.json',
(), {
'lam': 0.5
}
],
X=self.X,
y=self.y)
errors = trainer.run()
# load model
trained_model = LogisticRegression.load_model('/tmp/logistic.json')
loaded_model_error = classification_error(
self.y, trained_model.predict(self.X))
# check the errors
self.assertTrue(
np.abs(errors[errors.keys()[0]]['train'] - 0.06) < 1e-12)
self.assertTrue(
np.abs(errors[errors.keys()[0]]['train'] -
loaded_model_error) < 1e-12)
