removing leftovers from migration

Author: Jordan Stomps

tests/test_models.py

@@ -12,10 +12,6 @@
 import scripts.utils as utils
 # models
 from models.LogReg import LogReg
-from models.SSML.CoTraining import CoTraining
-from models.SSML.LabelProp import LabelProp
-from models.SSML.ShadowNN import ShadowNN
-from models.SSML.ShadowCNN import ShadowCNN
 # testing write
 import joblib
 import os
@@ -166,90 +162,4 @@ def test_LogReg():
     model_file = joblib.load(filename+ext)
     assert model_file.best['params'] == model.best['params']
 
-    os.remove(filename+ext)
-
-
-    # check default parameter settings
-    model = ShadowCNN()
-    assert model.params == {'binning': 1, 'batch_size': 1}
-    assert model.model is not None
-    assert model.eaat is not None
-    assert model.optimizer is not None
-
-    X, Ux, y, Uy = train_test_split(spectra,
-                                    labels,
-                                    test_size=0.5,
-                                    random_state=0)
-    X_train, X_test, y_train, y_test = train_test_split(X,
-                                                        y,
-                                                        test_size=0.2,
-                                                        random_state=0)
-
-    # normalization
-    normalizer = StandardScaler()
-    normalizer.fit(X_train)
-
-    X_train = normalizer.transform(X_train)
-    X_test = normalizer.transform(X_test)
-    Ux = normalizer.transform(Ux)
-
-    params = {'layer1': 2,
-              'kernel': 3,
-              'alpha': 0.1,
-              'xi': 1e-3,
-              'eps': 1.0,
-              'lr': 0.1,
-              'momentum': 0.9,
-              'binning': 20,
-              'batch_size': 4,
-              'drop_rate': 0.1}
-
-    # default behavior
-    model = ShadowCNN(params=params, random_state=0)
-    losscurve, evalcurve = model.train(X_train, y_train, Ux, X_test, y_test)
-
-    # testing train and predict methods
-    pred, acc = model.predict(X_test, y_test)
-
-    # test for agreement between training and testing
-    # (since the same data is used for diagnostics in this test)
-    assert evalcurve[-1] == acc
-
-    # Shadow/PyTorch reports accuracies as percentages
-    # rather than decimals
-    # uninteresting test if Shadow predicts all one class
-    # TODO: make the default params test meaningful
-    assert np.count_nonzero(pred == y_test) > 0
-
-    # testing hyperopt optimize methods
-    space = params
-    space['binning'] = scope.int(hp.quniform('binning',
-                                 10,
-                                 20,
-                                 1))
-    data_dict = {'trainx': X_train,
-                 'testx': X_test,
-                 'trainy': y_train,
-                 'testy': y_test,
-                 'Ux': Ux
-                 }
-    model.optimize(space, data_dict, max_evals=2, verbose=True)
-
-    assert model.best['accuracy'] >= model.worst['accuracy']
-    assert model.best['status'] == 'ok'
-
-    # testing model plotting method
-    filename = 'test_plot'
-    model.plot_training(losscurve=model.best['losscurve'],
-                        evalcurve=model.best['evalcurve'],
-                        filename=filename)
-    os.remove(filename+'.png')
-
-    # testing model write to file method
-    filename = 'test_LogReg'
-    ext = '.joblib'
-    model.save(filename)
-    model_file = joblib.load(filename+ext)
-    assert model_file.best['params'] == model.best['params']
-
-    os.remove(filename+ext)
+    os.remove(filename+ext)