diff options
Diffstat (limited to 'src/python/test/test_perslay.py')
| -rw-r--r-- | src/python/test/test_perslay.py | 183 |
1 files changed, 176 insertions, 7 deletions
diff --git a/src/python/test/test_perslay.py b/src/python/test/test_perslay.py index 72b02944..d20bfe14 100644 --- a/src/python/test/test_perslay.py +++ b/src/python/test/test_perslay.py @@ -6,8 +6,14 @@ import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tensorflow import random_uniform_initializer as rui +my_devices = tf.config.experimental.list_physical_devices(device_type='CPU') +tf.config.experimental.set_visible_devices(devices=my_devices, device_type='CPU') +tf.config.experimental.set_visible_devices([], 'GPU') + from gudhi.representations import DiagramScaler, Padding, PerslayModel +np.random.seed(0) +gauss_init = np.array(np.vstack([np.random.uniform(0.,10.,[2,3]), 1e-5*np.ones([2,3])]), dtype=np.float32) def test_perslay_image(): @@ -18,20 +24,21 @@ def test_perslay_image(): diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) perslayParameters = {} - perslayParameters["pweight"] = None - perslayParameters["perm_op"] = "sum" perslayParameters["layer"] = "Image" perslayParameters["layer_train"] = False perslayParameters["image_size"] = (2,2) perslayParameters["image_bnds"] = ((-.501, 1.501), (-.501, 1.501)) perslayParameters["lvariance_init"] = .1 + + perslayParameters["pweight"] = None + perslayParameters["perm_op"] = "sum" + perslayParameters["final_model"] = tf.keras.Sequential([tf.keras.layers.Flatten()]) model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") vector = model([diagrams, empty_feats]).numpy() - assert vector.shape == (1,4) assert np.abs(vector-np.array([[0,0,5.6e-5,3.3668644]])).sum() <= 1e-6 - + def test_perslay_landscape(): diag = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])] @@ -41,17 +48,179 @@ def test_perslay_landscape(): diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) perslayParameters = {} + perslayParameters["layer"] = "Landscape" + perslayParameters["layer_train"] = False + perslayParameters["lsample_num"] = 3 + perslayParameters["lsample_init"] = np.array(np.arange(-.1,1.1,.5), dtype=np.float32) + perslayParameters["final_model"] = "identity" + perslayParameters["pweight"] = None perslayParameters["perm_op"] = "topk" perslayParameters["keep"] = 3 - perslayParameters["layer"] = "Landscape" + + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,9) + assert np.abs(vector-np.array([[0.,0.,0.,0.1,0.025,0.,0.1,0.1,0.]])).sum() <= 1e-6 + + perslayParameters["pweight"] = "power" + perslayParameters["pweight_power"] = 2 + perslayParameters["pweight_init"] = 1. + perslayParameters["pweight_train"] = False + perslayParameters["perm_op"] = "sum" + + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,3) + assert np.abs(vector-np.array([[0., 0.03476562, 0.04531251]])).sum() <= 1e-6 + +def test_perslay_betti(): + + diag = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])] + diag = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diag) + diag = Padding(use=True).fit_transform(diag) + D = np.stack(np.array(diag, dtype=np.float32), 0) + diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) + perslayParameters = {} + + perslayParameters["layer"] = "BettiCurve" perslayParameters["layer_train"] = False perslayParameters["lsample_num"] = 3 perslayParameters["lsample_init"] = np.array(np.arange(-.1,1.1,.5), dtype=np.float32) + perslayParameters["theta"] = 1. perslayParameters["final_model"] = "identity" + + perslayParameters["pweight"] = "grid" + perslayParameters["pweight_size"] = [100,100] + perslayParameters["pweight_bnds"] = ((-.001, 10.001), (-.001, 10.001)) + perslayParameters["pweight_init"] = np.tile(np.arange(0.,100.,1, dtype=np.float32)[np.newaxis,:], [100,1]) + perslayParameters["pweight_train"] = False + perslayParameters["perm_op"] = "sum" model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,3) + assert np.abs(vector-np.array([[10.091741, 12.746357, 13.192123]])).sum() <= 1e-6 - assert vector.shape == (1,9) - assert np.abs(vector-np.array([[0.,0.,0.,0.1,0.025,0.,0.1,0.1,0.]])).sum() <= 1e-6 +def test_perslay_entropy(): + + diag = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])] + diag = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diag) + diag = Padding(use=True).fit_transform(diag) + D = np.stack(np.array(diag, dtype=np.float32), 0) + diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) + perslayParameters = {} + + perslayParameters["layer"] = "Entropy" + perslayParameters["layer_train"] = False + perslayParameters["lsample_num"] = 3 + perslayParameters["lsample_init"] = np.array(np.arange(-.1,1.1,.5), dtype=np.float32) + perslayParameters["theta"] = 1. + perslayParameters["final_model"] = "identity" + + perslayParameters["pweight"] = "gmix" + perslayParameters["pweight_num"] = 3 + perslayParameters["pweight_init"] = gauss_init + perslayParameters["pweight_train"] = False + perslayParameters["perm_op"] = "sum" + + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,3) + assert np.abs(vector-np.array([[1.4855406, 1.7884576, 1.6987829]])).sum() <= 1e-6 + +def test_perslay_rational(): + + diag = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])] + diag = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diag) + diag = Padding(use=True).fit_transform(diag) + D = np.stack(np.array(diag, dtype=np.float32), 0) + diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) + perslayParameters = {} + + perslayParameters["layer"] = "Rational" + perslayParameters["layer_train"] = False + perslayParameters["lnum"] = 3 + perslayParameters["lmean_init"] = gauss_init[:2,:] + perslayParameters["lvariance_init"] = gauss_init[2:,:] + perslayParameters["lalpha_init"] = rui(1., 1.) + + perslayParameters["pweight"] = "power" + perslayParameters["pweight_power"] = 2 + perslayParameters["pweight_init"] = 1. + perslayParameters["pweight_train"] = False + perslayParameters["perm_op"] = "sum" + + perslayParameters["final_model"] = tf.keras.Sequential([tf.keras.layers.Flatten()]) + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,3) + assert np.abs(vector-np.array([[0.7186792, 0.7186759, 0.718668]])).sum() <= 1e-6 + perslayParameters["layer"] = "RationalHat" + perslayParameters["layer_train"] = False + perslayParameters["lnum"] = 3 + perslayParameters["q"] = 1. + perslayParameters["lmean_init"] = gauss_init[:2,:] + perslayParameters["lr_init"] = rui(1., 1.) + + perslayParameters["pweight"] = "power" + perslayParameters["pweight_power"] = 2 + perslayParameters["pweight_init"] = 1. + perslayParameters["pweight_train"] = False + perslayParameters["perm_op"] = "sum" + + perslayParameters["final_model"] = tf.keras.Sequential([tf.keras.layers.Flatten()]) + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,3) + assert np.abs(vector-np.array([[-0.00675799, -0.00620097, -0.00510298]])).sum() <= 1e-6 + +def test_perslay_exponential(): + + diag = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])] + diag = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diag) + diag = Padding(use=True).fit_transform(diag) + D = np.stack(np.array(diag, dtype=np.float32), 0) + diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) + perslayParameters = {} + + perslayParameters["layer"] = "Exponential" + perslayParameters["layer_train"] = False + perslayParameters["lnum"] = 3 + perslayParameters["lmean_init"] = 1e3 * gauss_init[:2,:] + perslayParameters["lvariance_init"] = gauss_init[2:,:] + + perslayParameters["pweight"] = None + perslayParameters["perm_op"] = "max" + + perslayParameters["final_model"] = tf.keras.Sequential([tf.keras.layers.Flatten()]) + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,3) + assert np.abs(vector-np.array([[0.9940388, 0.99311596, 0.99222755]])).sum() <= 1e-6 + +def test_perslay_peq(): + + diag = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])] + diag = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diag) + diag = Padding(use=True).fit_transform(diag) + D = np.stack(np.array(diag, dtype=np.float32), 0) + diagrams, empty_feats = [D], np.empty([1,0], dtype=np.float32) + perslayParameters = {} + + perslayParameters["layer"] = "PermutationEquivariant" + perslayParameters["layer_train"] = False + perslayParameters["lpeq"] = [(5, "sum"), (5, "sum")] + perslayParameters["lweight_init"] = rui(1e-1, 1e-1) + perslayParameters["lbias_init"] = rui(0.1, 0.1) + perslayParameters["lgamma_init"] = rui(1e-1, 1e-1) + + perslayParameters["pweight"] = None + perslayParameters["perm_op"] = "topk" + perslayParameters["keep"] = 3 + + perslayParameters["final_model"] = tf.keras.Sequential([tf.keras.layers.Flatten()]) + model = PerslayModel(name="perslay", diagdim=2, perslay_parameters=[perslayParameters], rho="identity") + vector = model([diagrams, empty_feats]).numpy() + assert vector.shape == (1,15) + assert np.abs(vector-np.array([[0.4375, 0.41875, 0.375, 0.4375, 0.41875, 0.375, 0.4375, 0.41875, 0.375, 0.4375, 0.41875, 0.375, 0.4375, 0.41875, 0.375]])).sum() <= 1e-6 |