print("UMAP Projection")
s = time.time()
umap_proj = UMAP(n_components=2, random_state=420, n_neighbors=5,
min_dist=0.3).fit_transform(X_proj)
umap_proj = scaler.fit_transform(umap_proj)
print("\ttime: ", time.time() - s)
subset_size = 4659
print("\n\nILAMP tSNE")
s = time.time()
k_ilamp = 20
ilamp_tsne = ILAMP(n_neighbors=k_ilamp)
ilamp_tsne.fit(X_proj[:subset_size], tsne_proj[:subset_size])
ilamp_tsne_path = base_dir + "ilamp_tsne.joblib"
ilamp_tsne.save(ilamp_tsne_path)
print("\ttime: ", time.time() - s)
print("\n\nILAMP UMAP")
s = time.time()
ilamp_umap = ILAMP(n_neighbors=k_ilamp)
ilamp_umap.fit(X_proj[:subset_size], umap_proj[:subset_size])
ilamp_umap_path = base_dir + "ilamp_umap.joblib"
ilamp_umap.save(ilamp_umap_path)
print("\ttime: ", time.time() - s)
# print("\n\nNNInv TSNE")
# s = time.time()
# nninv_tsne = NNInv()
# nninv_tsne.fit(X_proj[:subset_size], tsne_proj[:subset_size])
# nninv_tsne_path = base_dir + "nninv_tsne.joblib"
def main():
if len(sys.argv) < 2:
print("Usage: ./fashion_mnist.py <base_dir>")
sys.exit(0)
base_dir = sys.argv[1]
print("Load dataset\n\n")
s = time.time()
X_train, y_train = data.LoadMNISTData('train', base_dir + 'orig/')
train_y_path = base_dir + "y_train.npy"
np.save(train_y_path, y_train)
X_test, y_test = data.LoadMNISTData('test', base_dir + 'orig/')
X_nd = np.copy(X_train)
X_nd = X_nd.reshape((X_nd.shape[0], X_nd.shape[1] * X_nd.shape[2]))
projection_size = 60000
X_proj = np.copy(X_train[:projection_size])
new_shape = (X_proj.shape[0], X_proj.shape[1] * X_proj.shape[2])
X_proj = np.reshape(X_proj, new_shape)
print("\ttime: ", time.time() - s)
scaler = MinMaxScaler(feature_range=(0, 1))
print("TSNE Projection")
s = time.time()
tsne = TSNE(n_components=2,
random_state=420,
perplexity=25.0,
n_iter=3000,
n_iter_without_progress=300,
n_jobs=4)
tsne_proj = tsne.fit_transform(X_proj)
tsne_proj = scaler.fit_transform(tsne_proj)
print("\ttime: ", time.time() - s)
print("UMAP Projection")
s = time.time()
umap_proj = UMAP(n_components=2,
random_state=420,
n_neighbors=5,
min_dist=0.3).fit_transform(X_proj)
umap_proj = scaler.fit_transform(umap_proj)
print("\ttime: ", time.time() - s)
subset_size = 15000
print("\n\nILAMP tSNE")
s = time.time()
k_ilamp = 20
ilamp_tsne = ILAMP(n_neighbors=k_ilamp)
ilamp_tsne.fit(X_proj[:subset_size], tsne_proj[:subset_size])
ilamp_tsne_path = base_dir + "ilamp_tsne.joblib"
ilamp_tsne.save(ilamp_tsne_path)
print("\ttime: ", time.time() - s)
print("\n\nILAMP UMAP")
s = time.time()
ilamp_umap = ILAMP(n_neighbors=k_ilamp)
ilamp_umap.fit(X_proj[:subset_size], umap_proj[:subset_size])
ilamp_umap_path = base_dir + "ilamp_umap.joblib"
ilamp_umap.save(ilamp_umap_path)
print("\ttime: ", time.time() - s)
print("\n\nRBFInv CTRL PTS TSNE")
s = time.time()
EPS = 50000
irbfcp_tsne = RBFInv(num_ctrl=400,
mode='rols',
kernel='gaussian',
eps=EPS,
normalize_c=True,
normalize_d=True)
irbfcp_tsne.fit(X_proj[:subset_size], tsne_proj[:subset_size])
irbfcp_tsne_path = base_dir + "irbfcp_tsne.joblib"
irbfcp_tsne.save(irbfcp_tsne_path)
print("\ttime: ", time.time() - s)
print("\n\nRBFInv CTRL PTS UMAP")
s = time.time()
EPS = 50000
irbfcp_umap = RBFInv(num_ctrl=400,
mode='rols',
kernel='gaussian',
eps=EPS,
normalize_c=True,
normalize_d=True)
irbfcp_umap.fit(X_proj[:subset_size], umap_proj[:subset_size])
irbfcp_umap_path = base_dir + "irbfcp_umap.joblib"
irbfcp_umap.save(irbfcp_umap_path)
print("\ttime: ", time.time() - s)
print("\n\nRBFInv CLUSTER TSNE")
s = time.time()
EPS = 50000
irbfc_tsne = RBFInv(num_ctrl=50,
mode='cluster',
kernel='gaussian',
eps=EPS,
normalize_c=True,
normalize_d=True)
irbfc_tsne.fit(X_proj[:subset_size], tsne_proj[:subset_size])
irbfc_tsne_path = base_dir + "irbfc_tsne.joblib"
irbfc_tsne.save(irbfc_tsne_path)
print("\ttime: ", time.time() - s)
print("\n\nRBFInv CLUSTER UMAP")
s = time.time()
EPS = 50000
irbfc_umap = RBFInv(num_ctrl=50,
mode='cluster',
kernel='gaussian',
eps=EPS,
normalize_c=True,
normalize_d=True)
irbfc_umap.fit(X_proj[:subset_size], tsne_proj[:subset_size])
irbfc_umap_path = base_dir + "irbfc_umap.joblib"
irbfc_umap.save(irbfc_umap_path)
print("\ttime: ", time.time() - s)
print("\n\nNNInv TSNE")
s = time.time()
nninv_tsne = NNInv()
nninv_tsne.fit(X_proj[:subset_size], tsne_proj[:subset_size])
nninv_tsne_path = base_dir + "nninv_tsne.joblib"
nninv_tsne.save(nninv_tsne_path, base_dir + 'nninv_tsne_keras.hdf5')
print("\ttime: ", time.time() - s)
print("\n\nNNInv UMAP")
s = time.time()
nninv_umap = NNInv()
nninv_umap.fit(X_proj[:subset_size], umap_proj[:subset_size])
nninv_umap_path = base_dir + "nninv_umap.joblib"
nninv_umap.save(nninv_umap_path, base_dir + 'nninv_umap_keras.hdf5')
print("\ttime: ", time.time() - s)
input_shape = (X_train.shape[1], X_train.shape[2], 1)
X_train = X_train.reshape((X_train.shape[0], ) + input_shape)
X_test = X_test.reshape((X_test.shape[0], ) + input_shape)
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
X_proj = X_proj.reshape((X_proj.shape[0], ) + input_shape)
print("\n\nTraining classifier")
s = time.time()
clf_keras = CNNModel(input_shape, 10)
clf_keras.fit(X_train,
y_train,
batch_size=128,
epochs=14,
verbose=1,
validation_data=(X_test, y_test))
print("\tAccuracy on test data: ",
clf_keras.evaluate(X_test, y_test, verbose=0))
clf_keras_path = base_dir + "mnist_cnn.hdf5"
clf_keras.save(clf_keras_path)
y_proj_pred = np.argmax(clf_keras.predict(X_proj), axis=1)
pred_path = base_dir + "y_pred_clf.npy"
np.save(pred_path, y_proj_pred)
clf = CLF(clf=clf_keras,
clf_type='keras_cnn',
clf_path=clf_keras_path,
shape=input_shape)
clf_path = base_dir + 'mnist_cnn.json'
clf.save_json(clf_path)
print("\ttime: ", time.time() - s)
N = 1
R = 500
print("\n\nGRID ILAMP tSNE")
s = time.time()
grid_ilamp_tsne_path = base_dir + 'grid_ilamp_tsne.joblib'
save_grid(grid_ilamp_tsne_path, R, N, clf, ilamp_tsne, X_nd, tsne_proj,
clf_path, ilamp_tsne_path)
ui_grid_ilamp_tsne_path = base_dir + 'mnist_500_' + 'ui_ilamp_tsne.json'
save_json_ui(ui_grid_ilamp_tsne_path, grid_ilamp_tsne_path, clf_path,
"ilamp", ilamp_tsne_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID ILAMP UMAP")
s = time.time()
grid_ilamp_umap_path = base_dir + 'grid_ilamp_umap.joblib'
save_grid(grid_ilamp_umap_path, R, N, clf, ilamp_umap, X_nd, umap_proj,
clf_path, ilamp_umap_path)
ui_grid_ilamp_umap_path = base_dir + 'mnist_500_' + 'ui_ilamp_umap.json'
save_json_ui(ui_grid_ilamp_umap_path, grid_ilamp_umap_path, clf_path,
"ilamp", ilamp_umap_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID NNInv tSNE")
s = time.time()
grid_nninv_tsne_path = base_dir + 'grid_nninv_tsne.joblib'
save_grid(grid_nninv_tsne_path, R, N, clf, nninv_tsne, X_nd, tsne_proj,
clf_path, nninv_tsne_path)
ui_grid_nninv_tsne_path = base_dir + 'mnist_500_' + '_ui_nninv_tsne.json'
save_json_ui(ui_grid_nninv_tsne_path, grid_nninv_tsne_path, clf_path,
"nninv", nninv_tsne_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID NNInv UMAP")
s = time.time()
grid_nninv_umap_path = base_dir + 'grid_nninv_umap.joblib'
save_grid(grid_nninv_umap_path, R, N, clf, nninv_umap, X_nd, umap_proj,
clf_path, nninv_umap_path)
ui_grid_nninv_umap_path = base_dir + 'mnist_500_' + 'ui_nninv_umap.json'
save_json_ui(ui_grid_nninv_umap_path, grid_nninv_umap_path, clf_path,
"nninv", nninv_umap_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID RBFInv CTRL PTS tSNE")
s = time.time()
grid_irbfcp_tsne_path = base_dir + 'grid_irbfcp_tsne.joblib'
save_grid(grid_irbfcp_tsne_path, R, N, clf, irbfcp_tsne, X_nd, tsne_proj,
clf_path, irbfcp_tsne_path)
ui_grid_irbfcp_tsne_path = base_dir + 'mnist_500_' + 'ui_irbfcp_tsne.json'
save_json_ui(ui_grid_irbfcp_tsne_path, grid_irbfcp_tsne_path, clf_path,
"rbf", irbfcp_tsne_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID RBFInv CTRL PTS UMAP")
s = time.time()
grid_irbfcp_umap_path = base_dir + 'grid_irbfcp_umap.joblib'
save_grid(grid_irbfcp_umap_path, R, N, clf, irbfcp_umap, X_nd, umap_proj,
clf_path, irbfcp_umap_path)
ui_grid_irbfcp_umap_path = base_dir + 'mnist_500_' + 'ui_irbfcp_umap.json'
save_json_ui(ui_grid_irbfcp_umap_path, grid_irbfcp_umap_path, clf_path,
"rbf", irbfcp_umap_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID RBFInv CLUSTER tSNE")
s = time.time()
grid_irbfc_tsne_path = base_dir + 'grid_irbfc_tsne.joblib'
save_grid(grid_irbfc_tsne_path, R, N, clf, irbfc_tsne, X_nd, tsne_proj,
clf_path, irbfc_tsne_path)
ui_grid_irbfc_tsne_path = base_dir + 'mnist_500_' + 'ui_irbfc_tsne.json'
save_json_ui(ui_grid_irbfc_tsne_path, grid_irbfc_tsne_path, clf_path,
"rbf", irbfc_tsne_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)
print("\n\nGRID RBFInv CLUSTER UMAP")
s = time.time()
grid_irbfc_umap_path = base_dir + 'grid_irbfc_umap.joblib'
save_grid(grid_irbfc_umap_path, R, N, clf, irbfc_umap, X_nd, umap_proj,
clf_path, irbfc_umap_path)
ui_grid_irbfc_umap_path = base_dir + 'mnist_500_' + 'ui_irbfc_umap.json'
save_json_ui(ui_grid_irbfc_umap_path, grid_irbfc_umap_path, clf_path,
"rbf", irbfc_umap_path, train_y_path, pred_path)
print("\ttime: ", time.time() - s)