def test_lmdb_creation(self):
params = nn_params.copy()
params.update({'nclasses': n_classes})
# Create dataset
X, Y = datasets.load_digits(return_X_y=True)
X = preprocessing.StandardScaler().fit_transform(X)
x_train, x_test, y_train, y_test = model_selection.train_test_split(
X, Y, test_size=test_size, random_state=seed)
# Save data in .svm format
tr_svm_f, tr_lmdb_f = os.path.abspath('x_train.svm'), os.path.abspath(
'x_train.lmdb')
te_svm_f, te_lmdb_f = os.path.abspath('x_test.svm'), os.path.abspath(
'x_test.lmdb')
vocab_path = os.path.abspath('vocab.dat')
datasets.dump_svmlight_file(x_train, y_train, tr_svm_f)
datasets.dump_svmlight_file(x_test, y_test, te_svm_f)
lmdb_utils.create_lmdb_from_svm(svm_path=tr_svm_f,
lmdb_path=tr_lmdb_f,
vocab_path=vocab_path,
**params)
lmdb_utils.create_lmdb_from_svm(svm_path=te_svm_f,
lmdb_path=te_lmdb_f,
**params)
tr_lmdb = SVMConnector(path=tr_svm_f,
lmdb_path=tr_lmdb_f,
vocab_path=vocab_path)
te_lmdb = SVMConnector(path=te_svm_f, lmdb_path=te_lmdb_f)
optimizer = GenericSolver(solver_type='SGD',
base_lr=0.01,
iterations=100)
clf = MLP(**params)
clf.fit(tr_lmdb, validation_data=[te_lmdb], solver=optimizer)
ytr_prob = clf.predict_proba(tr_lmdb)
acc = metrics.accuracy_score(y_train, ytr_prob.argmax(-1))
assert acc > 0.7
os_utils._remove_files([tr_svm_f, te_svm_f, vocab_path])
os_utils._remove_dirs([tr_lmdb_f, te_lmdb_f])
x_train, x_test, y_train, y_test = model_selection.train_test_split(
X, Y, test_size=test_size, random_state=seed)
# Save data in .svm format
tr_svm_f, tr_lmdb_f = os.path.abspath('x_train.svm'), os.path.abspath(
'x_train.lmdb')
te_svm_f, te_lmdb_f = os.path.abspath('x_test.svm'), os.path.abspath(
'x_test.lmdb')
vocab_path = os.path.abspath('vocab.dat')
datasets.dump_svmlight_file(x_train, y_train, tr_svm_f)
datasets.dump_svmlight_file(x_test, y_test, te_svm_f)
# create lmdb and vocab file
create_lmdb_from_svm(svm_path=tr_svm_f,
lmdb_path=tr_lmdb_f,
vocab_path=vocab_path,
**params)
create_lmdb_from_svm(svm_path=te_svm_f, lmdb_path=te_lmdb_f, **params)
tr_data = SVMConnector(path=tr_svm_f,
lmdb_path=tr_lmdb_f,
vocab_path=vocab_path)
te_data = SVMConnector(path=tr_svm_f, lmdb_path=tr_lmdb_f)
optimizer = GenericSolver(solver_type='SGD', base_lr=0.01, iterations=100)
clf = MLP(**params)
clf.fit(tr_data, validation_data=[te_data], solver=optimizer)
y_pred_lmdb = clf.predict_proba(te_data)
