def test_pca_transform_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "pca_transform_batch.csv"),
range(2))
from pca_transform_batch import main as get_results
_, result = get_results()
self.assertTrue(np.allclose(result.transformedData, testdata))
def test_univariate_outlier_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "univariate_outlier_batch.csv"),
range(1))
from univariate_outlier_batch import main as get_results
(_, result) = get_results()
self.assertTrue(np.allclose(result.weights, testdata))
def test_svm_multiclass_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "svm_multiclass_batch.csv"),
range(1))
from svm_multiclass_batch import main as get_results
(predict_result, _) = get_results()
self.assertTrue(np.allclose(predict_result.prediction, testdata))
def test_decision_tree_regression_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path,
"decision_tree_regression_batch.csv"), range(1))
from decision_tree_regression_batch import main as get_results
(_, predict_result, _) = get_results()
self.assertTrue(np.allclose(predict_result.prediction, testdata))
def test_log_reg_binary_dense_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "log_reg_binary_dense_batch.csv"),
range(1))
from log_reg_binary_dense_batch import main as get_results
(_, predict_result, _) = get_results()
self.assertTrue(np.allclose(predict_result.prediction, testdata))
def test_lbfgs_cr_entr_loss_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "lbfgs_cr_entr_loss_batch.csv"),
range(1))
from lbfgs_cr_entr_loss_batch import main as get_results
result = get_results()
self.assertTrue(np.allclose(result.minimum, testdata))
def test_adagrad_mse_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "adagrad_mse_batch.csv"),
range(1))
from adagrad_mse_batch import main as get_results
result = get_results()
self.assertTrue(np.allclose(result.minimum, testdata))
def test_gradient_boosted_classification_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path,
"gradient_boosted_classification_batch.csv"),
range(1))
from gradient_boosted_classification_batch import main as get_results
(_, predict_result, _) = get_results()
self.assertTrue(np.allclose(predict_result.prediction, testdata))
def test_gradient_boosted_regression_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path,
"gradient_boosted_regression_batch.csv"), range(1))
from gradient_boosted_regression_batch import main as get_results
(_, predict_result, _) = get_results()
#MSE
self.assertTrue(
np.square(predict_result.prediction - testdata).mean() < 1e-2)
def test_svm_batch(self):
testdata = read_csv(os.path.join(unittest_data_path, "svm_batch.csv"),
range(1))
from svm_batch import main as get_results
(predict_result, _) = get_results()
self.assertTrue(
np.absolute(predict_result.prediction - testdata).max() <
np.absolute(predict_result.prediction.max() -
predict_result.prediction.min()) * 0.05)
def test_svd_batch(self):
from svd_batch import main as get_results
(data, result) = get_results()
self.assertTrue(
np.allclose(
data,
np.matmul(
np.matmul(result.leftSingularMatrix,
np.diag(result.singularValues[0])),
result.rightSingularMatrix)))
def test_low_order_moms_dense_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "low_order_moms_dense_batch.csv"),
range(10))
from low_order_moms_dense_batch import main as get_results
res = get_results()
r = np.vstack(
(res.minimum, res.maximum, res.sum, res.sumSquares,
res.sumSquaresCentered, res.mean, res.secondOrderRawMoment,
res.variance, res.standardDeviation, res.variation))
self.assertTrue(np.allclose(r, testdata))
def test_cosine_distance_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "cosine_distance_batch.csv"),
range(1))
from cosine_distance_batch import main as get_results
result = get_results()
r = result.cosineDistance
self.assertTrue(
np.allclose(
np.array([[np.amin(r)], [np.amax(r)], [np.mean(r)],
[np.average(r)]]), testdata))
def test_kdtree_knn_classification_batch(self):
from kdtree_knn_classification_batch import main as get_results
(_, predict_result, test_labels) = get_results()
self.assertTrue(
np.count_nonzero(test_labels != predict_result.prediction) < 170)
def test_cholesky_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "cholesky_batch.csv"), range(5))
from cholesky_batch import main as get_results
result = get_results()
self.assertTrue(np.allclose(result.choleskyFactor, testdata))
def test_pca_batch(self):
testdata = read_csv(os.path.join(unittest_data_path, "pca_batch.csv"),
range(10))
from pca_batch import main as get_results
result = get_results()
self.assertTrue(np.allclose(result.eigenvectors, testdata))
def test_kmeans_batch(self):
testdata = read_csv(
os.path.join(unittest_data_path, "kmeans_batch.csv"), range(20))
from kmeans_batch import main as get_results
result = get_results()
self.assertTrue(np.allclose(result.centroids, testdata))
