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))