You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@systemds.apache.org by GitBox <gi...@apache.org> on 2021/06/25 08:47:05 UTC
[GitHub] [systemds] Baunsgaard commented on a change in pull request #1323: [WIP][SYSTEMDS-2835] Python end-to-end tutorial
Baunsgaard commented on a change in pull request #1323:
URL: https://github.com/apache/systemds/pull/1323#discussion_r658585499
##########
File path: src/main/python/tests/examples/tutorials/test_adult.py
##########
@@ -0,0 +1,243 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+from systemds.context import SystemDSContext
+from systemds.examples.tutorials.adult import DataManager
+from systemds.operator import OperationNode
+from systemds.operator.algorithm import kmeans, multiLogReg, multiLogRegPredict, l2svm, confusionMatrix, scale, scaleApply, split, winsorize
+from systemds.script_building import DMLScript
+
+
+class Test_DMLScript(unittest.TestCase):
+ """
+ Test class for adult dml script tutorial code.
+ """
+
+ sds: SystemDSContext = None
+ d: DataManager = None
+ neural_net_src_path: str = "tests/examples/tutorials/neural_net_source.dml"
+ dataset_path_train: str = "../../test/resources/datasets/adult/train_data.csv"
+ dataset_path_train_mtd: str = "../../test/resources/datasets/adult/train_data.csv.mtd"
+ dataset_path_test: str = "../../test/resources/datasets/adult/test_data.csv"
+ dataset_path_test_mtd: str = "../../test/resources/datasets/adult/test_data.csv.mtd"
+ dataset_jspec: str = "../../test/resources/datasets/adult/jspec.json"
+
+ @classmethod
+ def setUpClass(cls):
+ cls.sds = SystemDSContext()
+ cls.d = DataManager()
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.sds.close()
+
+ def test_train_data(self):
+ x = self.d.get_train_data()
+ self.assertEqual((32561, 14), x.shape)
+
+ def test_train_labels(self):
+ y = self.d.get_train_labels()
+ self.assertEqual((32561,), y.shape)
+
+ def test_test_data(self):
+ x_l = self.d.get_test_data()
+ self.assertEqual((16281, 14), x_l.shape)
+
+ def test_test_labels(self):
+ y_l = self.d.get_test_labels()
+ self.assertEqual((16281,), y_l.shape)
+
+ def test_preprocess(self):
+ #assumes certain preprocessing
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+ self.assertEqual((30162,104), train_data.shape)
+ self.assertEqual((30162, ), train_labels.shape)
+ self.assertEqual((15060,104), test_data.shape)
+ self.assertEqual((15060, ), test_labels.shape)
+
+ def test_multi_log_reg(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3503, 503],
+ [268, 726]])
+ )
+ )
+
+
+ def test_multi_log_reg_interpolated_standardized(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3583, 502],
+ [245, 670]])
+ )
+ )
+
+
+ def test_neural_net(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+ network = FFN_package.train(X, Y, 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ #probs = FFN_package.predict(Xt, network).compute(True)
+ # FFN_package.eval(Yt, Yt).compute()
+
+
+ def test_parse_dataset_with_systemdsread(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 30000
+ test_count = 10000
+ #self.sds.read(self.dataset_path_train, schema=self.dataset_path_train_mtd).compute(verbose=True)
+ print("")
+
+ SCHEMA = '"DOUBLE,STRING,DOUBLE,STRING,DOUBLE,STRING,STRING,STRING,STRING,STRING,DOUBLE,DOUBLE,DOUBLE,STRING,STRING"'
+ F1 = self.sds.read(
+ self.dataset_path_train,
+ schema=SCHEMA
+ )
+ F2 = self.sds.read(
+ self.dataset_path_test,
+ schema=SCHEMA
+ )
+ jspec = self.sds.read(self.dataset_jspec, data_type="scalar", value_type="string")
+ #scaling does not have effect yet. We need to replace labels in test set with the same string as in train dataset
+ X1, M1 = F1.rbind(F2).transform_encode(spec=jspec).compute()
Review comment:
you should not need to compute here.
use the output, X1 and call our builtin split
##########
File path: src/main/python/tests/examples/tutorials/test_adult.py
##########
@@ -0,0 +1,243 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+from systemds.context import SystemDSContext
+from systemds.examples.tutorials.adult import DataManager
+from systemds.operator import OperationNode
+from systemds.operator.algorithm import kmeans, multiLogReg, multiLogRegPredict, l2svm, confusionMatrix, scale, scaleApply, split, winsorize
+from systemds.script_building import DMLScript
+
+
+class Test_DMLScript(unittest.TestCase):
+ """
+ Test class for adult dml script tutorial code.
+ """
+
+ sds: SystemDSContext = None
+ d: DataManager = None
+ neural_net_src_path: str = "tests/examples/tutorials/neural_net_source.dml"
+ dataset_path_train: str = "../../test/resources/datasets/adult/train_data.csv"
+ dataset_path_train_mtd: str = "../../test/resources/datasets/adult/train_data.csv.mtd"
+ dataset_path_test: str = "../../test/resources/datasets/adult/test_data.csv"
+ dataset_path_test_mtd: str = "../../test/resources/datasets/adult/test_data.csv.mtd"
+ dataset_jspec: str = "../../test/resources/datasets/adult/jspec.json"
+
+ @classmethod
+ def setUpClass(cls):
+ cls.sds = SystemDSContext()
+ cls.d = DataManager()
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.sds.close()
+
+ def test_train_data(self):
+ x = self.d.get_train_data()
+ self.assertEqual((32561, 14), x.shape)
+
+ def test_train_labels(self):
+ y = self.d.get_train_labels()
+ self.assertEqual((32561,), y.shape)
+
+ def test_test_data(self):
+ x_l = self.d.get_test_data()
+ self.assertEqual((16281, 14), x_l.shape)
+
+ def test_test_labels(self):
+ y_l = self.d.get_test_labels()
+ self.assertEqual((16281,), y_l.shape)
+
+ def test_preprocess(self):
+ #assumes certain preprocessing
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+ self.assertEqual((30162,104), train_data.shape)
+ self.assertEqual((30162, ), train_labels.shape)
+ self.assertEqual((15060,104), test_data.shape)
+ self.assertEqual((15060, ), test_labels.shape)
+
+ def test_multi_log_reg(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3503, 503],
+ [268, 726]])
+ )
+ )
+
+
+ def test_multi_log_reg_interpolated_standardized(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3583, 502],
+ [245, 670]])
+ )
+ )
+
+
+ def test_neural_net(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+ network = FFN_package.train(X, Y, 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ #probs = FFN_package.predict(Xt, network).compute(True)
+ # FFN_package.eval(Yt, Yt).compute()
+
+
+ def test_parse_dataset_with_systemdsread(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 30000
+ test_count = 10000
+ #self.sds.read(self.dataset_path_train, schema=self.dataset_path_train_mtd).compute(verbose=True)
+ print("")
+
+ SCHEMA = '"DOUBLE,STRING,DOUBLE,STRING,DOUBLE,STRING,STRING,STRING,STRING,STRING,DOUBLE,DOUBLE,DOUBLE,STRING,STRING"'
+ F1 = self.sds.read(
+ self.dataset_path_train,
+ schema=SCHEMA
+ )
+ F2 = self.sds.read(
+ self.dataset_path_test,
+ schema=SCHEMA
+ )
+ jspec = self.sds.read(self.dataset_jspec, data_type="scalar", value_type="string")
+ #scaling does not have effect yet. We need to replace labels in test set with the same string as in train dataset
+ X1, M1 = F1.rbind(F2).transform_encode(spec=jspec).compute()
+ col_length = len(X1[0])
+ X = X1[0:train_count, 0:col_length-1]
+ Y = X1[0:train_count, col_length-1:col_length].flatten()-1
+ # Test data
+ Xt = X1[train_count:train_count+test_count, 0:col_length-1]
+ Yt = X1[train_count:train_count+test_count, col_length-1:col_length].flatten()-1
+
+ _ , mean , sigma = scale(self.sds.from_numpy(X), True, True).compute()
Review comment:
here also we should not need to compute, but simply get the values out, and call apply on the test data.
The first result from this call is the scaled and shifted X, therefore the following second scaling of x is not needed.
##########
File path: src/main/python/tests/examples/tutorials/test_adult.py
##########
@@ -0,0 +1,243 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+from systemds.context import SystemDSContext
+from systemds.examples.tutorials.adult import DataManager
+from systemds.operator import OperationNode
+from systemds.operator.algorithm import kmeans, multiLogReg, multiLogRegPredict, l2svm, confusionMatrix, scale, scaleApply, split, winsorize
+from systemds.script_building import DMLScript
+
+
+class Test_DMLScript(unittest.TestCase):
+ """
+ Test class for adult dml script tutorial code.
+ """
+
+ sds: SystemDSContext = None
+ d: DataManager = None
+ neural_net_src_path: str = "tests/examples/tutorials/neural_net_source.dml"
+ dataset_path_train: str = "../../test/resources/datasets/adult/train_data.csv"
+ dataset_path_train_mtd: str = "../../test/resources/datasets/adult/train_data.csv.mtd"
+ dataset_path_test: str = "../../test/resources/datasets/adult/test_data.csv"
+ dataset_path_test_mtd: str = "../../test/resources/datasets/adult/test_data.csv.mtd"
+ dataset_jspec: str = "../../test/resources/datasets/adult/jspec.json"
+
+ @classmethod
+ def setUpClass(cls):
+ cls.sds = SystemDSContext()
+ cls.d = DataManager()
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.sds.close()
+
+ def test_train_data(self):
+ x = self.d.get_train_data()
+ self.assertEqual((32561, 14), x.shape)
+
+ def test_train_labels(self):
+ y = self.d.get_train_labels()
+ self.assertEqual((32561,), y.shape)
+
+ def test_test_data(self):
+ x_l = self.d.get_test_data()
+ self.assertEqual((16281, 14), x_l.shape)
+
+ def test_test_labels(self):
+ y_l = self.d.get_test_labels()
+ self.assertEqual((16281,), y_l.shape)
+
+ def test_preprocess(self):
+ #assumes certain preprocessing
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+ self.assertEqual((30162,104), train_data.shape)
+ self.assertEqual((30162, ), train_labels.shape)
+ self.assertEqual((15060,104), test_data.shape)
+ self.assertEqual((15060, ), test_labels.shape)
+
+ def test_multi_log_reg(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3503, 503],
+ [268, 726]])
+ )
+ )
+
+
+ def test_multi_log_reg_interpolated_standardized(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3583, 502],
+ [245, 670]])
+ )
+ )
+
+
+ def test_neural_net(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+ network = FFN_package.train(X, Y, 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ #probs = FFN_package.predict(Xt, network).compute(True)
+ # FFN_package.eval(Yt, Yt).compute()
Review comment:
i will make this work.
##########
File path: src/main/python/tests/examples/tutorials/test_adult.py
##########
@@ -0,0 +1,243 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+from systemds.context import SystemDSContext
+from systemds.examples.tutorials.adult import DataManager
+from systemds.operator import OperationNode
+from systemds.operator.algorithm import kmeans, multiLogReg, multiLogRegPredict, l2svm, confusionMatrix, scale, scaleApply, split, winsorize
+from systemds.script_building import DMLScript
+
+
+class Test_DMLScript(unittest.TestCase):
+ """
+ Test class for adult dml script tutorial code.
+ """
+
+ sds: SystemDSContext = None
+ d: DataManager = None
+ neural_net_src_path: str = "tests/examples/tutorials/neural_net_source.dml"
+ dataset_path_train: str = "../../test/resources/datasets/adult/train_data.csv"
+ dataset_path_train_mtd: str = "../../test/resources/datasets/adult/train_data.csv.mtd"
+ dataset_path_test: str = "../../test/resources/datasets/adult/test_data.csv"
+ dataset_path_test_mtd: str = "../../test/resources/datasets/adult/test_data.csv.mtd"
+ dataset_jspec: str = "../../test/resources/datasets/adult/jspec.json"
+
+ @classmethod
+ def setUpClass(cls):
+ cls.sds = SystemDSContext()
+ cls.d = DataManager()
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.sds.close()
+
+ def test_train_data(self):
+ x = self.d.get_train_data()
+ self.assertEqual((32561, 14), x.shape)
+
+ def test_train_labels(self):
+ y = self.d.get_train_labels()
+ self.assertEqual((32561,), y.shape)
+
+ def test_test_data(self):
+ x_l = self.d.get_test_data()
+ self.assertEqual((16281, 14), x_l.shape)
+
+ def test_test_labels(self):
+ y_l = self.d.get_test_labels()
+ self.assertEqual((16281,), y_l.shape)
+
+ def test_preprocess(self):
+ #assumes certain preprocessing
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+ self.assertEqual((30162,104), train_data.shape)
+ self.assertEqual((30162, ), train_labels.shape)
+ self.assertEqual((15060,104), test_data.shape)
+ self.assertEqual((15060, ), test_labels.shape)
+
+ def test_multi_log_reg(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3503, 503],
+ [268, 726]])
+ )
+ )
+
+
+ def test_multi_log_reg_interpolated_standardized(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3583, 502],
+ [245, 670]])
+ )
+ )
+
+
+ def test_neural_net(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+ network = FFN_package.train(X, Y, 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ #probs = FFN_package.predict(Xt, network).compute(True)
+ # FFN_package.eval(Yt, Yt).compute()
+
+
+ def test_parse_dataset_with_systemdsread(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 30000
+ test_count = 10000
+ #self.sds.read(self.dataset_path_train, schema=self.dataset_path_train_mtd).compute(verbose=True)
+ print("")
+
+ SCHEMA = '"DOUBLE,STRING,DOUBLE,STRING,DOUBLE,STRING,STRING,STRING,STRING,STRING,DOUBLE,DOUBLE,DOUBLE,STRING,STRING"'
+ F1 = self.sds.read(
+ self.dataset_path_train,
+ schema=SCHEMA
+ )
+ F2 = self.sds.read(
+ self.dataset_path_test,
+ schema=SCHEMA
+ )
+ jspec = self.sds.read(self.dataset_jspec, data_type="scalar", value_type="string")
+ #scaling does not have effect yet. We need to replace labels in test set with the same string as in train dataset
+ X1, M1 = F1.rbind(F2).transform_encode(spec=jspec).compute()
+ col_length = len(X1[0])
+ X = X1[0:train_count, 0:col_length-1]
+ Y = X1[0:train_count, col_length-1:col_length].flatten()-1
+ # Test data
+ Xt = X1[train_count:train_count+test_count, 0:col_length-1]
+ Yt = X1[train_count:train_count+test_count, col_length-1:col_length].flatten()-1
+
+ _ , mean , sigma = scale(self.sds.from_numpy(X), True, True).compute()
+
+ mean_copy = np.array(mean)
+ sigma_copy = np.array(sigma)
+
+ numerical_cols = []
+ for count, col in enumerate(np.transpose(X)):
+ for entry in col:
+ if entry > 1 or entry < 0 or entry > 0 and entry < 1:
+ numerical_cols.append(count)
+ break
+
+ for x in range(0,105):
+ if not x in numerical_cols:
+ mean_copy[0][x] = 0
+ sigma_copy[0][x] = 1
+
+ mean = self.sds.from_numpy(mean_copy)
+ sigma = self.sds.from_numpy(sigma_copy)
+ X = self.sds.from_numpy(X)
+ Xt = self.sds.from_numpy(Xt)
+ X = scaleApply(winsorize(X, True), mean, sigma)
+ Xt = scaleApply(winsorize(Xt, True), mean, sigma)
+ #node = PROCESSING_split_package.m_split(X1,X1)
+ #X,Y = node.compute()
+
+ # Train data
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+
+ network = FFN_package.train(X, self.sds.from_numpy(Y), 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ # FFN_package.eval(Yt, Yt).compute()"""
Review comment:
i will make this work.
##########
File path: src/main/python/tests/examples/tutorials/test_adult.py
##########
@@ -0,0 +1,243 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+from systemds.context import SystemDSContext
+from systemds.examples.tutorials.adult import DataManager
+from systemds.operator import OperationNode
+from systemds.operator.algorithm import kmeans, multiLogReg, multiLogRegPredict, l2svm, confusionMatrix, scale, scaleApply, split, winsorize
+from systemds.script_building import DMLScript
+
+
+class Test_DMLScript(unittest.TestCase):
+ """
+ Test class for adult dml script tutorial code.
+ """
+
+ sds: SystemDSContext = None
+ d: DataManager = None
+ neural_net_src_path: str = "tests/examples/tutorials/neural_net_source.dml"
+ dataset_path_train: str = "../../test/resources/datasets/adult/train_data.csv"
+ dataset_path_train_mtd: str = "../../test/resources/datasets/adult/train_data.csv.mtd"
+ dataset_path_test: str = "../../test/resources/datasets/adult/test_data.csv"
+ dataset_path_test_mtd: str = "../../test/resources/datasets/adult/test_data.csv.mtd"
+ dataset_jspec: str = "../../test/resources/datasets/adult/jspec.json"
+
+ @classmethod
+ def setUpClass(cls):
+ cls.sds = SystemDSContext()
+ cls.d = DataManager()
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.sds.close()
+
+ def test_train_data(self):
+ x = self.d.get_train_data()
+ self.assertEqual((32561, 14), x.shape)
+
+ def test_train_labels(self):
+ y = self.d.get_train_labels()
+ self.assertEqual((32561,), y.shape)
+
+ def test_test_data(self):
+ x_l = self.d.get_test_data()
+ self.assertEqual((16281, 14), x_l.shape)
+
+ def test_test_labels(self):
+ y_l = self.d.get_test_labels()
+ self.assertEqual((16281,), y_l.shape)
+
+ def test_preprocess(self):
+ #assumes certain preprocessing
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+ self.assertEqual((30162,104), train_data.shape)
+ self.assertEqual((30162, ), train_labels.shape)
+ self.assertEqual((15060,104), test_data.shape)
+ self.assertEqual((15060, ), test_labels.shape)
+
+ def test_multi_log_reg(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3503, 503],
+ [268, 726]])
+ )
+ )
+
+
+ def test_multi_log_reg_interpolated_standardized(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3583, 502],
+ [245, 670]])
+ )
+ )
+
+
+ def test_neural_net(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+ network = FFN_package.train(X, Y, 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ #probs = FFN_package.predict(Xt, network).compute(True)
+ # FFN_package.eval(Yt, Yt).compute()
+
+
+ def test_parse_dataset_with_systemdsread(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 30000
+ test_count = 10000
+ #self.sds.read(self.dataset_path_train, schema=self.dataset_path_train_mtd).compute(verbose=True)
+ print("")
+
+ SCHEMA = '"DOUBLE,STRING,DOUBLE,STRING,DOUBLE,STRING,STRING,STRING,STRING,STRING,DOUBLE,DOUBLE,DOUBLE,STRING,STRING"'
+ F1 = self.sds.read(
+ self.dataset_path_train,
+ schema=SCHEMA
+ )
+ F2 = self.sds.read(
+ self.dataset_path_test,
+ schema=SCHEMA
+ )
+ jspec = self.sds.read(self.dataset_jspec, data_type="scalar", value_type="string")
+ #scaling does not have effect yet. We need to replace labels in test set with the same string as in train dataset
+ X1, M1 = F1.rbind(F2).transform_encode(spec=jspec).compute()
+ col_length = len(X1[0])
+ X = X1[0:train_count, 0:col_length-1]
+ Y = X1[0:train_count, col_length-1:col_length].flatten()-1
+ # Test data
+ Xt = X1[train_count:train_count+test_count, 0:col_length-1]
+ Yt = X1[train_count:train_count+test_count, col_length-1:col_length].flatten()-1
+
+ _ , mean , sigma = scale(self.sds.from_numpy(X), True, True).compute()
+
+ mean_copy = np.array(mean)
+ sigma_copy = np.array(sigma)
+
+ numerical_cols = []
+ for count, col in enumerate(np.transpose(X)):
+ for entry in col:
+ if entry > 1 or entry < 0 or entry > 0 and entry < 1:
+ numerical_cols.append(count)
+ break
+
+ for x in range(0,105):
+ if not x in numerical_cols:
+ mean_copy[0][x] = 0
+ sigma_copy[0][x] = 1
+
+ mean = self.sds.from_numpy(mean_copy)
+ sigma = self.sds.from_numpy(sigma_copy)
+ X = self.sds.from_numpy(X)
+ Xt = self.sds.from_numpy(Xt)
+ X = scaleApply(winsorize(X, True), mean, sigma)
Review comment:
also, maybe apply winsorize before getting the scaling? (i'm not sure what the best practice here is)
##########
File path: src/main/python/tests/examples/tutorials/test_adult.py
##########
@@ -0,0 +1,243 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+from systemds.context import SystemDSContext
+from systemds.examples.tutorials.adult import DataManager
+from systemds.operator import OperationNode
+from systemds.operator.algorithm import kmeans, multiLogReg, multiLogRegPredict, l2svm, confusionMatrix, scale, scaleApply, split, winsorize
+from systemds.script_building import DMLScript
+
+
+class Test_DMLScript(unittest.TestCase):
+ """
+ Test class for adult dml script tutorial code.
+ """
+
+ sds: SystemDSContext = None
+ d: DataManager = None
+ neural_net_src_path: str = "tests/examples/tutorials/neural_net_source.dml"
+ dataset_path_train: str = "../../test/resources/datasets/adult/train_data.csv"
+ dataset_path_train_mtd: str = "../../test/resources/datasets/adult/train_data.csv.mtd"
+ dataset_path_test: str = "../../test/resources/datasets/adult/test_data.csv"
+ dataset_path_test_mtd: str = "../../test/resources/datasets/adult/test_data.csv.mtd"
+ dataset_jspec: str = "../../test/resources/datasets/adult/jspec.json"
+
+ @classmethod
+ def setUpClass(cls):
+ cls.sds = SystemDSContext()
+ cls.d = DataManager()
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.sds.close()
+
+ def test_train_data(self):
+ x = self.d.get_train_data()
+ self.assertEqual((32561, 14), x.shape)
+
+ def test_train_labels(self):
+ y = self.d.get_train_labels()
+ self.assertEqual((32561,), y.shape)
+
+ def test_test_data(self):
+ x_l = self.d.get_test_data()
+ self.assertEqual((16281, 14), x_l.shape)
+
+ def test_test_labels(self):
+ y_l = self.d.get_test_labels()
+ self.assertEqual((16281,), y_l.shape)
+
+ def test_preprocess(self):
+ #assumes certain preprocessing
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+ self.assertEqual((30162,104), train_data.shape)
+ self.assertEqual((30162, ), train_labels.shape)
+ self.assertEqual((15060,104), test_data.shape)
+ self.assertEqual((15060, ), test_labels.shape)
+
+ def test_multi_log_reg(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset()
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3503, 503],
+ [268, 726]])
+ )
+ )
+
+
+ def test_multi_log_reg_interpolated_standardized(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+ Y = Y + 1.0
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+ Yt = Yt + 1.0
+
+ betas = multiLogReg(X, Y)
+
+ [_, y_pred, acc] = multiLogRegPredict(Xt, betas, Yt).compute()
+
+ self.assertGreater(acc, 80)
+
+ confusion_matrix_abs, _ = confusionMatrix(self.sds.from_numpy(y_pred), Yt).compute()
+
+ self.assertTrue(
+ np.allclose(
+ confusion_matrix_abs,
+ np.array([[3583, 502],
+ [245, 670]])
+ )
+ )
+
+
+ def test_neural_net(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 15000
+ test_count = 5000
+
+ train_data, train_labels, test_data, test_labels = self.d.get_preprocessed_dataset(interpolate=True, standardize=True, dimred=0.1)
+
+ # Train data
+ X = self.sds.from_numpy( train_data[:train_count])
+ Y = self.sds.from_numpy( train_labels[:train_count])
+
+ # Test data
+ Xt = self.sds.from_numpy(test_data[:test_count])
+ Yt = self.sds.from_numpy(test_labels[:test_count])
+
+ FFN_package = self.sds.source(self.neural_net_src_path, "fnn", print_imported_methods=True)
+
+ network = FFN_package.train(X, Y, 1, 16, 0.01, 1)
+
+ self.assertTrue(type(network) is not None) # sourcing and training seems to works
+
+ FFN_package.save_model(network, '"model/python_FFN/"').compute(verbose=True)
+
+ # TODO This does not work yet, not sure what the problem is
+ #probs = FFN_package.predict(Xt, network).compute(True)
+ # FFN_package.eval(Yt, Yt).compute()
+
+
+ def test_parse_dataset_with_systemdsread(self):
+ # Reduced because we want the tests to finish a bit faster.
+ train_count = 30000
+ test_count = 10000
+ #self.sds.read(self.dataset_path_train, schema=self.dataset_path_train_mtd).compute(verbose=True)
+ print("")
+
+ SCHEMA = '"DOUBLE,STRING,DOUBLE,STRING,DOUBLE,STRING,STRING,STRING,STRING,STRING,DOUBLE,DOUBLE,DOUBLE,STRING,STRING"'
+ F1 = self.sds.read(
+ self.dataset_path_train,
+ schema=SCHEMA
+ )
+ F2 = self.sds.read(
+ self.dataset_path_test,
+ schema=SCHEMA
+ )
+ jspec = self.sds.read(self.dataset_jspec, data_type="scalar", value_type="string")
+ #scaling does not have effect yet. We need to replace labels in test set with the same string as in train dataset
+ X1, M1 = F1.rbind(F2).transform_encode(spec=jspec).compute()
+ col_length = len(X1[0])
+ X = X1[0:train_count, 0:col_length-1]
+ Y = X1[0:train_count, col_length-1:col_length].flatten()-1
+ # Test data
+ Xt = X1[train_count:train_count+test_count, 0:col_length-1]
+ Yt = X1[train_count:train_count+test_count, col_length-1:col_length].flatten()-1
+
+ _ , mean , sigma = scale(self.sds.from_numpy(X), True, True).compute()
+
+ mean_copy = np.array(mean)
+ sigma_copy = np.array(sigma)
+
+ numerical_cols = []
+ for count, col in enumerate(np.transpose(X)):
+ for entry in col:
+ if entry > 1 or entry < 0 or entry > 0 and entry < 1:
+ numerical_cols.append(count)
+ break
+
+ for x in range(0,105):
+ if not x in numerical_cols:
+ mean_copy[0][x] = 0
+ sigma_copy[0][x] = 1
+
+ mean = self.sds.from_numpy(mean_copy)
+ sigma = self.sds.from_numpy(sigma_copy)
+ X = self.sds.from_numpy(X)
+ Xt = self.sds.from_numpy(Xt)
+ X = scaleApply(winsorize(X, True), mean, sigma)
Review comment:
second scaling not needed. if you use the output from the first.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org