This is an automated email from the ASF dual-hosted git repository.
mboehm7 pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemds.git
The following commit(s) were added to refs/heads/master by this push:
new 6bb9a0d [SYSTEMDS-2790] New outlierByArima (AR) built-in function
6bb9a0d is described below
commit 6bb9a0d082fe89451e1f90ba85dc1a8796bd19bc
Author: juulez <[email protected]>
AuthorDate: Sat Jan 9 23:03:40 2021 +0100
[SYSTEMDS-2790] New outlierByArima (AR) built-in function
DIA project WS2020/21.
Closes #1149.
---
scripts/builtin/outlierByArima.dml | 105 ++++++++++++++++
.../java/org/apache/sysds/common/Builtins.java | 1 +
.../functions/builtin/BuiltinOutlierByArima.java | 132 +++++++++++++++++++++
.../scripts/functions/builtin/outlierByArima.R | 30 +++++
.../scripts/functions/builtin/outlierByArima.dml | 25 ++++
5 files changed, 293 insertions(+)
diff --git a/scripts/builtin/outlierByArima.dml
b/scripts/builtin/outlierByArima.dml
new file mode 100644
index 0000000..b00c07e
--- /dev/null
+++ b/scripts/builtin/outlierByArima.dml
@@ -0,0 +1,105 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+
+# Built-in function for detecting and repairing outliers in time series,
+# by training an ARIMA model and classifying values that are more than
+# k standard-deviations away from the predicated values as outliers.
+#
+# INPUT PARAMETERS:
+#
---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+#
---------------------------------------------------------------------------------------------
+# X Double --- Matrix X
+# k Double 3 threshold values 1, 2, 3 for 68%, 95%,
99.7% respectively (3-sigma rule)
+# repairMethod Integer 1 values: 0 = delete rows having outliers,
1 = replace outliers as zeros
+# 2 = replace outliers as missing
values
+# p Int 0 non-seasonal AR order
+# d Int 0 non-seasonal differencing order
+# q Int 0 non-seasonal MA order
+# P Int 0 seasonal AR order
+# D Int 0 seasonal differencing order
+# Q Int 0 seasonal MA order
+# s Int 1 period in terms of number of time-steps
+# include_mean Bool FALSE
+# solver String "jacobi" solver, is either "cg" or "jacobi"
+#
---------------------------------------------------------------------------------------------
+
+
+#Output(s)
+#
---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+#
---------------------------------------------------------------------------------------------
+# X_corrected Double --- Matrix X with no outliers
+
+m_outlierByArima = function(Matrix[Double] X, Double k = 3, Integer
repairMethod = 1, Integer p=0, Integer d=0,
+ Integer q=0, Integer P=0, Integer D=0, Integer Q=0, Integer s=1, Boolean
include_mean=FALSE, String solver="jacobi")
+ return(Matrix[Double] X_corrected)
+{
+ outlierFilter = as.matrix(0)
+
+ if( k < 1 | k > 7)
+ stop("outlierBySd: invalid argument - k should be in range 1-7 found "+k)
+
+ features = transform_matrix(X,p)
+ X_adapted = X[p+1:nrow(X),]
+
+ # TODO replace by ARIMA once fully supported, LM only emulated the AR part
+ model = lm(X=features, y=X_adapted)
+ y_hat = lmpredict(X=features, w=model)
+
+ upperBound = sd(X) + k * y_hat
+ lowerBound = sd(X) - k * y_hat
+ outlierFilter = (X_adapted < lowerBound) | (X_adapted > upperBound)
+ outlierFilter = rbind(matrix(0.0, rows=p,cols=1), outlierFilter)
+ X_corrected = fix_outliers(X, outlierFilter, repairMethod)
+}
+
+transform_matrix = function(Matrix[Double] X, Integer p) return
(Matrix[Double] features){
+ nrows = nrow(X)
+ features = matrix(0, rows=nrows-p, cols=1)
+
+ for (i in 1:p){
+ features = cbind(features, X[p+1-i:nrows-i,])
+ }
+ features = features[,2:p+1]
+}
+
+fix_outliers = function(Matrix[Double] X, Matrix[Double] outlierFilter,
Integer repairMethod)
+ return (Matrix[Double] X_filtered)
+{
+ rows = nrow(X)
+ cols = ncol(X)
+ if(repairMethod == 0) {
+ sel = (outlierFilter == 0)
+ X = removeEmpty(target = X, margin = "rows", select = sel)
+ }
+ else if(repairMethod == 1)
+ X = (outlierFilter == 0) * X
+ else if (repairMethod == 2) {
+ outlierFilter = replace(target = (outlierFilter == 0), pattern = 0,
replacement = NaN)
+ X = outlierFilter * X
+ }
+ else{
+ stop("outlierBySd: invalid argument - repair required 0-1 found:
"+repairMethod)
+ }
+ X_filtered = X
+}
diff --git a/src/main/java/org/apache/sysds/common/Builtins.java
b/src/main/java/org/apache/sysds/common/Builtins.java
index 5bcc85e..1a230bf 100644
--- a/src/main/java/org/apache/sysds/common/Builtins.java
+++ b/src/main/java/org/apache/sysds/common/Builtins.java
@@ -166,6 +166,7 @@ public enum Builtins {
OUTLIER("outlier", true, false), //TODO parameterize opposite
OUTLIER_SD("outlierBySd", true),
OUTLIER_IQR("outlierByIQR", true),
+ OUTLIER_ARIMA("outlierByArima",true),
PCA("pca", true),
PCAINVERSE("pcaInverse", true),
PCATRANSFORM("pcaTransform", true),
diff --git
a/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinOutlierByArima.java
b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinOutlierByArima.java
new file mode 100644
index 0000000..8830ff2
--- /dev/null
+++
b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinOutlierByArima.java
@@ -0,0 +1,132 @@
+/*
+ * 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.
+ */
+
+package org.apache.sysds.test.functions.builtin;
+
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import org.junit.Test;
+import org.apache.sysds.common.Types;
+import org.apache.sysds.lops.LopProperties;
+import org.junit.runner.RunWith;
+import org.junit.runners.Parameterized;
+import org.junit.runners.Parameterized.Parameters;
+
+import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex;
+import org.apache.sysds.runtime.meta.MatrixCharacteristics;
+import org.apache.sysds.test.AutomatedTestBase;
+import org.apache.sysds.test.TestConfiguration;
+import org.apache.sysds.test.TestUtils;
+
+import java.util.concurrent.ThreadLocalRandom;
+
+@RunWith(value = Parameterized.class)
[email protected]
+public class BuiltinOutlierByArima extends AutomatedTestBase {
+ private final static String TEST_NAME = "outlierByArima";
+ private final static String TEST_DIR = "functions/builtin/";
+ private static final String TEST_CLASS_DIR = TEST_DIR +
BuiltinArimaTest.class.getSimpleName() + "/";
+
+ protected int max_func_invoc, p, d, q, P, D, Q, s, include_mean,
useJacobi, repairMethod;
+
+ public BuiltinOutlierByArima(int m, int p, int d, int q, int P, int D,
int Q, int s, int include_mean, int useJacobi, int repairMethod){
+ this.max_func_invoc = m;
+ this.p = p;
+ this.d = d;
+ this.q = q;
+ this.P = P;
+ this.D = D;
+ this.Q = Q;
+ this.s = s;
+ this.include_mean = include_mean;
+ this.useJacobi = useJacobi;
+ this.repairMethod = repairMethod;
+ }
+
+ @Parameters
+ public static Collection<Object[]> data() {
+ return Arrays.asList(new Object[][] {
+ {1, 2, 0, 0, 0, 0, 0, 24, 1, 1, 1},
+ {1, 2, 0, 0, 0, 0, 0, 24, 1, 1, 2},
+ {1, 2, 0, 0, 0, 0, 0, 24, 1, 1, 3}});
+ }
+
+ @Override
+ public void setUp() {
+ addTestConfiguration(TEST_NAME, new
TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[]{"B"}));
+ }
+
+ @Test
+ public void testOutlierByArima(){
+ Types.ExecMode platformOld =
setExecMode(LopProperties.ExecType.CP);
+ try {
+ loadTestConfiguration(getTestConfiguration(TEST_NAME));
+ String HOME = SCRIPT_DIR + TEST_DIR;
+ fullDMLScriptName = HOME + TEST_NAME + ".dml";
+ fullRScriptName = HOME + TEST_NAME + ".R";
+
+ programArgs = new String[]{
+ "-nvargs", "X=" + input("col.mtx"), "p=" + p,
"repairMethod=" + 1,
+ "outputfilename=" + output("result"),};
+ rCmd = getRCmd(input("bad.mtx"), expected("result"));
+
+ int timeSeriesLength = 3000;
+ int num_outliers = 10;
+ double[][] timeSeries =
getRandomMatrix(timeSeriesLength, 1, 1, 3, 1, System.currentTimeMillis());
+ double[][] comparisonSeries = deepCopy(timeSeries);
+ for(int i=0; i<num_outliers; i++) {
+ int r = ThreadLocalRandom.current().nextInt(0,
timeSeries.length);
+ double badValue =
ThreadLocalRandom.current().nextDouble(10, 50);
+ timeSeries[r][0] = badValue;
+ if (repairMethod == 1)
+ comparisonSeries[r][0] = 0.0;
+ else if (repairMethod == 2)
+ comparisonSeries[r][0] = Double.NaN;
+ }
+
+ MatrixCharacteristics mc = new
MatrixCharacteristics(timeSeriesLength,1,-1,-1);
+ writeInputMatrixWithMTD("col", timeSeries, true, mc);
+ writeInputMatrixWithMTD("bad", comparisonSeries, true,
mc);
+
+ runTest(true, false, null, -1);
+ runRScript(true);
+
+ HashMap<CellIndex, Double> time_series_SYSTEMDS =
readDMLMatrixFromOutputDir("result");
+ HashMap<CellIndex, Double> time_series_real =
readRMatrixFromExpectedDir("result");
+
+ double tol = Math.pow(10, -14);
+ if (repairMethod == 3)
+
TestUtils.compareScalars(time_series_real.size()-num_outliers,
time_series_SYSTEMDS.size(), tol);
+ else
+ TestUtils.compareMatrices(time_series_real,
time_series_SYSTEMDS, tol, "time_series_real", "time_series_SYSTEMDS");
+ }
+ finally {
+ rtplatform = platformOld;
+ }
+ }
+
+ private static double[][] deepCopy(double[][] input) {
+ double[][] result = new double[input.length][];
+ for (int r = 0; r < input.length; r++) {
+ result[r] = input[r].clone();
+ }
+ return result;
+ }
+}
diff --git a/src/test/scripts/functions/builtin/outlierByArima.R
b/src/test/scripts/functions/builtin/outlierByArima.R
new file mode 100644
index 0000000..ecc0a83
--- /dev/null
+++ b/src/test/scripts/functions/builtin/outlierByArima.R
@@ -0,0 +1,30 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+args <- commandArgs(TRUE)
+library(Matrix)
+
+X = as(readMM(args[1]),"CsparseMatrix")
+
+# (for whatever reason) NaNs are seen as NAs after reading. replace them by
zero instead of NAN since resulting NAN
+# will be seen as NA after reading it again in test file
+X[is.na(X)] = 0.0
+
+writeMM(X, file=args[2])
diff --git a/src/test/scripts/functions/builtin/outlierByArima.dml
b/src/test/scripts/functions/builtin/outlierByArima.dml
new file mode 100644
index 0000000..461f7db
--- /dev/null
+++ b/src/test/scripts/functions/builtin/outlierByArima.dml
@@ -0,0 +1,25 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+X = read($X)
+
+Y = outlierByArima(X=X, p=$p, repairMethod=$repairMethod)
+write(Y, $outputfilename, sparse=FALSE)
