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new 4b8b7ff IGNITE-12168: [ML] Flaky ML example tests (#6866)
4b8b7ff is described below
commit 4b8b7ff254db202f2f9af1130f0183057bb3f6aa
Author: Alexey Zinoviev <zaleslaw....@gmail.com>
AuthorDate: Fri Sep 13 22:13:51 2019 +0300
IGNITE-12168: [ML] Flaky ML example tests (#6866)
---
.../apache/ignite/ml/util/MLSandboxDatasets.java | 26 ++++++-------
.../org/apache/ignite/ml/util/SandboxMLCache.java | 45 ++++++++++++----------
2 files changed, 37 insertions(+), 34 deletions(-)
diff --git
a/modules/ml/src/main/java/org/apache/ignite/ml/util/MLSandboxDatasets.java
b/modules/ml/src/main/java/org/apache/ignite/ml/util/MLSandboxDatasets.java
index 4d423e9..12bff53 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/util/MLSandboxDatasets.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/util/MLSandboxDatasets.java
@@ -22,50 +22,50 @@ package org.apache.ignite.ml.util;
*/
public enum MLSandboxDatasets {
/** Movielens dataset with ratings. */
- MOVIELENS("ratings.csv", true, ","),
+ MOVIELENS("modules/ml/src/main/resources/datasets/ratings.csv", true, ","),
/** The full Iris dataset from Machine Learning Repository. */
- IRIS("iris.txt", false, "\t"),
+ IRIS("modules/ml/src/main/resources/datasets/iris.txt", false, "\t"),
/** The Titanic dataset from Kaggle competition. */
- TITANIC("titanic.csv", true, ";"),
+ TITANIC("modules/ml/src/main/resources/datasets/titanic.csv", true, ";"),
/** The 1st and 2nd classes from the Iris dataset. */
- TWO_CLASSED_IRIS("two_classed_iris.csv", false, "\t"),
+
TWO_CLASSED_IRIS("modules/ml/src/main/resources/datasets/two_classed_iris.csv",
false, "\t"),
/** The dataset is about different computers' properties based on
https://archive.ics.uci.edu/ml/datasets/Computer+Hardware. */
- CLEARED_MACHINES("cleared_machines.csv", false, ";"),
+
CLEARED_MACHINES("modules/ml/src/main/resources/datasets/cleared_machines.csv",
false, ";"),
/**
* The health data is related to death rate based on; doctor availability,
hospital availability,
* annual per capita income, and population density people per square mile.
*/
- MORTALITY_DATA("mortalitydata.csv", false, ";"),
+ MORTALITY_DATA("modules/ml/src/main/resources/datasets/mortalitydata.csv",
false, ";"),
/**
* The preprocessed Glass dataset from the Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets/Glass+Identification
* There are 3 classes with labels: 1 {building_windows_float_processed},
3 {vehicle_windows_float_processed}, 7 {headlamps}.
* Feature names: 'Na-Sodium', 'Mg-Magnesium', 'Al-Aluminum', 'Ba-Barium',
'Fe-Iron'.
*/
- GLASS_IDENTIFICATION("glass_identification.csv", false, ";"),
+
GLASS_IDENTIFICATION("modules/ml/src/main/resources/datasets/glass_identification.csv",
false, ";"),
/** The Wine recognition data. Could be found <a
href="https://archive.ics.uci.edu/ml/machine-learning-databases/wine/";>here</a>.
*/
- WINE_RECOGNITION("wine.txt", false, ","),
+ WINE_RECOGNITION("modules/ml/src/main/resources/datasets/wine.txt", false,
","),
/** The Boston house-prices dataset. Could be found <a
href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/";>here</a>.
*/
- BOSTON_HOUSE_PRICES("boston_housing_dataset.txt", false, ","),
+
BOSTON_HOUSE_PRICES("modules/ml/src/main/resources/datasets/boston_housing_dataset.txt",
false, ","),
/** Example from book Barber D. Bayesian reasoning and machine learning.
Chapter 10. */
- ENGLISH_VS_SCOTTISH("english_vs_scottish_binary_dataset.csv", true, ","),
+
ENGLISH_VS_SCOTTISH("modules/ml/src/main/resources/datasets/english_vs_scottish_binary_dataset.csv",
true, ","),
/** Wholesale customers dataset. Could be found <a
href="https://archive.ics.uci.edu/ml/datasets/Wholesale+customers";>here</a>. */
- WHOLESALE_CUSTOMERS("wholesale_customers.csv", true, ","),
+
WHOLESALE_CUSTOMERS("modules/ml/src/main/resources/datasets/wholesale_customers.csv",
true, ","),
/** Fraud detection problem [part of whole dataset]. Could be found <a
href="https://www.kaggle.com/mlg-ulb/creditcardfraud/";>here</a>. */
- FRAUD_DETECTION("fraud_detection.csv", false, ","),
+
FRAUD_DETECTION("modules/ml/src/main/resources/datasets/fraud_detection.csv",
false, ","),
/** A dataset with discrete and continious features. */
- MIXED_DATASET("mixed_dataset.csv", true, ",");
+ MIXED_DATASET("modules/ml/src/main/resources/datasets/mixed_dataset.csv",
true, ",");
/** Filename. */
private final String filename;
diff --git
a/modules/ml/src/main/java/org/apache/ignite/ml/util/SandboxMLCache.java
b/modules/ml/src/main/java/org/apache/ignite/ml/util/SandboxMLCache.java
index 23febff..ef2e260 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/util/SandboxMLCache.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/util/SandboxMLCache.java
@@ -17,6 +17,7 @@
package org.apache.ignite.ml.util;
+import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.nio.file.Paths;
@@ -31,13 +32,10 @@ import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
import org.apache.ignite.configuration.CacheConfiguration;
-import org.apache.ignite.internal.util.typedef.internal.A;
+import org.apache.ignite.internal.util.IgniteUtils;
import org.apache.ignite.ml.math.exceptions.knn.FileParsingException;
import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.springframework.core.io.Resource;
-import org.springframework.core.io.support.PathMatchingResourcePatternResolver;
-import org.springframework.core.io.support.ResourcePatternResolver;
/**
* Common utility code used in some ML examples to set up test cache.
@@ -46,10 +44,6 @@ public class SandboxMLCache {
/** */
private final Ignite ignite;
- /** Resource resolver. */
- private static final ResourcePatternResolver RESOURCE_RESOLVER =
- new
PathMatchingResourcePatternResolver(SandboxMLCache.class.getClassLoader());
-
/** */
public SandboxMLCache(Ignite ignite) {
this.ignite = ignite;
@@ -74,6 +68,7 @@ public class SandboxMLCache {
return cache;
}
+
/**
* Loads dataset as a list of rows.
*
@@ -84,10 +79,15 @@ public class SandboxMLCache {
public List<String> loadDataset(MLSandboxDatasets dataset) throws
IOException {
List<String> res = new ArrayList<>();
- Resource[] resources = RESOURCE_RESOLVER.getResources("classpath*:*/"
+ dataset.getFileName());
- A.ensure(resources.length == 1, "Cannot find resource");
+ String fileName = dataset.getFileName();
+
+ File file = IgniteUtils.resolveIgnitePath(fileName);
+
+ if (file == null)
+ throw new FileNotFoundException(fileName);
+
+ Scanner scanner = new Scanner(file);
- Scanner scanner = new Scanner(resources[0].getInputStream());
if (dataset.hasHeader() && scanner.hasNextLine())
scanner.nextLine();
@@ -99,6 +99,7 @@ public class SandboxMLCache {
return res;
}
+
/**
* Fills cache with data and returns it.
*
@@ -106,19 +107,23 @@ public class SandboxMLCache {
* @return Filled Ignite Cache.
* @throws FileNotFoundException If file not found.
*/
- public IgniteCache<Integer, Vector> fillCacheWith(MLSandboxDatasets
dataset) throws IOException {
+ public IgniteCache<Integer, Vector> fillCacheWith(MLSandboxDatasets
dataset) throws FileNotFoundException {
+
IgniteCache<Integer, Vector> cache = getCache();
String fileName = dataset.getFileName();
- Resource[] resources = RESOURCE_RESOLVER.getResources("classpath*:*/"
+ fileName);
- A.ensure(resources.length == 1, "Cannot find resource");
- Scanner scanner = new Scanner(resources[0].getInputStream());
+ File file = IgniteUtils.resolveIgnitePath(fileName);
+
+ if (file == null)
+ throw new FileNotFoundException(fileName);
+
+ Scanner scanner = new Scanner(file);
int cnt = 0;
while (scanner.hasNextLine()) {
String row = scanner.nextLine();
- if (dataset.hasHeader() && cnt == 0) {
+ if(dataset.hasHeader() && cnt == 0) {
cnt++;
continue;
}
@@ -129,11 +134,9 @@ public class SandboxMLCache {
NumberFormat format = NumberFormat.getInstance(Locale.FRANCE);
for (int i = 0; i < cells.length; i++)
- try {
- if (cells[i].equals(""))
- data[i] = Double.NaN;
- else
- data[i] = Double.valueOf(cells[i]);
+ try{
+ if(cells[i].equals("")) data[i] = Double.NaN;
+ else data[i] = Double.valueOf(cells[i]);
} catch (java.lang.NumberFormatException e) {
try {
data[i] = format.parse(cells[i]).doubleValue();
