IGNITE-14714 LineLength checkstyle rule added (apache#9106)

mrk-andreev · May 19, 2021 · b1529fb · b1529fb
1 parent cad0e8f
commit b1529fb
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diff --git a/checkstyle/checkstyle-suppressions.xml b/checkstyle/checkstyle-suppressions.xml
@@ -24,4 +24,6 @@
     <suppress checks="EmptyLineSeparator|MethodParamPad|SingleSpaceSeparator"
               files="BCrypt\.java|ConcurrentLinkedDeque8\.java"/>
     <suppress checks="NoWhitespaceBefore" files="ConcurrentLinkedHashMap\.java"/>
+    <suppress checks="LineLength"
+              files="ClusterTagGenerator\.java|PagesWriteSpeedBasedThrottle\.java|GridExecutorService\.java|CatboostClassificationModel\.java"/>
 </suppressions>
diff --git a/checkstyle/checkstyle.xml b/checkstyle/checkstyle.xml
@@ -135,4 +135,12 @@
         -->
         <module name="GenericWhitespace"/>
     </module>
+
+    <!--
+        Checks that the line length not exceeds 140 chars.
+        See: https://checkstyle.org/config_sizes.html#LineLength
+    -->
+    <module name="LineLength">
+        <property name="max" value="140"/>
+    </module>
 </module>
diff --git a/...s/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java b/...s/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
@@ -62,7 +62,8 @@ public static void main(String[] args) throws IOException {
             try {
                 dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS);
 
-                Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
+                Vectorizer<Integer, Vector, Integer, Double> vectorizer =
+                    new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
 
                 KMeansTrainer trainer = new KMeansTrainer();
 

diff --git a/.../src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java b/.../src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java
@@ -80,7 +80,8 @@ public static void main(String[] args) throws Exception {
 
                 Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1);
 
-                IgniteFunction<LabeledVector<Double>, LabeledVector<double[]>> func = lv -> new LabeledVector<>(lv.features(), new double[] {lv.label()});
+                IgniteFunction<LabeledVector<Double>, LabeledVector<double[]>> func =
+                    lv -> new LabeledVector<>(lv.features(), new double[] {lv.label()});
 
                 //NOTE: This class is part of Developer API and all lambdas should be loaded on server manually.
                 Preprocessor<Integer, Vector> preprocessor = new PatchedPreprocessor<>(func, vectorizer);
@@ -89,18 +90,19 @@ public static void main(String[] args) throws Exception {
                 SimpleLabeledDatasetDataBuilder<Integer, Vector, AlgorithmSpecificPartitionContext> builder =
                     new SimpleLabeledDatasetDataBuilder<>(preprocessor);
 
-                IgniteBiFunction<SimpleLabeledDatasetData, AlgorithmSpecificPartitionContext, SimpleLabeledDatasetData> builderFun = (data, ctx) -> {
-                    double[] features = data.getFeatures();
-                    int rows = data.getRows();
+                IgniteBiFunction<SimpleLabeledDatasetData, AlgorithmSpecificPartitionContext, SimpleLabeledDatasetData> builderFun =
+                    (data, ctx) -> {
+                        double[] features = data.getFeatures();
+                        int rows = data.getRows();
 
-                    // Makes a copy of features to supplement it by columns with values equal to 1.0.
-                    double[] a = new double[features.length + rows];
-                    Arrays.fill(a, 1.0);
+                        // Makes a copy of features to supplement it by columns with values equal to 1.0.
+                        double[] a = new double[features.length + rows];
+                        Arrays.fill(a, 1.0);
 
-                    System.arraycopy(features, 0, a, rows, features.length);
+                        System.arraycopy(features, 0, a, rows, features.length);
 
-                    return new SimpleLabeledDatasetData(a, data.getLabels(), rows);
-                };
+                        return new SimpleLabeledDatasetData(a, data.getLabels(), rows);
+                    };
 
                 try (AlgorithmSpecificDataset dataset = DatasetFactory.create(
                     ignite,

diff --git a/...pache/ignite/examples/ml/inference/catboost/CatboostClassificationModelParserExample.java b/...pache/ignite/examples/ml/inference/catboost/CatboostClassificationModelParserExample.java
@@ -53,7 +53,8 @@ public class CatboostClassificationModelParserExample {
     /**
      * Test expected results.
      */
-    private static final String TEST_ER_RES = "examples/src/main/resources/datasets/amazon-employee-access-challenge-sample-catboost-expected-results.csv";
+    private static final String TEST_ER_RES =
+        "examples/src/main/resources/datasets/amazon-employee-access-challenge-sample-catboost-expected-results.csv";
 
     /**
      * Parser.

diff --git a/...apache/ignite/examples/ml/inference/exchange/KMeansClusterizationExportImportExample.java b/...apache/ignite/examples/ml/inference/exchange/KMeansClusterizationExportImportExample.java
@@ -62,7 +62,8 @@ public static void main(String[] args) throws IOException {
             try {
                 dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS);
 
-                Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
+                Vectorizer<Integer, Vector, Integer, Double> vectorizer =
+                    new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
 
                 KMeansTrainer trainer = new KMeansTrainer()
                     .withDistance(new WeightedMinkowskiDistance(2, new double[] {5.9360, 2.7700, 4.2600, 1.3260}));

diff --git a/...g/apache/ignite/examples/ml/inference/spark/modelparser/DecisionTreeFromSparkExample.java b/...g/apache/ignite/examples/ml/inference/spark/modelparser/DecisionTreeFromSparkExample.java
@@ -50,15 +50,18 @@ public class DecisionTreeFromSparkExample {
         .toPath().toAbsolutePath().toString();
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Decision Tree model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Decision Tree model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...gnite/examples/ml/inference/spark/modelparser/DecisionTreeRegressionFromSparkExample.java b/...gnite/examples/ml/inference/spark/modelparser/DecisionTreeRegressionFromSparkExample.java
@@ -50,15 +50,18 @@ public class DecisionTreeRegressionFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/dtreg";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Decision tree regression model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Decision tree regression model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...n/java/org/apache/ignite/examples/ml/inference/spark/modelparser/GBTFromSparkExample.java b/...n/java/org/apache/ignite/examples/ml/inference/spark/modelparser/GBTFromSparkExample.java
@@ -48,15 +48,18 @@ public class GBTFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/gbt";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Gradient Boosted trees model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Gradient Boosted trees model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/.../apache/ignite/examples/ml/inference/spark/modelparser/GBTRegressionFromSparkExample.java b/.../apache/ignite/examples/ml/inference/spark/modelparser/GBTRegressionFromSparkExample.java
@@ -50,15 +50,18 @@ public class GBTRegressionFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/gbtreg";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> GBT Regression model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> GBT Regression model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...ava/org/apache/ignite/examples/ml/inference/spark/modelparser/KMeansFromSparkExample.java b/...ava/org/apache/ignite/examples/ml/inference/spark/modelparser/KMeansFromSparkExample.java
@@ -50,8 +50,9 @@ public class KMeansFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/kmeans";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.

diff --git a/...ache/ignite/examples/ml/inference/spark/modelparser/LinearRegressionFromSparkExample.java b/...ache/ignite/examples/ml/inference/spark/modelparser/LinearRegressionFromSparkExample.java
@@ -50,15 +50,17 @@ public class LinearRegressionFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/linreg";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Linear regression model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Linear regression model loaded from Spark through serialization over partitioned dataset usage example started.");
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...ava/org/apache/ignite/examples/ml/inference/spark/modelparser/LogRegFromSparkExample.java b/...ava/org/apache/ignite/examples/ml/inference/spark/modelparser/LogRegFromSparkExample.java
@@ -48,15 +48,18 @@ public class LogRegFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/logreg";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Logistic regression model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Logistic regression model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...g/apache/ignite/examples/ml/inference/spark/modelparser/RandomForestFromSparkExample.java b/...g/apache/ignite/examples/ml/inference/spark/modelparser/RandomForestFromSparkExample.java
@@ -48,15 +48,18 @@ public class RandomForestFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/rf";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Random Forest model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Random Forest model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...gnite/examples/ml/inference/spark/modelparser/RandomForestRegressionFromSparkExample.java b/...gnite/examples/ml/inference/spark/modelparser/RandomForestRegressionFromSparkExample.java
@@ -50,15 +50,18 @@ public class RandomForestRegressionFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/rfreg";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.
      */
     public static void main(String[] args) throws FileNotFoundException {
         System.out.println();
-        System.out.println(">>> Random Forest regression model loaded from Spark through serialization over partitioned dataset usage example started.");
+        System.out.println(
+            ">>> Random Forest regression model loaded from Spark through serialization over partitioned dataset usage example started."
+        );
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");

diff --git a/...n/java/org/apache/ignite/examples/ml/inference/spark/modelparser/SVMFromSparkExample.java b/...n/java/org/apache/ignite/examples/ml/inference/spark/modelparser/SVMFromSparkExample.java
@@ -48,8 +48,9 @@ public class SVMFromSparkExample {
     public static final String SPARK_MDL_PATH = "examples/src/main/resources/models/spark/serialized/svm";
 
     /** Learning environment. */
-    public static final LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
-        .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
+    public static final LearningEnvironment env =
+        LearningEnvironmentBuilder.defaultBuilder().withParallelismStrategyTypeDependency(ParallelismStrategy.ON_DEFAULT_POOL)
+            .withLoggingFactoryDependency(ConsoleLogger.Factory.HIGH).buildForTrainer();
 
     /**
      * Run example.

diff --git a/...rc/main/java/org/apache/ignite/examples/ml/multiclass/OneVsRestClassificationExample.java b/...rc/main/java/org/apache/ignite/examples/ml/multiclass/OneVsRestClassificationExample.java
@@ -145,7 +145,12 @@ public static void main(String[] args) throws IOException {
 
                         confusionMtxWithMinMaxScaling[idx1][idx2]++;
 
-                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithMinMaxScaling, groundTruth);
+                        System.out.printf(
+                            ">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n",
+                            prediction,
+                            predictionWithMinMaxScaling,
+                            groundTruth
+                        );
                     }
                     System.out.println(">>> ----------------------------------------------------------------");
                     System.out.println("\n>>> -----------------One-vs-Rest SVM model-------------");

diff --git a/.../main/java/org/apache/ignite/examples/ml/preprocessing/encoding/TargetEncoderExample.java b/.../main/java/org/apache/ignite/examples/ml/preprocessing/encoding/TargetEncoderExample.java
@@ -71,8 +71,8 @@ public static void main(String[] args) {
                 Set<Integer> targetEncodedfeaturesIndexies = new HashSet<>(Arrays.asList(1, 5, 6));
                 Integer targetIndex = 0;
 
-                final Vectorizer<Integer, Object[], Integer, Object> vectorizer = new ObjectArrayVectorizer<Integer>(featuresIndexies.toArray(new Integer[0]))
-                    .labeled(targetIndex);
+                final Vectorizer<Integer, Object[], Integer, Object> vectorizer =
+                    new ObjectArrayVectorizer<Integer>(featuresIndexies.toArray(new Integer[0])).labeled(targetIndex);
 
                 Preprocessor<Integer, Object[]> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
                     .withEncoderType(EncoderType.STRING_ENCODER)

diff --git a/...a/org/apache/ignite/examples/ml/regression/linear/BostonHousePricesPredictionExample.java b/...a/org/apache/ignite/examples/ml/regression/linear/BostonHousePricesPredictionExample.java
@@ -42,7 +42,8 @@
  * Description of model can be found in: https://en.wikipedia.org/wiki/Linear_regression . Original dataset can be
  * downloaded from: https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ . Copy of dataset are stored in:
  * modules/ml/src/main/resources/datasets/boston_housing_dataset.txt . Score for regression estimation: R^2 (coefficient
- * of determination). Description of score evaluation can be found in: https://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination
+ * of determination). Description of score evaluation can be found in:
+ * https://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination
  * .
  */
 public class BostonHousePricesPredictionExample {