http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java index c219441..679bd77 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java @@ -17,6 +17,7 @@ package org.apache.ignite.examples.ml.svm.binary; +import java.io.FileNotFoundException; import java.util.Arrays; import javax.cache.Cache; import org.apache.ignite.Ignite; @@ -24,9 +25,9 @@ import org.apache.ignite.IgniteCache; import org.apache.ignite.Ignition; import org.apache.ignite.cache.query.QueryCursor; import org.apache.ignite.cache.query.ScanQuery; -import org.apache.ignite.examples.ml.util.TestCache; -import org.apache.ignite.ml.math.primitives.vector.VectorUtils; -import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector; +import org.apache.ignite.examples.ml.util.MLSandboxDatasets; +import org.apache.ignite.examples.ml.util.SandboxMLCache; +import org.apache.ignite.ml.math.primitives.vector.Vector; import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationModel; import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer; @@ -46,22 +47,23 @@ import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer; */ public class SVMBinaryClassificationExample { /** Run example. */ - public static void main(String[] args) throws InterruptedException { + public static void main(String[] args) throws FileNotFoundException { System.out.println(); System.out.println(">>> SVM Binary classification model over cached dataset usage example started."); // Start ignite grid. try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { System.out.println(">>> Ignite grid started."); - IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data); + IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite) + .fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS); SVMLinearBinaryClassificationTrainer trainer = new SVMLinearBinaryClassificationTrainer(); SVMLinearBinaryClassificationModel mdl = trainer.fit( ignite, dataCache, - (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)), - (k, v) -> v[0] + (k, v) -> v.copyOfRange(1, v.size()), + (k, v) -> v.get(0) ); System.out.println(">>> SVM model " + mdl); @@ -76,13 +78,13 @@ public class SVMBinaryClassificationExample { // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix int[][] confusionMtx = {{0, 0}, {0, 0}}; - try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) { - for (Cache.Entry<Integer, double[]> observation : observations) { - double[] val = observation.getValue(); - double[] inputs = Arrays.copyOfRange(val, 1, val.length); - double groundTruth = val[0]; + try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) { + for (Cache.Entry<Integer, Vector> observation : observations) { + Vector val = observation.getValue(); + Vector inputs = val.copyOfRange(1, val.size()); + double groundTruth = val.get(0); - double prediction = mdl.apply(new DenseVector(inputs)); + double prediction = mdl.apply(inputs); totalAmount++; if(groundTruth != prediction) @@ -107,108 +109,4 @@ public class SVMBinaryClassificationExample { System.out.println(">>> Linear regression model over cache based dataset usage example completed."); } } - - /** The 1st and 2nd classes from the Iris dataset. */ - private static final double[][] data = { - {0, 5.1, 3.5, 1.4, 0.2}, - {0, 4.9, 3, 1.4, 0.2}, - {0, 4.7, 3.2, 1.3, 0.2}, - {0, 4.6, 3.1, 1.5, 0.2}, - {0, 5, 3.6, 1.4, 0.2}, - {0, 5.4, 3.9, 1.7, 0.4}, - {0, 4.6, 3.4, 1.4, 0.3}, - {0, 5, 3.4, 1.5, 0.2}, - {0, 4.4, 2.9, 1.4, 0.2}, - {0, 4.9, 3.1, 1.5, 0.1}, - {0, 5.4, 3.7, 1.5, 0.2}, - {0, 4.8, 3.4, 1.6, 0.2}, - {0, 4.8, 3, 1.4, 0.1}, - {0, 4.3, 3, 1.1, 0.1}, - {0, 5.8, 4, 1.2, 0.2}, - {0, 5.7, 4.4, 1.5, 0.4}, - {0, 5.4, 3.9, 1.3, 0.4}, - {0, 5.1, 3.5, 1.4, 0.3}, - {0, 5.7, 3.8, 1.7, 0.3}, - {0, 5.1, 3.8, 1.5, 0.3}, - {0, 5.4, 3.4, 1.7, 0.2}, - {0, 5.1, 3.7, 1.5, 0.4}, - {0, 4.6, 3.6, 1, 0.2}, - {0, 5.1, 3.3, 1.7, 0.5}, - {0, 4.8, 3.4, 1.9, 0.2}, - {0, 5, 3, 1.6, 0.2}, - {0, 5, 3.4, 1.6, 0.4}, - {0, 5.2, 3.5, 1.5, 0.2}, - {0, 5.2, 3.4, 1.4, 0.2}, - {0, 4.7, 3.2, 1.6, 0.2}, - {0, 4.8, 3.1, 1.6, 0.2}, - {0, 5.4, 3.4, 1.5, 0.4}, - {0, 5.2, 4.1, 1.5, 0.1}, - {0, 5.5, 4.2, 1.4, 0.2}, - {0, 4.9, 3.1, 1.5, 0.1}, - {0, 5, 3.2, 1.2, 0.2}, - {0, 5.5, 3.5, 1.3, 0.2}, - {0, 4.9, 3.1, 1.5, 0.1}, - {0, 4.4, 3, 1.3, 0.2}, - {0, 5.1, 3.4, 1.5, 0.2}, - {0, 5, 3.5, 1.3, 0.3}, - {0, 4.5, 2.3, 1.3, 0.3}, - {0, 4.4, 3.2, 1.3, 0.2}, - {0, 5, 3.5, 1.6, 0.6}, - {0, 5.1, 3.8, 1.9, 0.4}, - {0, 4.8, 3, 1.4, 0.3}, - {0, 5.1, 3.8, 1.6, 0.2}, - {0, 4.6, 3.2, 1.4, 0.2}, - {0, 5.3, 3.7, 1.5, 0.2}, - {0, 5, 3.3, 1.4, 0.2}, - {1, 7, 3.2, 4.7, 1.4}, - {1, 6.4, 3.2, 4.5, 1.5}, - {1, 6.9, 3.1, 4.9, 1.5}, - {1, 5.5, 2.3, 4, 1.3}, - {1, 6.5, 2.8, 4.6, 1.5}, - {1, 5.7, 2.8, 4.5, 1.3}, - {1, 6.3, 3.3, 4.7, 1.6}, - {1, 4.9, 2.4, 3.3, 1}, - {1, 6.6, 2.9, 4.6, 1.3}, - {1, 5.2, 2.7, 3.9, 1.4}, - {1, 5, 2, 3.5, 1}, - {1, 5.9, 3, 4.2, 1.5}, - {1, 6, 2.2, 4, 1}, - {1, 6.1, 2.9, 4.7, 1.4}, - {1, 5.6, 2.9, 3.6, 1.3}, - {1, 6.7, 3.1, 4.4, 1.4}, - {1, 5.6, 3, 4.5, 1.5}, - {1, 5.8, 2.7, 4.1, 1}, - {1, 6.2, 2.2, 4.5, 1.5}, - {1, 5.6, 2.5, 3.9, 1.1}, - {1, 5.9, 3.2, 4.8, 1.8}, - {1, 6.1, 2.8, 4, 1.3}, - {1, 6.3, 2.5, 4.9, 1.5}, - {1, 6.1, 2.8, 4.7, 1.2}, - {1, 6.4, 2.9, 4.3, 1.3}, - {1, 6.6, 3, 4.4, 1.4}, - {1, 6.8, 2.8, 4.8, 1.4}, - {1, 6.7, 3, 5, 1.7}, - {1, 6, 2.9, 4.5, 1.5}, - {1, 5.7, 2.6, 3.5, 1}, - {1, 5.5, 2.4, 3.8, 1.1}, - {1, 5.5, 2.4, 3.7, 1}, - {1, 5.8, 2.7, 3.9, 1.2}, - {1, 6, 2.7, 5.1, 1.6}, - {1, 5.4, 3, 4.5, 1.5}, - {1, 6, 3.4, 4.5, 1.6}, - {1, 6.7, 3.1, 4.7, 1.5}, - {1, 6.3, 2.3, 4.4, 1.3}, - {1, 5.6, 3, 4.1, 1.3}, - {1, 5.5, 2.5, 4, 1.3}, - {1, 5.5, 2.6, 4.4, 1.2}, - {1, 6.1, 3, 4.6, 1.4}, - {1, 5.8, 2.6, 4, 1.2}, - {1, 5, 2.3, 3.3, 1}, - {1, 5.6, 2.7, 4.2, 1.3}, - {1, 5.7, 3, 4.2, 1.2}, - {1, 5.7, 2.9, 4.2, 1.3}, - {1, 6.2, 2.9, 4.3, 1.3}, - {1, 5.1, 2.5, 3, 1.1}, - {1, 5.7, 2.8, 4.1, 1.3}, - }; }
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java index 520b8cc..987ac41 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java @@ -17,6 +17,7 @@ package org.apache.ignite.examples.ml.svm.multiclass; +import java.io.FileNotFoundException; import java.util.Arrays; import javax.cache.Cache; import org.apache.ignite.Ignite; @@ -24,11 +25,10 @@ import org.apache.ignite.IgniteCache; import org.apache.ignite.Ignition; import org.apache.ignite.cache.query.QueryCursor; import org.apache.ignite.cache.query.ScanQuery; -import org.apache.ignite.examples.ml.util.TestCache; +import org.apache.ignite.examples.ml.util.MLSandboxDatasets; +import org.apache.ignite.examples.ml.util.SandboxMLCache; import org.apache.ignite.ml.math.functions.IgniteBiFunction; import org.apache.ignite.ml.math.primitives.vector.Vector; -import org.apache.ignite.ml.math.primitives.vector.VectorUtils; -import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector; import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer; import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationModel; import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationTrainer; @@ -48,74 +48,70 @@ import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationTrainer; * <a href="https://en.wikipedia.org/wiki/Confusion_matrix";>confusion matrix</a>.</p> * <p> * You can change the test data used in this example and re-run it to explore this algorithm further.</p> + * NOTE: the smallest 3rd class could be classified via linear SVM here. */ public class SVMMultiClassClassificationExample { /** Run example. */ - public static void main(String[] args) throws InterruptedException { + public static void main(String[] args) throws FileNotFoundException { System.out.println(); System.out.println(">>> SVM Multi-class classification model over cached dataset usage example started."); // Start ignite grid. try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { System.out.println(">>> Ignite grid started."); - IgniteCache<Integer, Vector> dataCache = new TestCache(ignite).getVectors(data); + IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite) + .fillCacheWith(MLSandboxDatasets.GLASS_IDENTIFICATION); SVMLinearMultiClassClassificationTrainer trainer = new SVMLinearMultiClassClassificationTrainer(); SVMLinearMultiClassClassificationModel mdl = trainer.fit( ignite, dataCache, - (k, v) -> { - double[] arr = v.asArray(); - return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length)); - }, + (k, v) -> v.copyOfRange(1, v.size()), (k, v) -> v.get(0) ); System.out.println(">>> SVM Multi-class model"); System.out.println(mdl.toString()); - MinMaxScalerTrainer<Integer, Vector> normalizationTrainer = new MinMaxScalerTrainer<>(); + MinMaxScalerTrainer<Integer, Vector> minMaxScalerTrainer = new MinMaxScalerTrainer<>(); - IgniteBiFunction<Integer, Vector, Vector> preprocessor = normalizationTrainer.fit( + IgniteBiFunction<Integer, Vector, Vector> preprocessor = minMaxScalerTrainer.fit( ignite, dataCache, - (k, v) -> { - double[] arr = v.asArray(); - return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length)); - } + (k, v) -> v.copyOfRange(1, v.size()) ); - SVMLinearMultiClassClassificationModel mdlWithNormalization = trainer.fit( + SVMLinearMultiClassClassificationModel mdlWithScaling = trainer.fit( ignite, dataCache, preprocessor, (k, v) -> v.get(0) ); - System.out.println(">>> SVM Multi-class model with minmaxscaling"); - System.out.println(mdlWithNormalization.toString()); + System.out.println(">>> SVM Multi-class model with MinMaxScaling"); + System.out.println(mdlWithScaling.toString()); System.out.println(">>> ----------------------------------------------------------------"); - System.out.println(">>> | Prediction\t| Prediction with Normalization\t| Ground Truth\t|"); + System.out.println(">>> | Prediction\t| Prediction with MinMaxScaling\t| Ground Truth\t|"); System.out.println(">>> ----------------------------------------------------------------"); int amountOfErrors = 0; - int amountOfErrorsWithNormalization = 0; + int amountOfErrorsWithMinMaxScaling = 0; int totalAmount = 0; // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}}; - int[][] confusionMtxWithNormalization = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}}; + int[][] confusionMtxWithMinMaxScaling = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}}; try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) { for (Cache.Entry<Integer, Vector> observation : observations) { - double[] val = observation.getValue().asArray(); - double[] inputs = Arrays.copyOfRange(val, 1, val.length); - double groundTruth = val[0]; + Vector val = observation.getValue(); + Vector inputs = val.copyOfRange(1, val.size()); + double groundTruth = val.get(0); - double prediction = mdl.apply(new DenseVector(inputs)); - double predictionWithNormalization = mdlWithNormalization.apply(new DenseVector(inputs)); + double prediction = mdl.apply(inputs); + double predictionWithMinMaxScaling = mdlWithScaling.apply(inputs); totalAmount++; @@ -129,15 +125,15 @@ public class SVMMultiClassClassificationExample { confusionMtx[idx1][idx2]++; // Collect data for model with minmaxscaling - if(groundTruth != predictionWithNormalization) - amountOfErrorsWithNormalization++; + if (groundTruth != predictionWithMinMaxScaling) + amountOfErrorsWithMinMaxScaling++; - idx1 = (int)predictionWithNormalization == 1 ? 0 : ((int)predictionWithNormalization == 3 ? 1 : 2); + idx1 = (int)predictionWithMinMaxScaling == 1 ? 0 : ((int)predictionWithMinMaxScaling == 3 ? 1 : 2); idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2); - confusionMtxWithNormalization[idx1][idx2]++; + confusionMtxWithMinMaxScaling[idx1][idx2]++; - System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithNormalization, 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>>> -----------------SVM model-------------"); @@ -145,136 +141,13 @@ public class SVMMultiClassClassificationExample { System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount)); System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx)); - System.out.println("\n>>> -----------------SVM model with Normalization-------------"); - System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithNormalization); - System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithNormalization / (double)totalAmount)); - System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithNormalization)); + System.out.println("\n>>> -----------------SVM model with MinMaxScaling-------------"); + System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithMinMaxScaling); + System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithMinMaxScaling / (double)totalAmount)); + System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithMinMaxScaling)); System.out.println(">>> Linear regression model over cache based dataset usage example completed."); } } } - - /** 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'. - */ - private static final double[][] data = { - {1, 1.52101, 4.49, 1.10, 0.00, 0.00}, - {1, 1.51761, 3.60, 1.36, 0.00, 0.00}, - {1, 1.51618, 3.55, 1.54, 0.00, 0.00}, - {1, 1.51766, 3.69, 1.29, 0.00, 0.00}, - {1, 1.51742, 3.62, 1.24, 0.00, 0.00}, - {1, 1.51596, 3.61, 1.62, 0.00, 0.26}, - {1, 1.51743, 3.60, 1.14, 0.00, 0.00}, - {1, 1.51756, 3.61, 1.05, 0.00, 0.00}, - {1, 1.51918, 3.58, 1.37, 0.00, 0.00}, - {1, 1.51755, 3.60, 1.36, 0.00, 0.11}, - {1, 1.51571, 3.46, 1.56, 0.00, 0.24}, - {1, 1.51763, 3.66, 1.27, 0.00, 0.00}, - {1, 1.51589, 3.43, 1.40, 0.00, 0.24}, - {1, 1.51748, 3.56, 1.27, 0.00, 0.17}, - {1, 1.51763, 3.59, 1.31, 0.00, 0.00}, - {1, 1.51761, 3.54, 1.23, 0.00, 0.00}, - {1, 1.51784, 3.67, 1.16, 0.00, 0.00}, - {1, 1.52196, 3.85, 0.89, 0.00, 0.00}, - {1, 1.51911, 3.73, 1.18, 0.00, 0.00}, - {1, 1.51735, 3.54, 1.69, 0.00, 0.07}, - {1, 1.51750, 3.55, 1.49, 0.00, 0.19}, - {1, 1.51966, 3.75, 0.29, 0.00, 0.00}, - {1, 1.51736, 3.62, 1.29, 0.00, 0.00}, - {1, 1.51751, 3.57, 1.35, 0.00, 0.00}, - {1, 1.51720, 3.50, 1.15, 0.00, 0.00}, - {1, 1.51764, 3.54, 1.21, 0.00, 0.00}, - {1, 1.51793, 3.48, 1.41, 0.00, 0.00}, - {1, 1.51721, 3.48, 1.33, 0.00, 0.00}, - {1, 1.51768, 3.52, 1.43, 0.00, 0.00}, - {1, 1.51784, 3.49, 1.28, 0.00, 0.00}, - {1, 1.51768, 3.56, 1.30, 0.00, 0.14}, - {1, 1.51747, 3.50, 1.14, 0.00, 0.00}, - {1, 1.51775, 3.48, 1.23, 0.09, 0.22}, - {1, 1.51753, 3.47, 1.38, 0.00, 0.06}, - {1, 1.51783, 3.54, 1.34, 0.00, 0.00}, - {1, 1.51567, 3.45, 1.21, 0.00, 0.00}, - {1, 1.51909, 3.53, 1.32, 0.11, 0.00}, - {1, 1.51797, 3.48, 1.35, 0.00, 0.00}, - {1, 1.52213, 3.82, 0.47, 0.00, 0.00}, - {1, 1.52213, 3.82, 0.47, 0.00, 0.00}, - {1, 1.51793, 3.50, 1.12, 0.00, 0.00}, - {1, 1.51755, 3.42, 1.20, 0.00, 0.00}, - {1, 1.51779, 3.39, 1.33, 0.00, 0.00}, - {1, 1.52210, 3.84, 0.72, 0.00, 0.00}, - {1, 1.51786, 3.43, 1.19, 0.00, 0.30}, - {1, 1.51900, 3.48, 1.35, 0.00, 0.00}, - {1, 1.51869, 3.37, 1.18, 0.00, 0.16}, - {1, 1.52667, 3.70, 0.71, 0.00, 0.10}, - {1, 1.52223, 3.77, 0.79, 0.00, 0.00}, - {1, 1.51898, 3.35, 1.23, 0.00, 0.00}, - {1, 1.52320, 3.72, 0.51, 0.00, 0.16}, - {1, 1.51926, 3.33, 1.28, 0.00, 0.11}, - {1, 1.51808, 2.87, 1.19, 0.00, 0.00}, - {1, 1.51837, 2.84, 1.28, 0.00, 0.00}, - {1, 1.51778, 2.81, 1.29, 0.00, 0.09}, - {1, 1.51769, 2.71, 1.29, 0.00, 0.24}, - {1, 1.51215, 3.47, 1.12, 0.00, 0.31}, - {1, 1.51824, 3.48, 1.29, 0.00, 0.00}, - {1, 1.51754, 3.74, 1.17, 0.00, 0.00}, - {1, 1.51754, 3.66, 1.19, 0.00, 0.11}, - {1, 1.51905, 3.62, 1.11, 0.00, 0.00}, - {1, 1.51977, 3.58, 1.32, 0.69, 0.00}, - {1, 1.52172, 3.86, 0.88, 0.00, 0.11}, - {1, 1.52227, 3.81, 0.78, 0.00, 0.00}, - {1, 1.52172, 3.74, 0.90, 0.00, 0.07}, - {1, 1.52099, 3.59, 1.12, 0.00, 0.00}, - {1, 1.52152, 3.65, 0.87, 0.00, 0.17}, - {1, 1.52152, 3.65, 0.87, 0.00, 0.17}, - {1, 1.52152, 3.58, 0.90, 0.00, 0.16}, - {1, 1.52300, 3.58, 0.82, 0.00, 0.03}, - {3, 1.51769, 3.66, 1.11, 0.00, 0.00}, - {3, 1.51610, 3.53, 1.34, 0.00, 0.00}, - {3, 1.51670, 3.57, 1.38, 0.00, 0.10}, - {3, 1.51643, 3.52, 1.35, 0.00, 0.00}, - {3, 1.51665, 3.45, 1.76, 0.00, 0.17}, - {3, 1.52127, 3.90, 0.83, 0.00, 0.00}, - {3, 1.51779, 3.65, 0.65, 0.00, 0.00}, - {3, 1.51610, 3.40, 1.22, 0.00, 0.00}, - {3, 1.51694, 3.58, 1.31, 0.00, 0.00}, - {3, 1.51646, 3.40, 1.26, 0.00, 0.00}, - {3, 1.51655, 3.39, 1.28, 0.00, 0.00}, - {3, 1.52121, 3.76, 0.58, 0.00, 0.00}, - {3, 1.51776, 3.41, 1.52, 0.00, 0.00}, - {3, 1.51796, 3.36, 1.63, 0.00, 0.09}, - {3, 1.51832, 3.34, 1.54, 0.00, 0.00}, - {3, 1.51934, 3.54, 0.75, 0.15, 0.24}, - {3, 1.52211, 3.78, 0.91, 0.00, 0.37}, - {7, 1.51131, 3.20, 1.81, 1.19, 0.00}, - {7, 1.51838, 3.26, 2.22, 1.63, 0.00}, - {7, 1.52315, 3.34, 1.23, 0.00, 0.00}, - {7, 1.52247, 2.20, 2.06, 0.00, 0.00}, - {7, 1.52365, 1.83, 1.31, 1.68, 0.00}, - {7, 1.51613, 1.78, 1.79, 0.76, 0.00}, - {7, 1.51602, 0.00, 2.38, 0.64, 0.09}, - {7, 1.51623, 0.00, 2.79, 0.40, 0.09}, - {7, 1.51719, 0.00, 2.00, 1.59, 0.08}, - {7, 1.51683, 0.00, 1.98, 1.57, 0.07}, - {7, 1.51545, 0.00, 2.68, 0.61, 0.05}, - {7, 1.51556, 0.00, 2.54, 0.81, 0.01}, - {7, 1.51727, 0.00, 2.34, 0.66, 0.00}, - {7, 1.51531, 0.00, 2.66, 0.64, 0.00}, - {7, 1.51609, 0.00, 2.51, 0.53, 0.00}, - {7, 1.51508, 0.00, 2.25, 0.63, 0.00}, - {7, 1.51653, 0.00, 1.19, 0.00, 0.00}, - {7, 1.51514, 0.00, 2.42, 0.56, 0.00}, - {7, 1.51658, 0.00, 1.99, 1.71, 0.00}, - {7, 1.51617, 0.00, 2.27, 0.67, 0.00}, - {7, 1.51732, 0.00, 1.80, 1.55, 0.00}, - {7, 1.51645, 0.00, 1.87, 1.38, 0.00}, - {7, 1.51831, 0.00, 1.82, 2.88, 0.00}, - {7, 1.51640, 0.00, 2.74, 0.54, 0.00}, - {7, 1.51623, 0.00, 2.88, 1.06, 0.00}, - {7, 1.51685, 0.00, 1.99, 1.59, 0.00}, - {7, 1.52065, 0.00, 2.02, 1.64, 0.00}, - {7, 1.51651, 0.00, 1.94, 1.57, 0.00}, - {7, 1.51711, 0.00, 2.08, 1.67, 0.00}, - }; } http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java index 652b293..cc212e6 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java @@ -45,7 +45,7 @@ public class DecisionTreeClassificationTrainerExample { * * @param args Command line arguments, none required. */ - public static void main(String... args) throws InterruptedException { + public static void main(String... args) { System.out.println(">>> Decision tree classification trainer example started."); // Start ignite grid. http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java index 2a89c7e..2338522 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java @@ -45,7 +45,7 @@ public class DecisionTreeRegressionTrainerExample { * * @param args Command line arguments, none required. */ - public static void main(String... args) throws InterruptedException { + public static void main(String... args) { System.out.println(">>> Decision tree regression trainer example started."); // Start ignite grid. http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java index 5beb954..c478407 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java @@ -42,7 +42,7 @@ public class GDBOnTreesClassificationTrainerExample { * * @param args Command line arguments, none required. */ - public static void main(String... args) throws InterruptedException { + public static void main(String... args) { System.out.println(); System.out.println(">>> GDB classification trainer example started."); // Start ignite grid. http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java index ea235ee..83a78d3 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java @@ -17,7 +17,7 @@ package org.apache.ignite.examples.ml.tree.randomforest; -import java.util.Arrays; +import java.io.FileNotFoundException; import java.util.concurrent.atomic.AtomicInteger; import java.util.stream.Collectors; import java.util.stream.IntStream; @@ -27,10 +27,11 @@ import org.apache.ignite.IgniteCache; import org.apache.ignite.Ignition; import org.apache.ignite.cache.query.QueryCursor; import org.apache.ignite.cache.query.ScanQuery; -import org.apache.ignite.examples.ml.util.TestCache; +import org.apache.ignite.examples.ml.util.MLSandboxDatasets; +import org.apache.ignite.examples.ml.util.SandboxMLCache; import org.apache.ignite.ml.composition.ModelsComposition; import org.apache.ignite.ml.dataset.feature.FeatureMeta; -import org.apache.ignite.ml.math.primitives.vector.VectorUtils; +import org.apache.ignite.ml.math.primitives.vector.Vector; import org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer; import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies; @@ -54,18 +55,19 @@ public class RandomForestClassificationExample { /** * Run example. */ - public static void main(String[] args) throws InterruptedException { + public static void main(String[] args) throws FileNotFoundException { System.out.println(); System.out.println(">>> Random Forest multi-class classification algorithm over cached dataset usage example started."); // Start ignite grid. try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { System.out.println(">>> Ignite grid started."); - IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data); + IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite) + .fillCacheWith(MLSandboxDatasets.WINE_RECOGNITION); AtomicInteger idx = new AtomicInteger(0); RandomForestClassifierTrainer classifier = new RandomForestClassifierTrainer( - IntStream.range(0, data[0].length - 1).mapToObj( + IntStream.range(0, dataCache.get(1).size() - 1).mapToObj( x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList()) ).withAmountOfTrees(101) .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD) @@ -76,23 +78,23 @@ public class RandomForestClassificationExample { System.out.println(">>> Configured trainer: " + classifier.getClass().getSimpleName()); - ModelsComposition randomForest = classifier.fit(ignite, dataCache, - (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)), - (k, v) -> v[0] + ModelsComposition randomForestMdl = classifier.fit(ignite, dataCache, + (k, v) -> v.copyOfRange(1, v.size()), + (k, v) -> v.get(0) ); - System.out.println(">>> Trained model: " + randomForest.toString(true)); + System.out.println(">>> Trained model: " + randomForestMdl.toString(true)); int amountOfErrors = 0; int totalAmount = 0; - try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) { - for (Cache.Entry<Integer, double[]> observation : observations) { - double[] val = observation.getValue(); - double[] inputs = Arrays.copyOfRange(val, 1, val.length); - double groundTruth = val[0]; + try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) { + for (Cache.Entry<Integer, Vector> observation : observations) { + Vector val = observation.getValue(); + Vector inputs = val.copyOfRange(1, val.size()); + double groundTruth = val.get(0); - double prediction = randomForest.apply(VectorUtils.of(inputs)); + double prediction = randomForestMdl.apply(inputs); totalAmount++; if (groundTruth != prediction) @@ -107,186 +109,4 @@ public class RandomForestClassificationExample { } } } - - /** The Wine recognition dataset. */ - private static final double[][] data = { - {1, 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, .28, 2.29, 5.64, 1.04, 3.92, 1065}, - {1, 13.2, 1.78, 2.14, 11.2, 100, 2.65, 2.76, .26, 1.28, 4.38, 1.05, 3.4, 1050}, - {1, 13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, .3, 2.81, 5.68, 1.03, 3.17, 1185}, - {1, 14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, .24, 2.18, 7.8, .86, 3.45, 1480}, - {1, 13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, .39, 1.82, 4.32, 1.04, 2.93, 735}, - {1, 14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, .34, 1.97, 6.75, 1.05, 2.85, 1450}, - {1, 14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, .3, 1.98, 5.25, 1.02, 3.58, 1290}, - {1, 14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, .31, 1.25, 5.05, 1.06, 3.58, 1295}, - {1, 14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, .29, 1.98, 5.2, 1.08, 2.85, 1045}, - {1, 13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, .22, 1.85, 7.22, 1.01, 3.55, 1045}, - {1, 14.1, 2.16, 2.3, 18, 105, 2.95, 3.32, .22, 2.38, 5.75, 1.25, 3.17, 1510}, - {1, 14.12, 1.48, 2.32, 16.8, 95, 2.2, 2.43, .26, 1.57, 5, 1.17, 2.82, 1280}, - {1, 13.75, 1.73, 2.41, 16, 89, 2.6, 2.76, .29, 1.81, 5.6, 1.15, 2.9, 1320}, - {1, 14.75, 1.73, 2.39, 11.4, 91, 3.1, 3.69, .43, 2.81, 5.4, 1.25, 2.73, 1150}, - {1, 14.38, 1.87, 2.38, 12, 102, 3.3, 3.64, .29, 2.96, 7.5, 1.2, 3, 1547}, - {1, 13.63, 1.81, 2.7, 17.2, 112, 2.85, 2.91, .3, 1.46, 7.3, 1.28, 2.88, 1310}, - {1, 14.3, 1.92, 2.72, 20, 120, 2.8, 3.14, .33, 1.97, 6.2, 1.07, 2.65, 1280}, - {1, 13.83, 1.57, 2.62, 20, 115, 2.95, 3.4, .4, 1.72, 6.6, 1.13, 2.57, 1130}, - {1, 14.19, 1.59, 2.48, 16.5, 108, 3.3, 3.93, .32, 1.86, 8.7, 1.23, 2.82, 1680}, - {1, 13.64, 3.1, 2.56, 15.2, 116, 2.7, 3.03, .17, 1.66, 5.1, .96, 3.36, 845}, - {1, 14.06, 1.63, 2.28, 16, 126, 3, 3.17, .24, 2.1, 5.65, 1.09, 3.71, 780}, - {1, 12.93, 3.8, 2.65, 18.6, 102, 2.41, 2.41, .25, 1.98, 4.5, 1.03, 3.52, 770}, - {1, 13.71, 1.86, 2.36, 16.6, 101, 2.61, 2.88, .27, 1.69, 3.8, 1.11, 4, 1035}, - {1, 12.85, 1.6, 2.52, 17.8, 95, 2.48, 2.37, .26, 1.46, 3.93, 1.09, 3.63, 1015}, - {1, 13.5, 1.81, 2.61, 20, 96, 2.53, 2.61, .28, 1.66, 3.52, 1.12, 3.82, 845}, - {1, 13.05, 2.05, 3.22, 25, 124, 2.63, 2.68, .47, 1.92, 3.58, 1.13, 3.2, 830}, - {1, 13.39, 1.77, 2.62, 16.1, 93, 2.85, 2.94, .34, 1.45, 4.8, .92, 3.22, 1195}, - {1, 13.3, 1.72, 2.14, 17, 94, 2.4, 2.19, .27, 1.35, 3.95, 1.02, 2.77, 1285}, - {1, 13.87, 1.9, 2.8, 19.4, 107, 2.95, 2.97, .37, 1.76, 4.5, 1.25, 3.4, 915}, - {1, 14.02, 1.68, 2.21, 16, 96, 2.65, 2.33, .26, 1.98, 4.7, 1.04, 3.59, 1035}, - {1, 13.73, 1.5, 2.7, 22.5, 101, 3, 3.25, .29, 2.38, 5.7, 1.19, 2.71, 1285}, - {1, 13.58, 1.66, 2.36, 19.1, 106, 2.86, 3.19, .22, 1.95, 6.9, 1.09, 2.88, 1515}, - {1, 13.68, 1.83, 2.36, 17.2, 104, 2.42, 2.69, .42, 1.97, 3.84, 1.23, 2.87, 990}, - {1, 13.76, 1.53, 2.7, 19.5, 132, 2.95, 2.74, .5, 1.35, 5.4, 1.25, 3, 1235}, - {1, 13.51, 1.8, 2.65, 19, 110, 2.35, 2.53, .29, 1.54, 4.2, 1.1, 2.87, 1095}, - {1, 13.48, 1.81, 2.41, 20.5, 100, 2.7, 2.98, .26, 1.86, 5.1, 1.04, 3.47, 920}, - {1, 13.28, 1.64, 2.84, 15.5, 110, 2.6, 2.68, .34, 1.36, 4.6, 1.09, 2.78, 880}, - {1, 13.05, 1.65, 2.55, 18, 98, 2.45, 2.43, .29, 1.44, 4.25, 1.12, 2.51, 1105}, - {1, 13.07, 1.5, 2.1, 15.5, 98, 2.4, 2.64, .28, 1.37, 3.7, 1.18, 2.69, 1020}, - {1, 14.22, 3.99, 2.51, 13.2, 128, 3, 3.04, .2, 2.08, 5.1, .89, 3.53, 760}, - {1, 13.56, 1.71, 2.31, 16.2, 117, 3.15, 3.29, .34, 2.34, 6.13, .95, 3.38, 795}, - {1, 13.41, 3.84, 2.12, 18.8, 90, 2.45, 2.68, .27, 1.48, 4.28, .91, 3, 1035}, - {1, 13.88, 1.89, 2.59, 15, 101, 3.25, 3.56, .17, 1.7, 5.43, .88, 3.56, 1095}, - {1, 13.24, 3.98, 2.29, 17.5, 103, 2.64, 2.63, .32, 1.66, 4.36, .82, 3, 680}, - {1, 13.05, 1.77, 2.1, 17, 107, 3, 3, .28, 2.03, 5.04, .88, 3.35, 885}, - {1, 14.21, 4.04, 2.44, 18.9, 111, 2.85, 2.65, .3, 1.25, 5.24, .87, 3.33, 1080}, - {1, 14.38, 3.59, 2.28, 16, 102, 3.25, 3.17, .27, 2.19, 4.9, 1.04, 3.44, 1065}, - {1, 13.9, 1.68, 2.12, 16, 101, 3.1, 3.39, .21, 2.14, 6.1, .91, 3.33, 985}, - {1, 14.1, 2.02, 2.4, 18.8, 103, 2.75, 2.92, .32, 2.38, 6.2, 1.07, 2.75, 1060}, - {1, 13.94, 1.73, 2.27, 17.4, 108, 2.88, 3.54, .32, 2.08, 8.90, 1.12, 3.1, 1260}, - {1, 13.05, 1.73, 2.04, 12.4, 92, 2.72, 3.27, .17, 2.91, 7.2, 1.12, 2.91, 1150}, - {1, 13.83, 1.65, 2.6, 17.2, 94, 2.45, 2.99, .22, 2.29, 5.6, 1.24, 3.37, 1265}, - {1, 13.82, 1.75, 2.42, 14, 111, 3.88, 3.74, .32, 1.87, 7.05, 1.01, 3.26, 1190}, - {1, 13.77, 1.9, 2.68, 17.1, 115, 3, 2.79, .39, 1.68, 6.3, 1.13, 2.93, 1375}, - {1, 13.74, 1.67, 2.25, 16.4, 118, 2.6, 2.9, .21, 1.62, 5.85, .92, 3.2, 1060}, - {1, 13.56, 1.73, 2.46, 20.5, 116, 2.96, 2.78, .2, 2.45, 6.25, .98, 3.03, 1120}, - {1, 14.22, 1.7, 2.3, 16.3, 118, 3.2, 3, .26, 2.03, 6.38, .94, 3.31, 970}, - {1, 13.29, 1.97, 2.68, 16.8, 102, 3, 3.23, .31, 1.66, 6, 1.07, 2.84, 1270}, - {1, 13.72, 1.43, 2.5, 16.7, 108, 3.4, 3.67, .19, 2.04, 6.8, .89, 2.87, 1285}, - {2, 12.37, .94, 1.36, 10.6, 88, 1.98, .57, .28, .42, 1.95, 1.05, 1.82, 520}, - {2, 12.33, 1.1, 2.28, 16, 101, 2.05, 1.09, .63, .41, 3.27, 1.25, 1.67, 680}, - {2, 12.64, 1.36, 2.02, 16.8, 100, 2.02, 1.41, .53, .62, 5.75, .98, 1.59, 450}, - {2, 13.67, 1.25, 1.92, 18, 94, 2.1, 1.79, .32, .73, 3.8, 1.23, 2.46, 630}, - {2, 12.37, 1.13, 2.16, 19, 87, 3.5, 3.1, .19, 1.87, 4.45, 1.22, 2.87, 420}, - {2, 12.17, 1.45, 2.53, 19, 104, 1.89, 1.75, .45, 1.03, 2.95, 1.45, 2.23, 355}, - {2, 12.37, 1.21, 2.56, 18.1, 98, 2.42, 2.65, .37, 2.08, 4.6, 1.19, 2.3, 678}, - {2, 13.11, 1.01, 1.7, 15, 78, 2.98, 3.18, .26, 2.28, 5.3, 1.12, 3.18, 502}, - {2, 12.37, 1.17, 1.92, 19.6, 78, 2.11, 2, .27, 1.04, 4.68, 1.12, 3.48, 510}, - {2, 13.34, .94, 2.36, 17, 110, 2.53, 1.3, .55, .42, 3.17, 1.02, 1.93, 750}, - {2, 12.21, 1.19, 1.75, 16.8, 151, 1.85, 1.28, .14, 2.5, 2.85, 1.28, 3.07, 718}, - {2, 12.29, 1.61, 2.21, 20.4, 103, 1.1, 1.02, .37, 1.46, 3.05, .906, 1.82, 870}, - {2, 13.86, 1.51, 2.67, 25, 86, 2.95, 2.86, .21, 1.87, 3.38, 1.36, 3.16, 410}, - {2, 13.49, 1.66, 2.24, 24, 87, 1.88, 1.84, .27, 1.03, 3.74, .98, 2.78, 472}, - {2, 12.99, 1.67, 2.6, 30, 139, 3.3, 2.89, .21, 1.96, 3.35, 1.31, 3.5, 985}, - 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