diff --git a/extensions/timeseries/src/main/java/ai/djl/timeseries/dataset/M5Forecast.java b/extensions/timeseries/src/main/java/ai/djl/timeseries/dataset/M5Forecast.java index 3fbd084c7d0..a379f76a07d 100644 --- a/extensions/timeseries/src/main/java/ai/djl/timeseries/dataset/M5Forecast.java +++ b/extensions/timeseries/src/main/java/ai/djl/timeseries/dataset/M5Forecast.java @@ -22,7 +22,6 @@ import ai.djl.repository.Repository; import ai.djl.util.JsonUtils; import ai.djl.util.Progress; -import ai.djl.util.Utils; import org.apache.commons.csv.CSVFormat; @@ -35,7 +34,6 @@ import java.util.ArrayList; import java.util.List; import java.util.Map; -import java.util.Objects; import java.util.Set; /** @@ -109,9 +107,9 @@ private String getUsagePath(Usage usage) { // file as 'weekly_***' switch (usage) { case TRAIN: - return "fake_weekly_sales_train_validation.csv"; + return "weekly_sales_train_validation.csv"; case TEST: - return "fake_weekly_sales_train_evaluation.csv"; + return "weekly_sales_train_evaluation.csv"; case VALIDATION: default: throw new UnsupportedOperationException("Data not available."); @@ -122,11 +120,17 @@ private String getUsagePath(Usage usage) { public static class Builder extends CsvBuilder { Repository repository; - Usage usage = Usage.TRAIN; + String groupId; + String artifactId; + Usage usage; M5Features mf; List cardinality; Builder() { + repository = BasicDatasets.REPOSITORY; + groupId = BasicDatasets.GROUP_ID; + artifactId = ARTIFACT_ID; + usage = Usage.TRAIN; csvFormat = CSVFormat.DEFAULT .builder() @@ -139,8 +143,7 @@ public static class Builder extends CsvBuilder { } MRL getMrl() { - return repository.dataset( - Application.Tabular.ANY, BasicDatasets.GROUP_ID, ARTIFACT_ID, VERSION); + return repository.dataset(Application.Tabular.ANY, groupId, artifactId, VERSION); } /** {@inheritDoc} */ @@ -150,18 +153,46 @@ protected Builder self() { } /** - * Set the repository containing the path. + * Sets the optional repository. * - * @param repository the repository containing the path + * @param repository the repository * @return this builder */ - public Builder setRepository(Repository repository) { + public Builder optRepository(Repository repository) { this.repository = repository; return this; } /** - * Set the optional usage. + * Sets optional groupId. + * + * @param groupId the groupId} + * @return this builder + */ + public Builder optGroupId(String groupId) { + this.groupId = groupId; + return this; + } + + /** + * Sets the optional artifactId. + * + * @param artifactId the artifactId + * @return this builder + */ + public Builder optArtifactId(String artifactId) { + if (artifactId.contains(":")) { + String[] tokens = artifactId.split(":"); + groupId = tokens[0]; + this.artifactId = tokens[1]; + } else { + this.artifactId = artifactId; + } + return this; + } + + /** + * Sets the optional usage. * * @param usage the usage * @return this builder @@ -228,9 +259,8 @@ public M5Forecast build() { private void parseFeatures() { if (mf == null) { - String url = - "https://mlrepo.djl.ai/dataset/tabular/ai/djl/basicdataset/m5forecast/1.0/m5forecast_parser.json"; - try (InputStream is = Objects.requireNonNull(Utils.openUrl(url)); + try (InputStream is = + M5Forecast.class.getResourceAsStream("m5forecast_parser.json"); Reader reader = new InputStreamReader(is, StandardCharsets.UTF_8)) { mf = JsonUtils.GSON.fromJson(reader, M5Features.class); } catch (IOException e) { diff --git a/extensions/timeseries/src/main/resources/ai/djl/timeseries/dataset/m5forecast_parser.json b/extensions/timeseries/src/main/resources/ai/djl/timeseries/dataset/m5forecast_parser.json new file mode 100644 index 00000000000..b1f0bdc904a --- /dev/null +++ b/extensions/timeseries/src/main/resources/ai/djl/timeseries/dataset/m5forecast_parser.json @@ -0,0 +1,3100 @@ +{ + "featureArray": [ + "state_id", + "store_id", + "cat_id", + "dept_id", + "item_id" + ], + "categorical": [ + "state_id", + "store_id", + "cat_id", + "dept_id", + "item_id" + ], + "featureToMap": { + "state_id": { + "CA": 0, + "TX": 1, + "WI": 2 + }, + "store_id": { + "CA_1": 0, + "CA_2": 1, + "CA_3": 2, + "CA_4": 3, + "TX_1": 4, + "TX_2": 5, + "TX_3": 6, + "WI_1": 7, + "WI_2": 8, + "WI_3": 9 + }, + "cat_id": { + "FOODS": 0, + "HOBBIES": 1, + "HOUSEHOLD": 2 + }, + "dept_id": { + "FOODS_1": 0, + "FOODS_2": 1, + "FOODS_3": 2, + "HOBBIES_1": 3, + "HOBBIES_2": 4, + "HOUSEHOLD_1": 5, + "HOUSEHOLD_2": 6 + }, + "item_id": { + "FOODS_1_001": 0, + "FOODS_1_002": 1, + "FOODS_1_003": 2, + "FOODS_1_004": 3, + "FOODS_1_005": 4, + "FOODS_1_006": 5, + "FOODS_1_008": 6, + "FOODS_1_009": 7, + "FOODS_1_010": 8, + "FOODS_1_011": 9, + "FOODS_1_012": 10, + "FOODS_1_013": 11, + "FOODS_1_014": 12, + "FOODS_1_015": 13, + "FOODS_1_016": 14, + "FOODS_1_017": 15, + "FOODS_1_018": 16, + "FOODS_1_019": 17, + "FOODS_1_020": 18, + "FOODS_1_021": 19, + "FOODS_1_022": 20, + "FOODS_1_023": 21, + "FOODS_1_024": 22, + "FOODS_1_025": 23, + "FOODS_1_026": 24, + 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"HOUSEHOLD_2_511": 3043, + "HOUSEHOLD_2_512": 3044, + "HOUSEHOLD_2_513": 3045, + "HOUSEHOLD_2_514": 3046, + "HOUSEHOLD_2_515": 3047, + "HOUSEHOLD_2_516": 3048 + } + } +} \ No newline at end of file diff --git a/extensions/timeseries/src/test/java/ai/djl/timeseries/dataset/M5ForecastTest.java b/extensions/timeseries/src/test/java/ai/djl/timeseries/dataset/M5ForecastTest.java index 3582e14438b..7779f8fb200 100644 --- a/extensions/timeseries/src/test/java/ai/djl/timeseries/dataset/M5ForecastTest.java +++ b/extensions/timeseries/src/test/java/ai/djl/timeseries/dataset/M5ForecastTest.java @@ -21,7 +21,6 @@ import ai.djl.ndarray.NDManager; import ai.djl.nn.Blocks; import ai.djl.nn.Parameter; -import ai.djl.testing.TestRequirements; import ai.djl.timeseries.transform.TimeSeriesTransform; import ai.djl.training.DefaultTrainingConfig; import ai.djl.training.Trainer; @@ -38,14 +37,17 @@ import java.io.IOException; import java.time.LocalDateTime; +import java.util.List; public class M5ForecastTest { - // Here for the purpose of unittest, we use the fake M5 forecast data. The real data needs to be - // downloaded from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data + /* + * Here for the purpose of unittest, we use the M5 forecast unittest dataset. + * The real data needs to be downloaded from: + * https://www.kaggle.com/competitions/m5-forecasting-accuracy/data + */ @Test public void testM5Forecast() throws IOException, TranslateException { - TestRequirements.weekly(); TrainingConfig config = new DefaultTrainingConfig(Loss.softmaxCrossEntropyLoss()) .optInitializer(Initializer.ONES, Parameter.Type.WEIGHT); @@ -57,10 +59,14 @@ public void testM5Forecast() throws IOException, TranslateException { M5Forecast.Builder builder = M5Forecast.builder() .optUsage(Dataset.Usage.TEST) - .setRepository(BasicDatasets.REPOSITORY) + .optRepository(BasicDatasets.REPOSITORY) + .optGroupId(BasicDatasets.GROUP_ID) + .optArtifactId("m5forecast-unittest") .setTransformation(TimeSeriesTransform.identityTransformation()) .setContextLength(4) .setSampling(32, true); + List features = builder.getAvailableFeatures(); + Assert.assertEquals(features.size(), 5); for (int i = 1; i <= 277; i++) { builder.addFeature("w_" + i, FieldName.TARGET); } @@ -80,8 +86,8 @@ public void testM5Forecast() throws IOException, TranslateException { m5Forecast.prepare(); - long size = m5Forecast.size(); - Assert.assertEquals(size, 30490); + Assert.assertEquals(m5Forecast.getCardinality().size(), 5); + Assert.assertEquals(m5Forecast.size(), 64); Record record = m5Forecast.get(manager, 0); NDList data = record.getData(); diff --git a/extensions/timeseries/src/test/resources/mlrepo/dataset/tabular/ai/djl/basicdataset/m5forecast-unittest/metadata.json b/extensions/timeseries/src/test/resources/mlrepo/dataset/tabular/ai/djl/basicdataset/m5forecast-unittest/metadata.json new file mode 100644 index 00000000000..ec7fd8fe48b --- /dev/null +++ b/extensions/timeseries/src/test/resources/mlrepo/dataset/tabular/ai/djl/basicdataset/m5forecast-unittest/metadata.json @@ -0,0 +1,28 @@ +{ + "metadataVersion": "0.1", + "resourceType": "dataset", + "application": "tabular", + "groupId": "ai.djl.basicdataset", + "artifactId": "m5forecast-unittest", + "name": "m5forecast", + "description": "This dataset provides the unittest m5 dataset.", + "artifacts": [ + { + "version": "1.0", + "snapshot": false, + "name": "m5forecast", + "files": { + "train": { + "uri": "1.0/weekly_sales_train_evaluation.csv.gz", + "sha1Hash": "5505126866a94bccb0019035114bd95271a3505e", + "size": 13837 + }, + "test": { + "uri": "1.0/weekly_sales_train_validation.csv.gz", + "sha1Hash": "80812b80777009bc5a0e93980b683a87bb34e225", + "size": 13615 + } + } + } + ] +}