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[timeseries] add some basic block and deepAR model (deepjavalibrary#2027
) * feature: add TimeSeriesDataset and training transform * feature: some basic block and deepar model * feature: add train example * feature: add m5-demo and air passengers demo Co-authored-by: Carkham <1302112560@qq.com> Co-authored-by: Frank Liu <frankfliu2000@gmail.com> Co-authored-by: KexinFeng <fengx463@umn.edu>
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.../src/test/resources/mlrepo/model/timeseries/forecasting/ai/djl/mxnet/deepar/metadata.json
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| { | ||
| "metadataVersion": "0.2", | ||
| "resourceType": "model", | ||
| "application": "timeseries/forecasting", | ||
| "groupId": "ai.djl.mxnet", | ||
| "artifactId": "resnest", | ||
| "name": "deepar", | ||
| "description": "DeepAR model for timeseries forecasting", | ||
| "website": "http://www.djl.ai/engines/mxnet/model-zoo", | ||
| "licenses": { | ||
| "apache": { | ||
| "name": "The Apache License, Version 2.0", | ||
| "url": "https://www.apache.org/licenses/LICENSE-2.0" | ||
| } | ||
| }, | ||
| "artifacts": [ | ||
| { | ||
| "version": "0.0.1", | ||
| "snapshot": false, | ||
| "name": "airpassengers", | ||
| "properties": { | ||
| "dataset": "airpassengers" | ||
| }, | ||
| "arguments": { | ||
| "prediction_length": 12, | ||
| "freq": "M", | ||
| "use_feat_dynamic_real": false, | ||
| "use_feat_static_cat": false, | ||
| "use_feat_static_real": false, | ||
| "translatorFactory": "ai.djl.timeseries.translator.DeepARTranslatorFactory" | ||
| }, | ||
| "files": { | ||
| "model": { | ||
| "uri": "0.0.1/airpassengers.zip", | ||
| "sha1Hash": "1c99cdaefb79c3e63bc7ff1965b0fb2ba45e96c3", | ||
| "name": "", | ||
| "size": 106895 | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "version": "0.0.1", | ||
| "snapshot": false, | ||
| "name": "m5forecast", | ||
| "properties": { | ||
| "dataset": "m5forecast" | ||
| }, | ||
| "arguments": { | ||
| "prediction_length": 4, | ||
| "freq": "W", | ||
| "use_feat_dynamic_real": false, | ||
| "use_feat_static_cat": false, | ||
| "use_feat_static_real": false, | ||
| "translatorFactory": "ai.djl.timeseries.translator.DeepARTranslatorFactory" | ||
| }, | ||
| "files": { | ||
| "model": { | ||
| "uri": "0.0.1/m5forecast.zip", | ||
| "sha1Hash": "e251628df3a246911479de0ed36762515a5df241", | ||
| "name": "", | ||
| "size": 96363 | ||
| } | ||
| } | ||
| } | ||
| ] | ||
| } |
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examples/src/main/java/ai/djl/examples/inference/timeseries/AirPassengersDeepAR.java
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| /* | ||
| * Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance | ||
| * with the License. A copy of the License is located at | ||
| * | ||
| * http://aws.amazon.com/apache2.0/ | ||
| * | ||
| * or in the "license" file accompanying this file. This file 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 ai.djl.examples.inference.timeseries; | ||
|
|
||
| import ai.djl.ModelException; | ||
| import ai.djl.inference.Predictor; | ||
| import ai.djl.ndarray.NDArray; | ||
| import ai.djl.ndarray.NDList; | ||
| import ai.djl.ndarray.NDManager; | ||
| import ai.djl.repository.zoo.Criteria; | ||
| import ai.djl.repository.zoo.ZooModel; | ||
| import ai.djl.timeseries.Forecast; | ||
| import ai.djl.timeseries.SampleForecast; | ||
| import ai.djl.timeseries.TimeSeriesData; | ||
| import ai.djl.timeseries.dataset.FieldName; | ||
| import ai.djl.training.util.ProgressBar; | ||
| import ai.djl.translate.DeferredTranslatorFactory; | ||
| import ai.djl.translate.TranslateException; | ||
|
|
||
| import com.google.gson.GsonBuilder; | ||
|
|
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| import org.slf4j.Logger; | ||
| import org.slf4j.LoggerFactory; | ||
|
|
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| import java.io.IOException; | ||
| import java.io.InputStreamReader; | ||
| import java.io.OutputStream; | ||
| import java.io.Reader; | ||
| import java.net.URL; | ||
| import java.nio.charset.StandardCharsets; | ||
| import java.nio.file.Files; | ||
| import java.nio.file.Path; | ||
| import java.nio.file.Paths; | ||
| import java.time.LocalDateTime; | ||
| import java.time.ZoneId; | ||
| import java.util.Date; | ||
|
|
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| public final class AirPassengersDeepAR { | ||
|
|
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| private static final Logger logger = LoggerFactory.getLogger(AirPassengersDeepAR.class); | ||
|
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| private AirPassengersDeepAR() {} | ||
|
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| public static void main(String[] args) throws IOException, TranslateException, ModelException { | ||
| float[] results = predict(); | ||
| logger.info("{}", results); | ||
| } | ||
|
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| public static float[] predict() throws IOException, TranslateException, ModelException { | ||
| Criteria<TimeSeriesData, Forecast> criteria = | ||
| Criteria.builder() | ||
| .setTypes(TimeSeriesData.class, Forecast.class) | ||
| .optModelUrls("djl://ai.djl.mxnet/deepar/0.0.1/airpassengers") | ||
| .optEngine("MXNet") | ||
| .optTranslatorFactory(new DeferredTranslatorFactory()) | ||
| .optArgument("prediction_length", 12) | ||
| .optArgument("freq", "M") | ||
| .optArgument("use_feat_dynamic_real", false) | ||
| .optArgument("use_feat_static_cat", false) | ||
| .optArgument("use_feat_static_real", false) | ||
| .optProgress(new ProgressBar()) | ||
| .build(); | ||
|
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| String url = "https://resources.djl.ai/test-models/mxnet/timeseries/air_passengers.json"; | ||
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| try (ZooModel<TimeSeriesData, Forecast> model = criteria.loadModel(); | ||
| Predictor<TimeSeriesData, Forecast> predictor = model.newPredictor(); | ||
| NDManager manager = NDManager.newBaseManager(null, "MXNet")) { | ||
| TimeSeriesData data = getTimeSeriesData(manager, new URL(url)); | ||
|
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| // save data for plotting | ||
| NDArray target = data.get(FieldName.TARGET); | ||
| target.setName("target"); | ||
| saveNDArray(target); | ||
|
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| Forecast forecast = predictor.predict(data); | ||
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| // save data for plotting. Please see the corresponding python script from | ||
| // https://gist.github.com/Carkham/a5162c9298bc51fec648a458a3437008 | ||
| NDArray samples = ((SampleForecast) forecast).getSortedSamples(); | ||
| samples.setName("samples"); | ||
| saveNDArray(samples); | ||
| return forecast.mean().toFloatArray(); | ||
| } | ||
| } | ||
|
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| private static TimeSeriesData getTimeSeriesData(NDManager manager, URL url) throws IOException { | ||
| try (Reader reader = new InputStreamReader(url.openStream(), StandardCharsets.UTF_8)) { | ||
| AirPassengers passengers = | ||
| new GsonBuilder() | ||
| .setDateFormat("yyyy-MM") | ||
| .create() | ||
| .fromJson(reader, AirPassengers.class); | ||
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| LocalDateTime start = | ||
| passengers.start.toInstant().atZone(ZoneId.systemDefault()).toLocalDateTime(); | ||
| NDArray target = manager.create(passengers.target); | ||
| TimeSeriesData data = new TimeSeriesData(10); | ||
| data.setStartTime(start); | ||
| data.setField(FieldName.TARGET, target); | ||
| return data; | ||
| } | ||
| } | ||
|
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| private static void saveNDArray(NDArray array) throws IOException { | ||
| Path path = Paths.get("build").resolve(array.getName() + ".npz"); | ||
| try (OutputStream os = Files.newOutputStream(path)) { | ||
| new NDList(new NDList(array)).encode(os, true); | ||
| } | ||
| } | ||
|
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| private static final class AirPassengers { | ||
|
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| Date start; | ||
| float[] target; | ||
| } | ||
| } |
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examples/src/main/java/ai/djl/examples/inference/timeseries/package-info.java
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| /* | ||
| * Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance | ||
| * with the License. A copy of the License is located at | ||
| * | ||
| * http://aws.amazon.com/apache2.0/ | ||
| * | ||
| * or in the "license" file accompanying this file. This file 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. | ||
| */ | ||
|
|
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| /** Contains examples of time series forecasting. */ | ||
| package ai.djl.examples.inference.timeseries; |
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