software.amazon.awssdk.services.forecast.model.FeaturizationConfig Maven / Gradle / Ivy
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/*
* Copyright 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 software.amazon.awssdk.services.forecast.model;
import java.io.Serializable;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.Optional;
import java.util.function.BiConsumer;
import java.util.function.Consumer;
import java.util.function.Function;
import java.util.stream.Collectors;
import java.util.stream.Stream;
import software.amazon.awssdk.annotations.Generated;
import software.amazon.awssdk.core.SdkField;
import software.amazon.awssdk.core.SdkPojo;
import software.amazon.awssdk.core.protocol.MarshallLocation;
import software.amazon.awssdk.core.protocol.MarshallingType;
import software.amazon.awssdk.core.traits.ListTrait;
import software.amazon.awssdk.core.traits.LocationTrait;
import software.amazon.awssdk.core.util.DefaultSdkAutoConstructList;
import software.amazon.awssdk.core.util.SdkAutoConstructList;
import software.amazon.awssdk.utils.ToString;
import software.amazon.awssdk.utils.builder.CopyableBuilder;
import software.amazon.awssdk.utils.builder.ToCopyableBuilder;
/**
*
*
* This object belongs to the CreatePredictor operation. If you created your predictor with
* CreateAutoPredictor, see AttributeConfig.
*
*
*
* In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You
* can optionally tell the operation to modify data fields prior to training a model. These modifications are referred
* to as featurization.
*
*
* You define featurization using the FeaturizationConfig object. You specify an array of transformations,
* one for each field that you want to featurize. You then include the FeaturizationConfig object in your
* CreatePredictor request. Amazon Forecast applies the featurization to the
* TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training.
*
*
* You can create multiple featurization configurations. For example, you might call the CreatePredictor
* operation twice by specifying different featurization configurations.
*
*/
@Generated("software.amazon.awssdk:codegen")
public final class FeaturizationConfig implements SdkPojo, Serializable,
ToCopyableBuilder {
private static final SdkField FORECAST_FREQUENCY_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("ForecastFrequency").getter(getter(FeaturizationConfig::forecastFrequency))
.setter(setter(Builder::forecastFrequency))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("ForecastFrequency").build()).build();
private static final SdkField> FORECAST_DIMENSIONS_FIELD = SdkField
.> builder(MarshallingType.LIST)
.memberName("ForecastDimensions")
.getter(getter(FeaturizationConfig::forecastDimensions))
.setter(setter(Builder::forecastDimensions))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("ForecastDimensions").build(),
ListTrait
.builder()
.memberLocationName(null)
.memberFieldInfo(
SdkField. builder(MarshallingType.STRING)
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD)
.locationName("member").build()).build()).build()).build();
private static final SdkField> FEATURIZATIONS_FIELD = SdkField
.> builder(MarshallingType.LIST)
.memberName("Featurizations")
.getter(getter(FeaturizationConfig::featurizations))
.setter(setter(Builder::featurizations))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Featurizations").build(),
ListTrait
.builder()
.memberLocationName(null)
.memberFieldInfo(
SdkField. builder(MarshallingType.SDK_POJO)
.constructor(Featurization::builder)
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD)
.locationName("member").build()).build()).build()).build();
private static final List> SDK_FIELDS = Collections.unmodifiableList(Arrays.asList(FORECAST_FREQUENCY_FIELD,
FORECAST_DIMENSIONS_FIELD, FEATURIZATIONS_FIELD));
private static final long serialVersionUID = 1L;
private final String forecastFrequency;
private final List forecastDimensions;
private final List featurizations;
private FeaturizationConfig(BuilderImpl builder) {
this.forecastFrequency = builder.forecastFrequency;
this.forecastDimensions = builder.forecastDimensions;
this.featurizations = builder.featurizations;
}
/**
*
* The frequency of predictions in a forecast.
*
*
* Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute).
* For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that
* would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60
* minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
*
*
* -
*
* Minute - 1-59
*
*
* -
*
* Hour - 1-23
*
*
* -
*
* Day - 1-6
*
*
* -
*
* Week - 1-4
*
*
* -
*
* Month - 1-11
*
*
* -
*
* Year - 1
*
*
*
*
* Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify
* "3M".
*
*
* The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
*
*
* When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset
* frequency.
*
*
* @return The frequency of predictions in a forecast.
*
* Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min
* (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot
* specify a value that would overlap with the next larger frequency. That means, for example, you cannot
* specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each
* frequency are the following:
*
*
* -
*
* Minute - 1-59
*
*
* -
*
* Hour - 1-23
*
*
* -
*
* Day - 1-6
*
*
* -
*
* Week - 1-4
*
*
* -
*
* Month - 1-11
*
*
* -
*
* Year - 1
*
*
*
*
* Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you
* specify "3M".
*
*
* The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
*
*
* When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES
* dataset frequency.
*/
public final String forecastFrequency() {
return forecastFrequency;
}
/**
* For responses, this returns true if the service returned a value for the ForecastDimensions property. This DOES
* NOT check that the value is non-empty (for which, you should check the {@code isEmpty()} method on the property).
* This is useful because the SDK will never return a null collection or map, but you may need to differentiate
* between the service returning nothing (or null) and the service returning an empty collection or map. For
* requests, this returns true if a value for the property was specified in the request builder, and false if a
* value was not specified.
*/
public final boolean hasForecastDimensions() {
return forecastDimensions != null && !(forecastDimensions instanceof SdkAutoConstructList);
}
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, suppose that you are generating a forecast for item sales across all of your stores, and your
* dataset contains a store_id field. If you want the sales forecast for each item by store, you would
* specify store_id as the dimension.
*
*
* All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in
* the CreatePredictor request. All forecast dimensions specified in the
* RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
*
*
* Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
*
*
* This method will never return null. If you would like to know whether the service returned this field (so that
* you can differentiate between null and empty), you can use the {@link #hasForecastDimensions} method.
*
*
* @return An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, suppose that you are generating a forecast for item sales across all of your stores, and
* your dataset contains a store_id field. If you want the sales forecast for each item by
* store, you would specify store_id as the dimension.
*
*
* All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be
* specified in the CreatePredictor request. All forecast dimensions specified in the
* RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
*/
public final List forecastDimensions() {
return forecastDimensions;
}
/**
* For responses, this returns true if the service returned a value for the Featurizations property. This DOES NOT
* check that the value is non-empty (for which, you should check the {@code isEmpty()} method on the property).
* This is useful because the SDK will never return a null collection or map, but you may need to differentiate
* between the service returning nothing (or null) and the service returning an empty collection or map. For
* requests, this returns true if a value for the property was specified in the request builder, and false if a
* value was not specified.
*/
public final boolean hasFeaturizations() {
return featurizations != null && !(featurizations instanceof SdkAutoConstructList);
}
/**
*
* An array of featurization (transformation) information for the fields of a dataset.
*
*
* Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
*
*
* This method will never return null. If you would like to know whether the service returned this field (so that
* you can differentiate between null and empty), you can use the {@link #hasFeaturizations} method.
*
*
* @return An array of featurization (transformation) information for the fields of a dataset.
*/
public final List featurizations() {
return featurizations;
}
@Override
public Builder toBuilder() {
return new BuilderImpl(this);
}
public static Builder builder() {
return new BuilderImpl();
}
public static Class serializableBuilderClass() {
return BuilderImpl.class;
}
@Override
public final int hashCode() {
int hashCode = 1;
hashCode = 31 * hashCode + Objects.hashCode(forecastFrequency());
hashCode = 31 * hashCode + Objects.hashCode(hasForecastDimensions() ? forecastDimensions() : null);
hashCode = 31 * hashCode + Objects.hashCode(hasFeaturizations() ? featurizations() : null);
return hashCode;
}
@Override
public final boolean equals(Object obj) {
return equalsBySdkFields(obj);
}
@Override
public final boolean equalsBySdkFields(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (!(obj instanceof FeaturizationConfig)) {
return false;
}
FeaturizationConfig other = (FeaturizationConfig) obj;
return Objects.equals(forecastFrequency(), other.forecastFrequency())
&& hasForecastDimensions() == other.hasForecastDimensions()
&& Objects.equals(forecastDimensions(), other.forecastDimensions())
&& hasFeaturizations() == other.hasFeaturizations() && Objects.equals(featurizations(), other.featurizations());
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*/
@Override
public final String toString() {
return ToString.builder("FeaturizationConfig").add("ForecastFrequency", forecastFrequency())
.add("ForecastDimensions", hasForecastDimensions() ? forecastDimensions() : null)
.add("Featurizations", hasFeaturizations() ? featurizations() : null).build();
}
public final Optional getValueForField(String fieldName, Class clazz) {
switch (fieldName) {
case "ForecastFrequency":
return Optional.ofNullable(clazz.cast(forecastFrequency()));
case "ForecastDimensions":
return Optional.ofNullable(clazz.cast(forecastDimensions()));
case "Featurizations":
return Optional.ofNullable(clazz.cast(featurizations()));
default:
return Optional.empty();
}
}
@Override
public final List> sdkFields() {
return SDK_FIELDS;
}
private static Function getter(Function g) {
return obj -> g.apply((FeaturizationConfig) obj);
}
private static BiConsumer setter(BiConsumer s) {
return (obj, val) -> s.accept((Builder) obj, val);
}
public interface Builder extends SdkPojo, CopyableBuilder {
/**
*
* The frequency of predictions in a forecast.
*
*
* Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min
* (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a
* value that would overlap with the next larger frequency. That means, for example, you cannot specify a
* frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the
* following:
*
*
* -
*
* Minute - 1-59
*
*
* -
*
* Hour - 1-23
*
*
* -
*
* Day - 1-6
*
*
* -
*
* Week - 1-4
*
*
* -
*
* Month - 1-11
*
*
* -
*
* Year - 1
*
*
*
*
* Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify
* "3M".
*
*
* The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
*
*
* When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset
* frequency.
*
*
* @param forecastFrequency
* The frequency of predictions in a forecast.
*
* Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min
* (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot
* specify a value that would overlap with the next larger frequency. That means, for example, you cannot
* specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each
* frequency are the following:
*
*
* -
*
* Minute - 1-59
*
*
* -
*
* Hour - 1-23
*
*
* -
*
* Day - 1-6
*
*
* -
*
* Week - 1-4
*
*
* -
*
* Month - 1-11
*
*
* -
*
* Year - 1
*
*
*
*
* Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you
* specify "3M".
*
*
* The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
*
*
* When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES
* dataset frequency.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder forecastFrequency(String forecastFrequency);
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, suppose that you are generating a forecast for item sales across all of your stores, and your
* dataset contains a store_id field. If you want the sales forecast for each item by store, you
* would specify store_id as the dimension.
*
*
* All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified
* in the CreatePredictor request. All forecast dimensions specified in the
* RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
*
*
* @param forecastDimensions
* An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, suppose that you are generating a forecast for item sales across all of your stores, and
* your dataset contains a store_id field. If you want the sales forecast for each item by
* store, you would specify store_id as the dimension.
*
*
* All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be
* specified in the CreatePredictor request. All forecast dimensions specified in the
* RELATED_TIME_SERIES dataset must be specified in the CreatePredictor
* request.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder forecastDimensions(Collection forecastDimensions);
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, suppose that you are generating a forecast for item sales across all of your stores, and your
* dataset contains a store_id field. If you want the sales forecast for each item by store, you
* would specify store_id as the dimension.
*
*
* All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified
* in the CreatePredictor request. All forecast dimensions specified in the
* RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
*
*
* @param forecastDimensions
* An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, suppose that you are generating a forecast for item sales across all of your stores, and
* your dataset contains a store_id field. If you want the sales forecast for each item by
* store, you would specify store_id as the dimension.
*
*
* All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be
* specified in the CreatePredictor request. All forecast dimensions specified in the
* RELATED_TIME_SERIES dataset must be specified in the CreatePredictor
* request.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder forecastDimensions(String... forecastDimensions);
/**
*
* An array of featurization (transformation) information for the fields of a dataset.
*
*
* @param featurizations
* An array of featurization (transformation) information for the fields of a dataset.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder featurizations(Collection featurizations);
/**
*
* An array of featurization (transformation) information for the fields of a dataset.
*
*
* @param featurizations
* An array of featurization (transformation) information for the fields of a dataset.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder featurizations(Featurization... featurizations);
/**
*
* An array of featurization (transformation) information for the fields of a dataset.
*
* This is a convenience method that creates an instance of the
* {@link software.amazon.awssdk.services.forecast.model.Featurization.Builder} avoiding the need to create one
* manually via {@link software.amazon.awssdk.services.forecast.model.Featurization#builder()}.
*
*
* When the {@link Consumer} completes,
* {@link software.amazon.awssdk.services.forecast.model.Featurization.Builder#build()} is called immediately
* and its result is passed to {@link #featurizations(List)}.
*
* @param featurizations
* a consumer that will call methods on
* {@link software.amazon.awssdk.services.forecast.model.Featurization.Builder}
* @return Returns a reference to this object so that method calls can be chained together.
* @see #featurizations(java.util.Collection)
*/
Builder featurizations(Consumer... featurizations);
}
static final class BuilderImpl implements Builder {
private String forecastFrequency;
private List forecastDimensions = DefaultSdkAutoConstructList.getInstance();
private List featurizations = DefaultSdkAutoConstructList.getInstance();
private BuilderImpl() {
}
private BuilderImpl(FeaturizationConfig model) {
forecastFrequency(model.forecastFrequency);
forecastDimensions(model.forecastDimensions);
featurizations(model.featurizations);
}
public final String getForecastFrequency() {
return forecastFrequency;
}
public final void setForecastFrequency(String forecastFrequency) {
this.forecastFrequency = forecastFrequency;
}
@Override
public final Builder forecastFrequency(String forecastFrequency) {
this.forecastFrequency = forecastFrequency;
return this;
}
public final Collection getForecastDimensions() {
if (forecastDimensions instanceof SdkAutoConstructList) {
return null;
}
return forecastDimensions;
}
public final void setForecastDimensions(Collection forecastDimensions) {
this.forecastDimensions = ForecastDimensionsCopier.copy(forecastDimensions);
}
@Override
public final Builder forecastDimensions(Collection forecastDimensions) {
this.forecastDimensions = ForecastDimensionsCopier.copy(forecastDimensions);
return this;
}
@Override
@SafeVarargs
public final Builder forecastDimensions(String... forecastDimensions) {
forecastDimensions(Arrays.asList(forecastDimensions));
return this;
}
public final List getFeaturizations() {
List result = FeaturizationsCopier.copyToBuilder(this.featurizations);
if (result instanceof SdkAutoConstructList) {
return null;
}
return result;
}
public final void setFeaturizations(Collection featurizations) {
this.featurizations = FeaturizationsCopier.copyFromBuilder(featurizations);
}
@Override
public final Builder featurizations(Collection featurizations) {
this.featurizations = FeaturizationsCopier.copy(featurizations);
return this;
}
@Override
@SafeVarargs
public final Builder featurizations(Featurization... featurizations) {
featurizations(Arrays.asList(featurizations));
return this;
}
@Override
@SafeVarargs
public final Builder featurizations(Consumer... featurizations) {
featurizations(Stream.of(featurizations).map(c -> Featurization.builder().applyMutation(c).build())
.collect(Collectors.toList()));
return this;
}
@Override
public FeaturizationConfig build() {
return new FeaturizationConfig(this);
}
@Override
public List> sdkFields() {
return SDK_FIELDS;
}
}
}