src/main/java/org/apache/sysds/api/mlcontext/Script.java - systemds - Git at Google

 /*
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
  * distributed with this work for additional information
  * regarding copyright ownership.  The ASF licenses this file
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
  *
  *   http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License 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 org.apache.sysds.api.mlcontext;

 import java.util.Arrays;
 import java.util.Collections;
 import java.util.LinkedHashMap;
 import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Map;
 import java.util.Map.Entry;

 import org.apache.sysds.runtime.controlprogram.LocalVariableMap;
 import org.apache.sysds.runtime.controlprogram.caching.CacheableData;
 import org.apache.sysds.runtime.instructions.cp.Data;

 import java.util.Set;

 import scala.Tuple2;
 import scala.Tuple3;
 import scala.collection.JavaConversions;

 /**
  * A Script object encapsulates a DML or PYDML script.
  *
  */
 public class Script {
 	/**
 	 * The script content.
 	 */
 	private String scriptString;
 	/**
 	 * The optional name of the script.
 	 */
 	private String name;
 	/**
 	 * All inputs (input parameters ($) and input variables).
 	 */
 	private Map<String, Object> inputs = new LinkedHashMap<>();
 	/**
 	 * The input parameters ($).
 	 */
 	private Map<String, Object> inputParameters = new LinkedHashMap<>();
 	/**
 	 * The input variables.
 	 */
 	private Set<String> inputVariables = new LinkedHashSet<>();
 	/**
 	 * The input matrix or frame metadata if present.
 	 */
 	private Map<String, Metadata> inputMetadata = new LinkedHashMap<>();
 	/**
 	 * The output variables.
 	 */
 	private Set<String> outputVariables = new LinkedHashSet<>();
 	/**
 	 * The symbol table containing the data associated with variables.
 	 */
 	private LocalVariableMap symbolTable = new LocalVariableMap();
 	/**
 	 * The ScriptExecutor which is used to define the execution of the script.
 	 */
 	private ScriptExecutor scriptExecutor;
 	/**
 	 * The results of the execution of the script.
 	 */
 	private MLResults results;

 	/**
 	 * Script constructor, which by default creates a DML script.
 	 */
 	public Script() {

 	}

 	/**
 	 * Script constructor, specifying the script content. By default, the script
 	 * type is DML.
 	 *
 	 * @param scriptString
 	 *            the script content as a string
 	 */
 	public Script(String scriptString) {
 		this.scriptString = scriptString;
 	}

 	/**
 	 * Obtain the script string.
 	 *
 	 * @return the script string
 	 */
 	public String getScriptString() {
 		return scriptString;
 	}

 	/**
 	 * Set the script string.
 	 *
 	 * @param scriptString
 	 *            the script string
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script setScriptString(String scriptString) {
 		this.scriptString = scriptString;
 		return this;
 	}

 	/**
 	 * Obtain the input variable names as an unmodifiable set of strings.
 	 *
 	 * @return the input variable names
 	 */
 	public Set<String> getInputVariables() {
 		return Collections.unmodifiableSet(inputVariables);
 	}

 	/**
 	 * Obtain the output variable names as an unmodifiable set of strings.
 	 *
 	 * @return the output variable names
 	 */
 	public Set<String> getOutputVariables() {
 		return Collections.unmodifiableSet(outputVariables);
 	}

 	/**
 	 * Obtain the symbol table, which is essentially a
 	 * {@code HashMap<String, Data>} representing variables and their values.
 	 *
 	 * @return the symbol table
 	 */
 	public LocalVariableMap getSymbolTable() {
 		return symbolTable;
 	}

 	/**
 	 * Obtain an unmodifiable map of all inputs (parameters ($) and variables).
 	 *
 	 * @return all inputs to the script
 	 */
 	public Map<String, Object> getInputs() {
 		return Collections.unmodifiableMap(inputs);
 	}

 	/**
 	 * Obtain an unmodifiable map of input matrix/frame metadata.
 	 *
 	 * @return input matrix/frame metadata
 	 */
 	public Map<String, Metadata> getInputMetadata() {
 		return Collections.unmodifiableMap(inputMetadata);
 	}

 	/**
 	 * Pass a map of inputs to the script.
 	 *
 	 * @param inputs
 	 *            map of inputs (parameters ($) and variables).
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script in(Map<String, Object> inputs) {
 		for (Entry<String, Object> input : inputs.entrySet()) {
 			in(input.getKey(), input.getValue());
 		}

 		return this;
 	}

 	/**
 	 * Pass a Scala Map of inputs to the script.
 	 * <p>
 	 * Note that the {@code Map} value type is not explicitly specified on this
 	 * method because {@code [String, Any]} can't be recognized on the Java side
 	 * since {@code Any} doesn't have an equivalent in the Java class hierarchy
 	 * ({@code scala.Any} is a superclass of {@code scala.AnyRef}, which is
 	 * equivalent to {@code java.lang.Object}). Therefore, specifying
 	 * {@code scala.collection.Map<String, Object>} as an input parameter to
 	 * this Java method is not encompassing enough and would require types such
 	 * as a {@code scala.Double} to be cast using {@code asInstanceOf[AnyRef]}.
 	 *
 	 * @param inputs
 	 *            Scala Map of inputs (parameters ($) and variables).
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	@SuppressWarnings({ "rawtypes", "unchecked" })
 	public Script in(scala.collection.Map<String, ?> inputs) {
 		Map javaMap = JavaConversions.mapAsJavaMap(inputs);
 		in(javaMap);

 		return this;
 	}

 	/**
 	 * Pass a Scala Seq of inputs to the script. The inputs are either two-value
 	 * or three-value tuples, where the first value is the variable name, the
 	 * second value is the variable value, and the third optional value is the
 	 * metadata.
 	 *
 	 * @param inputs
 	 *            Scala Seq of inputs (parameters ($) and variables).
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script in(scala.collection.Seq<Object> inputs) {
 		List<Object> list = JavaConversions.seqAsJavaList(inputs);
 		for (Object obj : list) {
 			if (obj instanceof Tuple3) {
 				@SuppressWarnings("unchecked")
 				Tuple3<String, Object, MatrixMetadata> t3 = (Tuple3<String, Object, MatrixMetadata>) obj;
 				in(t3._1(), t3._2(), t3._3());
 			} else if (obj instanceof Tuple2) {
 				@SuppressWarnings("unchecked")
 				Tuple2<String, Object> t2 = (Tuple2<String, Object>) obj;
 				in(t2._1(), t2._2());
 			} else {
 				throw new MLContextException("Only Tuples of 2 or 3 values are permitted");
 			}
 		}
 		return this;
 	}

 	/**
 	 * Obtain an unmodifiable map of all input parameters ($).
 	 *
 	 * @return input parameters ($)
 	 */
 	public Map<String, Object> getInputParameters() {
 		return inputParameters;
 	}

 	/**
 	 * Register an input (parameter ($) or variable).
 	 *
 	 * @param name
 	 *            name of the input
 	 * @param value
 	 *            value of the input
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script in(String name, Object value) {
 		return in(name, value, null);
 	}

 	/**
 	 * Register an input (parameter ($) or variable) with optional matrix
 	 * metadata.
 	 *
 	 * @param name
 	 *            name of the input
 	 * @param value
 	 *            value of the input
 	 * @param metadata
 	 *            optional matrix/frame metadata
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script in(String name, Object value, Metadata metadata) {

 		if ((value != null) && (value instanceof Long)) {
 			// convert Long to Integer since Long not a supported value type
 			Long lng = (Long) value;
 			value = lng.intValue();
 		} else if ((value != null) && (value instanceof Float)) {
 			// convert Float to Double since Float not a supported value type
 			Float flt = (Float) value;
 			value = flt.doubleValue();
 		}

 		MLContextUtil.checkInputValueType(name, value);
 		if (inputs == null) {
 			inputs = new LinkedHashMap<>();
 		}
 		inputs.put(name, value);

 		if (name.startsWith("$")) {
 			MLContextUtil.checkInputParameterType(name, value);
 			if (inputParameters == null) {
 				inputParameters = new LinkedHashMap<>();
 			}
 			inputParameters.put(name, value);
 		} else {
 			Data data = MLContextUtil.convertInputType(name, value, metadata);
 			if (data != null) {
 				// store input variable name and data
 				symbolTable.put(name, data);
 				inputVariables.add(name);

 				// store matrix/frame meta data and disable variable cleanup
 				if (data instanceof CacheableData) {
 					if (metadata != null)
 						inputMetadata.put(name, metadata);
 					((CacheableData<?>) data).enableCleanup(false);
 				}
 			}
 		}
 		return this;
 	}

 	/**
 	 * Register an output variable.
 	 *
 	 * @param outputName
 	 *            name of the output variable
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script out(String outputName) {
 		outputVariables.add(outputName);
 		return this;
 	}

 	/**
 	 * Register output variables.
 	 *
 	 * @param outputNames
 	 *            names of the output variables
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script out(String... outputNames) {
 		outputVariables.addAll(Arrays.asList(outputNames));
 		return this;
 	}

 	/**
 	 * Register output variables.
 	 *
 	 * @param outputNames
 	 *            names of the output variables
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script out(scala.collection.Seq<String> outputNames) {
 		List<String> list = JavaConversions.seqAsJavaList(outputNames);
 		outputVariables.addAll(list);
 		return this;
 	}

 	/**
 	 * Register output variables.
 	 *
 	 * @param outputNames
 	 *            names of the output variables
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script out(List<String> outputNames) {
 		outputVariables.addAll(outputNames);
 		return this;
 	}

 	/**
 	 * Clear the inputs, outputs, and symbol table.
 	 */
 	public void clearIOS() {
 		clearInputs();
 		clearOutputs();
 		clearSymbolTable();
 	}

 	/**
 	 * Clear the inputs and outputs, but not the symbol table.
 	 */
 	public void clearIO() {
 		clearInputs();
 		clearOutputs();
 	}

 	/**
 	 * Clear the script string, inputs, outputs, and symbol table.
 	 */
 	public void clearAll() {
 		scriptString = null;
 		clearIOS();
 	}

 	/**
 	 * Clear the inputs.
 	 */
 	public void clearInputs() {
 		inputs.clear();
 		inputParameters.clear();
 		inputVariables.clear();
 		inputMetadata.clear();
 	}

 	/**
 	 * Clear the outputs.
 	 */
 	public void clearOutputs() {
 		outputVariables.clear();
 	}

 	/**
 	 * Clear the symbol table.
 	 */
 	public void clearSymbolTable() {
 		symbolTable.removeAll();
 	}

 	/**
 	 * Obtain the results of the script execution.
 	 *
 	 * @return the results of the script execution.
 	 */
 	public MLResults results() {
 		return results;
 	}

 	/**
 	 * Obtain the results of the script execution.
 	 *
 	 * @return the results of the script execution.
 	 */
 	public MLResults getResults() {
 		return results;
 	}

 	/**
 	 * Set the results of the script execution.
 	 *
 	 * @param results
 	 *            the results of the script execution.
 	 */
 	public void setResults(MLResults results) {
 		this.results = results;
 	}

 	/**
 	 * Obtain the script executor used by this Script.
 	 *
 	 * @return the ScriptExecutor used by this Script.
 	 */
 	public ScriptExecutor getScriptExecutor() {
 		return scriptExecutor;
 	}

 	/**
 	 * Set the ScriptExecutor used by this Script.
 	 *
 	 * @param scriptExecutor
 	 *            the script executor
 	 */
 	public void setScriptExecutor(ScriptExecutor scriptExecutor) {
 		this.scriptExecutor = scriptExecutor;
 	}

 	/**
 	 * Generate the script execution string, which adds read/load/write/save
 	 * statements to the beginning and end of the script to execute.
 	 *
 	 * @return the script execution string
 	 */
 	public String getScriptExecutionString() {
 		StringBuilder sb = new StringBuilder();

 		Set<String> ins = getInputVariables();
 		for (String in : ins) {
 			Object inValue = getInputs().get(in);
 			sb.append(in);
 			if (inValue instanceof String) {
 				String quotedString = MLContextUtil.quotedString((String) inValue);
 				sb.append(" = " + quotedString + ";\n");
 			} else if (MLContextUtil.isBasicType(inValue)) {
 				sb.append(" = read('', data_type='scalar', value_type='" + MLContextUtil.getBasicTypeString(inValue)
 						+ "');\n");
 			} else if (MLContextUtil.doesSymbolTableContainFrameObject(symbolTable, in)) {
 				sb.append(" = read('', data_type='frame');\n");
 			} else {
 				sb.append(" = read('');\n");
 			}
 		}

 		sb.append(getScriptString());
 		if (!getScriptString().endsWith("\n")) {
 			sb.append("\n");
 		}

 		Set<String> outs = getOutputVariables();
 		for (String out : outs) {
 			sb.append("write(");
 			sb.append(out);
 			sb.append(", '');\n");
 		}

 		return sb.toString();
 	}

 	@Override
 	public String toString() {
 		StringBuilder sb = new StringBuilder();

 		sb.append(MLContextUtil.displayInputs("Inputs", inputs, symbolTable));
 		sb.append("\n");
 		sb.append(MLContextUtil.displayOutputs("Outputs", outputVariables, symbolTable));
 		return sb.toString();
 	}

 	/**
 	 * Display information about the script as a String. This consists of the
 	 * script type, inputs, outputs, input parameters, input variables, output
 	 * variables, the symbol table, the script string, and the script execution
 	 * string.
 	 *
 	 * @return information about this script as a String
 	 */
 	public String info() {
 		StringBuilder sb = new StringBuilder();

 		sb.append(MLContextUtil.displayInputs("Inputs", inputs, symbolTable));
 		sb.append("\n");
 		sb.append(MLContextUtil.displayOutputs("Outputs", outputVariables, symbolTable));
 		sb.append("\n");
 		sb.append(MLContextUtil.displayMap("Input Parameters", inputParameters));
 		sb.append("\n");
 		sb.append(MLContextUtil.displaySet("Input Variables", inputVariables));
 		sb.append("\n");
 		sb.append(MLContextUtil.displaySet("Output Variables", outputVariables));
 		sb.append("\n");
 		sb.append(MLContextUtil.displaySymbolTable("Symbol Table", symbolTable));
 		sb.append("\nScript String:\n");
 		sb.append(scriptString);
 		sb.append("\nScript Execution String:\n");
 		sb.append(getScriptExecutionString());
 		sb.append("\n");

 		return sb.toString();
 	}

 	/**
 	 * Display the script inputs.
 	 *
 	 * @return the script inputs
 	 */
 	public String displayInputs() {
 		return MLContextUtil.displayInputs("Inputs", inputs, symbolTable);
 	}

 	/**
 	 * Display the script outputs.
 	 *
 	 * @return the script outputs as a String
 	 */
 	public String displayOutputs() {
 		return MLContextUtil.displayOutputs("Outputs", outputVariables, symbolTable);
 	}

 	/**
 	 * Display the script input parameters.
 	 *
 	 * @return the script input parameters as a String
 	 */
 	public String displayInputParameters() {
 		return MLContextUtil.displayMap("Input Parameters", inputParameters);
 	}

 	/**
 	 * Display the script input variables.
 	 *
 	 * @return the script input variables as a String
 	 */
 	public String displayInputVariables() {
 		return MLContextUtil.displaySet("Input Variables", inputVariables);
 	}

 	/**
 	 * Display the script output variables.
 	 *
 	 * @return the script output variables as a String
 	 */
 	public String displayOutputVariables() {
 		return MLContextUtil.displaySet("Output Variables", outputVariables);
 	}

 	/**
 	 * Display the script symbol table.
 	 *
 	 * @return the script symbol table as a String
 	 */
 	public String displaySymbolTable() {
 		return MLContextUtil.displaySymbolTable("Symbol Table", symbolTable);
 	}

 	/**
 	 * Obtain the script name.
 	 *
 	 * @return the script name
 	 */
 	public String getName() {
 		return name;
 	}

 	/**
 	 * Set the script name.
 	 *
 	 * @param name
 	 *            the script name
 	 * @return {@code this} Script object to allow chaining of methods
 	 */
 	public Script setName(String name) {
 		this.name = name;
 		return this;
 	}

 	/**
 	 * Execute the script and return the results as an MLResults object.
 	 *
 	 * @return results as an MLResults object
 	 */
 	public MLResults execute() {
 		MLContext ml = MLContext.getActiveMLContext();
 		if (ml == null) {
 			throw new MLContextException("No MLContext object exists. Please create one.");
 		}
 		return ml.execute(this);
 	}
 }
	/*
	* Licensed to the Apache Software Foundation (ASF) under one
	* or more contributor license agreements. See the NOTICE file
	* distributed with this work for additional information
	* regarding copyright ownership. The ASF licenses this file
	* to you under the Apache License, Version 2.0 (the
	* "License"); you may not use this file except in compliance
	* with the License. You may obtain a copy of the License at
	*
	* http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing,
	* software distributed under the License 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 org.apache.sysds.api.mlcontext;

	import java.util.Arrays;
	import java.util.Collections;
	import java.util.LinkedHashMap;
	import java.util.LinkedHashSet;
	import java.util.List;
	import java.util.Map;
	import java.util.Map.Entry;

	import org.apache.sysds.runtime.controlprogram.LocalVariableMap;
	import org.apache.sysds.runtime.controlprogram.caching.CacheableData;
	import org.apache.sysds.runtime.instructions.cp.Data;

	import java.util.Set;

	import scala.Tuple2;
	import scala.Tuple3;
	import scala.collection.JavaConversions;

	/**
	* A Script object encapsulates a DML or PYDML script.
	*
	*/
	public class Script {
	/**
	* The script content.
	*/
	private String scriptString;
	/**
	* The optional name of the script.
	*/
	private String name;
	/**
	* All inputs (input parameters ($) and input variables).
	*/
	private Map<String, Object> inputs = new LinkedHashMap<>();
	/**
	* The input parameters ($).
	*/
	private Map<String, Object> inputParameters = new LinkedHashMap<>();
	/**
	* The input variables.
	*/
	private Set<String> inputVariables = new LinkedHashSet<>();
	/**
	* The input matrix or frame metadata if present.
	*/
	private Map<String, Metadata> inputMetadata = new LinkedHashMap<>();
	/**
	* The output variables.
	*/
	private Set<String> outputVariables = new LinkedHashSet<>();
	/**
	* The symbol table containing the data associated with variables.
	*/
	private LocalVariableMap symbolTable = new LocalVariableMap();
	/**
	* The ScriptExecutor which is used to define the execution of the script.
	*/
	private ScriptExecutor scriptExecutor;
	/**
	* The results of the execution of the script.
	*/
	private MLResults results;

	/**
	* Script constructor, which by default creates a DML script.
	*/
	public Script() {

	}

	/**
	* Script constructor, specifying the script content. By default, the script
	* type is DML.
	*
	* @param scriptString
	* the script content as a string
	*/
	public Script(String scriptString) {
	this.scriptString = scriptString;
	}

	/**
	* Obtain the script string.
	*
	* @return the script string
	*/
	public String getScriptString() {
	return scriptString;
	}

	/**
	* Set the script string.
	*
	* @param scriptString
	* the script string
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script setScriptString(String scriptString) {
	this.scriptString = scriptString;
	return this;
	}

	/**
	* Obtain the input variable names as an unmodifiable set of strings.
	*
	* @return the input variable names
	*/
	public Set<String> getInputVariables() {
	return Collections.unmodifiableSet(inputVariables);
	}

	/**
	* Obtain the output variable names as an unmodifiable set of strings.
	*
	* @return the output variable names
	*/
	public Set<String> getOutputVariables() {
	return Collections.unmodifiableSet(outputVariables);
	}

	/**
	* Obtain the symbol table, which is essentially a
	* {@code HashMap<String, Data>} representing variables and their values.
	*
	* @return the symbol table
	*/
	public LocalVariableMap getSymbolTable() {
	return symbolTable;
	}

	/**
	* Obtain an unmodifiable map of all inputs (parameters ($) and variables).
	*
	* @return all inputs to the script
	*/
	public Map<String, Object> getInputs() {
	return Collections.unmodifiableMap(inputs);
	}

	/**
	* Obtain an unmodifiable map of input matrix/frame metadata.
	*
	* @return input matrix/frame metadata
	*/
	public Map<String, Metadata> getInputMetadata() {
	return Collections.unmodifiableMap(inputMetadata);
	}

	/**
	* Pass a map of inputs to the script.
	*
	* @param inputs
	* map of inputs (parameters ($) and variables).
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script in(Map<String, Object> inputs) {
	for (Entry<String, Object> input : inputs.entrySet()) {
	in(input.getKey(), input.getValue());
	}

	return this;
	}

	/**
	* Pass a Scala Map of inputs to the script.
	* <p>
	* Note that the {@code Map} value type is not explicitly specified on this
	* method because {@code [String, Any]} can't be recognized on the Java side
	* since {@code Any} doesn't have an equivalent in the Java class hierarchy
	* ({@code scala.Any} is a superclass of {@code scala.AnyRef}, which is
	* equivalent to {@code java.lang.Object}). Therefore, specifying
	* {@code scala.collection.Map<String, Object>} as an input parameter to
	* this Java method is not encompassing enough and would require types such
	* as a {@code scala.Double} to be cast using {@code asInstanceOf[AnyRef]}.
	*
	* @param inputs
	* Scala Map of inputs (parameters ($) and variables).
	* @return {@code this} Script object to allow chaining of methods
	*/
	@SuppressWarnings({ "rawtypes", "unchecked" })
	public Script in(scala.collection.Map<String, ?> inputs) {
	Map javaMap = JavaConversions.mapAsJavaMap(inputs);
	in(javaMap);

	return this;
	}

	/**
	* Pass a Scala Seq of inputs to the script. The inputs are either two-value
	* or three-value tuples, where the first value is the variable name, the
	* second value is the variable value, and the third optional value is the
	* metadata.
	*
	* @param inputs
	* Scala Seq of inputs (parameters ($) and variables).
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script in(scala.collection.Seq<Object> inputs) {
	List<Object> list = JavaConversions.seqAsJavaList(inputs);
	for (Object obj : list) {
	if (obj instanceof Tuple3) {
	@SuppressWarnings("unchecked")
	Tuple3<String, Object, MatrixMetadata> t3 = (Tuple3<String, Object, MatrixMetadata>) obj;
	in(t3._1(), t3._2(), t3._3());
	} else if (obj instanceof Tuple2) {
	@SuppressWarnings("unchecked")
	Tuple2<String, Object> t2 = (Tuple2<String, Object>) obj;
	in(t2._1(), t2._2());
	} else {
	throw new MLContextException("Only Tuples of 2 or 3 values are permitted");
	}
	}
	return this;
	}

	/**
	* Obtain an unmodifiable map of all input parameters ($).
	*
	* @return input parameters ($)
	*/
	public Map<String, Object> getInputParameters() {
	return inputParameters;
	}

	/**
	* Register an input (parameter ($) or variable).
	*
	* @param name
	* name of the input
	* @param value
	* value of the input
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script in(String name, Object value) {
	return in(name, value, null);
	}

	/**
	* Register an input (parameter ($) or variable) with optional matrix
	* metadata.
	*
	* @param name
	* name of the input
	* @param value
	* value of the input
	* @param metadata
	* optional matrix/frame metadata
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script in(String name, Object value, Metadata metadata) {

	if ((value != null) && (value instanceof Long)) {
	// convert Long to Integer since Long not a supported value type
	Long lng = (Long) value;
	value = lng.intValue();
	} else if ((value != null) && (value instanceof Float)) {
	// convert Float to Double since Float not a supported value type
	Float flt = (Float) value;
	value = flt.doubleValue();
	}

	MLContextUtil.checkInputValueType(name, value);
	if (inputs == null) {
	inputs = new LinkedHashMap<>();
	}
	inputs.put(name, value);

	if (name.startsWith("$")) {
	MLContextUtil.checkInputParameterType(name, value);
	if (inputParameters == null) {
	inputParameters = new LinkedHashMap<>();
	}
	inputParameters.put(name, value);
	} else {
	Data data = MLContextUtil.convertInputType(name, value, metadata);
	if (data != null) {
	// store input variable name and data
	symbolTable.put(name, data);
	inputVariables.add(name);

	// store matrix/frame meta data and disable variable cleanup
	if (data instanceof CacheableData) {
	if (metadata != null)
	inputMetadata.put(name, metadata);
	((CacheableData<?>) data).enableCleanup(false);
	}
	}
	}
	return this;
	}

	/**
	* Register an output variable.
	*
	* @param outputName
	* name of the output variable
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script out(String outputName) {
	outputVariables.add(outputName);
	return this;
	}

	/**
	* Register output variables.
	*
	* @param outputNames
	* names of the output variables
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script out(String... outputNames) {
	outputVariables.addAll(Arrays.asList(outputNames));
	return this;
	}

	/**
	* Register output variables.
	*
	* @param outputNames
	* names of the output variables
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script out(scala.collection.Seq<String> outputNames) {
	List<String> list = JavaConversions.seqAsJavaList(outputNames);
	outputVariables.addAll(list);
	return this;
	}

	/**
	* Register output variables.
	*
	* @param outputNames
	* names of the output variables
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script out(List<String> outputNames) {
	outputVariables.addAll(outputNames);
	return this;
	}

	/**
	* Clear the inputs, outputs, and symbol table.
	*/
	public void clearIOS() {
	clearInputs();
	clearOutputs();
	clearSymbolTable();
	}

	/**
	* Clear the inputs and outputs, but not the symbol table.
	*/
	public void clearIO() {
	clearInputs();
	clearOutputs();
	}

	/**
	* Clear the script string, inputs, outputs, and symbol table.
	*/
	public void clearAll() {
	scriptString = null;
	clearIOS();
	}

	/**
	* Clear the inputs.
	*/
	public void clearInputs() {
	inputs.clear();
	inputParameters.clear();
	inputVariables.clear();
	inputMetadata.clear();
	}

	/**
	* Clear the outputs.
	*/
	public void clearOutputs() {
	outputVariables.clear();
	}

	/**
	* Clear the symbol table.
	*/
	public void clearSymbolTable() {
	symbolTable.removeAll();
	}

	/**
	* Obtain the results of the script execution.
	*
	* @return the results of the script execution.
	*/
	public MLResults results() {
	return results;
	}

	/**
	* Obtain the results of the script execution.
	*
	* @return the results of the script execution.
	*/
	public MLResults getResults() {
	return results;
	}

	/**
	* Set the results of the script execution.
	*
	* @param results
	* the results of the script execution.
	*/
	public void setResults(MLResults results) {
	this.results = results;
	}

	/**
	* Obtain the script executor used by this Script.
	*
	* @return the ScriptExecutor used by this Script.
	*/
	public ScriptExecutor getScriptExecutor() {
	return scriptExecutor;
	}

	/**
	* Set the ScriptExecutor used by this Script.
	*
	* @param scriptExecutor
	* the script executor
	*/
	public void setScriptExecutor(ScriptExecutor scriptExecutor) {
	this.scriptExecutor = scriptExecutor;
	}

	/**
	* Generate the script execution string, which adds read/load/write/save
	* statements to the beginning and end of the script to execute.
	*
	* @return the script execution string
	*/
	public String getScriptExecutionString() {
	StringBuilder sb = new StringBuilder();

	Set<String> ins = getInputVariables();
	for (String in : ins) {
	Object inValue = getInputs().get(in);
	sb.append(in);
	if (inValue instanceof String) {
	String quotedString = MLContextUtil.quotedString((String) inValue);
	sb.append(" = " + quotedString + ";\n");
	} else if (MLContextUtil.isBasicType(inValue)) {
	sb.append(" = read('', data_type='scalar', value_type='" + MLContextUtil.getBasicTypeString(inValue)
	+ "');\n");
	} else if (MLContextUtil.doesSymbolTableContainFrameObject(symbolTable, in)) {
	sb.append(" = read('', data_type='frame');\n");
	} else {
	sb.append(" = read('');\n");
	}
	}

	sb.append(getScriptString());
	if (!getScriptString().endsWith("\n")) {
	sb.append("\n");
	}

	Set<String> outs = getOutputVariables();
	for (String out : outs) {
	sb.append("write(");
	sb.append(out);
	sb.append(", '');\n");
	}

	return sb.toString();
	}

	@Override
	public String toString() {
	StringBuilder sb = new StringBuilder();

	sb.append(MLContextUtil.displayInputs("Inputs", inputs, symbolTable));
	sb.append("\n");
	sb.append(MLContextUtil.displayOutputs("Outputs", outputVariables, symbolTable));
	return sb.toString();
	}

	/**
	* Display information about the script as a String. This consists of the
	* script type, inputs, outputs, input parameters, input variables, output
	* variables, the symbol table, the script string, and the script execution
	* string.
	*
	* @return information about this script as a String
	*/
	public String info() {
	StringBuilder sb = new StringBuilder();

	sb.append(MLContextUtil.displayInputs("Inputs", inputs, symbolTable));
	sb.append("\n");
	sb.append(MLContextUtil.displayOutputs("Outputs", outputVariables, symbolTable));
	sb.append("\n");
	sb.append(MLContextUtil.displayMap("Input Parameters", inputParameters));
	sb.append("\n");
	sb.append(MLContextUtil.displaySet("Input Variables", inputVariables));
	sb.append("\n");
	sb.append(MLContextUtil.displaySet("Output Variables", outputVariables));
	sb.append("\n");
	sb.append(MLContextUtil.displaySymbolTable("Symbol Table", symbolTable));
	sb.append("\nScript String:\n");
	sb.append(scriptString);
	sb.append("\nScript Execution String:\n");
	sb.append(getScriptExecutionString());
	sb.append("\n");

	return sb.toString();
	}

	/**
	* Display the script inputs.
	*
	* @return the script inputs
	*/
	public String displayInputs() {
	return MLContextUtil.displayInputs("Inputs", inputs, symbolTable);
	}

	/**
	* Display the script outputs.
	*
	* @return the script outputs as a String
	*/
	public String displayOutputs() {
	return MLContextUtil.displayOutputs("Outputs", outputVariables, symbolTable);
	}

	/**
	* Display the script input parameters.
	*
	* @return the script input parameters as a String
	*/
	public String displayInputParameters() {
	return MLContextUtil.displayMap("Input Parameters", inputParameters);
	}

	/**
	* Display the script input variables.
	*
	* @return the script input variables as a String
	*/
	public String displayInputVariables() {
	return MLContextUtil.displaySet("Input Variables", inputVariables);
	}

	/**
	* Display the script output variables.
	*
	* @return the script output variables as a String
	*/
	public String displayOutputVariables() {
	return MLContextUtil.displaySet("Output Variables", outputVariables);
	}

	/**
	* Display the script symbol table.
	*
	* @return the script symbol table as a String
	*/
	public String displaySymbolTable() {
	return MLContextUtil.displaySymbolTable("Symbol Table", symbolTable);
	}

	/**
	* Obtain the script name.
	*
	* @return the script name
	*/
	public String getName() {
	return name;
	}

	/**
	* Set the script name.
	*
	* @param name
	* the script name
	* @return {@code this} Script object to allow chaining of methods
	*/
	public Script setName(String name) {
	this.name = name;
	return this;
	}

	/**
	* Execute the script and return the results as an MLResults object.
	*
	* @return results as an MLResults object
	*/
	public MLResults execute() {
	MLContext ml = MLContext.getActiveMLContext();
	if (ml == null) {
	throw new MLContextException("No MLContext object exists. Please create one.");
	}
	return ml.execute(this);
	}
	}