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/*
* 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.mahout.classifier.sgd;
import com.google.common.base.Function;
import com.google.common.base.Preconditions;
import com.google.common.collect.Collections2;
import com.google.common.collect.ImmutableMap;
import com.google.common.collect.Lists;
import org.apache.commons.csv.CSVUtils;
import org.apache.mahout.math.Vector;
import org.apache.mahout.vectorizer.encoders.ConstantValueEncoder;
import org.apache.mahout.vectorizer.encoders.ContinuousValueEncoder;
import org.apache.mahout.vectorizer.encoders.Dictionary;
import org.apache.mahout.vectorizer.encoders.FeatureVectorEncoder;
import org.apache.mahout.vectorizer.encoders.StaticWordValueEncoder;
import org.apache.mahout.vectorizer.encoders.TextValueEncoder;
import java.io.IOException;
import java.lang.reflect.Constructor;
import java.lang.reflect.InvocationTargetException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.TreeMap;
/**
* Converts CSV data lines to vectors.
*
* Use of this class proceeds in a few steps.
*
*
At construction time, you tell the class about the target variable and provide
* a dictionary of the types of the predictor values. At this point,
* the class yet cannot decode inputs because it doesn't know the fields that are in the
* data records, nor their order.
*
Optionally, you tell the parser object about the possible values of the target
* variable. If you don't do this then you probably should set the number of distinct
* values so that the target variable values will be taken from a restricted range.
*
Later, when you get a list of the fields, typically from the first line of a CSV
* file, you tell the factory about these fields and it builds internal data structures
* that allow it to decode inputs. The most important internal state is the field numbers
* for various fields. After this point, you can use the factory for decoding data.
*
To encode data as a vector, you present a line of input to the factory and it
* mutates a vector that you provide. The factory also retains trace information so
* that it can approximately reverse engineer vectors later.
*
After converting data, you can ask for an explanation of the data in terms of
* terms and weights. In order to explain a vector accurately, the factory needs to
* have seen the particular values of categorical fields (typically during encoding vectors)
* and needs to have a reasonably small number of collisions in the vector encoding.
*
*/
public class CsvRecordFactory implements RecordFactory {
private static final String INTERCEPT_TERM = "Intercept Term";
private static final Map> TYPE_DICTIONARY =
ImmutableMap.>builder()
.put("continuous", ContinuousValueEncoder.class)
.put("numeric", ContinuousValueEncoder.class)
.put("n", ContinuousValueEncoder.class)
.put("word", StaticWordValueEncoder.class)
.put("w", StaticWordValueEncoder.class)
.put("text", TextValueEncoder.class)
.put("t", TextValueEncoder.class)
.build();
private final Map> traceDictionary = new TreeMap<>();
private int target;
private final Dictionary targetDictionary;
//Which column is used for identify a CSV file line
private String idName;
private int id = -1;
private List predictors;
private Map predictorEncoders;
private int maxTargetValue = Integer.MAX_VALUE;
private final String targetName;
private final Map typeMap;
private List variableNames;
private boolean includeBiasTerm;
private static final String CANNOT_CONSTRUCT_CONVERTER =
"Unable to construct type converter... shouldn't be possible";
/**
* Parse a single line of CSV-formatted text.
*
* Separated to make changing this functionality for the entire class easier
* in the future.
* @param line - CSV formatted text
* @return List
*/
private List parseCsvLine(String line) {
try {
return Arrays.asList(CSVUtils.parseLine(line));
}
catch (IOException e) {
List list = new ArrayList<>();
list.add(line);
return list;
}
}
private List parseCsvLine(CharSequence line) {
return parseCsvLine(line.toString());
}
/**
* Construct a parser for CSV lines that encodes the parsed data in vector form.
* @param targetName The name of the target variable.
* @param typeMap A map describing the types of the predictor variables.
*/
public CsvRecordFactory(String targetName, Map typeMap) {
this.targetName = targetName;
this.typeMap = typeMap;
targetDictionary = new Dictionary();
}
public CsvRecordFactory(String targetName, String idName, Map typeMap) {
this(targetName, typeMap);
this.idName = idName;
}
/**
* Defines the values and thus the encoding of values of the target variables. Note
* that any values of the target variable not present in this list will be given the
* value of the last member of the list.
* @param values The values the target variable can have.
*/
@Override
public void defineTargetCategories(List values) {
Preconditions.checkArgument(
values.size() <= maxTargetValue,
"Must have less than or equal to " + maxTargetValue + " categories for target variable, but found "
+ values.size());
if (maxTargetValue == Integer.MAX_VALUE) {
maxTargetValue = values.size();
}
for (String value : values) {
targetDictionary.intern(value);
}
}
/**
* Defines the number of target variable categories, but allows this parser to
* pick encodings for them as they appear.
* @param max The number of categories that will be expected. Once this many have been
* seen, all others will get the encoding max-1.
*/
@Override
public CsvRecordFactory maxTargetValue(int max) {
maxTargetValue = max;
return this;
}
@Override
public boolean usesFirstLineAsSchema() {
return true;
}
/**
* Processes the first line of a file (which should contain the variable names). The target and
* predictor column numbers are set from the names on this line.
*
* @param line Header line for the file.
*/
@Override
public void firstLine(String line) {
// read variable names, build map of name -> column
final Map vars = new HashMap<>();
variableNames = parseCsvLine(line);
int column = 0;
for (String var : variableNames) {
vars.put(var, column++);
}
// record target column and establish dictionary for decoding target
target = vars.get(targetName);
// record id column
if (idName != null) {
id = vars.get(idName);
}
// create list of predictor column numbers
predictors = new ArrayList<>(Collections2.transform(typeMap.keySet(), new Function() {
@Override
public Integer apply(String from) {
Integer r = vars.get(from);
Preconditions.checkArgument(r != null, "Can't find variable %s, only know about %s", from, vars);
return r;
}
}));
if (includeBiasTerm) {
predictors.add(-1);
}
Collections.sort(predictors);
// and map from column number to type encoder for each column that is a predictor
predictorEncoders = new HashMap<>();
for (Integer predictor : predictors) {
String name;
Class c;
if (predictor == -1) {
name = INTERCEPT_TERM;
c = ConstantValueEncoder.class;
} else {
name = variableNames.get(predictor);
c = TYPE_DICTIONARY.get(typeMap.get(name));
}
try {
Preconditions.checkArgument(c != null, "Invalid type of variable %s, wanted one of %s",
typeMap.get(name), TYPE_DICTIONARY.keySet());
Constructor constructor = c.getConstructor(String.class);
Preconditions.checkArgument(constructor != null, "Can't find correct constructor for %s", typeMap.get(name));
FeatureVectorEncoder encoder = constructor.newInstance(name);
predictorEncoders.put(predictor, encoder);
encoder.setTraceDictionary(traceDictionary);
} catch (InstantiationException e) {
throw new IllegalStateException(CANNOT_CONSTRUCT_CONVERTER, e);
} catch (IllegalAccessException e) {
throw new IllegalStateException(CANNOT_CONSTRUCT_CONVERTER, e);
} catch (InvocationTargetException e) {
throw new IllegalStateException(CANNOT_CONSTRUCT_CONVERTER, e);
} catch (NoSuchMethodException e) {
throw new IllegalStateException(CANNOT_CONSTRUCT_CONVERTER, e);
}
}
}
/**
* Decodes a single line of CSV data and records the target and predictor variables in a record.
* As a side effect, features are added into the featureVector. Returns the value of the target
* variable.
*
* @param line The raw data.
* @param featureVector Where to fill in the features. Should be zeroed before calling
* processLine.
* @return The value of the target variable.
*/
@Override
public int processLine(String line, Vector featureVector) {
List values = parseCsvLine(line);
int targetValue = targetDictionary.intern(values.get(target));
if (targetValue >= maxTargetValue) {
targetValue = maxTargetValue - 1;
}
for (Integer predictor : predictors) {
String value;
if (predictor >= 0) {
value = values.get(predictor);
} else {
value = null;
}
predictorEncoders.get(predictor).addToVector(value, featureVector);
}
return targetValue;
}
/***
* Decodes a single line of CSV data and records the target(if retrunTarget is true)
* and predictor variables in a record. As a side effect, features are added into the featureVector.
* Returns the value of the target variable. When used during classify against production data without
* target value, the method will be called with returnTarget = false.
* @param line The raw data.
* @param featureVector Where to fill in the features. Should be zeroed before calling
* processLine.
* @param returnTarget whether process and return target value, -1 will be returned if false.
* @return The value of the target variable.
*/
public int processLine(CharSequence line, Vector featureVector, boolean returnTarget) {
List values = parseCsvLine(line);
int targetValue = -1;
if (returnTarget) {
targetValue = targetDictionary.intern(values.get(target));
if (targetValue >= maxTargetValue) {
targetValue = maxTargetValue - 1;
}
}
for (Integer predictor : predictors) {
String value = predictor >= 0 ? values.get(predictor) : null;
predictorEncoders.get(predictor).addToVector(value, featureVector);
}
return targetValue;
}
/***
* Extract the raw target string from a line read from a CSV file.
* @param line the line of content read from CSV file
* @return the raw target value in the corresponding column of CSV line
*/
public String getTargetString(CharSequence line) {
List values = parseCsvLine(line);
return values.get(target);
}
/***
* Extract the corresponding raw target label according to a code
* @param code the integer code encoded during training process
* @return the raw target label
*/
public String getTargetLabel(int code) {
for (String key : targetDictionary.values()) {
if (targetDictionary.intern(key) == code) {
return key;
}
}
return null;
}
/***
* Extract the id column value from the CSV record
* @param line the line of content read from CSV file
* @return the id value of the CSV record
*/
public String getIdString(CharSequence line) {
List values = parseCsvLine(line);
return values.get(id);
}
/**
* Returns a list of the names of the predictor variables.
*
* @return A list of variable names.
*/
@Override
public Iterable getPredictors() {
return Lists.transform(predictors, new Function() {
@Override
public String apply(Integer v) {
if (v >= 0) {
return variableNames.get(v);
} else {
return INTERCEPT_TERM;
}
}
});
}
@Override
public Map> getTraceDictionary() {
return traceDictionary;
}
@Override
public CsvRecordFactory includeBiasTerm(boolean useBias) {
includeBiasTerm = useBias;
return this;
}
@Override
public List getTargetCategories() {
List r = targetDictionary.values();
if (r.size() > maxTargetValue) {
r.subList(maxTargetValue, r.size()).clear();
}
return r;
}
public String getIdName() {
return idName;
}
public void setIdName(String idName) {
this.idName = idName;
}
}
