问题
I have the following code, it reads in many files from a directory into a hash map, this is my feature vecteur. It's somewhat naive in the sense that it does no stemming but that's not my primary concern right now. I want to know how I can use this data structure as the input to the perceptron algorithm. I guess we call this a bag of words, isn't it?
public class BagOfWords
{
static Map<String, Integer> bag_of_words = new HashMap<>();
public static void main(String[] args) throws IOException
{
String path = "/home/flavius/atheism;
File file = new File( path );
new BagOfWords().iterateDirectory(file);
for (Map.Entry<String, Integer> entry : bag_of_words.entrySet())
{
System.out.println(entry.getKey()+" : "+entry.getValue());
}
}
private void iterateDirectory(File file) throws IOException
{
for (File f : file.listFiles())
{
if (f.isDirectory())
{
iterateDirectory(file);
}
else
{
String line;
BufferedReader br = new BufferedReader(new FileReader( f ));
while ((line = br.readLine()) != null)
{
String[] words = line.split(" ");//those are your words
String word;
for (int i = 0; i < words.length; i++)
{
word = words[i];
if (!bag_of_words.containsKey(word))
{
bag_of_words.put(word, 0);
}
bag_of_words.put(word, bag_of_words.get(word) + 1);
}
}
}
}
}
}
You can see that the path goes to a directory called 'atheism' there's also one called sports, I want to try to linearly seperate these two classes of documents, and then try to seperate the unseen test docs into either category.
How to do that? How to conceptualize that. I'd appreciate a solid reference, comprehensive explanation or some kind of pseudocode.
I've not found many informative and lucid references on the web.
回答1:
Let's establish some vocabulary up front (I guess you are using the 20-newsgroup dataset):
- "Class Label" is what you're trying to predict, in your binary case this is "atheism" vs. the rest
- "Feature vector" that's what you input to your classifier
- "Document" that is a single e-mail from the dataset
- "Token" a fraction of a document, usually a unigram/bigram/trigram
- "Dictionary" a set of "allowed" words for your vector
So the vectorization algorithm for bag of words usually follows the following steps:
- Go over all the documents (across all class labels) and collect all the tokens, this is your dictionary and the dimensionality of your feature vector
- Go over all the documents again and for each do:
- Create a new feature vector with the dimensionality of your dictionary (e.g. 200, for 200 entries in that dictionary)
- go over all the tokens in that document and set the word count (within this document) at this dimension of the feature vector
- You now have a list of feature vectors that you can feed into your algorithm
Example:
Document 1 = ["I", "am", "awesome"]
Document 2 = ["I", "am", "great", "great"]
Dictionary is:
["I", "am", "awesome", "great"]
So the documents as a vector would look like:
Document 1 = [1, 1, 1, 0]
Document 2 = [1, 1, 0, 2]
And with that you can do all kinds of fancy math stuff and feed this into your perceptron.
回答2:
This is the full and complete answer to my original question, posted here for the benefit of future perusers
Given the following files:
atheism/a_0.txt
Gott ist tot.politics/p_0.txt
L'Etat, c'est moi , et aussi moi .science/s_0.txt
If I have seen further it is by standing on the shoulders of giants.sports/s_1.txt
You miss 100% of the shots you don't take.Output data structures:
/data/train/politics/p_0.txt, [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0] /data/train/science/s_0.txt, [1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0] /data/train/atheism/a_0.txt, [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] /data/train/sports/s_1.txt, [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1]
The code looks like this, or you can find it on my GitHub page.
public class FileDictCreateur
{
static String PATH = "/home/matthias/Workbench/SUTD/ISTD_50.570/assignments/practice_data/data/train";
//the global list of all words across all articles
static Set<String> GLOBO_DICT = new HashSet<String>();
//is the globo dict full?
static boolean globo_dict_fixed = false;
// hash map of all the words contained in individual files
static Map<File, ArrayList<String> > fileDict = new HashMap<>();
//input to perceptron. final struc.
static Map<File, int[] > perceptron_input = new HashMap<>();
@SuppressWarnings("rawtypes")
public static void main(String[] args) throws IOException
{
//each of the diferent categories
String[] categories = { "/atheism", "/politics", "/science", "/sports"};
//cycle through all categories once to populate the global dict
for(int cycle = 0; cycle <= 3; cycle++)
{
String general_data_partition = PATH + categories[cycle];
File directory = new File( general_data_partition );
iterateDirectory( directory , globo_dict_fixed);
if(cycle == 3)
globo_dict_fixed = true;
}
//cycle through again to populate the file dicts
for(int cycle = 0; cycle <= 3; cycle++)
{
String general_data_partition = PATH + categories[cycle];
File directory = new File( general_data_partition );
iterateDirectory( directory , globo_dict_fixed);
}
perceptron_data_struc_generateur( GLOBO_DICT, fileDict, perceptron_input );
//print the output
for (Map.Entry<File, int[]> entry : perceptron_input.entrySet())
{
System.out.println(entry.getKey() + ", " + Arrays.toString(entry.getValue()));
}
}
private static void iterateDirectory(File directory, boolean globo_dict_fixed) throws IOException
{
for (File file : directory.listFiles())
{
if (file.isDirectory())
{
iterateDirectory(directory, globo_dict_fixed);
}
else
{
String line;
BufferedReader br = new BufferedReader(new FileReader( file ));
while ((line = br.readLine()) != null)
{
String[] words = line.split(" ");//those are your words
if(globo_dict_fixed == false)
{
populate_globo_dict( words );
}
else
{
create_file_dict( file, words );
}
}
}
}
}
@SuppressWarnings("unchecked")
public static void create_file_dict( File file, String[] words ) throws IOException
{
if (!fileDict.containsKey(file))
{
@SuppressWarnings("rawtypes")
ArrayList document_words = new ArrayList<String>();
String word;
for (int i = 0; i < words.length; i++)
{
word = words[i];
document_words.add(word);
}
fileDict.put(file, document_words);
}
}
public static void populate_globo_dict( String[] words ) throws IOException
{
String word;
for (int i = 0; i < words.length; i++)
{
word = words[i];
if (!GLOBO_DICT.contains(word))
{
GLOBO_DICT.add(word);
}
}
}
public static void perceptron_data_struc_generateur(Set<String> GLOBO_DICT,
Map<File, ArrayList<String> > fileDict,
Map<File, int[] > perceptron_input)
{
//create a new entry in the array list 'perceptron_input'
//with the key as the file name from fileDict
//create a new array which is the length of GLOBO_DICT
//iterate through the indicies of GLOBO_DICT
//for all words in globo dict, if that word appears in fileDict,
//increment the perceptron_input index that corresponds to that
//word in GLOBO_DICT by the number of times that word appears in fileDict
//so i can get the index later
List<String> GLOBO_DICT_list = new ArrayList<>(GLOBO_DICT);
for (Map.Entry<File, ArrayList<String>> entry : fileDict.entrySet())
{
int[] cross_czech = new int[GLOBO_DICT_list.size()];
//initialize to zero
Arrays.fill(cross_czech, 0);
for (String s : GLOBO_DICT_list)
{
for(String st : entry.getValue())
{
if( st.equals(s) )
{
cross_czech[ GLOBO_DICT_list.indexOf( s ) ] = cross_czech[ GLOBO_DICT_list.indexOf( s ) ] +1;
}
}
}
perceptron_input.put( entry.getKey() , cross_czech);
}
}
}
来源：https://stackoverflow.com/questions/28536678/run-perceptron-algorithm-on-a-hash-map-feature-vecteur-java