public void BuildIndexedFeatureIntoFile(string strFileName, List <string> features)
{
//Assign id for each feature
var feature2Id = new SortedDictionary <string, int>(StringComparer.Ordinal);
var maxId = 0;
foreach (var strFeature in features)
{
if (strFeature.StartsWith("U") == false)
{
Logger.WriteLine("Invalidated feature: {0}", strFeature);
continue;
}
feature2Id.Add(strFeature, maxId);
maxId++;
}
var da = new DoubleArrayTrieBuilder(4);
da.build(feature2Id);
da.save(strFileName + ".dart");
var swTemplate = new StreamWriter(strFileName + ".template");
swTemplate.WriteLine("MaxTemplateFeatureId:{0}", maxId);
foreach (var strTemplate in m_Templates)
{
swTemplate.WriteLine(strTemplate);
}
swTemplate.Close();
}
//Build double array trie-tree from text file
//strTextFileName: raw text file name used to build DA trie-tree
// text file format: key \t value
// key as string type
// value as non-netgive integer
//strDAFileName: double array trie-tree binary file name built from strTextFileName
private static void Build(string strTextFileName, string strDAFileName)
{
StreamReader sr = new StreamReader(strTextFileName);
//Load raw data from text file and sort them as ordinal
SortedDictionary<string, int> sdict = new SortedDictionary<string, int>(StringComparer.Ordinal);
Console.WriteLine("Loading key value pairs from raw text file and sort them...");
while (sr.EndOfStream == false)
{
string strLine = sr.ReadLine();
if (strLine.Length == 0)
{
continue;
}
string[] items = strLine.Split('\t');
sdict.Add(items[0], int.Parse(items[1]));
}
//test case for SearchAsKeyPrefix and SearchByPrefix
sdict.Add("TestSearchPrefix_case0", 1234567);
sdict.Add("TestSearchPrefix_case01", 2345678);
sdict.Add("TestSearchPrefix_case012", 3456789);
DoubleArrayTrieBuilder dab = new DoubleArrayTrieBuilder(4);
Console.WriteLine("Begin to build double array trie-tree...");
dab.build(sdict);
dab.save(strDAFileName);
Console.WriteLine("Done!");
}
//Build double array trie-tree from text file
//strTextFileName: raw text file name used to build DA trie-tree
// text file format: key \t value
// key as string type
// value as non-netgive integer
//strDAFileName: double array trie-tree binary file name built from strTextFileName
private static void Build(string strTextFileName, string strDAFileName)
{
StreamReader sr = new StreamReader(strTextFileName);
//Load raw data from text file and sort them as ordinal
SortedDictionary <string, int> sdict = new SortedDictionary <string, int>(StringComparer.Ordinal);
Console.WriteLine("Loading key value pairs from raw text file and sort them...");
while (sr.EndOfStream == false)
{
string strLine = sr.ReadLine();
if (strLine.Length == 0)
{
continue;
}
string[] items = strLine.Split('\t');
sdict.Add(items[0], int.Parse(items[1]));
}
//test case for SearchAsKeyPrefix and SearchByPrefix
sdict.Add("TestSearchPrefix_case0", 1234567);
sdict.Add("TestSearchPrefix_case01", 2345678);
sdict.Add("TestSearchPrefix_case012", 3456789);
DoubleArrayTrieBuilder dab = new DoubleArrayTrieBuilder(4);
Console.WriteLine("Begin to build double array trie-tree...");
dab.build(sdict);
dab.save(strDAFileName);
Console.WriteLine("Done!");
}
public void BuildIndexedFeatureIntoFile(string strFileName, List<string> features)
{
//Assign id for each feature
SortedDictionary<string, int> feature2Id = new SortedDictionary<string, int>(StringComparer.Ordinal);
int maxId = 0;
foreach (string strFeature in features)
{
if (strFeature.StartsWith("U") == false)
{
Logger.WriteLine("Invalidated feature: {0}", strFeature);
continue;
}
feature2Id.Add(strFeature, maxId);
maxId++;
}
DoubleArrayTrieBuilder da = new DoubleArrayTrieBuilder(4);
da.build(feature2Id);
da.save(strFileName + ".dart");
StreamWriter swTemplate = new StreamWriter(strFileName + ".template");
swTemplate.WriteLine("MaxTemplateFeatureId:{0}", maxId);
foreach (string strTemplate in m_Templates)
{
swTemplate.WriteLine(strTemplate);
}
swTemplate.Close();
}
static void Main(string[] args)
{
if (args.Length != 2)
{
Console.WriteLine("signDict.exe [raw dictionary] [binary dictionary]");
return;
}
DoubleArrayTrieBuilder daBuilder = new DoubleArrayTrieBuilder(4);
BTreeDictionary <string, int> dict = new BTreeDictionary <string, int>(StringComparer.Ordinal, 128);
string strLine = null;
StreamReader sr = new StreamReader(args[0], Encoding.UTF8);
StreamWriter sw = new StreamWriter(args[1] + ".prob", false, Encoding.UTF8);
BinaryWriter bw = new BinaryWriter(sw.BaseStream);
int index = 0;
while ((strLine = sr.ReadLine()) != null)
{
string[] items = strLine.Split('\t');
string strNGram = items[0].Trim();
if (dict.ContainsKey(strNGram) == true)
{
Console.WriteLine("duplicated line: {0}", strLine);
continue;
}
if (strNGram.Length == 0)
{
continue;
}
string[] vals = items[1].Split();
float prob = float.Parse(vals[0]);
float backoff = float.Parse(vals[1]);
//Write item into file
bw.Write(prob);
bw.Write(backoff);
dict.Add(strNGram, index);
index++;
}
sr.Close();
daBuilder.build(dict);
daBuilder.save(args[1] + ".da");
bw.Close();
}
public void Process(string strModelFileName, string strShrinkedModelFileName, int thread_num_ = 1)
{
var sr = new StreamReader(strModelFileName);
string strLine;
//读入版本号
strLine = sr.ReadLine();
var version = uint.Parse(strLine.Split(':')[1].Trim());
if (version == CRFSharp.Utils.MODEL_TYPE_SHRINKED)
{
Console.WriteLine("The input model has been shrinked");
return;
}
//读入cost_factor
strLine = sr.ReadLine();
var cost_factor_ = double.Parse(strLine.Split(':')[1].Trim());
//读入maxid
strLine = sr.ReadLine();
var maxid_ = long.Parse(strLine.Split(':')[1].Trim());
//读入xsize
strLine = sr.ReadLine();
var xsize_ = uint.Parse(strLine.Split(':')[1].Trim());
//读入空行
strLine = sr.ReadLine();
//读入待标注的标签
var y_ = new List<string>();
while (true)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
y_.Add(strLine);
}
//读入unigram和bigram模板
var unigram_templs_ = new List<string>();
var bigram_templs_ = new List<string>();
while (sr.EndOfStream == false)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
if (strLine[0] == 'U')
{
unigram_templs_.Add(strLine);
}
if (strLine[0] == 'B')
{
bigram_templs_.Add(strLine);
}
}
sr.Close();
//Load all features alpha data
var filename_alpha = strModelFileName + ".alpha";
var filename_shrink_alpha = strShrinkedModelFileName + ".alpha";
var sr_alpha = new StreamReader(filename_alpha);
var br_alpha = new BinaryReader(sr_alpha.BaseStream);
var sw_alpha = new StreamWriter(filename_shrink_alpha);
var bw_alpha = new BinaryWriter(sw_alpha.BaseStream);
long shrinked_alpha_size = 0;
//Only reserve non-zero feature weights and save them into file as two-tuples format
var alpha_ = new FixedBigArray<double>(maxid_ + 1, 0);
for (long i = 0; i < maxid_; i++)
{
alpha_[i] = br_alpha.ReadSingle();
if (alpha_[i] != 0)
{
bw_alpha.Write(i);
bw_alpha.Write((float)alpha_[i]);
shrinked_alpha_size++;
}
}
br_alpha.Close();
bw_alpha.Close();
//Only reserved lexical feature whose weights is non-zero
var varValue = new VarBigArray<int>(1024);
var varFeature = new VarBigArray<string>(1024);
var feaCnt = 0;
var filename_feature = strModelFileName + ".feature.raw_text";
var sr_fea = new StreamReader(filename_feature);
while (sr_fea.EndOfStream == false)
{
strLine = sr_fea.ReadLine();
var items = strLine.Split('\t');
var strFeature = items[0];
var key = int.Parse(items[1]);
var size = (strFeature[0] == 'U' ? y_.Count : y_.Count * y_.Count);
var hasAlpha = false;
for (var i = key; i < key + size; i++)
{
if (alpha_[i] != 0)
{
hasAlpha = true;
break;
}
}
if (hasAlpha == true)
{
varFeature[feaCnt] = strFeature;
varValue[feaCnt] = key;
feaCnt++;
}
}
sr_fea.Close();
Console.WriteLine("Shrink feature size from {0} to {1}", maxid_, shrinked_alpha_size);
maxid_ = shrinked_alpha_size;
//Build new lexical feature
var val = new FixedBigArray<int>(feaCnt, 0);
var fea = new FixedBigArray<string>(feaCnt, 0);
for (var i = 0; i < feaCnt; i++)
{
fea[i] = varFeature[i];
val[i] = varValue[i];
}
varFeature = null;
varValue = null;
var da = new DoubleArrayTrieBuilder(thread_num_);
if (da.build(fea, val, 0.95) == false)
{
Console.WriteLine("Build lexical dictionary failed.");
return;
}
da.save(strShrinkedModelFileName + ".feature");
var tofs = new StreamWriter(strShrinkedModelFileName);
// header
tofs.WriteLine("version: " + CRFSharp.Utils.MODEL_TYPE_SHRINKED);
tofs.WriteLine("cost-factor: " + cost_factor_);
tofs.WriteLine("maxid: " + maxid_);
tofs.WriteLine("xsize: " + xsize_);
tofs.WriteLine();
// y
for (var i = 0; i < y_.Count; ++i)
{
tofs.WriteLine(y_[i]);
}
tofs.WriteLine();
// template
for (var i = 0; i < unigram_templs_.Count; ++i)
{
tofs.WriteLine(unigram_templs_[i]);
}
for (var i = 0; i < bigram_templs_.Count; ++i)
{
tofs.WriteLine(bigram_templs_[i]);
}
tofs.Close();
}
public void Process(string strModelFileName, string strShrinkedModelFileName, int thread_num_ = 1)
{
var sr = new StreamReader(strModelFileName);
string strLine;
//读入版本号
strLine = sr.ReadLine();
var version = uint.Parse(strLine.Split(':')[1].Trim());
if (version == CRFSharp.Utils.MODEL_TYPE_SHRINKED)
{
Console.WriteLine("The input model has been shrinked");
return;
}
//读入cost_factor
strLine = sr.ReadLine();
var cost_factor_ = double.Parse(strLine.Split(':')[1].Trim());
//读入maxid
strLine = sr.ReadLine();
var maxid_ = long.Parse(strLine.Split(':')[1].Trim());
//读入xsize
strLine = sr.ReadLine();
var xsize_ = uint.Parse(strLine.Split(':')[1].Trim());
//读入空行
strLine = sr.ReadLine();
//读入待标注的标签
var y_ = new List <string>();
while (true)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
y_.Add(strLine);
}
//读入unigram和bigram模板
var unigram_templs_ = new List <string>();
var bigram_templs_ = new List <string>();
while (sr.EndOfStream == false)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
if (strLine[0] == 'U')
{
unigram_templs_.Add(strLine);
}
if (strLine[0] == 'B')
{
bigram_templs_.Add(strLine);
}
}
sr.Close();
//Load all features alpha data
var filename_alpha = strModelFileName + ".alpha";
var filename_shrink_alpha = strShrinkedModelFileName + ".alpha";
var sr_alpha = new StreamReader(filename_alpha);
var br_alpha = new BinaryReader(sr_alpha.BaseStream);
var sw_alpha = new StreamWriter(filename_shrink_alpha);
var bw_alpha = new BinaryWriter(sw_alpha.BaseStream);
long shrinked_alpha_size = 0;
//Only reserve non-zero feature weights and save them into file as two-tuples format
var alpha_ = new FixedBigArray <double>(maxid_ + 1, 0);
for (long i = 0; i < maxid_; i++)
{
alpha_[i] = br_alpha.ReadSingle();
if (alpha_[i] != 0)
{
bw_alpha.Write(i);
bw_alpha.Write((float)alpha_[i]);
shrinked_alpha_size++;
}
}
br_alpha.Close();
bw_alpha.Close();
//Only reserved lexical feature whose weights is non-zero
var varValue = new VarBigArray <int>(1024);
var varFeature = new VarBigArray <string>(1024);
var feaCnt = 0;
var filename_feature = strModelFileName + ".feature.raw_text";
var sr_fea = new StreamReader(filename_feature);
while (sr_fea.EndOfStream == false)
{
strLine = sr_fea.ReadLine();
var items = strLine.Split('\t');
var strFeature = items[0];
var key = int.Parse(items[1]);
var size = (strFeature[0] == 'U' ? y_.Count : y_.Count * y_.Count);
var hasAlpha = false;
for (var i = key; i < key + size; i++)
{
if (alpha_[i] != 0)
{
hasAlpha = true;
break;
}
}
if (hasAlpha == true)
{
varFeature[feaCnt] = strFeature;
varValue[feaCnt] = key;
feaCnt++;
}
}
sr_fea.Close();
Console.WriteLine("Shrink feature size from {0} to {1}", maxid_, shrinked_alpha_size);
maxid_ = shrinked_alpha_size;
//Build new lexical feature
var val = new FixedBigArray <int>(feaCnt, 0);
var fea = new FixedBigArray <string>(feaCnt, 0);
for (var i = 0; i < feaCnt; i++)
{
fea[i] = varFeature[i];
val[i] = varValue[i];
}
varFeature = null;
varValue = null;
var da = new DoubleArrayTrieBuilder(thread_num_);
if (da.build(fea, val, 0.95) == false)
{
Console.WriteLine("Build lexical dictionary failed.");
return;
}
da.save(strShrinkedModelFileName + ".feature");
var tofs = new StreamWriter(strShrinkedModelFileName);
// header
tofs.WriteLine("version: " + CRFSharp.Utils.MODEL_TYPE_SHRINKED);
tofs.WriteLine("cost-factor: " + cost_factor_);
tofs.WriteLine("maxid: " + maxid_);
tofs.WriteLine("xsize: " + xsize_);
tofs.WriteLine();
// y
for (var i = 0; i < y_.Count; ++i)
{
tofs.WriteLine(y_[i]);
}
tofs.WriteLine();
// template
for (var i = 0; i < unigram_templs_.Count; ++i)
{
tofs.WriteLine(unigram_templs_[i]);
}
for (var i = 0; i < bigram_templs_.Count; ++i)
{
tofs.WriteLine(bigram_templs_[i]);
}
tofs.Close();
}
//Build feature set into indexed data
public bool BuildFeatureSetIntoIndex(string filename, double max_slot_usage_rate_threshold, int debugLevel, string strRetrainModelFileName)
{
Console.WriteLine("Building {0} features into index...", featureLexicalDict.Size);
IList<string> keyList;
IList<int> valList;
featureLexicalDict.GenerateLexicalIdList(out keyList, out valList);
if (debugLevel > 0)
{
Console.Write("Debug: Writing raw feature set into file...");
var filename_featureset_raw_format = filename + ".feature.raw_text";
var sw = new StreamWriter(filename_featureset_raw_format);
// save feature and its id into lists in raw format
for (var i = 0; i < keyList.Count; i++)
{
sw.WriteLine("{0}\t{1}", keyList[i], valList[i]);
}
sw.Close();
Console.WriteLine("Done.");
}
//Build feature index
var filename_featureset = filename + ".feature";
var da = new DoubleArrayTrieBuilder(thread_num_);
if (da.build(keyList, valList, max_slot_usage_rate_threshold) == false)
{
Console.WriteLine("Build lexical dictionary failed.");
return false;
}
//Save indexed feature set into file
da.save(filename_featureset);
if (strRetrainModelFileName == null || strRetrainModelFileName.Length == 0)
{
//Clean up all data
featureLexicalDict.Clear();
featureLexicalDict = null;
keyList = null;
valList = null;
GC.Collect();
//Create weight matrix
alpha_ = new double[feature_size() + 1];
}
else
{
Console.WriteLine();
Console.WriteLine("Loading the existed model for re-training...");
//Create weight matrix
alpha_ = new double[feature_size() + 1];
var modelReader = new ModelReader();
modelReader.LoadModel(strRetrainModelFileName);
if (modelReader.y_.Count == y_.Count)
{
for (var i = 0; i < keyList.Count; i++)
{
var index = modelReader.get_id(keyList[i]);
if (index < 0)
{
continue;
}
var size = (keyList[i][0] == 'U' ? y_.Count : y_.Count * y_.Count);
for (var j = 0; j < size; j++)
{
alpha_[valList[i] + j + 1] = modelReader.GetAlpha(index + j);
}
}
}
else
{
Console.WriteLine("The number of tags isn't equal between two models, it cannot be re-trained.");
}
//Clean up all data
featureLexicalDict.Clear();
featureLexicalDict = null;
keyList = null;
valList = null;
GC.Collect();
}
return true;
}