src/word2phrase.c - ids-kl/dereko2vec - Gitiles

 //  Copyright 2013 Google Inc. 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.
 //  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.

 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
 #include <math.h>
 #include <pthread.h>

 #define MAX_STRING 60

 const int vocab_hash_size = 500000000; // Maximum 500M entries in the vocabulary

 typedef float real;                    // Precision of float numbers

 struct vocab_word {
   long long cn;
   char *word;
 };

 char train_file[MAX_STRING], output_file[MAX_STRING];
 struct vocab_word *vocab;
 int debug_mode = 2, min_count = 5, *vocab_hash, min_reduce = 1;
 long long vocab_max_size = 10000, vocab_size = 0;
 long long train_words = 0;
 real threshold = 100;

 unsigned long long next_random = 1;

 // Reads a single word from a file, assuming space + tab + EOL to be word boundaries
 void ReadWord(char *word, FILE *fin) {
   int a = 0, ch;
   while (!feof(fin)) {
     ch = fgetc(fin);
     if (ch == 13) continue;
     if ((ch == ' ') || (ch == '\t') || (ch == '\n')) {
       if (a > 0) {
         if (ch == '\n') ungetc(ch, fin);
         break;
       }
       if (ch == '\n') {
         strcpy(word, (char *)"</s>");
         return;
       } else continue;
     }
     word[a] = ch;
     a++;
     if (a >= MAX_STRING - 1) a--;   // Truncate too long words
   }
   word[a] = 0;
 }

 // Returns hash value of a word
 int GetWordHash(char *word) {
   unsigned long long a, hash = 1;
   for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];
   hash = hash % vocab_hash_size;
   return hash;
 }

 // Returns position of a word in the vocabulary; if the word is not found, returns -1
 int SearchVocab(char *word) {
   unsigned int hash = GetWordHash(word);
   while (1) {
     if (vocab_hash[hash] == -1) return -1;
     if (!strcmp(word, vocab[vocab_hash[hash]].word)) return vocab_hash[hash];
     hash = (hash + 1) % vocab_hash_size;
   }
   return -1;
 }

 // Reads a word and returns its index in the vocabulary
 int ReadWordIndex(FILE *fin) {
   char word[MAX_STRING];
   ReadWord(word, fin);
   if (feof(fin)) return -1;
   return SearchVocab(word);
 }

 // Adds a word to the vocabulary
 int AddWordToVocab(char *word) {
   unsigned int hash, length = strlen(word) + 1;
   if (length > MAX_STRING) length = MAX_STRING;
   vocab[vocab_size].word = (char *)calloc(length, sizeof(char));
   strcpy(vocab[vocab_size].word, word);
   vocab[vocab_size].cn = 0;
   vocab_size++;
   // Reallocate memory if needed
   if (vocab_size + 2 >= vocab_max_size) {
     vocab_max_size += 10000;
     vocab=(struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));
   }
   hash = GetWordHash(word);
   while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
   vocab_hash[hash]=vocab_size - 1;
   return vocab_size - 1;
 }

 // Used later for sorting by word counts
 int VocabCompare(const void *a, const void *b) {
     return ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;
 }

 // Sorts the vocabulary by frequency using word counts
 void SortVocab() {
   int a;
   unsigned int hash;
   // Sort the vocabulary and keep </s> at the first position
   qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);
   for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
   for (a = 0; a < vocab_size; a++) {
     // Words occuring less than min_count times will be discarded from the vocab
     if (vocab[a].cn < min_count) {
       vocab_size--;
       free(vocab[vocab_size].word);
     } else {
       // Hash will be re-computed, as after the sorting it is not actual
       hash = GetWordHash(vocab[a].word);
       while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
       vocab_hash[hash] = a;
     }
   }
   vocab = (struct vocab_word *)realloc(vocab, vocab_size * sizeof(struct vocab_word));
 }

 // Reduces the vocabulary by removing infrequent tokens
 void ReduceVocab() {
   int a, b = 0;
   unsigned int hash;
   for (a = 0; a < vocab_size; a++) if (vocab[a].cn > min_reduce) {
     vocab[b].cn = vocab[a].cn;
     vocab[b].word = vocab[a].word;
     b++;
   } else free(vocab[a].word);
   vocab_size = b;
   for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
   for (a = 0; a < vocab_size; a++) {
     // Hash will be re-computed, as it is not actual
     hash = GetWordHash(vocab[a].word);
     while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
     vocab_hash[hash] = a;
   }
   fflush(stdout);
   min_reduce++;
 }

 void LearnVocabFromTrainFile() {
   char word[MAX_STRING], last_word[MAX_STRING], bigram_word[MAX_STRING * 2];
   FILE *fin;
   long long a, i, start = 1;
   for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
   fin = fopen(train_file, "rb");
   if (fin == NULL) {
     printf("ERROR: training data file not found!\n");
     exit(1);
   }
   vocab_size = 0;
   AddWordToVocab((char *)"</s>");
   while (1) {
     ReadWord(word, fin);
     if (feof(fin)) break;
     if (!strcmp(word, "</s>")) {
       start = 1;
       continue;
     } else start = 0;
     train_words++;
     if ((debug_mode > 1) && (train_words % 100000 == 0)) {
       printf("Words processed: %lldK     Vocab size: %lldK  %c", train_words / 1000, vocab_size / 1000, 13);
       fflush(stdout);
     }
     i = SearchVocab(word);
     if (i == -1) {
       a = AddWordToVocab(word);
       vocab[a].cn = 1;
     } else vocab[i].cn++;
     if (start) continue;
     sprintf(bigram_word, "%s_%s", last_word, word);
     bigram_word[MAX_STRING - 1] = 0;
     strcpy(last_word, word);
     i = SearchVocab(bigram_word);
     if (i == -1) {
       a = AddWordToVocab(bigram_word);
       vocab[a].cn = 1;
     } else vocab[i].cn++;
     if (vocab_size > vocab_hash_size * 0.7) ReduceVocab();
   }
   SortVocab();
   if (debug_mode > 0) {
     printf("\nVocab size (unigrams + bigrams): %lld\n", vocab_size);
     printf("Words in train file: %lld\n", train_words);
   }
   fclose(fin);
 }

 void TrainModel() {
   long long pa = 0, pb = 0, pab = 0, oov, i, li = -1, cn = 0;
   char word[MAX_STRING], last_word[MAX_STRING], bigram_word[MAX_STRING * 2];
   real score;
   FILE *fo, *fin;
   printf("Starting training using file %s\n", train_file);
   LearnVocabFromTrainFile();
   fin = fopen(train_file, "rb");
   fo = fopen(output_file, "wb");
   word[0] = 0;
   while (1) {
     strcpy(last_word, word);
     ReadWord(word, fin);
     if (feof(fin)) break;
     if (!strcmp(word, "</s>")) {
       fprintf(fo, "\n");
       continue;
     }
     cn++;
     if ((debug_mode > 1) && (cn % 100000 == 0)) {
       printf("Words written: %lldK%c", cn / 1000, 13);
       fflush(stdout);
     }
     oov = 0;
     i = SearchVocab(word);
     if (i == -1) oov = 1; else pb = vocab[i].cn;
     if (li == -1) oov = 1;
     li = i;
     sprintf(bigram_word, "%s_%s", last_word, word);
     bigram_word[MAX_STRING - 1] = 0;
     i = SearchVocab(bigram_word);
     if (i == -1) oov = 1; else pab = vocab[i].cn;
     if (pa < min_count) oov = 1;
     if (pb < min_count) oov = 1;
     if (oov) score = 0; else score = (pab - min_count) / (real)pa / (real)pb * (real)train_words;
     if (score > threshold) {
       fprintf(fo, "_%s", word);
       pb = 0;
     } else fprintf(fo, " %s", word);
     pa = pb;
   }
   fclose(fo);
   fclose(fin);
 }

 int ArgPos(char *str, int argc, char **argv) {
   int a;
   for (a = 1; a < argc; a++) if (!strcmp(str, argv[a])) {
     if (a == argc - 1) {
       printf("Argument missing for %s\n", str);
       exit(1);
     }
     return a;
   }
   return -1;
 }

 int main(int argc, char **argv) {
   int i;
   if (argc == 1) {
     printf("WORD2PHRASE tool v0.1a\n\n");
     printf("Options:\n");
     printf("Parameters for training:\n");
     printf("\t-train <file>\n");
     printf("\t\tUse text data from <file> to train the model\n");
     printf("\t-output <file>\n");
     printf("\t\tUse <file> to save the resulting word vectors / word clusters / phrases\n");
     printf("\t-min-count <int>\n");
     printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
     printf("\t-threshold <float>\n");
     printf("\t\t The <float> value represents threshold for forming the phrases (higher means less phrases); default 100\n");
     printf("\t-debug <int>\n");
     printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
     printf("\nExamples:\n");
     printf("./word2phrase -train text.txt -output phrases.txt -threshold 100 -debug 2\n\n");
     return 0;
   }
   if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
   if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
   if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
   if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
   if ((i = ArgPos((char *)"-threshold", argc, argv)) > 0) threshold = atof(argv[i + 1]);
   vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));
   vocab_hash = (int *)calloc(vocab_hash_size, sizeof(int));
   TrainModel();
   return 0;
 }
	// Copyright 2013 Google Inc. 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.
	// 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.

	#include <stdio.h>
	#include <stdlib.h>
	#include <string.h>
	#include <math.h>
	#include <pthread.h>

	#define MAX_STRING 60

	const int vocab_hash_size = 500000000; // Maximum 500M entries in the vocabulary

	typedef float real; // Precision of float numbers

	struct vocab_word {
	long long cn;
	char *word;
	};

	char train_file[MAX_STRING], output_file[MAX_STRING];
	struct vocab_word *vocab;
	int debug_mode = 2, min_count = 5, *vocab_hash, min_reduce = 1;
	long long vocab_max_size = 10000, vocab_size = 0;
	long long train_words = 0;
	real threshold = 100;

	unsigned long long next_random = 1;

	// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
	void ReadWord(char word, FILE fin) {
	int a = 0, ch;
	while (!feof(fin)) {
	ch = fgetc(fin);
	if (ch == 13) continue;
	if ((ch == ' ') \|\| (ch == '\t') \|\| (ch == '\n')) {
	if (a > 0) {
	if (ch == '\n') ungetc(ch, fin);
	break;
	}
	if (ch == '\n') {
	strcpy(word, (char *)"</s>");
	return;
	} else continue;
	}
	word[a] = ch;
	a++;
	if (a >= MAX_STRING - 1) a--; // Truncate too long words
	}
	word[a] = 0;
	}

	// Returns hash value of a word
	int GetWordHash(char *word) {
	unsigned long long a, hash = 1;
	for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];
	hash = hash % vocab_hash_size;
	return hash;
	}

	// Returns position of a word in the vocabulary; if the word is not found, returns -1
	int SearchVocab(char *word) {
	unsigned int hash = GetWordHash(word);
	while (1) {
	if (vocab_hash[hash] == -1) return -1;
	if (!strcmp(word, vocab[vocab_hash[hash]].word)) return vocab_hash[hash];
	hash = (hash + 1) % vocab_hash_size;
	}
	return -1;
	}

	// Reads a word and returns its index in the vocabulary
	int ReadWordIndex(FILE *fin) {
	char word[MAX_STRING];
	ReadWord(word, fin);
	if (feof(fin)) return -1;
	return SearchVocab(word);
	}

	// Adds a word to the vocabulary
	int AddWordToVocab(char *word) {
	unsigned int hash, length = strlen(word) + 1;
	if (length > MAX_STRING) length = MAX_STRING;
	vocab[vocab_size].word = (char *)calloc(length, sizeof(char));
	strcpy(vocab[vocab_size].word, word);
	vocab[vocab_size].cn = 0;
	vocab_size++;
	// Reallocate memory if needed
	if (vocab_size + 2 >= vocab_max_size) {
	vocab_max_size += 10000;
	vocab=(struct vocab_word )realloc(vocab, vocab_max_size sizeof(struct vocab_word));
	}
	hash = GetWordHash(word);
	while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
	vocab_hash[hash]=vocab_size - 1;
	return vocab_size - 1;
	}

	// Used later for sorting by word counts
	int VocabCompare(const void a, const void b) {
	return ((struct vocab_word )b)->cn - ((struct vocab_word )a)->cn;
	}

	// Sorts the vocabulary by frequency using word counts
	void SortVocab() {
	int a;
	unsigned int hash;
	// Sort the vocabulary and keep </s> at the first position
	qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);
	for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
	for (a = 0; a < vocab_size; a++) {
	// Words occuring less than min_count times will be discarded from the vocab
	if (vocab[a].cn < min_count) {
	vocab_size--;
	free(vocab[vocab_size].word);
	} else {
	// Hash will be re-computed, as after the sorting it is not actual
	hash = GetWordHash(vocab[a].word);
	while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
	vocab_hash[hash] = a;
	}
	}
	vocab = (struct vocab_word )realloc(vocab, vocab_size sizeof(struct vocab_word));
	}

	// Reduces the vocabulary by removing infrequent tokens
	void ReduceVocab() {
	int a, b = 0;
	unsigned int hash;
	for (a = 0; a < vocab_size; a++) if (vocab[a].cn > min_reduce) {
	vocab[b].cn = vocab[a].cn;
	vocab[b].word = vocab[a].word;
	b++;
	} else free(vocab[a].word);
	vocab_size = b;
	for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
	for (a = 0; a < vocab_size; a++) {
	// Hash will be re-computed, as it is not actual
	hash = GetWordHash(vocab[a].word);
	while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
	vocab_hash[hash] = a;
	}
	fflush(stdout);
	min_reduce++;
	}

	void LearnVocabFromTrainFile() {
	char word[MAX_STRING], last_word[MAX_STRING], bigram_word[MAX_STRING * 2];
	FILE *fin;
	long long a, i, start = 1;
	for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
	fin = fopen(train_file, "rb");
	if (fin == NULL) {
	printf("ERROR: training data file not found!\n");
	exit(1);
	}
	vocab_size = 0;
	AddWordToVocab((char *)"</s>");
	while (1) {
	ReadWord(word, fin);
	if (feof(fin)) break;
	if (!strcmp(word, "</s>")) {
	start = 1;
	continue;
	} else start = 0;
	train_words++;
	if ((debug_mode > 1) && (train_words % 100000 == 0)) {
	printf("Words processed: %lldK Vocab size: %lldK %c", train_words / 1000, vocab_size / 1000, 13);
	fflush(stdout);
	}
	i = SearchVocab(word);
	if (i == -1) {
	a = AddWordToVocab(word);
	vocab[a].cn = 1;
	} else vocab[i].cn++;
	if (start) continue;
	sprintf(bigram_word, "%s_%s", last_word, word);
	bigram_word[MAX_STRING - 1] = 0;
	strcpy(last_word, word);
	i = SearchVocab(bigram_word);
	if (i == -1) {
	a = AddWordToVocab(bigram_word);
	vocab[a].cn = 1;
	} else vocab[i].cn++;
	if (vocab_size > vocab_hash_size * 0.7) ReduceVocab();
	}
	SortVocab();
	if (debug_mode > 0) {
	printf("\nVocab size (unigrams + bigrams): %lld\n", vocab_size);
	printf("Words in train file: %lld\n", train_words);
	}
	fclose(fin);
	}

	void TrainModel() {
	long long pa = 0, pb = 0, pab = 0, oov, i, li = -1, cn = 0;
	char word[MAX_STRING], last_word[MAX_STRING], bigram_word[MAX_STRING * 2];
	real score;
	FILE fo, fin;
	printf("Starting training using file %s\n", train_file);
	LearnVocabFromTrainFile();
	fin = fopen(train_file, "rb");
	fo = fopen(output_file, "wb");
	word[0] = 0;
	while (1) {
	strcpy(last_word, word);
	ReadWord(word, fin);
	if (feof(fin)) break;
	if (!strcmp(word, "</s>")) {
	fprintf(fo, "\n");
	continue;
	}
	cn++;
	if ((debug_mode > 1) && (cn % 100000 == 0)) {
	printf("Words written: %lldK%c", cn / 1000, 13);
	fflush(stdout);
	}
	oov = 0;
	i = SearchVocab(word);
	if (i == -1) oov = 1; else pb = vocab[i].cn;
	if (li == -1) oov = 1;
	li = i;
	sprintf(bigram_word, "%s_%s", last_word, word);
	bigram_word[MAX_STRING - 1] = 0;
	i = SearchVocab(bigram_word);
	if (i == -1) oov = 1; else pab = vocab[i].cn;
	if (pa < min_count) oov = 1;
	if (pb < min_count) oov = 1;
	if (oov) score = 0; else score = (pab - min_count) / (real)pa / (real)pb * (real)train_words;
	if (score > threshold) {
	fprintf(fo, "_%s", word);
	pb = 0;
	} else fprintf(fo, " %s", word);
	pa = pb;
	}
	fclose(fo);
	fclose(fin);
	}

	int ArgPos(char str, int argc, char *argv) {
	int a;
	for (a = 1; a < argc; a++) if (!strcmp(str, argv[a])) {
	if (a == argc - 1) {
	printf("Argument missing for %s\n", str);
	exit(1);
	}
	return a;
	}
	return -1;
	}

	int main(int argc, char **argv) {
	int i;
	if (argc == 1) {
	printf("WORD2PHRASE tool v0.1a\n\n");
	printf("Options:\n");
	printf("Parameters for training:\n");
	printf("\t-train <file>\n");
	printf("\t\tUse text data from <file> to train the model\n");
	printf("\t-output <file>\n");
	printf("\t\tUse <file> to save the resulting word vectors / word clusters / phrases\n");
	printf("\t-min-count <int>\n");
	printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
	printf("\t-threshold <float>\n");
	printf("\t\t The <float> value represents threshold for forming the phrases (higher means less phrases); default 100\n");
	printf("\t-debug <int>\n");
	printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
	printf("\nExamples:\n");
	printf("./word2phrase -train text.txt -output phrases.txt -threshold 100 -debug 2\n\n");
	return 0;
	}
	if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
	if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
	if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
	if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
	if ((i = ArgPos((char *)"-threshold", argc, argv)) > 0) threshold = atof(argv[i + 1]);
	vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));
	vocab_hash = (int *)calloc(vocab_hash_size, sizeof(int));
	TrainModel();
	return 0;
	}