uint add_implicit_edges(int type, sharder<als_edge_type>& shrd ){
switch(type){
case IMPLICIT_RATING_DISABLED: return 0;
case IMPLICIT_RATING_RANDOM: break;
default: assert(false);
};
uint added = 0;
uint toadd = implicitratingpercentage*N*M;
logstream(LOG_INFO)<<"Going to add: " << toadd << " implicit edges. " << std::endl;
assert(toadd >= 1);
for (uint j=0; j< toadd; j++){
ivec item = ::randi(1,0,N-1);
ivec user = ::randi(1,0,M-1);
shrd.preprocessing_add_edge(user[0], item[0], als_edge_type(implicitratingvalue));
added++;
}
logstream(LOG_INFO)<<"Finished adding " << toadd << " implicit edges. " << std::endl;
return added;
}
int convert_matrixmarket(std::string base_filename, size_t nodes = 0, size_t edges = 0, int tokens_per_row = 3, int type = TRAINING, int allow_square = true) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
FILE *f;
size_t nz;
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename, get_option_string("nshards", "auto")))) {
if (check_origfile_modification_earlier<als_edge_type>(base_filename, nshards)) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
read_global_mean(base_filename, type);
return nshards;
}
}
sharder<als_edge_type> sharderobj(base_filename);
sharderobj.start_preprocessing();
detect_matrix_size(base_filename, f, type == TRAINING?M:Me, type == TRAINING?N:Ne, nz, nodes, edges, type);
if (f == NULL){
if (type == TRAINING){
logstream(LOG_FATAL)<<"Failed to open training input file: " << base_filename << std::endl;
}
else if (type == VALIDATION){
logstream(LOG_INFO)<<"Validation file: " << base_filename << " is not found. " << std::endl;
return -1;
}
}
compute_matrix_size(nz, type);
uint I, J;
double val = 1.0;
bool active_edge = true;
int zero_entries = 0;
for (size_t i=0; i<nz; i++)
{
if (tokens_per_row == 3){
int rc = fscanf(f, "%u %u %lg\n", &I, &J, &val);
if (rc != 3)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
if (val == 0 && ! allow_zeros)
logstream(LOG_FATAL)<<"Encountered zero edge [ " << I << " " <<J << " 0] in line: " << i << " . Run with --allow_zeros=1 to ignore zero weights." << std::endl;
else if (val == 0){
zero_entries++;
continue;
}
}
else if (tokens_per_row == 2){
int rc = fscanf(f, "%u %u\n", &I, &J);
if (rc != 2)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
}
else assert(false);
if (I ==987654321 || J== 987654321) //hack - to be removed later
continue;
I-=(uint)input_file_offset; /* adjust from 1-based to 0-based */
J-=(uint)input_file_offset;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I+1 << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J+1 << " > " << N << " in line; " << i << std::endl;
if (minval != -1e100 && val < minval)
logstream(LOG_FATAL)<<"Found illegal rating value: " << val << " where min value is: " << minval << std::endl;
if (maxval != 1e100 && val > maxval)
logstream(LOG_FATAL)<<"Found illegal rating value: " << val << " where max value is: " << maxval << std::endl;
active_edge = decide_if_edge_is_active(i, type);
if (active_edge){
if (type == TRAINING)
globalMean += val;
else globalMean2 += val;
sharderobj.preprocessing_add_edge(I, (M==N && allow_square)?J:M + J, als_edge_type((float)val));
}
}
if (type == TRAINING){
uint toadd = 0;
if (implicitratingtype == IMPLICIT_RATING_RANDOM)
toadd = add_implicit_edges(implicitratingtype, sharderobj);
globalMean += implicitratingvalue * toadd;
L += toadd;
globalMean /= L;
logstream(LOG_INFO) << "Global mean is: " << globalMean << " Now creating shards." << std::endl;
}
else {
globalMean2 /= Le;
logstream(LOG_INFO) << "Global mean is: " << globalMean2 << " Now creating shards." << std::endl;
}
write_global_mean(base_filename, type);
sharderobj.end_preprocessing();
if (zero_entries)
logstream(LOG_WARNING)<<"Found " << zero_entries << " zero edges!" << std::endl;
fclose(f);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
logstream(LOG_INFO) << "Successfully finished sharding for " << base_filename<< std::endl;
logstream(LOG_INFO) << "Created " << nshards << " shards." << std::endl;
return nshards;
}
int convert_matrixmarket_and_item_similarity(std::string base_filename, std::string similarity_file, int tokens_per_row, vec & degrees) {
FILE *f = NULL, *fsim = NULL;
size_t nz, nz_sim;
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename, get_option_string("nshards", "auto")))) {
if (check_origfile_modification_earlier<als_edge_type>(base_filename, nshards)) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
read_global_mean(base_filename, TRAINING);
return nshards;
}
}
sharder<als_edge_type> sharderobj(base_filename);
sharderobj.start_preprocessing();
detect_matrix_size(base_filename, f, M, N, nz);
if (f == NULL)
logstream(LOG_FATAL)<<"Failed to open training input file: " << base_filename << std::endl;
uint N_row = 0 ,N_col = 0;
detect_matrix_size(similarity_file, fsim, N_row, N_col, nz_sim);
if (fsim == NULL || nz_sim == 0)
logstream(LOG_FATAL)<<"Failed to open item similarity input file: " << similarity_file << std::endl;
if (N_row != N || N_col != N)
logstream(LOG_FATAL)<<"Wrong item similarity file matrix size: " << N_row <<" x " << N_col << " Instead of " << N << " x " << N << std::endl;
L=nz + nz_sim;
degrees.resize(M+N);
uint I, J;
double val = 1.0;
int zero_entries = 0;
unsigned int actual_edges = 0;
logstream(LOG_INFO) << "Starting to read matrix-market input. Matrix dimensions: "
<< M << " x " << N << ", non-zeros: " << nz << std::endl;
for (size_t i=0; i<nz; i++){
if (tokens_per_row == 3){
int rc = fscanf(f, "%u %u %lg\n", &I, &J, &val);
if (rc != 3)
logstream(LOG_FATAL)<<"Error when reading input file in line: " << i << std::endl;
if (val == 0 && ! allow_zeros)
logstream(LOG_FATAL)<<"Zero weight encountered at input file line: " << i << " . Run with --allow_zeros=1 to ignore zero weights." << std::endl;
else if (val == 0) { zero_entries++; continue; }
}
else if (tokens_per_row == 2){
int rc = fscanf(f, "%u %u\n", &I, &J);
if (rc != 2)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
}
else assert(false);
I-=input_file_offset; /* adjust from 1-based to 0-based */
J-=input_file_offset;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
degrees[J+M]++;
degrees[I]++;
if (I< (uint)start_user || I >= (uint)end_user){
continue;
}
sharderobj.preprocessing_add_edge(I, M + J, als_edge_type((float)val, 0));
//std::cout<<"adding an edge: " <<I << " -> " << M+J << std::endl;
actual_edges++;
}
logstream(LOG_DEBUG)<<"Finished loading " << actual_edges << " ratings from file: " << base_filename << std::endl;
for (size_t i=0; i<nz_sim; i++){
if (tokens_per_row == 3){
int rc = fscanf(fsim, "%u %u %lg\n", &I, &J, &val);
if (rc != 3)
logstream(LOG_FATAL)<<"Error when reading input file: " << similarity_file << " line: " << i << std::endl;
}
else if (tokens_per_row == 2){
int rc = fscanf(fsim, "%u %u\n", &I, &J);
if (rc != 2)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
}
else assert(false);
I-=input_file_offset; /* adjust from 1-based to 0-based */
J-=input_file_offset;
if (I >= N)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
if (I == J)
logstream(LOG_FATAL)<<"Item similarity to itself found for item " << I << " in line; " << i << std::endl;
//std::cout<<"Adding an edge between "<<M+I<< " : " << M+J << " " << (I<J) << " " << val << std::endl;
sharderobj.preprocessing_add_edge(M+I, M+J, als_edge_type(I < J? val: 0, I>J? val: 0));
actual_edges++;
}
L = actual_edges;
logstream(LOG_DEBUG)<<"Finished loading " << nz_sim << " ratings from file: " << similarity_file << std::endl;
write_global_mean(base_filename, TRAINING);
sharderobj.end_preprocessing();
if (zero_entries)
logstream(LOG_WARNING)<<"Found " << zero_entries << " edges with zero weight!" << std::endl;
fclose(f);
fclose(fsim);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
logstream(LOG_INFO) << "Successfully finished sharding for " << base_filename << std::endl;
logstream(LOG_INFO) << "Created " << nshards << " shards." << std::endl;
return nshards;
}
int convert_matrixmarket4(std::string base_filename, bool add_time_edges = false, bool square = false, int type = TRAINING, int matlab_time_offset = 1) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
FILE *f = NULL;
size_t nz;
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename, get_option_string("nshards", "auto")))) {
if (check_origfile_modification_earlier<als_edge_type>(base_filename, nshards)) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
read_global_mean(base_filename, type);
}
if (type == TRAINING)
time_nodes_offset = M+N;
return nshards;
}
sharder<als_edge_type> sharderobj(base_filename);
sharderobj.start_preprocessing();
detect_matrix_size(base_filename, f, type == TRAINING? M:Me, type == TRAINING? N:Ne, nz);
if (f == NULL){
if (type == VALIDATION){
logstream(LOG_INFO)<< "Did not find validation file: " << base_filename << std::endl;
return -1;
}
else if (type == TRAINING)
logstream(LOG_FATAL)<<"Failed to open training input file: " << base_filename << std::endl;
}
if (type == TRAINING)
time_nodes_offset = M+N;
compute_matrix_size(nz, type);
uint I, J;
double val, time;
bool active_edge = true;
for (size_t i=0; i<nz; i++)
{
int rc = fscanf(f, "%d %d %lg %lg\n", &I, &J, &time, &val);
if (rc != 4)
logstream(LOG_FATAL)<<"Error when reading input file - line " << i << std::endl;
if (time < 0)
logstream(LOG_FATAL)<<"Time (third columns) should be >= 0 " << std::endl;
I-=input_file_offset; /* adjust from 1-based to 0-based */
J-=input_file_offset;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
K = std::max((int)time, (int)K);
time -= matlab_time_offset;
if (time < 0 && add_time_edges)
logstream(LOG_FATAL)<<"Time bins should be >= " << matlab_time_offset << " in row " << i << std::endl;
//only for tensor ALS we add edges between user and time bin and also item and time bin
//time bins are numbered beteen M+N to M+N+K
if (!weighted_als)
time += time_nodes_offset;
//avoid self edges
if (square && I == J)
continue;
active_edge = decide_if_edge_is_active(i, type);
if (active_edge){
if (type == TRAINING)
globalMean += val;
else globalMean2 += val;
sharderobj.preprocessing_add_edge(I, (square? J : (M + J)), als_edge_type(val, time));
}
//in case of a tensor, add besides of the user-> movie edge also
//time -> user and time-> movie edges
if (add_time_edges){
sharderobj.preprocessing_add_edge((uint)time, I, als_edge_type(val, M+J));
sharderobj.preprocessing_add_edge((uint)time, M+J , als_edge_type(val, I));
}
}
if (type == TRAINING){
uint toadd = 0;
if (implicitratingtype == IMPLICIT_RATING_RANDOM)
toadd = add_implicit_edges4(implicitratingtype, sharderobj);
globalMean += implicitratingvalue * toadd;
L += toadd;
globalMean /= L;
logstream(LOG_INFO) << "Global mean is: " << globalMean << " time bins: " << K << " . Now creating shards." << std::endl;
}
else {
globalMean2 /= Le;
logstream(LOG_INFO) << "Global mean is: " << globalMean2 << " time bins: " << K << " . Now creating shards." << std::endl;
}
write_global_mean(base_filename, type);
sharderobj.end_preprocessing();
fclose(f);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
return nshards;
}
int convert_matrixmarket_N(std::string base_filename, bool square, int limit_rating = 0) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
FILE *f;
size_t nz;
int nshards;
if (validation_only && (nshards = find_shards<als_edge_type>(base_filename, get_option_string("nshards", "auto")))) {
if (check_origfile_modification_earlier<als_edge_type>(base_filename, nshards)) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
FILE * infile = fopen((base_filename + ".gm").c_str(), "r");
int node_id_maps_size = 0;
assert( fscanf(infile, "%d\n%d\n%ld\n%d\n%lf\n%d\n%d\n%d\n", &M, &N, &L, &fc.total_features, &globalMean, &node_id_maps_size, &latent_factors_inmem_size,&num_feature_bins_size) ==8);
assert(node_id_maps_size >= 0);
assert(latent_factors_inmem_size >=M+N);
fclose(infile);
fc.node_id_maps.resize(node_id_maps_size);
for (int i=0; i < (int)fc.node_id_maps.size(); i++){
char buf[256];
sprintf(buf, "%s.map.%d", training.c_str(), i);
load_map_from_txt_file(fc.node_id_maps[i].string2nodeid, buf, 2);
assert(fc.node_id_maps[i].string2nodeid.size() > 0);
}
logstream(LOG_INFO)<<"Finished loading " << node_id_maps_size << " maps. "<<std::endl;
return nshards;
}
}
/**
* Create sharder object
*/
sharder<als_edge_type> sharderobj(base_filename);
sharderobj.start_preprocessing();
detect_matrix_size(base_filename, f, M, N, nz, 0, 0, 0);
if (f == NULL)
logstream(LOG_FATAL) << "Could not open file: " << base_filename << ", error: " << strerror(errno) << std::endl;
if (M == 0 && N == 0)
logstream(LOG_FATAL)<<"Failed to detect matrix size. Please prepare a file named: " << base_filename << ":info with matrix market header, as explained here: http://bickson.blogspot.co.il/2012/12/collaborative-filtering-3rd-generation_14.html " << std::endl;
logstream(LOG_INFO) << "Starting to read matrix-market input. Matrix dimensions: " << M << " x " << N << ", non-zeros: " << nz << std::endl;
if (has_header_titles){
char * linebuf = NULL;
size_t linesize;
char linebuf_debug[1024];
/* READ LINE */
int rc = getline(&linebuf, &linesize, f);
if (rc == -1)
logstream(LOG_FATAL)<<"Error header line " << " [ " << linebuf_debug << " ] " << std::endl;
strncpy(linebuf_debug, linebuf, 1024);
char *pch = strtok(linebuf,ptokens);
if (pch == NULL)
logstream(LOG_FATAL)<<"Error header line " << " [ " << linebuf_debug << " ] " << std::endl;
header_titles.push_back(std::string(pch));
while (pch != NULL){
pch = strtok(NULL, ptokens);
if (pch == NULL)
break;
header_titles.push_back(pch);
}
}
compute_matrix_size(nz, TRAINING);
uint I, J;
int val_array_len = std::max(1, fc.total_features);
assert(val_array_len < FEATURE_WIDTH);
std::vector<float> valarray; valarray.resize(val_array_len);
float val = 0.0f;
if (limit_rating > 0 && limit_rating < (int)nz)
nz = limit_rating;
char linebuf_debug[1024];
for (size_t i=0; i<nz; i++)
{
if (!read_line(f, base_filename, i,I, J, val, valarray, TRAINING, linebuf_debug))
logstream(LOG_FATAL)<<"Failed to read line: " <<i<< " in file: " << base_filename << std::endl;
if (I>= M || J >= N || I < 0 || J < 0){
if (i == 0)
logstream(LOG_FATAL)<<"Failed to parse first line, there are too many tokens. Did you forget the --has_header_titles=1 flag when file has string column headers? [ " << linebuf_debug << " ] " << " I : " << I << " J: " << J << std::endl;
else
logstream(LOG_FATAL)<<"Problem parsing input line number: " << i <<" in file: " << base_filename << ". Can not add edge from " << I << " to J " << J <<
" since matrix size is: " << M <<"x" <<N<< " [ original line: " << linebuf_debug << " ] . You probaably need to increase matrix size in the matrix market header." << std::endl;
}
bool active_edge = decide_if_edge_is_active(i, TRAINING);
if (active_edge){
//calc stats
globalMean += val;
sharderobj.preprocessing_add_edge(I, square?J:M+J, als_edge_type(val, &valarray[0], val_array_len));
}
}
sharderobj.end_preprocessing();
//calc stats
assert(L > 0);
//assert(globalMean != 0);
if (globalMean == 0)
logstream(LOG_WARNING)<<"Found global mean of the data to be zero (val_pos). Please verify this is correct." << std::endl;
globalMean /= L;
logstream(LOG_INFO)<<"Computed global mean is: " << globalMean << std::endl;
inputGlobalMean = globalMean;
fclose(f);
if (fc.hash_strings){
for (int i=0; i< fc.total_features+2; i++){
if (fc.node_id_maps[i].string2nodeid.size() == 0)
logstream(LOG_FATAL)<<"Failed sanity check for feature number : " << i << " no values find in data " << std::endl;
}
}
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
return nshards;
}
int convert_matrixmarket4(std::string base_filename, bool add_time_edges = false, bool square = false) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
int ret_code;
MM_typecode matcode;
FILE *f;
size_t nz;
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename, get_option_string("nshards", "auto")))) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
FILE * inf = fopen((base_filename + ".gm").c_str(), "r");
int rc = fscanf(inf,"%d\n%d\n%ld\n%lg\n%d\n",&M, &N, &L, &globalMean, &K);
if (rc != 5)
logstream(LOG_FATAL)<<"Failed to read global mean from file" << base_filename << ".gm" << std::endl;
fclose(inf);
if (K <= 0)
logstream(LOG_FATAL)<<"Incorrect number of time bins K in .gm file " << base_filename << ".gm" << std::endl;
logstream(LOG_INFO) << "Read matrix of size " << M << " x " << N << " Global mean is: " << globalMean << " time bins: " << K << " Now creating shards." << std::endl;
return nshards;
}
sharder<als_edge_type> sharderobj(base_filename);
sharderobj.start_preprocessing();
if ((f = fopen(base_filename.c_str(), "r")) == NULL) {
logstream(LOG_FATAL) << "Could not open file: " << base_filename << ", error: " << strerror(errno) << std::endl;
}
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << base_filename << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) !=0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error=" << ret_code << std::endl;
}
logstream(LOG_INFO) << "Starting to read matrix-market input. Matrix dimensions: "
<< M << " x " << N << ", non-zeros: " << nz << std::endl;
uint I, J;
double val, time;
if (!sharderobj.preprocessed_file_exists()) {
for (size_t i=0; i<nz; i++)
{
int rc = fscanf(f, "%d %d %lg %lg\n", &I, &J, &time, &val);
if (rc != 4)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
if (time < 0)
logstream(LOG_FATAL)<<"Time (third columns) should be >= 0 " << std::endl;
I--; /* adjust from 1-based to 0-based */
J--;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
K = std::max((int)time, (int)K);
//avoid self edges
if (square && I == J)
continue;
globalMean += val;
L++;
sharderobj.preprocessing_add_edge(I, (square? J : (M + J)), als_edge_type(val, time+M+N));
//in case of a tensor, add besides of the user-> movie edge also
//time -> user and time-> movie edges
if (add_time_edges){
sharderobj.preprocessing_add_edge((uint)time + M + N, I, als_edge_type(val, M+J));
sharderobj.preprocessing_add_edge((uint)time + M + N, M+J , als_edge_type(val, I));
}
}
uint toadd = 0;
if (implicitratingtype == IMPLICIT_RATING_RANDOM)
toadd = add_implicit_edges4(implicitratingtype, sharderobj);
globalMean += implicitratingvalue * toadd;
L += toadd;
sharderobj.end_preprocessing();
globalMean /= L;
logstream(LOG_INFO) << "Global mean is: " << globalMean << " time bins: " << K << " . Now creating shards." << std::endl;
FILE * outf = fopen((base_filename + ".gm").c_str(), "w");
fprintf(outf, "%d\n%d\n%ld\n%lg\n%d\n", M, N, L, globalMean, K);
fclose(outf);
} else {
logstream(LOG_INFO) << "Matrix already preprocessed, just run sharder." << std::endl;
}
fclose(f);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
return nshards;
}
int convert_matrixmarket(std::string base_filename, SharderPreprocessor<als_edge_type> * preprocessor = NULL) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
int ret_code;
MM_typecode matcode;
FILE *f;
size_t nz;
std::string suffix = "";
if (preprocessor != NULL) {
suffix = preprocessor->getSuffix();
}
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename+ suffix, get_option_string("nshards", "auto")))) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
FILE * inf = fopen((base_filename + ".gm").c_str(), "r");
int rc = fscanf(inf,"%d\n%d\n%ld\n%lg\n%d\n",&M, &N, &L, &globalMean, &K);
if (rc != 5)
logstream(LOG_FATAL)<<"Failed to read global mean from file" << base_filename+ suffix << ".gm" << std::endl;
fclose(inf);
logstream(LOG_INFO) << "Opened matrix size: " <<M << " x " << N << " Global mean is: " << globalMean << " time bins: " << K << " Now creating shards." << std::endl;
return nshards;
}
sharder<als_edge_type> sharderobj(base_filename + suffix);
sharderobj.start_preprocessing();
if ((f = fopen(base_filename.c_str(), "r")) == NULL) {
logstream(LOG_FATAL) << "Could not open file: " << base_filename << ", error: " << strerror(errno) << std::endl;
}
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << base_filename << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) !=0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error=" << ret_code << std::endl;
}
L=nz;
logstream(LOG_INFO) << "Starting to read matrix-market input. Matrix dimensions: "
<< M << " x " << N << ", non-zeros: " << nz << std::endl;
uint I, J;
double val;
if (!sharderobj.preprocessed_file_exists()) {
for (size_t i=0; i<nz; i++)
{
int rc = fscanf(f, "%d %d %lg\n", &I, &J, &val);
if (rc != 3)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
I--; /* adjust from 1-based to 0-based */
J--;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
globalMean += val;
sharderobj.preprocessing_add_edge(I, M + J, als_edge_type((float)val));
}
uint toadd = 0;
if (implicitratingtype == IMPLICIT_RATING_RANDOM)
toadd = add_implicit_edges(implicitratingtype, sharderobj);
globalMean += implicitratingvalue * toadd;
L += toadd;
sharderobj.end_preprocessing();
globalMean /= L;
logstream(LOG_INFO) << "Global mean is: " << globalMean << " Now creating shards." << std::endl;
if (preprocessor != NULL) {
preprocessor->reprocess(sharderobj.preprocessed_name(), base_filename);
}
FILE * outf = fopen((base_filename + ".gm").c_str(), "w");
fprintf(outf, "%d\n%d\n%ld\n%lg\n%d\n", M, N, L, globalMean, K);
fclose(outf);
} else {
logstream(LOG_INFO) << "Matrix already preprocessed, just run sharder." << std::endl;
}
fclose(f);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
logstream(LOG_INFO) << "Successfully finished sharding for " << base_filename + suffix << std::endl;
logstream(LOG_INFO) << "Created " << nshards << " shards." << std::endl;
return nshards;
}
