mldata-utils-0.5.0/0000700000175000017500000000000011547540067013141 5ustar sonnesonnemldata-utils-0.5.0/ml2h5/0000700000175000017500000000000011547540067014070 5ustar sonnesonnemldata-utils-0.5.0/ml2h5/indexsplit.py0000600000175000017500000001206411533700421016615 0ustar sonnesonneimport numpy import re import random def check_split_str(split_str): if split_str=='': return True if type(split_str) in [list,numpy.ndarray,numpy.matrix]: split_str=', '.join([str(i) for i in split_str]) else: split_str=str(split_str) split_str=split_str.replace(' ','') if re.match("((^|,)(\d+:\d+|\d+))+$",split_str)==None: return False tk=[] tk_sort=[] tk_pr=[] tk_po=[] tk_split=re.findall("(^|,)(\d+:\d+)|(\d+)",split_str) for i in range(len(tk_split)): if tk_split[i][2]!='': tk.append(tk_split[i][2]) tk_pr.append(int(tk[-1])) tk_po.append(int(tk[-1])+1) if tk_split[i][1]!='': tk.append(tk_split[i][1]) tk_pr.append(int(tk[-1].split(":")[0])) tk_po.append(int(tk[-1].split(":")[1])) tk=numpy.array(tk) tk_pr=numpy.array(tk_pr) tk_po=numpy.array(tk_po) sindex=tk_pr.argsort() tk=tk[sindex] tk_pr=tk_pr[sindex] tk_po=tk_po[sindex] for i in range(len(tk)): if tk_pr[i]>=tk_po[i]: return False return True def expand_split_str(split_str): tk=[] tk_sort=[] tk_pr=[] tk_po=[] if type(split_str) in [list,numpy.ndarray,numpy.matrix]: split_str=', '.join([str(i) for i in split_str]) else: split_str=str(split_str) tk_split=re.findall("(^|,)(\d+:\d+)|(\d+)",split_str) for i in range(len(tk_split)): if tk_split[i][2]!='': tk.append(tk_split[i][2]) tk_pr.append(int(tk[-1])) tk_po.append(int(tk[-1])+1) if tk_split[i][1]!='': tk.append(tk_split[i][1]) tk_pr.append(int(tk[-1].split(":")[0])) tk_po.append(int(tk[-1].split(":")[1])) tk=numpy.array(tk) split_pr=numpy.array(tk_pr) split_po=numpy.array(tk_po) out_set = set() for l in range(len(split_pr)): for i in range(split_po[l]-split_pr[l]): out_set.add(split_pr[l]+i) out = [i for i in out_set] out.sort() out_str=[str(i) for i in out] return out_str def reduce_split_str(split_str): tk=[] tk_sort=[] tk_pr=[] tk_po=[] if len(split_str)==0 : return [] if type(split_str) in [list,numpy.ndarray,numpy.matrix]: split_str=', '.join([str(i) for i in split_str]) else: split_str=str(split_str) split_str=split_str.replace(' ','') tk_split=re.findall("(^|,)(\d+:\d+)|(\d+)",split_str) for i in range(len(tk_split)): if tk_split[i][2]!='': tk.append(tk_split[i][2]) tk_pr.append(int(tk[-1])) tk_po.append(int(tk[-1])+1) if tk_split[i][1]!='': tk.append(tk_split[i][1]) tk_pr.append(int(tk[-1].split(":")[0])) tk_po.append(int(tk[-1].split(":")[1])) tk=numpy.array(tk) tk_pr=numpy.array(tk_pr) tk_po=numpy.array(tk_po) sindex=tk_pr.argsort() tk=tk[sindex] tk_pr=tk_pr[sindex] tk_po=tk_po[sindex] if len(tk) == 1: return tk cindex=[0] for i in range(1,len(tk)): if (tk_po[i]>=tk_po[i-1]): cindex.append(i) tk=tk[cindex] tk_pr=tk_pr[cindex] tk_po=tk_po[cindex] tk_reduce=[] akt=0 red_ind=0 aktstr=str(tk_pr[0]) for i in range(1,len(tk)): if (tk_pr[i]<=tk_po[i-1]): akt+=1 else: if akt>0: aktstr+=":"+str(tk_po[i-1]) tk_reduce.append(aktstr) aktstr=str(tk_pr[i]) akt=0 else: tk_reduce.append(tk[i-1]) aktstr=str(tk_pr[i]) if i==len(tk)-1: if akt>0: aktstr+=":"+str(tk_po[i]) tk_reduce.append(aktstr) aktstr=tk_pr[i] else: tk_reduce.append(tk[i]) return tk_reduce def check_split_intersec(split_lists): if len(split_lists)==0: return True if len(split_lists[0])>0 and type(split_lists[0][0])==list: max_lines=min([len(i) for i in split_lists]) for i in range(max_lines): split_line = [splits[i] for splits in split_lists] split_set=set() listsize=0 for sp in split_line: if len(sp): listsize+=len(sp) split_set|=set(sp) if not listsize==len(split_set): return i+1 else: split_set=set() listsize=0 for sp in split_lists: listsize+=len(sp) split_set|=set(sp) if not listsize==len(split_set): return 1 return 0 def main(): print check_split_str("1,2,3,4,5:7,27,28:32,32:30") x = reduce_split_str("0, 53,55,55 ,55:60,57, 61:62") print x y= expand_split_str("1,2,3:7,27:33") print y print check_split_intersec([[[1,2],[3,4]],[[5,6],[1,8],[1,2]]]) if __name__=="__main__": main() mldata-utils-0.5.0/ml2h5/__init__.py0000600000175000017500000000135211533700421016167 0ustar sonnesonne""" Utility functions around HDF5, mostly for mldata.org. """ __all__ = ['task', 'data', 'fileformat', 'converter'] VERSION_MLDATA = '0' NUM_EXTRACT = 10 # maximum value length LEN_EXTRACT = 6 PROGRAM_NAME='ml2h5' VERSION='0' ##workaround bugs #def import_h5py(): # h5py = __import__('h5py') # h5file=h5py.File # # def h5py_file(name, mode=None, driver=None):#, **driver_kwds): # if not mode: # mode='r' # file(name,mode) # if mode == 'r': # x=file(name).read(4) # if not x.endswith('HDF'): # raise Exception # # return h5file(name,mode,driver)#,driver_kwds) # # h5py.File=h5py_file # return h5py # #import __builtin__ #__builtin__.h5py=import_h5py() mldata-utils-0.5.0/ml2h5/fileformat.py0000600000175000017500000001345211533700421016564 0ustar sonnesonneimport h5py import os.path import gzip from ml2h5.converter.basehandler import ALLOWED_SEPERATORS from ml2h5.converter import AUTODETECTION_MAXBUFLEN def _try_suffix(fname): """Get format of given file by suffix. @param fname: name of file to determine format for @type fname: string """ suffix = fname.split('.')[-1] # just assume libsvm if no proper suffix if suffix.find('/') != -1: return '', False if suffix in ('svm', 'libsvm', 'light', 'svmlight'): return 'libsvm', True elif suffix in ('arff'): return 'arff', True elif suffix in ('h5', 'hdf5'): return 'h5', True elif suffix in ('csv', 'tsv'): return 'csv', True #elif suffix in ('uci', 'data'): # return 'uci', True elif suffix in ('bz2', 'gz', 'zip'): try: presuffix = fname.split('.')[-2] if presuffix == 'tar': return presuffix + '.' + suffix, True except IndexError: pass return suffix, True elif suffix in ('mat'): return 'matlab', True elif suffix in ('octave'): return 'octave', True elif suffix in ('xml'): return 'xml', True elif suffix in ('RData', 'rdata'): return 'rdata', True else: # unknown return suffix, False def _try_arff(fname): """Try if given file is in arff format @param fname: name of file to determine format for @type fname: string """ try: import arff arff.ArffFile.load(fname) return True except: return False def _try_csv(fname): """Try if given file is in csv format @param fname: name of file to determine format for @type fname: string """ if infer_seperator(fname) == ',': return True else: return False def _try_libsvm(fname): """Try if given file is in libsvm format @param fname: name of file to determine format for @type fname: string """ try: fp = open(fname, 'r') except: return False line = fp.readline(AUTODETECTION_MAXBUFLEN) attributes = line.split() if len(attributes) > 1: # 0th might be label, so look at 1st if len(attributes[1].split(':')) == 2: fp.close() return True fp.close() return False def _try_h5(fname): """Try if given file is in hdf5 format @param fname: name of file to determine format for @type fname: string """ return h5py.is_hdf5(fname) def _try_matlab(fname): try: return file(fname).read(6) == 'MATLAB' except: return False def _try_octave(fname): try: return file(fname).read(13)==('# Created by ') except: return False def _try_rdata(fname): try: return gzip.GzipFile(fname).read(4)==('RDX2') except: return False def infer_seperator(fname): """Infer seperator for variables in given file. @param fname: filename to retrieve data from @type fname: string @return: inferred seperator @rtype: string """ try: fp = open(fname, 'r') except: return None seperator = None minimum = 1 for i in xrange(100): # try the first 100 lines line = fp.readline(AUTODETECTION_MAXBUFLEN) if not line: break for s in ALLOWED_SEPERATORS: l = len(line.split(s)) if l > minimum: minimum = l seperator = s if seperator: break # stop processing if lines are too long anyways if not line.endswith('\n'): break fp.close() return seperator def get(fname, skip_suffix=False): """Get format of given file. By suffix it detects: libsvm, arff, csv, h5, tar.gz, tar.bz2, zip, matlab, octave. By deeper inspection it detects: h5, matlab, octave, arff, csv, libsvm @param fname: name of file to determine format for @type fname: string @param skip_suffix: if detection by suffix (first priority) shall be skipped @type skip_suffix: boolean """ if not skip_suffix: format, found = _try_suffix(fname) if found: return format if _try_h5(fname): return 'h5' elif _try_matlab(fname): return 'matlab' elif _try_rdata(fname): return 'rdata' elif _try_octave(fname): return 'octave' elif _try_libsvm(fname): return 'libsvm' elif _try_csv(fname): return 'csv' elif _try_arff(fname): return 'arff' return 'unknown' def get_filename(orig): """Convert a given filename to something that indicates HDF5. @param orig: original filename @type orig: string @return: HDF5-ified filename @rtype: string """ return os.path.splitext(orig)[0] + '.h5' def can_convert_h5_to(dst_type, h5_filename=None): """Whether conversion from this particular h5 file to dst type is supported @param h5_filename: h5 filename or None @type h5_filename: string @param dst_type: name of type @type dst_type: string @return: True if possible """ if dst_type in ('matlab', 'octave'): return True if h5_filename and h5_filename.endswith('.h5') and h5py.is_hdf5(h5_filename): try: h5 = h5py.File(h5_filename, 'r') if dst_type=='libsvm': # libsvm requires data/label ordering=set(('label','data')) if ordering.issubset(set(h5['data'].keys())): return True # TODO check if this is sparse data / ndarray data elif dst_type in ('csv', 'arff', 'rdata'): # csv/arff/RData support everything except sparse data for k in h5['data'].keys(): if k.endswith('_indptr') or k.endswith('_indices'): return False return True except: pass return False mldata-utils-0.5.0/ml2h5/data.py0000600000175000017500000003274411542125210015347 0ustar sonnesonne""" Handle Data objects and files. This module heavily relies on the functionality required for http://mldata.org """ import h5py, numpy, os, copy, tarfile, zipfile, bz2, gzip from scipy.sparse import csc_matrix import fileformat, converter from . import NUM_EXTRACT, LEN_EXTRACT def get_num_instattr(fname): """Retrieve number of instances and number of attributes from given HDF5 file. @param fname: filename to retrieve data from @type fname: string @return: number of instances and number of attributes @rtype: tuple containing 2 integers """ if not h5py.is_hdf5(fname): return (-1,-1) try: h5 = h5py.File(fname, 'r') num_inst = 0 num_attr = 0 for name in h5['/data_descr/ordering']: vname = '/data/' + name sp_indptr = vname+'_indptr' sp_indices = vname+'_indices' if sp_indptr in h5['/data'] and sp_indices in h5['/data']: A = csc_matrix( (h5[vname], h5[sp_indices], h5[sp_indptr]) ) num_inst += A.shape[1] num_attr += A.shape[0] else: if len(h5[vname].shape) == 2: num_attr += h5[vname].shape[0] num_inst = h5[vname].shape[1] else: num_inst = h5[vname].shape[0] num_attr += 1 h5.close() except: num_inst = -1 num_attr = -1 return (num_inst, num_attr) def _get_extract_data(h5): """Get extract data from given HDF5 object. @param h5: HDF5 file to get extract from @type h5: HDF5 file @return: data extract @rtype: list """ overlength=False overwidth=False cur_line=0 extract = [] try: for dset in list(h5['/data_descr/ordering']): if cur_line==NUM_EXTRACT: overwidth=True break dset = '/data/' + dset dset_indptr = dset+'_indptr' dset_indices = dset+'_indices' if dset_indptr in h5['/data'] and dset_indices in h5['/data']: # taking all data takes to long for quick viewing, but having just # this extract may result in less columns displayed than indicated # by attributes_names # reduce columns indices = h5[dset_indices][:h5[dset_indptr][NUM_EXTRACT+1]] data = (h5[dset][:h5[dset_indptr][NUM_EXTRACT+1]]) indptr = h5[dset_indptr][:NUM_EXTRACT+1] csc_shape = [min(NUM_EXTRACT,max(indices)),min(NUM_EXTRACT,len(indptr))] if len(indptr) NUM_EXTRACT): overwidth=True # reduce rows for i in xrange(len(indptr)-1): for j in range(indptr[i],indptr[i+1]): if indices[j]>=NUM_EXTRACT: indptr[i+1:]-=numpy.ones(len(indptr[i+1:])) data=data[indices < NUM_EXTRACT] indices=indices[indices < NUM_EXTRACT] # empty array exemption if data.shape==(0,) or indices.shape==(0,): data=numpy.array([0]) indices=data A=csc_matrix((data, indices, indptr),csc_shape).todense() if A.shape[0]+cur_line > NUM_EXTRACT: extract.extend(A[:-cur_line].tolist()) else: extract.extend(A.tolist()) cur_line+=A.shape[0] else: if type(h5[dset][0]) == numpy.ndarray: last = len(h5[dset][0]) if last > NUM_EXTRACT: last = NUM_EXTRACT overlength=True app_lines=xrange(len(h5[dset])) if len(app_lines) + cur_line > NUM_EXTRACT: app_lines = xrange(NUM_EXTRACT - cur_line ) overwidth=True for i in app_lines: extract.append(h5[dset][i][:last]) cur_line+=len(app_lines) else: cur_line+=1 last = len(h5[dset]) if last > NUM_EXTRACT: last = NUM_EXTRACT overlength=True extract.append(h5[dset][:last]) if overwidth: extract.append(['...' for i in extract[0]]) extract = numpy.matrix(extract).T # convert from numpy array to list, if necessary t = type(extract[0]) if t == numpy.ndarray or t == numpy.matrix: extract = [y for x in extract for y in x.tolist()] if overlength: extract.append(['...' for i in extract[0]]) # cut all values if necessary for l in range(len(extract)): for c in range(len(extract[l])): extract[l][c]=str(extract[l][c]) if len(extract[l][c])>LEN_EXTRACT: if extract[l][c][0]=='-': extract[l][c]=extract[l][c][:LEN_EXTRACT-1]+'...' else: extract[l][c]=extract[l][c][:LEN_EXTRACT-2]+'...' except KeyError: pass except ValueError: pass except IndexError: pass return extract def get_extract(fname): """Get an extract of an HDF5 Data file. @param fname: filename to get extract from @type fname: string @return: extract of an HDF5 file @rtype: dict with HDF5 attribute/dataset names as keys and their data as values """ format = fileformat.get(fname) if fname.endswith('.h5'): fname_h5 = fname else: return get_unparseable(fname) extract = { 'mldata': 0, 'name': '', 'comment': '', 'names': [], 'types': [], 'data': [], } if not h5py.is_hdf5(fname): return extract h5 = h5py.File(fname, 'r') attrs = ('mldata', 'name', 'comment') for a in attrs: if a in h5.attrs: extract[a] = h5.attrs[a] if '/data_descr/names' in h5: extract['names'] = h5['/data_descr/names'][:].tolist()[:NUM_EXTRACT] extract['names_cut'] = copy.copy(extract['names']) for i in xrange(len(extract['names_cut'])): if len(extract['names_cut'][i]) > LEN_EXTRACT: extract['names_cut'][i] = extract['names_cut'][i][:LEN_EXTRACT-2] + '...' if len(list(h5['/data_descr/names'])) > NUM_EXTRACT: extract['names_cut'].append('...') if '/data_descr/types' in h5: extract['types'] = h5['/data_descr/types'][:].tolist()[:NUM_EXTRACT] extract['data'] = _get_extract_data(h5) h5.close() return extract def is_binary(fname): """Return true if the given file is binary. @param fname: name of file to check if binary @type fname: string @return: if file is binary @rtype: boolean """ f = open(fname, 'rb') try: CHUNKSIZE = 1024 while 1: chunk = f.read(CHUNKSIZE) if '\0' in chunk: # found null byte f.close() return 1 if len(chunk) < CHUNKSIZE: break # done finally: f.close() return 0 def get_unparseable(fname): """Get data from unparseable files @param fname: filename to get data from @type fname: string @return: raw extract from unparseable file @rtype: dict with 'attribute' data """ if zipfile.is_zipfile(fname): intro = 'ZIP archive' f = zipfile.ZipFile(fname) data = ', '.join(f.namelist()) f.close() elif tarfile.is_tarfile(fname): intro = '(Zipped) TAR archive' f = tarfile.TarFile.open(fname) data = ', '.join(f.getnames()) f.close() else: intro = 'Format not HDF5, zip or tar archive. Will parse the following text:' if is_binary(fname): data = '' else: f = open(fname, 'r') i = 0 data = [] for l in f: # make sure we have unicode strings afterwards # otherwise this might lead to exceptions data.append(l.decode('utf-8', 'replace')) i += 1 if i > NUM_EXTRACT: break f.close() data = u"\n".join(data) return {'data': [[intro, data]]} def get_uncompressed(fname): """Get name of uncompressed Data file. The given file must be a zipfile/tarball and must contain exactly 1 file to be uncompressed. The returned name represents a file actually created in the file system which the user of this method might have to os.remove(). @param fname: (possibly) compressed filename @type fname: string @return: uncompressed filename if file is compressed, None otherwise @rtype: string """ src = None path = os.path.dirname(fname) # archives if zipfile.is_zipfile(fname): src = zipfile.ZipFile(fname) fnames = src.namelist() elif tarfile.is_tarfile(fname): src = tarfile.open(fname) fnames = src.getnames() if src: if len(fnames) == 1: src.extract(fnames[0], path) return os.path.join(path, fnames[0]) else: return None # gz/bz2 try: src = bz2.BZ2File(fname) except: try: src = gzip.open(fname, 'r') except: return None try: base = os.path.basename(fname) name = os.path.join(path, '.'.join(base.split('.')[:-1])) dst = open(name, 'w') dst.write(src.read()) dst.close() except: if os.path.exists(name) and not os.path.isdir(name): os.remove(name) name = None src.close() return name def _find_dset(fname, output_variables): """Find the dataset in given contents that contains the output_variable(s). This would be easy if all the data was just in one blob, but it may be in several datasets as defined by contents['ordering'], e.g. in contents['label'] or contents['data'] or contents['nameofvariable']. @param output_variables: index of output_variables to look for @type output_variables: integer @return: dataset corresponding to output_variables @rtype: list of integer """ if not h5py.is_hdf5(fname): return h5 = h5py.File(fname, 'r') dset = None ov = output_variables for name in h5['/data_descr/ordering']: path = '/data/' + name path_indptr = path+'_indptr' path_indices = path+'_indices' if path_indptr in h5['/data'] and path_indices in h5['/data']: A = csc_matrix( (h5['/data/data'], h5['/data/indices'], h5['/data/indptr']) ) if ov == 0: for i in xrange(A.shape[0]): if ov == 0: dset = A[i].todense().tolist() break # inner loop else: ov -= 1 else: data = h5[path][...] if len(data.shape) == 1: # datasets with shape (x,) if ov == 0: dset = data else: ov -= 1 else: # datasets with shape (x,y) for i in xrange(len(data)): if ov == 0: dset = data[i].tolist() break # inner loop else: ov -= 1 if dset is not None: break # outer loop h5.close() return dset def get_attribute_types(fname): if not h5py.is_hdf5(fname): return "" types=set() dt = h5py.special_dtype(vlen=str) try: h5 = h5py.File(fname, 'r') for o in h5['/data_descr/ordering']: indptr_name='/data/' + o + '_indptr' indices_name='/data/' + o + '_indices' if indptr_name in h5 and indices_name in h5: types += 'Sparse Matrix' else: if '/data_descr/types' in h5 and \ o in h5['/data_descr/types']: types += h5['/data_descr/types'][o] else: t=h5['/data/' + o].dtype if t==dt: types.add("String") elif t in (numpy.int64, numpy.int32): types.add("Integer") elif t in (numpy.float64, numpy.float32): types.add("Floating Point") else: types.add(str(t)) h5.close() except: pass return ','.join(list(types)) def get_correct(fname, test_idx, output_variables): """Get correct results from given file from given example indices at given attribute index. Used for solving tasks. @param fname: name of Data file @type fname: string @param test_idx: example indices for the test @type test_idx list of integer @param output_variables: index of attribute to get values for @type output_variables: integer @return: correct results @rtype: list of integer """ dset = _find_dset(fname, output_variables) correct = [] for idx in test_idx: correct.append(dset[idx]) return correct mldata-utils-0.5.0/ml2h5/converter/0000700000175000017500000000000011547540067016077 5ustar sonnesonnemldata-utils-0.5.0/ml2h5/converter/h5_mat.py0000700000175000017500000000566711472443701017641 0ustar sonnesonneimport h5py, numpy from scipy.io import savemat, loadmat from basehandler import BaseHandler class H5_MAT(BaseHandler): """Handle Matlab files.""" def __init__(self, *args, **kwargs): super(H5_MAT, self).__init__(*args, **kwargs) def read(self): matf = loadmat(self.fname, squeeze_me=False, chars_as_strings=True, mat_dtype=True, struct_as_record=True) for k in ('__header__', '__globals__', '__version__'): if matf.has_key(k): del matf[k] for k in matf.keys(): if matf[k].dtype == numpy.object: # asume cell of strings cell = [] for i in matf[k][0]: if len(i[0]): cell.append(str(i[0])) else: cell.append('') matf[k]=cell elif matf[k].shape[0] == 1: # row vectors are expanded matf[k]=matf[k][0] data = matf if matf.has_key('mldata_descr_ordering'): ordering = matf['mldata_descr_ordering'] del(matf['mldata_descr_ordering']) else: ordering = matf.keys() def strip_type(x): if x.startswith('double'): return int(x[6:]) elif x.startswith('int'): return int(x[3:]) elif x.startswith('str'): return int(x[3:]) else: return x ordering.sort(cmp=lambda x,y: cmp(strip_type(x),strip_type(y))) return { 'name': self.get_name(), 'comment': 'matlab', 'names': [], 'ordering': ordering, 'data': data, } def write(self, data): group=self.get_data_group(data) d=data[group] for k in d.keys(): if type(d[k])==list and len(d[k])>0 and type(d[k][0])==str: cell = array([ numpy.array(unicode(i)) for i in d[k] ], dtype=numpy.object) d[k] = cell elif type(d[k])==numpy.ndarray and len(d[k])>0 and type(d[k][0])==str: cell = numpy.empty((1,len(d[k])), dtype=numpy.object) for i in xrange(len(d[k])): cell[0,i]=numpy.array(unicode(d[k][i]), dtype='U') d[k] = cell elif type(d[k])==numpy.ndarray and len(d[k])>0 and d[k][0].dtype == numpy.object: cell = numpy.empty((1,len(d[k])), dtype=numpy.object) for i in xrange(len(d[k])): cell[0,i]=numpy.array(unicode(d[k][i]), dtype='U') d[k] = cell d['mldata_descr_ordering']=numpy.array(data['ordering'],dtype=numpy.object) savemat(self.fname, d, appendmat=False, oned_as='row', format='5', long_field_names=True) mldata-utils-0.5.0/ml2h5/converter/h5_octave.py0000700000175000017500000003423211542202071020316 0ustar sonnesonneimport h5py, numpy from basehandler import BaseHandler from scipy.sparse import csc_matrix import ml2h5.converter class H5_OCTAVE(BaseHandler): """Handle Octave files.""" def __init__(self, *args, **kwargs): super(H5_OCTAVE, self).__init__(*args, **kwargs) def _check_header(self, octf): """evidence of octave conformity (disabled) @param octf: octave file @type octf: opened File @return: if header exists @rtype: boolean """ octf.seek(0) header=octf.readline(15) if header.startswith('# Created by '): return True else: return False def _next_attr(self, octf): """Returns the next atribute in the octave file @param octf: octave file @type octf: opened File @return (name,data): name and proper matrix of the atribute """ name='' data=[] meta = self._read_meta(octf) # throw conversion exceptions ? if meta==None: return None if not meta.has_key('name'): return None if not meta.has_key('dtype'): return None if meta['dtype']=='scalar': data=self._read_matrix(octf,1,1) elif meta['dtype']=='matrix': data=self._read_matrix(octf,meta['columns'],meta['rows']) elif meta['dtype']=='int32 matrix': data=self._read_matrix(octf,1,1,mtype='int') elif meta['dtype']=='sparse matrix': data=self._read_sparse_matrix(octf,meta['columns'],meta['rows']) elif meta['dtype']=='cell': data=self._read_cellarray(octf,meta['columns'],meta['rows']) elif meta['dtype']=='string': data=self._read_sq_string(octf,meta['length'],meta['elements']) elif meta['dtype']=='sq_string': data=self._read_sq_string(octf,meta['length'],meta['elements']) else: # unsupported data type. format exception ? return None return { 'name':meta['name'], 'data':data, } def _read_meta(self,octf): """Returns the meta information of the next atribute in the octave file @param octf: octave file @type octf: opened File @return meta: dictionary of attribute meta information """ meta={} line = octf.readline() if not line: return None while not line.startswith('#'): if line == '': return None line = octf.readline() # metadata while line and line.startswith('#'): sp=line.split(': ') if sp[0]=='# name': if meta.has_key('name'): break meta['name']=sp[1][:-1] if sp[0]=='# type': meta['dtype']=sp[1][:-1] if sp[0]=='# rows': meta['rows']=int(sp[1]) if sp[0]=='# columns': meta['columns']=int(sp[1]) if sp[0]=='# elements': meta['elements']=int(sp[1]) if sp[0]=='# length': meta['length']=int(sp[1]) if sp[0]=='# ndims': meta['ndims']=int(sp[1]) lpos=octf.tell() line = octf.readline() octf.seek(lpos) return meta def _read_sq_string(self,octf,len,ele): """Returns the next string sequence atribute in the octave file @param octf: octave file @type octf: opened File @return data: array of strings """ data='' data=octf.readline()[:-1] return data def _read_cellarray(self,octf,col,row): """Returns the next cellarray atribute in the octave file @param octf: octave file @type octf: opened File @return data: array """ data=[] for r in range(row): for c in range(col): meta=self._read_meta(octf) if not meta['dtype']=='sq_string': return None else: data.append(self._read_sq_string(octf,meta['length'],meta['elements'])) return data def _read_sparse_matrix(self,octf,col,row): """Returns the next sparse matrix in the octave file @param octf: octave file @type octf: opened File @return data: csc_matrix """ tmp_data=numpy.array(self._read_matrix(octf,0,0)).T data=csc_matrix((tmp_data[2],tmp_data[0:2]-1),shape=(row,col)) data.sort_indices() return data def _read_matrix(self,octf,col,row,mtype='float'): """Returns the data of a matrix atribute in the octave file @param octf: octave file @type octf: opened File @return data: matrix """ data=[] line=octf.readline() lpos=octf.tell() if mtype=='int': if line.startswith(' '): line=line[1:] sp=line[:-1].split(' ') row=int(sp[0]) col=int(sp[1]) line=octf.readline() while line and not line.startswith('#'): if line.startswith(' '): line=line[1:] sp=line[:-1].split(' ') if mtype=='int': conv_sp=[] try: for i in sp: conv_sp.append(int(i)) except ValueError: raise ConversionError('unexpected data type') if mtype=='float': conv_sp=[] try: for i in sp: conv_sp.append(float(i)) except ValueError: raise ConversionError('unexpected data type') data.append(conv_sp) lpos=octf.tell() line = octf.readline() octf.seek(lpos) out = numpy.array(data) # unknown matrix shape ? if not (row==0 and col==0): out.shape=(row,col) if out.shape[0]==1: out.shape=(out.shape[1],) return out def read(self): data={} names=[] octf = open(self.fname, 'r') # header check if not self._check_header(octf): raise ml2h5.converter.ConversionError('Header check failed') attr=self._next_attr(octf) while attr and attr['name']!='': if attr['name']!='__nargin__': data[attr['name']]=attr['data'] names.append(attr['name']) attr=self._next_attr(octf) if (data.keys==[]): raise ml2h5.converter.ConversionError('empty conversion') return { 'name': self.get_name(), 'comment': 'octave', 'names':[], 'ordering': names, 'data': data, } def _oct_header(self): return '# Created by mldata.org for Octave 3.0.1\n' def _num_matrix(self,m): if type(m)!= numpy.ndarray: if type(m)==list: try: m=numpy.array(m) except: return None else: return None if len(m.shape) == 1: out=[] for i in m: try: if int(i)!=i: raise ValueError out.append(int(i)) except: try: out.append(float(i)) except: return None elif len(m.shape) == 2: out=[] for i in m: row=[] for j in i: try: if int(j)!=j: raise ValueError row.append(int(j)) except: try: row.append(float(j)) except: return None out.append(row) else: return None return out def _print_meta(self,of,attr,name): """Return a string of metainformation @return meta: string of attr informations """ attr_num=self._num_matrix(attr) of.write('# name: ' + str(name) + '\n') if type(attr) == numpy.ndarray: if attr.shape ==(): of.write('# type: sq_string\n') of.write('# elements: 1\n') elif attr.shape ==(1,1) or attr.shape==(1,): of.write('# type: scalar\n') elif len(attr.shape)==1: if attr_num==None: of.write('# type: cell\n') of.write('# rows: 1\n') of.write('# columns: ' + str(len(attr)) + '\n') else: if attr.dtype in ['int32', 'int64']: of.write('# type: int32 matrix\n') of.write('# ndims 2\n') else: of.write('# type: matrix\n') of.write('# rows: 1\n') try: of.write('# columns: ' + str(attr.shape[0]) + '\n') except IndexError: of.write('# columns: 1\n') else: if attr.dtype in ['int32', 'int64']: of.write('# type: int32 matrix\n') of.write('# ndims 2\n') else: of.write('# type: matrix\n') try: of.write('# rows: ' + str(attr.shape[0]) + '\n') except IndexError: of.write('# rows: 1\n') try: of.write('# columns: ' + str(attr.shape[1]) + '\n') except IndexError: of.write('# columns: 1\n') elif type(attr)== csc_matrix: of.write('# type: sparse matrix\n') of.write('# nnz: '+str(attr.nnz) + '\n') of.write('# rows: '+str(attr.shape[0]) + '\n') of.write('# columns: '+str(attr.shape[1]) + '\n') elif type(attr) == list: of.write('# type: cell\n') of.write('# rows: 1\n') of.write('# columns: ' + str(len(attr)) + '\n') else: of.write('# type: sq_string\n') of.write('# elements: 1\n') return True def _print_data(self, of, attr): """Return a string of data @return data: string of attr content """ if attr==None: return False attr_num=self._num_matrix(attr) # matrix or scalar or cell array if type(attr) == numpy.ndarray: # sq_string if attr.shape==(): of.write('# length: ' + str(len(str(attr))) + '\n') of.write(str(attr) + '\n\n') # scalar elif attr.shape==(1,1): of.write(str(attr_num[0][0]) + '\n') elif attr.shape==(1,): of.write(str(attr_num[0]) + '\n') # matrix elif len(attr.shape)==2: # int32 matrix if attr.dtype in ['int32','int64']: of.write(' ' + str(attr.shape[0]) + ' ' + str(attr.shape[1]) + '\n') for i in attr: for j in i: of.write(' ' + str(j) + '\n') # float matrix else: for i in attr_num: for j in i: of.write(' ' + str(j)) of.write('\n') # matrix (vector) or cell array elif len(attr.shape)==1: # matrix if attr_num!=None: # int32 matrix if attr.dtype in ['int32', 'int64']: of.write(' 1 ' + str(attr.shape[0]) + '\n') for i in attr: of.write(' ' + str(i) + '\n') # float matrix else: for i in attr_num: of.write(' ' + str(i)) of.write('\n') else: # cell array for i in attr: of.write('# name: \n') of.write('# type: sq_string\n') of.write('# elements: 1\n') of.write('# length: ' + str(len(i)) + '\n') of.write(str(i) + '\n\n') # sparse matrix elif type(attr) == csc_matrix: dat=self._num_matrix(attr.data) indptr=attr.indptr indices=attr.indices for i in xrange(attr.shape[1]): out=[] for j in xrange(indptr[i],indptr[i+1]): if dat[j]==int: out.append("%d %d %d\n" % (indices[j]+1, i+1, dat[j])) else: out.append("%d %d %.15g\n" % (indices[j]+1, i+1, dat[j])) of.write(''.join(out)) # more clean but slower code #indices=attr.nonzero() #for i in range(len(attr.data)): # of.write(str(indices[0][i]+1) + ' ' + str(indices[1][i]+1) + ' ' + str(dat[i]) + '\n') # cell array elif type(attr) == list: for i in attr: of.write('# name: \n') of.write('# type: sq_string\n') of.write('# elements: 1\n') of.write('# length: ' + str(len(i)) + '\n') of.write(str(i) + '\n\n') # single string else: of.write('# length: ' + str(len(attr)) + '\n') of.write(str(attr) + '\n\n') return True def write(self, data): group=self.get_data_group(data) of = open(self.fname,'w') of.writelines(self._oct_header()) for o in data['ordering']: self._print_meta(of,data[group][o], o) self._print_data(of,data[group][o]) of.close() mldata-utils-0.5.0/ml2h5/converter/__init__.py0000600000175000017500000001643011533700421020201 0ustar sonnesonne""" Convert from and to HDF5 (spec of mldata.org) """ ALLOWED_SEPERATORS = (None, ',', ' ', '\t') TO_H5 = ['libsvm', 'arff', 'csv', 'matlab', 'octave'] FROM_H5 = ['libsvm','arff', 'csv', 'matlab', 'octave', 'xml', 'rdata'] EPSILON = 1e-15 #1 MB is maximum line len for autodetection AUTODETECTION_MAXBUFLEN=1*1024*1024 import os, sys, numpy, h5py import subprocess from gettext import gettext as _ from scipy.sparse import csc_matrix import ml2h5.fileformat from h5_arff import H5_ARFF from h5_libsvm import H5_LibSVM from h5_csv import H5_CSV from h5_mat import H5_MAT from h5_octave import H5_OCTAVE from h5_rdata import H5_RData #from h5_uci import H5_UCI from basehandler import BaseHandler HANDLERS = { 'libsvm': H5_LibSVM, 'arff': H5_ARFF, 'csv': H5_CSV, 'matlab': H5_MAT, 'octave' : H5_OCTAVE, 'rdata': H5_RData, #'uci' : H5_UCI, 'h5': BaseHandler } class ConversionError(Exception): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) def print_error(self): print self.value class Converter(object): """Convert and verify conversion. @ivar fname_in: name of in-file @type fname_in: string @ivar fname_out: name of out-file @type fname_out: string @ivar format_in: format of in-file @type format_in: string @ivar format_out: format of out-file @type format_out: string @ivar handler_in: handler object to read data in a specific format @type handler_in: derivate of BaseHandler @ivar handler_out: handler object to write data in a specific format @type handler_out: derivate of BaseHandler """ def __init__(self, fname_in, fname_out, format_in=None, format_out=None, seperator=None, attribute_names_first=False, merge=True, type='data'): """ @param fname_in: name of in-file @type fname_in: string @param fname_out: name of out-file @type fname_out: string @param format_in: format of in-file @type format_in: string @param format_out: format of out-file @type format_out: string @param seperator: seperator to seperate variables in examples @type seperator: string @param attribute_names_first: first line contains attributes (for CSV only) @type attribute_names_first: boolean """ self.fname_in = fname_in self.fname_out = fname_out self.type = type if format_in: self.format_in = format_in else: self.format_in = ml2h5.fileformat.get(fname_in) if format_out: self.format_out = format_out else: self.format_out = ml2h5.fileformat.get(fname_out) # handle h5 -> xml via h5dump if self.format_in == 'h5' and self.format_out == 'xml': return try: self.handler_in = HANDLERS[self.format_in](fname_in, seperator,merge=merge) if self.format_in == 'csv': self.handler_in.attribute_names_first = attribute_names_first self.handler_out = HANDLERS[self.format_out](fname_out, seperator,merge=merge) if self.format_out == 'csv': self.handler_out.attribute_names_first = attribute_names_first except KeyError: raise ConversionError( 'Unknown conversion pair %s to %s!' % (self.format_in, self.format_out)) except Exception, e: # reformat all other exceptions to ConversionError raise ConversionError, ConversionError(str(e)), sys.exc_info()[2] def read(self): try: return self.handler_in.read() except Exception, e: # reformat all exceptions to ConversionError raise ConversionError, ConversionError(str(e)), sys.exc_info()[2] def run(self, verify=False, remove_out=True): """Convert to/from HDF5. @param verify: verify if data in output is same as data in input @type verify: boolean @param remove_out: if output file shall be removed before running. @type remove_out: boolean """ # sometimes it seems files are not properly overwritten when opened by # 'w' during run(). if remove_out and os.path.exists(self.fname_out): os.remove(self.fname_out) try: if self.format_in == 'h5' and self.format_out == 'xml': cmd = 'h5dump --xml ' + self.fname_in + ' > ' + self.fname_out if not subprocess.call(cmd, shell=True) == 0: raise ConversionError, 'Failed conversion of %s to XML' % (self.fname_in) else: data = self.handler_in.read() self.handler_out.write(data) except Exception, e: # reformat all exceptions to ConversionError raise ConversionError, ConversionError(str(e)), sys.exc_info()[2] def _compare(self, A, B): """Compare given matrices A and B. Used by verification process. @param A: list A to compare @type A: list of list @param B: list B to compare @type B: list of list """ if type(A) == csc_matrix: try: if all(A.indices==B.indices) and all(A.indptr==B.indptr) and all(A.data==B.data): return True else: return False except: return False if type(A[0])==numpy.ndarray: xrange_A0 = xrange(len(A[0])) for i in xrange(len(A)): Ai = A[i] Bi = B[i] for j in xrange_A0: try: if abs(Ai[j] - Bi[j]) > EPSILON: return False except TypeError: #string if str(Ai[j]) != str(Bi[j]): return False else: if type(A) == numpy.ndarray and \ type(B) == numpy.ndarray and \ A.shape != B.shape: return False for j in xrange(len(A)): try: if abs(A[j] - B[j]) > EPSILON: return False except TypeError: #string if str(A[j]) != str(B[j]): return False return True def verify(self): """Verify that data in given files is the same. @return: true if verification succeeds @rtype: boolean @raises: ConversionError """ if self.format_in == 'uci': raise ConversionError('Cannot verify UCI data format, %s!' % self.fname_in) if self.format_out == 'uci': raise ConversionError('Cannot verify UCI data format, %s!' % self.fname_out) data_in = self.handler_in.read() data_out = self.handler_out.read() for i in xrange(len(data_in['ordering'])): name_in = data_in['ordering'][i] name_out = data_out['ordering'][i] if not self._compare(data_in['data'][name_in], data_out['data'][name_out]): raise ConversionError( 'Verification failed! Data of %s != %s ("%s" not matching "%s")' % (self.fname_in, self.fname_out, name_in, name_out) ) return True mldata-utils-0.5.0/ml2h5/converter/arff.py0000600000175000017500000002514611465067567017411 0ustar sonnesonne# -*- coding: utf-8 -*- # Copyright (c) 2008, Mikio L. Braun, Cheng Soon Ong, Soeren Sonnenburg # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following disclaimer # in the documentation and/or other materials provided with the # distribution. # * Neither the names of the Technical University of Berlin, ETH # Zürich, or Fraunhofer FIRST nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import re, sys, numpy class ArffFile(object): """An ARFF File object describes a data set consisting of a number of data points made up of attributes. The whole data set is called a 'relation'. Supported attributes are: - 'numeric': floating point numbers - 'string': strings - 'nominal': taking one of a number of possible values Not all features of ARFF files are supported yet. The most notable exceptions are: - no sparse data - no support for date and relational attributes Also, parsing of strings might still be a bit brittle. You can either load or save from files, or write and parse from a string. You can also construct an empty ARFF file and then fill in your data by hand. To define attributes use the define_attribute method. Attributes are: - 'relation': name of the relation - 'attributes': names of the attributes - 'attribute_types': types of the attributes - 'attribute_data': additional data, for example for nominal attributes. - 'comment': the initial comment in the file. Typically contains some information on the data set. - 'data': the actual data, by data points. """ def __init__(self): """Construct an empty ARFF structure.""" self.relation = '' self.attributes = [] self.attribute_types = dict() self.attribute_data = dict() self.comment = [] self.data = [] pass @staticmethod def load(filename): """Load an ARFF File from a file.""" o = open(filename) s = o.read() a = ArffFile.parse(s) o.close() if not a.relation: raise Exception('Not an ARFF file: ' + filename) return a def _rm_ticks(self, item): """Remove ticks from item if it is a string. Some attributes are surrounded by unnecessary ticks. @param item: item to check for ticks @type item: any, preferably str @return: unmodified item or item with ticks removed @rtype: type(item) """ try: # a few attributes are designated strings by "'" if item[0] == "'" and item[-1] == "'": return item[1:-1] except TypeError: pass return item @staticmethod def parse(s): """Parse an ARFF File already loaded into a string.""" a = ArffFile() a.state = 'comment' a.lineno = 1 for l in s.splitlines(): a.__parseline(l) a.lineno += 1 return a def save(self, filename): """Save an arff structure to a file.""" o = open(filename, 'w') o.write(self.write()) o.close() def write(self): """Write an arff structure to a string.""" o = [] o.append('% ' + re.sub("\n", "\n% ", self.comment)) o.append("@relation " + self.esc(self.relation)) for a in self.attributes: at = self.attribute_types[a] if at == 'numeric': o.append("@attribute " + self.esc(a) + " numeric") elif at == 'string': o.append("@attribute " + self.esc(a) + " string") elif at == 'date': o.append("@attribute " + self.esc(a) + " date " + \ ''.join(self.attribute_data[a])) elif at == 'nominal': o.append("@attribute " + self.esc(a) + " {" + ','.join(self.attribute_data[a]) + "}") else: raise Exception("Type " + at + " not supported for writing!") o.append("\n@data") for d in self.data: line = [] for e, a in zip(d, self.attributes): at = self.attribute_types[a] if at == 'numeric': if type(e) in [numpy.int32,numpy.int64]: line.append(str(int(e))) else: line.append(str(float(e))) elif at == 'string': line.append(self.esc(e)) elif at == 'date': line.append(self.esc(e)) elif at == 'nominal': line.append(e) else: raise Exception("Type " + at + " not supported for writing!") o.append(','.join(map(str, line))) return "\n".join(o) + "\n" def esc(self, s): "Escape a string if it contains spaces" try: if re.match(r'\s', s): return "\'" + str(s) + "\'" except TypeError: pass return str(s) def define_attribute(self, name, atype, data=None): """Define a new attribute. atype has to be one of 'numeric', 'string', 'date' or 'nominal'. For nominal or date attributes, pass the possible values as data.""" n = self._rm_ticks(name) self.attributes.append(n) self.attribute_types[n] = atype self.attribute_data[n] = data def __parseline(self, l): if self.state == 'comment': if len(l) > 0 and l[0] == '%': self.comment.append(l[2:]) else: self.comment = '\n'.join(self.comment) self.state = 'in_header' self.__parseline(l) elif self.state == 'in_header': ll = l.lower().strip() if ll.startswith('@relation '): self.__parse_relation(l) if ll.startswith('@attribute '): self.__parse_attribute(l) if ll.startswith('@data'): self.state = 'data' elif self.state == 'data': if len(l) > 0 and l[0] != '%': self.__parse_data(l) def __parse_relation(self, l): l = l.split() self.relation = l[1] def __parse_attribute(self, l): p = re.compile(r'[a-zA-Z_][a-zA-Z0-9_/+-.*\(\)]*|\{[^\}]+\}|\'[^\']+\'|\"[^\"]+\"') l = [s.strip() for s in p.findall(l)] name = l[1] atype = l[2] atypel = atype.lower() if (atypel == 'real' or atypel == 'numeric' or atypel == 'integer'): self.define_attribute(name, 'numeric') elif atypel == 'string': self.define_attribute(name, 'string') elif atypel == 'date': self.define_attribute(name, 'date', l[3]) elif atype[0] == '{' and atype[-1] == '}': values = [s.strip () for s in atype[1:-1].split(',')] self.define_attribute(name, 'nominal', values) else: self.__print_warning("unsupported type " + atype + " for attribute " + name + ".") def __parse_data(self, line): l = [s.strip() for s in line.split(',')] if len(l) == 1: l = [s.strip() for s in line.split()] if not l[-1].strip(): # remove trailing empty item l.pop() if len(l) != len(self.attributes): self.__print_warning("contains wrong number of values") return datum = [] for n, v in zip(self.attributes, l): at = self.attribute_types[n] if at == 'numeric': if v == '?' or v == '': datum.append(numpy.nan) elif re.match(r'[+-]?[0-9]*(?:\.[0-9]*(?:[eE]-?[0-9]+)?)?', v): try: datum.append(int(v)) except ValueError: datum.append(float(v)) else: self.__print_warning('non-numeric value %s for numeric attribute %s' % (v, n)) return elif at == 'string': datum.append(self._rm_ticks(v)) elif at == 'date': datum.append(self._rm_ticks(v)) elif at == 'nominal': if v in self.attribute_data[n]: datum.append(v) elif v == '?': datum.append(numpy.nan) else: self.__print_warning('incorrect value %s for nomial attribute %s' % (v, n)) return self.data.append(datum) def __print_warning(self, msg): #print ('Warning (line %d): ' % self.lineno) + msg pass def dump(self): """Print an overview of the ARFF file.""" print "Relation " + self.relation print " With attributes" for n in self.attributes: if self.attribute_types[n] != 'nominal': print " %s of type %s" % (n, self.attribute_types[n]) else: print (" " + n + " of type nominal with values " + ', '.join(self.attribute_data[n])) for d in self.data: print d if __name__ == '__main__': if False: a = ArffFile.parse("""% yes % this is great @relation foobar @attribute foo {a,b,c} @attribute bar real @data a, 1 b, 2 c, d d, 3 """) a.dump() a = ArffFile.load('../examples/diabetes.arff') print a.write() mldata-utils-0.5.0/ml2h5/converter/h5_rdata.py0000600000175000017500000000341511533700421020130 0ustar sonnesonneimport h5py, numpy import rpy2.robjects as robjects import rpy2.robjects.numpy2ri import rpy2.rlike.container as rlc from basehandler import BaseHandler class H5_RData(BaseHandler): """Handle RData files.""" def __init__(self, *args, **kwargs): super(H5_RData, self).__init__(*args, **kwargs) def read(self): raise "Not supported..." def write(self, data): group=self.get_data_group(data) dest=robjects.globalenv if group == 'data': datavals = data['data'] ordering = data['ordering'] attrlist = [] nameind = 0 names = data['names'] types = data['types'] for cur_feat in ordering: if len(datavals[cur_feat].shape) > 1: for k in range(datavals[cur_feat].shape[0]): if str(types[nameind]).startswith('nominal'): attrlist.append((names[nameind], robjects.FactorVector(robjects.StrVector(datavals[cur_feat][k])))) else: attrlist.append((names[nameind], datavals[cur_feat][k])) nameind += 1 else: if str(types[nameind]).startswith('nominal'): attrlist.append((names[nameind], robjects.FactorVector(robjects.StrVector(datavals[cur_feat])))) else: attrlist.append((names[nameind], datavals[cur_feat])) nameind += 1 dest[data['name']] = robjects.DataFrame(rlc.OrdDict(attrlist)) elif group == 'task': d=data[group] for k in d.keys(): dest[k] = d[k] robjects.r.save(*list(robjects.r.ls(dest)), file=self.fname) mldata-utils-0.5.0/ml2h5/converter/basehandler.py0000600000175000017500000002577111542202071020720 0ustar sonnesonneimport os, h5py, numpy from scipy.sparse import csc_matrix import ml2h5.task from ml2h5 import VERSION_MLDATA from ml2h5.converter import ALLOWED_SEPERATORS class BaseHandler(object): """Base handler class. It is the base for classes to handle different data formats. It implicitely handles HDF5. @cvar str_type: string type to be used for variable length strings in h5py @type str_type: numpy.dtype @ivar fname: name of file to handle @type fname: string @ivar seperator: seperator to seperate variables in examples @type seperator: string """ str_type = h5py.new_vlen(numpy.str) def __init__(self, fname, seperator=None, compression=None, merge=False): """ @param fname: name of in-file @type fname: string @param seperator: seperator used to seperate examples @type seperator: string """ self.fname = fname self.compression = compression self.set_seperator(seperator) self.merge = merge def set_seperator(self, seperator): """Set the seperator to seperate variables in examples. @param seperator: seperator to use @type seperator: string """ if seperator in ALLOWED_SEPERATORS: self.seperator = seperator else: raise AttributeError(_("Seperator '%s' not allowed!" % seperator)) def warn(self, msg): """Print a warning message. @param msg: message to print @type msg: string """ return print 'WARNING: ' + msg def _convert_to_ndarray(self,path,val): """converts a attribut to a set of ndarrays depending on the datatype @param path: path of the attribute in the h5 file @type path: string @param val: data of the attribute @type val: csc_matrix/ndarray @rtype: list of (string,ndarray) tuples """ A=val out=[] dt = h5py.special_dtype(vlen=str) if type(A)==csc_matrix: # sparse out.append((path+'_indices', A.indices)) out.append((path+'_indptr', A.indptr)) out.append((path, A.data)) elif type(A)==list and len(A)>0 and type(A[0])==str: out.append((path, numpy.array(A, dtype=dt))) else: # dense out.append((path, numpy.array(A))) return out def get_data_as_list(self,data): """ this needs to `transpose' the data """ dl=[] group=self.get_data_group(data) lengths=dict() for o in data['ordering']: x=data[group][o] #if numpy.issubdtype(x.dtype, numpy.int): # data[group][o]=x.astype(numpy.float64) try: lengths[o]=data[group][o].shape[1] except (AttributeError, IndexError): lengths[o]=len(data[group][o]) l=set(lengths.values()) assert(len(l)==1) l=l.pop() for i in xrange(l): line=[] for o in data['ordering']: try: line.extend(data[group][o][:,i]) except: line.append(data[group][o][i]) dl.append(line) return dl def get_name(self): """Get dataset name from non-HDF5 file @return: comment @rtype: string """ # without str() it might barf return str(os.path.basename(self.fname).split('.')[0]) def get_data_group(self, data): if data and data.has_key('group'): return data['group'] else: return 'data' def get_descr_group(self, data): if data and data.has_key('group'): return data['group'] + '_descr' else: return 'data_descr' def get_datatype(self, values): """Get data type of given values. @param values: list of values to check @type values: list @return: data type to use for conversion @rtype: numpy.int32/numpy.double/self.str_type """ dtype = None for v in values: if isinstance(v, int): dtype = numpy.int32 elif isinstance(v, float): dtype = numpy.double else: # maybe int/double in string try: tmp = int(v) if not dtype: # a previous nan might set it to double dtype = numpy.int32 except ValueError: try: tmp = float(v) dtype = numpy.double except ValueError: return self.str_type return dtype def read(self): """Get data and description in-memory Retrieve contents from file. @return: example names, ordering and the examples @rtype: dict of: list of names, list of ordering and dict of examples """ # we want the exception handled elsewhere if not h5py.is_hdf5(self.fname): return h5 = h5py.File(self.fname, 'r') contents = { 'name': h5.attrs['name'], 'comment': h5.attrs['comment'], 'mldata': h5.attrs['mldata'], } if contents['comment']=='Task file': contents['task']=dict() contents['ordering']=list() group='task' for field in ml2h5.task.task_data_fields: if field in h5[group]: contents['ordering'].append(field) else: contents['data']=dict() contents['ordering']=h5['/data_descr/ordering'][...].tolist() group='data' contents['group']=group if '/%s_descr/names' % group in h5: contents['names']=h5['/%s_descr/names' % group][...].tolist() if '/%s_descr/types' % group in h5: contents['types'] = h5['/%s_descr/types' % group ][...] for name in contents['ordering']: vname='/%s/%s' % (group, name) sp_indices=vname+'_indices' sp_indptr=vname+'_indptr' if sp_indices in h5['/%s' % group] and sp_indptr in h5['/%s' % group]: contents[group][name] = csc_matrix((h5[vname], h5[sp_indices], h5[sp_indptr]) ) else: d = numpy.array(h5[vname],order='F') try: d=d['vlen'] except: pass contents[group][name] = d h5.close() return contents def read_data_as_array(self): """Read data from file, and return an array @return: an array with all data @rtype: numpy ndarray """ contents = self.read() #group = self.get_data_group(data) data = contents['data'] ordering = contents['ordering'] if len(data[ordering[0]].shape)>1: num_examples = data[ordering[0]].shape[1] else: num_examples = len(data[ordering[0]]) data_array = numpy.zeros((0, num_examples)) for cur_feat in ordering: data_array = numpy.vstack([data_array, data[cur_feat]]) return data_array.T def _get_merged(self, data): """Merge given data where appropriate. String arrays are not merged, but all int and all double are merged into one matrix. @param data: data structure as returned by read() @type data: dict @return: merged data structure @rtype: dict """ merged = {} ordering = [] path = '' idx = 0 merging = None group = self.get_data_group(data) for name in data['ordering']: val = data[group][name] if type(val) == csc_matrix: merging = None path = name merged[path] = val ordering.append(path) continue if name.endswith('_indices') or name.endswith('_indptr'): merging = None path = name merged[path] = val continue if len(val) < 1: continue t = type(val[0]) if t in [numpy.int32, numpy.int64]: if merging == 'int': merged[path].append(val) else: merging = 'int' path = 'int' + str(idx) ordering.append(path) merged[path] = [val] idx += 1 elif t == numpy.double: if merging == 'double': merged[path].append(val) else: merging = 'double' path = 'double' + str(idx) ordering.append(path) merged[path] = [val] idx += 1 else: # string or matrix merging = None if name.find('/') != -1: # / sep belongs to hdf5 path path = name.replace('/', '+') data['ordering'][data['ordering'].index(name)] = path else: path = name ordering.append(path) merged[path] = val data[group] = {} for k in merged: if len(merged[k])==1: merged[k] = merged[k][0] data[group][k] = numpy.array(merged[k]) data['ordering'] = ordering return data def write(self, data): """Write given data to HDF5 file. @param data: data to write to HDF5 file. @type data: dict of lists """ # we want the exception handled elsewhere h5 = h5py.File(self.fname, 'w') h5.attrs['name'] = data['name'] h5.attrs['mldata'] = VERSION_MLDATA h5.attrs['comment'] = data['comment'] data_group = self.get_data_group(data) descr_group = self.get_descr_group(data) try: group = h5.create_group('/%s' % data_group) for path, val in data[data_group].iteritems(): for path, val in self._convert_to_ndarray(path,val): group.create_dataset(path, data=val, compression=self.compression) group = h5.create_group('/%s' % descr_group) names = numpy.array(data['names']).astype(self.str_type) if names.size > 0: # simple 'if names' throws exception if array group.create_dataset('names', data=names, compression=self.compression) ordering = numpy.array(data['ordering']).astype(self.str_type) if ordering.size > 0: group.create_dataset('ordering', data=ordering, compression=self.compression) if 'types' in data: types = numpy.array(data['types']).astype(self.str_type) group.create_dataset('types', data=types, compression=self.compression) except: # just do some clean-up h5.close() os.remove(self.fname) raise else: h5.close() mldata-utils-0.5.0/ml2h5/converter/h5_arff.py0000700000175000017500000001003611472445351017763 0ustar sonnesonneimport h5py, numpy, copy import arff from basehandler import BaseHandler from scipy.sparse import csc_matrix import ml2h5.converter class H5_ARFF(BaseHandler): """Handle ARFF files. It uses the module arff provided by the dataformat project: http://mloss.org/software/view/163/ """ def __init__(self, *args, **kwargs): super(H5_ARFF, self).__init__(*args, **kwargs) def _get_types(self, af): """Get types from Arff file @param af: opened ARFF file @type af: arff.ArffFile @return: types in arff file @rtype: numpy.array """ # need to get it in the right order types = [] for name in af.attributes: t = af.attribute_types[name] if af.attribute_data[name]: t += ':' + ','.join(af.attribute_data[name]) types.append(t) return numpy.array(types) def read(self): data = {} names = [] ordering = [] af = arff.ArffFile.load(self.fname) for name in af.attributes: names.append(name) data[name] = [] for item in af.data: for i in xrange(len(data)): data[names[i]].append(item[i]) # conversion to proper data types for name, values in data.iteritems(): if af.attribute_types[name] == 'date': t = self.str_type else: t = self.get_datatype(values) data[name] = numpy.array(values).astype(t) ddict= { 'name': af.relation, 'comment': af.comment, 'types': self._get_types(af), 'names':names, 'ordering':copy.copy(names), 'data':data, } if self.merge == True: ddict = self._get_merged(ddict) return ddict def check_sparse(self, data): for k in data.keys(): d=data[k] if type(d)==csc_matrix: raise ml2h5.converter.ConversionError("Sparse matrices are not supported in ARFF files") def write(self, data): group=self.get_data_group(data) d=data[group] self.check_sparse(d) af = arff.ArffFile() af.data = self.get_data_as_list(data) if data.has_key('names') and len(data['names']): af.attributes = data['names'] else: attrs=[] idx=0 for o in data['ordering']: try: if len(d[o].shape)==1: num=1 else: num=d[o].shape[0] if d[o].dtype == numpy.double: prefix='double' elif d[o].dtype in [numpy.int32, numpy.int64 ]: prefix='int' else: prefix='str' except AttributeError: # list - so assume list of strings num=1 prefix='str' for i in xrange(num): attrs.append('%s%d' % (prefix,idx)) idx+=1 if len(attrs)==len(data['ordering']): attrs=data['ordering'] af.attributes = attrs af.relation = data['name'] af.comment = data['comment'] # handle arff types if 'types' in data: types = data['types'] else: types = [] for name in af.attributes: if name.startswith('int') or name.startswith('double'): types.append('numeric') elif name.startswith('date'): types.append('date') else: types.append('string') for i in xrange(len(types)): t = types[i].split(':') af.attribute_types[af.attributes[i]] = t[0] if len(t) == 1: af.attribute_data[af.attributes[i]] = None else: af.attribute_data[af.attributes[i]] = t[1].split(',') af.save(self.fname) mldata-utils-0.5.0/ml2h5/converter/h5_uci.py0000700000175000017500000001011111460302625017610 0ustar sonnesonneimport os, numpy from basehandler import BaseHandler class H5_UCI(BaseHandler): """Handle UCI files.""" def _get_comment(self): try: name = '.'.join(self.fname.split('.')[:-1]) if not name: name = '.'.join(self.fname.split('-')[:-1]) if os.path.exists(name + '.names'): fp = open(name + '.names', 'r') elif os.path.exists(name + '.info'): fp = open(name + '.info', 'r') else: return '' comment = ''.join(fp.readlines()) fp.close() return comment except ValueError: return '' except IOError: return '' def _ignore_line(self, line): line = line.strip() if not line: return True if line.startswith(';;;'): return True return False def _split(self, line): line=line.strip() items = line.split() for i in xrange(len(items)): if '\t' in items[i]: old=items.pop(i) new=old.split() for it in new[::-1]: items.insert(i, it) marker_start=line.find('"') if marker_start == -1: return items marker_start=line.find('"') marker_stop=line.find('"', marker_start+1) conc_item_idx=line conc_start=None for i in xrange(len(items)): if marker_start == -1 or marker_stop == -1: return items if conc_start and '"' in items[i]: for j in xrange(conc_start, i+1): items.pop(conc_start) conc_str=line[marker_start+1:marker_stop] marker_start=line.find('"', marker_stop+1) marker_stop=line.find('"', marker_start+1) items.insert(conc_start, conc_str) conc_start = None continue elif not conc_start and '"' in items[i]: conc_start = i return items def _parse(self): fp = open(self.fname, 'r') lineno = 0 num_items = None data = [] for line in fp: lineno += 1 if self._ignore_line(line): continue items = self._split(line) if not num_items: # do some init num_items = len(items) for i in xrange(num_items): data.append([]) for i in xrange(len(items)): item = items[i].strip() if not item: continue try: if item == '?': # missing value item = numpy.nan data[i].append(item) except IndexError: self.warn('Index Error in line ' + str(lineno) + ', column ' + str(i)) fp.close() return data def read(self): ordering = [] predata = self._parse() for i in xrange(len(predata)): arr = numpy.array(predata[i]) # everything is nan -> keep as str try: all_is_nan = numpy.isnan(arr).all() except AttributeError: all_is_nan = False if all_is_nan: arr = arr.astype(self.str_type) name = 'str' + str(i) else: try: arr = arr.astype(numpy.int) name = 'int' + str(i) except ValueError: try: arr = arr.astype(numpy.double) name = 'double' + str(i) except ValueError: # fallback to str arr = arr.astype(self.str_type) name = 'str' + str(i) data[name] = arr ordering.append(name) return { 'name': self.get_name(), 'comment': self._get_comment(), 'ordering':ordering, 'names':ordering, 'data':data, } mldata-utils-0.5.0/ml2h5/converter/h5_csv.py0000700000175000017500000000654311464536435017655 0ustar sonnesonneimport numpy, h5py, os, copy, numpy from scipy.sparse import csc_matrix from basehandler import BaseHandler import ml2h5.converter COMMENT = '# ' SEPERATOR = ',' class H5_CSV(BaseHandler): """Handle CSV files. @ivar attribute_names_first: first line contains attributes @type attribute_names_first: boolean """ def __init__(self, *args, **kwargs): super(H5_CSV, self).__init__(*args, **kwargs) self.attribute_names_first = False self.seperator = SEPERATOR # maybe more flexible in the future def _find_nan(self, value): """Find NaN values and make them proper numpy.nan. Used by map function in get_contents() @param value: value to check if it is NaN @type value: string """ if value == '?': return numpy.nan else: # str() important! when run thru webserver it will be unicode # otherwise and somehow results somewhere in nan being converted # to string 'na'... return str(value) def read(self): """ """ data = {} names = [] ordering = [] infile = open(self.fname, 'r') parsed = [] anf = self.attribute_names_first # copy value for line in infile: l = line.strip().split(self.seperator) if anf: names = l anf = False else: l = map(self._find_nan, l) parsed.append(l) infile.close() A = numpy.matrix(parsed).T for i in xrange(A.shape[0]): items = A[i].tolist()[0] t = self.get_datatype(items) if t == numpy.int32: name = 'int' + str(i) elif t == numpy.double: name = 'double' + str(i) else: name = 'str' + str(i) data[name] = numpy.array(items).astype(t) ordering.append(name) if not names: names = copy.copy(ordering) ddict = { 'name': self.get_name(), 'comment': 'CSV', 'names': names, 'ordering': ordering, 'data': data, } if self.merge == True: ddict = self._get_merged(ddict) return ddict def write(self, data): lengths=dict() for o in data['ordering']: try: lengths[o]=data['data'][o].shape[1] except (AttributeError, IndexError): lengths[o]=len(data['data'][o]) l=set(lengths.values()) assert(len(l)==1) l=l.pop() csv = open(self.fname, 'w') for i in xrange(l): line=[] for o in data['ordering']: d=data['data'][o] if type(d)==csc_matrix: raise ml2h5.converter.ConversionError("Sparse matrices are not supported in CSV files") try: line.extend(map(str, d[:,i])) except: try: if len(d[i]): line.append(str(d[i])) else: line.append('') except TypeError: line.append(str(d[i])) csv.write(self.seperator.join(line) + "\n") csv.close() return True mldata-utils-0.5.0/ml2h5/converter/h5_libsvm.py0000700000175000017500000001161611463271542020345 0ustar sonnesonneimport numpy, h5py, os, copy from scipy.sparse import csc_matrix from basehandler import BaseHandler import ml2h5.converter class H5_LibSVM(BaseHandler): """Handle LibSVM files.""" def __init__(self, *args, **kwargs): """Constructor. @ivar is_multilabel: if data is of type multilabel @type is_multilabel: boolean """ super(H5_LibSVM, self).__init__(*args, **kwargs) self.is_multilabel = False def convert_sparse(self, spmatrix): assert(type(spmatrix==csc_matrix)) A=spmatrix if A.nnz/numpy.double(A.shape[0]*A.shape[1]) < 0.5: # sparse return spmatrix return numpy.array(A.todense()) def get_comment(self): return 'LibSVM' def read(self): """Retrieves a SciPy Compressed Sparse Column matrix and labels from file. @return: compressed sparse column matrix + labels @rtype: list of scipy.sparse.csc_matrix and label tuple/2-d tuple (multilabel) """ indices_var = [] indptr_var = [0] ptr_var = 0 data_var = [] indices_lab = [] indptr_lab = [0] ptr_lab = 0 data_lab = [] label = [] for line in file(self.fname).xreadlines(): items = line.split() lab=items[0] dat=items[1:] lab=[int(float(i)) for i in lab.split(',')] if len(lab)>1: self.is_multilabel = True if self.is_multilabel: for idx in lab: indices_lab.append(idx) data_lab.append(1.) ptr_lab += 1 indptr_lab.append(ptr_lab) else: # only single label -> value is actual value label.append(lab[0]) for d in dat: v=d.split(':') indices_var.append(int(v[0]) - 1) data_var.append(numpy.double(v[1])) ptr_var += 1 indptr_var.append(ptr_var) if self.is_multilabel: data_lab=numpy.array(data_lab) indices_lab=numpy.array(indices_lab) indptr_lab=numpy.array(indptr_lab) label = csc_matrix( (data_lab, indices_lab, indptr_lab) ) label.sort_indices() else: label = numpy.array(label) data = csc_matrix( (numpy.array(data_var), numpy.array(indices_var), numpy.array(indptr_var)) ) data = self.convert_sparse(data) return { 'name': self.get_name(), 'comment': 'LibSVM', 'ordering': ['label', 'data'], 'names': [], 'data': { 'label' : label, 'data' : data} } def write(self, data): ordering=('label','data') if not set(data['data'].keys()).issubset(set(ordering)): raise ml2h5.converter.ConversionError('libsvm format needs data or label') t = type(data['data']['data']) if t not in (csc_matrix, numpy.ndarray): raise ml2h5.converter.ConversionError('libsvm format needs csc or dense matrix "data"') libsvm = open(self.fname, 'w') if 'label' in data['data'].keys(): if type(data['data']['label']) == csc_matrix: self.is_multilabel = True else: self.is_multilabel = False lengths=dict() for o in ordering: try: lengths[o]=data['data'][o].shape[1] except (AttributeError, IndexError): lengths[o]=len(data['data'][o]) l=set(lengths.values()) assert(len(l)==1) l=l.pop() for i in xrange(l): out = [] for o in ordering: d=data['data'][o] if o == 'label': if self.is_multilabel: labels = [] indptr=d.indptr indices=d.indices dat=d.data for j in xrange(indptr[i],indptr[i+1]): labels.append(str(indices[j])) out.append(','.join(labels)) else: out.append(str(d[i])) else: #data if type(d)==csc_matrix: indptr=d.indptr indices=d.indices dat=d.data for j in xrange(indptr[i],indptr[i+1]): if dat[j]==int: out.append('%d:%d' % (indices[j]+1,dat[j])) else: out.append('%d:%.15g' % (indices[j]+1,dat[j])) else: # dense for j in xrange(len(d)): out.append(str(j+1) + ':' + str(d[j,i])) libsvm.write(" ".join(out) + "\n") libsvm.close() return True mldata-utils-0.5.0/ml2h5/task.py0000600000175000017500000003520111547353355015411 0ustar sonnesonne""" Handle Task objects and files. This module heavily relies on the functionality required for http://mldata.org """ import os, h5py, numpy import ml2h5.data from . import VERSION_MLDATA,NUM_EXTRACT from indexsplit import reduce_split_str COMPRESSION = None task_data_fields = ['train_idx', 'val_idx', 'test_idx', 'data_split', 'input_variables', 'output_variables'] task_descr_fields = ['performance_measure','type'] def update_object(h5, name, value): if name in h5.keys(): del h5[name] h5[name] = value def get_splitnames(fnames): """Helper function to get names of splits. Get a name like test_idx, train_idx from given filenames. @param fnames: filenames to get splitnames from @type fnames: list of string @return: names of splits @rtype: list of strings """ names = [] for name in fnames: n = name.split(os.sep)[-1] if n.find('train') != -1 or n.find('.tr') != -1: names.append('train_idx') elif n.find('.val') != -1: names.append('val_idx') elif n.find('test') != -1 or n.find('.t') != -1 or n.find('.r') != -1: names.append('test_idx') else: names.append(0) # replace unknown name by test if train exists or train if test exists if 0 in names: if 'train_idx' in names: names[names.index(0)] = 'test_idx' elif 'test_idx' in names: names[names.index(0)] = 'train_idx' return names def get_splitdata(fnames): """Get split data. @param fnames: filenames of related data files @type fnames: list of strings """ names = get_splitnames(fnames) data = {} offset = 0 for i in xrange(len(fnames)): count = sum(1 for line in open(fnames[i])) if names[i] in data: # in case we have multiple train/test idx data[names[i]].extend(range(offset, offset+count)) else: data[names[i]] = range(offset, offset+count) offset += count return data def add_data(fname, splitnames=None, variables=None): """Update a Task file by given splitfiles and input/output variables @param fname: name of the Task file @type fname: string @param splitnames: names of files to contain split data @type splitnames: list of strings @param variables: 'input': list with indices of the variables to be used to predict the target(s), 'output': index of the variable to be predicted from the features @type variables: dict of list of integer and integer @return: if Task file could be updated @rtype: boolean """ try: h5 = h5py.File(fname, 'a') except: return False if not 'task' in h5: group = h5.create_group('task') else: group = h5['task'] if variables: if 'input' in variables: group.create_dataset('input_variables', data=variables['input'], compression=COMPRESSION) if 'output' in variables: group.create_dataset('output_variables', data=variables['output'], compression=COMPRESSION) if splitnames: data = get_splitdata(splitnames) for k,v in data.iteritems(): if v: group.create_dataset(k, data=v, compression=COMPRESSION) h5.close() return True def _encode(text): """Encode given (utf-8) text to something h5py can digest. A bit annoying that this kind of stuff is necessary. @param text: text @type text: string @return: encoded string @rtype: string """ try: return text.encode('ascii', 'ignore') except AttributeError: return str(text) def update_description(h5, task): """Update description group in Task file. @param h5: opened HDF5 file @type h5: h5py.File @param task: Task object as of mldata.org @type task: repository.Task @return: if update was successful @rtype: boolean """ if not 'task_descr' in h5: group = h5.create_group('task_descr') else: group = h5['task_descr'] update_object(group, 'pub_date', _encode(task.pub_date)) update_object(group, 'version', task.version) update_object(group, 'slug', _encode(task.slug.text)) update_object(group, 'summary', _encode(task.summary)) update_object(group, 'description', _encode(task.description)) update_object(group, 'urls', _encode(task.urls)) # Without saving task, it does not have a primary key # and the following line complains task.save() update_object(group, 'publications',\ ''.join([_encode(p.title) for p in task.publications.all()])) update_object(group, 'input', _encode(task.input)) update_object(group, 'output', _encode(task.output)) update_object(group, 'performance_measure', _encode(task.performance_measure)) update_object(group, 'type', _encode(task.type)) update_object(group, 'data', _encode(task.data.name)) if task.data_heldback: update_object(group, 'data_heldback', _encode(task.data_heldback.name)) update_object(group, 'license', _encode(task.license.name)) update_object(group, 'tags', _encode(task.tags)) return True def update_data(h5, taskinfo=None): """Update data group in Task file. @param h5: opened HDF5 file @type h5: h5py.File @param taskinfo: data to write to Task file @type taskinfo: dict with indices train_idx, test_idx, input_variables, output_variables @return: if update was successful @rtype: boolean """ if not 'task' in h5: group = h5.create_group('task') else: group = h5['task'] if not taskinfo: return True for name in taskinfo: if taskinfo[name] is not None: if name in group: del group[name] group.create_dataset(name, data=taskinfo[name]) return True def check_taskfile(fname): try: format = ml2h5.fileformat.get(fname) if not format in ('matlab','h5','octave'): return False c = ml2h5.converter.Converter(fname, '/tmp/dummy_does_not_exist.h5', format_in=format, format_out='h5', attribute_names_first=False, merge=False, type='data') except: return False return True def get_taskinfo(fname): taskinfo = None data_size = 0 format = ml2h5.fileformat.get(fname) if not format in ('matlab','h5','octave'): raise ml2h5.converter.ConversionError, 'Format not supported (only matlab, \ octave, h5) are supported' try: c = ml2h5.converter.Converter(fname, '/tmp/dummy_does_not_exist.h5', format_in=format, format_out='h5', attribute_names_first=False, merge=False, type='data') data = c.read() for g in ('data','task'): for f in task_data_fields: if data.has_key(g) and data[g].has_key(f): if not taskinfo: taskinfo=dict() taskinfo[f]=data[g][f] if f in ['train_idx','val_idx','test_idx']: data_size+=len(taskinfo[f]) if 'data_split' in taskinfo.keys(): data_size=len(taskinfo['data_split']) idx=conv_image2idx(taskinfo['data_split']) del taskinfo['datasplit'] for key in ['train_idx','val_idx','test_idx']: taskinfo[key]=idx[key] for key in ['train_idx','val_idx','test_idx']: if not key in taskinfo.keys(): taskinfo[key]=[] if len(taskinfo[key])>0 and type(taskinfo[key][0])!=list: taskinfo[key]=[taskinfo[key]] taskinfo['data_size']=data_size except ml2h5.converter.ConversionError: pass return taskinfo def update_or_create(fname, task, taskinfo=None): """Update or create Task file with data from given Task object. @param fname: full path of Task filename @type fname: string @param task: Task object as of mldata.org @type task: repository.Task @param taskinfo: data to write to Task file @type taskinfo: dict with indices train_idx, test_idx, input_variables, output_variables @return: if file could be updated / created @rtype: boolean """ try: h5 = h5py.File(fname, 'a') except: return False h5.attrs['name'] = _encode(task.name) h5.attrs['mldata'] = VERSION_MLDATA h5.attrs['comment'] = 'Task file' # convert train_idx and test_idx to data_split data_size=taskinfo['data_size'] taskinfo['data_split']=conv_idx2image(taskinfo['train_idx'],taskinfo['val_idx'],taskinfo['test_idx'],data_size) del taskinfo['train_idx'] del taskinfo['val_idx'] del taskinfo['test_idx'] error = False if not update_description(h5, task): error = True if not update_data(h5, taskinfo): error = True h5.close() return not error def get_extract(fname): """Get extract of Task file. @param fname: name of Task file @type fname: string @return: datasets from Task file @rtype: dict of lists """ extract = {} if not h5py.is_hdf5(fname): return extract h5 = h5py.File(fname, 'r') for t in task_data_fields: try: if len(h5['task'][t][...].shape) > 1: extract[t]=[] for l in h5['task'][t][...]: extract[t].append(reduce_split_str(l)) else: extract[t]=reduce_split_str(h5['task'][t][...]) except KeyError: pass for t in task_descr_fields: try: extract[t]=h5['task_descr'][t][...] except KeyError: pass #import pdb #pdb.set_trace() max_split_size=10 split_overflow=False split_string_overflow=False idx={} extract_reduce={} if 'train_idx' in h5['task'].keys(): idx['train_idx']=h5['task/train_idx'][...] for key in ['val_idx','test_idx']: if key in h5['task'].keys(): idx[key]=h5['task'][key][...] else: idx[key]=[] elif 'data_split' in h5['task'].keys(): idx=conv_image2idx(h5['task/data_split'][...]) try: for key in ['train_idx','val_idx','test_idx']: extract[key]=[reduce_split_str(i) for i in idx[key]] extract_reduce[key]=[reduce_split_str(i) for i in idx[key][:NUM_EXTRACT]] if len(idx['train_idx']) > NUM_EXTRACT: split_overflow=True for key in ['train_idx','val_idx','test_idx']: if split_overflow: extract_reduce[key].append(['...']) for i in range(len(extract_reduce[key])): if len(extract_reduce[key][i]) > max_split_size: extract_reduce[key][i] = extract_reduce[key][i][:max_split_size] extract_reduce[key][i][max_split_size-1] = '...' split_string_overflow=True num_split_reduce=len(extract_reduce['train_idx']) reduce_split_nr=[str(i) for i in range(num_split_reduce)] if split_overflow: reduce_split_nr[-1]='...' extract['split_idx']=zip(range(len(extract['train_idx'])),extract['train_idx'],extract['val_idx'],extract['test_idx']) extract['reduce_split_idx']=zip(reduce_split_nr,extract_reduce['train_idx'],extract_reduce['val_idx'],extract_reduce['test_idx']) extract['split_overflow']=split_overflow extract['split_string_overflow']=split_string_overflow except KeyError: extract['split']=[(0,[],[])] extract['split_overflow']=False h5.close() return extract def get_split_image(fname,split_nr,norm=1000): extract = {} if not h5py.is_hdf5(fname): return extract h5 = h5py.File(fname, 'r') path = '/task/data_split' if path in h5: try: image_data = h5[path][...][split_nr] except IndexError: return None else: return None h5.close() # normalize to length of norm image_norm=numpy.zeros([norm,1]) for i in xrange(len(image_norm)): image_norm[i]=image_data[int(i*(float(len(image_data))/norm))] return image_norm.T[0] def conv_idx2image(train_idx,val_idx,test_idx,last_idx): """Convert all idx to image. @param train_idx: Train Indices @type train_idx: list of lists of int @return: datasplit image @rtype: list of int """ # import pdb # pdb.set_trace() if train_idx==None: return None dim=len(train_idx) image_data=numpy.zeros([dim,last_idx], dtype=numpy.uint8) for split_nr in range(dim): train_split=numpy.array(train_idx[split_nr],dtype=int) try: test_split=numpy.array(test_idx[split_nr],dtype=int) except: test_split=numpy.array([],dtype=int) try: val_split=numpy.array(val_idx[split_nr],dtype=int) except: val_split=numpy.array([],dtype=int) try: image_data[split_nr][train_split]=1 image_data[split_nr][val_split]=2 image_data[split_nr][test_split]=3 except IndexError: raise ml2h5.converter.ConversionError, 'Index out of Range' return image_data def conv_image2idx(img): """Convert all images to idx. @param fname: name of Task file @type fname: string @return: datasets from Task file @rtype: dict of lists """ #import pdb #pdb.set_trace() train_idx=[] val_idx=[] test_idx=[] for split in img: train_idx.append(numpy.array(range(len(split)))[split==1]) val_idx.append(numpy.array(range(len(split)))[split==2]) test_idx.append(numpy.array(range(len(split)))[split==3]) return {'train_idx':train_idx,'val_idx':val_idx, 'test_idx':test_idx } def get_variables(fname): """Get input/output variables from given Data file. @param fname: name of Data file to retrieve variables from @type fname: string @return: input and output variables @rtype: dict of lists 'input' and 'output' """ # FIXME: this might be completely wrong bucket, num_attr = ml2h5.data.get_num_instattr(fname) return { 'input': range(num_attr)[1:], 'output': 0, } def get_test_output(fname): """Get test_idx and output_variables from given Task file.""" if not h5py.is_hdf5(fname): return None,None h5 = h5py.File(fname, 'r') test_idx = image2idx(h5['/task/data_split'][...])['test_idx'] #test_idx = h5['/task/test_idx'][:] output_variables = h5['/task/output_variables'][...] h5.close() return test_idx, output_variables mldata-utils-0.5.0/README0000600000175000017500000000024011450541742014011 0ustar sonnesonneThis package supplements http://mldata.org and should give you all tools necessary to work with Data, Task and Solution. See INSTALL for installation details. mldata-utils-0.5.0/INSTALL0000600000175000017500000000004511450541742014165 0ustar sonnesonneTo install: python setup.py install mldata-utils-0.5.0/CHANGELOG0000600000175000017500000000515611547537351014366 0ustar sonnesonne0.5.0 2011/04/08 by Soeren Sonnenburg * Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/... * Various bugfixes. 0.4.0 2010/11/12 by Cheng Soon Ong * Added clientapi to interact with mldata.org via python on a client. 0.3.6 2010/08/27 by Sebastian Henschel * Added a fix when data.get_correct internally receives an array of array with values instead an array with values. * Added support for sparse matrices in data.get_correct. 0.3.5 2010/08/25 by Sebastian Henschel * Introduced task.get_test_output to get test_idx and output_variables from Task file. * Introduced data.get_correct() to get the 'correct' results from Data file. * Fixed minor issus when converting to octave/matlab. 0.3.4 2010/08/24 by Sebastian Henschel * Fixed an issue with data extracts. * Fixed an issue when updating Task files. 0.3.3 2010/08/23 by Sebastian Henschel * Fixed a few issues when converting to arff/octave/matlab. 0.3.2 2010/08/21 by Sebastian Henschel * task.create now includes handling of input/output variables. * Fixed a little error handling octave files. 0.3.1 2010/08/20 by Sebastian Henschel * Had removed too much from data.get_extract and put it back in. * Added safeguard for illegal task files with no output_variables. 0.3.0 2010/08/19 by Sebastian Henschel * Restructured package into more different modules. * Revamped conversion structure. * Bugfix re Task vs output variables. 0.2.4 2010/08/17 by Sebastian Henschel * Caught a few more error conditions when handlings Task. * Temporarily removed author from package information because it threw ugly error message on older python installations. 0.2.3 2010/08/17 by Sebastian Henschel * Removed label_dims and improved support for input/output variables for Tasks. * Created new module 'data' for better encapsulation. 0.2.2 2010/08/16 by Sebastian Henschel * Added extract function (and script) for Task datasets. * Moved extract function for Data from website to this tool. 0.2.1 2010/08/02 by Sebastian Henschel * Improved handling of Task files. 0.2.0 2010/07/21 by Sebastian Henschel * Introduced CHANGELOG. * Moved Task-related code into its own-module. * Added code to save data from Task object into Task file. 0.1.0 2010/07/13 by Sebastian Henschel * Initial release as package. mldata-utils-0.5.0/mleval/0000700000175000017500000000000011547540067014421 5ustar sonnesonnemldata-utils-0.5.0/mleval/util.py0000600000175000017500000000144011451374163015745 0ustar sonnesonne# Some helper functions follow def register(dic, name, fun): dic[name]=(fun, fun.__doc__) def compute_area(a,b): """ Helper function to compute area """ return 0.5*numpy.sum(numpy.abs(a[:-1]-a[1:])*numpy.abs(b[:-1]+b[1:])) def alternative_calcprcscore(out, lab): """ For sanity checking only: auRPC compute function from PASCAL Image challenge. code translated from matlab code from image challenge """ idx = numpy.argsort(-out) tp=lab[idx]>0 fp=lab[idx]<0 fp=1.0*numpy.cumsum(fp) tp=1.0*numpy.cumsum(tp) rec=tp/numpy.sum(lab>0) prec=tp/(fp+tp) ap=0 for t in numpy.arange(0,1.01, 0.01): el=prec[rec>=t] if len(el)>0: p=numpy.max(el) else: p=0 ap=ap+p/101.0 return ap mldata-utils-0.5.0/mleval/classification.py0000600000175000017500000001057311453025065017766 0ustar sonnesonneimport numpy from util import register pm = dict() def calcbal(out, lab): """ Computes the Balanced Error. Expects labels to be +/-1 and predictions sign(output) """ pidx=(lab==+1) perr=numpy.mean(out[pidx]>0) nidx=(lab==-1) nerr=numpy.mean(out[nidx]<0) return 0.5 * (perr+nerr) register(pm, 'Balanced Error', calcbal) def accuracy(out, lab): """ Computes Accuracy. Expects labels to be +/-1 and predictions sign(output) """ return numpy.mean(numpy.sign(out) == lab) register(pm, 'Accuracy', accuracy) def error(out, lab): """ Computes Error Rate. Expects labels to be +/-1 and predictions sign(output) """ return numpy.mean(numpy.sign(out) != lab) register(pm, 'Error', error) def get_tp(out, lab): """ Computes True Positive Rate. Expects labels to be +/-1 and predictions sign(output) """ pidx=(lab==+1) return numpy.sum(numpy.sign(out[pidx])>0) register(pm,'True Positive Rate', get_tp) def get_tn(out, lab): """ Computes True Negative Rate. Expects labels to be +/-1 and predictions sign(output) """ nidx=(lab==-1) return numpy.sum(numpy.sign(out[nidx])<0) register(pm, 'True Negative Rate', get_tn) def get_fp(out, lab): """ Computes False Positive Rate. Expects labels to be +/-1 and predictions sign(output) """ nidx=(lab==-1) return numpy.sum(numpy.sign(out[nidx])>0) register(pm, 'False Positive Rate', get_fp) def get_fn(out, lab): """ Computes False Negative Rate. Expects labels to be +/-1 and predictions sign(output) """ pidx=(lab==+1) return numpy.sum(numpy.sign(out[pidx])<0) register(pm, 'False Negative Rate', get_fn) def calcwracc(out, lab): """ Computes Weighted Relative Accuracy. Expects labels to be +/-1 and predictions sign(output) """ tp=get_tp(out, lab) fp=get_fp(out, lab) tn=get_tn(out, lab) fn=get_fn(out, lab) return tp/(tp+fn) - fp/(fp+tn) register(pm, 'Weighted Relative Accuracy', calcwracc) def calcf1(out, lab): """ Computes the F1 score. Expects labels to be +/-1 and predictions sign(output) """ tp=get_tp(out, lab) fp=get_fp(out, lab) fn=get_fn(out, lab) return 2.0*tp/(2.0*tp+fp+fn) register(pm, 'F1 Score', calcf1) def calccc(out, lab): """ Computes the cross correlation coefficient. Expects labels to be +/-1 and predictions sign(output) """ tp=float(get_tp(out, lab)) fp=float(get_fp(out, lab)) tn=float(get_tn(out, lab)) fn=float(get_fn(out, lab)) return (tp*tn-fp*fn)/numpy.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)) register(pm, 'Cross Correlation Coefficient', calccc) def calcroc(out, lab): """ Computes the ROC curve. Expects labels to be +/-1 and real-valued predictions """ idx = numpy.argsort(out) tp=numpy.concatenate( ([1], 1-numpy.cumsum(lab[idx]>0)/float(numpy.sum(lab > 0))) ) fp=numpy.concatenate( ([1], 1-numpy.cumsum(lab[idx]<0)/float(numpy.sum(lab < 0))) ) score = 0.5*numpy.sum((fp[:-1]-fp[1:])*(tp[:-1]+tp[1:])) curve= {'x' : fp, 'y' : tp, 'x_name' : 'False Positive Rate', 'y_name' : 'True Positive Rate'} return (score, 'Curve', curve) register(pm, 'ROC Curve', calcroc) def calcrocscore(out, lab): """ Computes the area under the ROC curve. Expects labels to be +/-1 and real-valued predictions """ return calcroc(out,lab)[0] register(pm, 'Area under ROC Curve', calcrocscore) def calcprc(out, lab): """ Computes the precision recall curve. Expects labels to be +/-1 and real-valued predictions """ pmap=(lab==1) idx = numpy.argsort(-out) pmap=pmap[idx] recall=numpy.cumsum(pmap)/numpy.double(sum(lab==1)) precision=numpy.cumsum(pmap)/(numpy.arange(len(pmap),dtype=numpy.double)+1.0) score = 0.5*numpy.sum((precision[:-1]+precision[1:])*(recall[1:]-recall[:-1])) curve= {'x' : precision, 'y' : recall, 'x_name' : 'Precision', 'y_name' : 'Recall'} return (score, 'Curve', curve) register(pm, 'Precision Recall Curve', calcprc) def calcprcscore(out, lab): """ Computes the area under the precision recall curve. Expects labels to be +/-1 and real-valued predictions """ return calcprc(out,lab)[0] register(pm, 'Area under Precision Recall Curve', calcprcscore) mldata-utils-0.5.0/mleval/__init__.py0000600000175000017500000000024311474776561016544 0ustar sonnesonne""" Utility functions to perform evaluations, mostly for mldata.org. """ __all__ = ['evaluation', 'parse', 'multiclass', 'classification', 'regression','output'] mldata-utils-0.5.0/mleval/output.py0000600000175000017500000000601511474776561016350 0ustar sonnesonne"""A class to deal with standard prediction outputs. Currently supports: - binary classification - multiclass - regression """ from numpy import array, sign, unique class Outputs(object): """A container for the labels/predictions of a supervised learning method """ def str2label(str_label): raise NotImplementedError def label2str(preds): raise NotImplementedError class OutputsClassification(Outputs): """A container for labels/predictions for classification""" name2id = {} id2name = {} def str2label(self, str_label): """Convert labels into class ids""" self.init_id(str_label) int_label = [] for lab in str_label: int_label.append(self.name2id[str(lab[0])]) return array(int_label, dtype=float) class OutputsBinClass(OutputsClassification): """A container for labels/predictions for binary classification """ def init_id(self, str_label): """Discover the names of the labels""" all_names = unique(str_label) all_names = map(str, all_names) assert(len(all_names) == 2) self.name2id = {all_names[0]: 1, all_names[1]: -1, } self.id2name = {1: all_names[0], -1: all_names[1], } def label2str(self, preds): """Convert predictions into classification labels""" pred_label = [] for pred in preds: pred_label.append(self.id2name[sign(pred)]) return pred_label class OutputsMultiClass(OutputsClassification): """A container for labels/predictions for multiclass classification """ def init_id(self, str_label): """Discover the names of the labels""" all_names = unique(str_label) all_names = map(str, all_names) assert(len(all_names) > 2) self.name2id = {} self.id2name = {} for (idx, name) in enumerate(all_names): self.name2id[name] = idx self.id2name[idx] = name def label2str(self, preds): """Convert predictions into classification labels""" pred_label = [] for pred in preds: pred_label.append(self.id2name[pred]) return pred_label class OutputsRegression(Outputs): """A container for labels/predictions for regression """ def str2label(self, str_label): """Convert labels into floats""" float_label = [] for lab in str_label: float_label.append(float(lab[0])) return array(float_label, dtype=float) def label2str(self, preds): """Convert predictions into strings""" return map(str, preds) def init_output(task_type): """A factory for creating the right output class""" output_types = {'Binary Classification': OutputsBinClass, 'Multi Class Classification': OutputsMultiClass, 'Regression': OutputsRegression, } return output_types[task_type]() mldata-utils-0.5.0/mleval/parse.py0000600000175000017500000000421711450606061016101 0ustar sonnesonneimport numpy def parse_floats(fil): """ Parse file and return numpy array """ fil.seek(0) values=list() for l in fil.xreadlines(): values.append( [ float(e) for e in l.split() ] ) return numpy.array(values) def parse_strings(output_file, label_file): """ Parse file expecting a few string objects only converting them to +/-1 and return numpy array """ output_file.seek(0) label_file.seek(0) output_valid_values=set() label_valid_values=set() for l in output_file.xreadlines(): output_valid_values.add(l.strip()) for l in label_file.xreadlines(): label_valid_values.add(l.strip()) if len(output_valid_values)>2 or len(label_valid_values)>2: msg="Only Binary outputs are currently supported but found more than two:\n" msg+="outputs are: %s\n" % str(output_valid_values) msg+="labels are: %s\n" % str(label_valid_values) raise Exception(msg) if len(output_valid_values-label_valid_values): msg="Invalid outputs found\n" msg+="outputs are: %s\n" % str(output_valid_values) msg+="labels are: %s\n" % str(label_valid_values) raise Exception(msg) if len(output_valid_values-label_valid_values): msg="Invalid outputs found\n" msg+="outputs are: %s\n" % str(output_valid_values) msg+="labels are: %s\n" % str(label_valid_values) raise Exception(msg) map_dict=dict() i=-1 for k in label_valid_values: map_dict[k]=i i+=1 output_file.seek(0) label_file.seek(0) output_values=list() for l in output_file.xreadlines(): output_values.append( [ float(map_dict[e.strip()]) for e in l.split() ] ) label_values=list() for l in label_file.xreadlines(): label_values.append( [ float(map_dict[e.strip()]) for e in l.split() ] ) return numpy.array(output_values),numpy.array(label_values) def brute_force_parse(output_file, label_file): try: outputs=parse_floats(output_file) labels=parse_floats(label_file) return outputs,labels except: return parse_strings(output_file, label_file) mldata-utils-0.5.0/mleval/regression.py0000600000175000017500000000116711451413765017160 0ustar sonnesonneimport numpy from util import register pm = dict() # regression measures def mean_absolute_error(out, truth): """ Regression evaluation using Mean Absolute Error Expects out and truth to be real valued vectors """ return numpy.mean(numpy.abs(out-truth)) register(pm, 'Mean Absolute Error', mean_absolute_error) def root_mean_squared_error(out, truth): """ Regression evaluation using Root Mean Squared Error (RMSE) Expects out and truth to be real valued vectors """ return numpy.sqrt(numpy.mean((out-truth)**2)) register(pm, 'Root Mean Squared Error', root_mean_squared_error) mldata-utils-0.5.0/mleval/evaluation.py0000600000175000017500000000127011451415525017136 0ustar sonnesonneimport multiclass import classification import regression """ dictionaries of known performance measures: keys are human readable names, values are tuples of (functioname, description) pm_hierarchy - is a dictionary of tasks specific perf measures (e.g. for Classification, Regression, ...). To add a new measure just add a function, e.g. def calcbal(out, lab): "Computes the Balanced Error" ... and add it to the the appropriate dictionary register(pm, 'Balanced Error', calcbal) """ pm_hierarchy = { 'Regression': regression.pm, 'Binary Classification': classification.pm, 'Multi Class Classification' : multiclass.pm} mldata-utils-0.5.0/mleval/multiclass.py0000600000175000017500000000064311452347246017157 0ustar sonnesonneimport numpy from util import register pm = dict() def accuracy(out, lab): """ Computes Accuracy. Expects labels and predictions to be integers """ return numpy.mean(out == lab) register(pm, 'Accuracy', accuracy) def error(out, lab): """ Computes Error Rate. Expects labels and predictions to be integers """ return numpy.mean(out != lab) register(pm, 'Error', error) mldata-utils-0.5.0/scripts/0000700000175000017500000000000011547540067014630 5ustar sonnesonnemldata-utils-0.5.0/scripts/ml2h50000700000175000017500000000721011473010123015464 0ustar sonnesonne#!/usr/bin/env python """ Convert from and to HDF5 """ import sys, os, getopt import ml2h5.converter from ml2h5.converter import TO_H5,FROM_H5 from optparse import OptionParser def oParse(): parser=OptionParser() parser.add_option('-s','--seperator',dest='seperator',help='Seperator to use to seperate variables in examples. Default is ","',default=None) parser.add_option('-v', '--verify',action='store_true',dest='verify',help='Verify the converted data',default=False) parser.add_option('-f', '--first-line-attribute-names',action='store_true',dest='attribute_names_first',help='First line contains attributes names (for CSV)',default=False) parser.add_option('-t', '--type',dest='type',help='File type to convert, can either be data or task',default='data') parser.add_option('-i', '--format-in',dest='format_in',help='File format of in-file (if auto-detection fails)',default=None) parser.add_option('-o', '--format-out',dest='format_out',help='File format of out-file (if auto-detection fails)',default=None) parser.add_option('-m', '--merge-data',action='store_true',dest='merge',help='',default=False) parser.add_option('-n', '--no-conversion',action='store_false',dest='convert',help='',default=True) msg=[sys.argv[0] + """ [options] ") Convert to . The following types are supported:"""] for item in TO_H5: msg.append(' ' + item + ' -> ' + 'h5') msg.append('') for item in FROM_H5: msg.append(' h5 -> ' + item) parser.set_usage("\n".join(msg)) return parser class Options: """Option. Should not be instantiated. @cvar seperator: seperator to seperate variables in examples @type output: string @cvar verify: if converted data shall be verified against input data @type verify: boolean @cvar attribute_names_first: if first line in CSV files shall be treated as attribute names @type attribute_names_first: boolean @cvar format_in: file format of in-file @type format_in: string @cvar format_out: file format of out-file @type format_out: string """ seperator = None verify = False attribute_names_first = False format_in = None format_out = None merge = False type = None def rm_opt(option, value=None): """Remove given option and value from sys.argv. @param option: option to remove @type option: string @param value: value to remove @type value: string """ if not value: sys.argv.remove(option) return try: sys.argv.remove(option + value) except ValueError: try: sys.argv.remove(option) sys.argv.remove(value) except ValueError: sys.argv.remove(option + '=' + value) if __name__ == "__main__": op=oParse() (Options,args)=op.parse_args() if len(args)!=2: op.print_help() sys.exit(1) if Options.format_in == 'h5': seperator = None elif Options.seperator: seperator = Options.seperator else: seperator = ml2h5.fileformat.infer_seperator(args[0]) try: c = ml2h5.converter.Converter( args[0], args[1], format_in=Options.format_in, format_out=Options.format_out, seperator=seperator, attribute_names_first=Options.attribute_names_first, merge=Options.merge, type=Options.type ) if Options.convert: c.run(verify=Options.verify) if Options.verify: c.verify() except ml2h5.converter.ConversionError, e: e.print_error() sys.exit(1) mldata-utils-0.5.0/scripts/mleval0000700000175000017500000000414511451047767016045 0ustar sonnesonne#!/usr/bin/env python """ Perform evaluation """ import sys, os, getopt import mleval from mleval.evaluation import pm_hierarchy, pm from mleval.parse import brute_force_parse def usage(): msg = [mleval.PROGRAM_NAME + ', Version ' + mleval.VERSION + """ Usage: """ + sys.argv[0] + """ [options] Options: --pm="" Performance Measure Supported performance measures are: """] for pm_type in pm_hierarchy.keys(): p=pm_hierarchy[pm_type] msg.append(' %s\n %s\n\n' % (pm_type, len(pm_type)*'=')) for k in p.keys(): msg.append(' "%s"\n' % k) msg.append(' %s\n\n' % p[k][1]) print ''.join(msg) def parse_options(): """Parse given options.""" try: opts, args = getopt.getopt(sys.argv[1:], '', ['pm=']) except getopt.GetoptError, err: # print help information and exit print str(err) + "\n" usage() sys.exit(1) perf_measure=None for o, a in opts: if o in ('--pm'): perf_measure = a else: print 'Unhandled option: ', o sys.exit(2) if len(args)!=2 or not perf_measure: usage() sys.exit(1) # pm, outputs, true labels return (perf_measure, args[0], args[1]) if __name__ == "__main__": argc = len(sys.argv) if argc < 3: usage() sys.exit(1) perf_measure, outputfile, labelfile = parse_options() try: of=file(outputfile) except IOError: print "could not open output_file '%s'" % outputfile sys.exit(1) try: lf=file(labelfile) except IOError: print "could not open truth_file '%s'" % labelfile sys.exit(1) if not pm.has_key(perf_measure): print "not a valid performance measure" sys.exit(1) try: outputs, labels=brute_force_parse(of, lf) except Exception, err: print err sys.exit(2) if len(outputs)!=len(labels): print "outputs and label length don't match" sys.exit(2) print pm[perf_measure][0](outputs, labels) mldata-utils-0.5.0/scripts/ml2h5extract_data0000700000175000017500000000053311450541742020064 0ustar sonnesonne#!/usr/bin/env python """ Print an extract of an HDF5 file, similar to website output """ import sys import ml2h5.data def usage(): print """Usage: """ + sys.argv[0] + """ """ if __name__ == "__main__": argc = len(sys.argv) if argc < 2: usage() sys.exit(1) print ml2h5.data.get_extract(sys.argv[1]) mldata-utils-0.5.0/scripts/ml2h5extract_task0000700000175000017500000000053311450541742020115 0ustar sonnesonne#!/usr/bin/env python """ Print an extract of an HDF5 file, similar to website output """ import sys import ml2h5.task def usage(): print """Usage: """ + sys.argv[0] + """ """ if __name__ == "__main__": argc = len(sys.argv) if argc < 2: usage() sys.exit(1) print ml2h5.task.get_extract(sys.argv[1]) mldata-utils-0.5.0/setup.py0000600000175000017500000000413211547537703014660 0ustar sonnesonnefrom distutils.core import setup VERSION='0.5.0' DESCRIPTION='Utilities around hdf5 and for compute performance measures', LONG_DESCRIPTION=""" Utility functions to perform evaluations and to convert from/to the hdf5 based mldata format into/from typical machine learning formats like .csv,.arff,.octave,.mat. This code is actually used on mldata.org to perform the evaluations and data and task conversions. """ AUTHOR = ['Soeren Sonnenburg', 'Sebastian Henschel', 'Mikio Braun', 'Hagen Zahn','Cheng Soon Ong','Antti Honkela'] AUTHOR_EMAIL = ['Soeren.Sonnenburg@tu-berlin.de', 'mldata@kodeaffe.de', 'mikio@cs.tu-berlin.de', 'hzahn@informatik.hu-berlin.de', 'chengsoon.ong@inf.ethz.ch', 'antti.honkela@hiit.fi'] URL = 'http://mloss.org/software/view/262/' LICENSE = """ This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; version 3 of the License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, check http://www.gnu.org/licenses/gpl-3.0.html """ setup( name='mldata-utils', version=VERSION, description=DESCRIPTION, long_description=LONG_DESCRIPTION, maintainer=AUTHOR, maintainer_email=AUTHOR_EMAIL, license=LICENSE, url=URL, packages=['ml2h5', 'ml2h5.converter', 'mleval'], scripts=['scripts/ml2h5', 'scripts/ml2h5extract_data', 'scripts/ml2h5extract_task', 'scripts/mleval'], classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Console', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU General Public License (GPL)', 'Natural Language :: English', 'Operating System :: POSIX', 'Programming Language :: Python', 'Topic :: Utilities', ], platforms=['POSIX'], ) mldata-utils-0.5.0/PKG-INFO0000600000175000017500000000327711547540067014251 0ustar sonnesonneMetadata-Version: 1.0 Name: mldata-utils Version: 0.5.0 Summary: ('Utilities around hdf5 and for compute performance measures',) Home-page: http://mloss.org/software/view/262/ Author: ['Soeren Sonnenburg', 'Sebastian Henschel', 'Mikio Braun', 'Hagen Zahn', 'Cheng Soon Ong', 'Antti Honkela'] Author-email: ['Soeren.Sonnenburg@tu-berlin.de', 'mldata@kodeaffe.de', 'mikio@cs.tu-berlin.de', 'hzahn@informatik.hu-berlin.de', 'chengsoon.ong@inf.ethz.ch', 'antti.honkela@hiit.fi'] License: This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; version 3 of the License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, check http://www.gnu.org/licenses/gpl-3.0.html Description: Utility functions to perform evaluations and to convert from/to the hdf5 based mldata format into/from typical machine learning formats like .csv,.arff,.octave,.mat. This code is actually used on mldata.org to perform the evaluations and data and task conversions. Platform: POSIX Classifier: Development Status :: 3 - Alpha Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: GNU General Public License (GPL) Classifier: Natural Language :: English Classifier: Operating System :: POSIX Classifier: Programming Language :: Python Classifier: Topic :: Utilities mldata-utils-0.5.0/COPYING0000600000175000017500000010451311450541742014174 0ustar sonnesonne GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. 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