# # Collective Knowledge (program - features) # # See CK LICENSE.txt for licensing details # See CK COPYRIGHT.txt for copyright details # # Developer: Grigori Fursin, Grigori.Fursin@cTuning.org, http://fursin.net # cfg={} # Will be updated by CK (meta description of this module) work={} # Will be updated by CK (temporal data) ck=None # Will be updated by CK (initialized CK kernel) # Local settings ############################################################################## # Initialize module def init(i): """ Input: {} Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 } """ return {'return':0} ############################################################################## # extract program static milepost features def extract(i): """ Input: { (repo_uoa) - repository UOA (data_uoa) - program UOA (can be wildcards) (tags) - tags to process specific programs (target_repo_uoa) - repo, where to save features - if =='', use repo_uoa } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 dict - final dict with key 'features'={...} } """ import os import json o=i.get('out','') oo='' if o=='con':oo=o muoa=cfg['module_deps']['program'] duoa=i.get('data_uoa','') ruoa=i.get('repo_uoa','') truoa=i.get('target_repo_uoa','') tags=i.get('tags','') rx=ck.access({'action':'search', 'repo_uoa':ruoa, 'module_uoa':muoa, 'data_uoa':duoa, 'tags':tags}) if rx['return']>0: return rx lst=rx['lst'] feat1={} num=0 for q in lst: num+=1 duid=q['data_uid'] duoa=q['data_uoa'] xtruoa=truoa if truoa=='': xtruoa=q['repo_uid'] if o=='con': ck.out(str(num)+') Processing '+duoa+' ...') ii={'action':'pipeline', 'module_uoa':muoa, 'data_uoa':duoa, 'milepost':'yes', 'out':oo, 'quiet':'yes', 'skip_info_collection':'yes', 'no_run':'yes'} r=ck.access(ii) finish=False if r['return']>0: finish=True if o=='con': ck.out('') ck.out('CK WARNING: pipeline failed ('+r['error']+')') ck.out('') rx=ck.inp({'text':'Would you still like to record some extracted features (Y/n)? '}) x=rx['string'].strip() if x!='n' and x!='N': finish=False if not finish: feat=r.get('features',{}).get('program_static_milepost_features',{}) if len(feat)>0: ddd={} found=False ry=ck.access({'action':'load', 'module_uoa':work['self_module_uid'], 'data_uoa':duid}) if ry['return']==0: ddd=ry['dict'] found=True feat1=ddd.get('features',{}).get('program_static_milepost_features',{}) rz=ck.merge_dicts({'dict1':feat1, 'dict2':feat}) if rz['return']>0: return rz feat1=rz['dict1'] if 'features' not in ddd: ddd['features']={} ddd['features']['program_static_milepost_features']=feat1 ii={} ii['action']='add' if found: ii['action']='update' ii['module_uoa']=work['self_module_uid'] ii['data_uoa']=duoa ii['data_uid']=duid ii['repo_uoa']=xtruoa ii['dict']=ddd ii['substitute']='yes' ry=ck.access(ii) if ry['return']>0: return ry return {'return':0, 'dict':{'features':feat1}} ############################################################################## # Calculate similarity between programs based on features # # FGG: For now just simple Euclidean distance, however it has many drawbacks # since it doesn't take into account "importance" of different features, etc # We plan to use more SVN, decision trees, etc (see our papers) ... def calculate_similarity(i): """ Input: { features1 - MILEPOST features for program 1 features2 - MILEPOST features for program 2 } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 distance - basic Euclidean distance (can be > 1) } """ import math prog1=i['features1'] prog2=i['features2'] # Check which feature to use for normalization fd=cfg['milepost_features_description'] ftn=cfg['milepost_normalization_feature'] # Check that in vectors distance=None # if error if ftn in prog1 and ftn in prog2: x1=prog1.get(ftn,None) x2=prog2.get(ftn,None) if x1!=None and x1!='' and x2!=None and x2!='': ftnp1=float(x1) ftnp2=float(x2) distance=0.0 for q2 in fd: q=fd[q2] ftp1=float(prog1.get(q2,0)) ftp2=float(prog2.get(q2,0)) if q.get('use_for_euclidean_distance','')=='yes': if q.get('normalized','')!='yes': ftp1/=ftnp1 ftp2/=ftnp2 distance+=(ftp1-ftp2)*(ftp1-ftp2) if distance!=0.0: distance=math.sqrt(distance) return {'return':0, 'distance':distance} ############################################################################## # show features in HTML def show(i): """ Input: { } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 } """ h='
\n' h+=' Feature number\n' h+=' | \n' h+='\n' h+=' Meaning\n' h+=' | \n' h+='
\n' h+=' ft'+sft+'\n' h+=' | \n' h+='\n' h+=' '+desc+'\n' h+=' | \n' h+='