def step03():
assert isfile( cfg.dtadir+"/train_trn_.tsv.gz" );
with FeatureDiscretizer( cfg.dtadir+"/fdp.pickle", "w" ) as fdp:
with FeatureDiscretizer( cfg.dtadir+"/fdq.pickle", "w" ) as fdq:
with KPCAContinuousFrontend( cfg.dtadir+"/kpcacfe.pickle", "r" ) as kpcacfe:
with HomebrewContinuousFrontend( cfg.dtadir+"/hbcfe.pickle", "r" ) as hbcfe:
with CategoricalFrontend( cfg.dtadir+"/cfe.pickle", "r" ) as cfe:
with BinaryFrontend( cfg.dtadir+"/bfe.pickle", "r" ) as bfe:
rows = da_read( cfg.dtadir+"/train_trn_.tsv.gz" );
i = 0;
for ( id_, y, c, b, x ) in rows:
i += 1;
# print( i );
# if i >= 100:
# break;
c = cfe( c );
b = bfe( b );
xp = hbcfe( x );
xq = kpcacfe( x );
is_enough = [];
is_enough.append( fdp.train( xp ) );
is_enough.append( fdq.train( xq ) );
if all( is_enough ):
break;
def step02():
assert isfile( cfg.dtadir+"/train_trn_.tsv.gz" );
with KPCAContinuousFrontend( cfg.dtadir+"/kpcacfe.pickle", "w" ) as kpcacfe:
with HomebrewContinuousFrontend( cfg.dtadir+"/hbcfe.pickle", "w" ) as hbcfe:
with CategoricalFrontend( cfg.dtadir+"/cfe.pickle", "w" ) as cfe:
with BinaryFrontend( cfg.dtadir+"/bfe.pickle", "w" ) as bfe:
rows = da_read( cfg.dtadir+"/train_trn_.tsv.gz" );
i = 0;
for ( id_, y, c, b, x ) in rows:
i += 1;
# print( i );
# if i >= 100:
# break;
is_enough = [];
is_enough.append( bfe.train( b ) );
is_enough.append( cfe.train( [c] ) );
is_enough.append( hbcfe.train( x ) );
is_enough.append( kpcacfe.train( x ) );
if all( is_enough ):
break;
def step05( modelf ):
assert isfile( cfg.dtadir+"/train_trn_.tsv.gz" );
with FeatureSelector( cfg.dtadir+"/fs.pickle", "r" ) as fs:
with FeatureDiscretizer( cfg.dtadir+"/fdp.pickle", "r" ) as fdp:
with FeatureDiscretizer( cfg.dtadir+"/fdq.pickle", "r" ) as fdq:
with KPCAContinuousFrontend( cfg.dtadir+"/kpcacfe.pickle", "r" ) as kpcacfe:
with HomebrewContinuousFrontend( cfg.dtadir+"/hbcfe.pickle", "r" ) as hbcfe:
with CategoricalFrontend( cfg.dtadir+"/cfe.pickle", "r" ) as cfe:
with BinaryFrontend( cfg.dtadir+"/bfe.pickle", "r" ) as bfe:
if modelf == "mdlp.kch":
mdl_ \
= BKNNModel(
cfg.dtadir+"/"+modelf, "w",
cfe, bfe, hbcfe, fdp, fs, 7
);
elif modelf == "mdlq.kch":
fs.bypass_x = True;
mdl_ \
= BKNNModel(
cfg.dtadir+"/"+modelf, "w",
cfe, bfe, kpcacfe, fdq, fs, 7
);
with mdl_ as mdl:
rows = da_read( cfg.dtadir+"/train_trn_.tsv.gz" );
i = 0;
for ( id_, y, c, b, x ) in rows:
i += 1;
#print( i );
#if i > 10000:
# break;
if mdl.train( ( y, c, b, x ) ):
break;