def train_svm(mod_table=None, dat_table=None, label=None, arr=None,
step=None, mu=None, decay=None, epochs=None, database=None, noact=False):
qry = []
qry.append(iutil.make_model_table(mod_table))
for i in xrange(1, epochs+1):
qry.append(svm_epoch(mod_table, dat_table, label, arr, i, step=step, mu=mu))
step = step * decay
# qry.append(svm_loss(mod_table, dat_table, label, arr, epoch=epochs))
for q in qry:
print q
if not noact:
iutil.impala_shell_exec(qry, database=database)
def train_svm(mod_table=None,
dat_table=None,
label=None,
arr=None,
step=None,
mu=None,
decay=None,
epochs=None,
database=None,
noact=False):
qry = []
qry.append(iutil.make_model_table(mod_table))
for i in xrange(1, epochs + 1):
qry.append(
svm_epoch(mod_table, dat_table, label, arr, i, step=step, mu=mu))
step = step * decay
# qry.append(svm_loss(mod_table, dat_table, label, arr, epoch=epochs))
for q in qry:
print q
if not noact:
iutil.impala_shell_exec(qry, database=database)
def main():
# argument parsing
parser = optparse.OptionParser(
'usage: %prog LABEL_COL FEATURE_COL [FEATURE_COL ...]')
parser.add_option("-b",
"--db",
dest="database",
default=None,
help="the database which holds data table",
metavar="DB")
parser.add_option("-t",
"--table",
dest="table",
default=None,
help="data table to iterate over",
metavar="TABLE")
parser.add_option(
"-y",
"--history",
dest="history",
default='history',
help="name of table to story iteratoin history (default history)",
metavar="HIST")
parser.add_option("-n",
"--noact",
action="store_true",
dest="noact",
default=False,
help="just print queries, don't execute over impala")
parser.add_option("-s",
"--step",
dest="step",
default=0.1,
type="float",
help="step size for SGD (default 0.1)")
parser.add_option("-d",
"--decay",
dest="decay",
default=0.95,
type="float",
help="step size decay (default 0.95)")
parser.add_option("-u",
"--mu",
dest="mu",
default=0,
type="float",
help="regularizer weight (defualt 0)")
parser.add_option("-e",
"--epochs",
dest="epochs",
default=1,
type="int",
help="number of epochs to run (default 1)")
(options, args) = parser.parse_args()
# we need at least 1 label and 1 feature to train
if len(args) < 2:
parser.print_usage()
return
if options.database is None:
print 'use --db to specify a database to use.'
return
if options.table is None:
print 'use --table to specify the data table.'
return
# list of queries will submit to impala
qry = []
mod_table = options.history
dat_table = options.table
step = options.step
mu = options.mu
label = args[0]
# the query which creates an array out of the feature columns
arr = 'toarray(%s)' % (', '.join(
map(lambda f: '%s.%s' % (dat_table, f), args[1:])))
qry.append(iutil.make_model_table(mod_table))
# create all the queries
for i in xrange(1, options.epochs + 1):
qry.append(
logr_epoch(mod_table, dat_table, label, arr, i, step=step, mu=mu))
step = step * options.decay
# submit a query to to compute the loss
# qry.append(logr_loss(mod_table, dat_table, label, arr, epoch=options.epochs))
# print the query for reference
for q in qry:
print q
# submit the queries to impala
if not options.noact:
iutil.impala_shell_exec(qry, database=options.database)
def main():
# argument parsing
parser = optparse.OptionParser('usage: %prog LABEL_COL FEATURE_COL [FEATURE_COL ...]')
parser.add_option("-b", "--db", dest="database", default=None,
help="the database which holds data table", metavar="DB")
parser.add_option("-t", "--table", dest="table", default=None,
help="data table to iterate over", metavar="TABLE")
parser.add_option("-y", "--history", dest="history", default='history',
help="name of table to story iteratoin history (default history)", metavar="HIST")
parser.add_option("-n", "--noact", action="store_true", dest="noact", default=False,
help="just print queries, don't execute over impala")
parser.add_option("-s", "--step", dest="step", default=0.1, type="float",
help="step size for SGD (default 0.1)")
parser.add_option("-d", "--decay", dest="decay", default=0.95, type="float",
help="step size decay (default 0.95)")
parser.add_option("-u", "--mu", dest="mu", default=0, type="float",
help="regularizer weight (defualt 0)")
parser.add_option("-e", "--epochs", dest="epochs", default=1, type="int",
help="number of epochs to run (default 1)")
(options, args) = parser.parse_args()
# we need at least 1 label and 1 feature to train
if len(args) < 2:
parser.print_usage()
return
if options.database is None:
print 'use --db to specify a database to use.'
return
if options.table is None:
print 'use --table to specify the data table.'
return
# list of queries will submit to impala
qry = []
mod_table = options.history
dat_table = options.table
step = options.step
mu = options.mu
label = args[0]
# the query which creates an array out of the feature columns
arr = 'toarray(%s)' % (', '.join(map(lambda f: '%s.%s' % (dat_table, f), args[1:])))
qry.append(iutil.make_model_table(mod_table))
# create all the queries
for i in xrange(1, options.epochs+1):
qry.append(logr_epoch(mod_table, dat_table, label, arr, i, step=step, mu=mu))
step = step * options.decay
# submit a query to to compute the loss
# qry.append(logr_loss(mod_table, dat_table, label, arr, epoch=options.epochs))
# print the query for reference
for q in qry:
print q
# submit the queries to impala
if not options.noact:
iutil.impala_shell_exec(qry, database=options.database)
