def main():
import pandas as pd
'''
実行時に引数を取れるように設定
'''
ps = support.get_base_args()
ps.add_argument('--metric', '-m', default='', help='Metric function')
ps.add_argument('--leaf', '-l', default='', help='Leaf class')
args = ps.parse_args()
df = pd.read_csv(args.input,
sep=args.separator,
header=args.header,
index_col=args.indexcol)
x = df[df.columns[:-1]].values
##argsの引数によってどの関数を使うか定義
##metric
if args.metric == 'div':
mt = entropy.deviation_org
elif args.metric == 'infgain':
mt = entropy.infgain
elif args.metric == 'gini':
mt = entropy.gini
else:
mt = None
##leaf
if args.leaf == 'zeror':
lf = ZeroRule
elif args.leaf == 'linear':
lf = Linear
else:
lf = None
##回帰かクラス分類か
if not args.regression:
##クラス分類
y, clz = support.clz_to_prob(df[df.columns[-1]])
if mt is None:
mt = entropy.gini
if lf is None:
lf = ZeroRule
plf = DecisionStump(metric=mt, leaf=lf)
support.report_classifer(plf, x, y, clz, args.crossvalidate)
else:
y = df[df.columns[-1]].values.reshape((-1, 1))
if mt is None:
mt = entropy.deviation_org
if leaf is None:
lf = Linear
plf = DecisionStump(metric=mt, leaf=lf)
support.report_regressor(plf, x, y, args.crossvalidate)
def main():
import pandas as pd
ps = support.get_base_args()
ps.add_argument('--metric', '-m' , default='' , help='Metric function')
ps.add_argument('--leaf' , '-l', default='',help='Leaf class')
ps.add_argument('--depth' , '-d' , type=int , default=5,help='Max Tree Depth')
args = ps.parse_args()
df = pd.read_csv(args.input, sep=args.separator , header=args.header, index_col = args.indexcol)
x = df[df.columns[:-1]].values
##分岐関数の指定
if args.metric == 'div':
mt = entropy.deviation_org
elif args.metric == 'infgain':
mt = entropy.infgain
elif args.metric == 'gini':
mt = entropy.gini
else:
mt = None
##葉の関数の指定
if args.leaf == 'zeror':
lf = ZeroRule
elif args.leaf == 'linear':
lf = Linear
else:
lf =None
##回帰かクラス分類か
if not args.regression:
y,clz = support.clz_to_prob(df[df.columns[:-1]])
if mt is None:
mt = entropy.gini
if lf is None:
lf = ZeroRule
plf = DecisionTree(metric=mt , leaf=lf, max_depth=args.depth)
support.report_classifer(plf,x,y,clz,args.crossvalidate)
else:
y = df[df.columns[-1]].values.reshape((-1,1))
if mt is None:
mt = entropy.deviation_org
if lf is None:
lf = Linear
plf = DecisionTree(metric=mt ,leaf=lf , max_depth=args.depth)
support.report_regressor(plf,x,y,args.crossvalidate)
def main():
import pandas as pd
ps = support.get_base_args()
ps.add_argument('--epochs',
'-p',
type=int,
default=20,
help='Num of Epochs')
ps.add_argument('--learningrate',
'-l',
type=float,
default=0.01,
help='Learning rate')
ps.add_argument('--earlystop',
'-a',
action='store_true',
help='Early stopping')
ps.add_argument('--stoppingvalue',
'-v',
type=float,
default=0.01,
help='Early stopping value')
args = ps.parse_args()
df = pd.read_csv(args.input,
sep=args.separator,
header=args.header,
index_col=args.indexcol)
x = df[df.columns[:-1]].values
if not args.regression:
print('Not support')
else:
y = df[df.columns[-1]].values.reshape((-1, 1))
if args.earlystop:
plf = Linear(epochs=args.epochs,
lr=args.learningrate,
earlystop=args.stoppingvalue)
else:
plf = Linear(epochs=args.epochs, lr=args.learningrate)
support.report_regressor(plf, x, y, args.crossvalidate)
header=args.header,
index_col=args.indexcol)
x = df[df.columns[:-1]].values
if not args.regression:
y, clz = support.clz_to_prob(df[df.columns[-1]])
mt = entropy.gini
lf = ZeroRule
plf = PrunedTree(prunfnc=args.pruning,
pruntest=args.test,
splitratio=args.ratio,
critical=args.critical,
metric=mt,
leaf=lf,
max_depth=args.depth)
plf.fit(x, y)
support.report_classifier(plf, x, y, clz, args.crossvalidate)
else:
y = df[df.columns[-1]].values.reshape((-1, 1))
mt = entropy.deviation
lf = linear
plf = PrunedTree(prunfnc=args.pruning,
pruntest=args.test,
splitratio=args.ratio,
critical=args.critical,
metric=mt,
leaf=lf,
max_depth=args.depth)
plf.fit(x, y)
support.report_regressor(plf, x, y, args.crossvalidate)