예제 #1
0
파일: final.py 프로젝트: SiuTo/DreamPC
#! /usr/bin/env python

from format_data import formatData
from train_xgboost import train
import csv

def loadFinalData(flag):
	dataCSV = csv.reader(open("../data/data_"+flag+".csv", "r"))
	data = [row for row in dataCSV]
	formatData(data[1:], flag)

loadFinalData("train")
loadFinalData("test")
train("final")

예제 #2
0
from format_data import formatData
from train_xgboost import train
import csv

dataCSV = csv.reader(open("../data/data_train.csv", "r"))
data = [row for row in dataCSV]
del data[0]
m = (len(data)-1)//10+1
data_origin = []
for i in range(len(data)):
	data_origin.append(data[i][:2])

for k in range(10):
	left, right = k*m, min((k+1)*m, len(data))
	realFile = open("real_value_"+str(k)+".csv", "w")
	lines = ["LKADT_P,DEATH\n"]
	for i in range(left, right):
		lines.append("{},{}\n".format(data[i][0], data[i][1]))
	realFile.writelines(lines)

	formatData(data[:left]+data[right:], "train")
	for i in range(left, right):
		data[i][0], data[i][1] = 0, 1
	formatData(data[left:right], "test")
	for i in range(left, right):
		data[i][0], data[i][1] = data_origin[i]

	train(str(k))

예제 #3
0
파일: train.py 프로젝트: zsoltzombori/plcop
    value_train_dir = "{}/stage*/train_value".format(args.outdir)
    policy_train_dir = "{}/stage*/train_policy".format(args.outdir)
    value_train_dirs = glob.glob(value_train_dir)
    policy_train_dirs = glob.glob(policy_train_dir)

T0 = time.time()
if args.model_type == "xgboost":
    value_train_dirs = [d + "/svmlight" for d in value_train_dirs]
    policy_train_dirs = [d + "/svmlight" for d in policy_train_dirs]
    from train_xgboost import train
    if args.target_model in ("value", "all"):
        value_modelfile = "{}/value_xgb".format(args.outdir)
        print("\n\nTraining value model")
        train(args,
              value_train_dirs,
              value_modelfile,
              n_features,
              objective="reg:squarederror",
              oversample=True)
    if args.target_model in ("policy", "all"):
        policy_modelfile = "{}/policy_xgb".format(args.outdir)
        print("\n\nTraining policy model")
        train(args,
              policy_train_dirs,
              policy_modelfile,
              n_features,
              objective="reg:squarederror",
              oversample=True)

elif args.model_type == "nn":
    value_train_dirs = [d + "/svmlight" for d in value_train_dirs]
    policy_train_dirs = [d + "/svmlight" for d in policy_train_dirs]
예제 #4
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def train_xgboost(opt):
    import train_xgboost as xgboost
    team_data = get_team_representations(opt.team_data_type)
    xgboost.train(team_data, opt)