本文整理汇总了Python中sklearn.model_selection.GridSearchCV.save方法的典型用法代码示例。如果您正苦于以下问题：Python GridSearchCV.save方法的具体用法？Python GridSearchCV.save怎么用？Python GridSearchCV.save使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.model_selection.GridSearchCV的用法示例。
在下文中一共展示了GridSearchCV.save方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.model_selection import GridSearchCV [as 别名]
# 或者: from sklearn.model_selection.GridSearchCV import save [as 别名]
#.........这里部分代码省略.........
elif args.model == "gradientboosting":
if args.classify:
model = ensemble.GradientBoostingClassifier
else:
model = ensemble.GradientBoostingRegressor
elif args.model == "decisiontree":
model = DecisionTreeRegressor
elif args.model == "adaboost":
model = ensemble.AdaBoostRegressor
elif args.model == "linreg":
model = LinearRegression
elif args.model == "autolearn":
printd("AutoLearn disabled as it does not work properly")
sys.exit(-1)
#model = AutoSklearnClassifier
fitargs["dataset_name"] = "semeval"
elif args.model == "nn":
model = NNModel
fitargs["nb_epoch"] = 10
fitargs["batch_size"] = 32
fitargs["verbose"] = 2
predictargs["verbose"] = 0
elif args.model == "None":
printd("No Model specified, exiting")
sys.exit(-1)
else:
printd("Invalid model %s" % args.model)
sys.exit(-1)
if args.classify:
# Forest Classifiers do not allow non-binary labels, so we do it by sample weight instead
byweight = issubclass(model, ensemble.forest.ForestClassifier)
lintrainlabels = np.copy(fs.trainlabels)
fs.discretizeLabels(byweight=byweight)
if byweight:
fitargs["sample_weight"] = fs.trainweights
else:
lintrainlabels = np.array(fs.trainlabels)
fs.freeze()
printd("Train labels:" + str(fs.trainlabels.shape))
if (not args.force) and args.modelfile and os.path.exists(args.modelfile):
if args.model == "nn":
import keras
model = keras.models.load_model(args.modelfile)
else:
model = joblib.load(args.modelfile)
else:
params = default_params[args.model]
for param_name, param_value in params.items():
try:
pval = getattr(args, param_name)
if pval is not None:
params[param_name] = pval
except AttributeError:
pass
if "input_dim" in params:
# -1 for label
params["input_dim"] = len(fs.names) - 1
model = model(**params)
if args.gridsearch:
model = GridSearchCV(model, scoring=evalModel, cv=5, error_score=0,
param_grid=param_grids[args.model], n_jobs=16, pre_dispatch="2*n_jobs", verbose=10)
#model = Pipeline(steps=[('pca', kpca), ('dtree', dtree)])
printd("Training")
model.fit(fs.train, fs.trainlabels, **fitargs)
#X_kpca = kpca.fit_transform(X)
#dtree.fit(traindata, trainlabels)
if args.modelfile:
try:
if args.model == "nn":
model.save(args.modelfile)
else:
joblib.dump(model, args.modelfile)
except Exception:
printd("Could not save model, autolearn does not support saving")
printd("Evaluating")
print "Using Features: %s" % args.basefeatures
print "Using Matchers: %s" % args.matchers
print "Train Accuracy"
evalData(model=model, data=fs.train, labels=lintrainlabels, classify=args.classify, obs=model.oob_prediction_, **predictargs)
# trainobs = _
print "Test Accuracy"
testobs = evalData(model=model, data=fs.test, labels=fs.testlabels, classify=args.classify, **predictargs)
if args.writematches:
try:
fs.data.writer
except AttributeError:
fs.data = dataset.Dataset.load(args.input_data, args.dataset)
trainwriter = fs.data.writer(args.writematches + ".train")
testwriter = fs.data.writer(args.writematches + ".test")
for pair in fs.data.train():
trainwriter.write(pair, Match(score=pair.label, autop=0))
for pair, obs in izip(fs.data.test(), testobs):
testwriter.write(pair, Match(score=obs))