class OWAdaBoost(OWBaseLearner):
name = "自适应提升算法(AdaBoost)"
description = "一种集合元算法，组合弱学习器并适应每个训练样本的“难度”。"
icon = "icons/AdaBoost.svg"
replaces = [
"Orange.widgets.classify.owadaboost.OWAdaBoostClassification",
"Orange.widgets.regression.owadaboostregression.OWAdaBoostRegression",
]
priority = 80
keywords = ["boost", 'tisheng']
category = '模型(Model)'
LEARNER = SklAdaBoostLearner
class Inputs(OWBaseLearner.Inputs):
learner = Input("学习器(Learner)", Learner, replaces=['Learner'])
#: Algorithms for classification problems
algorithms = ["SAMME", "SAMME.R"]
#: Losses for regression problems
losses = ["Linear", "Square", "Exponential"]
Chinese_losses = ["线性的", "平方的", "指数"]
n_estimators = Setting(50)
learning_rate = Setting(1.)
algorithm_index = Setting(1)
loss_index = Setting(0)
use_random_seed = Setting(False)
random_seed = Setting(0)
DEFAULT_BASE_ESTIMATOR = SklTreeLearner()
class Error(OWBaseLearner.Error):
no_weight_support = Msg('The base learner does not support weights.')
def add_main_layout(self):
# this is part of init, pylint: disable=attribute-defined-outside-init
box = gui.widgetBox(self.controlArea, "参数")
self.base_estimator = self.DEFAULT_BASE_ESTIMATOR
self.base_label = gui.label(
box, self,
"基学习器(Base estimator): " + self.base_estimator.name.title())
self.n_estimators_spin = gui.spin(box,
self,
"n_estimators",
1,
10000,
label="基学习器数目:",
alignment=Qt.AlignRight,
controlWidth=80,
callback=self.settings_changed)
self.learning_rate_spin = gui.doubleSpin(
box,
self,
"learning_rate",
1e-5,
1.0,
1e-5,
label="学习率:",
decimals=5,
alignment=Qt.AlignRight,
controlWidth=80,
callback=self.settings_changed)
self.random_seed_spin = gui.spin(box,
self,
"random_seed",
0,
2**31 - 1,
controlWidth=80,
label="随机发生器的固定种子:",
alignment=Qt.AlignRight,
callback=self.settings_changed,
checked="use_random_seed",
checkCallback=self.settings_changed)
# Algorithms
box = gui.widgetBox(self.controlArea, "提升方法(Boosting method)")
self.cls_algorithm_combo = gui.comboBox(box,
self,
"algorithm_index",
label="分类算法:",
items=self.algorithms,
orientation=Qt.Horizontal,
callback=self.settings_changed)
self.reg_algorithm_combo = gui.comboBox(box,
self,
"loss_index",
label="回归损失函数:",
items=self.Chinese_losses,
orientation=Qt.Horizontal,
callback=self.settings_changed)
def create_learner(self):
if self.base_estimator is None:
return None
return self.LEARNER(base_estimator=self.base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
random_state=self.random_seed,
preprocessors=self.preprocessors,
algorithm=self.algorithms[self.algorithm_index],
loss=self.losses[self.loss_index].lower())
@Inputs.learner
def set_base_learner(self, learner):
# base_estimator is defined in add_main_layout
# pylint: disable=attribute-defined-outside-init
self.Error.no_weight_support.clear()
if learner and not learner.supports_weights:
# Clear the error and reset to default base learner
self.Error.no_weight_support()
self.base_estimator = None
self.base_label.setText("Base estimator: INVALID")
else:
self.base_estimator = learner or self.DEFAULT_BASE_ESTIMATOR
self.base_label.setText("Base estimator: %s" %
self.base_estimator.name.title())
self.learner = self.model = None
def get_learner_parameters(self):
return (("Base estimator", self.base_estimator),
("Number of estimators", self.n_estimators),
("Algorithm (classification)",
self.algorithms[self.algorithm_index].capitalize()),
("Loss (regression)",
self.losses[self.loss_index].capitalize()))
class OWAdaBoost(OWBaseLearner):
name = "AdaBoost"
description = "An ensemble meta-algorithm that combines weak learners " \
"and adapts to the 'hardness' of each training sample. "
icon = "icons/AdaBoost.svg"
replaces = [
"Orange.widgets.classify.owadaboost.OWAdaBoostClassification",
"Orange.widgets.regression.owadaboostregression.OWAdaBoostRegression",
]
priority = 80
keywords = ["boost"]
LEARNER = SklAdaBoostLearner
class Inputs(OWBaseLearner.Inputs):
learner = Input("Learner", Learner)
#: Algorithms for classification problems
algorithms = ["SAMME", "SAMME.R"]
#: Losses for regression problems
losses = ["Linear", "Square", "Exponential"]
n_estimators = Setting(50)
learning_rate = Setting(1.)
algorithm_index = Setting(1)
loss_index = Setting(0)
use_random_seed = Setting(False)
random_seed = Setting(0)
DEFAULT_BASE_ESTIMATOR = SklTreeLearner()
class Error(OWBaseLearner.Error):
no_weight_support = Msg('The base learner does not support weights.')
def add_main_layout(self):
box = gui.widgetBox(self.controlArea, "Parameters")
self.base_estimator = self.DEFAULT_BASE_ESTIMATOR
self.base_label = gui.label(
box, self, "Base estimator: " + self.base_estimator.name.title())
self.n_estimators_spin = gui.spin(box,
self,
"n_estimators",
1,
100,
label="Number of estimators:",
alignment=Qt.AlignRight,
controlWidth=80,
callback=self.settings_changed)
self.learning_rate_spin = gui.doubleSpin(
box,
self,
"learning_rate",
1e-5,
1.0,
1e-5,
label="Learning rate:",
decimals=5,
alignment=Qt.AlignRight,
controlWidth=80,
callback=self.settings_changed)
self.random_seed_spin = gui.spin(
box,
self,
"random_seed",
0,
2**31 - 1,
controlWidth=80,
label="Fixed seed for random generator:",
alignment=Qt.AlignRight,
callback=self.settings_changed,
checked="use_random_seed",
checkCallback=self.settings_changed)
# Algorithms
box = gui.widgetBox(self.controlArea, "Boosting method")
self.cls_algorithm_combo = gui.comboBox(
box,
self,
"algorithm_index",
label="Classification algorithm:",
items=self.algorithms,
orientation=Qt.Horizontal,
callback=self.settings_changed)
self.reg_algorithm_combo = gui.comboBox(
box,
self,
"loss_index",
label="Regression loss function:",
items=self.losses,
orientation=Qt.Horizontal,
callback=self.settings_changed)
def create_learner(self):
if self.base_estimator is None:
return None
return self.LEARNER(base_estimator=self.base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
random_state=self.random_seed,
preprocessors=self.preprocessors,
algorithm=self.algorithms[self.algorithm_index],
loss=self.losses[self.loss_index].lower())
@Inputs.learner
def set_base_learner(self, learner):
self.Error.no_weight_support.clear()
if learner and not learner.supports_weights:
# Clear the error and reset to default base learner
self.Error.no_weight_support()
self.base_estimator = None
self.base_label.setText("Base estimator: INVALID")
else:
self.base_estimator = learner or self.DEFAULT_BASE_ESTIMATOR
self.base_label.setText("Base estimator: %s" %
self.base_estimator.name.title())
if self.auto_apply:
self.apply()
def get_learner_parameters(self):
return (("Base estimator", self.base_estimator),
("Number of estimators", self.n_estimators),
("Algorithm (classification)",
self.algorithms[self.algorithm_index].capitalize()),
("Loss (regression)",
self.losses[self.loss_index].capitalize()))
class OWAdaBoost(OWBaseLearner):
name = "AdaBoost"
description = "一种集合元算法，结合弱学习者并适应每个训练样本的“硬度”。"
icon = "icons/AdaBoost.svg"
replaces = [
"Orange.widgets.classify.owadaboost.OWAdaBoostClassification",
"Orange.widgets.regression.owadaboostregression.OWAdaBoostRegression",
]
priority = 80
keywords = ["boost"]
LEARNER = SklAdaBoostLearner
class Inputs(OWBaseLearner.Inputs):
learner = Input("学习器", Learner)
#: Algorithms for classification problems
algorithms = ["SAMME", "SAMME.R"]
#: Losses for regression problems
losses = ["线性", "平方", "指数"]
n_estimators = Setting(50)
learning_rate = Setting(1.)
algorithm_index = Setting(1)
loss_index = Setting(0)
use_random_seed = Setting(False)
random_seed = Setting(0)
DEFAULT_BASE_ESTIMATOR = SklTreeLearner()
class Error(OWBaseLearner.Error):
no_weight_support = Msg('The base learner does not support weights.')
def add_main_layout(self):
box = gui.widgetBox(self.controlArea, "参数")
self.base_estimator = self.DEFAULT_BASE_ESTIMATOR
self.base_label = gui.label(
box, self, "基本估计量: " + self.base_estimator.name.title())
self.n_estimators_spin = gui.spin(box,
self,
"n_estimators",
1,
100,
label="估计数:",
alignment=Qt.AlignRight,
controlWidth=80,
callback=self.settings_changed)
self.learning_rate_spin = gui.doubleSpin(
box,
self,
"learning_rate",
1e-5,
1.0,
1e-5,
label="学习率:",
decimals=5,
alignment=Qt.AlignRight,
controlWidth=80,
callback=self.settings_changed)
self.random_seed_spin = gui.spin(box,
self,
"random_seed",
0,
2**31 - 1,
controlWidth=80,
label="随机发生器的固定种子:",
alignment=Qt.AlignRight,
callback=self.settings_changed,
checked="use_random_seed",
checkCallback=self.settings_changed)
# Algorithms
box = gui.widgetBox(self.controlArea, "助推方法")
self.cls_algorithm_combo = gui.comboBox(box,
self,
"algorithm_index",
label="分类算法:",
items=self.algorithms,
orientation=Qt.Horizontal,
callback=self.settings_changed)
self.reg_algorithm_combo = gui.comboBox(box,
self,
"loss_index",
label="回归损失函数:",
items=self.losses,
orientation=Qt.Horizontal,
callback=self.settings_changed)
def create_learner(self):
if self.base_estimator is None:
return None
return self.LEARNER(base_estimator=self.base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
random_state=self.random_seed,
preprocessors=self.preprocessors,
algorithm=self.algorithms[self.algorithm_index],
loss=self.losses[self.loss_index].lower())
@Inputs.learner
def set_base_learner(self, learner):
self.Error.no_weight_support.clear()
if learner and not learner.supports_weights:
# Clear the error and reset to default base learner
self.Error.no_weight_support()
self.base_estimator = None
self.base_label.setText("Base estimator: INVALID")
else:
self.base_estimator = learner or self.DEFAULT_BASE_ESTIMATOR
self.base_label.setText("Base estimator: %s" %
self.base_estimator.name.title())
if self.auto_apply:
self.apply()
def get_learner_parameters(self):
return (("Base estimator", self.base_estimator),
("Number of estimators", self.n_estimators),
("Algorithm (classification)",
self.algorithms[self.algorithm_index].capitalize()),
("Loss (regression)",
self.losses[self.loss_index].capitalize()))