model_eval

This module contains code for saving, loading, and evaluating pre-trained models.

Functions

load_model(model_path)	It loads a pre-trained TSVM-based estimator.
save_model(validator, params, output_file)	It saves an estimator with specified hyper-parameters and a evaluation method.

Classes

ModelThread(usr_in)

Evaluates a pre-trained model in a thread.

model_eval.save_model(validator, params, output_file)

It saves an estimator with specified hyper-parameters and a evaluation method.

Parameters:

validator : object

An evaluation method.

params : dict

Hyper-parameters of the estimator.

output_file : str

The full path and filename of the saved model.

model_eval.load_model(model_path)

It loads a pre-trained TSVM-based estimator.

Parameters:

model_path : str

The path at which the model is stored.

Returns:

object

A pre-trained estimator.

dict

Model information.

class model_eval.ModelThread(usr_in)

Bases: PyQt5.QtCore.QObject

Evaluates a pre-trained model in a thread.

Parameters:

usr_input : object

An instance of UserInput class which holds the user input.

Methods

blockSignals(self, bool)
childEvent(self, QChildEvent)
children(self)
connectNotify(self, QMetaMethod)
customEvent(self, QEvent)
deleteLater(self)
destroyed	destroyed(self, object: QObject = None) [signal]
disconnect(QMetaObject.Connection)
disconnectNotify(self, QMetaMethod)
dumpObjectInfo(self)
dumpObjectTree(self)
dynamicPropertyNames(self)
eval_model()	It evaluates a pre-trained model on test samples.
event(self, QEvent)
eventFilter(self, QObject, QEvent)
findChild(self, type, name, options, …)	findChild(self, Tuple, name: str = ‘’, options: Union[Qt.FindChildOptions, Qt.FindChildOption] = Qt.FindChildrenRecursively) -> QObject
findChildren(self, type, name, options, …)	findChildren(self, Tuple, name: str = ‘’, options: Union[Qt.FindChildOptions, Qt.FindChildOption] = Qt.FindChildrenRecursively) -> List[QObject] findChildren(self, type, QRegExp, options: Union[Qt.FindChildOptions, Qt.FindChildOption] = Qt.FindChildrenRecursively) -> List[QObject] findChildren(self, Tuple, QRegExp, options: Union[Qt.FindChildOptions, Qt.FindChildOption] = Qt.FindChildrenRecursively) -> List[QObject] findChildren(self, type, QRegularExpression, options: Union[Qt.FindChildOptions, Qt.FindChildOption] = Qt.FindChildrenRecursively) -> List[QObject] findChildren(self, Tuple, QRegularExpression, options: Union[Qt.FindChildOptions, Qt.FindChildOption] = Qt.FindChildrenRecursively) -> List[QObject]
inherits(self, str)
installEventFilter(self, QObject)
isSignalConnected(self, QMetaMethod)
isWidgetType(self)
isWindowType(self)
killTimer(self, int)
metaObject(self)
moveToThread(self, QThread)
objectName(self)
objectNameChanged	objectNameChanged(self, str) [signal]
parent(self)
property(self, str)
pyqtConfigure(…)	Each keyword argument is either the name of a Qt property or a Qt signal.
receivers(self, PYQT_SIGNAL)
removeEventFilter(self, QObject)
sender(self)
senderSignalIndex(self)
setObjectName(self, str)
setParent(self, QObject)
setProperty(self, str, Any)
sig_update_model_eval
signalsBlocked(self)
startTimer(self, int, timerType)
thread(self)
timerEvent(self, QTimerEvent)
tr(self, str, disambiguation, n)

eval_model()

It evaluates a pre-trained model on test samples.