class LarsImpl():
def __init__(self,
fit_intercept=True,
verbose=False,
normalize=True,
precompute='auto',
n_nonzero_coefs=500,
eps=2.220446049250313e-16,
copy_X=True,
fit_path=True,
positive=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'verbose': verbose,
'normalize': normalize,
'precompute': precompute,
'n_nonzero_coefs': n_nonzero_coefs,
'eps': eps,
'copy_X': copy_X,
'fit_path': fit_path,
'positive': positive
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def connectWidgets(self):
# LARS/ # LARSCV
lars = Lars()
self.fit_interceptCheckBox.setChecked(lars.fit_intercept)
self.normalizeCheckBox.setChecked(lars.normalize)
self.n_nonzero_coefsSpinBox.setValue(lars.n_nonzero_coefs)
def run(self):
if self.cVCheckBox.isChecked():
params = {
'fit_intercept': self.fit_interceptCheckBox.isChecked(),
'positive': self.positiveCheckBox.isChecked(),
'max_iter': self.max_iterSpinBox.value(),
'verbose': self.verboseCheckBox.isChecked(),
'normalize': self.normalizeCheckBox.isChecked(),
'precompute': self.precomputeComboBox.currentText(),
'copy_X': self.copy_XCheckBox.isChecked(),
'cv': self.cvSpinBox.value(),
'max_n_alphas': self.max_n_alphasSpinBox.value(),
'n_jobs': self.n_jobsSpinBox.value(),
'CV': self.cVCheckBox.isChecked(),
}
return params, self.getChangedValues(params, LarsCV())
else:
params = {
'fit_intercept': self.fit_interceptCheckBox.isChecked(),
'verbose': self.verboseCheckBox.isChecked(),
'normalize': self.normalizeCheckBox.isChecked(),
'precompute': {
'True': True,
'False': False,
'auto': 'auto',
'array-like': 'array-like'
}.get(self.precomputeComboBox.currentText()),
'n_nonzero_coefs': self.n_nonzero_coefsSpinBox.value(),
'copy_X': self.copy_XCheckBox.isChecked(),
'fit_path': self.fit_pathCheckBox.isChecked(),
'positive': self.positiveCheckBox.isChecked(),
'CV': self.cVCheckBox.isChecked(),
}
return params, self.getChangedValues(params, Lars())
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def connectWidgets(self):
lars = Lars()
self.fit_intercept_listWidget.setCurrentItem(
self.fit_intercept_listWidget.findItems(str(lars.fit_intercept),
QtCore.Qt.MatchExactly)[0])
self.normalize_list.setCurrentItem(
self.normalize_list.findItems(str(lars.normalize),
QtCore.Qt.MatchExactly)[0])
self.n_nonzero_coefsLineEdit.setText(str(lars.n_nonzero_coefs))
def run(self):
params = {
'fit_intercept': self.fit_interceptCheckBox.isChecked(),
'verbose': False,
'normalize': self.normalizeCheckBox.isChecked(),
'precompute': 'auto',
'n_nonzero_coefs': self.n_nonzero_coefsSpinBox.value(),
'copy_X': True,
'fit_path': True
}
return params, self.getChangedValues(params, Lars())
def connectWidgets(self):
# LARS/ # LARSCV
lars = Lars()
larscv = LarsCV()
self.fit_interceptCheckBox.setChecked(lars.fit_intercept)
self.verboseCheckBox.setChecked(lars.verbose)
self.normalizeCheckBox.setChecked(lars.normalize)
self.setComboBox(self.precomputeComboBox, ['True', 'False', 'auto', 'array-like'])
self.defaultComboItem(self.precomputeComboBox, lars.precompute)
self.n_nonzero_coefsSpinBox.setValue(lars.n_nonzero_coefs)
self.copy_XCheckBox.setChecked(lars.copy_X)
self.fit_pathCheckBox.setChecked(lars.fit_path)
self.positiveCheckBox.setChecked(lars.positive)
self.max_iterSpinBox.setValue(larscv.max_iter)
self.max_n_alphasSpinBox.setValue(larscv.max_n_alphas)
self.n_jobsSpinBox.setValue(larscv.n_jobs)
def __init__(self,
fit_intercept=True,
verbose=False,
normalize=True,
precompute='auto',
n_nonzero_coefs=500,
eps=2.220446049250313e-16,
copy_X=True,
fit_path=True,
positive=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'verbose': verbose,
'normalize': normalize,
'precompute': precompute,
'n_nonzero_coefs': n_nonzero_coefs,
'eps': eps,
'copy_X': copy_X,
'fit_path': fit_path,
'positive': positive
}
self._wrapped_model = SKLModel(**self._hyperparams)
'HuberRegressor':HuberRegressor(), 'Imputer':Imputer(), 'IncrementalPCA':IncrementalPCA(), 'IsolationForest':IsolationForest(), 'Isomap':Isomap(), 'KMeans':KMeans(), 'KNeighborsClassifier':KNeighborsClassifier(), 'KNeighborsRegressor':KNeighborsRegressor(), 'KernelCenterer':KernelCenterer(), 'KernelDensity':KernelDensity(), 'KernelPCA':KernelPCA(), 'KernelRidge':KernelRidge(), 'LSHForest':LSHForest(), 'LabelPropagation':LabelPropagation(), 'LabelSpreading':LabelSpreading(), 'Lars':Lars(), 'LarsCV':LarsCV(), 'Lasso':Lasso(), 'LassoCV':LassoCV(), 'LassoLars':LassoLars(), 'LassoLarsCV':LassoLarsCV(), 'LassoLarsIC':LassoLarsIC(), 'LatentDirichletAllocation':LatentDirichletAllocation(), 'LedoitWolf':LedoitWolf(), 'LinearDiscriminantAnalysis':LinearDiscriminantAnalysis(), 'LinearRegression':LinearRegression(), 'LinearSVC':LinearSVC(), 'LinearSVR':LinearSVR(), 'LocallyLinearEmbedding':LocallyLinearEmbedding(), 'LogisticRegression':LogisticRegression(), 'LogisticRegressionCV':LogisticRegressionCV(),