class RadiusNeighborsRegressorImpl():
def __init__(self,
radius=1.0,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None,
n_jobs=None):
self._hyperparams = {
'radius': radius,
'weights': weights,
'algorithm': algorithm,
'leaf_size': leaf_size,
'p': p,
'metric': metric,
'metric_params': metric_params,
'n_jobs': n_jobs
}
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 predict(self, X):
return self._sklearn_model.predict(X)
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 __init__(self, radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None):
self._hyperparams = {
'radius': radius,
'weights': weights,
'algorithm': algorithm,
'leaf_size': leaf_size,
'p': p,
'metric': metric,
'metric_params': metric_params,
'n_jobs': n_jobs}
self._wrapped_model = SKLModel(**self._hyperparams)
ExtraTreesRegressor(n_estimators=200, n_jobs=5, random_state=randomstate),
# GradientBoostingRegressor(random_state=randomstate), # learning_rate is a hyper-parameter in the range (0.0, 1.0]
# HistGradientBoostingClassifier(random_state=randomstate), # learning_rate is a hyper-parameter in the range (0.0, 1.0]
AdaBoostRegressor(n_estimators=200, random_state=randomstate),
GaussianProcessRegressor(normalize_y=True),
ARDRegression(),
# HuberRegressor(), # epsilon: greater than 1.0, default 1.35
LinearRegression(n_jobs=5),
PassiveAggressiveRegressor(
random_state=randomstate), # C: 0.25, 0.5, 1, 5, 10
SGDRegressor(random_state=randomstate),
TheilSenRegressor(n_jobs=5, random_state=randomstate),
RANSACRegressor(random_state=randomstate),
KNeighborsRegressor(
weights='distance'), # n_neighbors: 3, 6, 9, 12, 15, 20
RadiusNeighborsRegressor(weights='distance'), # radius: 1, 2, 5, 10, 15
MLPRegressor(max_iter=10000000, random_state=randomstate),
DecisionTreeRegressor(
random_state=randomstate), # max_depth = 2, 3, 4, 6, 8
ExtraTreeRegressor(random_state=randomstate), # max_depth = 2, 3, 4, 6, 8
SVR() # C: 0.25, 0.5, 1, 5, 10
]
selectors = [
reliefF.reliefF,
fisher_score.fisher_score,
# chi_square.chi_square,
JMI.jmi,
CIFE.cife,
DISR.disr,
MIM.mim,
'OneClassSVM':OneClassSVM(), 'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(), 'OrthogonalMatchingPursuitCV':OrthogonalMatchingPursuitCV(), 'PCA':PCA(), 'PLSCanonical':PLSCanonical(), 'PLSRegression':PLSRegression(), 'PLSSVD':PLSSVD(), 'PassiveAggressiveClassifier':PassiveAggressiveClassifier(), 'PassiveAggressiveRegressor':PassiveAggressiveRegressor(), 'Perceptron':Perceptron(), 'ProjectedGradientNMF':ProjectedGradientNMF(), 'QuadraticDiscriminantAnalysis':QuadraticDiscriminantAnalysis(), 'RANSACRegressor':RANSACRegressor(), 'RBFSampler':RBFSampler(), 'RadiusNeighborsClassifier':RadiusNeighborsClassifier(), 'RadiusNeighborsRegressor':RadiusNeighborsRegressor(), 'RandomForestClassifier':RandomForestClassifier(), 'RandomForestRegressor':RandomForestRegressor(), 'RandomizedLasso':RandomizedLasso(), 'RandomizedLogisticRegression':RandomizedLogisticRegression(), 'RandomizedPCA':RandomizedPCA(), 'Ridge':Ridge(), 'RidgeCV':RidgeCV(), 'RidgeClassifier':RidgeClassifier(), 'RidgeClassifierCV':RidgeClassifierCV(), 'RobustScaler':RobustScaler(), 'SGDClassifier':SGDClassifier(), 'SGDRegressor':SGDRegressor(), 'SVC':SVC(), 'SVR':SVR(), 'SelectFdr':SelectFdr(),