class TheilSenRegressorImpl():
def __init__(self,
fit_intercept=True,
copy_X=True,
max_subpopulation=10000,
n_subsamples=None,
max_iter=300,
tol=0.001,
random_state=None,
n_jobs=None,
verbose=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'copy_X': copy_X,
'max_subpopulation': max_subpopulation,
'n_subsamples': n_subsamples,
'max_iter': max_iter,
'tol': tol,
'random_state': random_state,
'n_jobs': n_jobs,
'verbose': verbose
}
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 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,
fit_intercept=True,
copy_X=True,
max_subpopulation=10000,
n_subsamples=None,
max_iter=300,
tol=0.001,
random_state=None,
n_jobs=None,
verbose=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'copy_X': copy_X,
'max_subpopulation': max_subpopulation,
'n_subsamples': n_subsamples,
'max_iter': max_iter,
'tol': tol,
'random_state': random_state,
'n_jobs': n_jobs,
'verbose': verbose
}
self._wrapped_model = SKLModel(**self._hyperparams)
classifiers = [
RandomForestRegressor(n_estimators=200, n_jobs=5,
random_state=randomstate),
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,
'RandomizedPCA':RandomizedPCA(),
'Ridge':Ridge(),
'RidgeCV':RidgeCV(),
'RidgeClassifier':RidgeClassifier(),
'RidgeClassifierCV':RidgeClassifierCV(),
'RobustScaler':RobustScaler(),
'SGDClassifier':SGDClassifier(),
'SGDRegressor':SGDRegressor(),
'SVC':SVC(),
'SVR':SVR(),
'SelectFdr':SelectFdr(),
'SelectFpr':SelectFpr(),
'SelectFwe':SelectFwe(),
'SelectKBest':SelectKBest(),
'SelectPercentile':SelectPercentile(),
'ShrunkCovariance':ShrunkCovariance(),
'SkewedChi2Sampler':SkewedChi2Sampler(),
'SparsePCA':SparsePCA(),
'SparseRandomProjection':SparseRandomProjection(),
'SpectralBiclustering':SpectralBiclustering(),
'SpectralClustering':SpectralClustering(),
'SpectralCoclustering':SpectralCoclustering(),
'SpectralEmbedding':SpectralEmbedding(),
'StandardScaler':StandardScaler(),
'TSNE':TSNE(),
'TheilSenRegressor':TheilSenRegressor(),
'VBGMM':VBGMM(),
'VarianceThreshold':VarianceThreshold(),}
