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Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.kernel_approximation import RBFSampler
class TPOTRBFSampler(Preprocessor):
"""Uses scikit-learn's RBFSampler to transform the feature set
Parameters
----------
gamma: float
Parameter of RBF kernel: exp(-gamma * x^2)
"""
import_hash = {'sklearn.kernel_approximation': ['RBFSampler']}
sklearn_class = RBFSampler
arg_types = (float, )
def __init__(self):
pass
def preprocess_args(self, gamma):
modify it under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.preprocessing import MinMaxScaler
class TPOTMaxAbsScaler(Preprocessor):
"""Uses scikit-learn's MinMaxScaler to transform all of the features by scaling them to the range [0, 1].
Parameters
----------
None
"""
import_hash = {'sklearn.preprocessing': ['MinMaxScaler']}
sklearn_class = MinMaxScaler
arg_types = ()
def __init__(self):
pass
def preprocess_args(self):
modify it under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.decomposition import PCA
class TPOTRandomizedPCA(Preprocessor):
"""Uses scikit-learn's randomized PCA to transform the feature set
Parameters
----------
iterated_power: int
Number of iterations for the power method. [1, 10]
"""
import_hash = {'sklearn.decomposition': ['PCA']}
sklearn_class = PCA
arg_types = (int, )
def __init__(self):
pass
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.cluster import FeatureAgglomeration
class TPOTFeatureAgglomeration(Preprocessor):
"""Uses scikit-learn's Nystroem to transform the feature set
Parameters
----------
affinity: int
Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
"manhattan", "cosine", or "precomputed". If linkage is "ward", only
"euclidean" is accepted.
Input integer is used to select one of the above strings.
linkage: int
Can be one of the following values:
"ward", "complete", "average"
Input integer is used to select one of the above strings.
"""
import_hash = {'sklearn.cluster': ['FeatureAgglomeration']}
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.preprocessing import Binarizer
class TPOTBinarizer(Preprocessor):
"""Uses scikit-learn's Binarizer to transform the feature set
Parameters
----------
threshold: float
Feature values below or equal to this value are replaced by 0, above it by 1
"""
import_hash = {'sklearn.preprocessing': ['Binarizer']}
sklearn_class = Binarizer
arg_types = (float, )
def __init__(self):
pass
def preprocess_args(self, threshold):
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.preprocessing import Normalizer
class TPOTNormalizer(Preprocessor):
"""Uses scikit-learn's Normalizer to normalize samples individually to unit norm
Parameters
----------
norm: 'l1', 'l2', or 'max'
The norm to use to normalize each non zero sample.
"""
import_hash = {'sklearn.preprocessing': ['Normalizer']}
sklearn_class = Normalizer
arg_types = (int, )
def __init__(self):
pass
def preprocess_args(self, norm):
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.preprocessing import StandardScaler
class TPOTStandardScaler(Preprocessor):
"""Uses scikit-learn's StandardScaler to transform the feature set
Parameters
----------
None
"""
import_hash = {'sklearn.preprocessing': ['StandardScaler']}
sklearn_class = StandardScaler
arg_types = ()
def __init__(self):
pass
def preprocess_args(self):
return {
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.kernel_approximation import Nystroem
class TPOTNystroem(Preprocessor):
"""Uses scikit-learn's Nystroem to transform the feature set
Parameters
----------
kernel: int
Kernel type is selected from scikit-learn's provided types:
'sigmoid', 'polynomial', 'additive_chi2', 'poly', 'laplacian', 'cosine', 'linear', 'rbf', 'chi2'
Input integer is used to select one of the above strings.
gamma: float
Gamma parameter for the kernels.
n_components: int
The number of components to keep
"""
import_hash = {'sklearn.kernel_approximation': ['Nystroem']}
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.decomposition import FastICA
class TPOTFastICA(Preprocessor):
"""Uses scikit-learn's FastICA to transform the feature set
Parameters
----------
tol: float
Tolerance on update at each iteration.
"""
import_hash = {'sklearn.decomposition': ['FastICA']}
sklearn_class = FastICA
arg_types = (float, )
def __init__(self):
pass
def preprocess_args(self, tol):
Free Software Foundation, either version 3 of the License, or (at your option)
any later version.
The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.
"""
from .base import Preprocessor
from sklearn.preprocessing import PolynomialFeatures
class TPOTPolynomialFeatures(Preprocessor):
"""Uses scikit-learn's PolynomialFeatures to transform the feature set
Parameters
----------
None
"""
import_hash = {'sklearn.preprocessing': ['PolynomialFeatures']}
sklearn_class = PolynomialFeatures
arg_types = ()
def __init__(self):
pass
def preprocess_args(self):
return {