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"""
Some of the docstrings for this module have been automatically
extracted from the `scikit-learn <http://scikit-learn.org/>`_ library
and are covered by their respective licenses.
"""
from __future__ import (print_function, division, unicode_literals,
absolute_import)
import sklearn
import sklearn.decomposition
import sklearn.cross_decomposition
from sympathy.api import node
from sympathy.api.nodeconfig import Ports, Tag, Tags
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.abstract_nodes import SyML_abstract
from sylib.machinelearning.utility import names_from_x
from sylib.machinelearning.utility import names_from_y
from sylib.machinelearning.utility import names_from_prefix
from sylib.machinelearning.descriptors import Descriptor
from sylib.machinelearning.descriptors import BoolType
from sylib.machinelearning.descriptors import FloatType
from sylib.machinelearning.descriptors import IntType
from sylib.machinelearning.descriptors import NoneType
from sylib.machinelearning.descriptors import StringSelectionType
from sylib.machinelearning.descriptors import UnionType
[docs]class PrincipalComponentAnalysis(SyML_abstract, node.Node):
name = 'Principal Component Analysis (PCA)'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'PCA.svg'
description = (
'Linear dimensionality reduction using Singular Value Decomposition '
'of the data to project it to a lower dimensional space.')
nodeid = 'org.sysess.sympathy.machinelearning.pca'
tags = Tags(Tag.MachineLearning.DimensionalityReduction)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_components',
'type': UnionType(
[IntType(min_value=1), FloatType(min_value=0, max_value=1),
StringSelectionType(['mle'])], default=1)},
{'name': 'svd_solver',
'type': StringSelectionType(
['auto', 'full', 'arpack', 'randomized'], default='auto')},
{'name': 'tol',
'type': FloatType(default=0.0)},
{'name': 'iterated_power',
'type': UnionType(
[IntType(min_value=0), StringSelectionType(['auto'])],
default='auto')},
{'name': 'whiten',
'type': BoolType(default=False)},
], doc_class=sklearn.decomposition.PCA)
descriptor.set_attributes([
{'name': 'components_', 'cnames': names_from_x},
{'name': 'explained_variance_'},
{'name': 'explained_variance_ratio_'},
{'name': 'mean_', 'cnames': names_from_x},
{'name': 'n_components_'},
{'name': 'noise_variance_'},
], doc_class=sklearn.decomposition.PCA)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
inputs = Ports([])
outputs = Ports([ModelPort('Model', 'model')])
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
model = node_context.output['model']
desc = self.__class__.descriptor
model.set_desc(desc)
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
skl = sklearn.decomposition.PCA(**kwargs)
model.set_skl(skl)
model.save()
[docs]class KernelPCA(SyML_abstract, node.Node):
name = 'Kernel Principal Component Analysis (KPCA)'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'PCA.svg'
description = (
'Non-linear dimensionality reduction through the use of kernels')
nodeid = 'org.sysess.sympathy.machinelearning.kpca'
tags = Tags(Tag.MachineLearning.DimensionalityReduction)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_components',
'type': UnionType(
[IntType(min_value=1), NoneType()], default=None)},
{'name': 'kernel',
'type': StringSelectionType(
['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'],
default='linear')},
{'name': 'degree',
'type': IntType(min_value=1, default=3)},
{'name': 'gamma',
'type': UnionType([
FloatType(min_value=0.0), NoneType()], default=None)},
{'name': 'coef0',
'type': FloatType(min_value=0.0, default=1)},
{'name': 'alpha',
'type': IntType(min_value=0.0, default=1)},
{'name': 'fit_inverse_transform',
'type': BoolType(default=False)},
{'name': 'remove_zero_eig',
'type': BoolType(default=False)},
{'name': 'eigen_solver',
'type': StringSelectionType([
'auto', 'dense', 'arpack'], default='auto')},
{'name': 'tol',
'type': FloatType(default=0.0)},
{'name': 'max_iter',
'type': UnionType([IntType(min_value=1), NoneType()], default=None)},
{'name': 'random_state',
'type': UnionType([NoneType(), IntType()], default=None)},
{'name': 'n_jobs',
'type': IntType(min_value=1)},
], doc_class=sklearn.decomposition.KernelPCA)
descriptor.set_attributes([
{'name': 'lambdas_'},
{'name': 'alphas_'},
{'name': 'dual_coef_', 'cnames': names_from_x},
{'name': 'X_transformed_fit_'},
{'name': 'X_fit_'},
], doc_class=sklearn.decomposition.KernelPCA)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
inputs = Ports([])
outputs = Ports([ModelPort('Model', 'model')])
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
model = node_context.output['model']
desc = self.__class__.descriptor
model.set_desc(desc)
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
kwargs['copy_X'] = True
skl = sklearn.decomposition.KernelPCA(**kwargs)
model.set_skl(skl)
model.save()
[docs]class PLSRegressionCrossDecomposition(SyML_abstract, node.Node):
name = 'Partial Least Squares cross-decomposition (PLS regression)'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'PCA.svg'
description = (
'Finds the fundamental relations between two matrices X and Y, ie. '
'it finds the (multidimensional) direction in X that best explains '
'maximum multidimensional direction in Y. See also PCA-analysis')
nodeid = 'org.sysess.sympathy.machinelearning.pls'
tags = Tags(Tag.MachineLearning.DimensionalityReduction)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_components',
'type': IntType(min_value=1, default=2)},
{'name': 'scale',
'type': BoolType(default=True)},
{'name': 'max_iter',
'type': IntType(min_value=1, default=500)},
{'name': 'tol',
'type': FloatType(default=0.0)},
], doc_class=sklearn.cross_decomposition.PLSRegression)
descriptor.set_attributes([
{'name': 'x_weights_',
'rnames': names_from_x,
'cnames': names_from_prefix('component ')},
{'name': 'y_weights_',
'rnames': names_from_y,
'cnames': names_from_prefix('component ')},
{'name': 'x_loadings_',
'rnames': names_from_x,
'cnames': names_from_prefix('component ')},
{'name': 'y_loadings_',
'rnames': names_from_y,
'cnames': names_from_prefix('component ')},
{'name': 'x_scores_',
'cnames': names_from_prefix('component ')},
{'name': 'y_scores_',
'cnames': names_from_prefix('component ')},
{'name': 'x_rotations_',
'rnames': names_from_x,
'cnames': names_from_prefix('component ')},
{'name': 'y_rotations_',
'rnames': names_from_y},
{'name': 'coef_'},
{'name': 'n_iter_'},
], doc_class=sklearn.cross_decomposition.PLSRegression)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
inputs = Ports([])
outputs = Ports([ModelPort('Model', 'model')])
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
model = node_context.output['model']
desc = self.__class__.descriptor
model.set_desc(desc)
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
skl = sklearn.cross_decomposition.PLSRegression(**kwargs)
model.set_skl(skl)
model.save()
