# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# Sympathy for Data is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# Sympathy for Data 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 Sympathy for Data. If not, see <http://www.gnu.org/licenses/>.
from packaging import version as pversion
import sklearn
import sklearn.svm
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.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 SupportVectorClassifier(SyML_abstract, node.Node):
name = 'Support Vector Classifier'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'svm.svg'
description = 'Support vector machine (SVM) based classifier'
nodeid = 'org.sysess.sympathy.machinelearning.svc'
tags = Tags(Tag.MachineLearning.Supervised)
descriptor = Descriptor()
descriptor.name = name
info = [
[
"Model",
{'name': 'C',
'dispname': 'Penalty parameter C',
'type': FloatType(min_value=0.0, default=1.0)},
{'name': 'kernel',
'dispname': 'Kernel',
'type': StringSelectionType([
'rbf', 'linear', 'poly', 'sigmoid', 'precomputed'],
default='rbf')},
],
[
"Advanced",
{'name': 'degree',
'dispname': 'Polynomial kernel degree',
'type': IntType(min_value=1, default=3)},
{'name': 'gamma',
'dispname': 'Kernel coefficient',
'type': UnionType([
FloatType(), StringSelectionType(['auto'])],
default='auto')},
{'name': 'coef0',
'dispname': 'Independent kernel function term',
'type': FloatType(default=0.0)},
{'name': 'probability',
'dispname': 'Enable probability estimates',
'type': BoolType(default=False)},
{'name': 'shrinking',
'dispname': 'Use shrinking heuristic',
'type': BoolType(default=True)},
{'name': 'class_weight',
'dispname': 'Class weight',
'type': UnionType([NoneType(), StringSelectionType(['balanced'])],
default=None)},
],
[
"Solver",
{'name': 'tol',
'dispname': 'Tolerance',
'type': FloatType(default=1e-3)},
{'name': 'max_iter',
'dispname': 'Hard iteration limit',
'type': IntType(min_value=-1)},
{'name': 'random_state',
'dispname': 'Random seed',
'type': UnionType([IntType(), NoneType()], default=None)},
]
]
descriptor.set_info(info, doc_class=sklearn.svm.SVC)
descriptor.set_attributes([
{'name': 'support_', },
{'name': 'support_vectors_', 'cnames': names_from_x},
{'name': 'n_support_'},
{'name': 'dual_coef_'},
{'name': 'coef_', 'cnames': names_from_x},
{'name': 'intercept_'},
], doc_class=sklearn.svm.SVC)
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.svm.SVC(**kwargs)
model.set_skl(skl)
model.save()
def _one_class_svm_info():
model_info = [
"Model",
{'name': 'kernel',
'dispname': 'Kernel',
'type': StringSelectionType(
['rbf', 'linear', 'poly', 'sigmoid', 'precomputed'],
default='rbf')},
{'name': 'nu',
'dispname': 'Upper/lower fraction bound',
'type': FloatType(min_value=0, max_value=1, default=0.5)},
]
advanced_info = [
"Advanced",
{'name': 'degree',
'dispname': 'Polynomial kernel degree',
'type': IntType(min_value=1, default=3)},
{'name': 'gamma',
'dispname': 'Kernel coefficient',
'type': UnionType([
FloatType(), StringSelectionType(['auto'])],
default='auto')},
{'name': 'coef0',
'dispname': 'Independent kernel function term',
'type': FloatType(default=0.0)},
{'name': 'shrinking',
'dispname': 'Use shrinking heuristic',
'type': BoolType(default=True)},
]
solver_info = [
"Solver",
{'name': 'tol',
'dispname': 'Tolerance',
'type': FloatType(default=1e-3)},
{'name': 'max_iter',
'dispname': 'Hard iteration limit',
'type': IntType(min_value=-1)},
]
# Earlier versions of sklearn and sympathy supported the random_state
# parameter. It is deprecated and ignored in sklearn>=0.20 and removed in
# sklearn>=0.22.
if pversion.parse(sklearn.__version__) < pversion.Version('0.20'):
solver_info.append({
'name': 'random_state',
'dispname': 'Random seed',
'type': UnionType([IntType(), NoneType()], default=None),
})
return [model_info, advanced_info, solver_info]
[docs]class OneClassSVM(SyML_abstract, node.Node):
name = 'One Class Support Vector Machines'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'outliers.svg'
description = (
'Unsupervised outlier detection based on support vector machines'
)
nodeid = 'org.sysess.sympathy.machinelearning.one_class_svm'
tags = Tags(Tag.MachineLearning.Unsupervised)
descriptor = Descriptor()
descriptor.name = name
info = _one_class_svm_info()
descriptor.set_info(info, doc_class=sklearn.svm.OneClassSVM)
descriptor.set_attributes([
{'name': 'support_', },
{'name': 'support_vectors_', 'cnames': names_from_x},
{'name': 'dual_coef_'},
{'name': 'coef_', 'cnames': names_from_x},
{'name': 'intercept_'},
], doc_class=sklearn.svm.OneClassSVM)
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.svm.OneClassSVM(**kwargs)
model.set_skl(skl)
model.save()
