# Copyright (c) 2017, System Engineering Software Society
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# names of its contributors may be used to endorse or promote products
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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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.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'
copyright = '(C) 2017 System Engineering Software Society'
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
descriptor.set_info([
{'name': 'C',
'type': FloatType(min_value=0.0, default=1.0)},
{'name': 'kernel',
'type': StringSelectionType([
'rbf', 'linear', 'poly', 'sigmoid', 'precomputed'],
default='rbf')},
{'name': 'degree',
'type': IntType(min_value=1, default=3)},
{'name': 'gamma',
'type': UnionType([
FloatType(), StringSelectionType(['auto'])],
default='auto')},
{'name': 'coef0',
'type': FloatType(default=0.0)},
{'name': 'probability',
'type': BoolType(default=False)},
{'name': 'shrinking',
'type': BoolType(default=True)},
{'name': 'tol',
'type': FloatType(default=1e-3)},
{'name': 'class_weight',
'type': UnionType([NoneType(), StringSelectionType(['balanced'])], default=None)},
{'name': 'max_iter',
'type': IntType(min_value=-1)},
{'name': 'random_state',
'type': UnionType([
IntType(), NoneType()], default=None)},
], 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()
[docs]class OneClassSVM(SyML_abstract, node.Node):
name = 'One Class SVM'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
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
descriptor.set_info([
{'name': 'kernel',
'type': StringSelectionType([
'rbf', 'linear', 'poly', 'sigmoid', 'precomputed'],
default='rbf')},
{'name': 'nu',
'type': FloatType(min_value=0, max_value=1, default=0.5)},
{'name': 'degree',
'type': IntType(min_value=1, default=3)},
{'name': 'gamma',
'type': UnionType([
FloatType(), StringSelectionType(['auto'])],
default='auto')},
{'name': 'coef0',
'type': FloatType(default=0.0)},
{'name': 'shrinking',
'type': BoolType(default=True)},
{'name': 'tol',
'type': FloatType(default=1e-3)},
{'name': 'max_iter',
'type': IntType(min_value=-1)},
{'name': 'random_state',
'type': UnionType([
IntType(), NoneType()], default=None)},
], 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()