# Copyright (c) 2017, Combine Control Systems AB
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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
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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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.linear_model
import sklearn.kernel_ridge
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.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 LinearRegression(SyML_abstract, node.Node):
name = 'Linear Regression'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'linear_regression.svg'
description = 'Ordinary linear regression'
nodeid = 'org.sysess.sympathy.machinelearning.linearregression'
tags = Tags(Tag.MachineLearning.Regression)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'fit_intercept',
'type': BoolType(default=True)},
{'name': 'normalize',
'type': BoolType(default=False)},
{'name': 'n_jobs',
'type': IntType(min_value=1, default=1)},
], doc_class=sklearn.linear_model.LinearRegression)
descriptor.set_attributes([
{'name': attr_name} for attr_name in [
'coef_', 'intercept_', 'residues_',
]], doc_class=sklearn.linear_model.LinearRegression)
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.linear_model.LinearRegression(**kwargs)
model.set_skl(skl)
model.save()
[docs]class LogisticRegression(SyML_abstract, node.Node):
name = 'Logistic Regression'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'logistic_regression.svg'
description = 'Logistic regression of a categorical dependent variable'
nodeid = 'org.sysess.sympathy.machinelearning.logisticregression'
tags = Tags(Tag.MachineLearning.Supervised)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
['Options',
{'name': 'penalty',
'type': StringSelectionType(['l1', 'l2'], default='l2')},
{'name': 'dual',
'type': BoolType(default=False)},
{'name': 'C',
'type': FloatType(min_value=0, default=1.0)},
{'name': 'fit_intercept',
'type': BoolType(default=True)},
{'name': 'intercept_scaling',
'type': FloatType(default=1.0)},
{'name': 'class_weight',
'type': UnionType([
NoneType(), StringSelectionType(['balanced'])], default=None)},
{'name': 'tol',
'type': FloatType(default=1e-4)},
{'name': 'multi_class',
'type': StringSelectionType(['ovr', 'multinomial'], default='ovr')}],
['Solver',
{'name': 'max_iter',
'type': IntType(min_value=0, default=100)},
{'name': 'solver',
'type': StringSelectionType(
['newton-cg', 'lbfgs', 'liblinear', 'sag'],
default='liblinear')},
{'name': 'n_jobs',
'desc': (
'Number of CPU cores used when parallelizing over classes if '
'multi_class="ovr". Ignored when the solver is set to '
'"liblinear" regardless of multi_class. If given -1 then all '
'cores are used'),
'type': IntType(min_value=1, default=1)}],
['Model state',
{'name': 'random_state',
'type': UnionType([NoneType(), IntType()], default=None)},
{'name': 'warm_start', 'type': BoolType(default=False)}],
], doc_class=sklearn.linear_model.LogisticRegression)
descriptor.set_attributes([
{'name': 'n_iter_'},
{'name': 'coef_', 'cnames': names_from_x},
{'name': 'intercept_'},
], doc_class=sklearn.linear_model.LogisticRegression)
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.linear_model.LogisticRegression(**kwargs)
model.set_skl(skl)
model.save()
[docs]class KernelRidge(SyML_abstract, node.Node):
name = 'Kernel Ridge Regression'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'kernel_ridge.svg'
description = (
'Kernel Ridge based classifier combining ridge regression '
'(linear least-squares L2-norm) regression with the kernel trick')
nodeid = 'org.sysess.sympathy.machinelearning.kernel_ridge'
tags = Tags(Tag.MachineLearning.Regression)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'alpha',
'type': FloatType(min_value=0, default=1.0)},
{'name': 'kernel',
'type': StringSelectionType(
['linear', 'rbf', 'poly', 'sigmoid', 'cosine',
'laplacian', 'chi2'], default='rbf')},
{'name': 'gamma',
'type': UnionType([NoneType(), FloatType()], default=None)},
{'name': 'coef0',
'type': FloatType(default=1.0)},
{'name': 'degree',
'type': IntType(min_value=1, default=3)},
], doc_class=sklearn.kernel_ridge.KernelRidge)
descriptor.set_attributes([
{'name': 'dual_coef_', 'cnames': names_from_y},
{'name': 'X_fit_', 'cnames': names_from_x},
], doc_class=sklearn.kernel_ridge.KernelRidge)
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.kernel_ridge.KernelRidge(**kwargs)
model.set_skl(skl)
model.save()
[docs]class SupportVectorRegression(SyML_abstract, node.Node):
name = 'Epsilon Support Vector Regression'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'svm.svg'
description = 'Support vector machine based regressor (SVR)'
nodeid = 'org.sysess.sympathy.machinelearning.svr'
tags = Tags(Tag.MachineLearning.Regression)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'C',
'type': FloatType(default=1.0)},
{'name': 'kernel',
'type': StringSelectionType(
['linear', 'rbf', 'poly', 'sigmoid'], default='rbf')},
{'name': 'gamma',
'type': UnionType([
StringSelectionType(['auto']), FloatType()], default='auto')},
{'name': 'epsilon',
'type': FloatType(default=0.1)},
{'name': 'coef0',
'type': FloatType(default=0.0)},
{'name': 'tol',
'type': FloatType(default=1e-3)},
{'name': 'degree',
'type': IntType(min_value=1, default=3)},
{'name': 'shrinking',
'type': BoolType(default=True)},
{'name': 'max_iter',
'type': IntType(default=-1)},
], doc_class=sklearn.svm.SVR)
descriptor.set_attributes([
{'name': 'support_', },
{'name': 'support_vectors_', 'cnames': names_from_x},
{'name': 'dual_coef_'},
{'name': 'intercept_'},
{'name': 'coef_', 'cnames': names_from_x},
], doc_class=sklearn.svm.SVR)
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.SVR(**kwargs)
model.set_skl(skl)
model.save()
