# Copyright (c) 2017, System Engineering Software Society
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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 copy
import sklearn
import sklearn.base
import sklearn.exceptions
import sklearn.ensemble
import sklearn.multioutput
from sympathy.api import node
from sympathy.api.nodeconfig import Ports
from sympathy.api.nodeconfig import Tag
from sympathy.api.nodeconfig import Tags
from sympathy.api.exceptions import SyNodeError
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.ensemble import VotingClassifierDescriptor
from sylib.machinelearning.ensemble import MultiOutputClassifierDescriptor
from sylib.machinelearning.abstract_nodes import SyML_abstract
from sylib.machinelearning.descriptors import IntListType
from sylib.machinelearning.descriptors import IntType
from sylib.machinelearning.descriptors import StringSelectionType
from sylib.machinelearning.descriptors import StringType
[docs]class VotingClassifier(SyML_abstract, node.Node):
name = 'Voting Classifier'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'votingclassifier.svg'
description = (
'Uses voting to select answer from multiple classifiers. '
'Add additional input ports for models by right-clicking on '
'node and selecting "Create Input Port > models"')
nodeid = 'org.sysess.sympathy.machinelearning.votingclassifier'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('models','models', n=(1,)),])
outputs = Ports([ModelPort('Output model','out-model')])
descriptor = VotingClassifierDescriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'names',
'type': StringType(default=''),
'desc': 'Comma separated list of model names, eg. Rescale, SVC'},
{'name': 'copies',
'type': IntListType(default=[1]),
'desc': 'Number of copies to make of each input model'},
{'name': 'n_jobs',
'type': IntType(default=1)},
{'name': 'voting',
'type': StringSelectionType(['hard', 'soft'])},
], doc_class=sklearn.ensemble.VotingClassifier)
descriptor.set_attributes([
{'name': attr_name} for attr_name in [
'classes_',
]], doc_class=sklearn.ensemble.VotingClassifier)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
out_model = node_context.output['out-model']
models = node_context.input.group('models')
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
names_raw = kwargs['names']
copies = kwargs['copies']
del kwargs['names']
del kwargs['copies']
name_list = [x.strip() for x in names_raw.split(',')]
name_list = list(filter(lambda x: x != "", name_list))
if len(name_list) < len(models):
for i in range(len(name_list), len(models)):
model = models[i]
model.load()
desc = model.get_desc()
name_list.append(desc.name)
else:
name_list = name_list[:len(models)]
descs = []
skls = []
names = []
for i, model in enumerate(models):
model.load()
desc = model.get_desc()
names.append(name_list[i])
descs.append(model.get_desc())
skls.append(model.get_skl())
if len(copies) == 1 and copies[0] > 1:
skls_ = []
descs_ = []
names_ = []
for skl, desc, name in zip(skls, descs, names):
skls_ += [copy.deepcopy(skl) for _ in range(copies[0])]
descs_ += [copy.deepcopy(desc) for _ in range(copies[0])]
names_ += ['{}-{}'.format(name,i+1) for i in range(copies[0])]
skls = skls_
descs = descs_
names = names_
elif len(copies) > 1:
if len(copies) != len(skls):
raise SyNodeError(
'Need to specify exactly one number as copies, or one '
'number for each of the {} different inputs'
.format(len(skls)))
skls_ = []
descs_ = []
names_ = []
for copies, skl, desc, name in zip(copies, skls, descs, names):
skls_ += [copy.deepcopy(skl) for _ in range(copies)]
descs_ += [copy.deepcopy(desc) for _ in range(copies)]
names_ += ["{}-{}".format(name, i+1) for i in range(copies)]
skls = skls_
descs = descs_
names = names_
skl = sklearn.ensemble.VotingClassifier(
list(zip(names, skls)), **kwargs)
desc = self.__class__.descriptor.new(skl)
out_model.set_skl(skl)
out_model.set_desc(desc)
desc.set_children(names, descs)
out_model.save()
[docs]class MultiOutputClassifier(SyML_abstract, node.Node):
name = 'Multi-output classifier'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'multioutput.svg'
description = (
'Fits one classifier for each target of outputs. Useful for '
'extending classifiers that do not natively support multiple outputs')
nodeid = 'org.sysess.sympathy.machinelearning.multioutput_classifier'
tags = Tags(Tag.MachineLearning.Supervised)
inputs = Ports([ModelPort('model','model', n=1)])
outputs = Ports([ModelPort('Output model','out-model')])
descriptor = MultiOutputClassifierDescriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_jobs',
'type': IntType(default=1)},
], doc_class=sklearn.multioutput.MultiOutputClassifier)
descriptor.set_attributes([
], doc_class=sklearn.multioutput.MultiOutputClassifier)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
out_model = node_context.output['out-model']
model = node_context.input['model']
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
model.load()
child_desc = model.get_desc()
child_skl = model.get_skl()
skl = sklearn.multioutput.MultiOutputClassifier(child_skl, **kwargs)
desc = self.__class__.descriptor.new(skl)
out_model.set_skl(skl)
out_model.set_desc(desc)
desc.set_child(child_desc)
out_model.save()
[docs]class MultiOutputRegressor(SyML_abstract, node.Node):
name = 'Multi-output regressor'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'multioutput.svg'
description = (
'Fits one regressor for each target of outputs. Useful for '
'extending regressors that do not natively support multiple outputs')
nodeid = 'org.sysess.sympathy.machinelearning.multioutput_regressor'
tags = Tags(Tag.MachineLearning.Regression)
inputs = Ports([ModelPort('model','model', n=1)])
outputs = Ports([ModelPort('Output model','out-model')])
descriptor = MultiOutputClassifierDescriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_jobs',
'type': IntType(default=1)},
], doc_class=sklearn.multioutput.MultiOutputRegressor)
descriptor.set_attributes([
], doc_class=sklearn.multioutput.MultiOutputRegressor)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
out_model = node_context.output['out-model']
model = node_context.input['model']
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
model.load()
child_desc = model.get_desc()
child_skl = model.get_skl()
skl = sklearn.multioutput.MultiOutputRegressor(child_skl, **kwargs)
desc = self.__class__.descriptor.new(skl)
out_model.set_skl(skl)
out_model.set_desc(desc)
desc.set_child(child_desc)
out_model.save()
