# Copyright (c) 2017, Combine Control Systems AB
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# * Redistributions of source code must retain the above copyright
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# documentation and/or other materials provided with the distribution.
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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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from __future__ import (print_function, division, unicode_literals,
absolute_import)
import sklearn
import sklearn.base
import sklearn.exceptions
import sklearn.pipeline
from sympathy.api import node
from sympathy.api.nodeconfig import Ports, Tag, Tags
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.pipeline import PipelineDescriptor
from sylib.machinelearning.abstract_nodes import SyML_abstract
[docs]class Pipeline(node.Node):
name = 'Pipeline'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'pipeline.svg'
description = 'Applies one model on the output of another'
nodeid = 'org.sysess.sympathy.machinelearning.pipeline'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('models', 'models', n=(2, ))])
outputs = Ports([ModelPort('Output model', 'out-model')])
descriptor = PipelineDescriptor()
descriptor.name = name
descriptor.set_info([])
parameters = node.parameters()
parameters.set_string(
'names', value='', label='Model names',
description=(
'Comma separated list of model names, eg. Rescale, SVC. '
'If fewer names are given than models then default names '
'will be used.'))
parameters.set_boolean(
'flatten', value=True, label='Flatten',
description=(
'Flattens multiple pipeline objects into a single pipeline '
'containing all models'))
__doc__ = SyML_abstract.generate_docstring2(
description, parameters, inputs, outputs)
def execute(self, node_context):
out_model = node_context.output['out-model']
models = node_context.input.group('models')
names_raw = node_context.parameters['names'].value
flatten = node_context.parameters['flatten'].value
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()
if flatten and isinstance(desc, PipelineDescriptor):
names += [tpl[0] for tpl in desc.models]
descs += [tpl[1] for tpl in desc.models]
skls += [tpl[2] for tpl in desc.models]
else:
names.append(name_list[i])
descs.append(model.get_desc())
skls.append(model.get_skl())
skl = sklearn.pipeline.Pipeline(list(zip(names, skls)))
desc = self.__class__.descriptor.new(skl)
out_model.set_desc(desc)
out_model.set_skl(skl)
desc.set_steps(names, descs)
out_model.save()
[docs]class SplitPipeline(node.Node):
name = 'Pipeline decomposition'
author = 'Mathias Broxvall'
copyright = '(C) 2017 Combine Control Systems AB'
version = '0.1'
icon = 'pipeline_split.svg'
description = 'Pick out given model from a fitted pipeline'
nodeid = 'org.sysess.sympathy.machinelearning.pipeline_split'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('model', 'model')])
outputs = Ports([ModelPort('Output model', 'out-model')])
parameters = node.parameters()
parameters.set_string(
'name', value='A', label='Model name or index',
description=(
'Index (0 to N) or name of model to pick out from pipeline'))
__doc__ = SyML_abstract.generate_docstring2(
description, parameters, inputs, outputs)
def execute(self, node_context):
out_model = node_context.output['out-model']
model = node_context.input['model']
name = node_context.parameters['name'].value
model.load()
desc = model.get_desc()
out_desc = None, None
try:
index = int(name)
_, out_desc, out_skl = list(desc.get_models())[index]
except ValueError:
for n, d, s in desc.get_models():
if n == name:
out_desc, out_skl = d, s
break
if out_desc is not None:
out_model.set_desc(out_desc)
out_model.set_skl(out_skl)
out_model.save()
