# Copyright (c) 2017, System Engineering Software Society
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#
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# modification, are permitted provided that the following conditions are met:
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# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the System Engineering Software Society nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
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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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from __future__ import (print_function, division, unicode_literals,
absolute_import)
import copy
import PySide.QtGui as QtGui
import sklearn, sklearn.base, sklearn.exceptions, sklearn.pipeline
from sympathy.api import node
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags
from sympathy.api.exceptions import SyNodeError, SyDataError, sywarn
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.descriptors import *
from sylib.machinelearning.utility import *
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 System Engineering Software Society'
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())
desc = self.__class__.descriptor.new()
out_model.set_desc(desc)
out_model.set_skl(sklearn.pipeline.Pipeline(list(zip(names, skls))))
desc.models = list(zip(names, descs, skls))
out_model.save()
[docs]class SplitPipeline(node.Node):
name = 'Pipeline decomposition'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
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 = desc.models[index]
except ValueError:
for n, d, s in desc.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()