# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# Sympathy for Data is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# Sympathy for Data is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Sympathy for Data. If not, see <http://www.gnu.org/licenses/>.
import warnings
import sklearn
# Ignore a warning from numpy>=1.15.2 when importing sklearn.ensemble
# See issue #2768 for details.
with warnings.catch_warnings():
warnings.simplefilter('ignore', DeprecationWarning)
import sklearn.ensemble
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.decisiontrees import IsolationForestDescriptor
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 IsolationForest(SyML_abstract, node.Node):
name = 'Isolation Forest'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'isolation_forest.svg'
description = (
'Predicts outliers based on minimum path length of random trees with '
'single nodes in the leafs.')
nodeid = 'org.sysess.sympathy.machinelearning.isolation_forest'
tags = Tags(Tag.MachineLearning.Unsupervised)
descriptor = IsolationForestDescriptor()
descriptor.name = name
info = [
[
"Model",
{'name': 'n_estimators',
'dispname': 'Number of estimators',
'type': IntType(min_value=0, default=100)},
{'name': 'max_samples',
'dispname': 'Number of samples',
'type': UnionType([
IntType(),
FloatType(),
StringSelectionType(['auto'])],
default='auto'),
'desc': (
'The number of samples to draw from X to train each base '
'estimator expressed as number of samples (int), or a '
'fraction of all samples (float). If "auto" then a maximum of '
'256 samples will be used (less when fewer input samples given)'
)},
{'name': 'contamination',
'dispname': 'Contamination',
'type': FloatType(min_value=0, max_value=0.5, default=0.1)},
{'name': 'max_features',
'dispname': 'Number of features',
'type': UnionType([
IntType(min_value=1),
FloatType(min_value=0.0, max_value=1.0)],
default=1.0)},
{'name': 'bootstrap',
'dispname': 'Bootstrap',
'type': BoolType(default=False)},
],
[
"Solver",
{'name': 'n_jobs',
'dispname': 'Number of jobs',
'type': IntType(min_value=-1, default=1)},
{'name': 'random_state',
'dispname': 'Random seed',
'type': UnionType([
IntType(), NoneType()], default=None)},
]
]
descriptor.set_info(info, doc_class=sklearn.ensemble.IsolationForest)
descriptor.set_attributes([
{'name': 'estimators_samples_', },
{'name': 'max_samples_'},
], doc_class=sklearn.ensemble.IsolationForest)
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)
from distutils.version import LooseVersion
if LooseVersion(sklearn.__version__) < LooseVersion('0.22'):
skl = sklearn.ensemble.IsolationForest(behaviour='new', **kwargs)
else:
skl = sklearn.ensemble.IsolationForest(**kwargs)
model.set_skl(skl)
model.save()
