# Copyright (c) 2017, System Engineering Software Society
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# 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.
#
# 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
# ARE DISCLAIMED.
# IN NO EVENT SHALL SYSTEM ENGINEERING SOFTWARE SOCIETY BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
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, sklearn.tree
from sympathy.api import node
from sympathy.api.nodeconfig import Ports, Tag, Tags
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.decisiontrees import DecisionTreeDescriptor
from sylib.machinelearning.abstract_nodes import SyML_abstract
from sylib.machinelearning.utility import names_from_x
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 DecisionTreeClassifier(SyML_abstract, node.Node):
name = 'Decision Tree Classifier'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'tree.svg'
description = (
'Decision Trees (DTs) are a non-parametric supervised learning method'
'used for classification and regression. The goal is to create a model'
'that predicts the value of a target variable by learning simple'
'decision rules inferred from the data features.'
)
nodeid = 'org.sysess.sympathy.machinelearning.decision_tree_classifier'
tags = Tags(Tag.MachineLearning.Supervised)
descriptor = DecisionTreeDescriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'max_depth',
'type': UnionType(
[IntType(min_value=1), NoneType()], default=3)},
{'name': 'criterion',
'type': StringSelectionType(['gini', 'entropy']),},
{'name': 'splitter',
'type': StringSelectionType(['best', 'random']),},
{'name': 'max_features',
'type': UnionType([
IntType(min_value=1),
FloatType(min_value=0, max_value=1),
NoneType(),
StringSelectionType(['auto', 'sqrt', 'log2'])
], default=None),},
{'name': 'min_samples_split',
'type': UnionType([IntType(min_value=0),
FloatType(min_value=0, max_value=1)], default=2),},
{'name': 'min_samples_leaf',
'type': UnionType([IntType(min_value=0),
FloatType(min_value=0, max_value=1)], default=1),},
{'name': 'max_leaf_nodes',
'type': UnionType([IntType(min_value=0), NoneType()], default=None),},
{'name': 'min_impurity_split',
'type': FloatType(default=1e-7),
'deprecated': True,
},
{'name': 'min_impurity_decrease',
'type': FloatType(default=0),},
{'name': 'presort',
'type': BoolType(default=False),},
{'name': 'random_state',
'type': UnionType([NoneType(), IntType()], default=None)},
], doc_class=sklearn.tree.DecisionTreeClassifier)
descriptor.set_attributes([
{'name': 'classes_'},
{'name': 'feature_importances_', 'cnames': names_from_x},
{'name': 'max_features_'},
{'name': 'n_classes_'},
{'name': 'n_features_'},
{'name': 'n_outputs_'},
], doc_class = sklearn.tree.DecisionTreeClassifier)
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.tree.DecisionTreeClassifier(**kwargs)
model.set_skl(skl)
model.save()