# 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 inspect
import sklearn
import 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 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'
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)
# Test for existance of 'impurity_decrease' parameter (scikit-learn 0.19+)
param_impurity_decrease = (
'min_impurity_decrease' in inspect.signature(
sklearn.tree.DecisionTreeClassifier.__init__).parameters
)
descriptor = DecisionTreeDescriptor()
descriptor.name = name
info = [
[
"Tree options",
{'name': 'max_depth',
'dispname': 'Maximum tree depth',
'type': UnionType([IntType(min_value=1), NoneType()], default=3)},
{'name': 'criterion',
'dispname': 'Split quality criterion',
'type': StringSelectionType(['gini', 'entropy'])},
{'name': 'max_features',
'dispname': 'Number of features to consider',
'type': UnionType([IntType(min_value=1),
FloatType(min_value=0, max_value=1),
NoneType(),
StringSelectionType(['auto', 'sqrt', 'log2'])],
default=None)},
{'name': 'min_samples_split',
'dispname': 'Minimum samples required to split',
'type': UnionType([IntType(min_value=0), FloatType(
min_value=0, max_value=1)], default=2)},
{'name': 'min_samples_leaf',
'dispname': 'Minimum samples required for leaf node',
'type': UnionType([IntType(min_value=0), FloatType(
min_value=0, max_value=1)], default=1)},
{'name': 'max_leaf_nodes',
'dispname': 'Maximum of leaf nodes',
'type': UnionType([IntType(min_value=0), NoneType()],
default=None)},
],
[
"Advanced options",
{'name': 'min_weight_fraction_leaf',
'dispname': 'Min. weighted fraction of weights for leaf node',
'type': FloatType(default=0.)},
{'name': 'splitter',
'dispname': 'Splitting strategy',
'type': StringSelectionType(['best', 'random'])},
{'name': 'min_impurity_decrease',
'dispname': 'Node splitting threshold',
'type': FloatType(default=0.)},
],
[
"Model state",
# {'name': 'presort',
# 'dispname': 'Presort data',
# 'type': BoolType(default=False)},
{'name': 'random_state',
'dispname': 'Random seed',
'type': UnionType([NoneType(), IntType()], default=None)},
]
]
descriptor.set_info(info, 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()
