# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# SYMPATHY FOR DATA COMMERCIAL LICENSE
# You should have received a link to the License with Sympathy for Data.
import inspect
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.decisiontrees import RandomForestDescriptor
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 RandomForestClassifier(SyML_abstract, node.Node):
name = 'Random Forest Classifier'
author = 'Mathias Broxvall'
icon = 'forest.svg'
description = (
'A random forest is a meta estimator that fits a number of decision '
'tree classifiers on various sub-samples of the dataset and use '
'averaging to improve the predictive accuracy and control '
'over-fitting. The sub-sample size is always the same as the original '
'input sample size but the samples are drawn with replacement if '
'bootstrap is True (default).')
nodeid = 'org.sysess.sympathy.machinelearning.random_forest_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.ensemble.RandomForestClassifier).parameters
)
descriptor = RandomForestDescriptor()
descriptor.name = name
info = [
[
"Basic",
{'name': 'n_estimators',
'dispname': 'Trees in forest',
'type': IntType(min_value=1, default=3)},
{'name': 'criterion',
'dispname': 'Split quality criterion',
'type': StringSelectionType(['gini', 'entropy'])},
{'name': 'bootstrap',
'dispname': 'Bootstrap',
'type': BoolType(default=True)},
{'name': 'oob_score',
'dispname': 'Use out-of-bad samples',
'type': BoolType(default=True)},
{'name': 'n_jobs',
'dispname': 'Number of jobs',
'type': IntType(min_value=-1, default=1)},
],
[
"Node growth",
{'name': 'max_features',
'dispname': 'Maximum number of features',
'type': UnionType([
IntType(min_value=1),
FloatType(min_value=0.0, max_value=1.0),
StringSelectionType([
'auto', 'sqrt', 'log2']),
NoneType()], default="None")},
{'name': 'max_depth',
'dispname': 'Maximum tree depth',
'type': UnionType([IntType(min_value=1), NoneType()])},
{'name': 'min_samples_split',
'dispname': 'Minimum samples for split',
'type': UnionType([
IntType(min_value=1),
FloatType(min_value=0.0, max_value=1.0)
], default="2")},
{'name': 'min_samples_leaf',
'dispname': 'Minimum number of samples for leaf node',
'type': UnionType([
IntType(min_value=1),
FloatType(min_value=0.0, max_value=1.0)
], default="1")},
{'name': 'min_weight_fraction_leaf',
'dispname': 'Minimum leaf weight fraction',
'type': FloatType(min_value=0.0, default=0.0)},
{'name': 'max_leaf_nodes',
'dispname': 'Maximum leaf nodes',
'type': UnionType([IntType(min_value=0),
NoneType()], default="None")},
{'name': 'min_impurity_split',
'dispname': 'Growth threshold',
'type': FloatType(min_value=0.0, default=1e-7),
'deprecated': param_impurity_decrease},
],
[
"Model state",
{'name': 'random_state',
'dispname': 'Random Seed',
'type': UnionType([NoneType(), IntType()], default=None)},
{'name': 'warm_start',
'dispname': 'Warm start',
'type': BoolType(default=False)},
],
]
if param_impurity_decrease:
info[1].append({'name': 'min_impurity_decrease',
'dispname': 'Minimum impurity decrease',
'type': FloatType(default=0)})
descriptor.set_info(info,
doc_class=sklearn.ensemble.RandomForestClassifier)
descriptor.set_attributes([
{'name': 'classes_'},
{'name': 'feature_importances_', 'cnames': names_from_x},
{'name': 'n_classes_'},
{'name': 'n_features_'},
{'name': 'n_outputs_'},
{'name': 'oob_score_'},
{'name': 'oob_decision_function_'},
], doc_class=sklearn.ensemble.RandomForestClassifier)
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.ensemble.RandomForestClassifier(**kwargs)
model.set_skl(skl)
model.save()
