# 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
# 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):
"""Random Forest classifiers are ensemble learning methods that build multiple decision trees
during training and combine their outputs to improve prediction accuracy and control
overfitting. Each tree is trained on a random subset of the data and features, making the
model robust to noise and capable of handling high-dimensional datasets. They are particularly
good for classification tasks with complex, nonlinear relationships and when interpretability
(like feature importance) is valuable.
The subset sample size is always the same as the original input sample size but the samples are
drawn with replacement if bootstrap is True (default).
"""
name = 'Random Forest Classifier'
author = 'Mathias Broxvall'
icon = 'forest.svg'
description = 'A random forest classifier model (meta estimator).'
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-bag 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()
