# Copyright (c) 2017, System Engineering Software Society
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"""
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.neural_network
from sympathy.api import node
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.descriptors import *
from sylib.machinelearning.decisiontrees import *
from sylib.machinelearning.abstract_nodes import SyML_abstract
from sylib.machinelearning.neuralnetwork import MLPClassifierDescriptor
[docs]class MLPClassifier(SyML_abstract, node.Node):
name = 'Multi-Layer Perceptron Classifier'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'neuralnetwork.svg'
description = 'Multi-layer perceptron classifier'
nodeid = 'org.sysess.sympathy.machinelearning.mlp__classifier'
tags = Tags(Tag.MachineLearning.Supervised)
descriptor = MLPClassifierDescriptor()
descriptor.name = name
descriptor.set_info([
["Architecture",
{'name': 'max_iter', 'type': IntType(default=200, min_value=0)},
{'name': 'hidden_layer_sizes',
'type': IntListType(default=[100], min_value=1)
},
{'name': 'activation',
'type': StringSelectionType([
'identity', 'logistic', 'tanh', 'relu'], default='relu')},
],
["Solving methods",
{'name': 'solver',
'type': StringSelectionType([
'lbfgs', 'sgd', 'adam'], default='adam')},
{'name': 'batch_size',
'type': UnionType([IntType(min_value=1),
StringSelectionType(['auto'])], default='auto')},
{'name': 'learning_rate',
'type': StringSelectionType(['constant', 'invscaling', 'adaptive'])},
{'name': 'shuffle',
'type': BoolType(default=True)},
{'name': 'early_stopping',
'type': BoolType(default=False)},
{'name': 'validation_fraction',
'type': FloatType(default=0.1, min_value=0.0, max_value=1.0)},
],
["Solver parameters",
{'name': 'alpha',
'type': FloatType(default=1e-5, min_value=0.0)},
{'name': 'tol', 'type': FloatType(default=1e-4, min_value=0.0)},
{'name': 'learning_rate_init',
'type': FloatType(default=1e-3, min_value=0.0)},
{'name': 'power_t', 'type': FloatType(default=0.5, min_value=0.0)},
{'name': 'momentum',
'type': FloatType(default=0.9, min_value=0.0, max_value=1.0)},
{'name': 'nesterovs_momentum',
'type': BoolType(default=True)},
{'name': 'beta_1',
'type': FloatType(default=0.9, min_value=0.0, max_value=1.0)},
{'name': 'beta_2',
'type': FloatType(default=0.999, min_value=0.0, max_value=1.0)},
{'name': 'epsilon', 'type': FloatType(default=1e-8, min_value=0.0)},
],
["Model state",
{'name': 'random_state',
'type': UnionType([NoneType(), IntType()], default=None)},
{'name': 'warm_start', 'type': BoolType(default=False)},
]
], doc_class = sklearn.neural_network.MLPClassifier)
descriptor.set_attributes([
{'name': attr_name} for attr_name in [
'classes_', 'loss_', 'coefs_', 'intercepts_', 'n_iter_',
'n_layers_', 'n_outputs_', 'out_activation_',
]], doc_class = sklearn.neural_network.MLPClassifier)
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.neural_network.MLPClassifier(**kwargs)
model.set_skl(skl)
model.save()