# Copyright (c) 2017, System Engineering Software Society
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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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
import sklearn.neural_network
from sympathy.api import node
from sympathy.api.nodeconfig import Ports, Tag, Tags
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.abstract_nodes import SyML_abstract
from sylib.machinelearning.neuralnetwork import MLPClassifierDescriptor
from sylib.machinelearning.descriptors import BoolType
from sylib.machinelearning.descriptors import FloatType
from sylib.machinelearning.descriptors import IntListType
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 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()
