# Copyright (c) 2017, System Engineering Software Society
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#
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# modification, are permitted provided that the following conditions are met:
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# names of its contributors may be used to endorse or promote products
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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.cluster
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.descriptors import Descriptor
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 KMeansClustering(SyML_abstract, node.Node):
name = 'K-means Clustering'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'dataset_blobs.svg'
description = (
'Clusters data by trying to separate samples in n groups of equal '
'variance')
nodeid = 'org.sysess.sympathy.machinelearning.k_means'
tags = Tags(Tag.MachineLearning.Unsupervised)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_clusters',
'type': IntType(default=8)},
{'name': 'max_iter',
'type': IntType(default=300)},
{'name': 'n_init',
'type': IntType(default=10)},
{'name': 'init',
'type': StringSelectionType(
['k-means++', 'random'], default='k-means++')},
{'name': 'algorithm',
'type': StringSelectionType(
['auto', 'full', 'elkan'], default='auto')},
{'name': 'precompute_distances',
'type': UnionType([
StringSelectionType(['auto']), BoolType()], default='auto')},
{'name': 'tol',
'type': FloatType(min_value=0, default=1e-4)},
{'name': 'n_jobs',
'type': IntType(min_value=1)},
{'name': 'random_state',
'type': UnionType([NoneType(), IntType()], default=None)},
], doc_class=sklearn.cluster.KMeans)
descriptor.set_attributes([
{'name': attr_name} for attr_name in [
'cluster_centers_', 'labels_', 'inertia_'
]], doc_class=sklearn.cluster.KMeans)
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.cluster.KMeans(**kwargs)
model.set_skl(skl)
model.save()
[docs]class MiniBatchKMeansClustering(SyML_abstract, node.Node):
name = 'Mini-batch K-means Clustering'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'dataset_blobs.svg'
description = (
'Variant of the KMeans algorithm which uses mini-batches to reduce the '
'computation time')
nodeid = 'org.sysess.sympathy.machinelearning.mini_batch_k_means'
tags = Tags(Tag.MachineLearning.Unsupervised)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'n_clusters',
'type': IntType(default=8)},
{'name': 'max_iter',
'type': IntType(default=300)},
{'name': 'max_no_improvement',
'type': UnionType([IntType(), NoneType()], default=10)},
{'name': 'batch_size',
'type': IntType(default=100, min_value=1)},
{'name': 'init_size',
'type': IntType(default=300, min_value=1)},
{'name': 'n_init',
'type': IntType(default=3)},
{'name': 'init',
'type': StringSelectionType(
['k-means++', 'random'], default='k-means++')},
{'name': 'compute_labels',
'type': BoolType(default=True)},
{'name': 'reassignment_ratio',
'type': FloatType(default=0.01)},
{'name': 'tol',
'type': FloatType(min_value=0, default=1e-4)},
{'name': 'random_state',
'type': UnionType([NoneType(), IntType()], default=None)},
], doc_class=sklearn.cluster.MiniBatchKMeans)
descriptor.set_attributes([
{'name': attr_name} for attr_name in [
'cluster_centers_', 'labels_', 'inertia_'
]], doc_class=sklearn.cluster.MiniBatchKMeans)
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.cluster.MiniBatchKMeans(**kwargs)
model.set_skl(skl)
model.save()
