# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# Sympathy for Data is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# Sympathy for Data is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Sympathy for Data. If not, see <http://www.gnu.org/licenses/>.
import sklearn
import sklearn.cluster
from sympathy.api import node as synode
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, synode.Node):
name = 'K-means Clustering'
author = 'Mathias Broxvall'
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
info = [
[
"Model",
{'name': 'n_clusters',
'dispname': 'Number of clusters/centroids',
'type': IntType(default=8)},
{'name': 'n_init',
'dispname': 'Number of runs',
'type': IntType(default=10)},
{'name': 'init',
'dispname': 'Initialization method',
'type': StringSelectionType(
['k-means++', 'random'], default='k-means++')},
{'name': 'algorithm',
'dispname': 'K-means algorithm',
'type': StringSelectionType(
['auto', 'full', 'elkan'], default='auto')},
],
[
"Solver",
{'name': 'max_iter',
'dispname': 'Maximum number of iterations',
'type': IntType(default=300)},
{'name': 'tol',
'dispname': 'Tolerance',
'type': FloatType(min_value=0, default=1e-4)},
{'name': 'precompute_distances',
'dispname': 'Precompute distances',
'type': UnionType([
StringSelectionType(['auto']), BoolType()], default='auto')},
{'name': 'n_jobs',
'dispname': 'Number of jobs',
'type': IntType(min_value=-1, default=1)},
{'name': 'random_state',
'dispname': 'Random seed',
'type': UnionType([NoneType(), IntType()], default=None)},
]
]
descriptor.set_info(info, 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 = synode.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, synode.Node):
name = 'Mini-batch K-means Clustering'
author = 'Mathias Broxvall'
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
info = [
[
"Model",
{'name': 'n_clusters',
'dispname': 'Number of clusters/centroids',
'type': IntType(default=8)},
{'name': 'max_no_improvement',
'dispname': 'Consecutive batches without improvement',
'type': UnionType([IntType(), NoneType()], default=10)},
{'name': 'batch_size',
'dispname': 'Mini-batch size',
'type': IntType(default=100, min_value=1)},
{'name': 'init',
'dispname': 'Initialization method',
'type': StringSelectionType(
['k-means++', 'random'], default='k-means++')},
{'name': 'compute_labels',
'dispname': 'Compute label assignment',
'type': BoolType(default=True)},
],
[
"Solver",
{'name': 'max_iter',
'dispname': 'Maximum number of iterations',
'type': IntType(default=300)},
{'name': 'tol',
'dispname': 'Tolerance',
'type': FloatType(min_value=0, default=1e-4)},
{'name': 'init_size',
'dispname': 'Number of random samples',
'type': IntType(default=300, min_value=1)},
{'name': 'n_init',
'dispname': 'Number of random initializations',
'type': IntType(default=3)},
{'name': 'reassignment_ratio',
'dispname': 'Reassignment ratio',
'type': FloatType(default=0.01)},
{'name': 'random_state',
'dispname': 'Random seed',
'type': UnionType([NoneType(), IntType()], default=None)},
]
]
descriptor.set_info(info, 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 = synode.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()
