# 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 sklearn
import sklearn.feature_extraction
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.count_vectorizer import CountVectorizerDescriptor
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 StringListType
from sylib.machinelearning.descriptors import StringSelectionType
from sylib.machinelearning.descriptors import StringType
from sylib.machinelearning.descriptors import UnionType
from packaging import version as pversion
sklearn_version = pversion.parse(sklearn.__version__)
[docs]
class CountVectorizer(SyML_abstract, node.Node):
name = 'Text Count Vectorizer'
author = 'Mathias Broxvall'
icon = 'count_vectorizer.svg'
description = (
'Convert a collection of text documents to a matrix of token counts')
nodeid = 'org.sysess.sympathy.machinelearning.count_vectorizer'
tags = Tags(Tag.MachineLearning.Processing)
descriptor = CountVectorizerDescriptor()
descriptor.name = name
info = [
[
"Model",
{'name': 'encoding',
'dispname': 'Encoding',
'type': StringType(default='utf-8')},
{'name': 'decode_error',
'dispname': 'Decoding error behavior',
'type': StringSelectionType(['strict', 'ignore', 'replace'],
default='strict')},
{'name': 'strip_accents',
'dispname': 'Strip accents',
'type': UnionType([StringSelectionType(['ascii', 'unicode']),
NoneType()], default=None)},
{'name': 'lowercase',
'dispname': 'Lowercase',
'type': BoolType(default=True)},
],
[
"Advanced",
{'name': 'analyzer',
'dispname': 'Analyzer',
'type': StringSelectionType(['word', 'char', 'char_wb'],
default='word')},
{'name': 'ngram_range',
'dispname': 'N-gram range',
'type': IntListType(min_value=1, min_length=2, max_length=2,
default=[1, 3])},
{'name': 'stop_words',
'dispname': 'Stop words',
'type': UnionType([StringSelectionType('english'),
StringListType(min_length=1),
NoneType()], default='english')},
{'name': 'max_df',
'dispname': 'Maximum document frequency',
'type': UnionType([
IntType(min_value=0),
FloatType(min_value=0.0, max_value=1.0)], default=1.0)},
{'name': 'min_df',
'dispname': 'Minimum document frequency',
'type': UnionType([
IntType(min_value=0),
FloatType(min_value=0.0, max_value=1.0)], default=0.0)},
{'name': 'max_features',
'dispname': 'Maximum features',
'type': UnionType([IntType(min_value=1),
NoneType()], default=None)},
{'name': 'binary',
'dispname': 'Binary',
'type': BoolType(default=False)},
# {'name': 'token_pattern',
# 'type': UnionType([StringType(), NoneType()], default=None)},
]
]
descriptor.set_info(
info, doc_class=sklearn.feature_extraction.text.CountVectorizer)
descriptor.set_attributes([
{'name': attr_name} for attr_name in [
'vocabulary_', 'stop_words_'
]], doc_class=sklearn.feature_extraction.text.CountVectorizer)
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)
# Test for tuple requirement from sklearn 1.2.0
params_120 = sklearn_version >= pversion.Version('1.2.0')
if params_120:
kwargs["ngram_range"] = tuple(kwargs["ngram_range"])
skl = sklearn.feature_extraction.text.CountVectorizer(**kwargs)
model.set_skl(skl)
model.save()
