1108 lines
37 KiB
Python
1108 lines
37 KiB
Python
# =============================================================================
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# Federal University of Rio Grande do Sul (UFRGS)
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# Connectionist Artificial Intelligence Laboratory (LIAC)
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# Renato de Pontes Pereira - rppereira@inf.ufrgs.br
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# =============================================================================
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# Copyright (c) 2011 Renato de Pontes Pereira, renato.ppontes at gmail dot com
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# =============================================================================
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'''
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The liac-arff module implements functions to read and write ARFF files in
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Python. It was created in the Connectionist Artificial Intelligence Laboratory
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(LIAC), which takes place at the Federal University of Rio Grande do Sul
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(UFRGS), in Brazil.
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ARFF (Attribute-Relation File Format) is an file format specially created for
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describe datasets which are commonly used for machine learning experiments and
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software. This file format was created to be used in Weka, the best
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representative software for machine learning automated experiments.
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An ARFF file can be divided into two sections: header and data. The Header
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describes the metadata of the dataset, including a general description of the
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dataset, its name and its attributes. The source below is an example of a
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header section in a XOR dataset::
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%
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% XOR Dataset
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%
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% Created by Renato Pereira
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% rppereira@inf.ufrgs.br
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% http://inf.ufrgs.br/~rppereira
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%
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%
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@RELATION XOR
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@ATTRIBUTE input1 REAL
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@ATTRIBUTE input2 REAL
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@ATTRIBUTE y REAL
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The Data section of an ARFF file describes the observations of the dataset, in
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the case of XOR dataset::
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@DATA
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0.0,0.0,0.0
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0.0,1.0,1.0
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1.0,0.0,1.0
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1.0,1.0,0.0
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%
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%
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%
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Notice that several lines are starting with an ``%`` symbol, denoting a
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comment, thus, lines with ``%`` at the beginning will be ignored, except by the
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description part at the beginning of the file. The declarations ``@RELATION``,
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``@ATTRIBUTE``, and ``@DATA`` are all case insensitive and obligatory.
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For more information and details about the ARFF file description, consult
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http://www.cs.waikato.ac.nz/~ml/weka/arff.html
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ARFF Files in Python
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~~~~~~~~~~~~~~~~~~~~
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This module uses built-ins python objects to represent a deserialized ARFF
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file. A dictionary is used as the container of the data and metadata of ARFF,
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and have the following keys:
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- **description**: (OPTIONAL) a string with the description of the dataset.
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- **relation**: (OBLIGATORY) a string with the name of the dataset.
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- **attributes**: (OBLIGATORY) a list of attributes with the following
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template::
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(attribute_name, attribute_type)
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the attribute_name is a string, and attribute_type must be an string
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or a list of strings.
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- **data**: (OBLIGATORY) a list of data instances. Each data instance must be
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a list with values, depending on the attributes.
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The above keys must follow the case which were described, i.e., the keys are
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case sensitive. The attribute type ``attribute_type`` must be one of these
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strings (they are not case sensitive): ``NUMERIC``, ``INTEGER``, ``REAL`` or
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``STRING``. For nominal attributes, the ``atribute_type`` must be a list of
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strings.
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In this format, the XOR dataset presented above can be represented as a python
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object as::
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xor_dataset = {
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'description': 'XOR Dataset',
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'relation': 'XOR',
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'attributes': [
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('input1', 'REAL'),
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('input2', 'REAL'),
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('y', 'REAL'),
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],
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'data': [
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[0.0, 0.0, 0.0],
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[0.0, 1.0, 1.0],
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[1.0, 0.0, 1.0],
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[1.0, 1.0, 0.0]
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]
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}
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Features
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~~~~~~~~
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This module provides several features, including:
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- Read and write ARFF files using python built-in structures, such dictionaries
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and lists;
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- Supports `scipy.sparse.coo <http://docs.scipy
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.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html#scipy.sparse.coo_matrix>`_
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and lists of dictionaries as used by SVMLight
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- Supports the following attribute types: NUMERIC, REAL, INTEGER, STRING, and
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NOMINAL;
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- Has an interface similar to other built-in modules such as ``json``, or
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``zipfile``;
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- Supports read and write the descriptions of files;
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- Supports missing values and names with spaces;
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- Supports unicode values and names;
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- Fully compatible with Python 2.7+, Python 3.5+, pypy and pypy3;
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- Under `MIT License <http://opensource.org/licenses/MIT>`_
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'''
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__author__ = 'Renato de Pontes Pereira, Matthias Feurer, Joel Nothman'
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__author_email__ = ('renato.ppontes@gmail.com, '
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'feurerm@informatik.uni-freiburg.de, '
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'joel.nothman@gmail.com')
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__version__ = '2.4.0'
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import re
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import csv
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from typing import TYPE_CHECKING
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from typing import Optional, List, Dict, Any, Iterator, Union, Tuple
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# CONSTANTS ===================================================================
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_SIMPLE_TYPES = ['NUMERIC', 'REAL', 'INTEGER', 'STRING']
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_TK_DESCRIPTION = '%'
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_TK_COMMENT = '%'
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_TK_RELATION = '@RELATION'
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_TK_ATTRIBUTE = '@ATTRIBUTE'
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_TK_DATA = '@DATA'
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_RE_RELATION = re.compile(r'^([^\{\}%,\s]*|\".*\"|\'.*\')$', re.UNICODE)
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_RE_ATTRIBUTE = re.compile(r'^(\".*\"|\'.*\'|[^\{\}%,\s]*)\s+(.+)$', re.UNICODE)
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_RE_QUOTE_CHARS = re.compile(r'["\'\\\s%,\000-\031]', re.UNICODE)
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_RE_ESCAPE_CHARS = re.compile(r'(?=["\'\\%])|[\n\r\t\000-\031]')
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_RE_SPARSE_LINE = re.compile(r'^\s*\{.*\}\s*$', re.UNICODE)
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_RE_NONTRIVIAL_DATA = re.compile('["\'{}\\s]', re.UNICODE)
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ArffDenseDataType = Iterator[List]
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ArffSparseDataType = Tuple[List, ...]
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if TYPE_CHECKING:
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# typing_extensions is available when mypy is installed
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from typing_extensions import TypedDict
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class ArffContainerType(TypedDict):
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description: str
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relation: str
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attributes: List
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data: Union[ArffDenseDataType, ArffSparseDataType]
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else:
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ArffContainerType = Dict[str, Any]
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def _build_re_values():
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quoted_re = r'''
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" # open quote followed by zero or more of:
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(?:
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(?<!\\) # no additional backslash
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(?:\\\\)* # maybe escaped backslashes
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\\" # escaped quote
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\\[^"] # escaping a non-quote
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[^"\\] # non-quote char
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)*
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" # close quote
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'''
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# a value is surrounded by " or by ' or contains no quotables
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value_re = r'''(?:
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%s| # a value may be surrounded by "
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%s| # or by '
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[^,\s"'{}]+ # or may contain no characters requiring quoting
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)''' % (quoted_re,
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quoted_re.replace('"', "'"))
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# This captures (value, error) groups. Because empty values are allowed,
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# we cannot just look for empty values to handle syntax errors.
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# We presume the line has had ',' prepended...
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dense = re.compile(r'''(?x)
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, # may follow ','
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\s*
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((?=,)|$|{value_re}) # empty or value
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(\S.*) # error
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'''.format(value_re=value_re))
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# This captures (key, value) groups and will have an empty key/value
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# in case of syntax errors.
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# It does not ensure that the line starts with '{' or ends with '}'.
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sparse = re.compile(r'''(?x)
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(?:^\s*\{|,) # may follow ',', or '{' at line start
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\s*
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(\d+) # attribute key
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\s+
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(%(value_re)s) # value
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(?!}\s*$) # not an error if it's }$
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(?!^\s*{\s*}\s*$) # not an error if it's ^{}$
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\S.* # error
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''' % {'value_re': value_re})
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return dense, sparse
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_RE_DENSE_VALUES, _RE_SPARSE_KEY_VALUES = _build_re_values()
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_ESCAPE_SUB_MAP = {
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'\\\\': '\\',
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'\\"': '"',
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"\\'": "'",
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'\\t': '\t',
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'\\n': '\n',
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'\\r': '\r',
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'\\b': '\b',
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'\\f': '\f',
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'\\%': '%',
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}
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_UNESCAPE_SUB_MAP = {chr(i): '\\%03o' % i for i in range(32)}
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_UNESCAPE_SUB_MAP.update({v: k for k, v in _ESCAPE_SUB_MAP.items()})
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_UNESCAPE_SUB_MAP[''] = '\\'
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_ESCAPE_SUB_MAP.update({'\\%d' % i: chr(i) for i in range(10)})
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def _escape_sub_callback(match):
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s = match.group()
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if len(s) == 2:
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try:
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return _ESCAPE_SUB_MAP[s]
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except KeyError:
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raise ValueError('Unsupported escape sequence: %s' % s)
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if s[1] == 'u':
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return chr(int(s[2:], 16))
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else:
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return chr(int(s[1:], 8))
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def _unquote(v):
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if v[:1] in ('"', "'"):
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return re.sub(r'\\([0-9]{1,3}|u[0-9a-f]{4}|.)', _escape_sub_callback,
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v[1:-1])
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elif v in ('?', ''):
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return None
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else:
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return v
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def _parse_values(s):
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'''(INTERNAL) Split a line into a list of values'''
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if not _RE_NONTRIVIAL_DATA.search(s):
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# Fast path for trivial cases (unfortunately we have to handle missing
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# values because of the empty string case :(.)
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return [None if s in ('?', '') else s
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for s in next(csv.reader([s]))]
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# _RE_DENSE_VALUES tokenizes despite quoting, whitespace, etc.
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values, errors = zip(*_RE_DENSE_VALUES.findall(',' + s))
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if not any(errors):
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return [_unquote(v) for v in values]
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if _RE_SPARSE_LINE.match(s):
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try:
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return {int(k): _unquote(v)
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for k, v in _RE_SPARSE_KEY_VALUES.findall(s)}
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except ValueError:
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# an ARFF syntax error in sparse data
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for match in _RE_SPARSE_KEY_VALUES.finditer(s):
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if not match.group(1):
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raise BadLayout('Error parsing %r' % match.group())
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raise BadLayout('Unknown parsing error')
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else:
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# an ARFF syntax error
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for match in _RE_DENSE_VALUES.finditer(s):
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if match.group(2):
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raise BadLayout('Error parsing %r' % match.group())
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raise BadLayout('Unknown parsing error')
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DENSE = 0 # Constant value representing a dense matrix
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COO = 1 # Constant value representing a sparse matrix in coordinate format
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LOD = 2 # Constant value representing a sparse matrix in list of
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# dictionaries format
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DENSE_GEN = 3 # Generator of dictionaries
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LOD_GEN = 4 # Generator of dictionaries
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_SUPPORTED_DATA_STRUCTURES = [DENSE, COO, LOD, DENSE_GEN, LOD_GEN]
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# EXCEPTIONS ==================================================================
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class ArffException(Exception):
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message: Optional[str] = None
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def __init__(self):
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self.line = -1
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def __str__(self):
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return self.message%self.line
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class BadRelationFormat(ArffException):
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'''Error raised when the relation declaration is in an invalid format.'''
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message = 'Bad @RELATION format, at line %d.'
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class BadAttributeFormat(ArffException):
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'''Error raised when some attribute declaration is in an invalid format.'''
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message = 'Bad @ATTRIBUTE format, at line %d.'
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class BadDataFormat(ArffException):
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'''Error raised when some data instance is in an invalid format.'''
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def __init__(self, value):
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super().__init__()
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self.message = (
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'Bad @DATA instance format in line %d: ' +
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('%s' % value)
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)
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class BadAttributeType(ArffException):
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'''Error raised when some invalid type is provided into the attribute
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declaration.'''
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message = 'Bad @ATTRIBUTE type, at line %d.'
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class BadAttributeName(ArffException):
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'''Error raised when an attribute name is provided twice the attribute
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declaration.'''
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def __init__(self, value, value2):
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super().__init__()
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self.message = (
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('Bad @ATTRIBUTE name %s at line' % value) +
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' %d, this name is already in use in line' +
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(' %d.' % value2)
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)
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class BadNominalValue(ArffException):
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'''Error raised when a value in used in some data instance but is not
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declared into it respective attribute declaration.'''
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def __init__(self, value):
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super().__init__()
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self.message = (
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('Data value %s not found in nominal declaration, ' % value)
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+ 'at line %d.'
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)
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class BadNominalFormatting(ArffException):
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'''Error raised when a nominal value with space is not properly quoted.'''
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def __init__(self, value):
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super().__init__()
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self.message = (
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('Nominal data value "%s" not properly quoted in line ' % value) +
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'%d.'
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)
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class BadNumericalValue(ArffException):
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'''Error raised when and invalid numerical value is used in some data
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instance.'''
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message = 'Invalid numerical value, at line %d.'
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class BadStringValue(ArffException):
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'''Error raise when a string contains space but is not quoted.'''
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message = 'Invalid string value at line %d.'
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class BadLayout(ArffException):
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'''Error raised when the layout of the ARFF file has something wrong.'''
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message = 'Invalid layout of the ARFF file, at line %d.'
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def __init__(self, msg=''):
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super().__init__()
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if msg:
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self.message = BadLayout.message + ' ' + msg.replace('%', '%%')
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class BadObject(ArffException):
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'''Error raised when the object representing the ARFF file has something
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wrong.'''
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def __init__(self, msg='Invalid object.'):
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self.msg = msg
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def __str__(self):
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return '%s' % self.msg
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# =============================================================================
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# INTERNAL ====================================================================
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def _unescape_sub_callback(match):
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return _UNESCAPE_SUB_MAP[match.group()]
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|
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def encode_string(s):
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if _RE_QUOTE_CHARS.search(s):
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return "'%s'" % _RE_ESCAPE_CHARS.sub(_unescape_sub_callback, s)
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return s
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class EncodedNominalConversor:
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def __init__(self, values):
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self.values = {v: i for i, v in enumerate(values)}
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self.values[0] = 0
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def __call__(self, value):
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try:
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return self.values[value]
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except KeyError:
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raise BadNominalValue(value)
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|
|
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class NominalConversor:
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def __init__(self, values):
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self.values = set(values)
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self.zero_value = values[0]
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def __call__(self, value):
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if value not in self.values:
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if value == 0:
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# Sparse decode
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# See issue #52: nominals should take their first value when
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# unspecified in a sparse matrix. Naturally, this is consistent
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# with EncodedNominalConversor.
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return self.zero_value
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raise BadNominalValue(value)
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return str(value)
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|
|
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class DenseGeneratorData:
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'''Internal helper class to allow for different matrix types without
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making the code a huge collection of if statements.'''
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def decode_rows(self, stream, conversors):
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for row in stream:
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values = _parse_values(row)
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if isinstance(values, dict):
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if values and max(values) >= len(conversors):
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raise BadDataFormat(row)
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# XXX: int 0 is used for implicit values, not '0'
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values = [values[i] if i in values else 0 for i in
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range(len(conversors))]
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else:
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if len(values) != len(conversors):
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raise BadDataFormat(row)
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yield self._decode_values(values, conversors)
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|
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@staticmethod
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def _decode_values(values, conversors):
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try:
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values = [None if value is None else conversor(value)
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for conversor, value
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in zip(conversors, values)]
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except ValueError as exc:
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if 'float: ' in str(exc):
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raise BadNumericalValue()
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return values
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def encode_data(self, data, attributes):
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|
'''(INTERNAL) Encodes a line of data.
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|
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|
Data instances follow the csv format, i.e, attribute values are
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delimited by commas. After converted from csv.
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:param data: a list of values.
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|
:param attributes: a list of attributes. Used to check if data is valid.
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:return: a string with the encoded data line.
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'''
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|
current_row = 0
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for inst in data:
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if len(inst) != len(attributes):
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raise BadObject(
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|
'Instance %d has %d attributes, expected %d' %
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(current_row, len(inst), len(attributes))
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)
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new_data = []
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for value in inst:
|
|
if value is None or value == '' or value != value:
|
|
s = '?'
|
|
else:
|
|
s = encode_string(str(value))
|
|
new_data.append(s)
|
|
|
|
current_row += 1
|
|
yield ','.join(new_data)
|
|
|
|
|
|
class _DataListMixin:
|
|
"""Mixin to return a list from decode_rows instead of a generator"""
|
|
def decode_rows(self, stream, conversors):
|
|
return list(super().decode_rows(stream, conversors))
|
|
|
|
|
|
class Data(_DataListMixin, DenseGeneratorData):
|
|
pass
|
|
|
|
|
|
class COOData:
|
|
def decode_rows(self, stream, conversors):
|
|
data, rows, cols = [], [], []
|
|
for i, row in enumerate(stream):
|
|
values = _parse_values(row)
|
|
if not isinstance(values, dict):
|
|
raise BadLayout()
|
|
if not values:
|
|
continue
|
|
row_cols, values = zip(*sorted(values.items()))
|
|
try:
|
|
values = [value if value is None else conversors[key](value)
|
|
for key, value in zip(row_cols, values)]
|
|
except ValueError as exc:
|
|
if 'float: ' in str(exc):
|
|
raise BadNumericalValue()
|
|
raise
|
|
except IndexError:
|
|
# conversor out of range
|
|
raise BadDataFormat(row)
|
|
|
|
data.extend(values)
|
|
rows.extend([i] * len(values))
|
|
cols.extend(row_cols)
|
|
|
|
return data, rows, cols
|
|
|
|
def encode_data(self, data, attributes):
|
|
num_attributes = len(attributes)
|
|
new_data = []
|
|
current_row = 0
|
|
|
|
row = data.row
|
|
col = data.col
|
|
data = data.data
|
|
|
|
# Check if the rows are sorted
|
|
if not all(row[i] <= row[i + 1] for i in range(len(row) - 1)):
|
|
raise ValueError("liac-arff can only output COO matrices with "
|
|
"sorted rows.")
|
|
|
|
for v, col, row in zip(data, col, row):
|
|
if row > current_row:
|
|
# Add empty rows if necessary
|
|
while current_row < row:
|
|
yield " ".join(["{", ','.join(new_data), "}"])
|
|
new_data = []
|
|
current_row += 1
|
|
|
|
if col >= num_attributes:
|
|
raise BadObject(
|
|
'Instance %d has at least %d attributes, expected %d' %
|
|
(current_row, col + 1, num_attributes)
|
|
)
|
|
|
|
if v is None or v == '' or v != v:
|
|
s = '?'
|
|
else:
|
|
s = encode_string(str(v))
|
|
new_data.append("%d %s" % (col, s))
|
|
|
|
yield " ".join(["{", ','.join(new_data), "}"])
|
|
|
|
class LODGeneratorData:
|
|
def decode_rows(self, stream, conversors):
|
|
for row in stream:
|
|
values = _parse_values(row)
|
|
|
|
if not isinstance(values, dict):
|
|
raise BadLayout()
|
|
try:
|
|
yield {key: None if value is None else conversors[key](value)
|
|
for key, value in values.items()}
|
|
except ValueError as exc:
|
|
if 'float: ' in str(exc):
|
|
raise BadNumericalValue()
|
|
raise
|
|
except IndexError:
|
|
# conversor out of range
|
|
raise BadDataFormat(row)
|
|
|
|
def encode_data(self, data, attributes):
|
|
current_row = 0
|
|
|
|
num_attributes = len(attributes)
|
|
for row in data:
|
|
new_data = []
|
|
|
|
if len(row) > 0 and max(row) >= num_attributes:
|
|
raise BadObject(
|
|
'Instance %d has %d attributes, expected %d' %
|
|
(current_row, max(row) + 1, num_attributes)
|
|
)
|
|
|
|
for col in sorted(row):
|
|
v = row[col]
|
|
if v is None or v == '' or v != v:
|
|
s = '?'
|
|
else:
|
|
s = encode_string(str(v))
|
|
new_data.append("%d %s" % (col, s))
|
|
|
|
current_row += 1
|
|
yield " ".join(["{", ','.join(new_data), "}"])
|
|
|
|
class LODData(_DataListMixin, LODGeneratorData):
|
|
pass
|
|
|
|
|
|
def _get_data_object_for_decoding(matrix_type):
|
|
if matrix_type == DENSE:
|
|
return Data()
|
|
elif matrix_type == COO:
|
|
return COOData()
|
|
elif matrix_type == LOD:
|
|
return LODData()
|
|
elif matrix_type == DENSE_GEN:
|
|
return DenseGeneratorData()
|
|
elif matrix_type == LOD_GEN:
|
|
return LODGeneratorData()
|
|
else:
|
|
raise ValueError("Matrix type %s not supported." % str(matrix_type))
|
|
|
|
def _get_data_object_for_encoding(matrix):
|
|
# Probably a scipy.sparse
|
|
if hasattr(matrix, 'format'):
|
|
if matrix.format == 'coo':
|
|
return COOData()
|
|
else:
|
|
raise ValueError('Cannot guess matrix format!')
|
|
elif isinstance(matrix[0], dict):
|
|
return LODData()
|
|
else:
|
|
return Data()
|
|
|
|
# =============================================================================
|
|
|
|
# ADVANCED INTERFACE ==========================================================
|
|
class ArffDecoder:
|
|
'''An ARFF decoder.'''
|
|
|
|
def __init__(self):
|
|
'''Constructor.'''
|
|
self._conversors = []
|
|
self._current_line = 0
|
|
|
|
def _decode_comment(self, s):
|
|
'''(INTERNAL) Decodes a comment line.
|
|
|
|
Comments are single line strings starting, obligatorily, with the ``%``
|
|
character, and can have any symbol, including whitespaces or special
|
|
characters.
|
|
|
|
This method must receive a normalized string, i.e., a string without
|
|
padding, including the "\r\n" characters.
|
|
|
|
:param s: a normalized string.
|
|
:return: a string with the decoded comment.
|
|
'''
|
|
res = re.sub(r'^\%( )?', '', s)
|
|
return res
|
|
|
|
def _decode_relation(self, s):
|
|
'''(INTERNAL) Decodes a relation line.
|
|
|
|
The relation declaration is a line with the format ``@RELATION
|
|
<relation-name>``, where ``relation-name`` is a string. The string must
|
|
start with alphabetic character and must be quoted if the name includes
|
|
spaces, otherwise this method will raise a `BadRelationFormat` exception.
|
|
|
|
This method must receive a normalized string, i.e., a string without
|
|
padding, including the "\r\n" characters.
|
|
|
|
:param s: a normalized string.
|
|
:return: a string with the decoded relation name.
|
|
'''
|
|
_, v = s.split(' ', 1)
|
|
v = v.strip()
|
|
|
|
if not _RE_RELATION.match(v):
|
|
raise BadRelationFormat()
|
|
|
|
res = str(v.strip('"\''))
|
|
return res
|
|
|
|
def _decode_attribute(self, s):
|
|
'''(INTERNAL) Decodes an attribute line.
|
|
|
|
The attribute is the most complex declaration in an arff file. All
|
|
attributes must follow the template::
|
|
|
|
@attribute <attribute-name> <datatype>
|
|
|
|
where ``attribute-name`` is a string, quoted if the name contains any
|
|
whitespace, and ``datatype`` can be:
|
|
|
|
- Numerical attributes as ``NUMERIC``, ``INTEGER`` or ``REAL``.
|
|
- Strings as ``STRING``.
|
|
- Dates (NOT IMPLEMENTED).
|
|
- Nominal attributes with format:
|
|
|
|
{<nominal-name1>, <nominal-name2>, <nominal-name3>, ...}
|
|
|
|
The nominal names follow the rules for the attribute names, i.e., they
|
|
must be quoted if the name contains whitespaces.
|
|
|
|
This method must receive a normalized string, i.e., a string without
|
|
padding, including the "\r\n" characters.
|
|
|
|
:param s: a normalized string.
|
|
:return: a tuple (ATTRIBUTE_NAME, TYPE_OR_VALUES).
|
|
'''
|
|
_, v = s.split(' ', 1)
|
|
v = v.strip()
|
|
|
|
# Verify the general structure of declaration
|
|
m = _RE_ATTRIBUTE.match(v)
|
|
if not m:
|
|
raise BadAttributeFormat()
|
|
|
|
# Extracts the raw name and type
|
|
name, type_ = m.groups()
|
|
|
|
# Extracts the final name
|
|
name = str(name.strip('"\''))
|
|
|
|
# Extracts the final type
|
|
if type_[:1] == "{" and type_[-1:] == "}":
|
|
try:
|
|
type_ = _parse_values(type_.strip('{} '))
|
|
except Exception:
|
|
raise BadAttributeType()
|
|
if isinstance(type_, dict):
|
|
raise BadAttributeType()
|
|
|
|
else:
|
|
# If not nominal, verify the type name
|
|
type_ = str(type_).upper()
|
|
if type_ not in ['NUMERIC', 'REAL', 'INTEGER', 'STRING']:
|
|
raise BadAttributeType()
|
|
|
|
return (name, type_)
|
|
|
|
def _decode(self, s, encode_nominal=False, matrix_type=DENSE):
|
|
'''Do the job the ``encode``.'''
|
|
|
|
# Make sure this method is idempotent
|
|
self._current_line = 0
|
|
|
|
# If string, convert to a list of lines
|
|
if isinstance(s, str):
|
|
s = s.strip('\r\n ').replace('\r\n', '\n').split('\n')
|
|
|
|
# Create the return object
|
|
obj: ArffContainerType = {
|
|
'description': '',
|
|
'relation': '',
|
|
'attributes': [],
|
|
'data': []
|
|
}
|
|
attribute_names = {}
|
|
|
|
# Create the data helper object
|
|
data = _get_data_object_for_decoding(matrix_type)
|
|
|
|
# Read all lines
|
|
STATE = _TK_DESCRIPTION
|
|
s = iter(s)
|
|
for row in s:
|
|
self._current_line += 1
|
|
# Ignore empty lines
|
|
row = row.strip(' \r\n')
|
|
if not row: continue
|
|
|
|
u_row = row.upper()
|
|
|
|
# DESCRIPTION -----------------------------------------------------
|
|
if u_row.startswith(_TK_DESCRIPTION) and STATE == _TK_DESCRIPTION:
|
|
obj['description'] += self._decode_comment(row) + '\n'
|
|
# -----------------------------------------------------------------
|
|
|
|
# RELATION --------------------------------------------------------
|
|
elif u_row.startswith(_TK_RELATION):
|
|
if STATE != _TK_DESCRIPTION:
|
|
raise BadLayout()
|
|
|
|
STATE = _TK_RELATION
|
|
obj['relation'] = self._decode_relation(row)
|
|
# -----------------------------------------------------------------
|
|
|
|
# ATTRIBUTE -------------------------------------------------------
|
|
elif u_row.startswith(_TK_ATTRIBUTE):
|
|
if STATE != _TK_RELATION and STATE != _TK_ATTRIBUTE:
|
|
raise BadLayout()
|
|
|
|
STATE = _TK_ATTRIBUTE
|
|
|
|
attr = self._decode_attribute(row)
|
|
if attr[0] in attribute_names:
|
|
raise BadAttributeName(attr[0], attribute_names[attr[0]])
|
|
else:
|
|
attribute_names[attr[0]] = self._current_line
|
|
obj['attributes'].append(attr)
|
|
|
|
if isinstance(attr[1], (list, tuple)):
|
|
if encode_nominal:
|
|
conversor = EncodedNominalConversor(attr[1])
|
|
else:
|
|
conversor = NominalConversor(attr[1])
|
|
else:
|
|
CONVERSOR_MAP = {'STRING': str,
|
|
'INTEGER': lambda x: int(float(x)),
|
|
'NUMERIC': float,
|
|
'REAL': float}
|
|
conversor = CONVERSOR_MAP[attr[1]]
|
|
|
|
self._conversors.append(conversor)
|
|
# -----------------------------------------------------------------
|
|
|
|
# DATA ------------------------------------------------------------
|
|
elif u_row.startswith(_TK_DATA):
|
|
if STATE != _TK_ATTRIBUTE:
|
|
raise BadLayout()
|
|
|
|
break
|
|
# -----------------------------------------------------------------
|
|
|
|
# COMMENT ---------------------------------------------------------
|
|
elif u_row.startswith(_TK_COMMENT):
|
|
pass
|
|
# -----------------------------------------------------------------
|
|
else:
|
|
# Never found @DATA
|
|
raise BadLayout()
|
|
|
|
def stream():
|
|
for row in s:
|
|
self._current_line += 1
|
|
row = row.strip()
|
|
# Ignore empty lines and comment lines.
|
|
if row and not row.startswith(_TK_COMMENT):
|
|
yield row
|
|
|
|
# Alter the data object
|
|
obj['data'] = data.decode_rows(stream(), self._conversors)
|
|
if obj['description'].endswith('\n'):
|
|
obj['description'] = obj['description'][:-1]
|
|
|
|
return obj
|
|
|
|
def decode(self, s, encode_nominal=False, return_type=DENSE):
|
|
'''Returns the Python representation of a given ARFF file.
|
|
|
|
When a file object is passed as an argument, this method reads lines
|
|
iteratively, avoiding to load unnecessary information to the memory.
|
|
|
|
:param s: a string or file object with the ARFF file.
|
|
:param encode_nominal: boolean, if True perform a label encoding
|
|
while reading the .arff file.
|
|
:param return_type: determines the data structure used to store the
|
|
dataset. Can be one of `arff.DENSE`, `arff.COO`, `arff.LOD`,
|
|
`arff.DENSE_GEN` or `arff.LOD_GEN`.
|
|
Consult the sections on `working with sparse data`_ and `loading
|
|
progressively`_.
|
|
'''
|
|
try:
|
|
return self._decode(s, encode_nominal=encode_nominal,
|
|
matrix_type=return_type)
|
|
except ArffException as e:
|
|
e.line = self._current_line
|
|
raise e
|
|
|
|
|
|
class ArffEncoder:
|
|
'''An ARFF encoder.'''
|
|
|
|
def _encode_comment(self, s=''):
|
|
'''(INTERNAL) Encodes a comment line.
|
|
|
|
Comments are single line strings starting, obligatorily, with the ``%``
|
|
character, and can have any symbol, including whitespaces or special
|
|
characters.
|
|
|
|
If ``s`` is None, this method will simply return an empty comment.
|
|
|
|
:param s: (OPTIONAL) string.
|
|
:return: a string with the encoded comment line.
|
|
'''
|
|
if s:
|
|
return '%s %s'%(_TK_COMMENT, s)
|
|
else:
|
|
return '%s' % _TK_COMMENT
|
|
|
|
def _encode_relation(self, name):
|
|
'''(INTERNAL) Decodes a relation line.
|
|
|
|
The relation declaration is a line with the format ``@RELATION
|
|
<relation-name>``, where ``relation-name`` is a string.
|
|
|
|
:param name: a string.
|
|
:return: a string with the encoded relation declaration.
|
|
'''
|
|
for char in ' %{},':
|
|
if char in name:
|
|
name = '"%s"'%name
|
|
break
|
|
|
|
return '%s %s'%(_TK_RELATION, name)
|
|
|
|
def _encode_attribute(self, name, type_):
|
|
'''(INTERNAL) Encodes an attribute line.
|
|
|
|
The attribute follow the template::
|
|
|
|
@attribute <attribute-name> <datatype>
|
|
|
|
where ``attribute-name`` is a string, and ``datatype`` can be:
|
|
|
|
- Numerical attributes as ``NUMERIC``, ``INTEGER`` or ``REAL``.
|
|
- Strings as ``STRING``.
|
|
- Dates (NOT IMPLEMENTED).
|
|
- Nominal attributes with format:
|
|
|
|
{<nominal-name1>, <nominal-name2>, <nominal-name3>, ...}
|
|
|
|
This method must receive a the name of the attribute and its type, if
|
|
the attribute type is nominal, ``type`` must be a list of values.
|
|
|
|
:param name: a string.
|
|
:param type_: a string or a list of string.
|
|
:return: a string with the encoded attribute declaration.
|
|
'''
|
|
for char in ' %{},':
|
|
if char in name:
|
|
name = '"%s"'%name
|
|
break
|
|
|
|
if isinstance(type_, (tuple, list)):
|
|
type_tmp = ['%s' % encode_string(type_k) for type_k in type_]
|
|
type_ = '{%s}'%(', '.join(type_tmp))
|
|
|
|
return '%s %s %s'%(_TK_ATTRIBUTE, name, type_)
|
|
|
|
def encode(self, obj):
|
|
'''Encodes a given object to an ARFF file.
|
|
|
|
:param obj: the object containing the ARFF information.
|
|
:return: the ARFF file as an string.
|
|
'''
|
|
data = [row for row in self.iter_encode(obj)]
|
|
|
|
return '\n'.join(data)
|
|
|
|
def iter_encode(self, obj):
|
|
'''The iterative version of `arff.ArffEncoder.encode`.
|
|
|
|
This encodes iteratively a given object and return, one-by-one, the
|
|
lines of the ARFF file.
|
|
|
|
:param obj: the object containing the ARFF information.
|
|
:return: (yields) the ARFF file as strings.
|
|
'''
|
|
# DESCRIPTION
|
|
if obj.get('description', None):
|
|
for row in obj['description'].split('\n'):
|
|
yield self._encode_comment(row)
|
|
|
|
# RELATION
|
|
if not obj.get('relation'):
|
|
raise BadObject('Relation name not found or with invalid value.')
|
|
|
|
yield self._encode_relation(obj['relation'])
|
|
yield ''
|
|
|
|
# ATTRIBUTES
|
|
if not obj.get('attributes'):
|
|
raise BadObject('Attributes not found.')
|
|
|
|
attribute_names = set()
|
|
for attr in obj['attributes']:
|
|
# Verify for bad object format
|
|
if not isinstance(attr, (tuple, list)) or \
|
|
len(attr) != 2 or \
|
|
not isinstance(attr[0], str):
|
|
raise BadObject('Invalid attribute declaration "%s"'%str(attr))
|
|
|
|
if isinstance(attr[1], str):
|
|
# Verify for invalid types
|
|
if attr[1] not in _SIMPLE_TYPES:
|
|
raise BadObject('Invalid attribute type "%s"'%str(attr))
|
|
|
|
# Verify for bad object format
|
|
elif not isinstance(attr[1], (tuple, list)):
|
|
raise BadObject('Invalid attribute type "%s"'%str(attr))
|
|
|
|
# Verify attribute name is not used twice
|
|
if attr[0] in attribute_names:
|
|
raise BadObject('Trying to use attribute name "%s" for the '
|
|
'second time.' % str(attr[0]))
|
|
else:
|
|
attribute_names.add(attr[0])
|
|
|
|
yield self._encode_attribute(attr[0], attr[1])
|
|
yield ''
|
|
attributes = obj['attributes']
|
|
|
|
# DATA
|
|
yield _TK_DATA
|
|
if 'data' in obj:
|
|
data = _get_data_object_for_encoding(obj.get('data'))
|
|
yield from data.encode_data(obj.get('data'), attributes)
|
|
|
|
yield ''
|
|
|
|
# =============================================================================
|
|
|
|
# BASIC INTERFACE =============================================================
|
|
def load(fp, encode_nominal=False, return_type=DENSE):
|
|
'''Load a file-like object containing the ARFF document and convert it into
|
|
a Python object.
|
|
|
|
:param fp: a file-like object.
|
|
:param encode_nominal: boolean, if True perform a label encoding
|
|
while reading the .arff file.
|
|
:param return_type: determines the data structure used to store the
|
|
dataset. Can be one of `arff.DENSE`, `arff.COO`, `arff.LOD`,
|
|
`arff.DENSE_GEN` or `arff.LOD_GEN`.
|
|
Consult the sections on `working with sparse data`_ and `loading
|
|
progressively`_.
|
|
:return: a dictionary.
|
|
'''
|
|
decoder = ArffDecoder()
|
|
return decoder.decode(fp, encode_nominal=encode_nominal,
|
|
return_type=return_type)
|
|
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def loads(s, encode_nominal=False, return_type=DENSE):
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'''Convert a string instance containing the ARFF document into a Python
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|
object.
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|
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:param s: a string object.
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|
:param encode_nominal: boolean, if True perform a label encoding
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|
while reading the .arff file.
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|
:param return_type: determines the data structure used to store the
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|
dataset. Can be one of `arff.DENSE`, `arff.COO`, `arff.LOD`,
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|
`arff.DENSE_GEN` or `arff.LOD_GEN`.
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|
Consult the sections on `working with sparse data`_ and `loading
|
|
progressively`_.
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:return: a dictionary.
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|
'''
|
|
decoder = ArffDecoder()
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return decoder.decode(s, encode_nominal=encode_nominal,
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|
return_type=return_type)
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|
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def dump(obj, fp):
|
|
'''Serialize an object representing the ARFF document to a given file-like
|
|
object.
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|
|
|
:param obj: a dictionary.
|
|
:param fp: a file-like object.
|
|
'''
|
|
encoder = ArffEncoder()
|
|
generator = encoder.iter_encode(obj)
|
|
|
|
last_row = next(generator)
|
|
for row in generator:
|
|
fp.write(last_row + '\n')
|
|
last_row = row
|
|
fp.write(last_row)
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|
|
|
return fp
|
|
|
|
def dumps(obj):
|
|
'''Serialize an object representing the ARFF document, returning a string.
|
|
|
|
:param obj: a dictionary.
|
|
:return: a string with the ARFF document.
|
|
'''
|
|
encoder = ArffEncoder()
|
|
return encoder.encode(obj)
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|
# =============================================================================
|