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utils/venv/lib/python3.6/site-packages/scipy/io/arff/__init__.py
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"""
Module to read ARFF files, which are the standard data format for WEKA.
ARFF is a text file format which support numerical, string and data values.
The format can also represent missing data and sparse data.
Notes
-----
The ARFF support in ``scipy.io`` provides file reading functionality only.
For more extensive ARFF functionality, see `liac-arff
<https://github.com/renatopp/liac-arff>`_.
See the `WEKA website <http://weka.wikispaces.com/ARFF>`_
for more details about the ARFF format and available datasets.
"""
from __future__ import division, print_function, absolute_import
from .arffread import *
from . import arffread
__all__ = arffread.__all__
from scipy._lib._testutils import PytestTester
test = PytestTester(__name__)
del PytestTester
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# Last Change: Mon Aug 20 08:00 PM 2007 J
from __future__ import division, print_function, absolute_import
import re
import itertools
import datetime
from functools import partial
import numpy as np
from scipy._lib.six import next
"""A module to read arff files."""
__all__ = ['MetaData', 'loadarff', 'ArffError', 'ParseArffError']
# An Arff file is basically two parts:
# - header
# - data
#
# A header has each of its components starting by @META where META is one of
# the keyword (attribute of relation, for now).
# TODO:
# - both integer and reals are treated as numeric -> the integer info
# is lost!
# - Replace ValueError by ParseError or something
# We know can handle the following:
# - numeric and nominal attributes
# - missing values for numeric attributes
r_meta = re.compile(r'^\s*@')
# Match a comment
r_comment = re.compile(r'^%')
# Match an empty line
r_empty = re.compile(r'^\s+$')
# Match a header line, that is a line which starts by @ + a word
r_headerline = re.compile(r'^@\S*')
r_datameta = re.compile(r'^@[Dd][Aa][Tt][Aa]')
r_relation = re.compile(r'^@[Rr][Ee][Ll][Aa][Tt][Ii][Oo][Nn]\s*(\S*)')
r_attribute = re.compile(r'^@[Aa][Tt][Tt][Rr][Ii][Bb][Uu][Tt][Ee]\s*(..*$)')
# To get attributes name enclosed with ''
r_comattrval = re.compile(r"'(..+)'\s+(..+$)")
# To get normal attributes
r_wcomattrval = re.compile(r"(\S+)\s+(..+$)")
#-------------------------
# Module defined exception
#-------------------------
class ArffError(IOError):
pass
class ParseArffError(ArffError):
pass
#------------------
# Various utilities
#------------------
# An attribute is defined as @attribute name value
def parse_type(attrtype):
"""Given an arff attribute value (meta data), returns its type.
Expect the value to be a name."""
uattribute = attrtype.lower().strip()
if uattribute[0] == '{':
return 'nominal'
elif uattribute[:len('real')] == 'real':
return 'numeric'
elif uattribute[:len('integer')] == 'integer':
return 'numeric'
elif uattribute[:len('numeric')] == 'numeric':
return 'numeric'
elif uattribute[:len('string')] == 'string':
return 'string'
elif uattribute[:len('relational')] == 'relational':
return 'relational'
elif uattribute[:len('date')] == 'date':
return 'date'
else:
raise ParseArffError("unknown attribute %s" % uattribute)
def get_nominal(attribute):
"""If attribute is nominal, returns a list of the values"""
return attribute.split(',')
def read_data_list(ofile):
"""Read each line of the iterable and put it in a list."""
data = [next(ofile)]
if data[0].strip()[0] == '{':
raise ValueError("This looks like a sparse ARFF: not supported yet")
data.extend([i for i in ofile])
return data
def get_ndata(ofile):
"""Read the whole file to get number of data attributes."""
data = [next(ofile)]
loc = 1
if data[0].strip()[0] == '{':
raise ValueError("This looks like a sparse ARFF: not supported yet")
for i in ofile:
loc += 1
return loc
def maxnomlen(atrv):
"""Given a string containing a nominal type definition, returns the
string len of the biggest component.
A nominal type is defined as seomthing framed between brace ({}).
Parameters
----------
atrv : str
Nominal type definition
Returns
-------
slen : int
length of longest component
Examples
--------
maxnomlen("{floup, bouga, fl, ratata}") returns 6 (the size of
ratata, the longest nominal value).
>>> maxnomlen("{floup, bouga, fl, ratata}")
6
"""
nomtp = get_nom_val(atrv)
return max(len(i) for i in nomtp)
def get_nom_val(atrv):
"""Given a string containing a nominal type, returns a tuple of the
possible values.
A nominal type is defined as something framed between braces ({}).
Parameters
----------
atrv : str
Nominal type definition
Returns
-------
poss_vals : tuple
possible values
Examples
--------
>>> get_nom_val("{floup, bouga, fl, ratata}")
('floup', 'bouga', 'fl', 'ratata')
"""
r_nominal = re.compile('{(.+)}')
m = r_nominal.match(atrv)
if m:
return tuple(i.strip() for i in m.group(1).split(','))
else:
raise ValueError("This does not look like a nominal string")
def get_date_format(atrv):
r_date = re.compile(r"[Dd][Aa][Tt][Ee]\s+[\"']?(.+?)[\"']?$")
m = r_date.match(atrv)
if m:
pattern = m.group(1).strip()
# convert time pattern from Java's SimpleDateFormat to C's format
datetime_unit = None
if "yyyy" in pattern:
pattern = pattern.replace("yyyy", "%Y")
datetime_unit = "Y"
elif "yy":
pattern = pattern.replace("yy", "%y")
datetime_unit = "Y"
if "MM" in pattern:
pattern = pattern.replace("MM", "%m")
datetime_unit = "M"
if "dd" in pattern:
pattern = pattern.replace("dd", "%d")
datetime_unit = "D"
if "HH" in pattern:
pattern = pattern.replace("HH", "%H")
datetime_unit = "h"
if "mm" in pattern:
pattern = pattern.replace("mm", "%M")
datetime_unit = "m"
if "ss" in pattern:
pattern = pattern.replace("ss", "%S")
datetime_unit = "s"
if "z" in pattern or "Z" in pattern:
raise ValueError("Date type attributes with time zone not "
"supported, yet")
if datetime_unit is None:
raise ValueError("Invalid or unsupported date format")
return pattern, datetime_unit
else:
raise ValueError("Invalid or no date format")
def go_data(ofile):
"""Skip header.
the first next() call of the returned iterator will be the @data line"""
return itertools.dropwhile(lambda x: not r_datameta.match(x), ofile)
#----------------
# Parsing header
#----------------
def tokenize_attribute(iterable, attribute):
"""Parse a raw string in header (eg starts by @attribute).
Given a raw string attribute, try to get the name and type of the
attribute. Constraints:
* The first line must start with @attribute (case insensitive, and
space like characters before @attribute are allowed)
* Works also if the attribute is spread on multilines.
* Works if empty lines or comments are in between
Parameters
----------
attribute : str
the attribute string.
Returns
-------
name : str
name of the attribute
value : str
value of the attribute
next : str
next line to be parsed
Examples
--------
If attribute is a string defined in python as r"floupi real", will
return floupi as name, and real as value.
>>> iterable = iter([0] * 10) # dummy iterator
>>> tokenize_attribute(iterable, r"@attribute floupi real")
('floupi', 'real', 0)
If attribute is r"'floupi 2' real", will return 'floupi 2' as name,
and real as value.
>>> tokenize_attribute(iterable, r" @attribute 'floupi 2' real ")
('floupi 2', 'real', 0)
"""
sattr = attribute.strip()
mattr = r_attribute.match(sattr)
if mattr:
# atrv is everything after @attribute
atrv = mattr.group(1)
if r_comattrval.match(atrv):
name, type = tokenize_single_comma(atrv)
next_item = next(iterable)
elif r_wcomattrval.match(atrv):
name, type = tokenize_single_wcomma(atrv)
next_item = next(iterable)
else:
# Not sure we should support this, as it does not seem supported by
# weka.
raise ValueError("multi line not supported yet")
#name, type, next_item = tokenize_multilines(iterable, atrv)
else:
raise ValueError("First line unparsable: %s" % sattr)
if type == 'relational':
raise ValueError("relational attributes not supported yet")
return name, type, next_item
def tokenize_single_comma(val):
# XXX we match twice the same string (here and at the caller level). It is
# stupid, but it is easier for now...
m = r_comattrval.match(val)
if m:
try:
name = m.group(1).strip()
type = m.group(2).strip()
except IndexError:
raise ValueError("Error while tokenizing attribute")
else:
raise ValueError("Error while tokenizing single %s" % val)
return name, type
def tokenize_single_wcomma(val):
# XXX we match twice the same string (here and at the caller level). It is
# stupid, but it is easier for now...
m = r_wcomattrval.match(val)
if m:
try:
name = m.group(1).strip()
type = m.group(2).strip()
except IndexError:
raise ValueError("Error while tokenizing attribute")
else:
raise ValueError("Error while tokenizing single %s" % val)
return name, type
def read_header(ofile):
"""Read the header of the iterable ofile."""
i = next(ofile)
# Pass first comments
while r_comment.match(i):
i = next(ofile)
# Header is everything up to DATA attribute ?
relation = None
attributes = []
while not r_datameta.match(i):
m = r_headerline.match(i)
if m:
isattr = r_attribute.match(i)
if isattr:
name, type, i = tokenize_attribute(ofile, i)
attributes.append((name, type))
else:
isrel = r_relation.match(i)
if isrel:
relation = isrel.group(1)
else:
raise ValueError("Error parsing line %s" % i)
i = next(ofile)
else:
i = next(ofile)
return relation, attributes
#--------------------
# Parsing actual data
#--------------------
def safe_float(x):
"""given a string x, convert it to a float. If the stripped string is a ?,
return a Nan (missing value).
Parameters
----------
x : str
string to convert
Returns
-------
f : float
where float can be nan
Examples
--------
>>> safe_float('1')
1.0
>>> safe_float('1\\n')
1.0
>>> safe_float('?\\n')
nan
"""
if '?' in x:
return np.nan
else:
return float(x)
def safe_nominal(value, pvalue):
svalue = value.strip()
if svalue in pvalue:
return svalue
elif svalue == '?':
return svalue
else:
raise ValueError("%s value not in %s" % (str(svalue), str(pvalue)))
def safe_date(value, date_format, datetime_unit):
date_str = value.strip().strip("'").strip('"')
if date_str == '?':
return np.datetime64('NaT', datetime_unit)
else:
dt = datetime.datetime.strptime(date_str, date_format)
return np.datetime64(dt).astype("datetime64[%s]" % datetime_unit)
class MetaData(object):
"""Small container to keep useful information on a ARFF dataset.
Knows about attributes names and types.
Examples
--------
::
data, meta = loadarff('iris.arff')
# This will print the attributes names of the iris.arff dataset
for i in meta:
print(i)
# This works too
meta.names()
# Getting attribute type
types = meta.types()
Notes
-----
Also maintains the list of attributes in order, i.e. doing for i in
meta, where meta is an instance of MetaData, will return the
different attribute names in the order they were defined.
"""
def __init__(self, rel, attr):
self.name = rel
# We need the dictionary to be ordered
# XXX: may be better to implement an ordered dictionary
self._attributes = {}
self._attrnames = []
for name, value in attr:
tp = parse_type(value)
self._attrnames.append(name)
if tp == 'nominal':
self._attributes[name] = (tp, get_nom_val(value))
elif tp == 'date':
self._attributes[name] = (tp, get_date_format(value)[0])
else:
self._attributes[name] = (tp, None)
def __repr__(self):
msg = ""
msg += "Dataset: %s\n" % self.name
for i in self._attrnames:
msg += "\t%s's type is %s" % (i, self._attributes[i][0])
if self._attributes[i][1]:
msg += ", range is %s" % str(self._attributes[i][1])
msg += '\n'
return msg
def __iter__(self):
return iter(self._attrnames)
def __getitem__(self, key):
return self._attributes[key]
def names(self):
"""Return the list of attribute names."""
return self._attrnames
def types(self):
"""Return the list of attribute types."""
attr_types = [self._attributes[name][0] for name in self._attrnames]
return attr_types
def loadarff(f):
"""
Read an arff file.
The data is returned as a record array, which can be accessed much like
a dictionary of numpy arrays. For example, if one of the attributes is
called 'pressure', then its first 10 data points can be accessed from the
``data`` record array like so: ``data['pressure'][0:10]``
Parameters
----------
f : file-like or str
File-like object to read from, or filename to open.
Returns
-------
data : record array
The data of the arff file, accessible by attribute names.
meta : `MetaData`
Contains information about the arff file such as name and
type of attributes, the relation (name of the dataset), etc...
Raises
------
ParseArffError
This is raised if the given file is not ARFF-formatted.
NotImplementedError
The ARFF file has an attribute which is not supported yet.
Notes
-----
This function should be able to read most arff files. Not
implemented functionality include:
* date type attributes
* string type attributes
It can read files with numeric and nominal attributes. It cannot read
files with sparse data ({} in the file). However, this function can
read files with missing data (? in the file), representing the data
points as NaNs.
Examples
--------
>>> from scipy.io import arff
>>> from io import StringIO
>>> content = \"\"\"
... @relation foo
... @attribute width numeric
... @attribute height numeric
... @attribute color {red,green,blue,yellow,black}
... @data
... 5.0,3.25,blue
... 4.5,3.75,green
... 3.0,4.00,red
... \"\"\"
>>> f = StringIO(content)
>>> data, meta = arff.loadarff(f)
>>> data
array([(5.0, 3.25, 'blue'), (4.5, 3.75, 'green'), (3.0, 4.0, 'red')],
dtype=[('width', '<f8'), ('height', '<f8'), ('color', '|S6')])
>>> meta
Dataset: foo
\twidth's type is numeric
\theight's type is numeric
\tcolor's type is nominal, range is ('red', 'green', 'blue', 'yellow', 'black')
"""
if hasattr(f, 'read'):
ofile = f
else:
ofile = open(f, 'rt')
try:
return _loadarff(ofile)
finally:
if ofile is not f: # only close what we opened
ofile.close()
def _loadarff(ofile):
# Parse the header file
try:
rel, attr = read_header(ofile)
except ValueError as e:
msg = "Error while parsing header, error was: " + str(e)
raise ParseArffError(msg)
# Check whether we have a string attribute (not supported yet)
hasstr = False
for name, value in attr:
type = parse_type(value)
if type == 'string':
hasstr = True
meta = MetaData(rel, attr)
# XXX The following code is not great
# Build the type descriptor descr and the list of convertors to convert
# each attribute to the suitable type (which should match the one in
# descr).
# This can be used once we want to support integer as integer values and
# not as numeric anymore (using masked arrays ?).
acls2dtype = {'real': float, 'integer': float, 'numeric': float}
acls2conv = {'real': safe_float,
'integer': safe_float,
'numeric': safe_float}
descr = []
convertors = []
if not hasstr:
for name, value in attr:
type = parse_type(value)
if type == 'date':
date_format, datetime_unit = get_date_format(value)
descr.append((name, "datetime64[%s]" % datetime_unit))
convertors.append(partial(safe_date, date_format=date_format,
datetime_unit=datetime_unit))
elif type == 'nominal':
n = maxnomlen(value)
descr.append((name, 'S%d' % n))
pvalue = get_nom_val(value)
convertors.append(partial(safe_nominal, pvalue=pvalue))
else:
descr.append((name, acls2dtype[type]))
convertors.append(safe_float)
#dc.append(acls2conv[type])
#sdescr.append((name, acls2sdtype[type]))
else:
# How to support string efficiently ? Ideally, we should know the max
# size of the string before allocating the numpy array.
raise NotImplementedError("String attributes not supported yet, sorry")
ni = len(convertors)
def generator(row_iter, delim=','):
# TODO: this is where we are spending times (~80%). I think things
# could be made more efficiently:
# - We could for example "compile" the function, because some values
# do not change here.
# - The function to convert a line to dtyped values could also be
# generated on the fly from a string and be executed instead of
# looping.
# - The regex are overkill: for comments, checking that a line starts
# by % should be enough and faster, and for empty lines, same thing
# --> this does not seem to change anything.
# 'compiling' the range since it does not change
# Note, I have already tried zipping the converters and
# row elements and got slightly worse performance.
elems = list(range(ni))
for raw in row_iter:
# We do not abstract skipping comments and empty lines for
# performance reasons.
if r_comment.match(raw) or r_empty.match(raw):
continue
row = raw.split(delim)
yield tuple([convertors[i](row[i]) for i in elems])
a = generator(ofile)
# No error should happen here: it is a bug otherwise
data = np.fromiter(a, descr)
return data, meta
#-----
# Misc
#-----
def basic_stats(data):
nbfac = data.size * 1. / (data.size - 1)
return np.nanmin(data), np.nanmax(data), np.mean(data), np.std(data) * nbfac
def print_attribute(name, tp, data):
type = tp[0]
if type == 'numeric' or type == 'real' or type == 'integer':
min, max, mean, std = basic_stats(data)
print("%s,%s,%f,%f,%f,%f" % (name, type, min, max, mean, std))
else:
msg = name + ",{"
for i in range(len(tp[1])-1):
msg += tp[1][i] + ","
msg += tp[1][-1]
msg += "}"
print(msg)
def test_weka(filename):
data, meta = loadarff(filename)
print(len(data.dtype))
print(data.size)
for i in meta:
print_attribute(i, meta[i], data[i])
# make sure nose does not find this as a test
test_weka.__test__ = False
if __name__ == '__main__':
import sys
filename = sys.argv[1]
test_weka(filename)
utils/venv/lib/python3.6/site-packages/scipy/io/arff/setup.py
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from __future__ import division, print_function, absolute_import
def configuration(parent_package='io',top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('arff', parent_package, top_path)
config.add_data_dir('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
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% 1. Title: Iris Plants Database
%
% 2. Sources:
% (a) Creator: R.A. Fisher
% (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
% (c) Date: July, 1988
%
% 3. Past Usage:
% - Publications: too many to mention!!! Here are a few.
% 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
% Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
% to Mathematical Statistics" (John Wiley, NY, 1950).
% 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
% (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
% 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
% Structure and Classification Rule for Recognition in Partially Exposed
% Environments". IEEE Transactions on Pattern Analysis and Machine
% Intelligence, Vol. PAMI-2, No. 1, 67-71.
% -- Results:
% -- very low misclassification rates (0% for the setosa class)
% 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
% Transactions on Information Theory, May 1972, 431-433.
% -- Results:
% -- very low misclassification rates again
% 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
% conceptual clustering system finds 3 classes in the data.
%
% 4. Relevant Information:
% --- This is perhaps the best known database to be found in the pattern
% recognition literature. Fisher's paper is a classic in the field
% and is referenced frequently to this day. (See Duda & Hart, for
% example.) The data set contains 3 classes of 50 instances each,
% where each class refers to a type of iris plant. One class is
% linearly separable from the other 2; the latter are NOT linearly
% separable from each other.
% --- Predicted attribute: class of iris plant.
% --- This is an exceedingly simple domain.
%
% 5. Number of Instances: 150 (50 in each of three classes)
%
% 6. Number of Attributes: 4 numeric, predictive attributes and the class
%
% 7. Attribute Information:
% 1. sepal length in cm
% 2. sepal width in cm
% 3. petal length in cm
% 4. petal width in cm
% 5. class:
% -- Iris Setosa
% -- Iris Versicolour
% -- Iris Virginica
%
% 8. Missing Attribute Values: None
%
% Summary Statistics:
% Min Max Mean SD Class Correlation
% sepal length: 4.3 7.9 5.84 0.83 0.7826
% sepal width: 2.0 4.4 3.05 0.43 -0.4194
% petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
% petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
%
% 9. Class Distribution: 33.3% for each of 3 classes.
@RELATION iris
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength REAL
@ATTRIBUTE petalwidth REAL
@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}
@DATA
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
%
%
%
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/missing.arff
+8
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@@ -0,0 +1,8 @@
% This arff file contains some missing data
@relation missing
@attribute yop real
@attribute yap real
@data
1,5
2,4
?,?
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/nodata.arff
+11
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@@ -0,0 +1,11 @@
@RELATION iris
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength REAL
@ATTRIBUTE petalwidth REAL
@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}
@DATA
% This file has no data
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test1.arff
+10
View File
@@ -0,0 +1,10 @@
@RELATION test1
@ATTRIBUTE attr0 REAL
@ATTRIBUTE attr1 REAL
@ATTRIBUTE attr2 REAL
@ATTRIBUTE attr3 REAL
@ATTRIBUTE class {class0, class1, class2, class3}
@DATA
0.1, 0.2, 0.3, 0.4,class1
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test2.arff
+15
View File
@@ -0,0 +1,15 @@
@RELATION test2
@ATTRIBUTE attr0 REAL
@ATTRIBUTE attr1 real
@ATTRIBUTE attr2 integer
@ATTRIBUTE attr3 Integer
@ATTRIBUTE attr4 Numeric
@ATTRIBUTE attr5 numeric
@ATTRIBUTE attr6 string
@ATTRIBUTE attr7 STRING
@ATTRIBUTE attr8 {bla}
@ATTRIBUTE attr9 {bla, bla}
@DATA
0.1, 0.2, 0.3, 0.4,class1
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test3.arff
+6
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@@ -0,0 +1,6 @@
@RELATION test3
@ATTRIBUTE attr0 crap
@DATA
0.1, 0.2, 0.3, 0.4,class1
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test4.arff
+11
View File
@@ -0,0 +1,11 @@
@RELATION test5
@ATTRIBUTE attr0 REAL
@ATTRIBUTE attr1 REAL
@ATTRIBUTE attr2 REAL
@ATTRIBUTE attr3 REAL
@ATTRIBUTE class {class0, class1, class2, class3}
@DATA
0.1, 0.2, 0.3, 0.4,class1
-0.1, -0.2, -0.3, -0.4,class2
1, 2, 3, 4,class3
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test5.arff
+26
View File
@@ -0,0 +1,26 @@
@RELATION test4
@ATTRIBUTE attr0 REAL
@ATTRIBUTE attr1 REAL
@ATTRIBUTE attr2 REAL
@ATTRIBUTE attr3 REAL
@ATTRIBUTE class {class0, class1, class2, class3}
@DATA
% lsdflkjhaksjdhf
% lsdflkjhaksjdhf
0.1, 0.2, 0.3, 0.4,class1
% laksjdhf
% lsdflkjhaksjdhf
-0.1, -0.2, -0.3, -0.4,class2
% lsdflkjhaksjdhf
% lsdflkjhaksjdhf
% lsdflkjhaksjdhf
1, 2, 3, 4,class3
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test6.arff
+12
View File
@@ -0,0 +1,12 @@
@RELATION test6
@ATTRIBUTE attr0 REAL
@ATTRIBUTE attr1 REAL
@ATTRIBUTE attr2 REAL
@ATTRIBUTE attr3 REAL
@ATTRIBUTE class {C}
@DATA
0.1, 0.2, 0.3, 0.4,C
-0.1, -0.2, -0.3, -0.4,C
1, 2, 3, 4,C
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test7.arff
+15
View File
@@ -0,0 +1,15 @@
@RELATION test7
@ATTRIBUTE attr_year DATE yyyy
@ATTRIBUTE attr_month DATE yyyy-MM
@ATTRIBUTE attr_date DATE yyyy-MM-dd
@ATTRIBUTE attr_datetime_local DATE "yyyy-MM-dd HH:mm"
@ATTRIBUTE attr_datetime_missing DATE "yyyy-MM-dd HH:mm"
@DATA
1999,1999-01,1999-01-31,"1999-01-31 00:01",?
2004,2004-12,2004-12-01,"2004-12-01 23:59","2004-12-01 23:59"
1817,1817-04,1817-04-28,"1817-04-28 13:00",?
2100,2100-09,2100-09-10,"2100-09-10 12:00",?
2013,2013-11,2013-11-30,"2013-11-30 04:55","2013-11-30 04:55"
1631,1631-10,1631-10-15,"1631-10-15 20:04","1631-10-15 20:04"
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/data/test8.arff
+12
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@@ -0,0 +1,12 @@
@RELATION test8
@ATTRIBUTE attr_datetime_utc DATE "yyyy-MM-dd HH:mm Z"
@ATTRIBUTE attr_datetime_full DATE "yy-MM-dd HH:mm:ss z"
@DATA
"1999-01-31 00:01 UTC","99-01-31 00:01:08 +0430"
"2004-12-01 23:59 UTC","04-12-01 23:59:59 -0800"
"1817-04-28 13:00 UTC","17-04-28 13:00:33 +1000"
"2100-09-10 12:00 UTC","21-09-10 12:00:21 -0300"
"2013-11-30 04:55 UTC","13-11-30 04:55:48 -1100"
"1631-10-15 20:04 UTC","31-10-15 20:04:10 +0000"
utils/venv/lib/python3.6/site-packages/scipy/io/arff/tests/test_arffread.py
+259
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@@ -0,0 +1,259 @@
from __future__ import division, print_function, absolute_import
import datetime
import os
import sys
from os.path import join as pjoin
if sys.version_info[0] >= 3:
from io import StringIO
else:
from cStringIO import StringIO
import numpy as np
from numpy.testing import (assert_array_almost_equal,
assert_array_equal, assert_equal, assert_)
import pytest
from pytest import raises as assert_raises
from scipy.io.arff.arffread import loadarff
from scipy.io.arff.arffread import read_header, parse_type, ParseArffError
data_path = pjoin(os.path.dirname(__file__), 'data')
test1 = pjoin(data_path, 'test1.arff')
test2 = pjoin(data_path, 'test2.arff')
test3 = pjoin(data_path, 'test3.arff')
test4 = pjoin(data_path, 'test4.arff')
test5 = pjoin(data_path, 'test5.arff')
test6 = pjoin(data_path, 'test6.arff')
test7 = pjoin(data_path, 'test7.arff')
test8 = pjoin(data_path, 'test8.arff')
expect4_data = [(0.1, 0.2, 0.3, 0.4, 'class1'),
(-0.1, -0.2, -0.3, -0.4, 'class2'),
(1, 2, 3, 4, 'class3')]
expected_types = ['numeric', 'numeric', 'numeric', 'numeric', 'nominal']
missing = pjoin(data_path, 'missing.arff')
expect_missing_raw = np.array([[1, 5], [2, 4], [np.nan, np.nan]])
expect_missing = np.empty(3, [('yop', float), ('yap', float)])
expect_missing['yop'] = expect_missing_raw[:, 0]
expect_missing['yap'] = expect_missing_raw[:, 1]
class TestData(object):
def test1(self):
# Parsing trivial file with nothing.
self._test(test4)
def test2(self):
# Parsing trivial file with some comments in the data section.
self._test(test5)
def test3(self):
# Parsing trivial file with nominal attribute of 1 character.
self._test(test6)
def _test(self, test_file):
data, meta = loadarff(test_file)
for i in range(len(data)):
for j in range(4):
assert_array_almost_equal(expect4_data[i][j], data[i][j])
assert_equal(meta.types(), expected_types)
def test_filelike(self):
# Test reading from file-like object (StringIO)
f1 = open(test1)
data1, meta1 = loadarff(f1)
f1.close()
f2 = open(test1)
data2, meta2 = loadarff(StringIO(f2.read()))
f2.close()
assert_(data1 == data2)
assert_(repr(meta1) == repr(meta2))
@pytest.mark.skipif(sys.version_info < (3, 6),
reason='Passing path-like objects to IO functions requires Python >= 3.6')
def test_path(self):
# Test reading from `pathlib.Path` object
from pathlib import Path
with open(test1) as f1:
data1, meta1 = loadarff(f1)
data2, meta2 = loadarff(Path(test1))
assert_(data1 == data2)
assert_(repr(meta1) == repr(meta2))
class TestMissingData(object):
def test_missing(self):
data, meta = loadarff(missing)
for i in ['yop', 'yap']:
assert_array_almost_equal(data[i], expect_missing[i])
class TestNoData(object):
def test_nodata(self):
# The file nodata.arff has no data in the @DATA section.
# Reading it should result in an array with length 0.
nodata_filename = os.path.join(data_path, 'nodata.arff')
data, meta = loadarff(nodata_filename)
expected_dtype = np.dtype([('sepallength', '<f8'),
('sepalwidth', '<f8'),
('petallength', '<f8'),
('petalwidth', '<f8'),
('class', 'S15')])
assert_equal(data.dtype, expected_dtype)
assert_equal(data.size, 0)
class TestHeader(object):
def test_type_parsing(self):
# Test parsing type of attribute from their value.
ofile = open(test2)
rel, attrs = read_header(ofile)
ofile.close()
expected = ['numeric', 'numeric', 'numeric', 'numeric', 'numeric',
'numeric', 'string', 'string', 'nominal', 'nominal']
for i in range(len(attrs)):
assert_(parse_type(attrs[i][1]) == expected[i])
def test_badtype_parsing(self):
# Test parsing wrong type of attribute from their value.
ofile = open(test3)
rel, attrs = read_header(ofile)
ofile.close()
for name, value in attrs:
assert_raises(ParseArffError, parse_type, value)
def test_fullheader1(self):
# Parsing trivial header with nothing.
ofile = open(test1)
rel, attrs = read_header(ofile)
ofile.close()
# Test relation
assert_(rel == 'test1')
# Test numerical attributes
assert_(len(attrs) == 5)
for i in range(4):
assert_(attrs[i][0] == 'attr%d' % i)
assert_(attrs[i][1] == 'REAL')
# Test nominal attribute
assert_(attrs[4][0] == 'class')
assert_(attrs[4][1] == '{class0, class1, class2, class3}')
def test_dateheader(self):
ofile = open(test7)
rel, attrs = read_header(ofile)
ofile.close()
assert_(rel == 'test7')
assert_(len(attrs) == 5)
assert_(attrs[0][0] == 'attr_year')
assert_(attrs[0][1] == 'DATE yyyy')
assert_(attrs[1][0] == 'attr_month')
assert_(attrs[1][1] == 'DATE yyyy-MM')
assert_(attrs[2][0] == 'attr_date')
assert_(attrs[2][1] == 'DATE yyyy-MM-dd')
assert_(attrs[3][0] == 'attr_datetime_local')
assert_(attrs[3][1] == 'DATE "yyyy-MM-dd HH:mm"')
assert_(attrs[4][0] == 'attr_datetime_missing')
assert_(attrs[4][1] == 'DATE "yyyy-MM-dd HH:mm"')
def test_dateheader_unsupported(self):
ofile = open(test8)
rel, attrs = read_header(ofile)
ofile.close()
assert_(rel == 'test8')
assert_(len(attrs) == 2)
assert_(attrs[0][0] == 'attr_datetime_utc')
assert_(attrs[0][1] == 'DATE "yyyy-MM-dd HH:mm Z"')
assert_(attrs[1][0] == 'attr_datetime_full')
assert_(attrs[1][1] == 'DATE "yy-MM-dd HH:mm:ss z"')
class TestDateAttribute(object):
def setup_method(self):
self.data, self.meta = loadarff(test7)
def test_year_attribute(self):
expected = np.array([
'1999',
'2004',
'1817',
'2100',
'2013',
'1631'
], dtype='datetime64[Y]')
assert_array_equal(self.data["attr_year"], expected)
def test_month_attribute(self):
expected = np.array([
'1999-01',
'2004-12',
'1817-04',
'2100-09',
'2013-11',
'1631-10'
], dtype='datetime64[M]')
assert_array_equal(self.data["attr_month"], expected)
def test_date_attribute(self):
expected = np.array([
'1999-01-31',
'2004-12-01',
'1817-04-28',
'2100-09-10',
'2013-11-30',
'1631-10-15'
], dtype='datetime64[D]')
assert_array_equal(self.data["attr_date"], expected)
def test_datetime_local_attribute(self):
expected = np.array([
datetime.datetime(year=1999, month=1, day=31, hour=0, minute=1),
datetime.datetime(year=2004, month=12, day=1, hour=23, minute=59),
datetime.datetime(year=1817, month=4, day=28, hour=13, minute=0),
datetime.datetime(year=2100, month=9, day=10, hour=12, minute=0),
datetime.datetime(year=2013, month=11, day=30, hour=4, minute=55),
datetime.datetime(year=1631, month=10, day=15, hour=20, minute=4)
], dtype='datetime64[m]')
assert_array_equal(self.data["attr_datetime_local"], expected)
def test_datetime_missing(self):
expected = np.array([
'nat',
'2004-12-01T23:59',
'nat',
'nat',
'2013-11-30T04:55',
'1631-10-15T20:04'
], dtype='datetime64[m]')
assert_array_equal(self.data["attr_datetime_missing"], expected)
def test_datetime_timezone(self):
assert_raises(ValueError, loadarff, test8)