Module idict.function.dataset
Functions to be used directly within an idict workflow
Expand source code
# Copyright (c) 2021. Davi Pereira dos Santos
# This file is part of the i-dict project.
# Please respect the license - more about this in the section (*) below.
#
# i-dict is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# i-dict is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with i-dict. If not, see <http://www.gnu.org/licenses/>.
#
# (*) Removing authorship by any means, e.g. by distribution of derived
# works or verbatim, obfuscated, compiled or rewritten versions of any
# part of this work is illegal and it is unethical regarding the effort and
# time spent here.
#
"""
Functions to be used directly within an idict workflow
"""
from io import StringIO
from idict.function import isplit
import arff as liacarff
import pandas as pd
# library arff2pandas simply disappeared...
def liac2pandas(arff):
attrs = arff['attributes']
attrs_t = []
for attr in attrs:
if isinstance(attr[1], list):
attrs_t.append("%s@{%s}" % (attr[0], ','.join(attr[1])))
else:
attrs_t.append("%s@%s" % (attr[0], attr[1]))
df = pd.DataFrame(data=arff['data'], columns=attrs_t)
return df
def load(fp):
data = liacarff.load(fp)
return liac2pandas(data)
def loads(s):
data = liacarff.loads(s)
return liac2pandas(data)
def df2liac(df, relation='data', description=''):
attrs = []
for col in df.columns:
attr = col.split('@')
if attr[1].count('{') > 0 and attr[1].count('}') > 0:
vals = attr[1].replace('{', '').replace('}', '').split(',')
attrs.append((attr[0], vals))
else:
attrs.append((attr[0], attr[1]))
data = list(df.values)
result = {'attributes': attrs, 'data': data, 'description': description, 'relation': relation}
return result
def dump(df, fp):
arff = df2liac(df)
liacarff.dump(arff, fp)
def dumps(df):
arff = df2liac(df)
return liacarff.dumps(arff)
def Xy2M(input=["X", "y"], output="M", **kwargs):
"""
>>> from idict import idict
>>> from idict.function.dataset import df2Xy
>>> d = idict.fromtoy(output_format="df")
>>> d = d >> df2Xy >> Xy2M
>>> d.M
array([[ 5.1, 6.4, 0. ],
[ 1.1, 2.5, 1. ],
[ 6.1, 3.6, 0. ],
[ 1.1, 3.5, 1. ],
[ 3.1, 2.5, 0. ],
[ 4.7, 4.9, 1. ],
[ 9.1, 3.5, 0. ],
[ 8.3, 2.9, 1. ],
[ 9.1, 7.2, 0. ],
[ 2.5, 4.5, 1. ],
[ 7.1, 6.6, 0. ],
[ 0.1, 4.3, 1. ],
[ 2.1, 0.1, 0. ],
[ 0.1, 4. , 1. ],
[ 5.1, 4.5, 0. ],
[31.1, 4.7, 1. ],
[ 1.1, 3.2, 0. ],
[ 2.2, 8.5, 1. ],
[ 3.1, 2.5, 0. ],
[ 1.1, 8.5, 1. ]])
"""
import numpy
return {output: numpy.column_stack((kwargs[input[0]], kwargs[input[1]])), "_history": ...}
def df2Xy(input="df", Xout="X", yout="y", **kwargs):
"""
>>> from idict import let, idict
>>> d = idict.fromminiarff()
>>> d >>= df2Xy
>>> d.show(colored=False)
{
"X": "→(input Xout yout df)",
"y": "→(input Xout yout df)",
"_history": "idict-pandas-1.3.4--sklearn-1.0.1--df2Xy",
"df": "«{'attr1@REAL': {0: 5.1, 1: 3.1}, 'attr2@REAL': {0: 3.5, 1: 4.5}, 'class@{0,1}': {0: '0', 1: '1'}}»",
"_id": "0GLFP05fQeW7dVFF0m50N8yVTgl-1.0.1--df2Xy",
"_ids": {
"X": "NeRaymrp6pcRWPAFc4t8A9.BnboEM0.1BllNL8yo",
"y": "0u5TDWnV.PZO-bmzBnZveLLnojWlh-1ZsEFsvVoa",
"_history": "OdE7kT5FmO.bsbvUAU9a3FA-v5EAQaOdihxMcNv6",
"df": "q3_b71eb05c4be05eba7b6ae5a9245d5dd70b81b (content: 6X_dc8ccea3b2e46f1c78967fae98b692701dc99)"
}
}
>>> d.y
array([0, 1])
"""
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df = kwargs[input]
X_ = df.drop(df.columns[[-1]], axis=1)
y_ = le.fit_transform(df[df.columns[-1]])
return {Xout: X_, yout: y_, "_history": ...}
def df2arff(input="df", output="arff", **kwargs):
"""
>>> from idict import let, idict
>>> d = idict.fromminiarff()
>>> d >>= let(df2arff, output="a")
>>> d.show(colored=False)
{
"a": "→(input output df)",
"_history": "idict---------arff2pandas-1.0.1--df2arff",
"df": "«{'attr1@REAL': {0: 5.1, 1: 3.1}, 'attr2@REAL': {0: 3.5, 1: 4.5}, 'class@{0,1}': {0: '0', 1: '1'}}»",
"_id": "Ojq9k7ZSbVjwLGlZ7uuqIyhoJPo.p36mAmav2Wul",
"_ids": {
"a": "wVPgESiPwobfFLLm591BKw6i2Zn.p36mAmav2Wul",
"_history": "D2NpAYrhyJW-.nOhh91ttSjrbGw.nZ8qxps9giws",
"df": "q3_b71eb05c4be05eba7b6ae5a9245d5dd70b81b (content: 6X_dc8ccea3b2e46f1c78967fae98b692701dc99)"
}
}
>>> d.a
'@RELATION data\\n\\n@ATTRIBUTE attr1 REAL\\n@ATTRIBUTE attr2 REAL\\n@ATTRIBUTE class {0, 1}\\n\\n@DATA\\n5.1,3.5,0\\n3.1,4.5,1\\n'
"""
return {output: dumps(kwargs[input]), "_history": ...}
def openml(Xout="X", yout="y", name="iris", version=1):
"""
#>>> from idict import Ø
#>>> (Ø >> openml).show(colored=False)
#{
#"X": "→(Xout yout name version)",
#"y": "→(Xout yout name version)",
#"_history": "idict--------------sklearn-1.0.1--openml",
#"_id": "idict--------------sklearn-1.0.1--openml",
#"_ids": {
#"X": "KkSoAvgPmGq52PvPBqGFCEp9cSUCfb7eht9VpUdr",
#"y": "HOE9E-HnFYen6JNRC9cHgAamJlOWGYTPIVKvQu8W",
#"_history": "Efw0ebrPTxTiuJ.ZpVt-MvB62ja.kmrOYzrn-ljf"
#}
#}
#>>> (Ø >> openml).X.head()
#sepallength sepalwidth petallength petalwidth
#0 5.1 3.5 1.4 0.2
#1 4.9 3.0 1.4 0.2
#2 4.7 3.2 1.3 0.2
#3 4.6 3.1 1.5 0.2
#4 5.0 3.6 1.4 0.2
#>>> (Ø >> openml).y.head()
#0 Iris-setosa
#1 Iris-setosa
#2 Iris-setosa
#3 Iris-setosa
#4 Iris-setosa
#Name: class, dtype: category
#Categories (3, object): ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
"""
from sklearn.datasets import fetch_openml
X, y = fetch_openml(name=name, version=version, as_frame=True, return_X_y=True)
return {Xout: X, yout: y, "_history": ...}
def arff2df(input="arff", output="df", **kwargs):
r"""
>>> from idict import let, idict
>>> d = idict.fromminiarff(output=["arff"], output_format="arff")
>>> d.arff
'@RELATION mini\n@ATTRIBUTE attr1\tREAL\n@ATTRIBUTE attr2 \tREAL\n@ATTRIBUTE class \t{0,1}\n@DATA\n5.1,3.5,0\n3.1,4.5,1'
>>> d >>= arff2df # doctest: +SKIP
>>> d.show(colored=False) # doctest: +SKIP +ELLIPSIS
{
"df": "→(input output arff)",
"_name": "→(input output arff)",
"_history": "idict---------arff2pandas-1.0.1--arff2df",
"arff": "@RELATION mini\n@ATTRIBUTE attr1\tREAL\n@ATTRIBUTE attr2 \tREAL\n@ATTRIBUTE class \t{0,1}\n@DATA\n5.1,3.5,0\n3.1,4.5,1",
"_id": "XraBH1sOCC.9ohqV3hfIRTE5FV3.0.1--arff2df",
"_ids": {
"df": "VNy1otmyPSLXWCKAYbZRtNqMdW3.0.1--arff2df",
"_name": "K4gZ2YDXBHOHwPzYufHChxkfhSz7ZAr--YbuHM-o",
"_history": "VaU.REn8zFBQtEWngL.FYvWCowT0aeZPmXg35hCs",
"arff": "Z._c3e2b235b697e9734b9ec13084129dc30e45b (content: Ev_8bb973161e5ae900c5743b3c332b4a64d1955)"
}
}
>>> d.df # doctest: +SKIP
attr1@REAL attr2@REAL class@{0,1}
0 5.1 3.5 0
1 3.1 4.5 1
"""
relation = "<Unnamed>"
with StringIO() as f:
f.write(kwargs[input])
text = f.getvalue()
df = loads(text)
for line in isplit(text, "\n"):
if line[:9].upper() == "@RELATION":
relation = line[9:].strip()
break
return {output: df, "_name": relation, "_history": ...}
Xy2M.metadata = {
"id": "idict--pandas-1.3.4--sklearn-1.0.1--Xy2M",
"name": "Xy2M",
"description": "X,y (pandas/numpy) to M (numpy) column concatenator.",
"parameters": ...,
"code": ...,
}
df2Xy.metadata = {
"id": "idict-pandas-1.3.4--sklearn-1.0.1--df2Xy",
"name": "df2Xy",
"description": "DataFrame (pandas) to X,y (pandas) converter.",
"parameters": ...,
"code": ...,
}
df2arff.metadata = {
"id": "idict---------arff2pandas-1.0.1--df2arff",
"name": "df2arff",
"description": "DataFrame (pandas) to ARFF converter.",
"parameters": ...,
"code": ...,
}
openml.metadata = {
"id": "idict--------------sklearn-1.0.1--openml",
"name": "openml",
"description": "Fetch DataFrame+Series (pandas) from OpenML.",
"parameters": ...,
"code": ...,
}
arff2df.metadata = {
"id": "idict---------arff2pandas-1.0.1--arff2df",
"name": "arff2df",
"description": "ARFF to DataFrame (pandas) converter.",
"parameters": ...,
"code": ...,
}Functions
def Xy2M(input=['X', 'y'], output='M', **kwargs)-
>>> from idict import idict >>> from idict.function.dataset import df2Xy >>> d = idict.fromtoy(output_format="df") >>> d = d >> df2Xy >> Xy2M >>> d.M array([[ 5.1, 6.4, 0. ], [ 1.1, 2.5, 1. ], [ 6.1, 3.6, 0. ], [ 1.1, 3.5, 1. ], [ 3.1, 2.5, 0. ], [ 4.7, 4.9, 1. ], [ 9.1, 3.5, 0. ], [ 8.3, 2.9, 1. ], [ 9.1, 7.2, 0. ], [ 2.5, 4.5, 1. ], [ 7.1, 6.6, 0. ], [ 0.1, 4.3, 1. ], [ 2.1, 0.1, 0. ], [ 0.1, 4. , 1. ], [ 5.1, 4.5, 0. ], [31.1, 4.7, 1. ], [ 1.1, 3.2, 0. ], [ 2.2, 8.5, 1. ], [ 3.1, 2.5, 0. ], [ 1.1, 8.5, 1. ]])Expand source code
def Xy2M(input=["X", "y"], output="M", **kwargs): """ >>> from idict import idict >>> from idict.function.dataset import df2Xy >>> d = idict.fromtoy(output_format="df") >>> d = d >> df2Xy >> Xy2M >>> d.M array([[ 5.1, 6.4, 0. ], [ 1.1, 2.5, 1. ], [ 6.1, 3.6, 0. ], [ 1.1, 3.5, 1. ], [ 3.1, 2.5, 0. ], [ 4.7, 4.9, 1. ], [ 9.1, 3.5, 0. ], [ 8.3, 2.9, 1. ], [ 9.1, 7.2, 0. ], [ 2.5, 4.5, 1. ], [ 7.1, 6.6, 0. ], [ 0.1, 4.3, 1. ], [ 2.1, 0.1, 0. ], [ 0.1, 4. , 1. ], [ 5.1, 4.5, 0. ], [31.1, 4.7, 1. ], [ 1.1, 3.2, 0. ], [ 2.2, 8.5, 1. ], [ 3.1, 2.5, 0. ], [ 1.1, 8.5, 1. ]]) """ import numpy return {output: numpy.column_stack((kwargs[input[0]], kwargs[input[1]])), "_history": ...} def arff2df(input='arff', output='df', **kwargs)-
>>> from idict import let, idict >>> d = idict.fromminiarff(output=["arff"], output_format="arff") >>> d.arff '@RELATION mini\n@ATTRIBUTE attr1\tREAL\n@ATTRIBUTE attr2 \tREAL\n@ATTRIBUTE class \t{0,1}\n@DATA\n5.1,3.5,0\n3.1,4.5,1' >>> d >>= arff2df # doctest: +SKIP >>> d.show(colored=False) # doctest: +SKIP +ELLIPSIS { "df": "→(input output arff)", "_name": "→(input output arff)", "_history": "idict---------arff2pandas-1.0.1--arff2df", "arff": "@RELATION mini\n@ATTRIBUTE attr1\tREAL\n@ATTRIBUTE attr2 \tREAL\n@ATTRIBUTE class \t{0,1}\n@DATA\n5.1,3.5,0\n3.1,4.5,1", "_id": "XraBH1sOCC.9ohqV3hfIRTE5FV3.0.1--arff2df", "_ids": { "df": "VNy1otmyPSLXWCKAYbZRtNqMdW3.0.1--arff2df", "_name": "K4gZ2YDXBHOHwPzYufHChxkfhSz7ZAr--YbuHM-o", "_history": "VaU.REn8zFBQtEWngL.FYvWCowT0aeZPmXg35hCs", "arff": "Z._c3e2b235b697e9734b9ec13084129dc30e45b (content: Ev_8bb973161e5ae900c5743b3c332b4a64d1955)" } } >>> d.df # doctest: +SKIP attr1@REAL attr2@REAL class@{0,1} 0 5.1 3.5 0 1 3.1 4.5 1Expand source code
def arff2df(input="arff", output="df", **kwargs): r""" >>> from idict import let, idict >>> d = idict.fromminiarff(output=["arff"], output_format="arff") >>> d.arff '@RELATION mini\n@ATTRIBUTE attr1\tREAL\n@ATTRIBUTE attr2 \tREAL\n@ATTRIBUTE class \t{0,1}\n@DATA\n5.1,3.5,0\n3.1,4.5,1' >>> d >>= arff2df # doctest: +SKIP >>> d.show(colored=False) # doctest: +SKIP +ELLIPSIS { "df": "→(input output arff)", "_name": "→(input output arff)", "_history": "idict---------arff2pandas-1.0.1--arff2df", "arff": "@RELATION mini\n@ATTRIBUTE attr1\tREAL\n@ATTRIBUTE attr2 \tREAL\n@ATTRIBUTE class \t{0,1}\n@DATA\n5.1,3.5,0\n3.1,4.5,1", "_id": "XraBH1sOCC.9ohqV3hfIRTE5FV3.0.1--arff2df", "_ids": { "df": "VNy1otmyPSLXWCKAYbZRtNqMdW3.0.1--arff2df", "_name": "K4gZ2YDXBHOHwPzYufHChxkfhSz7ZAr--YbuHM-o", "_history": "VaU.REn8zFBQtEWngL.FYvWCowT0aeZPmXg35hCs", "arff": "Z._c3e2b235b697e9734b9ec13084129dc30e45b (content: Ev_8bb973161e5ae900c5743b3c332b4a64d1955)" } } >>> d.df # doctest: +SKIP attr1@REAL attr2@REAL class@{0,1} 0 5.1 3.5 0 1 3.1 4.5 1 """ relation = "<Unnamed>" with StringIO() as f: f.write(kwargs[input]) text = f.getvalue() df = loads(text) for line in isplit(text, "\n"): if line[:9].upper() == "@RELATION": relation = line[9:].strip() break return {output: df, "_name": relation, "_history": ...} def df2Xy(input='df', Xout='X', yout='y', **kwargs)-
>>> from idict import let, idict >>> d = idict.fromminiarff() >>> d >>= df2Xy >>> d.show(colored=False) { "X": "→(input Xout yout df)", "y": "→(input Xout yout df)", "_history": "idict-pandas-1.3.4--sklearn-1.0.1--df2Xy", "df": "«{'attr1@REAL': {0: 5.1, 1: 3.1}, 'attr2@REAL': {0: 3.5, 1: 4.5}, 'class@{0,1}': {0: '0', 1: '1'}}»", "_id": "0GLFP05fQeW7dVFF0m50N8yVTgl-1.0.1--df2Xy", "_ids": { "X": "NeRaymrp6pcRWPAFc4t8A9.BnboEM0.1BllNL8yo", "y": "0u5TDWnV.PZO-bmzBnZveLLnojWlh-1ZsEFsvVoa", "_history": "OdE7kT5FmO.bsbvUAU9a3FA-v5EAQaOdihxMcNv6", "df": "q3_b71eb05c4be05eba7b6ae5a9245d5dd70b81b (content: 6X_dc8ccea3b2e46f1c78967fae98b692701dc99)" } } >>> d.y array([0, 1])Expand source code
def df2Xy(input="df", Xout="X", yout="y", **kwargs): """ >>> from idict import let, idict >>> d = idict.fromminiarff() >>> d >>= df2Xy >>> d.show(colored=False) { "X": "→(input Xout yout df)", "y": "→(input Xout yout df)", "_history": "idict-pandas-1.3.4--sklearn-1.0.1--df2Xy", "df": "«{'attr1@REAL': {0: 5.1, 1: 3.1}, 'attr2@REAL': {0: 3.5, 1: 4.5}, 'class@{0,1}': {0: '0', 1: '1'}}»", "_id": "0GLFP05fQeW7dVFF0m50N8yVTgl-1.0.1--df2Xy", "_ids": { "X": "NeRaymrp6pcRWPAFc4t8A9.BnboEM0.1BllNL8yo", "y": "0u5TDWnV.PZO-bmzBnZveLLnojWlh-1ZsEFsvVoa", "_history": "OdE7kT5FmO.bsbvUAU9a3FA-v5EAQaOdihxMcNv6", "df": "q3_b71eb05c4be05eba7b6ae5a9245d5dd70b81b (content: 6X_dc8ccea3b2e46f1c78967fae98b692701dc99)" } } >>> d.y array([0, 1]) """ from sklearn.preprocessing import LabelEncoder le = LabelEncoder() df = kwargs[input] X_ = df.drop(df.columns[[-1]], axis=1) y_ = le.fit_transform(df[df.columns[-1]]) return {Xout: X_, yout: y_, "_history": ...} def df2arff(input='df', output='arff', **kwargs)-
>>> from idict import let, idict >>> d = idict.fromminiarff() >>> d >>= let(df2arff, output="a") >>> d.show(colored=False) { "a": "→(input output df)", "_history": "idict---------arff2pandas-1.0.1--df2arff", "df": "«{'attr1@REAL': {0: 5.1, 1: 3.1}, 'attr2@REAL': {0: 3.5, 1: 4.5}, 'class@{0,1}': {0: '0', 1: '1'}}»", "_id": "Ojq9k7ZSbVjwLGlZ7uuqIyhoJPo.p36mAmav2Wul", "_ids": { "a": "wVPgESiPwobfFLLm591BKw6i2Zn.p36mAmav2Wul", "_history": "D2NpAYrhyJW-.nOhh91ttSjrbGw.nZ8qxps9giws", "df": "q3_b71eb05c4be05eba7b6ae5a9245d5dd70b81b (content: 6X_dc8ccea3b2e46f1c78967fae98b692701dc99)" } } >>> d.a '@RELATION data\n\n@ATTRIBUTE attr1 REAL\n@ATTRIBUTE attr2 REAL\n@ATTRIBUTE class {0, 1}\n\n@DATA\n5.1,3.5,0\n3.1,4.5,1\n'Expand source code
def df2arff(input="df", output="arff", **kwargs): """ >>> from idict import let, idict >>> d = idict.fromminiarff() >>> d >>= let(df2arff, output="a") >>> d.show(colored=False) { "a": "→(input output df)", "_history": "idict---------arff2pandas-1.0.1--df2arff", "df": "«{'attr1@REAL': {0: 5.1, 1: 3.1}, 'attr2@REAL': {0: 3.5, 1: 4.5}, 'class@{0,1}': {0: '0', 1: '1'}}»", "_id": "Ojq9k7ZSbVjwLGlZ7uuqIyhoJPo.p36mAmav2Wul", "_ids": { "a": "wVPgESiPwobfFLLm591BKw6i2Zn.p36mAmav2Wul", "_history": "D2NpAYrhyJW-.nOhh91ttSjrbGw.nZ8qxps9giws", "df": "q3_b71eb05c4be05eba7b6ae5a9245d5dd70b81b (content: 6X_dc8ccea3b2e46f1c78967fae98b692701dc99)" } } >>> d.a '@RELATION data\\n\\n@ATTRIBUTE attr1 REAL\\n@ATTRIBUTE attr2 REAL\\n@ATTRIBUTE class {0, 1}\\n\\n@DATA\\n5.1,3.5,0\\n3.1,4.5,1\\n' """ return {output: dumps(kwargs[input]), "_history": ...} def df2liac(df, relation='data', description='')-
Expand source code
def df2liac(df, relation='data', description=''): attrs = [] for col in df.columns: attr = col.split('@') if attr[1].count('{') > 0 and attr[1].count('}') > 0: vals = attr[1].replace('{', '').replace('}', '').split(',') attrs.append((attr[0], vals)) else: attrs.append((attr[0], attr[1])) data = list(df.values) result = {'attributes': attrs, 'data': data, 'description': description, 'relation': relation} return result def dump(df, fp)-
Expand source code
def dump(df, fp): arff = df2liac(df) liacarff.dump(arff, fp) def dumps(df)-
Expand source code
def dumps(df): arff = df2liac(df) return liacarff.dumps(arff) def liac2pandas(arff)-
Expand source code
def liac2pandas(arff): attrs = arff['attributes'] attrs_t = [] for attr in attrs: if isinstance(attr[1], list): attrs_t.append("%s@{%s}" % (attr[0], ','.join(attr[1]))) else: attrs_t.append("%s@%s" % (attr[0], attr[1])) df = pd.DataFrame(data=arff['data'], columns=attrs_t) return df def load(fp)-
Expand source code
def load(fp): data = liacarff.load(fp) return liac2pandas(data) def loads(s)-
Expand source code
def loads(s): data = liacarff.loads(s) return liac2pandas(data) def openml(Xout='X', yout='y', name='iris', version=1)-
>>> from idict import Ø
>>> (Ø >> openml).show(colored=False)
{
#"X": "→(Xout yout name version)", #"y": "→(Xout yout name version)", #"_history": "idict--------------sklearn-1.0.1--openml", #"_id": "idict--------------sklearn-1.0.1--openml", #"_ids": { #"X": "KkSoAvgPmGq52PvPBqGFCEp9cSUCfb7eht9VpUdr", #"y": "HOE9E-HnFYen6JNRC9cHgAamJlOWGYTPIVKvQu8W", #"_history": "Efw0ebrPTxTiuJ.ZpVt-MvB62ja.kmrOYzrn-ljf" #}}
>>> (Ø >> openml).X.head()
#sepallength sepalwidth petallength petalwidth
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
>>> (Ø >> openml).y.head()
0 Iris-setosa
1 Iris-setosa
2 Iris-setosa
3 Iris-setosa
4 Iris-setosa
Name: class, dtype: category
Categories (3, object): ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
Expand source code
def openml(Xout="X", yout="y", name="iris", version=1): """ #>>> from idict import Ø #>>> (Ø >> openml).show(colored=False) #{ #"X": "→(Xout yout name version)", #"y": "→(Xout yout name version)", #"_history": "idict--------------sklearn-1.0.1--openml", #"_id": "idict--------------sklearn-1.0.1--openml", #"_ids": { #"X": "KkSoAvgPmGq52PvPBqGFCEp9cSUCfb7eht9VpUdr", #"y": "HOE9E-HnFYen6JNRC9cHgAamJlOWGYTPIVKvQu8W", #"_history": "Efw0ebrPTxTiuJ.ZpVt-MvB62ja.kmrOYzrn-ljf" #} #} #>>> (Ø >> openml).X.head() #sepallength sepalwidth petallength petalwidth #0 5.1 3.5 1.4 0.2 #1 4.9 3.0 1.4 0.2 #2 4.7 3.2 1.3 0.2 #3 4.6 3.1 1.5 0.2 #4 5.0 3.6 1.4 0.2 #>>> (Ø >> openml).y.head() #0 Iris-setosa #1 Iris-setosa #2 Iris-setosa #3 Iris-setosa #4 Iris-setosa #Name: class, dtype: category #Categories (3, object): ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] """ from sklearn.datasets import fetch_openml X, y = fetch_openml(name=name, version=version, as_frame=True, return_X_y=True) return {Xout: X, yout: y, "_history": ...}