Merge branch 'lorenzovolpi-devel' into devel
This commit is contained in:
commit
9642808cf3
|
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@ -217,9 +217,9 @@ are summarized below.
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| cmc.1 | 2 | 1473 | 9 | [0.573, 0.427] | dense |
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| cmc.2 | 2 | 1473 | 9 | [0.774, 0.226] | dense |
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| cmc.3 | 2 | 1473 | 9 | [0.653, 0.347] | dense |
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| ctg.1 | 2 | 2126 | 22 | [0.222, 0.778] | dense |
|
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| ctg.2 | 2 | 2126 | 22 | [0.861, 0.139] | dense |
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||||
| ctg.3 | 2 | 2126 | 22 | [0.917, 0.083] | dense |
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||||
| ctg.1 | 2 | 2126 | 21 | [0.222, 0.778] | dense |
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| ctg.2 | 2 | 2126 | 21 | [0.861, 0.139] | dense |
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| ctg.3 | 2 | 2126 | 21 | [0.917, 0.083] | dense |
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| german | 2 | 1000 | 24 | [0.300, 0.700] | dense |
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| haberman | 2 | 306 | 3 | [0.735, 0.265] | dense |
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| ionosphere | 2 | 351 | 34 | [0.641, 0.359] | dense |
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|
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@ -242,24 +242,15 @@ are summarized below.
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| wine-q-white | 2 | 4898 | 11 | [0.335, 0.665] | dense |
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| yeast | 2 | 1484 | 8 | [0.711, 0.289] | dense |
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### Issues:
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#### Notes:
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All datasets will be downloaded automatically the first time they are requested, and
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stored in the _quapy_data_ folder for faster further reuse.
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However, some datasets require special actions that at the moment are not fully
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automated.
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* Datasets with ids "ctg.1", "ctg.2", and "ctg.3" (_Cardiotocography Data Set_) load
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an Excel file, which requires the user to install the _xlrd_ Python module in order
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to open it.
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* The dataset with id "pageblocks.5" (_Page Blocks Classification (5)_) needs to
|
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open a "unix compressed file" (extension .Z), which is not directly doable with
|
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standard Pythons packages like gzip or zip. This file would need to be uncompressed using
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OS-dependent software manually. Information on how to do it will be printed the first
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time the dataset is invoked.
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* It is a good idea to ignore datasets _acute.a_, _acute.b_ and _balance.2_, since the former two
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are very easy (many classifiers would score 100% accuracy) while the latter is extremely difficult
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(probably there is some problem with this dataset, the errors it tends to produce are orders of magnitude
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greater than for other datasets, and this has a disproportionate impact in the average performance).
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However, notice that it is a good idea to ignore datasets:
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* _acute.a_ and _acute.b_: these are very easy and many classifiers would score 100% accuracy
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* _balance.2_: this is extremely difficult; probably there is some problem with this dataset,
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the errors it tends to produce are orders of magnitude greater than for other datasets,
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and this has a disproportionate impact in the average performance.
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### Multiclass datasets
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|
|
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@ -1,6 +1,7 @@
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import os
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import pandas as pd
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import math
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from typing import Optional
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from quapy.data import LabelledCollection
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from quapy.protocol import AbstractProtocol
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from pathlib import Path
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@ -66,7 +67,7 @@ class IFCBTrainSamplesFromDir(AbstractProtocol):
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class IFCBTestSamples(AbstractProtocol):
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def __init__(self, path_dir:str, test_prevalences: pd.DataFrame, samples: list = None, classes: list=None):
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def __init__(self, path_dir:str, test_prevalences: Optional[pd.DataFrame]=None, samples: list=None, classes: list=None):
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self.path_dir = path_dir
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self.test_prevalences = test_prevalences
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self.classes = classes
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|
|
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|
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@ -3,6 +3,7 @@ def warn(*args, **kwargs):
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import warnings
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warnings.warn = warn
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import os
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from contextlib import contextmanager
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import zipfile
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from os.path import join
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import pandas as pd
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@ -270,7 +271,7 @@ def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, verbose=Fals
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>>> import quapy as qp
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>>> collection = qp.datasets.fetch_UCIBinaryLabelledCollection("yeast")
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>>> for data in qp.train.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
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>>> for data in qp.datasets.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
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>>> ...
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The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`
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@ -278,323 +279,298 @@ def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, verbose=Fals
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:param dataset_name: a dataset name
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:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
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~/quay_data/ directory)
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:param test_split: proportion of documents to be included in the test set. The rest conforms the training set
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:param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
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:return: a :class:`quapy.data.base.LabelledCollection` instance
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"""
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assert dataset_name in UCI_BINARY_DATASETS, \
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f'Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository. ' \
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f'Valid ones are {UCI_BINARY_DATASETS}'
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assert dataset_name in UCI_BINARY_DATASETS, (
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f"Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository. "
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f"Valid ones are {UCI_BINARY_DATASETS}"
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)
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if data_home is None:
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data_home = get_quapy_home()
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dataset_fullname = {
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'acute.a': 'Acute Inflammations (urinary bladder)',
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'acute.b': 'Acute Inflammations (renal pelvis)',
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'balance.1': 'Balance Scale Weight & Distance Database (left)',
|
||||
'balance.2': 'Balance Scale Weight & Distance Database (balanced)',
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'balance.3': 'Balance Scale Weight & Distance Database (right)',
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'breast-cancer': 'Breast Cancer Wisconsin (Original)',
|
||||
'cmc.1': 'Contraceptive Method Choice (no use)',
|
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'cmc.2': 'Contraceptive Method Choice (long term)',
|
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'cmc.3': 'Contraceptive Method Choice (short term)',
|
||||
'ctg.1': 'Cardiotocography Data Set (normal)',
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'ctg.2': 'Cardiotocography Data Set (suspect)',
|
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'ctg.3': 'Cardiotocography Data Set (pathologic)',
|
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'german': 'Statlog German Credit Data',
|
||||
'haberman': "Haberman's Survival Data",
|
||||
'ionosphere': 'Johns Hopkins University Ionosphere DB',
|
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'iris.1': 'Iris Plants Database(x)',
|
||||
'iris.2': 'Iris Plants Database(versicolour)',
|
||||
'iris.3': 'Iris Plants Database(virginica)',
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'mammographic': 'Mammographic Mass',
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'pageblocks.5': 'Page Blocks Classification (5)',
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'semeion': 'Semeion Handwritten Digit (8)',
|
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'sonar': 'Sonar, Mines vs. Rocks',
|
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'spambase': 'Spambase Data Set',
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'spectf': 'SPECTF Heart Data',
|
||||
'tictactoe': 'Tic-Tac-Toe Endgame Database',
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||||
'transfusion': 'Blood Transfusion Service Center Data Set',
|
||||
'wdbc': 'Wisconsin Diagnostic Breast Cancer',
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||||
'wine.1': 'Wine Recognition Data (1)',
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||||
'wine.2': 'Wine Recognition Data (2)',
|
||||
'wine.3': 'Wine Recognition Data (3)',
|
||||
'wine-q-red': 'Wine Quality Red (6-10)',
|
||||
'wine-q-white': 'Wine Quality White (6-10)',
|
||||
'yeast': 'Yeast',
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||||
# mapping bewteen dataset names and UCI api ids
|
||||
identifiers = {
|
||||
"acute.a": 184,
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||||
"acute.b": 184,
|
||||
"balance.1": 12,
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||||
"balance.2": 12,
|
||||
"balance.3": 12,
|
||||
"breast-cancer": 15,
|
||||
"cmc.1": 30,
|
||||
"cmc.2": 30,
|
||||
"cmc.3": 30,
|
||||
# "ctg.1": , # not python importable
|
||||
# "ctg.2": , # not python importable
|
||||
# "ctg.3": , # not python importable
|
||||
# "german": , # not python importable
|
||||
"haberman": 43,
|
||||
"ionosphere": 52,
|
||||
"iris.1": 53,
|
||||
"iris.2": 53,
|
||||
"iris.3": 53,
|
||||
"mammographic": 161,
|
||||
"pageblocks.5": 78,
|
||||
# "semeion": , # not python importable
|
||||
"sonar": 151,
|
||||
"spambase": 94,
|
||||
"spectf": 96,
|
||||
"tictactoe": 101,
|
||||
"transfusion": 176,
|
||||
"wdbc": 17,
|
||||
"wine.1": 109,
|
||||
"wine.2": 109,
|
||||
"wine.3": 109,
|
||||
"wine-q-red": 186,
|
||||
"wine-q-white": 186,
|
||||
"yeast": 110,
|
||||
}
|
||||
|
||||
# the identifier is an alias for the dataset group, it's part of the url data-folder, and is the name we use
|
||||
# to download the raw dataset
|
||||
identifier_map = {
|
||||
'acute.a': 'acute',
|
||||
'acute.b': 'acute',
|
||||
'balance.1': 'balance-scale',
|
||||
'balance.2': 'balance-scale',
|
||||
'balance.3': 'balance-scale',
|
||||
'breast-cancer': 'breast-cancer-wisconsin',
|
||||
'cmc.1': 'cmc',
|
||||
'cmc.2': 'cmc',
|
||||
'cmc.3': 'cmc',
|
||||
'ctg.1': '00193',
|
||||
'ctg.2': '00193',
|
||||
'ctg.3': '00193',
|
||||
'german': 'statlog/german',
|
||||
'haberman': 'haberman',
|
||||
'ionosphere': 'ionosphere',
|
||||
'iris.1': 'iris',
|
||||
'iris.2': 'iris',
|
||||
'iris.3': 'iris',
|
||||
'mammographic': 'mammographic-masses',
|
||||
'pageblocks.5': 'page-blocks',
|
||||
'semeion': 'semeion',
|
||||
'sonar': 'undocumented/connectionist-bench/sonar',
|
||||
'spambase': 'spambase',
|
||||
'spectf': 'spect',
|
||||
'tictactoe': 'tic-tac-toe',
|
||||
'transfusion': 'blood-transfusion',
|
||||
'wdbc': 'breast-cancer-wisconsin',
|
||||
'wine-q-red': 'wine-quality',
|
||||
'wine-q-white': 'wine-quality',
|
||||
'wine.1': 'wine',
|
||||
'wine.2': 'wine',
|
||||
'wine.3': 'wine',
|
||||
'yeast': 'yeast',
|
||||
# mapping between dataset names and dataset groups
|
||||
groups = {
|
||||
"acute.a": "acute",
|
||||
"acute.b": "acute",
|
||||
"balance.1": "balance",
|
||||
"balance.2": "balance",
|
||||
"balance.3": "balance",
|
||||
"breast-cancer": "breast-cancer",
|
||||
"cmc.1": "cmc",
|
||||
"cmc.2": "cmc",
|
||||
"cmc.3": "cmc",
|
||||
"ctg.1": "ctg",
|
||||
"ctg.2": "ctg",
|
||||
"ctg.3": "ctg",
|
||||
"german": "german",
|
||||
"haberman": "haberman",
|
||||
"ionosphere": "ionosphere",
|
||||
"iris.1": "iris",
|
||||
"iris.2": "iris",
|
||||
"iris.3": "iris",
|
||||
"mammographic": "mammographic",
|
||||
"pageblocks.5": "pageblocks",
|
||||
"semeion": "semeion",
|
||||
"sonar": "sonar",
|
||||
"spambase": "spambase",
|
||||
"spectf": "spectf",
|
||||
"tictactoe": "tictactoe",
|
||||
"transfusion": "transfusion",
|
||||
"wdbc": "wdbc",
|
||||
"wine-q-red": "wine-quality",
|
||||
"wine-q-white": "wine-quality",
|
||||
"wine.1": "wine",
|
||||
"wine.2": "wine",
|
||||
"wine.3": "wine",
|
||||
"yeast": "yeast",
|
||||
}
|
||||
|
||||
# the filename is the name of the file within the data_folder indexed by the identifier
|
||||
file_name = {
|
||||
'acute': 'diagnosis.data',
|
||||
'00193': 'CTG.xls',
|
||||
'statlog/german': 'german.data-numeric',
|
||||
'mammographic-masses': 'mammographic_masses.data',
|
||||
'page-blocks': 'page-blocks.data.Z',
|
||||
'undocumented/connectionist-bench/sonar': 'sonar.all-data',
|
||||
'spect': ['SPECTF.train', 'SPECTF.test'],
|
||||
'blood-transfusion': 'transfusion.data',
|
||||
'wine-quality': ['winequality-red.csv', 'winequality-white.csv'],
|
||||
'breast-cancer-wisconsin': 'breast-cancer-wisconsin.data' if dataset_name=='breast-cancer' else 'wdbc.data'
|
||||
# mapping between dataset short names and full names
|
||||
full_names = {
|
||||
"acute.a": "Acute Inflammations (urinary bladder)",
|
||||
"acute.b": "Acute Inflammations (renal pelvis)",
|
||||
"balance.1": "Balance Scale Weight & Distance Database (left)",
|
||||
"balance.2": "Balance Scale Weight & Distance Database (balanced)",
|
||||
"balance.3": "Balance Scale Weight & Distance Database (right)",
|
||||
"breast-cancer": "Breast Cancer Wisconsin (Original)",
|
||||
"cmc.1": "Contraceptive Method Choice (no use)",
|
||||
"cmc.2": "Contraceptive Method Choice (long term)",
|
||||
"cmc.3": "Contraceptive Method Choice (short term)",
|
||||
"ctg.1": "Cardiotocography Data Set (normal)",
|
||||
"ctg.2": "Cardiotocography Data Set (suspect)",
|
||||
"ctg.3": "Cardiotocography Data Set (pathologic)",
|
||||
"german": "Statlog German Credit Data",
|
||||
"haberman": "Haberman's Survival Data",
|
||||
"ionosphere": "Johns Hopkins University Ionosphere DB",
|
||||
"iris.1": "Iris Plants Database(x)",
|
||||
"iris.2": "Iris Plants Database(versicolour)",
|
||||
"iris.3": "Iris Plants Database(virginica)",
|
||||
"mammographic": "Mammographic Mass",
|
||||
"pageblocks.5": "Page Blocks Classification (5)",
|
||||
"semeion": "Semeion Handwritten Digit (8)",
|
||||
"sonar": "Sonar, Mines vs. Rocks",
|
||||
"spambase": "Spambase Data Set",
|
||||
"spectf": "SPECTF Heart Data",
|
||||
"tictactoe": "Tic-Tac-Toe Endgame Database",
|
||||
"transfusion": "Blood Transfusion Service Center Data Set",
|
||||
"wdbc": "Wisconsin Diagnostic Breast Cancer",
|
||||
"wine.1": "Wine Recognition Data (1)",
|
||||
"wine.2": "Wine Recognition Data (2)",
|
||||
"wine.3": "Wine Recognition Data (3)",
|
||||
"wine-q-red": "Wine Quality Red (6-10)",
|
||||
"wine-q-white": "Wine Quality White (6-10)",
|
||||
"yeast": "Yeast",
|
||||
}
|
||||
|
||||
# the filename containing the dataset description (if any)
|
||||
desc_name = {
|
||||
'acute': 'diagnosis.names',
|
||||
'00193': None,
|
||||
'statlog/german': 'german.doc',
|
||||
'mammographic-masses': 'mammographic_masses.names',
|
||||
'undocumented/connectionist-bench/sonar': 'sonar.names',
|
||||
'spect': 'SPECTF.names',
|
||||
'blood-transfusion': 'transfusion.names',
|
||||
'wine-quality': 'winequality.names',
|
||||
'breast-cancer-wisconsin': 'breast-cancer-wisconsin.names' if dataset_name == 'breast-cancer' else 'wdbc.names'
|
||||
# mapping between dataset names and values of positive class
|
||||
pos_class = {
|
||||
"acute.a": "yes",
|
||||
"acute.b": "yes",
|
||||
"balance.1": "L",
|
||||
"balance.2": "B",
|
||||
"balance.3": "R",
|
||||
"breast-cancer": 2,
|
||||
"cmc.1": 1,
|
||||
"cmc.2": 2,
|
||||
"cmc.3": 3,
|
||||
"ctg.1": 1, # 1==Normal
|
||||
"ctg.2": 2, # 2==Suspect
|
||||
"ctg.3": 3, # 3==Pathologic
|
||||
"german": 1,
|
||||
"haberman": 2,
|
||||
"ionosphere": "b",
|
||||
"iris.1": "Iris-setosa", # 1==Setosa
|
||||
"iris.2": "Iris-versicolor", # 2==Versicolor
|
||||
"iris.3": "Iris-virginica", # 3==Virginica
|
||||
"mammographic": 1,
|
||||
"pageblocks.5": 5, # 5==block "graphic"
|
||||
"semeion": 1,
|
||||
"sonar": "R",
|
||||
"spambase": 1,
|
||||
"spectf": 0,
|
||||
"tictactoe": "negative",
|
||||
"transfusion": 1,
|
||||
"wdbc": "M",
|
||||
"wine.1": 1,
|
||||
"wine.2": 2,
|
||||
"wine.3": 3,
|
||||
"wine-q-red": 1,
|
||||
"wine-q-white": 1,
|
||||
"yeast": "NUC",
|
||||
}
|
||||
|
||||
identifier = identifier_map[dataset_name]
|
||||
filename = file_name.get(identifier, f'{identifier}.data')
|
||||
descfile = desc_name.get(identifier, f'{identifier}.names')
|
||||
fullname = dataset_fullname[dataset_name]
|
||||
|
||||
URL = f'http://archive.ics.uci.edu/ml/machine-learning-databases/{identifier}'
|
||||
data_dir = join(data_home, 'uci_datasets', identifier)
|
||||
if isinstance(filename, str): # filename could be a list of files, in which case it will be processed later
|
||||
data_path = join(data_dir, filename)
|
||||
download_file_if_not_exists(f'{URL}/{filename}', data_path)
|
||||
|
||||
if descfile:
|
||||
try:
|
||||
download_file_if_not_exists(f'{URL}/{descfile}', f'{data_dir}/{descfile}')
|
||||
if verbose:
|
||||
print(open(f'{data_dir}/{descfile}', 'rt').read())
|
||||
except Exception:
|
||||
print('could not read the description file')
|
||||
elif verbose:
|
||||
print('no file description available')
|
||||
identifier = identifiers.get(dataset_name, None)
|
||||
dataset_group = groups[dataset_name]
|
||||
fullname = full_names[dataset_name]
|
||||
|
||||
if verbose:
|
||||
print(f'Loading {dataset_name} ({fullname})')
|
||||
if identifier == 'acute':
|
||||
df = pd.read_csv(data_path, header=None, encoding='utf-16', sep='\t')
|
||||
print(f"Loading UCI Binary {dataset_name} ({fullname})")
|
||||
|
||||
df[0] = df[0].apply(lambda x: float(x.replace(',', '.'))).astype(float, copy=False)
|
||||
[_df_replace(df, col) for col in range(1, 6)]
|
||||
X = df.loc[:, 0:5].values
|
||||
if dataset_name == 'acute.a':
|
||||
y = binarize(df[6], pos_class='yes')
|
||||
elif dataset_name == 'acute.b':
|
||||
y = binarize(df[7], pos_class='yes')
|
||||
file = join(data_home, "uci_datasets", dataset_group + ".pkl")
|
||||
|
||||
if identifier == 'balance-scale':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
if dataset_name == 'balance.1':
|
||||
y = binarize(df[0], pos_class='L')
|
||||
elif dataset_name == 'balance.2':
|
||||
y = binarize(df[0], pos_class='B')
|
||||
elif dataset_name == 'balance.3':
|
||||
y = binarize(df[0], pos_class='R')
|
||||
X = df.loc[:, 1:].astype(float).values
|
||||
@contextmanager
|
||||
def download_tmp_file(url_group: str, filename: str):
|
||||
"""
|
||||
Download a data file for a group of datasets temporarely.
|
||||
When used as a context, the file is removed once the context exits.
|
||||
|
||||
if identifier == 'breast-cancer-wisconsin' and dataset_name=='breast-cancer':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
Xy = df.loc[:, 1:10]
|
||||
Xy[Xy=='?']=np.nan
|
||||
Xy = Xy.dropna(axis=0)
|
||||
X = Xy.loc[:, 1:9]
|
||||
X = X.astype(float).values
|
||||
y = binarize(Xy[10], pos_class=2)
|
||||
|
||||
if identifier == 'breast-cancer-wisconsin' and dataset_name=='wdbc':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
X = df.loc[:, 2:32].astype(float).values
|
||||
y = df[1].values
|
||||
y = binarize(y, pos_class='M')
|
||||
|
||||
if identifier == 'cmc':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
X = df.loc[:, 0:8].astype(float).values
|
||||
y = df[9].astype(int).values
|
||||
if dataset_name == 'cmc.1':
|
||||
y = binarize(y, pos_class=1)
|
||||
elif dataset_name == 'cmc.2':
|
||||
y = binarize(y, pos_class=2)
|
||||
elif dataset_name == 'cmc.3':
|
||||
y = binarize(y, pos_class=3)
|
||||
|
||||
if identifier == '00193':
|
||||
df = pd.read_excel(data_path, sheet_name='Data', skipfooter=3)
|
||||
df = df[list(range(1,24))] # select columns numbered (number 23 is the target label)
|
||||
# replaces the header with the first row
|
||||
new_header = df.iloc[0] # grab the first row for the header
|
||||
df = df[1:] # take the data less the header row
|
||||
df.columns = new_header # set the header row as the df header
|
||||
X = df.iloc[:, 0:22].astype(float).values
|
||||
y = df['NSP'].astype(int).values
|
||||
if dataset_name == 'ctg.1':
|
||||
y = binarize(y, pos_class=1) # 1==Normal
|
||||
elif dataset_name == 'ctg.2':
|
||||
y = binarize(y, pos_class=2) # 2==Suspect
|
||||
elif dataset_name == 'ctg.3':
|
||||
y = binarize(y, pos_class=3) # 3==Pathologic
|
||||
|
||||
if identifier == 'statlog/german':
|
||||
df = pd.read_csv(data_path, header=None, delim_whitespace=True)
|
||||
X = df.iloc[:, 0:24].astype(float).values
|
||||
y = df[24].astype(int).values
|
||||
y = binarize(y, pos_class=1)
|
||||
|
||||
if identifier == 'haberman':
|
||||
df = pd.read_csv(data_path, header=None)
|
||||
X = df.iloc[:, 0:3].astype(float).values
|
||||
y = df[3].astype(int).values
|
||||
y = binarize(y, pos_class=2)
|
||||
|
||||
if identifier == 'ionosphere':
|
||||
df = pd.read_csv(data_path, header=None)
|
||||
X = df.iloc[:, 0:34].astype(float).values
|
||||
y = df[34].values
|
||||
y = binarize(y, pos_class='b')
|
||||
|
||||
if identifier == 'iris':
|
||||
df = pd.read_csv(data_path, header=None)
|
||||
X = df.iloc[:, 0:4].astype(float).values
|
||||
y = df[4].values
|
||||
if dataset_name == 'iris.1':
|
||||
y = binarize(y, pos_class='Iris-setosa') # 1==Setosa
|
||||
elif dataset_name == 'iris.2':
|
||||
y = binarize(y, pos_class='Iris-versicolor') # 2==Versicolor
|
||||
elif dataset_name == 'iris.3':
|
||||
y = binarize(y, pos_class='Iris-virginica') # 3==Virginica
|
||||
|
||||
if identifier == 'mammographic-masses':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
df[df == '?'] = np.nan
|
||||
Xy = df.dropna(axis=0)
|
||||
X = Xy.iloc[:, 0:5]
|
||||
X = X.astype(float).values
|
||||
y = binarize(Xy.iloc[:,5], pos_class=1)
|
||||
|
||||
if identifier == 'page-blocks':
|
||||
data_path_ = data_path.replace('.Z', '')
|
||||
if not os.path.exists(data_path_):
|
||||
raise FileNotFoundError(f'Warning: file {data_path_} does not exist. If this is the first time you '
|
||||
f'attempt to load this dataset, then you have to manually unzip the {data_path} '
|
||||
f'and name the extracted file {data_path_} (unfortunately, neither zipfile, nor '
|
||||
f'gzip can handle unix compressed files automatically -- there is a repo in GitHub '
|
||||
f'https://github.com/umeat/unlzw where the problem seems to be solved anyway).')
|
||||
df = pd.read_csv(data_path_, header=None, delim_whitespace=True)
|
||||
X = df.iloc[:, 0:10].astype(float).values
|
||||
y = df[10].values
|
||||
y = binarize(y, pos_class=5) # 5==block "graphic"
|
||||
|
||||
if identifier == 'semeion':
|
||||
df = pd.read_csv(data_path, header=None, delim_whitespace=True )
|
||||
X = df.iloc[:, 0:256].astype(float).values
|
||||
y = df[263].values # 263 stands for digit 8 (labels are one-hot vectors from col 256-266)
|
||||
y = binarize(y, pos_class=1)
|
||||
|
||||
if identifier == 'undocumented/connectionist-bench/sonar':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
X = df.iloc[:, 0:60].astype(float).values
|
||||
y = df[60].values
|
||||
y = binarize(y, pos_class='R')
|
||||
|
||||
if identifier == 'spambase':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
X = df.iloc[:, 0:57].astype(float).values
|
||||
y = df[57].values
|
||||
y = binarize(y, pos_class=1)
|
||||
|
||||
if identifier == 'spect':
|
||||
dfs = []
|
||||
for file in filename:
|
||||
data_path = join(data_dir, file)
|
||||
download_file_if_not_exists(f'{URL}/{file}', data_path)
|
||||
dfs.append(pd.read_csv(data_path, header=None, sep=','))
|
||||
df = pd.concat(dfs)
|
||||
X = df.iloc[:, 1:45].astype(float).values
|
||||
y = df[0].values
|
||||
y = binarize(y, pos_class=0)
|
||||
|
||||
if identifier == 'tic-tac-toe':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
X = df.iloc[:, 0:9].replace('o',0).replace('b',1).replace('x',2).values
|
||||
y = df[9].values
|
||||
y = binarize(y, pos_class='negative')
|
||||
|
||||
if identifier == 'blood-transfusion':
|
||||
df = pd.read_csv(data_path, sep=',')
|
||||
X = df.iloc[:, 0:4].astype(float).values
|
||||
y = df.iloc[:, 4].values
|
||||
y = binarize(y, pos_class=1)
|
||||
|
||||
if identifier == 'wine':
|
||||
df = pd.read_csv(data_path, header=None, sep=',')
|
||||
X = df.iloc[:, 1:14].astype(float).values
|
||||
y = df[0].values
|
||||
if dataset_name == 'wine.1':
|
||||
y = binarize(y, pos_class=1)
|
||||
elif dataset_name == 'wine.2':
|
||||
y = binarize(y, pos_class=2)
|
||||
elif dataset_name == 'wine.3':
|
||||
y = binarize(y, pos_class=3)
|
||||
|
||||
if identifier == 'wine-quality':
|
||||
filename = filename[0] if dataset_name=='wine-q-red' else filename[1]
|
||||
:param url_group: identifier of the dataset group in the URL
|
||||
:param filename: name of the file to be downloaded
|
||||
"""
|
||||
data_dir = join(data_home, "uci_datasets", "tmp")
|
||||
os.makedirs(data_dir, exist_ok=True)
|
||||
data_path = join(data_dir, filename)
|
||||
download_file_if_not_exists(f'{URL}/{filename}', data_path)
|
||||
df = pd.read_csv(data_path, sep=';')
|
||||
X = df.iloc[:, 0:11].astype(float).values
|
||||
y = df.iloc[:, 11].values > 5
|
||||
url = f"http://archive.ics.uci.edu/ml/machine-learning-databases/{url_group}/{filename}"
|
||||
download_file_if_not_exists(url, data_path)
|
||||
try:
|
||||
yield data_path
|
||||
finally:
|
||||
os.remove(data_path)
|
||||
|
||||
if identifier == 'yeast':
|
||||
df = pd.read_csv(data_path, header=None, delim_whitespace=True)
|
||||
X = df.iloc[:, 1:9].astype(float).values
|
||||
y = df.iloc[:, 9].values
|
||||
y = binarize(y, pos_class='NUC')
|
||||
def download(id: int | None, group: str) -> dict:
|
||||
"""
|
||||
Download the data to be pickled for a dataset group. Use the `fetch_ucirepo` api when possible.
|
||||
|
||||
:param id: numeric identifier for the group; can be None
|
||||
:param group: group name
|
||||
:return: a dictionary with X and y as keys and, optionally, extra data.
|
||||
"""
|
||||
|
||||
# use the fetch_ucirepo api, when possible, to download data
|
||||
# fall back to direct download when needed
|
||||
if group == "german":
|
||||
with download_tmp_file("statlog/german", "german.data-numeric") as tmp:
|
||||
df = pd.read_csv(tmp, header=None, delim_whitespace=True)
|
||||
X, y = df.iloc[:, 0:24].astype(float).values, df[24].astype(int).values
|
||||
elif group == "ctg":
|
||||
with download_tmp_file("00193", "CTG.xls") as tmp:
|
||||
df = pd.read_excel(tmp, sheet_name="Data", skipfooter=3)
|
||||
df = df[list(range(1, 24))] # select columns numbered (number 23 is the target label)
|
||||
# replaces the header with the first row
|
||||
new_header = df.iloc[0] # grab the first row for the header
|
||||
df = df[1:] # take the data less the header row
|
||||
df.columns = new_header # set the header row as the df header
|
||||
X = df.iloc[:, 0:21].astype(float).values # column 21 is skipped, it is a class column
|
||||
y = df["NSP"].astype(int).values
|
||||
elif group == "semeion":
|
||||
with download_tmp_file("semeion", "semeion.data") as tmp:
|
||||
df = pd.read_csv(tmp, header=None, delim_whitespace=True)
|
||||
X = df.iloc[:, 0:256].astype(float).values
|
||||
y = df[263].values # 263 stands for digit 8 (labels are one-hot vectors from col 256-266)
|
||||
else:
|
||||
df = fetch_ucirepo(id=id)
|
||||
X, y = df.data.features.to_numpy(), df.data.targets.to_numpy().squeeze()
|
||||
|
||||
# transform data when needed before returning (returned data will be pickled)
|
||||
if group == "acute":
|
||||
_array_replace(X)
|
||||
data = {"X": X, "y": y}
|
||||
elif group == "balance":
|
||||
# features' order is reversed to match data retrieved via direct download
|
||||
X = X[:, np.arange(X.shape[1])[::-1]]
|
||||
data = {"X": X, "y": y}
|
||||
elif group == "breast-cancer":
|
||||
# remove rows with nan values
|
||||
Xy = np.hstack([X, y[:, np.newaxis]])
|
||||
nan_rows = np.isnan(Xy).sum(axis=-1) > 0
|
||||
Xy = Xy[~nan_rows]
|
||||
data = {"X": Xy[:, :-1], "y": Xy[:, -1]}
|
||||
elif group == "mammographic":
|
||||
# remove rows with nan values
|
||||
Xy = np.hstack([X, y[:, np.newaxis]])
|
||||
nan_rows = np.isnan(Xy).sum(axis=-1) > 0
|
||||
Xy = Xy[~nan_rows]
|
||||
data = {"X": Xy[:, :-1], "y": Xy[:, -1]}
|
||||
elif group == "tictactoe":
|
||||
_array_replace(X, repl={"o": 0, "b": 1, "x": 2})
|
||||
data = {"X": X, "y": y}
|
||||
elif group == "wine-quality":
|
||||
# add color data to split the final datasets
|
||||
color = df.data.original["color"].to_numpy()
|
||||
data = {"X": X, "y": y, "color": color}
|
||||
else:
|
||||
data = {"X": X, "y": y}
|
||||
|
||||
return data
|
||||
|
||||
def binarize_data(name, data: dict) -> LabelledCollection:
|
||||
"""
|
||||
Filter and transform data to extract a binary dataset.
|
||||
|
||||
:param name: name of the dataset
|
||||
:param data: dictionary containing X and y fields, plus additional data when needed
|
||||
:return: a :class:`quapy.data.base.LabelledCollection` with the extracted dataset
|
||||
"""
|
||||
if name == "acute.a":
|
||||
X, y = data["X"], data["y"][:, 0]
|
||||
# X, y = Xy[:, :-2], Xy[:, -2]
|
||||
elif name == "acute.b":
|
||||
X, y = data["X"], data["y"][:, 1]
|
||||
# X, y = Xy[:, :-2], Xy[:, -1]
|
||||
elif name == "wine-q-red":
|
||||
X, y, color = data["X"], data["y"], data["color"]
|
||||
# X, y, color = Xy[:, :-2], Xy[:, -2], Xy[:, -1]
|
||||
red_idx = color == "red"
|
||||
X, y = X[red_idx, :], y[red_idx]
|
||||
y = (y > 5).astype(int)
|
||||
elif name == "wine-q-white":
|
||||
X, y, color = data["X"], data["y"], data["color"]
|
||||
# X, y, color = Xy[:, :-2], Xy[:, -2], Xy[:, -1]
|
||||
white_idx = color == "white"
|
||||
X, y = X[white_idx, :], y[white_idx]
|
||||
y = (y > 5).astype(int)
|
||||
else:
|
||||
X, y = data["X"], data["y"]
|
||||
# X, y = Xy[:, :-1], Xy[:, -1]
|
||||
|
||||
y = binarize(y, pos_class=pos_class[name])
|
||||
|
||||
return LabelledCollection(X, y)
|
||||
|
||||
data = pickled_resource(file, download, identifier, dataset_group)
|
||||
data = binarize_data(dataset_name, data)
|
||||
|
||||
data = LabelledCollection(X, y)
|
||||
if verbose:
|
||||
data.stats()
|
||||
|
||||
return data
|
||||
|
||||
|
||||
|
|
@ -671,7 +647,6 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_clas
|
|||
:param dataset_name: a dataset name
|
||||
:param data_home: specify the quapy home directory where the dataset will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:param test_split: proportion of instances to be included in the test set. The rest conforms the training set
|
||||
:param min_class_support: minimum number of istances per class. Classes with fewer instances
|
||||
are discarded (deafult is 100)
|
||||
:param verbose: set to True (default is False) to get information (stats) about the dataset
|
||||
|
|
@ -760,6 +735,8 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_clas
|
|||
return LabelledCollection(X, y)
|
||||
|
||||
def filter_classes(data: LabelledCollection, min_ipc):
|
||||
if min_ipc is None:
|
||||
min_ipc = 0
|
||||
classes = data.classes_
|
||||
# restrict classes to only those with at least min_ipc instances
|
||||
classes = classes[data.counts() >= min_ipc]
|
||||
|
|
@ -788,6 +765,11 @@ def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
|
|||
df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False)
|
||||
|
||||
|
||||
def _array_replace(arr, repl={"yes": 1, "no": 0}):
|
||||
for k, v in repl.items():
|
||||
arr[arr == k] = v
|
||||
|
||||
|
||||
def fetch_lequa2022(task, data_home=None):
|
||||
"""
|
||||
Loads the official datasets provided for the `LeQua <https://lequa2022.github.io/index>`_ competition.
|
||||
|
|
@ -805,7 +787,6 @@ def fetch_lequa2022(task, data_home=None):
|
|||
See `4.lequa2022_experiments.py` provided in the example folder, that can serve as a guide on how to use these
|
||||
datasets.
|
||||
|
||||
|
||||
:param task: a string representing the task name; valid ones are T1A, T1B, T2A, and T2B
|
||||
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
|
|
|
|||
Loading…
Reference in New Issue