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Merge branch 'AICGijon-uci_multiclass'

This commit is contained in:
Alejandro Moreo Fernandez 2023-10-18 17:51:37 +02:00
commit 34c60e0870
1 changed files with 111 additions and 1 deletions

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@ -6,7 +6,8 @@ import os
import zipfile import zipfile
from os.path import join from os.path import join
import pandas as pd import pandas as pd
import scipy
from ucimlrepo import fetch_ucirepo
from quapy.data.base import Dataset, LabelledCollection from quapy.data.base import Dataset, LabelledCollection
from quapy.data.preprocessing import text2tfidf, reduce_columns from quapy.data.preprocessing import text2tfidf, reduce_columns
@ -45,6 +46,12 @@ UCI_DATASETS = ['acute.a', 'acute.b',
'wine-q-red', 'wine-q-white', 'wine-q-red', 'wine-q-white',
'yeast'] 'yeast']
UCI_MULTICLASS_DATASETS = ['dry-bean',
'wine-quality',
'academic-success',
'digits',
'letter']
LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B'] LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B']
_TXA_SAMPLE_SIZE = 250 _TXA_SAMPLE_SIZE = 250
@ -549,6 +556,109 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) ->
return data return data
def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, verbose=False) -> Dataset:
"""
Loads a UCI multiclass dataset as an instance of :class:`quapy.data.base.Dataset`.
The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria:
- It has more than 1000 instances
- It is suited for classification
- It has more than two classes
- It is available for Python import (requires ucimlrepo package)
>>> import quapy as qp
>>> dataset = qp.datasets.fetch_UCIMulticlassDataset("dry-bean")
>>> train, test = dataset.train_test
>>> ...
The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`
The datasets are downloaded only once and pickled into disk, saving time for consecutive calls.
:param dataset_name: a dataset name
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
~/quay_data/ directory)
:param test_split: proportion of documents to be included in the test set. The rest conforms the training set
:param verbose: set to True (default is False) to get information (stats) about the dataset
:return: a :class:`quapy.data.base.Dataset` instance
"""
data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, verbose)
return Dataset(*data.split_stratified(1 - test_split, random_state=0))
def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=False) -> LabelledCollection:
"""
Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.
The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria:
- It has more than 1000 instances
- It is suited for classification
- It has more than two classes
- It is available for Python import (requires ucimlrepo package)
>>> import quapy as qp
>>> collection = qp.datasets.fetch_UCIMulticlassLabelledCollection("dry-bean")
>>> X, y = collection.Xy
>>> ...
The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`
The datasets are downloaded only once and pickled into disk, saving time for consecutive calls.
: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 documents to be included in the test set. The rest conforms the training set
:param verbose: set to True (default is False) to get information (stats) about the dataset
:return: a :class:`quapy.data.base.LabelledCollection` instance
"""
assert dataset_name in UCI_MULTICLASS_DATASETS, \
f'Name {dataset_name} does not match any known dataset from the ' \
f'UCI Machine Learning datasets repository (multiclass). ' \
f'Valid ones are {UCI_MULTICLASS_DATASETS}'
if data_home is None:
data_home = get_quapy_home()
identifiers = {
"dry-bean": 602,
"wine-quality": 186,
"academic-success": 697,
"digits": 80,
"letter": 59
}
full_names = {
"dry-bean": "Dry Bean Dataset",
"wine-quality": "Wine Quality",
"academic-success": "Predict students' dropout and academic success",
"digits": "Optical Recognition of Handwritten Digits",
"letter": "Letter Recognition"
}
identifier = identifiers[dataset_name]
fullname = full_names[dataset_name]
if verbose:
print(f'Loading UCI Muticlass {dataset_name} ({fullname})')
file = join(data_home, 'uci_multiclass', dataset_name+'.pkl')
def download(id):
data = fetch_ucirepo(id=id)
X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze()
classes = np.sort(np.unique(y))
y = np.searchsorted(classes, y)
return LabelledCollection(X, y)
data = pickled_resource(file, download, identifier)
if verbose:
data.stats()
return data
def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float): 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) df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False)