Datasets

QuaPy makes available several datasets that have been used in quantification literature, as well as an interface to allow anyone import their custom datasets.

A Dataset object in QuaPy is roughly a pair of LabelledCollection objects, one playing the role of the training set, another the test set. LabelledCollection is a data class consisting of the (iterable) instances and labels. This class handles most of the sampling functionality in QuaPy. Take a look at the following code:

import quapy as qp
import quapy.functional as F

instances = [
    '1st positive document', '2nd positive document',
    'the only negative document',
    '1st neutral document', '2nd neutral document', '3rd neutral document'
]
labels = [2, 2, 0, 1, 1, 1]

data = qp.data.LabelledCollection(instances, labels)
print(F.strprev(data.prevalence(), prec=2))

Output the class prevalences (showing 2 digit precision):

[0.17, 0.50, 0.33]

One can easily produce new samples at desired class prevalences:

sample_size = 10
prev = [0.4, 0.1, 0.5]
sample = data.sampling(sample_size, *prev)

print('instances:', sample.instances)
print('labels:', sample.labels)
print('prevalence:', F.strprev(sample.prevalence(), prec=2))

Which outputs:

instances: ['the only negative document' '2nd positive document'
 '2nd positive document' '2nd neutral document' '1st positive document'
 'the only negative document' 'the only negative document'
 'the only negative document' '2nd positive document'
 '1st positive document']
labels: [0 2 2 1 2 0 0 0 2 2]
prevalence: [0.40, 0.10, 0.50]

Samples can be made consistent across different runs (e.g., to test different methods on the same exact samples) by sampling and retaining the indexes, that can then be used to generate the sample:

index = data.sampling_index(sample_size, *prev)
for method in methods:
    sample = data.sampling_from_index(index)
    ...

QuaPy also implements the artificial sampling protocol that produces (via a Python’s generator) a series of LabelledCollection objects with equidistant prevalences ranging across the entire prevalence spectrum in the simplex space, e.g.:

for sample in data.artificial_sampling_generator(sample_size=100, n_prevalences=5):
    print(F.strprev(sample.prevalence(), prec=2))

produces one sampling for each (valid) combination of prevalences originating from splitting the range [0,1] into n_prevalences=5 points (i.e., [0, 0.25, 0.5, 0.75, 1]), that is:

[0.00, 0.00, 1.00]
[0.00, 0.25, 0.75]
[0.00, 0.50, 0.50]
[0.00, 0.75, 0.25]
[0.00, 1.00, 0.00]
[0.25, 0.00, 0.75]
...
[1.00, 0.00, 0.00]

See the Evaluation wiki for further details on how to use the artificial sampling protocol to properly evaluate a quantification method.

Reviews Datasets

Three datasets of reviews about Kindle devices, Harry Potter’s series, and the well-known IMDb movie reviews can be fetched using a unified interface. For example:

import quapy as qp
data = qp.datasets.fetch_reviews('kindle')

These datasets have been used in:

Esuli, A., Moreo, A., & Sebastiani, F. (2018, October). 
A recurrent neural network for sentiment quantification. 
In Proceedings of the 27th ACM International Conference on 
Information and Knowledge Management (pp. 1775-1778).

The list of reviews ids is available in:

qp.datasets.REVIEWS_SENTIMENT_DATASETS

Some statistics of the fhe available datasets are summarized below:

Dataset	classes	train size	test size	train prev	test prev	type
hp	2	9533	18399	[0.018, 0.982]	[0.065, 0.935]	text
kindle	2	3821	21591	[0.081, 0.919]	[0.063, 0.937]	text
imdb	2	25000	25000	[0.500, 0.500]	[0.500, 0.500]	text

Twitter Sentiment Datasets

11 Twitter datasets for sentiment analysis. Text is not accessible, and the documents were made available in tf-idf format. Each dataset presents two splits: a train/val split for model selection purposes, and a train+val/test split for model evaluation. The following code exemplifies how to load a twitter dataset for model selection.

import quapy as qp
data = qp.datasets.fetch_twitter('gasp', for_model_selection=True)

The datasets were used in:

Gao, W., & Sebastiani, F. (2015, August). 
Tweet sentiment: From classification to quantification. 
In 2015 IEEE/ACM International Conference on Advances in 
Social Networks Analysis and Mining (ASONAM) (pp. 97-104). IEEE.

Three of the datasets (semeval13, semeval14, and semeval15) share the same training set (semeval), meaning that the training split one would get when requesting any of them is the same. The dataset “semeval” can only be requested with “for_model_selection=True”. The lists of the Twitter dataset’s ids can be consulted in:

# a list of 11 dataset ids that can be used for model selection or model evaluation
qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST

# 9 dataset ids in which "semeval13", "semeval14", and "semeval15" are replaced with "semeval"
qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN  

Some details can be found below:

Dataset	classes	train size	test size	features	train prev	test prev	type
gasp	3	8788	3765	694582	[0.421, 0.496, 0.082]	[0.407, 0.507, 0.086]	sparse
hcr	3	1594	798	222046	[0.546, 0.211, 0.243]	[0.640, 0.167, 0.193]	sparse
omd	3	1839	787	199151	[0.463, 0.271, 0.266]	[0.437, 0.283, 0.280]	sparse
sanders	3	2155	923	229399	[0.161, 0.691, 0.148]	[0.164, 0.688, 0.148]	sparse
semeval13	3	11338	3813	1215742	[0.159, 0.470, 0.372]	[0.158, 0.430, 0.412]	sparse
semeval14	3	11338	1853	1215742	[0.159, 0.470, 0.372]	[0.109, 0.361, 0.530]	sparse
semeval15	3	11338	2390	1215742	[0.159, 0.470, 0.372]	[0.153, 0.413, 0.434]	sparse
semeval16	3	8000	2000	889504	[0.157, 0.351, 0.492]	[0.163, 0.341, 0.497]	sparse
sst	3	2971	1271	376132	[0.261, 0.452, 0.288]	[0.207, 0.481, 0.312]	sparse
wa	3	2184	936	248563	[0.305, 0.414, 0.281]	[0.282, 0.446, 0.272]	sparse
wb	3	4259	1823	404333	[0.270, 0.392, 0.337]	[0.274, 0.392, 0.335]	sparse

UCI Machine Learning

A set of 32 datasets from the UCI Machine Learning repository used in:

Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
Using ensembles for problems with characterizable changes 
in data distribution: A case study on quantification.
Information Fusion, 34, 87-100.

The list does not exactly coincide with that used in Pérez-Gállego et al. 2017 since we were unable to find the datasets with ids “diabetes” and “phoneme”.

These dataset can be loaded by calling, e.g.:

import quapy as qp
data = qp.datasets.fetch_UCIDataset('yeast', verbose=True)

This call will return a Dataset object in which the training and test splits are randomly drawn, in a stratified manner, from the whole collection at 70% and 30%, respectively. The verbose=True option indicates that the dataset description should be printed in standard output. The original data is not split, and some papers submit the entire collection to a kFCV validation. In order to accommodate with these practices, one could first instantiate the entire collection, and then creating a generator that will return one training+test dataset at a time, following a kFCV protocol:

import quapy as qp
collection = qp.datasets.fetch_UCILabelledCollection("yeast")
for data in qp.data.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
    ...

Above code will allow to conduct a 2x5FCV evaluation on the “yeast” dataset.

All datasets come in numerical form (dense matrices); some statistics are summarized below.

Dataset	classes	instances	features	prev	type
acute.a	2	120	6	[0.508, 0.492]	dense
acute.b	2	120	6	[0.583, 0.417]	dense
balance.1	2	625	4	[0.539, 0.461]	dense
balance.2	2	625	4	[0.922, 0.078]	dense
balance.3	2	625	4	[0.539, 0.461]	dense
breast-cancer	2	683	9	[0.350, 0.650]	dense
cmc.1	2	1473	9	[0.573, 0.427]	dense
cmc.2	2	1473	9	[0.774, 0.226]	dense
cmc.3	2	1473	9	[0.653, 0.347]	dense
ctg.1	2	2126	22	[0.222, 0.778]	dense
ctg.2	2	2126	22	[0.861, 0.139]	dense
ctg.3	2	2126	22	[0.917, 0.083]	dense
german	2	1000	24	[0.300, 0.700]	dense
haberman	2	306	3	[0.735, 0.265]	dense
ionosphere	2	351	34	[0.641, 0.359]	dense
iris.1	2	150	4	[0.667, 0.333]	dense
iris.2	2	150	4	[0.667, 0.333]	dense
iris.3	2	150	4	[0.667, 0.333]	dense
mammographic	2	830	5	[0.514, 0.486]	dense
pageblocks.5	2	5473	10	[0.979, 0.021]	dense
semeion	2	1593	256	[0.901, 0.099]	dense
sonar	2	208	60	[0.534, 0.466]	dense
spambase	2	4601	57	[0.606, 0.394]	dense
spectf	2	267	44	[0.794, 0.206]	dense
tictactoe	2	958	9	[0.653, 0.347]	dense
transfusion	2	748	4	[0.762, 0.238]	dense
wdbc	2	569	30	[0.627, 0.373]	dense
wine.1	2	178	13	[0.669, 0.331]	dense
wine.2	2	178	13	[0.601, 0.399]	dense
wine.3	2	178	13	[0.730, 0.270]	dense
wine-q-red	2	1599	11	[0.465, 0.535]	dense
wine-q-white	2	4898	11	[0.335, 0.665]	dense
yeast	2	1484	8	[0.711, 0.289]	dense

Issues:

All datasets will be downloaded automatically the first time they are requested, and stored in the quapy_data folder for faster further reuse. However, some datasets require special actions that at the moment are not fully automated.

• Datasets with ids “ctg.1”, “ctg.2”, and “ctg.3” (Cardiotocography Data Set) load an Excel file, which requires the user to install the xlrd Python module in order to open it.

• The dataset with id “pageblocks.5” (Page Blocks Classification (5)) needs to open a “unix compressed file” (extension .Z), which is not directly doable with standard Pythons packages like gzip or zip. This file would need to be uncompressed using OS-dependent software manually. Information on how to do it will be printed the first time the dataset is invoked.

Adding Custom Datasets

QuaPy provides data loaders for simple formats dealing with text, following the format:

class-id \t first document's pre-processed text \n
class-id \t second document's pre-processed text \n
...

and sparse representations of the form:

{-1, 0, or +1} col(int):val(float) col(int):val(float) ... \n
...

The code in charge in loading a LabelledCollection is:

@classmethod
def load(cls, path:str, loader_func:callable):
    return LabelledCollection(*loader_func(path))

indicating that any loader_func (e.g., a user-defined one) which returns valid arguments for initializing a LabelledCollection object will allow to load any collection. In particular, the LabelledCollection receives as arguments the instances (as an iterable) and the labels (as an iterable) and, additionally, the number of classes can be specified (it would otherwise be inferred from the labels, but that requires at least one positive example for all classes to be present in the collection).

The same loader_func can be passed to a Dataset, along with two paths, in order to create a training and test pair of LabelledCollection, e.g.:

import quapy as qp
train_path = '../my_data/train.dat'
test_path = '../my_data/test.dat'
def my_custom_loader(path):
    with open(path, 'rb') as fin:
        ...
    return instances, labels
data = qp.data.Dataset.load(train_path, test_path, my_custom_loader)

Data Processing

QuaPy implements a number of preprocessing functions in the package qp.data.preprocessing, including:

• text2tfidf: tfidf vectorization

• reduce_columns: reducing the number of columns based on term frequency

• standardize: transforms the column values into z-scores (i.e., subtract the mean and normalizes by the standard deviation, so that the column values have zero mean and unit variance).

• index: transforms textual tokens into lists of numeric ids)