20 KiB
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 prevalence values:
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)
...However, generating samples for evaluation purposes is tackled in QuaPy by means of the evaluation protocols (see the dedicated entries in the manuals for evaluation and protocols).
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_DATASETSSome 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
Binary datasets
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_UCIBinaryDataset('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_UCIBinaryLabelledCollection("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 | 21 | [0.222, 0.778] | dense |
| ctg.2 | 2 | 2126 | 21 | [0.861, 0.139] | dense |
| ctg.3 | 2 | 2126 | 21 | [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 |
Notes:
All datasets will be downloaded automatically the first time they are requested, and stored in the quapy_data folder for faster further reuse.
However, notice that it is a good idea to ignore datasets: * acute.a and acute.b: these are very easy and many classifiers would score 100% accuracy * balance.2: this is extremely difficult; probably there is some problem with this dataset, the errors it tends to produce are orders of magnitude greater than for other datasets, and this has a disproportionate impact in the average performance.
Multiclass datasets
A collection of 24 multiclass datasets from the UCI Machine Learning repository. Some of the datasets were first used in this paper and can be instantiated as follows:
import quapy as qp
data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', verbose=True)A dataset can be instantiated filtering classes with a minimum number
of instances using the min_class_support parameter
(default: 100) as folows:
import quapy as qp
data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', min_class_support=50, verbose=True)There are no pre-defined train-test partitions for these datasets,
but you can easily create your own with the
split_stratified method, e.g.,
data.split_stratified(). This can be also achieved using
the method fetch_UCIMulticlassDataset as shown below:
data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, verbose=True)
train, test = data.train_testThis method tries to respect the min_test_split value
while generating the train-test partition, but the resulting training
set will not be bigger than max_train_instances, which
defaults to 25000. A bigger value can be passed as a
parameter:
data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, max_train_instances=30000, verbose=True)
train, test = data.train_testThe datasets correspond to a part of the datasets that can be retrieved from the platform using the following filters: * datasets for classification * more than 2 classes * containing at least 1,000 instances * can be imported using the Python API.
Some statistics about these datasets are displayed below :
| Dataset | classes | instances | features | prevs | type |
|---|---|---|---|---|---|
| dry-bean | 7 | 13611 | 16 | [0.097, 0.038, 0.120, 0.261, 0.142, 0.149, 0.194] | dense |
| wine-quality | 5 | 6462 | 11 | [0.033, 0.331, 0.439, 0.167, 0.030] | dense |
| academic-success | 3 | 4424 | 36 | [0.321, 0.179, 0.499] | dense |
| digits | 10 | 5620 | 64 | [0.099, 0.102, 0.099, 0.102, 0.101, 0.099, 0.099, 0.101, 0.099, 0.100] | dense |
| letter | 26 | 20000 | 16 | [0.039, 0.038, 0.037, 0.040, 0.038, 0.039, 0.039, 0.037, 0.038, 0.037, 0.037, 0.038, 0.040, 0.039, 0.038, 0.040, 0.039, 0.038, 0.037, 0.040, 0.041, 0.038, 0.038, 0.039, 0.039, 0.037] | dense |
| abalone | 11 | 3842 | 9 | [0.030, 0.067, 0.102, 0.148, 0.179, 0.165, 0.127, 0.069, 0.053, 0.033, 0.027] | dense |
| obesity | 7 | 2111 | 23 | [0.129, 0.136, 0.166, 0.141, 0.153, 0.137, 0.137] | dense |
| nursery | 4 | 12958 | 19 | [0.333, 0.329, 0.312, 0.025] | dense |
| yeast | 4 | 1299 | 8 | [0.356, 0.125, 0.188, 0.330] | dense |
| hand_digits | 10 | 10992 | 16 | [0.104, 0.104, 0.104, 0.096, 0.104, 0.096, 0.096, 0.104, 0.096, 0.096] | dense |
| satellite | 6 | 6435 | 36 | [0.238, 0.109, 0.211, 0.097, 0.110, 0.234] | dense |
| shuttle | 4 | 57927 | 7 | [0.787, 0.003, 0.154, 0.056] | dense |
| cmc | 3 | 1473 | 9 | [0.427, 0.226, 0.347] | dense |
| isolet | 26 | 7797 | 617 | [0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038] | dense |
| waveform-v1 | 3 | 5000 | 21 | [0.331, 0.329, 0.339] | dense |
| molecular | 3 | 3190 | 227 | [0.240, 0.241, 0.519] | dense |
| poker_hand | 8 | 1024985 | 10 | [0.501, 0.423, 0.048, 0.021, 0.004, 0.002, 0.001, 0.000] | dense |
| connect-4 | 3 | 67557 | 84 | [0.095, 0.246, 0.658] | dense |
| mhr | 3 | 1014 | 6 | [0.268, 0.400, 0.331] | dense |
| chess | 15 | 27870 | 20 | [0.100, 0.051, 0.102, 0.078, 0.017, 0.007, 0.163, 0.061, 0.025, 0.021, 0.014, 0.071, 0.150, 0.129, 0.009] | dense |
| page_block | 3 | 5357 | 10 | [0.917, 0.061, 0.021] | dense |
| phishing | 3 | 1353 | 9 | [0.519, 0.076, 0.405] | dense |
| image_seg | 7 | 2310 | 19 | [0.143, 0.143, 0.143, 0.143, 0.143, 0.143, 0.143] | dense |
| hcv | 4 | 1385 | 28 | [0.243, 0.240, 0.256, 0.261] | dense |
Values shown above refer to datasets obtained through
fetchUCIMulticlassLabelledCollection using all default
parameters.
LeQua 2022 Datasets
QuaPy also provides the datasets used for the LeQua 2022 competition. In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide raw documents instead. Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B are multiclass quantification problems consisting of estimating the class prevalence values of 28 different merchandise products.
Every task consists of a training set, a set of validation samples (for model selection) and a set of test samples (for evaluation). QuaPy returns this data as a LabelledCollection (training) and two generation protocols (for validation and test samples), as follows:
training, val_generator, test_generator = fetch_lequa2022(task=task)See the lequa2022_experiments.py in the examples folder
for further details on how to carry out experiments using these
datasets.
The datasets are downloaded only once, and stored for fast reuse.
Some statistics are summarized below:
| Dataset | classes | train size | validation samples | test samples | docs by sample | type |
|---|---|---|---|---|---|---|
| T1A | 2 | 5000 | 1000 | 5000 | 250 | vector |
| T1B | 28 | 20000 | 1000 | 5000 | 1000 | vector |
| T2A | 2 | 5000 | 1000 | 5000 | 250 | text |
| T2B | 28 | 20000 | 1000 | 5000 | 1000 | text |
For further details on the datasets, we refer to the original paper:
Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).
A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify.IFCB Plankton dataset
IFCB is a dataset of plankton species in water samples hosted in
Zenodo <https://zenodo.org/records/10036244>.
This dataset is based on the data available publicly at
WHOI-Plankton repo <https://github.com/hsosik/WHOI-Plankton>
and in the scripts for the processing are available at
P. González's repo <https://github.com/pglez82/IFCB_Zenodo>_.
This dataset comes with precomputed features for testing quantification algorithms.
Some statistics:
| Training | Validation | Test | |
|---|---|---|---|
| samples | 200 | 86 | 678 |
| total instances | 584474 | 246916 | 2626429 |
| mean per sample | 2922.3 | 2871.1 | 3873.8 |
| min per sample | 266 | 59 | 33 |
| max per sample | 6645 | 7375 | 9112 |
The number of features is 512, while the number of classes is 50. In terms of prevalence, the mean is 0.020, the minimum is 0, and the maximum is 0.978.
The dataset can be loaded for model selection
(for_model_selection=True, thus returning the training and
validation) or for test (for_model_selection=False, thus
returning the training+validation and the test).
Additionally, the training can be interpreted as a list (a generator)
of samples (single_sample_train=False) or as a single
training set (single_sample_train=True).
Example:
train, val_gen = qp.datasets.fetch_IFCB(for_model_selection=True, single_sample_train=True)
# ... model selection
train, test_gen = qp.datasets.fetch_IFCB(for_model_selection=False, single_sample_train=True)
# ... train and evaluationAdding 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)