Merge branch 'lorenzovolpi-ucimulti_wiki' into devel
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@ -263,39 +263,76 @@ greater than for other datasets, and this has a disproportionate impact in the a
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### Multiclass datasets
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A collection of 5 multiclass datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php). The datasets were first used
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in [this paper](https://arxiv.org/abs/2401.00490) and can be instantiated as follows:
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A collection of 24 multiclass datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php).
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Some of the datasets were first used in [this paper](https://arxiv.org/abs/2401.00490) and can be instantiated as follows:
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```python
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import quapy as qp
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data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', test_split=0.4, verbose=True)
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data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', verbose=True)
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```
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A dataset can be instantiated filtering classes with a minimum number of instances using the `min_class_support` parameter
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(default: `100`) as folows:
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```python
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import quapy as qp
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data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', min_class_support=50, verbose=True)
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```
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There are no pre-defined train-test partitions for these datasets, but you can easily create your own with the
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`split_stratified` method, e.g., `data.split_stratified()`. This is equivalent
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to calling the following method directly:
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`split_stratified` method, e.g., `data.split_stratified()`. This can be also achieved using the method `fetch_UCIMulticlassDataset`
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as shown below:
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```python
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data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, verbose=True)
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train, test = data.train_test
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```
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The datasets correspond to all the datasets that can be retrieved from the platform
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using the following filters:
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This method tries to respect the `min_test_split` value while generating the train-test partition, but the resulting training set
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will not be bigger than `max_train_instances`, which defaults to `25000`. A bigger value can be passed as a parameter:
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```python
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data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, max_train_instances=30000, verbose=True)
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train, test = data.train_test
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```
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The datasets correspond to a part of the datasets that can be retrieved from the platform using the following filters:
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* datasets for classification
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* more than 2 classes
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* containing at least 1,000 instances
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* can be imported using the Python API.
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Some statistics about these datasets are displayed below:
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Some statistics about these datasets are displayed below :
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| **Dataset** | **classes** | **train size** | **test size** |
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|------------------|:-----------:|:--------------:|:-------------:|
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| dry-bean | 7 | 9527 | 4084 |
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| wine-quality | 7 | 3428 | 1470 |
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| academic-success | 3 | 3096 | 1328 |
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| digits | 10 | 3933 | 1687 |
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| letter | 26 | 14000 | 6000 |
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| **Dataset** | **classes** | **instances** | **features** | **prevs** | **type** |
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|:------------|:-----------:|:-------------:|:------------:|:----------|:--------:|
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| dry-bean | 7 | 13611 | 16 | [0.097, 0.038, 0.120, 0.261, 0.142, 0.149, 0.194] | dense |
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| wine-quality | 5 | 6462 | 11 | [0.033, 0.331, 0.439, 0.167, 0.030] | dense |
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| academic-success | 3 | 4424 | 36 | [0.321, 0.179, 0.499] | dense |
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| 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 |
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| 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 |
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| 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 |
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| obesity | 7 | 2111 | 23 | [0.129, 0.136, 0.166, 0.141, 0.153, 0.137, 0.137] | dense |
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| nursery | 4 | 12958 | 19 | [0.333, 0.329, 0.312, 0.025] | dense |
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| yeast | 4 | 1299 | 8 | [0.356, 0.125, 0.188, 0.330] | dense |
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| 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 |
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| satellite | 6 | 6435 | 36 | [0.238, 0.109, 0.211, 0.097, 0.110, 0.234] | dense |
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| shuttle | 4 | 57927 | 7 | [0.787, 0.003, 0.154, 0.056] | dense |
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| cmc | 3 | 1473 | 9 | [0.427, 0.226, 0.347] | dense |
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| 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 |
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| waveform-v1 | 3 | 5000 | 21 | [0.331, 0.329, 0.339] | dense |
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| molecular | 3 | 3190 | 227 | [0.240, 0.241, 0.519] | dense |
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| poker_hand | 8 | 1024985 | 10 | [0.501, 0.423, 0.048, 0.021, 0.004, 0.002, 0.001, 0.000] | dense |
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| connect-4 | 3 | 67557 | 84 | [0.095, 0.246, 0.658] | dense |
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| mhr | 3 | 1014 | 6 | [0.268, 0.400, 0.331] | dense |
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| 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 |
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| page_block | 3 | 5357 | 10 | [0.917, 0.061, 0.021] | dense |
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| phishing | 3 | 1353 | 9 | [0.519, 0.076, 0.405] | dense |
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| image_seg | 7 | 2310 | 19 | [0.143, 0.143, 0.143, 0.143, 0.143, 0.143, 0.143] | dense |
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| hcv | 4 | 1385 | 28 | [0.243, 0.240, 0.256, 0.261] | dense |
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Values shown above refer to datasets obtained through `fetchUCIMulticlassLabelledCollection` using all default parameters.
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## LeQua 2022 Datasets
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@ -1432,12 +1432,10 @@ class AggregativeMedianEstimator(BinaryQuantifier):
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def _delayed_fit_classifier(self, args):
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with qp.util.temp_seed(self.random_state):
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print('enter job')
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cls_params, training, kwargs = args
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model = deepcopy(self.base_quantifier)
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model.set_params(**cls_params)
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predictions = model.classifier_fit_predict(training, **kwargs)
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print('exit job')
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return (model, predictions)
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def _delayed_fit_aggregation(self, args):
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@ -1467,7 +1465,6 @@ class AggregativeMedianEstimator(BinaryQuantifier):
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backend='threading'
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)
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else:
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print('only 1')
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model = self.base_quantifier
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model.set_params(**cls_configs[0])
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predictions = model.classifier_fit_predict(training, **kwargs)
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