Meta-feature Extractor

## Project description

# pymfe: Python Meta-Feature Extractor

The pymfe (**py**thon **m**eta-**f**eature **e**xtractor) provides a
comprehensive set of meta-features implemented in python. The package brings
cutting edge meta-features, following recent literature propose. The pymfe
architecture was thought to systematically make the extraction, which can
produce a robust set of meta-features. Moreover, pymfe follows recent
meta-feature formalization aiming to make MtL reproducible.

Here, you can use different measures and summary functions, setting their hyperparameters, and also measuring automatically the elapsed time. Moreover, you can extract meta-features from specific models, or even extract meta-features with confidence intervals using bootstrap. There are a lot of other interesting features and you can see more about it looking at the documentation.

## Meta-feature

In the Meta-learning (MtL) literature, meta-features are measures used to characterize data sets and/or their relations with algorithm bias. According to Brazdil et al. (2008), "Meta-learning is the study of principled methods that exploit meta-knowledge to obtain efficient models and solutions by adapting the machine learning and data mining process".

Meta-features are used in MtL and AutoML tasks in general, to represent/understand a dataset, to understanding a learning bias, to create machine learning (or data mining) recommendations systems, and to create surrogates models, to name a few.

Pinto et al. (2016) and Rivolli et al. (2018) defined a meta-feature as follows. Let be a dataset, be a characterization measure, and be a summarization function. Both and have also hyperparameters associated, and respectively. Thus, a meta-feature for a given dataset is:

.

The measure can extract more than one value from each data set, i.e., can vary according to , which can be mapped to a vector of fixed length using a summarization function .

In this package, We provided the following meta-features groups:

**General**: General information related to the dataset, also known as simple measures, such as the number of instances, attributes and classes.**Statistical**: Standard statistical measures to describe the numerical properties of data distribution.**Information-theoretic**: Particularly appropriate to describe discrete (categorical) attributes and their relationship with the classes.**Model-based**: Measures designed to extract characteristics from simple machine learning models.**Landmarking**: Performance of simple and efficient learning algorithms.**Relative Landmarking**: Relative performance of simple and efficient learning algorithms.**Subsampling Landmarking**: Performance of simple and efficient learning algorithms from a subsample of the dataset.**Clustering**: Clustering measures extract information about dataset based on external validation indexes.**Concept**: Estimate the variability of class labels among examples and the examples density.**Itemset**: Compute the correlation between binary attributes.**Complexity**: Estimate the difficulty in separating the data points into their expected classes.

In the pymfe package, you can use different measures and summary functions, setting their hyperparameters, and automatically measure the elapsed time. Moreover, you can extract meta-features from specific models, or even obtain meta-features with confidence intervals using bootstrap. There are many other exciting features. You can see more about it looking at the documentation.

## Dependencies

The main `pymfe`

requirement is:

- Python (>= 3.6)

## Installation

The installation process is similar to other packages available on pip:

pip install -U pymfe

It is possible to install the development version using:

pip install -U git+https://github.com/ealcobaca/pymfe

or

```
git clone https://github.com/ealcobaca/pymfe.git
cd pymfe
python3 setup.py install
```

## Example of use

The simplest way to extract meta-features is by instantiating the `MFE`

class.
It computes five meta-features groups by default using mean and standard
deviation as summary functions: General, Statistical, Information-theoretic,
Model-based, and Landmarking. The `fit`

method can be called by passing the `X`

and `y`

. Then the `extract`

method is used to extract the related measures.
A simple example using `pymfe`

for supervised tasks is given next:

# Load a dataset from sklearn.datasets import load_iris from pymfe.mfe import MFE data = load_iris() y = data.target X = data.data # Extract default measures mfe = MFE() mfe.fit(X, y) ft = mfe.extract() print(ft) # Extract general, statistical and information-theoretic measures mfe = MFE(groups=["general", "statistical", "info-theory"]) mfe.fit(X, y) ft = mfe.extract() print(ft) # Extract all available measures mfe = MFE(groups="all") mfe.fit(X, y) ft = mfe.extract() print(ft)

You can simply omit the target attribute for unsupervised tasks while fitting
the data into the `MFE`

model. The `pymfe`

package automatically finds and
extracts only the metafeatures suitable for this type of task. Examples are
given next:

# Load a dataset from sklearn.datasets import load_iris from pymfe.mfe import MFE data = load_iris() y = data.target X = data.data # Extract default unsupervised measures mfe = MFE() mfe.fit(X) ft = mfe.extract() print(ft) # Extract all available unsupervised measures mfe = MFE(groups="all") mfe.fit(X) ft = mfe.extract() print(ft)

Several measures return more than one value. To aggregate the returned values,
summarization function can be used. This method can compute `min`

, `max`

,
`mean`

, `median`

, `kurtosis`

, `standard deviation`

, among others. The default
methods are the `mean`

and the `sd`

. Next, it is possible to see an example of
the use of this method:

## Extract default measures using min, median and max mfe = MFE(summary=["min", "median", "max"]) mfe.fit(X, y) ft = mfe.extract() print(ft) ## Extract default measures using quantile mfe = MFE(summary=["quantiles"]) mfe.fit(X, y) ft = mfe.extract() print(ft)

You can easily list all available metafeature groups, metafeatures, summary methods and metafeatures filtered by groups of interest:

from pymfe.mfe import MFE # Check all available meta-feature groups in the package print(MFE.valid_groups()) # Check all available meta-features in the package print(MFE.valid_metafeatures()) # Check available meta-features filtering by groups of interest print(MFE.valid_metafeatures(groups=["general", "statistical", "info-theory"])) # Check all available summary functions in the package print(MFE.valid_summary())

It is possible to pass custom arguments to every metafeature using `MFE`

`extract`

method kwargs. The keywords must be the target metafeature name, and
the value must be a dictionary in the format {`argument`

: `value`

}, i.e., each
key in the dictionary is a target argument with its respective value. In the
example below, the extraction of metafeatures `min`

and `max`

happens as
usual, but the metafeatures `sd,`

`nr_norm`

and `nr_cor_attr`

will receive user
custom argument values, which will interfere in each metafeature result.

# Extract measures with custom user arguments mfe = MFE(features=["sd", "nr_norm", "nr_cor_attr", "min", "max"]) mfe.fit(X, y) ft = mfe.extract( sd={"ddof": 0}, nr_norm={"method": "all", "failure": "hard", "threshold": 0.025}, nr_cor_attr={"threshold": 0.6}, ) print(ft)

If you want to extract metafeatures from a pre-fitted machine learning model
(from `sklearn package`

), you can use the `extract_from_model`

method without
needing to use the training data:

import sklearn.tree from sklearn.datasets import load_iris from pymfe.mfe import MFE # Extract from model iris = load_iris() model = sklearn.tree.DecisionTreeClassifier().fit(iris.data, iris.target) extractor = MFE() ft = extractor.extract_from_model(model) print(ft) # Extract specific metafeatures from model extractor = MFE(features=["tree_shape", "nodes_repeated"], summary="histogram") ft = extractor.extract_from_model( model, arguments_fit={"verbose": 1}, arguments_extract={"verbose": 1, "histogram": {"bins": 5}}) print(ft)

You can also extract your metafeatures with confidence intervals using bootstrap. Keep in mind that this method extracts each metafeature several times, and may be very expensive depending mainly on your data and the number of metafeature extract methods called.

# Extract metafeatures with confidence interval mfe = MFE(features=["mean", "nr_cor_attr", "sd", "max"]) mfe.fit(X, y) ft = mfe.extract_with_confidence( sample_num=256, confidence=0.99, verbose=1, ) print(ft)

## Documentation

We write a great Documentation to guide you on how to use the pymfe library. You can find in the documentation interesting pages like:

## Developer notes

- We are glad to accept any contributions, please check Contributing and the Documentation.
- To submit bugs and feature requests, report at project issues.

## License

This project is licensed under the MIT License - see the License file for details.

## Cite Us

If you use the `pymfe`

in scientific publication, we would appreciate citations
to the following paper:

You can also use the bibtex format:

@article{JMLR:v21:19-348, author = {Edesio Alcobaça and Felipe Siqueira and Adriano Rivolli and Luís P. F. Garcia and Jefferson T. Oliva and André C. P. L. F. de Carvalho }, title = {MFE: Towards reproducible meta-feature extraction}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {111}, pages = {1-5}, url = {http://jmlr.org/papers/v21/19-348.html} }

## Acknowledgments

We would like to thank every Contributor that directly or indirectly has make this project to happen. Thank you all.

## References

- Brazdil, P., Carrier, C. G., Soares, C., & Vilalta, R. (2008). Metalearning: Applications to data mining. Springer Science and Business Media.
- Pinto, F., Soares, C., & Mendes-Moreira, J. (2016, April). Towards automatic generation of metafeatures. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 215-226). Springer, Cham.
- Rivolli, A., Garcia, L. P. F., Soares, C., Vanschoren, J., and de Carvalho, A. C. P. L. F. (2018). Characterizing classification datasets: a study of meta-features for meta-learning. arXiv:1808.10406.

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