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https://github.com/ealcobaca/pymfe
Python Meta-Feature Extractor package.
https://github.com/ealcobaca/pymfe
automl machine-learning meta-feature meta-features meta-learning metalearning
Last synced: 3 months ago
JSON representation
Python Meta-Feature Extractor package.
- Host: GitHub
- URL: https://github.com/ealcobaca/pymfe
- Owner: ealcobaca
- License: mit
- Created: 2018-11-19T15:20:11.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-22T14:24:16.000Z (5 months ago)
- Last Synced: 2024-07-26T02:25:51.144Z (4 months ago)
- Topics: automl, machine-learning, meta-feature, meta-features, meta-learning, metalearning
- Language: Python
- Homepage: https://pymfe.readthedocs.io
- Size: 1.8 MB
- Stars: 123
- Watchers: 6
- Forks: 28
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
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# 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.
> "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." - ([Brazdil et al. (2008)](https://www.springer.com/gp/book/9783540732624))
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)](https://link.springer.com/chapter/10.1007/978-3-319-31753-3_18) and
[Rivolli et al. (2018)](https://arxiv.org/abs/1808.10406v2) defined a meta-feature as
follows. Let $D \in \mathcal{D}$ be a dataset, $m\colon \mathcal{D} \to \mathbb{R}^{k'}$
be a characterization measure, and $\sigma\colon \mathbb{R}^{k'} \to \mathbb{R}^{k}$
be a summarization function. Both $m$ and $\sigma$ have also hyperparameters associated,
$h_m$ and $h_\sigma$ respectively. Thus, a meta-feature $f\colon \mathcal{D} \to \mathbb{R}^{k}$
for a given dataset $D$ is
$$
f\big(D\big) = \sigma\big(m(D,h_m), h_\sigma\big).
$$
The measure $m$ can extract more than one value from each data set, i.e.,
$k'$ can vary according to $D$, which can be mapped to a vector of fixed length
$k$ using a summarization function $\sigma$.
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; and
- **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](https://pymfe.readthedocs.io/en/latest/api.html).
## Dependencies
The main `pymfe` requirement is:
* Python (>= 3.6)
## Installation
The installation process is similar to other packages available on pip:
```bash
pip install -U pymfe
```
It is possible to install the development version using:
```bash
pip install -U git+https://github.com/ealcobaca/pymfe
```
or
```bash
git clone https://github.com/ealcobaca/pymfe.git
cd pymfe
python 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:
```python
# 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:
```python
# 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:
```python
## 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:
```python
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.
```python
# 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:
```python
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.
```python
# 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](https://pymfe.readthedocs.io/en/latest/?badge=latest)
to guide you on how to use the pymfe library.
You can find in the documentation interesting pages like:
* [Getting started](https://pymfe.readthedocs.io/en/latest/install.html)
* [API documentation](https://pymfe.readthedocs.io/en/latest/api.html)
* [Examples](https://pymfe.readthedocs.io/en/latest/auto_examples/index.html)
* [News about pymfe](https://pymfe.readthedocs.io/en/latest/new.html)
## Developer notes
* We are glad to accept any contributions, please check
[Contributing](https://github.com/ealcobaca/pymfe/blob/master/CONTRIBUTING.md)
and the [Documentation](https://pymfe.readthedocs.io/en/latest/?badge=latest).
* To submit bugs and feature requests, report at
[project issues](https://github.com/ealcobaca/pymfe/issues).
## License
This project is licensed under the MIT License - see the
[License](https://github.com/ealcobaca/pymfe/blob/master/LICENCE) file for
details.
## Cite Us
If you use the `pymfe` in scientific publication, we would appreciate citations
to the following paper:
[Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia,
Jefferson T. Oliva, & André C. P. L. F. de Carvalho (2020).
MFE: Towards reproducible meta-feature extraction.
Journal of Machine Learning Research, 21(111), 1-5.](http://jmlr.org/papers/v21/19-348.html)
You can also use the bibtex format:
```bibtex
@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](https://github.com/ealcobaca/pymfe/graphs/contributors)
that directly or indirectly has make this project to happen. Thank you all.
## References
1. [Brazdil, P., Carrier, C. G., Soares, C., & Vilalta, R. (2008). Metalearning:
Applications to data mining. Springer Science
and Business Media.](https://www.springer.com/gp/book/9783540732624)
2. [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.](https://link.springer.com/chapter/10.1007/978-3-319-31753-3_18)
3. [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.](https://arxiv.org/abs/1808.10406v2)