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https://github.com/automl/Auto-PyTorch

Automatic architecture search and hyperparameter optimization for PyTorch
https://github.com/automl/Auto-PyTorch

automl deep-learning pytorch tabular-data

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Automatic architecture search and hyperparameter optimization for PyTorch

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README 

        
# Auto-PyTorch



Copyright (C) 2021  [AutoML Groups Freiburg and Hannover](http://www.automl.org/)



While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed **Auto-PyTorch**, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL).



Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series data (forecasting).

The newest features in Auto-PyTorch for tabular data are described in the paper ["Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL"](https://arxiv.org/abs/2006.13799) (see below for bibtex ref).

Details about Auto-PyTorch for multi-horizontal time series forecasting tasks can be found in the paper ["Efficient Automated Deep Learning for Time Series Forecasting"](https://arxiv.org/abs/2205.05511) (also see below for bibtex ref).



Also, find the documentation [here](https://automl.github.io/Auto-PyTorch/master).



***From v0.1.0, AutoPyTorch has been updated to further improve usability, robustness and efficiency by using SMAC as the underlying optimization package as well as changing the code structure. Therefore, moving from v0.0.2 to v0.1.0 will break compatibility. 

In case you would like to use the old API, you can find it at [`master_old`](https://github.com/automl/Auto-PyTorch/tree/master-old).***



## Workflow



The rough description of the workflow of Auto-Pytorch is drawn in the following figure.



![AutoPyTorch Workflow](https://raw.githubusercontent.com/automl/Auto-PyTorch/master/figs/apt_workflow.png)



In the figure, **Data** is provided by user and

**Portfolio** is a set of configurations of neural networks that work well on diverse datasets.

The current version only supports the *greedy portfolio* as described in the paper *Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL*

This portfolio is used to warm-start the optimization of SMAC.

In other words, we evaluate the portfolio on a provided data as initial configurations.

Then API starts the following procedures:

1. **Validate input data**: Process each data type, e.g. encoding categorical data, so that Auto-Pytorch can handled.

2. **Create dataset**: Create a dataset that can be handled in this API with a choice of cross validation or holdout splits.

3. **Evaluate baselines** 

   * ***Tabular dataset*** *1: Train each algorithm in the predefined pool with a fixed hyperparameter configuration and dummy model from `sklearn.dummy` that represents the worst possible performance.

   * ***Time Series Forecasting dataset*** : Train a dummy predictor that repeats the last observed value in each series

4. **Search by [SMAC](https://github.com/automl/SMAC3)**:\

    a. Determine budget and cut-off rules by [Hyperband](https://jmlr.org/papers/volume18/16-558/16-558.pdf)\

    b. Sample a pipeline hyperparameter configuration *2 by SMAC\

    c. Update the observations by obtained results\

    d. Repeat a. -- c. until the budget runs out

5. Build the best ensemble for the provided dataset from the observations and [model selection of the ensemble](https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf).



*1: Baselines are a predefined pool of machine learning algorithms, e.g. LightGBM and support vector machine, to solve either regression or classification task on the provided dataset



*2: A pipeline hyperparameter configuration specifies the choice of components, e.g. target algorithm, the shape of neural networks, in each step and 

(which specifies the choice of components in each step and their corresponding hyperparameters.



## Installation



### PyPI Installation



```sh



pip install autoPyTorch



```



Auto-PyTorch for Time Series Forecasting requires additional dependencies 



```sh



pip install autoPyTorch[forecasting]



```



### Manual Installation



We recommend using Anaconda for developing as follows:



```sh

# Following commands assume the user is in a cloned directory of Auto-Pytorch



# We also need to initialize the automl_common repository as follows

# You can find more information about this here:

# https://github.com/automl/automl_common/

git submodule update --init --recursive



# Create the environment

conda create -n auto-pytorch python=3.8

conda activate auto-pytorch

conda install swig

python setup.py install



```



Similarly, to install all the dependencies for Auto-PyTorch-TimeSeriesForecasting:



```sh



git submodule update --init --recursive



conda create -n auto-pytorch python=3.8

conda activate auto-pytorch

conda install swig

pip install -e[forecasting]



```



## Examples



In a nutshell:



```py

from autoPyTorch.api.tabular_classification import TabularClassificationTask



# data and metric imports

import sklearn.model_selection

import sklearn.datasets

import sklearn.metrics

X, y = sklearn.datasets.load_digits(return_X_y=True)

X_train, X_test, y_train, y_test = \

        sklearn.model_selection.train_test_split(X, y, random_state=1)



# initialise Auto-PyTorch api

api = TabularClassificationTask()



# Search for an ensemble of machine learning algorithms

api.search(

    X_train=X_train,

    y_train=y_train,

    X_test=X_test,

    y_test=y_test,

    optimize_metric='accuracy',

    total_walltime_limit=300,

    func_eval_time_limit_secs=50

)



# Calculate test accuracy

y_pred = api.predict(X_test)

score = api.score(y_pred, y_test)

print("Accuracy score", score)

```



For Time Series Forecasting Tasks

```py



from autoPyTorch.api.time_series_forecasting import TimeSeriesForecastingTask



# data and metric imports

from sktime.datasets import load_longley

targets, features = load_longley()



# define the forecasting horizon

forecasting_horizon = 3



# Dataset optimized by APT-TS can be a list of np.ndarray/ pd.DataFrame where each series represents an element in the 

# list, or a single pd.DataFrame that records the series

# index information: to which series the timestep belongs? This id can be stored as the DataFrame's index or a separate

# column

# Within each series, we take the last forecasting_horizon as test targets. The items before that as training targets

# Normally the value to be forecasted should follow the training sets

y_train = [targets[: -forecasting_horizon]]

y_test = [targets[-forecasting_horizon:]]



# same for features. For uni-variant models, X_train, X_test can be omitted and set as None

X_train = [features[: -forecasting_horizon]]

# Here x_test indicates the 'known future features': they are the features known previously, features that are unknown

# could be replaced with NAN or zeros (which will not be used by our networks). If no feature is known beforehand,

# we could also omit X_test

known_future_features = list(features.columns)

X_test = [features[-forecasting_horizon:]]



start_times = [targets.index.to_timestamp()[0]]

freq = '1Y'



# initialise Auto-PyTorch api

api = TimeSeriesForecastingTask()



# Search for an ensemble of machine learning algorithms

api.search(

    X_train=X_train,

    y_train=y_train,

    X_test=X_test, 

    optimize_metric='mean_MAPE_forecasting',

    n_prediction_steps=forecasting_horizon,

    memory_limit=16 * 1024,  # Currently, forecasting models use much more memories

    freq=freq,

    start_times=start_times,

    func_eval_time_limit_secs=50,

    total_walltime_limit=60,

    min_num_test_instances=1000,  # proxy validation sets. This only works for the tasks with more than 1000 series

    known_future_features=known_future_features,

)



# our dataset could directly generate sequences for new datasets

test_sets = api.dataset.generate_test_seqs()



# Calculate test accuracy

y_pred = api.predict(test_sets)

score = api.score(y_pred, y_test)

print("Forecasting score", score)

```



For more examples including customising the search space, parellising the code, etc, checkout the `examples` folder



```sh

$ cd examples/

```



Code for the [paper](https://arxiv.org/abs/2006.13799) is available under `examples/ensemble` in the [TPAMI.2021.3067763](https://github.com/automl/Auto-PyTorch/tree/TPAMI.2021.3067763`) branch.



## Contributing



If you want to contribute to Auto-PyTorch, clone the repository and checkout our current development branch



```sh

$ git checkout development

```



## License



This program is free software: you can redistribute it and/or modify

it under the terms of the Apache license 2.0 (please see the LICENSE file).



This program is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.



You should have received a copy of the Apache license 2.0

along with this program (see LICENSE file).



## Reference



Please refer to the branch `TPAMI.2021.3067763` to reproduce the paper *Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL*.



```bibtex

  @article{zimmer-tpami21a,

  author = {Lucas Zimmer and Marius Lindauer and Frank Hutter},

  title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},

  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},

  year = {2021},

  note = {also available under https://arxiv.org/abs/2006.13799},

  pages = {3079 - 3090}

}

```



```bibtex

@incollection{mendoza-automlbook18a,

  author    = {Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter},

  title     = {Towards Automatically-Tuned Deep Neural Networks},

  year      = {2018},

  month     = dec,

  editor    = {Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin},

  booktitle = {AutoML: Methods, Sytems, Challenges},

  publisher = {Springer},

  chapter   = {7},

  pages     = {141--156}

}

```



```bibtex

@article{deng-ecml22,

  author    = {Difan Deng and Florian Karl and Frank Hutter and Bernd Bischl and Marius Lindauer},

  title     = {Efficient Automated Deep Learning for Time Series Forecasting},

  year      = {2022},

  booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track

               - European Conference, {ECML} {PKDD} 2022},

  url       = {https://doi.org/10.48550/arXiv.2205.05511},

}

```



## Contact



Auto-PyTorch is developed by the [AutoML Groups of the University of Freiburg and Hannover](http://www.automl.org/).