### Install AutoFolio Dependencies and Package

Source: https://github.com/automl/autofolio/blob/master/doc/installation.rst

Use these commands to install AutoFolio from the command line. Ensure you have Python 3.5 or higher.

```bash
cat requirements.txt | xargs -n 1 -L 1 pip install
python setup.py install
```

--------------------------------

### Install AutoFolio Requirements

Source: https://github.com/automl/autofolio/blob/master/README.md

Installs all requirements for AutoFolio using pip from a requirements file. Ensure you have the development branch of SMAC installed.

```bash
cat requirements.txt | xargs -n 1 -L 1 pip install
```

--------------------------------

### Get AutoFolio Command-Line Help

Source: https://github.com/automl/autofolio/blob/master/README.md

Displays all available command-line options and arguments for the AutoFolio script. Use this to understand how to run AutoFolio and its various settings.

```bash
python3 scripts/autofolio --help
```

--------------------------------

### AutFolio Tuned Performance Output

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Example output after tuning AutFolio, indicating improved average solution quality.

```bash
INFO:AutoFolio:>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
INFO:AutoFolio:CV Stats
INFO:Stats:Number of instances: 10
INFO:Stats:Average Solution Quality: 2.0000
```

--------------------------------

### Install Anaconda Dependencies for SMAC

Source: https://github.com/automl/autofolio/blob/master/README.md

Installs necessary packages (gxx_linux-64, gcc_linux-64, swig) using conda, which are prerequisites for installing SMAC.

```bash
conda install gxx_linux-64 gcc_linux-64 swig
```

--------------------------------

### AutFolio Cross-Validation Output

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Example output from AutFolio during cross-validation, showing performance statistics.

```bash
INFO:AutoFolio:>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
INFO:AutoFolio:CV Stats
INFO:Stats:Number of instances: 10
INFO:Stats:Average Solution Quality: 6.0000
```

--------------------------------

### Predict with AutoFolio

Source: https://github.com/automl/autofolio/blob/master/README.md

To predict for new instances, first train and save AutoFolio's state using `python3 scripts/autofolio --save [filename]`. Then, load the saved state and provide the feature vector using `python3 scripts/autofolio --load [filename] --feature_vec "[space-separated feature vector]"`. Ensure the feature vector is quoted.

```bash
python3 scripts/autofolio --load [filename] --feature_vec "[space-separated feature vector]"
```

--------------------------------

### AutFolio ASlib Scenario Input

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Specify the directory containing ASlib scenario files for advanced input format.

```bash
-s SCENARIO, --scenario SCENARIO
                        directory with ASlib scenario files (required if not
                        using --load or csv input files (default: None)
```

--------------------------------

### Tune AutFolio Parameters

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Run AutFolio with the tune flag to automatically optimize its parameters for better performance. This is useful when default parameters yield suboptimal results.

```bash
cd examples/toy_example_csv/
python ../../scripts/autofolio --perf perf.csv --feature_csv feats.csv -t
```

--------------------------------

### AutFolio CSV Input Options

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Command-line arguments for specifying objective, runtime cutoff, and maximization for CSV input.

```bash
--objective {runtime,solution_quality}
                        Are the objective values in the performance data
                        runtimes or an arbitrary solution quality (or cost)
                        value (default: solution_quality)
  --runtime_cutoff RUNTIME_CUTOFF
                        cutoff time for each algorithm run for the performance
                        data (default: None)
  --maximize            Set this parameter to indicate maximization of the
                        performance metric (default: minimization) (default:
                        False)
```

--------------------------------

### Run AutFolio with CSV Input

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Execute AutFolio using CSV files for performance and features. This command initiates a 10-fold cross-validation.

```bash
cd examples/toy_example_csv/
python ../../scripts/autofolio --perf perf.csv --feature_csv feats.csv 
```

--------------------------------

### Enable Self-Tuning Mode in AutoFolio

Source: https://github.com/automl/autofolio/blob/master/README.md

Use the `--tune` option to enable algorithm configuration for optimizing AutoFolio's performance.

```bash
python3 scripts/autofolio --tune
```

--------------------------------

### Save Outer Cross-Validation Choices in AutoFolio

Source: https://github.com/automl/autofolio/blob/master/README.md

Utilize the `--out-template` option to save the learned model and solver choices for each instance. The template string uses placeholders like "${fold}" and "${type}". Ensure the template is enclosed in single quotes to prevent shell interpretation.

```bash
python3 scripts/autofolio -s examples/asp-aslib/data/ --outer-cv --out-template 'asp.fold-${fold}.${type}'
```

--------------------------------

### Parallelize Outer Cross-Validation Folds in AutoFolio

Source: https://github.com/automl/autofolio/blob/master/README.md

Run a specific outer cross-validation fold using the `--outer-cv-fold` option, typically combined with `--out-template`. This is useful for parallelizing outer CV calls across a cluster. Fold numbers range from 1 to 10.

```bash
python3 scripts/autofolio -s examples/asp-aslib/data/ --outer-cv --outer-cv-fold 1 --out-template 'asp.fold-${fold}.${type}'
```

--------------------------------

### Save AutFolio Internal State

Source: https://github.com/automl/autofolio/blob/master/doc/manual.rst

Save the trained internal state of AutFolio to a file for later prediction. Use this command before predicting on new instances.

```python
python3 scripts/autofolio --save [filename]
```

--------------------------------

### Enable Outer Cross-Validation in AutoFolio

Source: https://github.com/automl/autofolio/blob/master/README.md

Use the `--outer-cv` flag to enable the outer cross-validation mode for evaluating AutoFolio. This mode uses a 10-fold cross-validation scheme where the test subset is not seen during training.

```bash
python3 scripts/autofolio -s examples/asp-aslib/data/ --outer-cv
```

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