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-[Utilities, classes and scripts](#utilities-classes-and-scripts)
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-[Datasets](#datasets)
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-[Experiments parallelization with HTCondor](#experiments-parallelization-with-htcondor)
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-[External software](#external-software)
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-[Other contributors](#other-contributors)
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-[References](#references)
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<!-- /TOC -->
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1. A first *'how to' tutorial* ([Jupyter Notebook](doc/orca_tutorial_1.ipynb), [MD](doc/orca_tutorial_1.md)) to get started with ORCA.
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1. A specific *tutorial for naive approaches and decomposition methods* ([Jupyter Notebook](doc/orca_tutorial_2.ipynb), [MD](doc/orca_tutorial_2.md)) covering the different considerations needed for these methods.
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1. A *tutorial for threshold models* ([Jupyter Notebook](doc/orca_tutorial_3.ipynb), [MD](doc/orca_tutorial_3.md)) examining the differences of these models.
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- A guide about how to [paralelize ORCA experiments](doc/orca_parallel.md).
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- A guide about how to [parallelize ORCA experiments](doc/orca_parallel.md).
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- Some notes about the [use of ORCA with HTCondor](doc/orca_condor.md).
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- An example about how to [add a new method to ORCA](doc/orca_addmethod.md).
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- An additional branch that includes other methods ready to use in ORCA. Visit [orca-extra-methods branch](https://github.com/ayrna/orca/tree/orca-extra-methods).
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-[Experiment.m](src/Utils/Experiment.m): Class that runs the different experiments.
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-[Utilities.m](src/Utils/Utilities.m): Class that pre-process the experiment files, run the different algorithms and produces the results.
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-[runtests_single.m](src/runtests_single.m): Script to run all the methods using the ORCA API. Reference performance is compared with `toy` dataset in order to check that the installation is correct.
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-[runtestscv.m](src/runtestscv.m): This script runs full experiment tests using the ORCA configuration files to describe experiments.
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-[runtests_cv.m](src/runtests_cv.m): This script runs full experiment tests using the ORCA configuration files to describe experiments.
Copy file name to clipboardexpand all lines: doc/orca_quick_install.md
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## Installation testing
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We provide a set of basic tests to for checking that all the algorithms work, both using ORCA's API and experiment scripts (see [tutorial](orca_tutorial_1_md) for more information).
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We provide a set of basic tests to for checking that all the algorithms work, both using ORCA's API and experiment scripts (see [tutorial](orca_tutorial_1.md) for more information).
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The way to run the tests checking the API (see [single test scripts](../src/tests/singletests/)) is the following (running time is ~12 seconds):
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To run the tests checking the experiment scripts (running time is ~123 seconds):
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## Use ORCA as a toolbox
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The first of three tutorials cvers the basic use of ORCA as a toolbox but also as an experimental framework: *'how to' tutorial* ([HTML](doc/orca_tutorial_1.html), [Jupyter Notebook](doc/orca_tutorial_1.ipynb), [MD](doc/orca_tutorial_1.md)).
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The first of three tutorials covers the basic use of ORCA as a toolbox but also as an experimental framework: *'how to' tutorial* ([Jupyter Notebook](orca_tutorial_1.ipynb), [MD](orca_tutorial_1.md)).
Copy file name to clipboardexpand all lines: doc/orca_tutorial_1.ipynb
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The whole example is available at [exampleProjections.m](../code-examples/exampleProjections.m)."
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"The whole example is available at [exampleProjections.m](../src/code-examples/exampleProjections.m)."
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The whole example is available at [exampleProjectionsThresholds.m](../code-examples/exampleProjectionsThresholds.m)."
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"The whole example is available at [exampleProjectionsThresholds.m](../src/code-examples/exampleProjectionsThresholds.m)."
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]
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},
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{
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"\n",
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"---\n",
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"\n",
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"***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca-tutorial.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?\n",
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"***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca_tutorial_1.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?\n",
As can be observed, ORCA analyses all the files included in the folder of the dataset, where training and test partitions are included (a pair of files `train_dataset.X` and `test_dataset.X` for each dataset, where `X` is the number of partition). For each partition, a model is trained on training data and tested on test data.
Once the experiments are finished, the corresponding results can be found in the `Experiments` subfolder, as described in the [corresponding section](#Experimental-results-and-reports) of this tutorial.
The whole example is available at [exampleProjectionsThresholds.m](../code-examples/exampleProjectionsThresholds.m).
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The whole example is available at [exampleProjectionsThresholds.m](../src/code-examples/exampleProjectionsThresholds.m).
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## Using ORCA with your own datasets
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---
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***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca-tutorial.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?
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***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca_tutorial_1.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?
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