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‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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.static_storage/
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.media/
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local_settings.py
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/

‎CONTRIBUTING.md‎

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This project has adopted the [Microsoft Open Source Code of
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Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct
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FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com)
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with any additional questions or comments.

‎LICENSE.txt‎

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FigureQA Code
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Copyright (c) Microsoft Corporation
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All rights reserved.
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MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

‎README.md‎

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# FigureQA
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Code to generate the FigureQA dataset, see https://datasets.maluuba.com/FigureQA.
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## Data Generation
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Data generation consists of 3 parts:
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1. Generate the source numerical data, styles, and question-answer pairs for the figures.
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1. Generate the figure images and bounding box annotatations.
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1. Aggregrate the figure images, questions & answers, annotations, and source data.
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### Code Map
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All data generation source code lives in the `figureqa/generation` subpackage:
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- `questions` subpackage contains code to generate questions
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- `categorical.py` for questions for bar graphs and pie charts.
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- `lines.py` for line plots.
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- `utils.py` for balancing and question encoding augmentation.
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- `source_data_generation.py` to generate source data, questions, and answers.
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- `figure_generation.py` to generate figure images and bounding boxes.
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- `json_combiner.py` aggregates the generated data into the documented format. Allows for generating a data split in multiple batches.
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- `data_utils.py` has misc. utilities for reconciling data formats, placing legends, etc.
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- `figure.py` defines the figure objects in Bokeh.
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- `generate_dataset.py` generates a whole dataset end-to-end.
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- `show_bounding_boxes.py` generates images with bounding boxes visualized.
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Each runnable module (script) can have its command line arguments displayed with `--help`.
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There are some additional files used for data generation in these directories:
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- `config` contains `.yaml` files that configure visual apsects, source data parameters, color splits, and dataset generation.
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- `resources` contains the colors and other misc. resources for data generation.
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And `docs` contains additional documentation on annotations, question format, and file formats.
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### Prerequisites
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1. Install the FigureQA fork of Bokeh from https://www.github.com/Maluuba/bokeh.
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1. `pip install -r requirements.txt`.
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1. Make sure you have enough space. The whole dataset unzipped is > 6GB, plus you need room for intermediate data.
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### Generate a whole dataset
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#### Using a single script
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This is done with the end-to-end script `generate_dataset.py`. It does the source data synthesis, figure generation, and aggregation.
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This script must be run from the root directory, `FigureQA`.
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The config for the actual dataset is in `config/figureqa_generation_config.yaml`.
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A sample config is provided in `config/sample_figureqa_generation_config.yaml`.
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Note that this does not generate the test sets.
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#### With individual scripts
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1. `cd FigureQA`
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1. `python figureqa/generation/source_data_generation.py CONFIG_FILE.yaml SOURCE_DATA.json --<figure_type> <N_figures> ...`
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1. `python figureqa/generation/figure_generation.py SOURCE_DATA.json RAW_GENERATED_DIR`
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1. `python figureqa/generation/json_combiner.py FINAL_AGGREGATE_DIR RAW_GENERATED_DIR1 RAW_GENERATED_DIR2 ...`

‎config/color_scheme1_source_data.yaml‎

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vbar_categorical:
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y_range: [0, 99]
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n_points_range: [2, 10]
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x_distn: ["linear"]
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shape: ["random", "random", "random", "random", "linear_inc", "linear_dec", "cluster"]
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color_sources: ["resources/color_split2.txt"]
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hbar_categorical:
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y_range: [0, 99]
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n_points_range: [2, 10]
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x_distn: ["linear"]
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shape: ["random", "random", "random", "random", "linear_inc", "linear_dec", "cluster"]
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color_sources: ["resources/color_split1.txt"]
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line:
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x_range: [0, 100]
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y_range: [0, 100]
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n_points_range: [5, 20]
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x_distn: ["linear"]
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shape: ["linear", "linear_with_noise", "quadratic"]
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n_classes_range: [2, 7]
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color_sources: ["resources/color_split2.txt"]
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solid_pr: 0.5
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dot_line:
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x_range: [0, 100]
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y_range: [0, 100]
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n_points_range: [5, 20]
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x_distn: ["linear"]
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shape: ["linear", "linear_with_noise", "quadratic"]
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n_classes_range: [2, 7]
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color_sources: ["resources/color_split1.txt"]
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pie:
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color_sources: ["resources/color_split2.txt"]
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n_classes_range: [2, 7]

‎config/color_scheme2_source_data.yaml‎

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vbar_categorical:
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y_range: [0, 99]
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n_points_range: [2, 10]
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x_distn: ["linear"]
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shape: ["random", "random", "random", "random", "linear_inc", "linear_dec", "cluster"]
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color_sources: ["resources/color_split1.txt"]
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hbar_categorical:
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y_range: [0, 99]
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n_points_range: [2, 10]
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x_distn: ["linear"]
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shape: ["random", "random", "random", "random", "linear_inc", "linear_dec", "cluster"]
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color_sources: ["resources/color_split2.txt"]
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line:
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x_range: [0, 100]
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y_range: [0, 100]
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n_points_range: [5, 20]
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x_distn: ["linear"]
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shape: ["linear", "linear_with_noise", "quadratic"]
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n_classes_range: [2, 7]
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color_sources: ["resources/color_split1.txt"]
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solid_pr: 0.5
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dot_line:
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x_range: [0, 100]
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y_range: [0, 100]
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n_points_range: [5, 20]
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x_distn: ["linear"]
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shape: ["linear", "linear_with_noise", "quadratic"]
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n_classes_range: [2, 7]
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color_sources: ["resources/color_split2.txt"]
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pie:
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color_sources: ["resources/color_split1.txt"]
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n_classes_range: [2, 7]

‎config/common_source_data.yaml‎

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figure_height_px: 400
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figure_width_ratio_range: [1.0, 2.0]
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figure_min_width_side_legend: 1.33
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draw_gridlines_pr: 0.5
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draw_legend_pr: 1.0
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legend_inside_pr: 0.5
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legend_border_pr: 0.5
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legend_label_font_sizes: ['8pt', '9pt', '10pt', '11pt']
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legend_horizontal_pr: 0.5
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legend_horizontal_max_classes: 3

‎config/figureqa_generation_config.yaml‎

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working_directory: figureqa_generation
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destination_directory: figureqa_final
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common_config_yaml: config/common_source_data.yaml
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colors: resources/x11_colors_refined.txt
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splits:
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- name: figureqa-train1
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partitions:
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- name: train1_1
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data_config_yaml: config/color_scheme1_source_data.yaml
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seed: 123
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vbar: 20000
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hbar: 20000
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pie: 20000
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line: 0
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dot_line: 0
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- name: train1_2
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data_config_yaml: config/color_scheme1_source_data.yaml
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seed: 456
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vbar: 0
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hbar: 0
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pie: 0
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line: 20000
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dot_line: 20000
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- name: figureqa-validation1
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partitions:
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- name: validation1
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data_config_yaml: config/color_scheme1_source_data.yaml
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seed: 654
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vbar: 4000
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hbar: 4000
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pie: 4000
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line: 4000
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dot_line: 4000
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- name: figureqa-validation2
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partitions:
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- name: validation2
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data_config_yaml: config/color_scheme2_source_data.yaml
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seed: 321
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vbar: 4000
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hbar: 4000
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pie: 4000
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line: 4000
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dot_line: 4000
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‎config/sample_figureqa_generation_config.yaml‎

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working_directory: sample_generation
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destination_directory: sample_final
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common_config_yaml: config/common_source_data.yaml
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colors: resources/x11_colors_refined.txt
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splits:
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- name: figureqa-train1
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partitions:
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- name: train1_1
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data_config_yaml: config/color_scheme1_source_data.yaml
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seed: 1
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vbar: 1
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hbar: 1
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pie: 1
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line: 0
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dot_line: 0
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- name: train1_2
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data_config_yaml: config/color_scheme1_source_data.yaml
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seed: 456
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vbar: 0
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hbar: 0
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pie: 0
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line: 1
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dot_line: 1
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- name: figureqa-validation1
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partitions:
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- name: validation1
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data_config_yaml: config/color_scheme1_source_data.yaml
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seed: 1001
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vbar: 1
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hbar: 1
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pie: 1
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line: 1
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dot_line: 1
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