retrieve_data.sh: Shell script to download and unzip dataset - visualization pairs and features from Amazon S3 storage
requirements.txt: Python dependencies
data/: Placeholder directory for raw data
features/: Placeholder directory for extracted features
results/: Placeholder directory for intermediate results and figures
models/: Placeholder directory for trained models
feature_extraction/
    └───features/
        └───aggregate_single_field_features.py: Functions to aggregate single - column features
        └───aggregation_helpers.py: Helper functions used in aggregate_single_field_features.py
        └───dateparser.py: Functions to detect and mark dates
        └───helpers.py: Helper functions used in all feature extraction scripts
        └───single_field_features.py: Functions to extract single - column features
        └───transform.py: Functions to transform single - column features
        └───type_detection.py: Functions used to detect data types
    └───outcomes/
        └───chart_outcomes.py: Functions to extract design choices of visualizations
        └───field_encoding_outcomes.py: Functions to extract design choices of encodings
    └───extract.py: Top -level entry point to extract features and outcomes
    └───general_helpers.py: Helpers used in top -level extraction function
helpers/
    └───analysis.py: Helpers functions when training baseline models
    └───processing.py: Helper functions when processing data
    └───util.py: Misc helper functions
neural_network/
    └───agg.py: Top-level entry point to load features and train neural network
    └───evaluate.py: Functions to evaluate trained neural network
    └───nets.py: Class definitions for neural network
    └───paper_ground_truth.py: Script to evaluate best network against benchmarking ground truth
    └───paper_tasks.py: Script to evaluate best network for Plotly test set
    └───save_field.py: Script to prepare training, validation, and testing splits
    └───train.py: Helper functions for model training
    └───train_field.py: Script to train network
    └───util.py: Helper functions
notebooks/
    └───Descriptive Statistics.ipynb: Notebook to generate visualizations of number of charts per user, number of rows per dataset, and number of columns per dataset
    └───Plotly Performance.ipynb: Notebook to train baseline models and assess performance on a hold-out setfrom the Plotly corpus
    └───Model Feature Importances.ipynb: Notebook to extract feature importances from trained models
    └───Benchmarking.ipynb: Notebook to generate predictions of trained models on benchmarking datasets, bootstrap crowdsourced consensus, and compare predictions
preprocessing/: Scripts to preprocess features before ML modeling
    └───deduplication.py: Helper functions to deduplicate charts
    └───impute.py: Helper function to impute missing values
    └───preprocess.py: Helper functions to prepare features for learning
docs/: Landing page and miscellaneous material for documentation