Yeezy-Taught-Me

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Required dependencies

System libraries

The below instructions were performed in a fresh Amazon EC2 AMI (Linux) instance. YMMV.

This will make your life easier

sudo yum install -y python-devel
sudo yum install -y gcc libxml2 libxml2-devel libxslt libxslt-devel python-devel

HD5F client binary (Linux)

Download and copy the compiled headers and files:

wget http://www.hdfgroup.org/ftp/HDF5/current/bin/linux-x86_64/hdf5-1.8.12-linux-x86_64-shared.tar.gz
tar xvfz hdf5-1.8.12-linux-x86_64-shared.tar.gz
cd hdf5-1.8.12-linux-x86_64-shared
sudo cp -a bin/* /usr/bin
sudo cp -a include/* /usr/include
sudo cp -a share/* /usr/share
cd /usr/lib

Edit ~/.bashrc:

export LD_LIBRARY_PATH="/usr/lib/"

Source .bashrc (or restart terminal session):

. ~/.bashrc

Python libraries

Install all dependencies using sudo pip install -r requirements.txt

The Million Song Susbet

Download and uncompress the tar ball:

wget http://static.echonest.com/millionsongsubset_full.tar.gz
mkdir assets
mkdir data
tar -xf millionsongsubset_full.tar.gz -C assets/data/

Create datasets

Create the Million Song Dataset:

make data_subset

This will combine song metadata, audio features read from H5 files, and any lyrics found on Lyrics Wikia into one serialized DataFrame for a target artist ID set in src/data/make_song_features_df.py.