Add environment, license, README

.gitignore

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 # ploffer ignores
 .snakemake/
+.vscode/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

LICENSE.txt

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+MIT License
+
+Copyright (c) 2019 Nana Mensah
+
+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:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+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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 # snakepot
-:tea: A snakemake wrapper for the TPOT auto-ML classifier
+
+![snakepot_logo](_assets/snakepot.png)
+
+`snakepot` is a snakemake workflow designed to train and evaluate a binary classifier using the [TPOT auto-ML library](http://automl.info/tpot/).
+
+## Quick Start
+
+1. [Install snakepot](#Setup) (requires conda)
+
+1. Edit parameters in [config.json](#config.json)
+
+1. Run `snakemake`
+
+1. View outputs in new directory
+
+## Features
+
+I developed `snakepot` during my elective at the William Harvey Research Institute. We used `snakepot` to quickly train a baseline model on a variety of gene-phenotype datasets. The workflow takes the following steps:
+
+1. Clean the data set (simple N/A drop by rows and explicit drop by columns)
+2. Split data into train/test/validate sets
+3. Call the TPOT automated machine learning algorithm to train a classifier
+4. Save classifier and re-run it on the houldout/validation data
+5. Evaluate the classifier on the holdout set
+6. Call the classifier for predictions on the unlabelled data
+
+An example dataset (/test/data.csv) and config file (config.json) are provided.
+
+## Installation
+
+```bash
+# Build conda environment
+conda env create -f environment.yaml
+conda activate snakepot
+# Install python helper scripts to path
+pip install . 
+# Run the workflow in Snakefile using config.json
+snakemake 
+```
+
+## config.json
+
+|Parameter|Description|
+|-----|-----|
+|directory|Output directory for new files|
+|input|Input CSV file. All data must be encoded as binary or continuous variables|
+|drop_columns|Features to drop from the data. Skipped if not found|
+|encode_columns|Categorical features to encode. Skipped if not found|
+|target_column|The name of the target variable|
+|target_1|Target variable value to label as '1'|
+|target_0|Target variable value to label as '0'|
+|to_predict|Target variable value for final predictions|
+|perc_split|Percentage of training data (target '1' or '0') to split for holdout set|
+|TPOT_max_time|Maximum time to run TPOT in muntes|
+
+## License
+
+MIT License. Copyright (c) 2019 Nana Mensah

environment.yaml

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+name: snakepot
+channels:
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - snakemake-minimal=5.4.4=py_1
+  - appdirs=1.4.3=py_1
+  - asn1crypto=0.24.0=py37_1003
+  - attrs=19.1.0=py_0
+  - blas=1.0=mkl
+  - category_encoders=1.3.0=py_0
+  - certifi=2019.3.9=py37_0
+  - cffi=1.12.2=py37hf0e25f4_1
+  - chardet=3.0.4=py37_1003
+  - configargparse=0.13.0=py_1
+  - cryptography=2.6.1=py37h72c5cf5_0
+  - datrie=0.7.1=py37h14c3975_0
+  - docutils=0.14=py37_1001
+  - gitdb2=2.0.5=py_0
+  - gitpython=2.1.11=py_0
+  - idna=2.8=py37_1000
+  - jedi=0.13.3=py37_0
+  - jsonschema=3.0.1=py37_0
+  - patsy=0.5.1=py_0
+  - pexpect=4.6.0=py37_0
+  - pip=19.0.3=py37_0
+  - psutil=5.6.1=py37h14c3975_0
+  - ptyprocess=0.6.0=py37_0
+  - pycparser=2.19=py37_1
+  - pyopenssl=19.0.0=py37_0
+  - pyrsistent=0.14.11=py37h14c3975_0
+  - pysocks=1.6.8=py37_1002
+  - pyyaml=5.1=py37h14c3975_0
+  - ratelimiter=1.2.0=py37_1000
+  - requests=2.21.0=py37_1000
+  - setuptools=41.0.0=py37_0
+  - smmap2=2.0.5=py_0
+  - statsmodels=0.9.0=py37h3010b51_1000
+  - traitlets=4.3.2=py37_0
+  - urllib3=1.24.1=py37_1000
+  - wheel=0.33.1=py37_0
+  - wrapt=1.11.1=py37h14c3975_0
+  - yaml=0.1.7=h14c3975_1001
+  - _libgcc_mutex=0.1=main
+  - backcall=0.1.0=py37_0
+  - ca-certificates=2019.1.23=0
+  - decorator=4.4.0=py37_1
+  - intel-openmp=2019.3=199
+  - ipython=7.4.0=py37h39e3cac_0
+  - ipython_genutils=0.2.0=py37_0
+  - joblib=0.13.2=py37_0
+  - libedit=3.1.20181209=hc058e9b_0
+  - libffi=3.2.1=hd88cf55_4
+  - libgcc-ng=8.2.0=hdf63c60_1
+  - libgfortran-ng=7.3.0=hdf63c60_0
+  - libstdcxx-ng=8.2.0=hdf63c60_1
+  - mkl=2019.3=199
+  - mkl_fft=1.0.10=py37ha843d7b_0
+  - mkl_random=1.0.2=py37hd81dba3_0
+  - ncurses=6.1=he6710b0_1
+  - numpy=1.16.2=py37h7e9f1db_0
+  - numpy-base=1.16.2=py37hde5b4d6_0
+  - openssl=1.1.1b=h7b6447c_1
+  - pandas=0.24.2=py37he6710b0_0
+  - parso=0.3.4=py37_0
+  - pickleshare=0.7.5=py37_0
+  - prompt_toolkit=2.0.9=py37_0
+  - pygments=2.3.1=py37_0
+  - python=3.7.3=h0371630_0
+  - python-dateutil=2.8.0=py37_0
+  - pytz=2018.9=py37_0
+  - readline=7.0=h7b6447c_5
+  - scikit-learn=0.20.3=py37hd81dba3_0
+  - scipy=1.2.1=py37h7c811a0_0
+  - six=1.12.0=py37_0
+  - sqlite=3.27.2=h7b6447c_0
+  - tk=8.6.8=hbc83047_0
+  - wcwidth=0.1.7=py37_0
+  - xz=5.2.4=h14c3975_4
+  - zlib=1.2.11=h7b6447c_3
+  - pip:
+    - beautifulsoup4==4.8.0
+    - deap==1.2.2
+    - logbook==1.5.2
+    - snakemake==5.4.4
+    - snowballstemmer==1.2.1
+    - soupsieve==1.9.3
+    - stopit==1.1.2
+    - tpot==0.10.0
+    - tqdm==4.31.1
+    - update-checker==0.16
+prefix: /home/nana/miniconda3/envs/snakepot
+