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    "info": {
        "author": "Sayed Hadi Hashemi",
        "author_email": "SayedHadiHashemi@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# GenoML-core\nGenoML is an Automated Machine Learning (AutoML) for Genomic. This is the core package of GenoML. \nPlease note this repo is under development for \"functional testing\". This is not the end product, just for preliminary evaluation of software logic. \nPackage website and on-going documentation: https://genoml.github.io\n\n## Goals \n\nPlease test the code in different environments and on different datasets. The goal is to resolve the following issues:\n\n - **Dependencies:** did you need to install any package not already [listed](https://github.com/GenoML/genoml-core/blob/master/otherPackages/readMe_otherPackages.txt)? do you get dependency errors? \n - **Errors:** do you get any error? is the error not clear? \n - **Wrong output:** any discrepancies in the expected output? \n -  **Corner cases:** did the code break on a particular case you were testing? \n - **Usability:** could we improve the way user interacts with the code? is there any particular feature or document you fit helpful? \n\n## Install\nFor now just download or clone the genoml-core repo and run the genoml with `python genoml.py`.\n\n## Step-by-step examples \nPlease refer to the following quick examples for running GonML (for full `usage`, please refer to [Usage](#usage)):\n\n### Step 1 - genoml data-prune:\nTo perform `data-prune` only on `genotype` and `phenotype` data:\n\n    python genoml.py data-prune --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno\n\nTo perform `data-prune`  on `genotype`, `phenotype` , `GWAS`, and `covariance` data:\n\n    python genoml.py data-prune --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno  --gwas-file=./exampleData/example_GWAS.txt  \n\nTo perform `data-prune`  on `genotype`, `phenotype` , `GWAS`, `covariance`, and `additional` data:\n\n    python genoml.py data-prune --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno --cov-file=./exampleData/training.cov --gwas-file=./exampleData/example_GWAS.txt --addit-file=./exampleData/training.addit  \n\nTo perform `data-prune`  on `genotype`, `phenotype` , `GWAS`, and `additional` data, as well as `Heritability estimate`:\n\n    python genoml.py data-prune --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno  --gwas-file=./exampleData/example_GWAS.txt --addit-file=./exampleData/training.addit --herit=0.2  \n\nTo perform `data-prune`  on `genotype`, `phenotype` , `GWAS`, `covariance`, and `additional` data, as well as `Heritability estimate`:\n\n    python genoml.py data-prune --geno-prefix=./exampleData/training --pheno-file=./exampleData/example.pheno --cov-file=./exampleData/training.cov --gwas-file=./exampleData/example_GWAS.txt --addit-file=./exampleData/training.addit --herit=0.5 \n\n### Step 2 - genoml model-train:\nTo perform `model-train`  on the output of `data-prune` with the prefix given to you from the prune step `prune-prefix=./tmp/20181225-230052`:\n\n    python genoml.py model-train --prune-prefix=./tmp/20181225-230052 --pheno-file=./exampleData/training.pheno  \n\n### Step 3 - genoml model-tune:\nTo perform `model-tune`  after `model-train`  on the output of `data-prune` with the prefix given to you from the prune step `prune-prefix=./tmp/20181225-230052`:\n\n    python genoml.py model-tune --prune-prefix=./tmp/20181225-230052 --pheno-file=./exampleData/training.pheno\n\n### Step 4 - genoml model-validation:\nTo perform external `model-validate` only when `genotype` and `phenotype` data present:\n\n    python genoml.py model-validate --prune-prefix=./tmp/20181225-230052 --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno --valid-geno-prefix=./exampleData/validation --valid-pheno-file=./exampleData/validation.pheno\n\nTo perform external `model-validate`  when `genotype`, `phenotype`, and `GWAS` data present:\n\n    python genoml.py model-validate --prune-prefix=./tmp/20181225-230052 --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno --valid-geno-prefix=./exampleData/validation --valid-pheno-file=./exampleData/validation.pheno --gwas-file=./exampleData/example_GWAS.txt\n\nTo perform external `model-validate`  when `genotype`, `phenotype`, `GWAS`, and `additional` data present:\n\n    python genoml.py model-validate --prune-prefix=./tmp/20181225-230052 --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno --valid-geno-prefix=./exampleData/validation --valid-pheno-file=./exampleData/validation.pheno --gwas-file=./exampleData/example_GWAS.txt --valid-addit-file=./exampleData/validation.addit\n\nTo perform external `model-validate`  when `genotype`, `phenotype`, `GWAS`, `additional`, and `covariance` data present:\n\n    python genoml.py model-validate --prune-prefix=./tmp/20181225-230052 --geno-prefix=./exampleData/training --pheno-file=./exampleData/training.pheno --valid-geno-prefix=./exampleData/validation --valid-pheno-file=./exampleData/validation.pheno --gwas-file=./exampleData/example_GWAS.txt --valid-addit-file=./exampleData/validation.addit --valid-cov-file=./exampleData/validation.cov\n\n\n## Usage \nFull GenoML usage:\n\n     Usage:\n       genoml data-prune  (--geno-prefix=geno_prefix) (--pheno-file=<pheno_file>) [--gwas-file=<gwas_file>] [--cov-file=<cov_file>] [--herit=<herit>] [--addit-file=<addit_file>] [--temp-dir=<directory>]\n       genoml model-train (--prune-prefix=prune_prefix) (--pheno-file=<pheno_file>) [--n-cores=<n_cores>] [--train-speed=<train_speed>] [--cv-reps=<cv_reps>] [--grid-search=<grid_search>] [--impute-data=<impute_data>]\n       genoml model-tune (--prune-prefix=prune_prefix) (--pheno-file=<pheno_file>) [--cv-reps=<cv_reps>] [--grid-search=<grid_search>] [--impute-data=<impute_data>] [--best-model-name=<best_model_name>]\n       genoml model-validate (--prune-prefix=prune_prefix) (--pheno-file=<pheno_file>) (--geno-prefix=geno_prefix) (--valid-geno-prefix=valid_geno_prefix) (--valid-pheno-file=<valid_pheno_file>) [--valid-cov-file=<valid_cov_file>] [--gwas-file=<gwas_file>] [--valid-addit-file=<valid_addit_file>] [--n-cores=<n_cores>] [--impute-data=<impute_data>]  [--best-model-name=<best_model_name>]\n       genoml -h | --help\n       genoml --version\n\n     Options:\n       --geno-prefix=geno_prefix               Prefix with path to genotype files in PLINK format, *.bed, *.bim and *.fam.\n       --pheno-file=<pheno_file>               Path to the phenotype file in PLINK format, *.pheno.\n       --gwas-file=<gwas_file>                 Path to the GWAS file, if available.\n       --cov-file=<cov_file>                   Path to the covariance file, if available.\n       --herit=<herit>                         Heritability estimate of phenotype between 0 and 1, if available.\n       --addit-file=<addit_file>               Path to the additional file, if avialable.\n       --temp-dir=<directory>                  Directory for temporary files [default: ./tmp/].\n       --n-cores=<n_cores>                     Number of cores to be allocated for computation [default: 1].\n       --prune-prefix=prune_prefix             Prefix given to you at the end of pruning stage.\n       --train-speed=<train_speed>             Training speed: (ALL, FAST, FURIOUS, BOOSTED). Run all models, only  the fastest models, run slightly slower models, or just run boosted models which usually perform best when using genotype data [default: BOOSTED].\n       --cv-reps=<cv_reps>                     Number of cross-validation. An integer greater than 5. Effects the speed [default: 5].\n       --impute-data=<impute_data>             Imputation: (knn, median). Governs secondary imputation and data transformation [default: median].\n       --grid-search=<grid_search>             Grid search length for parameters, integer greater than 10, 30 or greater recommended, effects speed of initial tune [default: 10].\n       --best-model-name=<best_model_name>     Name for the best model [default: best_model].\n       --valid-geno-prefix=valid_geno_prefix   Prefix with path to the validation genotype files in PLINK format, *.bed, *.bim and *.fam.\n       --valid-pheno-file=<valid_pheno_file>   Path to the validation phenotype file in PLINK format, *.pheno.\n       --valid-cov-file=<valid_cov_file>       Path to the validation covariance file, if available.\n       --valid-addit-file=<valid_addit_file>   Path to the the validation additional file, if avialable.\n       -h --help                               Show this screen.\n       --version                               Show version.\n\n     Examples:\n       genoml data-prune --geno-prefix=./exampleData/example --pheno-file=./exampleData/training.pheno\n       genoml data-prune --geno-prefix=./exampleData/example --pheno-file=./exampleData/training.pheno  --gwas-file=./exampleData/example_GWAS.txt\n       genoml data-prune --geno-prefix=./exampleData/example --pheno-file=./exampleData/training.pheno --cov-file=./exampleData/training.cov --gwas-file=./exampleData/example_GWAS.txt --addit-file=./exampleData/training.addit\n       genoml data-prune --geno-prefix=./exampleData/example --pheno-file=./exampleData/training.pheno  --gwas-file=./exampleData/example_GWAS.txt --addit-file=./exampleData/training.addit --herit=0.2\n       genoml data-prune --geno-prefix=./exampleData/example --pheno-file=./exampleData/training.pheno --cov-file=./exampleData/training.cov --gwas-file=./exampleData/example_GWAS.txt --addit-file=./exampleData/training.addit --herit=0.5\n       genoml model-train --prune-prefix=./tmp/20181225-230052 --pheno-file=./exampleData/training.pheno\n       genoml model-tune --prune-prefix=./tmp/20181225-230052 --pheno-file=./exampleData/training.pheno\n       genoml model-validate --prune-prefix=./tmp/20181225-230052 --pheno-file=./exampleData/training.pheno --valid-geno-prefix=./exampleData/validation --valid-pheno-file=./exampleData/validation.pheno\n\n     Help:\n       For help using this tool, please open an issue on the Github repository:\n       https://github.com/GenoML/genoml-core/issues\n\n\n\n\n## Report issues \nPlease report any issue or suggestions on the [GenoML-core issues page](https://github.com/GenoML/genoml-core/issues).\n\n\n",
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