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Merge #406
406: Multiple improvements r=DilumAluthge a=DilumAluthge 1. Remove unnecessary comments. 2. Better approach to generating examples. 3. Store the output of the examples in a subdirectory of the directory containing the generated example files. Co-authored-by: Dilum Aluthge <[email protected]>
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‎.gitignore

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##### Beginning of file
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### Directories to ignore:
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*.ipynb_checkpoints/
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scratch.jl
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##### End of file

‎.travis.yml

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##### Beginning of file
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############################################################################
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addons:
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apt:
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packages:
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# install: ./ci/travis/install.sh "travis-7"
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# script: ./ci/travis/script.sh "travis-7"
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############################################################################
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‎CONTRIBUTING.md

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<!-- Beginning of file -->
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# Contributing to PredictMD
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This document provides information on contributing to the
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After you have entered the correct information in all of the above fields, click the white "Save" button, and then click the blue "Publish" button. Congratulations, you are finished!
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<!-- End of file -->

‎Project.toml

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NumericalIntegration = "e7bfaba1-d571-5449-8927-abc22e82249b"
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PGFPlotsX = "8314cec4-20b6-5062-9cdb-752b83310925"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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PredictMDAPI = "df4d2c64-811c-11e9-2782-c5e8d6893c98"
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ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
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Query = "1a8c2f83-1ff3-5112-b086-8aa67b057ba1"
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RData = "df47a6cb-8c03-5eed-afd8-b6050d6c41da"

‎ci/travis/before_install.sh

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#!/bin/bash
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set -ev
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export COMPILED_MODULES=$COMP_MODS
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:
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fi
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‎ci/travis/install.sh

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#!/bin/bash
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set -ev
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export COMPILED_MODULES=$COMP_MODS
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fi
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julia $JULIA_FLAGS -e 'import Pkg;Pkg.add("Coverage");'
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julia $JULIA_FLAGS -e 'import Pkg;cd(Pkg.dir("PredictMD"));import Coverage;Coverage.Codecov.submit(Coverage.Codecov.process_folder());'
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cat Project.toml
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cat Manifest.toml
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else
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:
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fi
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mkdir -p $HOME/predictmd_cache_travis
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find $HOME/predictmd_cache_travis
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‎ci/travis/script.sh

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#!/bin/bash
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set -ev
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true
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‎codecov.yml

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codecov:
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branch: master
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# ignore src/toplevel/utils/openbrowserwindow.jl
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# - "src/toplevel/utils/openbrowserwindow.jl"
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‎docs/make.jl

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import Pkg
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Pkg.add("Documenter")
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ENV["PREDICTMD_IS_DEPLOY_DOCS"] = "false"
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‎docs/mkdocs.yml

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site_name: PredictMD documentation
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# repo_url: https://github.com/bcbi/PredictMD.jl
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site_description: PredictMD - uniform interface for machine learning in Julia
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"generate_examples/generate_examples.md"
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- "Boston housing (single label regression)":
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- "1. Preprocess data":
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"examples/cpu/boston_housing/01_preprocess_data.md"
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"examples/cpu_examples/boston_housing/01_preprocess_data.md"
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- "2. Linear regression":
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"examples/cpu/boston_housing/02_linear_regression.md"
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"examples/cpu_examples/boston_housing/02_linear_regression.md"
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- "3. Random forest regression":
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"examples/cpu/boston_housing/\
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"examples/cpu_examples/boston_housing/\
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03_random_forest_regression.md"
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- "4. Knet neural network regression":
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"examples/cpu/boston_housing/04_knet_mlp_regression.md"
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"examples/cpu_examples/boston_housing/04_knet_mlp_regression.md"
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- "5. Compare models":
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"examples/cpu/boston_housing/05_compare_models.md"
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"examples/cpu_examples/boston_housing/05_compare_models.md"
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- "6. Directly access model output":
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"examples/cpu/boston_housing/06_get_model_output.md"
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"examples/cpu_examples/boston_housing/06_get_model_output.md"
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- "Breast cancer biopsy (single label binary classification)":
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- "1. Preprocess data":
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"examples/cpu/breast_cancer_biopsy/\
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"examples/cpu_examples/breast_cancer_biopsy/\
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01_preprocess_data.md"
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- "2. Apply SMOTE algorithm":
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"examples/cpu/breast_cancer_biopsy/02_smote.md"
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"examples/cpu_examples/breast_cancer_biopsy/02_smote.md"
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- "3. Logistic classifier":
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"examples/cpu/breast_cancer_biopsy/\
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"examples/cpu_examples/breast_cancer_biopsy/\
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03_logistic_classifier.md"
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- "4. Random forest classifier":
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"examples/cpu/breast_cancer_biopsy/\
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"examples/cpu_examples/breast_cancer_biopsy/\
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04_random_forest_classifier.md"
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- "5. C-SVC support vector machine classifier":
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"examples/cpu_examples/breast_cancer_biopsy/\
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05_c_svc_svm_classifier.md"
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- "6. nu-SVC support vector machine classifier":
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"examples/cpu/breast_cancer_biopsy/\
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"examples/cpu_examples/breast_cancer_biopsy/\
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06_nu_svc_svm_classifier.md"
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- "7. Knet neural network classifier":
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"examples/cpu/breast_cancer_biopsy/\
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"examples/cpu_examples/breast_cancer_biopsy/\
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07_knet_mlp_classifier.md"
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- "8. Compare models":
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"examples/cpu_examples/breast_cancer_biopsy/\
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08_compare_models.md"
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09_get_model_output.md"
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- "Library":
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- "Internals": "library/internals.md"
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##### End of file

‎docs/src/generate_examples/generate_examples.md

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# Generating example files locally
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You can generate the example files using the `generate_examples`
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PredictMD.generate_examples(output_directory; notebooks = true)
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‎docs/src/index.md

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# PredictMD
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[PredictMD](https://predictmd.net) is a free and open-source Julia package that provides a uniform
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‎docs/src/library/internals.md

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# Documentation of internals
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```@contents
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```@index
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```
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‎docs/src/requirements_for_plotting.md

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# Requirements for plotting
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There are no requirements in order to run PredictMD--you can train, run,
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* GNU/Linux: [http://www.cityinthesky.co.uk/opensource/pdf2svg/](
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http://www.cityinthesky.co.uk/opensource/pdf2svg/)
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‎src/PredictMD.jl

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__precompile__(true)
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"""
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include(
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"toplevel", "utils", "linearly_dependent_columns.jl",
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"toplevel", "utils", "make_directory.jl",
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‎src/base/abstract-types.jl

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abstract type Fittable end
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abstract type AbstractNonExistentUnderlyingObject end
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‎src/base/backends.jl

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module Backends # begin submodule PredictMD.Backends
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abstract type AbstractBackend end
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‎src/base/concrete-types.jl

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struct FeatureContrastsNotYetGenerated <: AbstractNonExistentFeatureContrasts
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‎src/base/interface.jl

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import PredictMDAPI
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"""
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"""
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‎src/base/next_version_number.jl

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function next_major_version(
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‎src/init.jl

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import Requires
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‎src/package_directory.jl

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function is_filesystem_root(path::AbstractString)::Bool
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‎src/predictmd_glue_modules/PredictMD_Glue_MLJ/PredictMD_Glue_MLJ.jl

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"""
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‎src/registry_url_list.jl

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# function registry_url_list()::Vector{String}
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# registry_url_list_raw::Vector{String} = String[
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# "https://github.com/JuliaRegistries/General",
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# return registry_url_list
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‎src/submodules/Cleaning/Cleaning.jl

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"""
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"""
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‎src/submodules/Cleaning/hcup/hcup.jl

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import CSV
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‎src/submodules/Compilation/Compilation.jl

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"""
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‎src/submodules/Compilation/buildexecutable/buildexecutable.jl

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‎src/submodules/Compilation/packagecompiler/packagecompiler.jl

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# import PackageCompiler
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# import SnoopCompile
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