├── .gitignore ├── LICENSE ├── Makefile ├── README.md ├── data ├── external │ └── .gitkeep ├── interim │ └── .gitkeep ├── processed │ ├── .gitkeep │ ├── Skyserver_results.csv │ ├── bank-additional_results.csv │ ├── income_evaluation_results.csv │ ├── sonar_results.csv │ ├── sonar_results_hypertuned.csv │ └── weatherAUS_results.csv └── raw │ ├── .gitkeep │ ├── Skyserver.csv │ ├── bank-additional-full.csv │ ├── income_evaluation.csv │ ├── sonar.csv │ └── weatherAUS.csv ├── docs ├── Makefile ├── commands.rst ├── conf.py ├── getting-started.rst ├── index.rst └── make.bat ├── models └── .gitkeep ├── notebooks ├── .gitkeep └── File_for_extracting_info_inside │ └── Story line plots EXTRACTOR.ipynb ├── references └── .gitkeep ├── reports ├── .gitkeep └── figures │ └── .gitkeep ├── requirements.txt ├── setup.py ├── src ├── __init__.py ├── data │ ├── .gitkeep │ ├── __init__.py │ ├── bank_additional.py │ ├── income_evaluation.py │ ├── skyserver.py │ ├── sonar.py │ └── weather_aus.py ├── features │ ├── .gitkeep │ └── __init__.py ├── models │ ├── .gitkeep │ ├── __init__.py │ ├── expirament.py │ └── expirament_utils.py └── visualization │ ├── .gitkeep │ └── __init__.py ├── test_environment.py └── tox.ini /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *.cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | 59 | # DotEnv configuration 60 | .env 61 | 62 | # Database 63 | *.db 64 | *.rdb 65 | 66 | # Pycharm 67 | .idea 68 | 69 | # VS Code 70 | .vscode/ 71 | 72 | # Spyder 73 | .spyproject/ 74 | 75 | # Jupyter NB Checkpoints 76 | .ipynb_checkpoints/ 77 | 78 | # exclude data from source control by default 79 | # /data/ 80 | 81 | # Mac OS-specific storage files 82 | .DS_Store 83 | 84 | # vim 85 | *.swp 86 | *.swo 87 | 88 | # Mypy cache 89 | .mypy_cache/ 90 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | The MIT License (MIT) 3 | Copyright (c) 2019, Shay Geller 4 | 5 | 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: 6 | 7 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 8 | 9 | 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. 10 | 11 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: clean data lint requirements sync_data_to_s3 sync_data_from_s3 2 | 3 | ################################################################################# 4 | # GLOBALS # 5 | ################################################################################# 6 | 7 | PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST)))) 8 | BUCKET = [OPTIONAL] your-bucket-for-syncing-data (do not include 's3://') 9 | PROFILE = default 10 | PROJECT_NAME = normalization_vs_standartization 11 | PYTHON_INTERPRETER = python3 12 | 13 | ifeq (,$(shell which conda)) 14 | HAS_CONDA=False 15 | else 16 | HAS_CONDA=True 17 | endif 18 | 19 | ################################################################################# 20 | # COMMANDS # 21 | ################################################################################# 22 | 23 | ## Install Python Dependencies 24 | requirements: test_environment 25 | $(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel 26 | $(PYTHON_INTERPRETER) -m pip install -r requirements.txt 27 | 28 | ## Make Dataset 29 | data: requirements 30 | $(PYTHON_INTERPRETER) src/data/make_dataset.py 31 | 32 | ## Delete all compiled Python files 33 | clean: 34 | find . -type f -name "*.py[co]" -delete 35 | find . -type d -name "__pycache__" -delete 36 | 37 | ## Lint using flake8 38 | lint: 39 | flake8 src 40 | 41 | ## Upload Data to S3 42 | sync_data_to_s3: 43 | ifeq (default,$(PROFILE)) 44 | aws s3 sync data/ s3://$(BUCKET)/data/ 45 | else 46 | aws s3 sync data/ s3://$(BUCKET)/data/ --profile $(PROFILE) 47 | endif 48 | 49 | ## Download Data from S3 50 | sync_data_from_s3: 51 | ifeq (default,$(PROFILE)) 52 | aws s3 sync s3://$(BUCKET)/data/ data/ 53 | else 54 | aws s3 sync s3://$(BUCKET)/data/ data/ --profile $(PROFILE) 55 | endif 56 | 57 | ## Set up python interpreter environment 58 | create_environment: 59 | ifeq (True,$(HAS_CONDA)) 60 | @echo ">>> Detected conda, creating conda environment." 61 | ifeq (3,$(findstring 3,$(PYTHON_INTERPRETER))) 62 | conda create --name $(PROJECT_NAME) python=3 63 | else 64 | conda create --name $(PROJECT_NAME) python=2.7 65 | endif 66 | @echo ">>> New conda env created. Activate with:\nsource activate $(PROJECT_NAME)" 67 | else 68 | $(PYTHON_INTERPRETER) -m pip install -q virtualenv virtualenvwrapper 69 | @echo ">>> Installing virtualenvwrapper if not already intalled.\nMake sure the following lines are in shell startup file\n\ 70 | export WORKON_HOME=$$HOME/.virtualenvs\nexport PROJECT_HOME=$$HOME/Devel\nsource /usr/local/bin/virtualenvwrapper.sh\n" 71 | @bash -c "source `which virtualenvwrapper.sh`;mkvirtualenv $(PROJECT_NAME) --python=$(PYTHON_INTERPRETER)" 72 | @echo ">>> New virtualenv created. Activate with:\nworkon $(PROJECT_NAME)" 73 | endif 74 | 75 | ## Test python environment is setup correctly 76 | test_environment: 77 | $(PYTHON_INTERPRETER) test_environment.py 78 | 79 | ################################################################################# 80 | # PROJECT RULES # 81 | ################################################################################# 82 | 83 | 84 | 85 | ################################################################################# 86 | # Self Documenting Commands # 87 | ################################################################################# 88 | 89 | .DEFAULT_GOAL := help 90 | 91 | # Inspired by 92 | # sed script explained: 93 | # /^##/: 94 | # * save line in hold space 95 | # * purge line 96 | # * Loop: 97 | # * append newline + line to hold space 98 | # * go to next line 99 | # * if line starts with doc comment, strip comment character off and loop 100 | # * remove target prerequisites 101 | # * append hold space (+ newline) to line 102 | # * replace newline plus comments by `---` 103 | # * print line 104 | # Separate expressions are necessary because labels cannot be delimited by 105 | # semicolon; see 106 | .PHONY: help 107 | help: 108 | @echo "$$(tput bold)Available rules:$$(tput sgr0)" 109 | @echo 110 | @sed -n -e "/^## / { \ 111 | h; \ 112 | s/.*//; \ 113 | :doc" \ 114 | -e "H; \ 115 | n; \ 116 | s/^## //; \ 117 | t doc" \ 118 | -e "s/:.*//; \ 119 | G; \ 120 | s/\\n## /---/; \ 121 | s/\\n/ /g; \ 122 | p; \ 123 | }" ${MAKEFILE_LIST} \ 124 | | LC_ALL='C' sort --ignore-case \ 125 | | awk -F '---' \ 126 | -v ncol=$$(tput cols) \ 127 | -v indent=19 \ 128 | -v col_on="$$(tput setaf 6)" \ 129 | -v col_off="$$(tput sgr0)" \ 130 | '{ \ 131 | printf "%s%*s%s ", col_on, -indent, $$1, col_off; \ 132 | n = split($$2, words, " "); \ 133 | line_length = ncol - indent; \ 134 | for (i = 1; i <= n; i++) { \ 135 | line_length -= length(words[i]) + 1; \ 136 | if (line_length <= 0) { \ 137 | line_length = ncol - indent - length(words[i]) - 1; \ 138 | printf "\n%*s ", -indent, " "; \ 139 | } \ 140 | printf "%s ", words[i]; \ 141 | } \ 142 | printf "\n"; \ 143 | }' \ 144 | | more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars') 145 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Normalization_vs_Standartization 2 | ============================== 3 | 4 | Code that produce the results for my article "Normalization vs Standardization — Quantitative analysis" - https://towardsdatascience.com/normalization-vs-standardization-quantitative-analysis-a91e8a79cebf 5 | 6 | Project Organization 7 | ------------ 8 | 9 | ├── LICENSE 10 | ├── Makefile <- Makefile with commands like `make data` or `make train` 11 | ├── README.md <- The top-level README for developers using this project. 12 | ├── data 13 | │   ├── external <- Data from third party sources. 14 | │   ├── interim <- Intermediate data that has been transformed. 15 | │   ├── processed <- The final, canonical data sets for modeling. 16 | │   └── raw <- The original, immutable data dump. 17 | │ 18 | ├── docs <- A default Sphinx project; see sphinx-doc.org for details 19 | │ 20 | ├── models <- Trained and serialized models, model predictions, or model summaries 21 | │ 22 | ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), 23 | │ the creator's initials, and a short `-` delimited description, e.g. 24 | │ `1.0-jqp-initial-data-exploration`. 25 | │ 26 | ├── references <- Data dictionaries, manuals, and all other explanatory materials. 27 | │ 28 | ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. 29 | │   └── figures <- Generated graphics and figures to be used in reporting 30 | │ 31 | ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. 32 | │ generated with `pip freeze > requirements.txt` 33 | │ 34 | ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported 35 | ├── src <- Source code for use in this project. 36 | │   ├── __init__.py <- Makes src a Python module 37 | │ │ 38 | │   ├── data <- Scripts to download or generate data 39 | │   │   └── make_dataset.py 40 | │ │ 41 | │   ├── features <- Scripts to turn raw data into features for modeling 42 | │   │   └── build_features.py 43 | │ │ 44 | │   ├── models <- Scripts to train models and then use trained models to make 45 | │ │ │ predictions 46 | │   │   ├── predict_model.py 47 | │   │   └── train_model.py 48 | │ │ 49 | │   └── visualization <- Scripts to create exploratory and results oriented visualizations 50 | │   └── visualize.py 51 | │ 52 | └── tox.ini <- tox file with settings for running tox; see tox.testrun.org 53 | 54 | 55 | -------- 56 | 57 |

Project based on the cookiecutter data science project template. #cookiecutterdatascience

58 | -------------------------------------------------------------------------------- /data/external/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/data/external/.gitkeep -------------------------------------------------------------------------------- /data/interim/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/data/interim/.gitkeep -------------------------------------------------------------------------------- /data/processed/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/data/processed/.gitkeep -------------------------------------------------------------------------------- /data/processed/Skyserver_results.csv: -------------------------------------------------------------------------------- 1 | Dataset,Classifier_Name,CV_mean,CV_std,Test_score 2 | Skyserver,_LR,0.7965,0.013167194082263689,0.798 3 | Skyserver,StandardScaler_LR,0.9552499999999998,0.006118619125260207,0.9565 4 | Skyserver,MinMaxScaler_LR,0.895125,0.00959247752147484,0.8945 5 | Skyserver,MaxAbsScaler_LR,0.87475,0.008958236433584451,0.8755 6 | Skyserver,RobustScaler_LR,0.97475,0.005910795208768465,0.9785 7 | Skyserver,QuantileTransformer-Normal_LR,0.9710000000000001,0.005147815070493497,0.9715 8 | Skyserver,QuantileTransformer-Uniform_LR,0.9546249999999998,0.004331930862790868,0.9565 9 | Skyserver,PowerTransformer-Yeo-Johnson_LR,0.9780000000000001,0.005279678020485721,0.9785 10 | Skyserver,Normalizer_LR,0.797875,0.01245052709727584,0.7985 11 | ,,,, 12 | Skyserver,_LR-PCA,0.7965,0.013167194082263689,0.798 13 | Skyserver,StandardScaler_LR-PCA,0.8474999999999999,0.01096870548424014,0.8415 14 | Skyserver,MinMaxScaler_LR-PCA,0.796875,0.013029413839463386,0.7975 15 | Skyserver,MaxAbsScaler_LR-PCA,0.797125,0.01263737017737472,0.7975 16 | Skyserver,RobustScaler_LR-PCA,0.9572500000000002,0.007842193570679062,0.965 17 | Skyserver,QuantileTransformer-Normal_LR-PCA,0.821875,0.013325844250928334,0.816 18 | Skyserver,QuantileTransformer-Uniform_LR-PCA,0.821,0.012597122687344128,0.8205 19 | Skyserver,PowerTransformer-Yeo-Johnson_LR-PCA,0.625375,0.01834606565452114,0.9205 20 | Skyserver,Normalizer_LR-PCA,0.797875,0.01245052709727584,0.7985 21 | ,,,, 22 | Skyserver,_LDA,0.9187500000000002,0.009953014618697183,0.912 23 | Skyserver,StandardScaler_LDA,0.9115000000000002,0.008173891362135902,0.9065 24 | Skyserver,MinMaxScaler_LDA,0.9115000000000002,0.008173891362135902,0.9065 25 | Skyserver,MaxAbsScaler_LDA,0.9115000000000002,0.008173891362135902,0.9065 26 | Skyserver,RobustScaler_LDA,0.9115000000000002,0.008173891362135902,0.9065 27 | Skyserver,QuantileTransformer-Normal_LDA,0.96975,0.004322904116447628,0.968 28 | Skyserver,QuantileTransformer-Uniform_LDA,0.96775,0.00604152298679728,0.969 29 | Skyserver,PowerTransformer-Yeo-Johnson_LDA,0.9627500000000001,0.005777110004145669,0.975 30 | Skyserver,Normalizer_LDA,0.90525,0.008511022265274597,0.912 31 | ,,,, 32 | Skyserver,_LDA-PCA,0.78625,0.013601470508735457,0.744 33 | Skyserver,StandardScaler_LDA-PCA,0.8376250000000001,0.012911356435324676,0.834 34 | Skyserver,MinMaxScaler_LDA-PCA,0.790875,0.013953606164715979,0.792 35 | Skyserver,MaxAbsScaler_LDA-PCA,0.7775000000000001,0.012221906561580318,0.771 36 | Skyserver,RobustScaler_LDA-PCA,0.85275,0.010706306552681927,0.8485 37 | Skyserver,QuantileTransformer-Normal_LDA-PCA,0.8246249999999999,0.015602984490154443,0.8155 38 | Skyserver,QuantileTransformer-Uniform_LDA-PCA,0.812875,0.012699040318071267,0.808 39 | Skyserver,PowerTransformer-Yeo-Johnson_LDA-PCA,0.6236250000000001,0.018911388764445626,0.907 40 | Skyserver,Normalizer_LDA-PCA,0.797875,0.01245052709727584,0.7985 41 | ,,,, 42 | Skyserver,_KNN,0.7827500000000001,0.012695963925594602,0.793 43 | Skyserver,StandardScaler_KNN,0.90425,0.006736096792653728,0.902 44 | Skyserver,MinMaxScaler_KNN,0.877375,0.009754005587449684,0.8865 45 | Skyserver,MaxAbsScaler_KNN,0.868625,0.009754005587449687,0.876 46 | Skyserver,RobustScaler_KNN,0.952625,0.0051371806470086314,0.9525 47 | Skyserver,QuantileTransformer-Normal_KNN,0.9438749999999999,0.009260973220995726,0.9505 48 | Skyserver,QuantileTransformer-Uniform_KNN,0.9552500000000002,0.006169481339626548,0.956 49 | Skyserver,PowerTransformer-Yeo-Johnson_KNN,0.955875,0.00450867219921785,0.965 50 | Skyserver,Normalizer_KNN,0.7825,0.014097872179871697,0.7925 51 | ,,,, 52 | Skyserver,_KNN-PCA,0.782125,0.012918615444388762,0.794 53 | Skyserver,StandardScaler_KNN-PCA,0.8457500000000001,0.008699856320652651,0.8485 54 | Skyserver,MinMaxScaler_KNN-PCA,0.804875,0.013421647626129955,0.8125 55 | Skyserver,MaxAbsScaler_KNN-PCA,0.7785,0.014435200033252035,0.7905 56 | Skyserver,RobustScaler_KNN-PCA,0.970375,0.006276195105316598,0.972 57 | Skyserver,QuantileTransformer-Normal_KNN-PCA,0.8605,0.008771402396424412,0.8585 58 | Skyserver,QuantileTransformer-Uniform_KNN-PCA,0.8665,0.011575836902790213,0.8695 59 | Skyserver,PowerTransformer-Yeo-Johnson_KNN-PCA,0.8225,0.013062350477613132,0.932 60 | Skyserver,Normalizer_KNN-PCA,0.7821250000000001,0.01326473237573983,0.794 61 | ,,,, 62 | Skyserver,_CART,0.9851249999999998,0.0032811011871016734,0.985 63 | Skyserver,StandardScaler_CART,0.9847499999999998,0.002893959225697557,0.985 64 | Skyserver,MinMaxScaler_CART,0.98475,0.002947456530637926,0.984 65 | Skyserver,MaxAbsScaler_CART,0.98475,0.002947456530637926,0.984 66 | Skyserver,RobustScaler_CART,0.9847499999999998,0.002893959225697557,0.985 67 | Skyserver,QuantileTransformer-Normal_CART,0.985125,0.003033665274877908,0.985 68 | Skyserver,QuantileTransformer-Uniform_CART,0.985,0.0032113081446663,0.985 69 | Skyserver,PowerTransformer-Yeo-Johnson_CART,0.984375,0.003801726581436387,0.9855 70 | Skyserver,Normalizer_CART,0.719125,0.015612995388457653,0.7385 71 | ,,,, 72 | Skyserver,_CART-PCA,0.7074999999999999,0.011250000000000013,0.722 73 | Skyserver,StandardScaler_CART-PCA,0.80375,0.02020983671383813,0.8105 74 | Skyserver,MinMaxScaler_CART-PCA,0.7462500000000001,0.01644118304745738,0.764 75 | Skyserver,MaxAbsScaler_CART-PCA,0.7183750000000001,0.020325860990373815,0.7345 76 | Skyserver,RobustScaler_CART-PCA,0.9710000000000001,0.003944933459514874,0.9795 77 | Skyserver,QuantileTransformer-Normal_CART-PCA,0.8182499999999999,0.014771171246722462,0.8255 78 | Skyserver,QuantileTransformer-Uniform_CART-PCA,0.818,0.015483862567202009,0.8195 79 | Skyserver,PowerTransformer-Yeo-Johnson_CART-PCA,0.803125,0.009391651878130904,0.909 80 | Skyserver,Normalizer_CART-PCA,0.718375,0.015125000000000008,0.741 81 | ,,,, 82 | Skyserver,_NB,0.79625,0.012784267675545583,0.7975 83 | Skyserver,StandardScaler_NB,0.9420000000000002,0.006523994175349941,0.947 84 | Skyserver,MinMaxScaler_NB,0.9417500000000001,0.006642665127793205,0.947 85 | Skyserver,MaxAbsScaler_NB,0.9417500000000001,0.006642665127793205,0.947 86 | Skyserver,RobustScaler_NB,0.9420000000000002,0.006523994175349941,0.947 87 | Skyserver,QuantileTransformer-Normal_NB,0.924625,0.011096874559983086,0.9145 88 | Skyserver,QuantileTransformer-Uniform_NB,0.9241249999999999,0.012209755321053728,0.9155 89 | Skyserver,PowerTransformer-Yeo-Johnson_NB,0.985125,0.002820128543169633,0.9855 90 | Skyserver,Normalizer_NB,0.797875,0.01245052709727584,0.7985 91 | ,,,, 92 | Skyserver,_NB-PCA,0.79625,0.012784267675545583,0.7975 93 | Skyserver,StandardScaler_NB-PCA,0.8404999999999999,0.013876689086377897,0.8365 94 | Skyserver,MinMaxScaler_NB-PCA,0.7816249999999999,0.014958797578682584,0.7825 95 | Skyserver,MaxAbsScaler_NB-PCA,0.768125,0.01295726147764258,0.7635 96 | Skyserver,RobustScaler_NB-PCA,0.8800000000000001,0.011712706775122468,0.874 97 | Skyserver,QuantileTransformer-Normal_NB-PCA,0.835375,0.013451881838612774,0.8365 98 | Skyserver,QuantileTransformer-Uniform_NB-PCA,0.826375,0.013115472732616236,0.825 99 | Skyserver,PowerTransformer-Yeo-Johnson_NB-PCA,0.6285000000000001,0.016953981833185985,0.912 100 | Skyserver,Normalizer_NB-PCA,0.79425,0.013706750161872791,0.796 101 | ,,,, 102 | Skyserver,_SVM,0.5335000000000001,0.014819750335278943,0.532 103 | Skyserver,StandardScaler_SVM,0.9544999999999998,0.005367727638395973,0.958 104 | Skyserver,MinMaxScaler_SVM,0.8618750000000001,0.009830596370515898,0.8605 105 | Skyserver,MaxAbsScaler_SVM,0.857375,0.009770011514834585,0.8555 106 | Skyserver,RobustScaler_SVM,0.982375,0.00472526454285896,0.983 107 | Skyserver,QuantileTransformer-Normal_SVM,0.97725,0.0027838821814150224,0.979 108 | Skyserver,QuantileTransformer-Uniform_SVM,0.9752500000000002,0.004062019202317976,0.975 109 | Skyserver,PowerTransformer-Yeo-Johnson_SVM,0.985,0.0029047375096555726,0.9855 110 | Skyserver,Normalizer_SVM,0.7965,0.013167194082263689,0.798 111 | ,,,, 112 | Skyserver,_SVM-PCA,0.5015,0.014945735177635125,0.5945 113 | Skyserver,StandardScaler_SVM-PCA,0.8557500000000001,0.009522998477370469,0.854 114 | Skyserver,MinMaxScaler_SVM-PCA,0.79675,0.01321930406640228,0.798 115 | Skyserver,MaxAbsScaler_SVM-PCA,0.7966249999999999,0.013370326286220535,0.798 116 | Skyserver,RobustScaler_SVM-PCA,0.9796249999999999,0.004543195461346563,0.983 117 | Skyserver,QuantileTransformer-Normal_SVM-PCA,0.861,0.01083685840084663,0.861 118 | Skyserver,QuantileTransformer-Uniform_SVM-PCA,0.8460000000000001,0.010304731922762465,0.8425 119 | Skyserver,PowerTransformer-Yeo-Johnson_SVM-PCA,0.82875,0.010383279828647608,0.939 120 | Skyserver,Normalizer_SVM-PCA,0.7965,0.013167194082263689,0.798 121 | ,,,, 122 | Skyserver,_RF,0.933,0.0059476886266851565,0.9285 123 | Skyserver,StandardScaler_RF,0.933,0.0059476886266851565,0.9285 124 | Skyserver,MinMaxScaler_RF,0.933,0.0059476886266851565,0.9285 125 | Skyserver,MaxAbsScaler_RF,0.933,0.0059476886266851565,0.9285 126 | Skyserver,RobustScaler_RF,0.933,0.0059476886266851565,0.9285 127 | Skyserver,QuantileTransformer-Normal_RF,0.933,0.0059476886266851565,0.9285 128 | Skyserver,QuantileTransformer-Uniform_RF,0.933,0.0059476886266851565,0.9285 129 | Skyserver,PowerTransformer-Yeo-Johnson_RF,0.86725,0.013226394066411287,0.968 130 | Skyserver,Normalizer_RF,0.797875,0.01245052709727584,0.7985 131 | ,,,, 132 | Skyserver,_RF-PCA,0.79775,0.012534951136721676,0.798 133 | Skyserver,StandardScaler_RF-PCA,0.851125,0.012125000000000004,0.85 134 | Skyserver,MinMaxScaler_RF-PCA,0.788625,0.013281213235243226,0.792 135 | Skyserver,MaxAbsScaler_RF-PCA,0.7481249999999999,0.012288841483231866,0.7575 136 | Skyserver,RobustScaler_RF-PCA,0.854125,0.015592967164718839,0.847 137 | Skyserver,QuantileTransformer-Normal_RF-PCA,0.812625,0.010192675065948094,0.802 138 | Skyserver,QuantileTransformer-Uniform_RF-PCA,0.83025,0.011149551560488882,0.8275 139 | Skyserver,PowerTransformer-Yeo-Johnson_RF-PCA,0.6759999999999999,0.020545680811304354,0.9165 140 | Skyserver,Normalizer_RF-PCA,0.797875,0.01245052709727584,0.799 141 | ,,,, 142 | Skyserver,_MLP,0.6162500000000001,0.180593639146012,0.796 143 | Skyserver,StandardScaler_MLP,0.984875,0.0050140427800329105,0.9845 144 | Skyserver,MinMaxScaler_MLP,0.978,0.006451743950281963,0.9795 145 | Skyserver,MaxAbsScaler_MLP,0.9737499999999999,0.005916079783099614,0.9755 146 | Skyserver,RobustScaler_MLP,0.988875,0.0031349840509961174,0.9865 147 | Skyserver,QuantileTransformer-Normal_MLP,0.9852500000000001,0.002610076627227627,0.984 148 | Skyserver,QuantileTransformer-Uniform_MLP,0.9845,0.002861380785564882,0.983 149 | Skyserver,PowerTransformer-Yeo-Johnson_MLP,0.9879999999999999,0.004190763653560053,0.985 150 | Skyserver,Normalizer_MLP,0.79775,0.012434830115445885,0.7985 151 | ,,,, 152 | Skyserver,_MLP-PCA,0.7368750000000001,0.12075575814428065,0.0815 153 | Skyserver,StandardScaler_MLP-PCA,0.861375,0.012060394894032283,0.8625 154 | Skyserver,MinMaxScaler_MLP-PCA,0.798875,0.012654668111017366,0.7965 155 | Skyserver,MaxAbsScaler_MLP-PCA,0.79725,0.01230853362509118,0.798 156 | Skyserver,RobustScaler_MLP-PCA,0.985,0.0038324274291889494,0.9855 157 | Skyserver,QuantileTransformer-Normal_MLP-PCA,0.858125,0.012161028944953623,0.858 158 | Skyserver,QuantileTransformer-Uniform_MLP-PCA,0.863875,0.01031458312293813,0.867 159 | Skyserver,PowerTransformer-Yeo-Johnson_MLP-PCA,0.8735000000000002,0.010647769719523427,0.9435 160 | Skyserver,Normalizer_MLP-PCA,0.797875,0.01245052709727584,0.7985 161 | -------------------------------------------------------------------------------- /data/processed/bank-additional_results.csv: -------------------------------------------------------------------------------- 1 | Dataset,Classifier_Name,CV_mean,CV_std,Test_score 2 | bank-additional,_LR,0.9210873456107214,0.003897271369549948,0.9212626085062509 3 | bank-additional,StandardScaler_LR,0.9231531159544251,0.003564947422550041,0.921920334246011 4 | bank-additional,MinMaxScaler_LR,0.9210733782902587,0.003942193815962733,0.9201661523236585 5 | bank-additional,MaxAbsScaler_LR,0.9165376102286389,0.004985130143325203,0.9180410101246853 6 | bank-additional,RobustScaler_LR,0.9231482040854135,0.0035800904173932183,0.9219591023323591 7 | bank-additional,QuantileTransformer-Normal_LR,0.9076247445593933,0.0040924063081824065,0.9106669353798147 8 | bank-additional,QuantileTransformer-Uniform_LR,0.8996548546437353,0.0047972037900587995,0.9063201746753838 9 | bank-additional,PowerTransformer-Yeo-Johnson_LR,0.9217336022236824,0.0035453664615846254,0.9231407891169991 10 | bank-additional,Normalizer_LR,0.8535708837850938,0.009187942567939533,0.8580692960307399 11 | ,,,, 12 | bank-additional,_LR-PCA,0.9200117204864583,0.003998830038322409,0.919965505281338 13 | bank-additional,StandardScaler_LR-PCA,0.8998072683608633,0.0039900369531599466,0.9086079356793058 14 | bank-additional,MinMaxScaler_LR-PCA,0.7466635736886131,0.012402777891160788,0.759791161349816 15 | bank-additional,MaxAbsScaler_LR-PCA,0.7466496023214724,0.0111048871294481,0.758644839346234 16 | bank-additional,RobustScaler_LR-PCA,0.9080041208517411,0.005384149509774018,0.9096147221659937 17 | bank-additional,QuantileTransformer-Normal_LR-PCA,0.726416995400031,0.009056343434269974,0.7427781861744127 18 | bank-additional,QuantileTransformer-Uniform_LR-PCA,0.7494839085940346,0.008894221339577173,0.7623695350315531 19 | bank-additional,PowerTransformer-Yeo-Johnson_LR-PCA,0.8596494525541487,0.00770810008120292,0.8728251399498324 20 | bank-additional,Normalizer_LR-PCA,0.8533506748402233,0.009198188839739675,0.8578503007338117 21 | ,,,, 22 | bank-additional,_LDA,0.9220916201531082,0.0033370582669893984,0.9210082484282648 23 | bank-additional,StandardScaler_LDA,0.9220916201531082,0.0033370582669893984,0.9210082484282648 24 | bank-additional,MinMaxScaler_LDA,0.9220916201531082,0.0033370582669893984,0.9210082484282648 25 | bank-additional,MaxAbsScaler_LDA,0.9220916201531082,0.0033370582669893984,0.9210082484282648 26 | bank-additional,RobustScaler_LDA,0.9220916201531082,0.0033370582669893984,0.9210082484282648 27 | bank-additional,QuantileTransformer-Normal_LDA,0.9020021674574343,0.0033204571453490455,0.9063460693895476 28 | 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bank-additional,QuantileTransformer-Uniform_LDA-PCA,0.7464112177585368,0.017213739367050326,0.7619882168692669 39 | bank-additional,PowerTransformer-Yeo-Johnson_LDA-PCA,0.8538661091247846,0.006855242491440687,0.8666993383974517 40 | bank-additional,Normalizer_LDA-PCA,0.8508803729236416,0.009887234680164265,0.8553827564286957 41 | ,,,, 42 | bank-additional,_KNN,0.8727778436225033,0.00511707994617484,0.8672861126204031 43 | bank-additional,StandardScaler_KNN,0.8838173180115083,0.00721461624743626,0.8763216662109197 44 | bank-additional,MinMaxScaler_KNN,0.8816727032481386,0.008897416918167832,0.8823128152496487 45 | bank-additional,MaxAbsScaler_KNN,0.8835549773983195,0.010145053800564373,0.8836881944962339 46 | bank-additional,RobustScaler_KNN,0.8836239485603246,0.009070068491769254,0.8791299849544313 47 | bank-additional,QuantileTransformer-Normal_KNN,0.882663926695742,0.006683031321981791,0.8832449509746475 48 | bank-additional,QuantileTransformer-Uniform_KNN,0.8824194600692371,0.009615072408634632,0.8836555671563876 49 | bank-additional,PowerTransformer-Yeo-Johnson_KNN,0.8834516418290681,0.008083038002048362,0.8820155439310484 50 | bank-additional,Normalizer_KNN,0.8691055321646486,0.005329680771909551,0.8653197420117027 51 | ,,,, 52 | bank-additional,_KNN-PCA,0.8704185378858075,0.00684966523544737,0.867116169310591 53 | bank-additional,StandardScaler_KNN-PCA,0.8658232650729574,0.006780532746533046,0.8646331621618742 54 | bank-additional,MinMaxScaler_KNN-PCA,0.8428630344796556,0.010555740011252302,0.8567820327972091 55 | bank-additional,MaxAbsScaler_KNN-PCA,0.8247700473131564,0.013432908024296662,0.8456479715708592 56 | bank-additional,RobustScaler_KNN-PCA,0.8738334625064331,0.008429394641055165,0.8631272735559036 57 | bank-additional,QuantileTransformer-Normal_KNN-PCA,0.8210473482255128,0.008222243661572586,0.8282265997606441 58 | bank-additional,QuantileTransformer-Uniform_KNN-PCA,0.8512203740062173,0.017152779543242392,0.8665027605244996 59 | bank-additional,PowerTransformer-Yeo-Johnson_KNN-PCA,0.8678393821427897,0.007817237616156084,0.8710092546228723 60 | bank-additional,Normalizer_KNN-PCA,0.8637997975310456,0.006599980670237412,0.864032478778172 61 | ,,,, 62 | bank-additional,_CART,0.7342370235839132,0.012635459171485862,0.7290399305370593 63 | bank-additional,StandardScaler_CART,0.7345910282461101,0.01219296658223303,0.7302589057100952 64 | bank-additional,MinMaxScaler_CART,0.7338046716116862,0.011384861991283437,0.7293076078966153 65 | bank-additional,MaxAbsScaler_CART,0.7343886729223029,0.011823398524538859,0.7299854575285256 66 | bank-additional,RobustScaler_CART,0.7336562708062218,0.012373403480758098,0.7293076078966153 67 | bank-additional,QuantileTransformer-Normal_CART,0.734538292485085,0.008447144607876949,0.733419984445415 68 | bank-additional,QuantileTransformer-Uniform_CART,0.734996171815874,0.008343462020119954,0.7317966078220385 69 | bank-additional,PowerTransformer-Yeo-Johnson_CART,0.734201483929158,0.012107033121063903,0.724978603567611 70 | bank-additional,Normalizer_CART,0.7276111814834864,0.012394481641076906,0.7321952384503656 71 | ,,,, 72 | bank-additional,_CART-PCA,0.7104657853001799,0.01769259306928543,0.7115292589554278 73 | bank-additional,StandardScaler_CART-PCA,0.7125492866082068,0.012769221728030177,0.7084054686085038 74 | bank-additional,MinMaxScaler_CART-PCA,0.6798956927132466,0.013898347990895852,0.6778848487216002 75 | bank-additional,MaxAbsScaler_CART-PCA,0.6858076262736268,0.015515918463085516,0.694188086774223 76 | bank-additional,RobustScaler_CART-PCA,0.7198500873116493,0.009891736260565267,0.7133126649123368 77 | bank-additional,QuantileTransformer-Normal_CART-PCA,0.6996178569748233,0.011924589190876664,0.7100016927744575 78 | bank-additional,QuantileTransformer-Uniform_CART-PCA,0.6912498560951882,0.012508930926690151,0.6982556284750706 79 | bank-additional,PowerTransformer-Yeo-Johnson_CART-PCA,0.7074721755500567,0.00774619865632865,0.7087102124017628 80 | bank-additional,Normalizer_CART-PCA,0.7062563499608465,0.012609719804908185,0.7122984799358876 81 | ,,,, 82 | bank-additional,_NB,0.8678252283287715,0.007757472274397123,0.8752144082332762 83 | bank-additional,StandardScaler_NB,0.8678306733115665,0.007741378652934503,0.8751996852386517 84 | bank-additional,MinMaxScaler_NB,0.8678383585991171,0.007694044172381936,0.8752468136184298 85 | bank-additional,MaxAbsScaler_NB,0.8678552869662475,0.007724554607821728,0.8752322385936003 86 | bank-additional,RobustScaler_NB,0.8678336400341532,0.007740954882608079,0.8752183294328494 87 | bank-additional,QuantileTransformer-Normal_NB,0.8391757719436445,0.006969602056046144,0.8456187475363028 88 | bank-additional,QuantileTransformer-Uniform_NB,0.8329445428724054,0.007115332783898826,0.8431637066788831 89 | bank-additional,PowerTransformer-Yeo-Johnson_NB,0.8476644283781601,0.0078086192080433795,0.8553841621417504 90 | bank-additional,Normalizer_NB,0.8886768963207305,0.006885214660470077,0.8927221944039007 91 | ,,,, 92 | bank-additional,_NB-PCA,0.9189974851164557,0.004391127515592527,0.9139489054378307 93 | bank-additional,StandardScaler_NB-PCA,0.8734175366058305,0.005927569836263667,0.8757744739081901 94 | bank-additional,MinMaxScaler_NB-PCA,0.7532656808745319,0.011771094712928564,0.7690852921574824 95 | bank-additional,MaxAbsScaler_NB-PCA,0.7579018592524307,0.012185599216258032,0.7683424837854698 96 | bank-additional,RobustScaler_NB-PCA,0.8887278934557642,0.006194449108239268,0.8937121863188311 97 | bank-additional,QuantileTransformer-Normal_NB-PCA,0.7409654264402395,0.010186989567665389,0.7573023093941881 98 | bank-additional,QuantileTransformer-Uniform_NB-PCA,0.7505059774801547,0.00977283650292974,0.7628443701044194 99 | bank-additional,PowerTransformer-Yeo-Johnson_NB-PCA,0.8320109834048273,0.007472053328821655,0.8399024523921982 100 | bank-additional,Normalizer_NB-PCA,0.856767201607189,0.010343794824774624,0.8504274699414158 101 | ,,,, 102 | bank-additional,_SVM,0.8681964301770961,0.006390655010469922,0.8718027426497483 103 | bank-additional,StandardScaler_SVM,0.8983586110444868,0.008102722866540221,0.903992461826752 104 | bank-additional,MinMaxScaler_SVM,0.9296984245541253,0.0030396337970192903,0.928522302599415 105 | bank-additional,MaxAbsScaler_SVM,0.9236760533156266,0.004536049889251716,0.9235502215403775 106 | bank-additional,RobustScaler_SVM,0.9037200972917926,0.007300608059587111,0.9061309213072953 107 | bank-additional,QuantileTransformer-Normal_SVM,0.9192892852272816,0.0065718505324866535,0.9141949792072843 108 | bank-additional,QuantileTransformer-Uniform_SVM,0.9158062761191964,0.005233598674739286,0.9182416571670058 109 | bank-additional,PowerTransformer-Yeo-Johnson_SVM,0.8964309239567596,0.007172914139485657,0.8993574559612296 110 | bank-additional,Normalizer_SVM,0.6046240749498619,0.015840790861860708,0.6052266482947368 111 | ,,,, 112 | bank-additional,_SVM-PCA,0.8553285421541699,0.008102747149456016,0.8578645058341531 113 | bank-additional,StandardScaler_SVM-PCA,0.8304297797247301,0.011903604121573036,0.8403204670636991 114 | bank-additional,MinMaxScaler_SVM-PCA,0.6181931806600621,0.020425538723556715,0.6555994138028594 115 | bank-additional,MaxAbsScaler_SVM-PCA,0.6272563593895579,0.016697136249975143,0.6351175827180748 116 | bank-additional,RobustScaler_SVM-PCA,0.8511213927185504,0.009969918722112486,0.8613792323800528 117 | bank-additional,QuantileTransformer-Normal_SVM-PCA,0.8056879781227136,0.013812645431078052,0.821846882039663 118 | bank-additional,QuantileTransformer-Uniform_SVM-PCA,0.6689892527023987,0.061674784545478645,0.7367420543179362 119 | bank-additional,PowerTransformer-Yeo-Johnson_SVM-PCA,0.8321073428692813,0.011024305741299376,0.8344601233239461 120 | bank-additional,Normalizer_SVM-PCA,0.5775039342401701,0.018163611432690192,0.5816375994800933 121 | ,,,, 122 | bank-additional,_RF,0.9096337417951202,0.005633107491428639,0.9079131435058424 123 | bank-additional,StandardScaler_RF,0.9096380372523056,0.005632582865457635,0.9079131435058424 124 | bank-additional,MinMaxScaler_RF,0.909632024435521,0.005635237249839063,0.9079131435058424 125 | bank-additional,MaxAbsScaler_RF,0.909632024435521,0.005635237249839063,0.9079131435058424 126 | bank-additional,RobustScaler_RF,0.9096337417951202,0.005633107491428639,0.9079131435058424 127 | bank-additional,QuantileTransformer-Normal_RF,0.9094311829385298,0.00519131569775238,0.9082175913595111 128 | bank-additional,QuantileTransformer-Uniform_RF,0.909432900298129,0.0051890702111160114,0.9082175913595111 129 | bank-additional,PowerTransformer-Yeo-Johnson_RF,0.9076977848758689,0.005513205209135352,0.906467848531015 130 | bank-additional,Normalizer_RF,0.9208723199934934,0.004453227138890796,0.9170503523456766 131 | ,,,, 132 | bank-additional,_RF-PCA,0.9150632338972524,0.0050521555818843975,0.913028755266245 133 | bank-additional,StandardScaler_RF-PCA,0.8805263316054621,0.008357641547741658,0.8847572762667103 134 | bank-additional,MinMaxScaler_RF-PCA,0.7756511796060368,0.014370486623871212,0.7876661552830544 135 | bank-additional,MaxAbsScaler_RF-PCA,0.7670411002733567,0.013669665500070143,0.7758034907850331 136 | bank-additional,RobustScaler_RF-PCA,0.906858607218372,0.006477785662950265,0.8992900557195059 137 | bank-additional,QuantileTransformer-Normal_RF-PCA,0.7834133131783669,0.013913781883907466,0.799624556238584 138 | bank-additional,QuantileTransformer-Uniform_RF-PCA,0.7911135358691685,0.01789980439957459,0.8146369057976933 139 | bank-additional,PowerTransformer-Yeo-Johnson_RF-PCA,0.8610801428203209,0.007399030502991918,0.8723207109179217 140 | bank-additional,Normalizer_RF-PCA,0.9024644164566775,0.008277149821609571,0.9121757093967923 141 | ,,,, 142 | bank-additional,_MLP,0.911731013776987,0.007258988426040562,0.9196586159260483 143 | bank-additional,StandardScaler_MLP,0.9441797103557711,0.0024581284544371545,0.9418566006958131 144 | bank-additional,MinMaxScaler_MLP,0.943317218520318,0.002189848719968423,0.9402255296430849 145 | bank-additional,MaxAbsScaler_MLP,0.9370901243417575,0.0019765984730432855,0.9347225329587922 146 | bank-additional,RobustScaler_MLP,0.9353946463563441,0.005743218517483912,0.9398844592650991 147 | bank-additional,QuantileTransformer-Normal_MLP,0.9421955829558172,0.0029800658865285073,0.9393206943453046 148 | bank-additional,QuantileTransformer-Uniform_MLP,0.9420418996085391,0.003365271759156128,0.9408140055186814 149 | bank-additional,PowerTransformer-Yeo-Johnson_MLP,0.9442348503617897,0.00292537657487322,0.9420658299862567 150 | bank-additional,Normalizer_MLP,0.9240930543337853,0.004205444946202325,0.9254608075362791 151 | ,,,, 152 | bank-additional,_MLP-PCA,0.9102665878084378,0.0177971303577235,0.9242174173470319 153 | bank-additional,StandardScaler_MLP-PCA,0.9199004673809755,0.0037551507669812464,0.923057334152494 154 | bank-additional,MinMaxScaler_MLP-PCA,0.7860350018068564,0.015353084945215612,0.7969792262245093 155 | bank-additional,MaxAbsScaler_MLP-PCA,0.782411949797432,0.01683664647162354,0.7918673137089871 156 | bank-additional,RobustScaler_MLP-PCA,0.9084697444698063,0.011875332853160931,0.9161523236584763 157 | bank-additional,QuantileTransformer-Normal_MLP-PCA,0.7895168647541365,0.02535630778509932,0.8121678817946251 158 | bank-additional,QuantileTransformer-Uniform_MLP-PCA,0.8998237058051644,0.020732488515295543,0.9147819753849288 159 | bank-additional,PowerTransformer-Yeo-Johnson_MLP-PCA,0.9187067344731237,0.004582117445471884,0.9253502740992486 160 | bank-additional,Normalizer_MLP-PCA,0.8920179172606459,0.021955314774262415,0.868535495586357 161 | -------------------------------------------------------------------------------- /data/processed/income_evaluation_results.csv: -------------------------------------------------------------------------------- 1 | Dataset,Classifier_Name,CV_mean,CV_std,Test_score 2 | income_evaluation,_LR,0.5825199454659367,0.012783067364140428,0.5867214208069234 3 | income_evaluation,StandardScaler_LR,0.832951645903074,0.010684574426441313,0.8254362564132187 4 | income_evaluation,MinMaxScaler_LR,0.8310425151393657,0.01050505744312951,0.8235322459525173 5 | income_evaluation,MaxAbsScaler_LR,0.8310742409644813,0.010519963148188578,0.8235928991161967 6 | income_evaluation,RobustScaler_LR,0.832865059710404,0.01067432492982942,0.8254487283841052 7 | income_evaluation,QuantileTransformer-Normal_LR,0.8292755645240814,0.009811633061924578,0.8199393993498821 8 | income_evaluation,QuantileTransformer-Uniform_LR,0.832903834779968,0.009531892496017796,0.8223014593518775 9 | income_evaluation,PowerTransformer-Yeo-Johnson_LR,0.8290771729737955,0.010339895209993049,0.8204814705897797 10 | income_evaluation,Normalizer_LR,0.6835736925863337,0.006551501221939774,0.6856206052713112 11 | ,,,, 12 | income_evaluation,_LR-PCA,0.7638851514374903,0.007354152856217676,0.7603376884580444 13 | income_evaluation,StandardScaler_LR-PCA,0.8275973424379508,0.009932001317058932,0.8184306847243299 14 | income_evaluation,MinMaxScaler_LR-PCA,0.8079742391909941,0.011075674058446058,0.7957379993383291 15 | income_evaluation,MaxAbsScaler_LR-PCA,0.8084325760225459,0.011054166899473215,0.7962840090953489 16 | income_evaluation,RobustScaler_LR-PCA,0.7559155874356168,0.008361787720410995,0.7529234299757912 17 | income_evaluation,QuantileTransformer-Normal_LR-PCA,0.8249725938908232,0.00945496973748973,0.8157194095375131 18 | income_evaluation,QuantileTransformer-Uniform_LR-PCA,0.8293068940244057,0.009162450392041126,0.8177631060721431 19 | income_evaluation,PowerTransformer-Yeo-Johnson_LR-PCA,0.8270563475340257,0.009715718074887258,0.8189295635597894 20 | income_evaluation,Normalizer_LR-PCA,0.6831243542315597,0.006575886951504007,0.6851206761646196 21 | ,,,, 22 | income_evaluation,_LDA,0.8215766062388257,0.010578158541449929,0.8125726656409017 23 | income_evaluation,StandardScaler_LDA,0.8215766062388257,0.010578158541449929,0.8125726656409017 24 | income_evaluation,MinMaxScaler_LDA,0.8215766062388257,0.010578158541449929,0.8125726656409017 25 | income_evaluation,MaxAbsScaler_LDA,0.8215766062388257,0.010578158541449929,0.8125726656409017 26 | income_evaluation,RobustScaler_LDA,0.8215766062388257,0.010578158541449929,0.8125726656409017 27 | income_evaluation,QuantileTransformer-Normal_LDA,0.822684470472568,0.009756893746572837,0.8150176970702685 28 | income_evaluation,QuantileTransformer-Uniform_LDA,0.8301427667056718,0.009451381102103154,0.820624307477406 29 | income_evaluation,PowerTransformer-Yeo-Johnson_LDA,0.8257420483552327,0.010204990081515104,0.8185602619376453 30 | income_evaluation,Normalizer_LDA,0.6762334204194185,0.012515820229942853,0.6803524447688615 31 | ,,,, 32 | income_evaluation,_LDA-PCA,0.7513485312318913,0.005824830445985662,0.7452363635408844 33 | income_evaluation,StandardScaler_LDA-PCA,0.8227136120118471,0.009557278861612957,0.813666129277886 34 | income_evaluation,MinMaxScaler_LDA-PCA,0.8072288222943456,0.011152632170064897,0.7953260304473628 35 | income_evaluation,MaxAbsScaler_LDA-PCA,0.8077172726449369,0.011111907826550695,0.7959151013249172 36 | income_evaluation,RobustScaler_LDA-PCA,0.7458792805845004,0.0076544396566776965,0.7402578153308091 37 | income_evaluation,QuantileTransformer-Normal_LDA-PCA,0.8192865296959996,0.00922605229859111,0.8116518403377672 38 | income_evaluation,QuantileTransformer-Uniform_LDA-PCA,0.8271743657976118,0.009453198844119612,0.8161070909062266 39 | income_evaluation,PowerTransformer-Yeo-Johnson_LDA-PCA,0.8265231351546518,0.01023129126790285,0.8185340051568317 40 | income_evaluation,Normalizer_LDA-PCA,0.683425923764917,0.0060950686797816185,0.6850115792403388 41 | ,,,, 42 | income_evaluation,_KNN,0.6620775410376454,0.011484746639190321,0.6831688127208851 43 | income_evaluation,StandardScaler_KNN,0.7918790561655635,0.00991359208358048,0.783765104213163 44 | income_evaluation,MinMaxScaler_KNN,0.7831442543000776,0.009188210711981152,0.7811698183555904 45 | income_evaluation,MaxAbsScaler_KNN,0.7862105198010502,0.009856761359424286,0.7800122225314686 46 | income_evaluation,RobustScaler_KNN,0.8210281926606202,0.011605804652164434,0.813619851701702 47 | income_evaluation,QuantileTransformer-Normal_KNN,0.7861599948992819,0.008345509095364906,0.7807310019062423 48 | income_evaluation,QuantileTransformer-Uniform_KNN,0.7780513124067836,0.01099336006752381,0.7695609078544534 49 | income_evaluation,PowerTransformer-Yeo-Johnson_KNN,0.7764930635828511,0.010118531159174986,0.7689837838121695 50 | income_evaluation,Normalizer_KNN,0.695792341893906,0.012596501246790237,0.6914349068146849 51 | ,,,, 52 | income_evaluation,_KNN-PCA,0.661950700825486,0.011784225031347613,0.6827875642634711 53 | income_evaluation,StandardScaler_KNN-PCA,0.7785985730635014,0.012482593379908287,0.7774727979750771 54 | income_evaluation,MinMaxScaler_KNN-PCA,0.7723804669683986,0.009802930974589227,0.7676478388043713 55 | income_evaluation,MaxAbsScaler_KNN-PCA,0.7745789348754701,0.010488226486900946,0.7716411013144144 56 | income_evaluation,RobustScaler_KNN-PCA,0.7902218540540056,0.01530877676656584,0.783189686861632 57 | income_evaluation,QuantileTransformer-Normal_KNN-PCA,0.7897262325033306,0.009739625755474796,0.7835981110871884 58 | income_evaluation,QuantileTransformer-Uniform_KNN-PCA,0.7729688100640376,0.011519377206011056,0.7630822441145426 59 | income_evaluation,PowerTransformer-Yeo-Johnson_KNN-PCA,0.7697915392651262,0.010557879372377019,0.7631033808230976 60 | income_evaluation,Normalizer_KNN-PCA,0.6412830540607404,0.00892957704539917,0.6503824300125507 61 | ,,,, 62 | income_evaluation,_CART,0.6954789767792127,0.014677293821555795,0.6804490697222557 63 | income_evaluation,StandardScaler_CART,0.6953744709205741,0.014789160151642378,0.6800210185530414 64 | income_evaluation,MinMaxScaler_CART,0.6958231607103633,0.015034547872565446,0.6797191312156365 65 | income_evaluation,MaxAbsScaler_CART,0.6959117734241331,0.015039244317366737,0.6804490697222557 66 | income_evaluation,RobustScaler_CART,0.6958405488339017,0.014939796912236916,0.6801471823848508 67 | income_evaluation,QuantileTransformer-Normal_CART,0.6961041354421601,0.014944094352062877,0.6808020264983432 68 | income_evaluation,QuantileTransformer-Uniform_CART,0.6955955360165976,0.01524177336109587,0.6801216476655096 69 | income_evaluation,PowerTransformer-Yeo-Johnson_CART,0.6790927801683887,0.01706692356021663,0.673702980669758 70 | income_evaluation,Normalizer_CART,0.62081255426407,0.011673395263795225,0.6136514254806305 71 | ,,,, 72 | income_evaluation,_CART-PCA,0.6592675912998048,0.011592914559889247,0.6639453701418391 73 | income_evaluation,StandardScaler_CART-PCA,0.6619664720158044,0.007726326135571722,0.6558331407836073 74 | income_evaluation,MinMaxScaler_CART-PCA,0.6734058540212426,0.010075371810247365,0.6764694607382358 75 | income_evaluation,MaxAbsScaler_CART-PCA,0.6726229856559123,0.008851265913497984,0.6776179323310245 76 | income_evaluation,RobustScaler_CART-PCA,0.7030087107061477,0.009661851993588341,0.7019373565723349 77 | income_evaluation,QuantileTransformer-Normal_CART-PCA,0.6785833573390953,0.012249630035425734,0.6858186470405981 78 | income_evaluation,QuantileTransformer-Uniform_CART-PCA,0.6462304960986607,0.012723498318926111,0.656457855241116 79 | income_evaluation,PowerTransformer-Yeo-Johnson_CART-PCA,0.6385840354378615,0.009412719311590913,0.6253765222368677 80 | income_evaluation,Normalizer_CART-PCA,0.6088661623818583,0.011600401540516291,0.6096765427171567 81 | ,,,, 82 | income_evaluation,_NB,0.823600119246494,0.0070664709317719634,0.8186809118454841 83 | income_evaluation,StandardScaler_NB,0.8311323099507912,0.008651466358561954,0.8261660636359341 84 | income_evaluation,MinMaxScaler_NB,0.8311318724024824,0.008651293311315065,0.826165866710078 85 | income_evaluation,MaxAbsScaler_NB,0.8311318724024824,0.008651293311315061,0.826165866710078 86 | income_evaluation,RobustScaler_NB,0.8295441510706892,0.008889135118818184,0.8246884632956459 87 | income_evaluation,QuantileTransformer-Normal_NB,0.8253208668801708,0.006445710700407912,0.8195320253955585 88 | income_evaluation,QuantileTransformer-Uniform_NB,0.82761266239516,0.006623734379480541,0.8213009447189735 89 | income_evaluation,PowerTransformer-Yeo-Johnson_NB,0.8258994199390539,0.007230062366486679,0.8206442626308245 90 | income_evaluation,Normalizer_NB,0.6800961386617652,0.007066709549324545,0.6821741402217122 91 | ,,,, 92 | income_evaluation,_NB-PCA,0.768173094161517,0.006360096471647163,0.7637002630929437 93 | income_evaluation,StandardScaler_NB-PCA,0.768794965992085,0.010274451011235324,0.7611622170175447 94 | income_evaluation,MinMaxScaler_NB-PCA,0.8134152269002903,0.010668842978909751,0.803802244429624 95 | income_evaluation,MaxAbsScaler_NB-PCA,0.8120518359065632,0.011259802514802313,0.8022521753742904 96 | income_evaluation,RobustScaler_NB-PCA,0.7534649124969053,0.008314316229945927,0.7520915495176628 97 | income_evaluation,QuantileTransformer-Normal_NB-PCA,0.8148180094983685,0.008291722193368803,0.8090121148786674 98 | income_evaluation,QuantileTransformer-Uniform_NB-PCA,0.8216405247776514,0.0080532681516789,0.8120644656482536 99 | income_evaluation,PowerTransformer-Yeo-Johnson_NB-PCA,0.8158377408443922,0.007728752473840101,0.8102040414437027 100 | income_evaluation,Normalizer_NB-PCA,0.6305634889355487,0.009621097902097071,0.6350033083543825 101 | ,,,, 102 | income_evaluation,_SVM,0.582268294633715,0.009868192127969824,0.5890528260173189 103 | income_evaluation,StandardScaler_SVM,0.817295744207138,0.012731627049593887,0.804793437905339 104 | income_evaluation,MinMaxScaler_SVM,0.8397529899564887,0.008736589077365936,0.8339410246446145 105 | income_evaluation,MaxAbsScaler_SVM,0.8361934879352614,0.008955837411940757,0.8311125785734165 106 | income_evaluation,RobustScaler_SVM,0.8226686689011526,0.01618837218237851,0.810818187546934 107 | income_evaluation,QuantileTransformer-Normal_SVM,0.8217555180319449,0.012486419154863428,0.8111008417923928 108 | income_evaluation,QuantileTransformer-Uniform_SVM,0.8187949750139817,0.007320898674432559,0.8073400174345025 109 | income_evaluation,PowerTransformer-Yeo-Johnson_SVM,0.8091014722702445,0.013343707885908454,0.7984113334768704 110 | income_evaluation,Normalizer_SVM,0.6840817037517477,0.005753878575686027,0.6847715922637021 111 | ,,,, 112 | income_evaluation,_SVM-PCA,0.5857854257734179,0.01016779463163553,0.5998841419546597 113 | income_evaluation,StandardScaler_SVM-PCA,0.8089968895447395,0.01645726087252031,0.8015295231243468 114 | income_evaluation,MinMaxScaler_SVM-PCA,0.8188000774570214,0.011354023186959552,0.8060716836372994 115 | income_evaluation,MaxAbsScaler_SVM-PCA,0.81752980846612,0.011600424948552377,0.8051861737043592 116 | income_evaluation,RobustScaler_SVM-PCA,0.6882703138059386,0.023288551982767344,0.6911857956067154 117 | income_evaluation,QuantileTransformer-Normal_SVM-PCA,0.8131781688531312,0.011934110095644048,0.7993999012745041 118 | income_evaluation,QuantileTransformer-Uniform_SVM-PCA,0.810606554454791,0.01084744784025697,0.7973639504902141 119 | income_evaluation,PowerTransformer-Yeo-Johnson_SVM-PCA,0.8003920792142301,0.013583027928717632,0.789784143004931 120 | income_evaluation,Normalizer_SVM-PCA,0.6840693736703545,0.006465255682734179,0.6859633875448334 121 | ,,,, 122 | income_evaluation,_RF,0.8360620583149428,0.01219907599276449,0.8276351305224574 123 | income_evaluation,StandardScaler_RF,0.8360620583149428,0.01219907599276449,0.8276351305224574 124 | income_evaluation,MinMaxScaler_RF,0.8360601916414978,0.012201391554857279,0.8276351305224574 125 | income_evaluation,MaxAbsScaler_RF,0.8360620583149428,0.01219907599276449,0.8276351305224574 126 | income_evaluation,RobustScaler_RF,0.836053166050732,0.012187475970062549,0.8276351305224574 127 | income_evaluation,QuantileTransformer-Normal_RF,0.8360552766979398,0.012188265450640324,0.8276351305224574 128 | income_evaluation,QuantileTransformer-Uniform_RF,0.8360571402501231,0.012207227242239644,0.8276327674121842 129 | income_evaluation,PowerTransformer-Yeo-Johnson_RF,0.8360677706976632,0.011261744600785209,0.8278149238290788 130 | income_evaluation,Normalizer_RF,0.7005505355544029,0.008662773892904829,0.6968675004069801 131 | ,,,, 132 | income_evaluation,_RF-PCA,0.7720458887134218,0.00865197205388236,0.7667283263402773 133 | income_evaluation,StandardScaler_RF-PCA,0.7999447444750707,0.012458875127784172,0.7811958782105478 134 | income_evaluation,MinMaxScaler_RF-PCA,0.837051183476399,0.010007256645048897,0.826853072305923 135 | income_evaluation,MaxAbsScaler_RF-PCA,0.8339801679992549,0.009125596533868473,0.8230345486721947 136 | income_evaluation,RobustScaler_RF-PCA,0.8018570694185982,0.010869626403196117,0.7890397632688643 137 | income_evaluation,QuantileTransformer-Normal_RF-PCA,0.8309088020722296,0.009096644865669797,0.8204665698666681 138 | income_evaluation,QuantileTransformer-Uniform_RF-PCA,0.8136643673604341,0.00910447815820526,0.8041362963235255 139 | income_evaluation,PowerTransformer-Yeo-Johnson_RF-PCA,0.8128468851546149,0.010506558934548924,0.8066497923088637 140 | income_evaluation,Normalizer_RF-PCA,0.6837307636358465,0.008516090190224024,0.6826487971768709 141 | ,,,, 142 | income_evaluation,_MLP,0.601911690543415,0.07147092670890845,0.6303977377157651 143 | income_evaluation,StandardScaler_MLP,0.8558531691968915,0.008362748761089025,0.8457523092838726 144 | income_evaluation,MinMaxScaler_MLP,0.8516279850301676,0.008628958030020312,0.8430281682744569 145 | income_evaluation,MaxAbsScaler_MLP,0.8517546104228938,0.008694889893260341,0.8420569299521602 146 | income_evaluation,RobustScaler_MLP,0.796533187413554,0.06814912998249403,0.8281874419068724 147 | income_evaluation,QuantileTransformer-Normal_MLP,0.855898425173843,0.008345201115502512,0.8436357501824846 148 | income_evaluation,QuantileTransformer-Uniform_MLP,0.844676919456426,0.00923274174188682,0.8352871441549781 149 | income_evaluation,PowerTransformer-Yeo-Johnson_MLP,0.8433744248911689,0.008638281031733879,0.8313416689860157 150 | income_evaluation,Normalizer_MLP,0.6853458383206288,0.006472493084334999,0.6875588808309746 151 | ,,,, 152 | income_evaluation,_MLP-PCA,0.6444717717121733,0.04742324081820761,0.6583190015071392 153 | income_evaluation,StandardScaler_MLP-PCA,0.8495612826331769,0.009288334176185303,0.8381299658136713 154 | income_evaluation,MinMaxScaler_MLP-PCA,0.852545245015628,0.008503713912899993,0.843163128127839 155 | income_evaluation,MaxAbsScaler_MLP-PCA,0.852918067431465,0.00834427462834088,0.8434431566952166 156 | income_evaluation,RobustScaler_MLP-PCA,0.7197427100738297,0.06273585949118352,0.7516881140804612 157 | income_evaluation,QuantileTransformer-Normal_MLP-PCA,0.8485427604717153,0.008510797787314273,0.8392500800831815 158 | income_evaluation,QuantileTransformer-Uniform_MLP-PCA,0.8388253070233972,0.008842477087824619,0.8263946289129167 159 | income_evaluation,PowerTransformer-Yeo-Johnson_MLP-PCA,0.8375053605576003,0.00904819754310638,0.8255113508063456 160 | income_evaluation,Normalizer_MLP-PCA,0.6847193718754545,0.006438422031315545,0.6865367043538994 161 | -------------------------------------------------------------------------------- /data/processed/sonar_results.csv: -------------------------------------------------------------------------------- 1 | Dataset,Classifier_Name,CV_mean,CV_std,Test_score 2 | sonar,_LR,0.7536764705882353,0.11373314925189718,0.7142857142857143 3 | sonar,StandardScaler_LR,0.7415441176470589,0.10502164981119722,0.8333333333333334 4 | sonar,MinMaxScaler_LR,0.7779411764705882,0.1205097484893602,0.7619047619047619 5 | sonar,MaxAbsScaler_LR,0.7779411764705882,0.1205097484893602,0.7619047619047619 6 | sonar,RobustScaler_LR,0.7352941176470588,0.07678456470647357,0.8095238095238095 7 | sonar,QuantileTransformer-Normal_LR,0.7180147058823529,0.13492246347632048,0.8095238095238095 8 | sonar,QuantileTransformer-Uniform_LR,0.8080882352941178,0.11689557786574935,0.7857142857142857 9 | sonar,PowerTransformer-Yeo-Johnson_LR,0.7595588235294117,0.0890465159729476,0.8333333333333334 10 | sonar,Normalizer_LR,0.6985294117647058,0.09667090483092856,0.6904761904761905 11 | ,,,, 12 | sonar,_LR-PCA,0.6985294117647058,0.10151320503189597,0.6190476190476191 13 | sonar,StandardScaler_LR-PCA,0.7525735294117647,0.14654367492037843,0.7380952380952381 14 | sonar,MinMaxScaler_LR-PCA,0.7529411764705882,0.1454860752679988,0.7142857142857143 15 | sonar,MaxAbsScaler_LR-PCA,0.7588235294117647,0.1422390313575092,0.7142857142857143 16 | sonar,RobustScaler_LR-PCA,0.7338235294117647,0.13051418806856682,0.7380952380952381 17 | sonar,QuantileTransformer-Normal_LR-PCA,0.7411764705882353,0.15509477792114182,0.7142857142857143 18 | sonar,QuantileTransformer-Uniform_LR-PCA,0.7886029411764706,0.1173162859235945,0.7380952380952381 19 | sonar,PowerTransformer-Yeo-Johnson_LR-PCA,0.7823529411764706,0.11677989232995187,0.7142857142857143 20 | sonar,Normalizer_LR-PCA,0.63125,0.06864375244066241,0.5714285714285714 21 | ,,,, 22 | sonar,_LDA,0.6985294117647058,0.13226099747577588,0.8333333333333334 23 | sonar,StandardScaler_LDA,0.6985294117647058,0.13226099747577588,0.8333333333333334 24 | sonar,MinMaxScaler_LDA,0.6985294117647058,0.13226099747577588,0.8333333333333334 25 | sonar,MaxAbsScaler_LDA,0.6985294117647058,0.13226099747577588,0.8333333333333334 26 | sonar,RobustScaler_LDA,0.6985294117647058,0.13226099747577588,0.8333333333333334 27 | sonar,QuantileTransformer-Normal_LDA,0.6944852941176471,0.15607862574194714,0.7380952380952381 28 | sonar,QuantileTransformer-Uniform_LDA,0.7419117647058824,0.09838663931575134,0.7857142857142857 29 | sonar,PowerTransformer-Yeo-Johnson_LDA,0.7474264705882353,0.09627931742418938,0.7857142857142857 30 | sonar,Normalizer_LDA,0.6922794117647059,0.11501008399447077,0.8333333333333334 31 | ,,,, 32 | sonar,_LDA-PCA,0.7044117647058823,0.09288019220375368,0.6428571428571429 33 | sonar,StandardScaler_LDA-PCA,0.7408088235294118,0.1426854291935664,0.7142857142857143 34 | sonar,MinMaxScaler_LDA-PCA,0.7529411764705882,0.1454860752679988,0.7142857142857143 35 | sonar,MaxAbsScaler_LDA-PCA,0.7588235294117647,0.1422390313575092,0.7142857142857143 36 | sonar,RobustScaler_LDA-PCA,0.7338235294117647,0.12120314883367192,0.6904761904761905 37 | sonar,QuantileTransformer-Normal_LDA-PCA,0.7533088235294118,0.1562949762258071,0.7142857142857143 38 | sonar,QuantileTransformer-Uniform_LDA-PCA,0.7827205882352941,0.09922997758518891,0.7380952380952381 39 | sonar,PowerTransformer-Yeo-Johnson_LDA-PCA,0.7823529411764706,0.11677989232995187,0.6904761904761905 40 | sonar,Normalizer_LDA-PCA,0.6422794117647058,0.12146883010406911,0.5714285714285714 41 | ,,,, 42 | sonar,_KNN,0.7533088235294118,0.08050463273007,0.7619047619047619 43 | sonar,StandardScaler_KNN,0.7955882352941176,0.06995348800759733,0.8333333333333334 44 | sonar,MinMaxScaler_KNN,0.8132352941176471,0.07409342508035653,0.7857142857142857 45 | sonar,MaxAbsScaler_KNN,0.8077205882352942,0.06353064575326611,0.7619047619047619 46 | sonar,RobustScaler_KNN,0.7595588235294117,0.06412675326592687,0.8333333333333334 47 | sonar,QuantileTransformer-Normal_KNN,0.7827205882352942,0.07182679480219824,0.8333333333333334 48 | sonar,QuantileTransformer-Uniform_KNN,0.7886029411764707,0.11126107988835932,0.9047619047619048 49 | sonar,PowerTransformer-Yeo-Johnson_KNN,0.8128676470588235,0.10381001515944809,0.7857142857142857 50 | sonar,Normalizer_KNN,0.7654411764705882,0.08463551509398941,0.7142857142857143 51 | ,,,, 52 | sonar,_KNN-PCA,0.7415441176470587,0.0954474358955291,0.7142857142857143 53 | sonar,StandardScaler_KNN-PCA,0.7529411764705882,0.07809017389861267,0.7857142857142857 54 | sonar,MinMaxScaler_KNN-PCA,0.7591911764705882,0.10114304017639851,0.8095238095238095 55 | sonar,MaxAbsScaler_KNN-PCA,0.7768382352941176,0.11196105527335777,0.8095238095238095 56 | sonar,RobustScaler_KNN-PCA,0.7577205882352941,0.13474603257924794,0.7142857142857143 57 | sonar,QuantileTransformer-Normal_KNN-PCA,0.7408088235294118,0.10286564981452605,0.7857142857142857 58 | sonar,QuantileTransformer-Uniform_KNN-PCA,0.7397058823529411,0.08993161088670767,0.8333333333333334 59 | sonar,PowerTransformer-Yeo-Johnson_KNN-PCA,0.7768382352941177,0.0915588813986703,0.8095238095238095 60 | sonar,Normalizer_KNN-PCA,0.7415441176470587,0.13718753817853824,0.6904761904761905 61 | ,,,, 62 | sonar,_CART,0.7345588235294118,0.066923883841075,0.8095238095238095 63 | sonar,StandardScaler_CART,0.7345588235294118,0.066923883841075,0.8095238095238095 64 | sonar,MinMaxScaler_CART,0.7345588235294118,0.066923883841075,0.8095238095238095 65 | sonar,MaxAbsScaler_CART,0.7345588235294118,0.066923883841075,0.8095238095238095 66 | sonar,RobustScaler_CART,0.7345588235294118,0.066923883841075,0.8095238095238095 67 | sonar,QuantileTransformer-Normal_CART,0.7345588235294118,0.066923883841075,0.8095238095238095 68 | sonar,QuantileTransformer-Uniform_CART,0.7404411764705883,0.06672971355610838,0.8095238095238095 69 | sonar,PowerTransformer-Yeo-Johnson_CART,0.7286764705882354,0.05524011756174394,0.8095238095238095 70 | sonar,Normalizer_CART,0.7158088235294118,0.12151333192551399,0.7619047619047619 71 | ,,,, 72 | sonar,_CART-PCA,0.6566176470588235,0.07202880527210857,0.7142857142857143 73 | sonar,StandardScaler_CART-PCA,0.6915441176470589,0.13897301974218942,0.7619047619047619 74 | sonar,MinMaxScaler_CART-PCA,0.7415441176470589,0.12321443123276525,0.7619047619047619 75 | sonar,MaxAbsScaler_CART-PCA,0.7165441176470588,0.1317126054099433,0.7619047619047619 76 | sonar,RobustScaler_CART-PCA,0.7154411764705882,0.12383977118573677,0.6428571428571429 77 | sonar,QuantileTransformer-Normal_CART-PCA,0.7058823529411764,0.08323776238285834,0.7619047619047619 78 | sonar,QuantileTransformer-Uniform_CART-PCA,0.7099264705882353,0.0804038317981181,0.8095238095238095 79 | sonar,PowerTransformer-Yeo-Johnson_CART-PCA,0.7102941176470589,0.12564496239163137,0.8095238095238095 80 | sonar,Normalizer_CART-PCA,0.5713235294117648,0.07340430533369614,0.7619047619047619 81 | ,,,, 82 | sonar,_NB,0.6569852941176471,0.13065754404120297,0.6904761904761905 83 | sonar,StandardScaler_NB,0.6569852941176471,0.13065754404120297,0.6904761904761905 84 | sonar,MinMaxScaler_NB,0.6569852941176471,0.13065754404120297,0.6904761904761905 85 | sonar,MaxAbsScaler_NB,0.6569852941176471,0.13065754404120297,0.6904761904761905 86 | sonar,RobustScaler_NB,0.6569852941176471,0.13065754404120297,0.6904761904761905 87 | sonar,QuantileTransformer-Normal_NB,0.7522058823529412,0.11866744990864202,0.7619047619047619 88 | sonar,QuantileTransformer-Uniform_NB,0.7522058823529412,0.11109026380742268,0.7857142857142857 89 | sonar,PowerTransformer-Yeo-Johnson_NB,0.7518382352941176,0.13682448487982607,0.7619047619047619 90 | sonar,Normalizer_NB,0.6621323529411764,0.15463438084318223,0.6428571428571429 91 | ,,,, 92 | sonar,_NB-PCA,0.6985294117647058,0.07671412013686724,0.7142857142857143 93 | sonar,StandardScaler_NB-PCA,0.7522058823529412,0.12980804966482418,0.7857142857142857 94 | sonar,MinMaxScaler_NB-PCA,0.7525735294117647,0.14079538409216721,0.7619047619047619 95 | sonar,MaxAbsScaler_NB-PCA,0.7525735294117647,0.14079538409216721,0.7619047619047619 96 | sonar,RobustScaler_NB-PCA,0.6613970588235294,0.13345588235294115,0.6428571428571429 97 | sonar,QuantileTransformer-Normal_NB-PCA,0.7231617647058823,0.16508171733918317,0.7142857142857143 98 | sonar,QuantileTransformer-Uniform_NB-PCA,0.7349264705882353,0.16314957009806488,0.7619047619047619 99 | sonar,PowerTransformer-Yeo-Johnson_NB-PCA,0.7584558823529411,0.1563693315945355,0.7619047619047619 100 | sonar,Normalizer_NB-PCA,0.6382352941176471,0.06601696463316957,0.5714285714285714 101 | ,,,, 102 | sonar,_SVM,0.6084558823529411,0.11580882352941177,0.7380952380952381 103 | sonar,StandardScaler_SVM,0.8492647058823529,0.07715334346808633,0.8333333333333334 104 | sonar,MinMaxScaler_SVM,0.7113970588235293,0.0861934396563473,0.7142857142857143 105 | sonar,MaxAbsScaler_SVM,0.7051470588235293,0.08072846322757855,0.6904761904761905 106 | sonar,RobustScaler_SVM,0.776470588235294,0.13247339290208285,0.7857142857142857 107 | sonar,QuantileTransformer-Normal_SVM,0.8319852941176471,0.09419301325297297,0.8095238095238095 108 | sonar,QuantileTransformer-Uniform_SVM,0.7716911764705883,0.11427201600509422,0.7857142857142857 109 | sonar,PowerTransformer-Yeo-Johnson_SVM,0.8136029411764707,0.11486896871674457,0.8333333333333334 110 | sonar,Normalizer_SVM,0.5242647058823529,0.10295693156745431,0.6190476190476191 111 | ,,,, 112 | sonar,_SVM-PCA,0.6992647058823529,0.06756708828035377,0.6904761904761905 113 | sonar,StandardScaler_SVM-PCA,0.7764705882352941,0.06865851887808876,0.7380952380952381 114 | sonar,MinMaxScaler_SVM-PCA,0.7588235294117647,0.11758040391188934,0.7619047619047619 115 | sonar,MaxAbsScaler_SVM-PCA,0.7588235294117647,0.12881505142664207,0.7619047619047619 116 | sonar,RobustScaler_SVM-PCA,0.7878676470588235,0.11186443402773742,0.7380952380952381 117 | sonar,QuantileTransformer-Normal_SVM-PCA,0.7290441176470589,0.08732759213301867,0.7619047619047619 118 | sonar,QuantileTransformer-Uniform_SVM-PCA,0.7466911764705882,0.11911367549660877,0.8333333333333334 119 | sonar,PowerTransformer-Yeo-Johnson_SVM-PCA,0.7400735294117646,0.07557897202337913,0.8571428571428571 120 | sonar,Normalizer_SVM-PCA,0.5893382352941177,0.06464841197802369,0.5952380952380952 121 | ,,,, 122 | sonar,_RF,0.7102941176470587,0.12573099407851795,0.8095238095238095 123 | sonar,StandardScaler_RF,0.7102941176470587,0.12573099407851795,0.8095238095238095 124 | sonar,MinMaxScaler_RF,0.7102941176470587,0.12573099407851795,0.8095238095238095 125 | sonar,MaxAbsScaler_RF,0.7102941176470587,0.12573099407851795,0.8095238095238095 126 | sonar,RobustScaler_RF,0.7102941176470587,0.12573099407851795,0.8095238095238095 127 | sonar,QuantileTransformer-Normal_RF,0.7165441176470588,0.1349765494498362,0.8095238095238095 128 | sonar,QuantileTransformer-Uniform_RF,0.7165441176470588,0.1349765494498362,0.8095238095238095 129 | sonar,PowerTransformer-Yeo-Johnson_RF,0.7102941176470587,0.12573099407851795,0.8095238095238095 130 | sonar,Normalizer_RF,0.7227941176470589,0.1169464432474767,0.7619047619047619 131 | ,,,, 132 | sonar,_RF-PCA,0.7106617647058824,0.09587132853438558,0.7857142857142857 133 | sonar,StandardScaler_RF-PCA,0.6746323529411764,0.1204205478635223,0.7619047619047619 134 | sonar,MinMaxScaler_RF-PCA,0.7283088235294117,0.11761775829981647,0.7380952380952381 135 | sonar,MaxAbsScaler_RF-PCA,0.7286764705882354,0.12398811891958837,0.8095238095238095 136 | sonar,RobustScaler_RF-PCA,0.7033088235294118,0.14889524327142936,0.7857142857142857 137 | sonar,QuantileTransformer-Normal_RF-PCA,0.7658088235294118,0.10454437001947146,0.8333333333333334 138 | sonar,QuantileTransformer-Uniform_RF-PCA,0.6683823529411765,0.13727372069869626,0.8095238095238095 139 | sonar,PowerTransformer-Yeo-Johnson_RF-PCA,0.7102941176470587,0.1276725543798216,0.7619047619047619 140 | sonar,Normalizer_RF-PCA,0.6430147058823529,0.08589812128611679,0.6428571428571429 141 | ,,,, 142 | sonar,_MLP,0.7783088235294118,0.1251734307251965,0.8333333333333334 143 | sonar,StandardScaler_MLP,0.8198529411764707,0.08441483905038918,0.8809523809523809 144 | sonar,MinMaxScaler_MLP,0.7665441176470588,0.14323380313875525,0.8095238095238095 145 | sonar,MaxAbsScaler_MLP,0.7665441176470588,0.14323380313875525,0.8095238095238095 146 | sonar,RobustScaler_MLP,0.807720588235294,0.08760575048078201,0.8809523809523809 147 | sonar,QuantileTransformer-Normal_MLP,0.8084558823529411,0.11721485407392244,0.8333333333333334 148 | sonar,QuantileTransformer-Uniform_MLP,0.8077205882352942,0.10552239160366762,0.8333333333333334 149 | sonar,PowerTransformer-Yeo-Johnson_MLP,0.8386029411764706,0.11076431636712616,0.8809523809523809 150 | sonar,Normalizer_MLP,0.7235294117647059,0.1317777534243844,0.7619047619047619 151 | ,,,, 152 | sonar,_MLP-PCA,0.7047794117647059,0.08494238455430611,0.7380952380952381 153 | sonar,StandardScaler_MLP-PCA,0.7768382352941176,0.09057931225152063,0.7619047619047619 154 | sonar,MinMaxScaler_MLP-PCA,0.7470588235294118,0.11029166663943293,0.7857142857142857 155 | sonar,MaxAbsScaler_MLP-PCA,0.7408088235294118,0.10774317169413938,0.7857142857142857 156 | sonar,RobustScaler_MLP-PCA,0.7705882352941176,0.08906169375280062,0.8095238095238095 157 | sonar,QuantileTransformer-Normal_MLP-PCA,0.7286764705882354,0.11968365887075827,0.8095238095238095 158 | sonar,QuantileTransformer-Uniform_MLP-PCA,0.7761029411764706,0.10370579998161619,0.7857142857142857 159 | sonar,PowerTransformer-Yeo-Johnson_MLP-PCA,0.7702205882352942,0.1229090911199444,0.7857142857142857 160 | sonar,Normalizer_MLP-PCA,0.61875,0.0974098209968769,0.5714285714285714 161 | -------------------------------------------------------------------------------- /data/processed/sonar_results_hypertuned.csv: -------------------------------------------------------------------------------- 1 | Dataset,Classifier_Name,CV_mean,CV_std,Test_acc 2 | sonar,_LR,0.759926471,0.081033448,0.714285714 3 | sonar,StandardScaler_LR,0.740808824,0.089274669,0.833333333 4 | sonar,MinMaxScaler_LR,0.711029412,0.108080732,0.761904762 5 | sonar,MaxAbsScaler_LR,0.735661765,0.108346156,0.761904762 6 | sonar,RobustScaler_LR,0.758455882,0.068525506,0.80952381 7 | sonar,QuantileTransformer-Normal_LR,0.795220588,0.10674498,0.80952381 8 | sonar,QuantileTransformer-Uniform_LR,0.813602941,0.080195108,0.785714286 9 | sonar,PowerTransformer-Yeo-Johnson_LR,0.789338235,0.097686944,0.833333333 10 | ,,,, 11 | sonar,_LR-PCA,0.688602941,0.102124518,0.619047619 12 | sonar,StandardScaler_LR-PCA,0.747794118,0.092778268,0.738095238 13 | sonar,MinMaxScaler_LR-PCA,0.765808824,0.07956905,0.714285714 14 | sonar,MaxAbsScaler_LR-PCA,0.765808824,0.07956905,0.714285714 15 | sonar,RobustScaler_LR-PCA,0.723529412,0.078055549,0.738095238 16 | sonar,QuantileTransformer-Normal_LR-PCA,0.765808824,0.064543789,0.714285714 17 | sonar,QuantileTransformer-Uniform_LR-PCA,0.777573529,0.095930524,0.738095238 18 | sonar,PowerTransformer-Yeo-Johnson_LR-PCA,0.777205882,0.065791363,0.714285714 19 | ,,,, 20 | sonar,_LDA,0.769852941,0.090162771,0.833333333 21 | sonar,StandardScaler_LDA,0.783088235,0.072006283,0.833333333 22 | sonar,MinMaxScaler_LDA,0.782352941,0.111000191,0.833333333 23 | sonar,MaxAbsScaler_LDA,0.765441176,0.109984861,0.833333333 24 | sonar,RobustScaler_LDA,0.776102941,0.083293766,0.833333333 25 | sonar,QuantileTransformer-Normal_LDA,0.770955882,0.091233527,0.738095238 26 | sonar,QuantileTransformer-Uniform_LDA,0.783088235,0.047310906,0.785714286 27 | sonar,PowerTransformer-Yeo-Johnson_LDA,0.777941176,0.082997087,0.785714286 28 | ,,,, 29 | sonar,_LDA-PCA,0.676102941,0.097567877,0.642857143 30 | sonar,StandardScaler_LDA-PCA,0.753308824,0.076085178,0.714285714 31 | sonar,MinMaxScaler_LDA-PCA,0.753676471,0.083383779,0.714285714 32 | sonar,MaxAbsScaler_LDA-PCA,0.759558824,0.077523855,0.714285714 33 | sonar,RobustScaler_LDA-PCA,0.729779412,0.067895364,0.69047619 34 | sonar,QuantileTransformer-Normal_LDA-PCA,0.777573529,0.08388881,0.714285714 35 | sonar,QuantileTransformer-Uniform_LDA-PCA,0.764705882,0.073859553,0.738095238 36 | sonar,PowerTransformer-Yeo-Johnson_LDA-PCA,0.770955882,0.065106755,0.69047619 37 | ,,,, 38 | sonar,_KNN,0.85,0.103357875,0.761904762 39 | sonar,StandardScaler_KNN,0.825,0.096547786,0.833333333 40 | sonar,MinMaxScaler_KNN,0.837132353,0.066013893,0.785714286 41 | sonar,MaxAbsScaler_KNN,0.843014706,0.055902908,0.761904762 42 | sonar,RobustScaler_KNN,0.837867647,0.103614526,0.833333333 43 | sonar,QuantileTransformer-Normal_KNN,0.805882353,0.100439669,0.833333333 44 | sonar,QuantileTransformer-Uniform_KNN,0.890808824,0.106580242,0.904761905 45 | sonar,PowerTransformer-Yeo-Johnson_KNN,0.873897059,0.106084494,0.785714286 46 | ,,,, 47 | sonar,_KNN-PCA,0.770955882,0.058801696,0.714285714 48 | sonar,StandardScaler_KNN-PCA,0.775735294,0.088693662,0.785714286 49 | sonar,MinMaxScaler_KNN-PCA,0.752941176,0.094160721,0.80952381 50 | sonar,MaxAbsScaler_KNN-PCA,0.740808824,0.101116309,0.80952381 51 | sonar,RobustScaler_KNN-PCA,0.789338235,0.08537094,0.714285714 52 | sonar,QuantileTransformer-Normal_KNN-PCA,0.73125,0.106076849,0.785714286 53 | sonar,QuantileTransformer-Uniform_KNN-PCA,0.770955882,0.094950503,0.833333333 54 | sonar,PowerTransformer-Yeo-Johnson_KNN-PCA,0.777205882,0.111696961,0.80952381 55 | ,,,, 56 | sonar,_CART,0.734191176,0.097270964,0.80952381 57 | sonar,StandardScaler_CART,0.734191176,0.097270964,0.80952381 58 | sonar,MinMaxScaler_CART,0.734191176,0.097270964,0.80952381 59 | sonar,MaxAbsScaler_CART,0.734191176,0.097270964,0.80952381 60 | sonar,RobustScaler_CART,0.734191176,0.097270964,0.80952381 61 | sonar,QuantileTransformer-Normal_CART,0.640073529,0.133617832,0.80952381 62 | sonar,QuantileTransformer-Uniform_CART,0.651102941,0.123196878,0.80952381 63 | sonar,PowerTransformer-Yeo-Johnson_CART,0.650367647,0.107103987,0.80952381 64 | ,,,, 65 | sonar,_CART-PCA,0.704411765,0.094195166,0.714285714 66 | sonar,StandardScaler_CART-PCA,0.736029412,0.090336501,0.761904762 67 | sonar,MinMaxScaler_CART-PCA,0.657352941,0.107436007,0.761904762 68 | sonar,MaxAbsScaler_CART-PCA,0.722794118,0.094924164,0.761904762 69 | sonar,RobustScaler_CART-PCA,0.685661765,0.126389872,0.642857143 70 | sonar,QuantileTransformer-Normal_CART-PCA,0.687132353,0.102973341,0.761904762 71 | sonar,QuantileTransformer-Uniform_CART-PCA,0.716911765,0.12270823,0.80952381 72 | sonar,PowerTransformer-Yeo-Johnson_CART-PCA,0.740441176,0.110651382,0.80952381 73 | ,,,, 74 | sonar,_SVM,0.788970588,0.077279377,0.738095238 75 | sonar,StandardScaler_SVM,0.861029412,0.074002157,0.833333333 76 | sonar,MinMaxScaler_SVM,0.734926471,0.109832982,0.714285714 77 | sonar,MaxAbsScaler_SVM,0.759191176,0.074447395,0.69047619 78 | sonar,RobustScaler_SVM,0.771323529,0.111048888,0.785714286 79 | sonar,QuantileTransformer-Normal_SVM,0.836764706,0.084097205,0.80952381 80 | sonar,QuantileTransformer-Uniform_SVM,0.813602941,0.071043454,0.785714286 81 | sonar,PowerTransformer-Yeo-Johnson_SVM,0.873161765,0.058783304,0.833333333 82 | ,,,, 83 | sonar,_SVM-PCA,0.669852941,0.108649503,0.69047619 84 | sonar,StandardScaler_SVM-PCA,0.717647059,0.103749453,0.738095238 85 | sonar,MinMaxScaler_SVM-PCA,0.736764706,0.096014322,0.761904762 86 | sonar,MaxAbsScaler_SVM-PCA,0.742647059,0.10271772,0.761904762 87 | sonar,RobustScaler_SVM-PCA,0.759191176,0.088300374,0.738095238 88 | sonar,QuantileTransformer-Normal_SVM-PCA,0.735294118,0.09486434,0.761904762 89 | sonar,QuantileTransformer-Uniform_SVM-PCA,0.753308824,0.110434347,0.833333333 90 | sonar,PowerTransformer-Yeo-Johnson_SVM-PCA,0.727941176,0.080564214,0.857142857 91 | ,,,, 92 | sonar,_RF,0.770588235,0.072477774,0.80952381 93 | sonar,StandardScaler_RF,0.776470588,0.078659289,0.80952381 94 | sonar,MinMaxScaler_RF,0.776470588,0.078659289,0.80952381 95 | sonar,MaxAbsScaler_RF,0.776470588,0.078659289,0.80952381 96 | sonar,RobustScaler_RF,0.776470588,0.078659289,0.80952381 97 | sonar,QuantileTransformer-Normal_RF,0.795220588,0.108714796,0.80952381 98 | sonar,QuantileTransformer-Uniform_RF,0.745955882,0.091106027,0.80952381 99 | sonar,PowerTransformer-Yeo-Johnson_RF,0.776470588,0.074129901,0.80952381 100 | ,,,, 101 | sonar,_RF-PCA,0.699264706,0.104321108,0.785714286 102 | sonar,StandardScaler_RF-PCA,0.722426471,0.094553935,0.761904762 103 | sonar,MinMaxScaler_RF-PCA,0.722426471,0.133007471,0.738095238 104 | sonar,MaxAbsScaler_RF-PCA,0.758455882,0.072791332,0.80952381 105 | sonar,RobustScaler_RF-PCA,0.710661765,0.117048685,0.785714286 106 | sonar,QuantileTransformer-Normal_RF-PCA,0.772794118,0.119389668,0.833333333 107 | sonar,QuantileTransformer-Uniform_RF-PCA,0.741176471,0.13665595,0.80952381 108 | sonar,PowerTransformer-Yeo-Johnson_RF-PCA,0.772058824,0.11085641,0.761904762 109 | ,,,, 110 | sonar,_MLP,0.734191176,0.134952514,0.833333333 111 | sonar,StandardScaler_MLP,0.776838235,0.085117242,0.880952381 112 | sonar,MinMaxScaler_MLP,0.745955882,0.07377624,0.80952381 113 | sonar,MaxAbsScaler_MLP,0.727941176,0.103164149,0.80952381 114 | sonar,RobustScaler_MLP,0.782720588,0.078222474,0.880952381 115 | sonar,QuantileTransformer-Normal_MLP,0.69375,0.103413439,0.833333333 116 | sonar,QuantileTransformer-Uniform_MLP,0.758455882,0.092264763,0.80952381 117 | sonar,PowerTransformer-Yeo-Johnson_MLP,0.806985294,0.074719234,0.880952381 118 | ,,,, 119 | sonar,_MLP-PCA,0.650735294,0.097907387,0.738095238 120 | sonar,StandardScaler_MLP-PCA,0.741544118,0.09450818,0.761904762 121 | sonar,MinMaxScaler_MLP-PCA,0.741911765,0.104334064,0.785714286 122 | sonar,MaxAbsScaler_MLP-PCA,0.754411765,0.092305041,0.785714286 123 | sonar,RobustScaler_MLP-PCA,0.709558824,0.084126131,0.80952381 124 | sonar,QuantileTransformer-Normal_MLP-PCA,0.734558824,0.061536694,0.80952381 125 | sonar,QuantileTransformer-Uniform_MLP-PCA,0.740808824,0.084258578,0.785714286 126 | sonar,PowerTransformer-Yeo-Johnson_MLP-PCA,0.680514706,0.111409192,0.785714286 127 | -------------------------------------------------------------------------------- /data/processed/weatherAUS_results.csv: -------------------------------------------------------------------------------- 1 | Dataset,Classifier_Name,CV_mean,CV_std,Test_score 2 | weatherAUS,_LR,1.0,0.0,1.0 3 | weatherAUS,StandardScaler_LR,0.9936192112168254,0.000990295018677697,0.9944168734491315 4 | weatherAUS,MinMaxScaler_LR,0.8874953673437471,0.004163161869895122,0.8923254165189649 5 | weatherAUS,MaxAbsScaler_LR,0.8868972920350906,0.005014558968264108,0.8898440269408011 6 | weatherAUS,RobustScaler_LR,1.0,0.0,1.0 7 | weatherAUS,QuantileTransformer-Normal_LR,0.9999556933983165,8.861320336728262e-05,0.9997341368309111 8 | weatherAUS,QuantileTransformer-Uniform_LR,0.9938186154682761,0.0012670425828027,0.9950372208436724 9 | weatherAUS,PowerTransformer-Yeo-Johnson_LR,1.0,0.0,1.0 10 | weatherAUS,Normalizer_LR,0.8269895190784933,0.003827638840460483,0.8347217298830202 11 | ,,,, 12 | weatherAUS,_LR-PCA,0.8501640610726204,0.004697572243791872,0.8522686990428926 13 | weatherAUS,StandardScaler_LR-PCA,0.8532878532072118,0.00509808539720156,0.8577632045373981 14 | weatherAUS,MinMaxScaler_LR-PCA,0.8253500422792776,0.005885776954794296,0.8232009925558312 15 | weatherAUS,MaxAbsScaler_LR-PCA,0.8207418366338135,0.007404669703654039,0.8185926976249557 16 | weatherAUS,RobustScaler_LR-PCA,1.0,0.0,1.0 17 | weatherAUS,QuantileTransformer-Normal_LR-PCA,0.9541164936699877,0.00296245532550185,0.956398440269408 18 | weatherAUS,QuantileTransformer-Uniform_LR-PCA,0.8852578594148485,0.005725260244911056,0.8890464374335342 19 | weatherAUS,PowerTransformer-Yeo-Johnson_LR-PCA,0.8716323522034164,0.004855194217325859,0.8743353420772776 20 | weatherAUS,Normalizer_LR-PCA,0.8209854640377756,0.003422564523299251,0.8273661822048919 21 | ,,,, 22 | weatherAUS,_LDA,0.8801398087796579,0.0029925795384450455,0.8829315845444877 23 | weatherAUS,StandardScaler_LDA,0.8801398087796579,0.0029925795384450455,0.8829315845444877 24 | weatherAUS,MinMaxScaler_LDA,0.8801398087796579,0.0029925795384450455,0.8829315845444877 25 | weatherAUS,MaxAbsScaler_LDA,0.8801398087796579,0.0029925795384450455,0.8829315845444877 26 | weatherAUS,RobustScaler_LDA,0.8801398087796579,0.0029925795384450455,0.8829315845444877 27 | weatherAUS,QuantileTransformer-Normal_LDA,0.8839730366888704,0.0037423206739179518,0.8826657213753988 28 | weatherAUS,QuantileTransformer-Uniform_LDA,0.884482572425782,0.003969031245683899,0.8830202056008508 29 | weatherAUS,PowerTransformer-Yeo-Johnson_LDA,0.9610289273119422,0.0027211202833996935,0.9630450194966323 30 | weatherAUS,Normalizer_LDA,0.883950593770313,0.003457791496216723,0.8884260900389933 31 | ,,,, 32 | weatherAUS,_LDA-PCA,0.8498094806310054,0.005012319891962318,0.8516483516483516 33 | weatherAUS,StandardScaler_LDA-PCA,0.8528669061297933,0.003980337721014812,0.859181141439206 34 | weatherAUS,MinMaxScaler_LDA-PCA,0.8237548328115469,0.006642173226823206,0.8213399503722084 35 | weatherAUS,MaxAbsScaler_LDA-PCA,0.8192796009676568,0.0073892933357718096,0.8177951081176887 36 | weatherAUS,RobustScaler_LDA-PCA,0.860399509475948,0.003206489250084653,0.8654732364409784 37 | weatherAUS,QuantileTransformer-Normal_LDA-PCA,0.8723414492725736,0.0036911691594401614,0.8708791208791209 38 | weatherAUS,QuantileTransformer-Uniform_LDA-PCA,0.8851470781843149,0.005747368213924541,0.8873626373626373 39 | weatherAUS,PowerTransformer-Yeo-Johnson_LDA-PCA,0.8690180712618624,0.00479381087659197,0.8702587734845799 40 | weatherAUS,Normalizer_LDA-PCA,0.8475939394695049,0.004277102934280114,0.8501417936901808 41 | ,,,, 42 | weatherAUS,_KNN,0.8903312451077923,0.00408718200170553,0.8934774902516838 43 | weatherAUS,StandardScaler_KNN,0.8903090427192023,0.004273152234173719,0.8938319744771358 44 | weatherAUS,MinMaxScaler_KNN,0.8446028364275469,0.0039829307436939805,0.847217298830202 45 | weatherAUS,MaxAbsScaler_KNN,0.839551481316074,0.004239922425336697,0.8470400567174761 46 | weatherAUS,RobustScaler_KNN,0.9910935385698638,0.0016034573777241926,0.9908720311946119 47 | weatherAUS,QuantileTransformer-Normal_KNN,0.9174715519317453,0.0033977600643027408,0.923697270471464 48 | weatherAUS,QuantileTransformer-Uniform_KNN,0.9192661361982777,0.0034344386406382705,0.923697270471464 49 | weatherAUS,PowerTransformer-Yeo-Johnson_KNN,0.9737238240808981,0.001611014293319918,0.9750974831619993 50 | weatherAUS,Normalizer_KNN,0.8928345926467929,0.0043570457335146305,0.8961361219425735 51 | ,,,, 52 | weatherAUS,_KNN-PCA,0.8358073329438385,0.005556481462871903,0.83649415101028 53 | weatherAUS,StandardScaler_KNN-PCA,0.8391304949684567,0.006561086701240172,0.8426976249556895 54 | weatherAUS,MinMaxScaler_KNN-PCA,0.8139399976889488,0.005423573446898781,0.8102623183268345 55 | weatherAUS,MaxAbsScaler_KNN-PCA,0.8076259240645912,0.0059016236509324945,0.803704360155973 56 | weatherAUS,RobustScaler_KNN-PCA,0.995458146264015,0.0006592281932342026,0.9957461892945764 57 | weatherAUS,QuantileTransformer-Normal_KNN-PCA,0.9540943452779207,0.003152626430296587,0.95586671393123 58 | weatherAUS,QuantileTransformer-Uniform_KNN-PCA,0.8778135060227232,0.007087144521450284,0.8773484579936193 59 | weatherAUS,PowerTransformer-Yeo-Johnson_KNN-PCA,0.8577851745378707,0.006242282162252784,0.8605104572846508 60 | weatherAUS,Normalizer_KNN-PCA,0.8338134131487032,0.005763357319443337,0.8402162353775257 61 | ,,,, 62 | weatherAUS,_CART,1.0,0.0,1.0 63 | weatherAUS,StandardScaler_CART,1.0,0.0,1.0 64 | weatherAUS,MinMaxScaler_CART,1.0,0.0,1.0 65 | weatherAUS,MaxAbsScaler_CART,1.0,0.0,1.0 66 | weatherAUS,RobustScaler_CART,1.0,0.0,1.0 67 | weatherAUS,QuantileTransformer-Normal_CART,1.0,0.0,1.0 68 | weatherAUS,QuantileTransformer-Uniform_CART,1.0,0.0,1.0 69 | weatherAUS,PowerTransformer-Yeo-Johnson_CART,1.0,0.0,1.0 70 | weatherAUS,Normalizer_CART,1.0,0.0,1.0 71 | ,,,, 72 | weatherAUS,_CART-PCA,0.7801313853220366,0.0045135719803459625,0.7873094647288196 73 | weatherAUS,StandardScaler_CART-PCA,0.7918732925440324,0.00707389590951061,0.7960829493087558 74 | weatherAUS,MinMaxScaler_CART-PCA,0.7591279306244816,0.006362079764100196,0.7575327897908543 75 | weatherAUS,MaxAbsScaler_CART-PCA,0.7532125624187536,0.007855375237513468,0.7516838000708969 76 | weatherAUS,RobustScaler_CART-PCA,0.9970090736739362,0.0009920034338585788,0.9973413683091102 77 | weatherAUS,QuantileTransformer-Normal_CART-PCA,0.9427951359146487,0.0029298627329795972,0.9415987238567883 78 | weatherAUS,QuantileTransformer-Uniform_CART-PCA,0.843871674415495,0.006701194178255928,0.8437610776320453 79 | weatherAUS,PowerTransformer-Yeo-Johnson_CART-PCA,0.8183268961296373,0.006230918878202215,0.8234668557249203 80 | weatherAUS,Normalizer_CART-PCA,0.7796218544938999,0.0037483556916837833,0.7840304856433888 81 | ,,,, 82 | weatherAUS,_NB,0.9495302400655969,0.0032974279976292343,0.9511697979439915 83 | weatherAUS,StandardScaler_NB,0.9495302400655969,0.0032974279976292343,0.9511697979439915 84 | weatherAUS,MinMaxScaler_NB,0.9495302400655969,0.0032974279976292343,0.9511697979439915 85 | weatherAUS,MaxAbsScaler_NB,0.9495302400655969,0.0032974279976292343,0.9511697979439915 86 | weatherAUS,RobustScaler_NB,0.9495302400655969,0.0032974279976292343,0.9511697979439915 87 | weatherAUS,QuantileTransformer-Normal_NB,0.8911290339207142,0.004292969271937924,0.8882488479262672 88 | weatherAUS,QuantileTransformer-Uniform_NB,0.9155663336979245,0.00390046982170262,0.9139489542715349 89 | weatherAUS,PowerTransformer-Yeo-Johnson_NB,0.9783764835912911,0.0023906567498747167,0.9775788727401631 90 | weatherAUS,Normalizer_NB,0.9416208440716873,0.003967970125976163,0.9424849344204183 91 | ,,,, 92 | weatherAUS,_NB-PCA,0.8438276868841758,0.004712164083795965,0.8468628146047501 93 | weatherAUS,StandardScaler_NB-PCA,0.8339463378625289,0.003742605397636434,0.8419000354484225 94 | weatherAUS,MinMaxScaler_NB-PCA,0.8231789304388316,0.006152645912557017,0.8192130450194967 95 | weatherAUS,MaxAbsScaler_NB-PCA,0.8144497641382777,0.007317728733306743,0.8115030131159163 96 | weatherAUS,RobustScaler_NB-PCA,0.8714769992973579,0.003926703574750151,0.880716058135413 97 | weatherAUS,QuantileTransformer-Normal_NB-PCA,0.8934111478865615,0.004107952287374199,0.8927685218007798 98 | weatherAUS,QuantileTransformer-Uniform_NB-PCA,0.8800514213799332,0.0041599129112294886,0.8834633108826657 99 | weatherAUS,PowerTransformer-Yeo-Johnson_NB-PCA,0.8636343822763383,0.0033719606624128947,0.8681318681318682 100 | weatherAUS,Normalizer_NB-PCA,0.8436504064809179,0.0045703498083992135,0.847394540942928 101 | ,,,, 102 | weatherAUS,_SVM,0.7797102173497505,0.003848674361286306,0.7798652959943283 103 | weatherAUS,StandardScaler_SVM,0.9780219767812988,0.0020325096314785587,0.9794399149237859 104 | weatherAUS,MinMaxScaler_SVM,0.8687077728780569,0.0031993868826987954,0.8741580999645516 105 | weatherAUS,MaxAbsScaler_SVM,0.8677108203436517,0.004514800157044259,0.8702587734845799 106 | weatherAUS,RobustScaler_SVM,0.9982054157334677,0.0005191367697776155,0.9980503367600142 107 | weatherAUS,QuantileTransformer-Normal_SVM,0.984579839799188,0.0020268408976832917,0.9866182204891882 108 | weatherAUS,QuantileTransformer-Uniform_SVM,0.9939293427022864,0.0014472349398301119,0.9956575682382134 109 | weatherAUS,PowerTransformer-Yeo-Johnson_SVM,0.9970090196774131,0.0006364914832238614,0.9971641261963843 110 | weatherAUS,Normalizer_SVM,0.7799317601757183,0.003907459701105226,0.7801311591634172 111 | ,,,, 112 | weatherAUS,_SVM-PCA,0.7796438212612979,0.004697609209325043,0.7816377171215881 113 | weatherAUS,StandardScaler_SVM-PCA,0.8543291859749234,0.004612286083890741,0.8598014888337469 114 | weatherAUS,MinMaxScaler_SVM-PCA,0.8241094574321354,0.0054549749754192935,0.825859624246721 115 | weatherAUS,MaxAbsScaler_SVM-PCA,0.8204981405070036,0.005995743997871908,0.8191244239631337 116 | weatherAUS,RobustScaler_SVM-PCA,0.9978066121393413,0.0007444770666104936,0.9984934420418291 117 | weatherAUS,QuantileTransformer-Normal_SVM-PCA,0.9649725241146019,0.002033946513002881,0.967919177596597 118 | weatherAUS,QuantileTransformer-Uniform_SVM-PCA,0.8857674344219589,0.005882228778133284,0.8897554058844381 119 | weatherAUS,PowerTransformer-Yeo-Johnson_SVM-PCA,0.8733604765674233,0.0058857903102743445,0.8764622474299893 120 | weatherAUS,Normalizer_SVM-PCA,0.7805742598966546,0.003685372829501679,0.781549096065225 121 | ,,,, 122 | weatherAUS,_RF,0.9697578334474295,0.0033755229388277358,0.9712867777383907 123 | weatherAUS,StandardScaler_RF,0.9697578334474295,0.0033755229388277358,0.9712867777383907 124 | weatherAUS,MinMaxScaler_RF,0.9697578334474295,0.0033755229388277358,0.9712867777383907 125 | weatherAUS,MaxAbsScaler_RF,0.9697578334474295,0.0033755229388277358,0.9712867777383907 126 | weatherAUS,RobustScaler_RF,0.9697578334474295,0.0033755229388277358,0.9712867777383907 127 | weatherAUS,QuantileTransformer-Normal_RF,0.9696692104265127,0.0033260196008407417,0.9716412619638426 128 | weatherAUS,QuantileTransformer-Uniform_RF,0.9696692104265127,0.0033260196008407417,0.9716412619638426 129 | weatherAUS,PowerTransformer-Yeo-Johnson_RF,0.9697578334474295,0.0033755229388277358,0.9712867777383907 130 | weatherAUS,Normalizer_RF,0.9480013383283717,0.005038052453577101,0.9510811768876285 131 | ,,,, 132 | weatherAUS,_RF-PCA,0.8307559729235907,0.004484485692427932,0.8356965615030131 133 | weatherAUS,StandardScaler_RF-PCA,0.8456663863101731,0.004305869654433713,0.8535093938319744 134 | weatherAUS,MinMaxScaler_RF-PCA,0.8012674113016294,0.00497977167131171,0.8029953917050692 135 | weatherAUS,MaxAbsScaler_RF-PCA,0.8089329786318086,0.005411919148550766,0.8099964551577454 136 | weatherAUS,RobustScaler_RF-PCA,0.9473812520733438,0.004643022909579236,0.9546260191421482 137 | weatherAUS,QuantileTransformer-Normal_RF-PCA,0.9073910637324891,0.0042047625337148465,0.9037575327897909 138 | weatherAUS,QuantileTransformer-Uniform_RF-PCA,0.859424788782782,0.004110568059672514,0.8662708259482453 139 | weatherAUS,PowerTransformer-Yeo-Johnson_RF-PCA,0.8607320789711915,0.005088929680219763,0.8675115207373272 140 | weatherAUS,Normalizer_RF-PCA,0.8389311938012778,0.0044016287039452175,0.8395958879829848 141 | ,,,, 142 | weatherAUS,_MLP,0.9915360891653424,0.010429271353534348,0.9988479262672811 143 | weatherAUS,StandardScaler_MLP,0.9999335302799249,0.00010153417534669389,0.999822757887274 144 | weatherAUS,MinMaxScaler_MLP,0.998426919289237,0.001960539794371112,1.0 145 | weatherAUS,MaxAbsScaler_MLP,0.9984270174647337,0.002719463393103566,1.0 146 | weatherAUS,RobustScaler_MLP,0.9999113769790829,0.00017724604319342623,1.0 147 | weatherAUS,QuantileTransformer-Normal_MLP,0.9990694877330208,0.000393805702125357,0.9989365473236441 148 | weatherAUS,QuantileTransformer-Uniform_MLP,0.9989144342622274,0.0016080645462367362,1.0 149 | weatherAUS,PowerTransformer-Yeo-Johnson_MLP,1.0,0.0,1.0 150 | weatherAUS,Normalizer_MLP,0.9985154048644584,0.002332516039400516,0.9983161999291031 151 | ,,,, 152 | weatherAUS,_MLP-PCA,0.8507399879892098,0.005954054672660673,0.8540411201701524 153 | weatherAUS,StandardScaler_MLP-PCA,0.8550824718351681,0.004592021330530892,0.859269762495569 154 | weatherAUS,MinMaxScaler_MLP-PCA,0.8303349473057748,0.004389302319947125,0.8320630981921304 155 | weatherAUS,MaxAbsScaler_MLP-PCA,0.8267236352894178,0.005405480593908296,0.8271889400921659 156 | weatherAUS,RobustScaler_MLP-PCA,0.999800610474874,0.00040183043754344017,1.0 157 | weatherAUS,QuantileTransformer-Normal_MLP-PCA,0.9648839747253074,0.002482327827248719,0.967653314427508 158 | weatherAUS,QuantileTransformer-Uniform_MLP-PCA,0.8879385953501533,0.005461538713292528,0.892059553349876 159 | weatherAUS,PowerTransformer-Yeo-Johnson_MLP-PCA,0.8732496707930155,0.006220590282830204,0.8749556894718185 160 | weatherAUS,Normalizer_MLP-PCA,0.8487237921738616,0.004748569925069982,0.8512052463665367 161 | -------------------------------------------------------------------------------- /data/raw/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/data/raw/.gitkeep -------------------------------------------------------------------------------- /data/raw/sonar.csv: -------------------------------------------------------------------------------- 1 | 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60 2 | 0.02,0.0371,0.0428,0.0207,0.0954,0.0986,0.1539,0.1601,0.3109,0.2111,0.1609,0.1582,0.2238,0.0645,0.066,0.2273,0.31,0.2999,0.5078,0.4797,0.5783,0.5071,0.4328,0.555,0.6711,0.6415,0.7104,0.808,0.6791,0.3857,0.1307,0.2604,0.5121,0.7547,0.8537,0.8507,0.6692,0.6097,0.4943,0.2744,0.051,0.2834,0.2825,0.4256,0.2641,0.1386,0.1051,0.1343,0.0383,0.0324,0.0232,0.0027,0.0065,0.0159,0.0072,0.0167,0.018,0.0084,0.009,0.0032,R 3 | 0.0453,0.0523,0.0843,0.0689,0.1183,0.2583,0.2156,0.3481,0.3337,0.2872,0.4918,0.6552,0.6919,0.7797,0.7464,0.9444,1.0,0.8874,0.8024,0.7818,0.5212,0.4052,0.3957,0.3914,0.325,0.32,0.3271,0.2767,0.4423,0.2028,0.3788,0.2947,0.1984,0.2341,0.1306,0.4182,0.3835,0.1057,0.184,0.197,0.1674,0.0583,0.1401,0.1628,0.0621,0.0203,0.053,0.0742,0.0409,0.0061,0.0125,0.0084,0.0089,0.0048,0.0094,0.0191,0.014,0.0049,0.0052,0.0044,R 4 | 0.0262,0.0582,0.1099,0.1083,0.0974,0.228,0.2431,0.3771,0.5598,0.6194,0.6333,0.706,0.5544,0.532,0.6479,0.6931,0.6759,0.7551,0.8929,0.8619,0.7974,0.6737,0.4293,0.3648,0.5331,0.2413,0.507,0.8533,0.6036,0.8514,0.8512,0.5045,0.1862,0.2709,0.4232,0.3043,0.6116,0.6756,0.5375,0.4719,0.4647,0.2587,0.2129,0.2222,0.2111,0.0176,0.1348,0.0744,0.013,0.0106,0.0033,0.0232,0.0166,0.0095,0.018,0.0244,0.0316,0.0164,0.0095,0.0078,R 5 | 0.01,0.0171,0.0623,0.0205,0.0205,0.0368,0.1098,0.1276,0.0598,0.1264,0.0881,0.1992,0.0184,0.2261,0.1729,0.2131,0.0693,0.2281,0.406,0.3973,0.2741,0.369,0.5556,0.4846,0.314,0.5334,0.5256,0.252,0.209,0.3559,0.626,0.734,0.612,0.3497,0.3953,0.3012,0.5408,0.8814,0.9857,0.9167,0.6121,0.5006,0.321,0.3202,0.4295,0.3654,0.2655,0.1576,0.0681,0.0294,0.0241,0.0121,0.0036,0.015,0.0085,0.0073,0.005,0.0044,0.004,0.0117,R 6 | 0.0762,0.0666,0.0481,0.0394,0.059,0.0649,0.1209,0.2467,0.3564,0.4459,0.4152,0.3952,0.4256,0.4135,0.4528,0.5326,0.7306,0.6193,0.2032,0.4636,0.4148,0.4292,0.573,0.5399,0.3161,0.2285,0.6995,1.0,0.7262,0.4724,0.5103,0.5459,0.2881,0.0981,0.1951,0.4181,0.4604,0.3217,0.2828,0.243,0.1979,0.2444,0.1847,0.0841,0.0692,0.0528,0.0357,0.0085,0.023,0.0046,0.0156,0.0031,0.0054,0.0105,0.011,0.0015,0.0072,0.0048,0.0107,0.0094,R 7 | 0.0286,0.0453,0.0277,0.0174,0.0384,0.099,0.1201,0.1833,0.2105,0.3039,0.2988,0.425,0.6343,0.8198,1.0,0.9988,0.9508,0.9025,0.7234,0.5122,0.2074,0.3985,0.589,0.2872,0.2043,0.5782,0.5389,0.375,0.3411,0.5067,0.558,0.4778,0.3299,0.2198,0.1407,0.2856,0.3807,0.4158,0.4054,0.3296,0.2707,0.265,0.0723,0.1238,0.1192,0.1089,0.0623,0.0494,0.0264,0.0081,0.0104,0.0045,0.0014,0.0038,0.0013,0.0089,0.0057,0.0027,0.0051,0.0062,R 8 | 0.0317,0.0956,0.1321,0.1408,0.1674,0.171,0.0731,0.1401,0.2083,0.3513,0.1786,0.0658,0.0513,0.3752,0.5419,0.544,0.515,0.4262,0.2024,0.4233,0.7723,0.9735,0.939,0.5559,0.5268,0.6826,0.5713,0.5429,0.2177,0.2149,0.5811,0.6323,0.2965,0.1873,0.2969,0.5163,0.6153,0.4283,0.5479,0.6133,0.5017,0.2377,0.1957,0.1749,0.1304,0.0597,0.1124,0.1047,0.0507,0.0159,0.0195,0.0201,0.0248,0.0131,0.007,0.0138,0.0092,0.0143,0.0036,0.0103,R 9 | 0.0519,0.0548,0.0842,0.0319,0.1158,0.0922,0.1027,0.0613,0.1465,0.2838,0.2802,0.3086,0.2657,0.3801,0.5626,0.4376,0.2617,0.1199,0.6676,0.9402,0.7832,0.5352,0.6809,0.9174,0.7613,0.822,0.8872,0.6091,0.2967,0.1103,0.1318,0.0624,0.099,0.4006,0.3666,0.105,0.1915,0.393,0.4288,0.2546,0.1151,0.2196,0.1879,0.1437,0.2146,0.236,0.1125,0.0254,0.0285,0.0178,0.0052,0.0081,0.012,0.0045,0.0121,0.0097,0.0085,0.0047,0.0048,0.0053,R 10 | 0.0223,0.0375,0.0484,0.0475,0.0647,0.0591,0.0753,0.0098,0.0684,0.1487,0.1156,0.1654,0.3833,0.3598,0.1713,0.1136,0.0349,0.3796,0.7401,0.9925,0.9802,0.889,0.6712,0.4286,0.3374,0.7366,0.9611,0.7353,0.4856,0.1594,0.3007,0.4096,0.317,0.3305,0.3408,0.2186,0.2463,0.2726,0.168,0.2792,0.2558,0.174,0.2121,0.1099,0.0985,0.1271,0.1459,0.1164,0.0777,0.0439,0.0061,0.0145,0.0128,0.0145,0.0058,0.0049,0.0065,0.0093,0.0059,0.0022,R 11 | 0.0164,0.0173,0.0347,0.007,0.0187,0.0671,0.1056,0.0697,0.0962,0.0251,0.0801,0.1056,0.1266,0.089,0.0198,0.1133,0.2826,0.3234,0.3238,0.4333,0.6068,0.7652,0.9203,0.9719,0.9207,0.7545,0.8289,0.8907,0.7309,0.6896,0.5829,0.4935,0.3101,0.0306,0.0244,0.1108,0.1594,0.1371,0.0696,0.0452,0.062,0.1421,0.1597,0.1384,0.0372,0.0688,0.0867,0.0513,0.0092,0.0198,0.0118,0.009,0.0223,0.0179,0.0084,0.0068,0.0032,0.0035,0.0056,0.004,R 12 | 0.0039,0.0063,0.0152,0.0336,0.031,0.0284,0.0396,0.0272,0.0323,0.0452,0.0492,0.0996,0.1424,0.1194,0.0628,0.0907,0.1177,0.1429,0.1223,0.1104,0.1847,0.3715,0.4382,0.5707,0.6654,0.7476,0.7654,0.8555,0.972,0.9221,0.7502,0.7209,0.7757,0.6055,0.5021,0.4499,0.3947,0.4281,0.4427,0.3749,0.1972,0.0511,0.0793,0.1269,0.1533,0.069,0.0402,0.0534,0.0228,0.0073,0.0062,0.0062,0.012,0.0052,0.0056,0.0093,0.0042,0.0003,0.0053,0.0036,R 13 | 0.0123,0.0309,0.0169,0.0313,0.0358,0.0102,0.0182,0.0579,0.1122,0.0835,0.0548,0.0847,0.2026,0.2557,0.187,0.2032,0.1463,0.2849,0.5824,0.7728,0.7852,0.8515,0.5312,0.3653,0.5973,0.8275,1.0,0.8673,0.6301,0.4591,0.394,0.2576,0.2817,0.2641,0.2757,0.2698,0.3994,0.4576,0.394,0.2522,0.1782,0.1354,0.0516,0.0337,0.0894,0.0861,0.0872,0.0445,0.0134,0.0217,0.0188,0.0133,0.0265,0.0224,0.0074,0.0118,0.0026,0.0092,0.0009,0.0044,R 14 | 0.0079,0.0086,0.0055,0.025,0.0344,0.0546,0.0528,0.0958,0.1009,0.124,0.1097,0.1215,0.1874,0.3383,0.3227,0.2723,0.3943,0.6432,0.7271,0.8673,0.9674,0.9847,0.948,0.8036,0.6833,0.5136,0.309,0.0832,0.4019,0.2344,0.1905,0.1235,0.1717,0.2351,0.2489,0.3649,0.3382,0.1589,0.0989,0.1089,0.1043,0.0839,0.1391,0.0819,0.0678,0.0663,0.1202,0.0692,0.0152,0.0266,0.0174,0.0176,0.0127,0.0088,0.0098,0.0019,0.0059,0.0058,0.0059,0.0032,R 15 | 0.009,0.0062,0.0253,0.0489,0.1197,0.1589,0.1392,0.0987,0.0955,0.1895,0.1896,0.2547,0.4073,0.2988,0.2901,0.5326,0.4022,0.1571,0.3024,0.3907,0.3542,0.4438,0.6414,0.4601,0.6009,0.869,0.8345,0.7669,0.5081,0.462,0.538,0.5375,0.3844,0.3601,0.7402,0.7761,0.3858,0.0667,0.3684,0.6114,0.351,0.2312,0.2195,0.3051,0.1937,0.157,0.0479,0.0538,0.0146,0.0068,0.0187,0.0059,0.0095,0.0194,0.008,0.0152,0.0158,0.0053,0.0189,0.0102,R 16 | 0.0124,0.0433,0.0604,0.0449,0.0597,0.0355,0.0531,0.0343,0.1052,0.212,0.164,0.1901,0.3026,0.2019,0.0592,0.239,0.3657,0.3809,0.5929,0.6299,0.5801,0.4574,0.4449,0.3691,0.6446,0.894,0.8978,0.498,0.3333,0.235,0.1553,0.3666,0.434,0.3082,0.3024,0.4109,0.5501,0.4129,0.5499,0.5018,0.3132,0.2802,0.2351,0.2298,0.1155,0.0724,0.0621,0.0318,0.045,0.0167,0.0078,0.0083,0.0057,0.0174,0.0188,0.0054,0.0114,0.0196,0.0147,0.0062,R 17 | 0.0298,0.0615,0.065,0.0921,0.1615,0.2294,0.2176,0.2033,0.1459,0.0852,0.2476,0.3645,0.2777,0.2826,0.3237,0.4335,0.5638,0.4555,0.4348,0.6433,0.3932,0.1989,0.354,0.9165,0.9371,0.462,0.2771,0.6613,0.8028,0.42,0.5192,0.6962,0.5792,0.8889,0.7863,0.7133,0.7615,0.4401,0.3009,0.3163,0.2809,0.2898,0.0526,0.1867,0.1553,0.1633,0.1252,0.0748,0.0452,0.0064,0.0154,0.0031,0.0153,0.0071,0.0212,0.0076,0.0152,0.0049,0.02,0.0073,R 18 | 0.0352,0.0116,0.0191,0.0469,0.0737,0.1185,0.1683,0.1541,0.1466,0.2912,0.2328,0.2237,0.247,0.156,0.3491,0.3308,0.2299,0.2203,0.2493,0.4128,0.3158,0.6191,0.5854,0.3395,0.2561,0.5599,0.8145,0.6941,0.6985,0.866,0.593,0.3664,0.675,0.8697,0.7837,0.7552,0.5789,0.4713,0.1252,0.6087,0.7322,0.5977,0.3431,0.1803,0.2378,0.3424,0.2303,0.0689,0.0216,0.0469,0.0426,0.0346,0.0158,0.0154,0.0109,0.0048,0.0095,0.0015,0.0073,0.0067,R 19 | 0.0192,0.0607,0.0378,0.0774,0.1388,0.0809,0.0568,0.0219,0.1037,0.1186,0.1237,0.1601,0.352,0.4479,0.3769,0.5761,0.6426,0.679,0.7157,0.5466,0.5399,0.6362,0.7849,0.7756,0.578,0.4862,0.4181,0.2457,0.0716,0.0613,0.1816,0.4493,0.5976,0.3785,0.2495,0.5771,0.8852,0.8409,0.357,0.3133,0.6096,0.6378,0.2709,0.1419,0.126,0.1288,0.079,0.0829,0.052,0.0216,0.036,0.0331,0.0131,0.012,0.0108,0.0024,0.0045,0.0037,0.0112,0.0075,R 20 | 0.027,0.0092,0.0145,0.0278,0.0412,0.0757,0.1026,0.1138,0.0794,0.152,0.1675,0.137,0.1361,0.1345,0.2144,0.5354,0.683,0.56,0.3093,0.3226,0.443,0.5573,0.5782,0.6173,0.8132,0.9819,0.9823,0.9166,0.7423,0.7736,0.8473,0.7352,0.6671,0.6083,0.6239,0.5972,0.5715,0.5242,0.2924,0.1536,0.2003,0.2031,0.2207,0.1778,0.1353,0.1373,0.0749,0.0472,0.0325,0.0179,0.0045,0.0084,0.001,0.0018,0.0068,0.0039,0.012,0.0132,0.007,0.0088,R 21 | 0.0126,0.0149,0.0641,0.1732,0.2565,0.2559,0.2947,0.411,0.4983,0.592,0.5832,0.5419,0.5472,0.5314,0.4981,0.6985,0.8292,0.7839,0.8215,0.9363,1.0,0.9224,0.7839,0.547,0.4562,0.5922,0.5448,0.3971,0.0882,0.2385,0.2005,0.0587,0.2544,0.2009,0.0329,0.1547,0.1212,0.2446,0.3171,0.3195,0.3051,0.0836,0.1266,0.1381,0.1136,0.0516,0.0073,0.0278,0.0372,0.0121,0.0153,0.0092,0.0035,0.0098,0.0121,0.0006,0.0181,0.0094,0.0116,0.0063,R 22 | 0.0473,0.0509,0.0819,0.1252,0.1783,0.307,0.3008,0.2362,0.383,0.3759,0.3021,0.2909,0.2301,0.1411,0.1582,0.243,0.4474,0.5964,0.6744,0.7969,0.8319,0.7813,0.8626,0.7369,0.4122,0.2596,0.3392,0.3788,0.4488,0.6281,0.7449,0.7328,0.7704,0.787,0.6048,0.586,0.6385,0.7279,0.6286,0.5316,0.4069,0.1791,0.1625,0.2527,0.1903,0.1643,0.0604,0.0209,0.0436,0.0175,0.0107,0.0193,0.0118,0.0064,0.0042,0.0054,0.0049,0.0082,0.0028,0.0027,R 23 | 0.0664,0.0575,0.0842,0.0372,0.0458,0.0771,0.0771,0.113,0.2353,0.1838,0.2869,0.4129,0.3647,0.1984,0.284,0.4039,0.5837,0.6792,0.6086,0.4858,0.3246,0.2013,0.2082,0.1686,0.2484,0.2736,0.2984,0.4655,0.699,0.7474,0.7956,0.7981,0.6715,0.6942,0.744,0.8169,0.8912,1.0,0.8753,0.7061,0.6803,0.5898,0.4618,0.3639,0.1492,0.1216,0.1306,0.1198,0.0578,0.0235,0.0135,0.0141,0.019,0.0043,0.0036,0.0026,0.0024,0.0162,0.0109,0.0079,R 24 | 0.0099,0.0484,0.0299,0.0297,0.0652,0.1077,0.2363,0.2385,0.0075,0.1882,0.1456,0.1892,0.3176,0.134,0.2169,0.2458,0.2589,0.2786,0.2298,0.0656,0.1441,0.1179,0.1668,0.1783,0.2476,0.257,0.1036,0.5356,0.7124,0.6291,0.4756,0.6015,0.7208,0.6234,0.5725,0.7523,0.8712,0.9252,0.9709,0.9297,0.8995,0.7911,0.56,0.2838,0.4407,0.5507,0.4331,0.2905,0.1981,0.0779,0.0396,0.0173,0.0149,0.0115,0.0202,0.0139,0.0029,0.016,0.0106,0.0134,R 25 | 0.0115,0.015,0.0136,0.0076,0.0211,0.1058,0.1023,0.044,0.0931,0.0734,0.074,0.0622,0.1055,0.1183,0.1721,0.2584,0.3232,0.3817,0.4243,0.4217,0.4449,0.4075,0.3306,0.4012,0.4466,0.5218,0.7552,0.9503,1.0,0.9084,0.8283,0.7571,0.7262,0.6152,0.568,0.5757,0.5324,0.3672,0.1669,0.0866,0.0646,0.1891,0.2683,0.2887,0.2341,0.1668,0.1015,0.1195,0.0704,0.0167,0.0107,0.0091,0.0016,0.0084,0.0064,0.0026,0.0029,0.0037,0.007,0.0041,R 26 | 0.0293,0.0644,0.039,0.0173,0.0476,0.0816,0.0993,0.0315,0.0736,0.086,0.0414,0.0472,0.0835,0.0938,0.1466,0.0809,0.1179,0.2179,0.3326,0.3258,0.2111,0.2302,0.3361,0.4259,0.4609,0.2606,0.0874,0.2862,0.5606,0.8344,0.8096,0.725,0.8048,0.9435,1.0,0.896,0.5516,0.3037,0.2338,0.2382,0.3318,0.3821,0.1575,0.2228,0.1582,0.1433,0.1634,0.1133,0.0567,0.0133,0.017,0.0035,0.0052,0.0083,0.0078,0.0075,0.0105,0.016,0.0095,0.0011,R 27 | 0.0201,0.0026,0.0138,0.0062,0.0133,0.0151,0.0541,0.021,0.0505,0.1097,0.0841,0.0942,0.1204,0.042,0.0031,0.0162,0.0624,0.2127,0.3436,0.3813,0.3825,0.4764,0.6313,0.7523,0.8675,0.8788,0.7901,0.8357,0.9631,0.9619,0.9236,0.8903,0.9708,0.9647,0.7892,0.5307,0.2718,0.1953,0.1374,0.3105,0.379,0.4105,0.3355,0.2998,0.2748,0.2024,0.1043,0.0453,0.0337,0.0122,0.0072,0.0108,0.007,0.0063,0.003,0.0011,0.0007,0.0024,0.0057,0.0044,R 28 | 0.0151,0.032,0.0599,0.105,0.1163,0.1734,0.1679,0.1119,0.0889,0.1205,0.0847,0.1518,0.2305,0.2793,0.3404,0.4527,0.695,0.8807,0.9154,0.7542,0.6736,0.7146,0.8335,0.7701,0.6993,0.6543,0.504,0.4926,0.4992,0.4161,0.1631,0.0404,0.0637,0.2962,0.3609,0.1866,0.0476,0.1497,0.2405,0.198,0.3175,0.2379,0.1716,0.1559,0.1556,0.0422,0.0493,0.0476,0.0219,0.0059,0.0086,0.0061,0.0015,0.0084,0.0128,0.0054,0.0011,0.0019,0.0023,0.0062,R 29 | 0.0177,0.03,0.0288,0.0394,0.063,0.0526,0.0688,0.0633,0.0624,0.0613,0.168,0.3476,0.4561,0.5188,0.6308,0.7201,0.5153,0.3818,0.2644,0.3345,0.4865,0.6628,0.7389,0.9213,1.0,0.775,0.5593,0.6172,0.8635,0.6592,0.477,0.4983,0.333,0.3076,0.2876,0.2226,0.0794,0.0603,0.1049,0.0606,0.153,0.0983,0.1643,0.1901,0.1107,0.1917,0.1467,0.0392,0.0356,0.027,0.0168,0.0102,0.0122,0.0044,0.0075,0.0124,0.0099,0.0057,0.0032,0.0019,R 30 | 0.01,0.0275,0.019,0.0371,0.0416,0.0201,0.0314,0.0651,0.1896,0.2668,0.3376,0.3282,0.2432,0.1268,0.1278,0.4441,0.6795,0.7051,0.7966,0.9401,0.9857,0.8193,0.5789,0.6394,0.7043,0.6875,0.4081,0.1811,0.2064,0.3917,0.3791,0.2042,0.2227,0.3341,0.3984,0.5077,0.5534,0.3352,0.2723,0.2278,0.2044,0.1986,0.0835,0.0908,0.138,0.1948,0.1211,0.0843,0.0589,0.0247,0.0118,0.0088,0.0104,0.0036,0.0088,0.0047,0.0117,0.002,0.0091,0.0058,R 31 | 0.0189,0.0308,0.0197,0.0622,0.008,0.0789,0.144,0.1451,0.1789,0.2522,0.2607,0.371,0.3906,0.2672,0.2716,0.4183,0.6988,0.5733,0.2226,0.2631,0.7473,0.7263,0.3393,0.2824,0.6053,0.5897,0.4967,0.8616,0.8339,0.4084,0.2268,0.1745,0.0507,0.1588,0.304,0.1369,0.1605,0.2061,0.0734,0.0202,0.1638,0.1583,0.183,0.1886,0.1008,0.0663,0.0183,0.0404,0.0108,0.0143,0.0091,0.0038,0.0096,0.0142,0.019,0.014,0.0099,0.0092,0.0052,0.0075,R 32 | 0.024,0.0218,0.0324,0.0569,0.033,0.0513,0.0897,0.0713,0.0569,0.0389,0.1934,0.2434,0.2906,0.2606,0.3811,0.4997,0.3015,0.3655,0.6791,0.7307,0.5053,0.4441,0.6987,0.8133,0.7781,0.8943,0.8929,0.8913,0.861,0.8063,0.554,0.2446,0.3459,0.1615,0.2467,0.5564,0.4681,0.0979,0.1582,0.0751,0.3321,0.3745,0.2666,0.1078,0.1418,0.1687,0.0738,0.0634,0.0144,0.0226,0.0061,0.0162,0.0146,0.0093,0.0112,0.0094,0.0054,0.0019,0.0066,0.0023,R 33 | 0.0084,0.0153,0.0291,0.0432,0.0951,0.0752,0.0414,0.0259,0.0692,0.1753,0.197,0.1167,0.1683,0.0814,0.2179,0.5121,0.7231,0.7776,0.6222,0.3501,0.3733,0.2622,0.3776,0.7361,0.8673,0.8223,0.7772,0.7862,0.5652,0.3635,0.3534,0.3865,0.337,0.1693,0.2627,0.3195,0.1388,0.1048,0.1681,0.191,0.1174,0.0933,0.0856,0.0951,0.0986,0.0956,0.0426,0.0407,0.0106,0.0179,0.0056,0.0236,0.0114,0.0136,0.0117,0.006,0.0058,0.0031,0.0072,0.0045,R 34 | 0.0195,0.0213,0.0058,0.019,0.0319,0.0571,0.1004,0.0668,0.0691,0.0242,0.0728,0.0639,0.3002,0.3854,0.4767,0.4602,0.3175,0.416,0.6428,1.0,0.8631,0.5212,0.3156,0.5952,0.7732,0.6042,0.4375,0.5487,0.472,0.6235,0.3851,0.159,0.3891,0.5294,0.3504,0.448,0.4041,0.5031,0.6475,0.5493,0.3548,0.2028,0.1882,0.0845,0.1315,0.159,0.0562,0.0617,0.0343,0.037,0.0261,0.0157,0.0074,0.0271,0.0203,0.0089,0.0095,0.0095,0.0021,0.0053,R 35 | 0.0442,0.0477,0.0049,0.0581,0.0278,0.0678,0.1664,0.149,0.0974,0.1268,0.1109,0.2375,0.2007,0.214,0.1109,0.2036,0.2468,0.6682,0.8345,0.8252,0.8017,0.8982,0.9664,0.8515,0.6626,0.3241,0.2054,0.5669,0.5726,0.4877,0.7532,0.76,0.5185,0.412,0.556,0.5569,0.1336,0.3831,0.4611,0.433,0.2556,0.1466,0.3489,0.2659,0.0944,0.137,0.1344,0.0416,0.0719,0.0637,0.021,0.0204,0.0216,0.0135,0.0055,0.0073,0.008,0.0105,0.0059,0.0105,R 36 | 0.0311,0.0491,0.0692,0.0831,0.0079,0.02,0.0981,0.1016,0.2025,0.0767,0.1767,0.2555,0.2812,0.2722,0.3227,0.3463,0.5395,0.7911,0.9064,0.8701,0.7672,0.2957,0.4148,0.6043,0.3178,0.3482,0.6158,0.8049,0.6289,0.4999,0.583,0.666,0.4124,0.126,0.2487,0.4676,0.5382,0.315,0.2139,0.1848,0.1679,0.2328,0.1015,0.0713,0.0615,0.0779,0.0761,0.0845,0.0592,0.0068,0.0089,0.0087,0.0032,0.013,0.0188,0.0101,0.0229,0.0182,0.0046,0.0038,R 37 | 0.0206,0.0132,0.0533,0.0569,0.0647,0.1432,0.1344,0.2041,0.1571,0.1573,0.2327,0.1785,0.1507,0.1916,0.2061,0.2307,0.236,0.1299,0.3812,0.5858,0.4497,0.4876,1.0,0.8675,0.4718,0.5341,0.6197,0.7143,0.5605,0.3728,0.2481,0.1921,0.1386,0.3325,0.2883,0.3228,0.2607,0.204,0.2396,0.1319,0.0683,0.0334,0.0716,0.0976,0.0787,0.0522,0.05,0.0231,0.0221,0.0144,0.0307,0.0386,0.0147,0.0018,0.01,0.0096,0.0077,0.018,0.0109,0.007,R 38 | 0.0094,0.0166,0.0398,0.0359,0.0681,0.0706,0.102,0.0893,0.0381,0.1328,0.1303,0.0273,0.0644,0.0712,0.1204,0.0717,0.1224,0.2349,0.3684,0.3918,0.4925,0.8793,0.9606,0.8786,0.6905,0.6937,0.5674,0.654,0.7802,0.7575,0.5836,0.6316,0.8108,0.9039,0.8647,0.6695,0.4027,0.237,0.2685,0.3662,0.3267,0.22,0.2996,0.2205,0.1163,0.0635,0.0465,0.0422,0.0174,0.0172,0.0134,0.0141,0.0191,0.0145,0.0065,0.0129,0.0217,0.0087,0.0077,0.0122,R 39 | 0.0333,0.0221,0.027,0.0481,0.0679,0.0981,0.0843,0.1172,0.0759,0.092,0.1475,0.0522,0.1119,0.097,0.1174,0.1678,0.1642,0.1205,0.0494,0.1544,0.3485,0.6146,0.9146,0.9364,0.8677,0.8772,0.8553,0.8833,1.0,0.8296,0.6601,0.5499,0.5716,0.6859,0.6825,0.5142,0.275,0.1358,0.1551,0.2646,0.1994,0.1883,0.2746,0.1651,0.0575,0.0695,0.0598,0.0456,0.0021,0.0068,0.0036,0.0022,0.0032,0.006,0.0054,0.0063,0.0143,0.0132,0.0051,0.0041,R 40 | 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0.0211,0.0319,0.0415,0.0286,0.0121,0.0438,0.1299,0.139,0.0695,0.0568,0.0869,0.1935,0.1478,0.1871,0.1994,0.3283,0.6861,0.5814,0.25,0.1734,0.3363,0.5588,0.6592,0.7012,0.8099,0.8901,0.8745,0.7887,0.8725,0.9376,0.892,0.7508,0.6832,0.761,0.9017,1.0,0.9123,0.7388,0.5915,0.4057,0.3019,0.2331,0.2931,0.2298,0.2391,0.191,0.1096,0.03,0.0171,0.0383,0.0053,0.009,0.0042,0.0153,0.0106,0.002,0.0105,0.0049,0.007,0.008,R 45 | 0.0093,0.0269,0.0217,0.0339,0.0305,0.1172,0.145,0.0638,0.074,0.136,0.2132,0.3738,0.3738,0.2673,0.2333,0.5367,0.7312,0.7659,0.6271,0.4395,0.433,0.4326,0.5544,0.736,0.8589,0.8989,0.942,0.9401,0.9379,0.8575,0.7284,0.67,0.7547,0.8773,0.9919,0.9922,0.9419,0.8388,0.6605,0.4816,0.2917,0.1769,0.1136,0.0701,0.1578,0.1938,0.1106,0.0693,0.0176,0.0205,0.0309,0.0212,0.0091,0.0056,0.0086,0.0092,0.007,0.0116,0.006,0.011,R 46 | 0.0257,0.0447,0.0388,0.0239,0.1315,0.1323,0.1608,0.2145,0.0847,0.0561,0.0891,0.0861,0.1531,0.1524,0.1849,0.2871,0.2009,0.2748,0.5017,0.2172,0.4978,0.5265,0.3647,0.5768,0.5161,0.5715,0.4006,0.365,0.6685,0.8659,0.8052,0.4082,0.3379,0.5092,0.6776,0.7313,0.6062,0.704,0.8849,0.8979,0.7751,0.7247,0.7733,0.7762,0.6009,0.4514,0.3096,0.1859,0.0956,0.0206,0.0206,0.0096,0.0153,0.0096,0.0131,0.0198,0.0025,0.0199,0.0255,0.018,R 47 | 0.0408,0.0653,0.0397,0.0604,0.0496,0.1817,0.1178,0.1024,0.0583,0.2176,0.2459,0.3332,0.3087,0.2613,0.3232,0.3731,0.4203,0.5364,0.7062,0.8196,0.8835,0.8299,0.7609,0.7605,0.8367,0.8905,0.7652,0.5897,0.3037,0.0823,0.2787,0.7241,0.8032,0.805,0.7676,0.7468,0.6253,0.173,0.2916,0.5003,0.522,0.4824,0.4004,0.3877,0.1651,0.0442,0.0663,0.0418,0.0475,0.0235,0.0066,0.0062,0.0129,0.0184,0.0069,0.0198,0.0199,0.0102,0.007,0.0055,R 48 | 0.0308,0.0339,0.0202,0.0889,0.157,0.175,0.092,0.1353,0.1593,0.2795,0.3336,0.294,0.1608,0.3335,0.4985,0.7295,0.735,0.8253,0.8793,0.9657,1.0,0.8707,0.6471,0.5973,0.8218,0.7755,0.6111,0.4195,0.299,0.1354,0.2438,0.5624,0.5555,0.6963,0.7298,0.7022,0.5468,0.1421,0.4738,0.641,0.4375,0.3178,0.2377,0.2808,0.1374,0.1136,0.1034,0.0688,0.0422,0.0117,0.007,0.0167,0.0127,0.0138,0.009,0.0051,0.0029,0.0122,0.0056,0.002,R 49 | 0.0373,0.0281,0.0232,0.0225,0.0179,0.0733,0.0841,0.1031,0.0993,0.0802,0.1564,0.2565,0.2624,0.1179,0.0597,0.1563,0.2241,0.3586,0.1792,0.3256,0.6079,0.6988,0.8391,0.8553,0.771,0.6215,0.5736,0.4402,0.4056,0.4411,0.513,0.5965,0.7272,0.6539,0.5902,0.5393,0.4897,0.4081,0.4145,0.6003,0.7196,0.6633,0.6287,0.4087,0.3212,0.2518,0.1482,0.0988,0.0317,0.0269,0.0066,0.0008,0.0045,0.0024,0.0006,0.0073,0.0096,0.0054,0.0085,0.006,R 50 | 0.019,0.0038,0.0642,0.0452,0.0333,0.069,0.0901,0.1454,0.074,0.0349,0.1459,0.3473,0.3197,0.2823,0.0166,0.0572,0.2164,0.4563,0.3819,0.5627,0.6484,0.7235,0.8242,0.8766,1.0,0.8582,0.6563,0.5087,0.4817,0.453,0.4521,0.4532,0.5385,0.5308,0.5356,0.5271,0.426,0.2436,0.1205,0.3845,0.4107,0.5067,0.4216,0.2479,0.1586,0.1124,0.0651,0.0789,0.0325,0.007,0.0026,0.0093,0.0118,0.0112,0.0094,0.014,0.0072,0.0022,0.0055,0.0122,R 51 | 0.0119,0.0582,0.0623,0.06,0.1397,0.1883,0.1422,0.1447,0.0487,0.0864,0.2143,0.372,0.2665,0.2113,0.1103,0.1136,0.1934,0.4142,0.3279,0.6222,0.7468,0.7676,0.7867,0.8253,1.0,0.9481,0.7539,0.6008,0.5437,0.5387,0.5619,0.5141,0.6084,0.5621,0.5956,0.6078,0.5025,0.2829,0.0477,0.2811,0.3422,0.5147,0.4372,0.247,0.1708,0.1343,0.0838,0.0755,0.0304,0.0074,0.0069,0.0025,0.0103,0.0074,0.0123,0.0069,0.0076,0.0073,0.003,0.0138,R 52 | 0.0353,0.0713,0.0326,0.0272,0.037,0.0792,0.1083,0.0687,0.0298,0.088,0.1078,0.0979,0.225,0.2819,0.2099,0.124,0.1699,0.0939,0.1091,0.141,0.1268,0.3151,0.143,0.2264,0.5756,0.7876,0.7158,0.5998,0.5583,0.6295,0.7659,0.894,0.8436,0.6807,0.838,1.0,0.9497,0.7866,0.5647,0.348,0.2585,0.2304,0.2948,0.3363,0.3017,0.2193,0.1316,0.1078,0.0559,0.0035,0.0098,0.0163,0.0242,0.0043,0.0202,0.0108,0.0037,0.0096,0.0093,0.0053,R 53 | 0.0131,0.0068,0.0308,0.0311,0.0085,0.0767,0.0771,0.064,0.0726,0.0901,0.075,0.0844,0.1226,0.1619,0.2317,0.2934,0.3526,0.3657,0.3221,0.3093,0.4084,0.4285,0.4663,0.5956,0.6948,0.8386,0.8875,0.6404,0.3308,0.3425,0.492,0.4592,0.3034,0.4366,0.5175,0.5122,0.4746,0.4902,0.4603,0.446,0.4196,0.2873,0.2296,0.0949,0.0095,0.0527,0.0383,0.0107,0.0108,0.0077,0.0109,0.0062,0.0028,0.004,0.0075,0.0039,0.0053,0.0013,0.0052,0.0023,R 54 | 0.0087,0.0046,0.0081,0.023,0.0586,0.0682,0.0993,0.0717,0.0576,0.0818,0.1315,0.1862,0.2789,0.2579,0.224,0.2568,0.2933,0.2991,0.3924,0.4691,0.5665,0.6464,0.6774,0.7577,0.8856,0.9419,1.0,0.8564,0.679,0.5587,0.4147,0.2946,0.2025,0.0688,0.1171,0.2157,0.2216,0.2776,0.2309,0.1444,0.1513,0.1745,0.1756,0.1424,0.0908,0.0138,0.0469,0.048,0.0159,0.0045,0.0015,0.0052,0.0038,0.0079,0.0114,0.005,0.003,0.0064,0.0058,0.003,R 55 | 0.0293,0.0378,0.0257,0.0062,0.013,0.0612,0.0895,0.1107,0.0973,0.0751,0.0528,0.1209,0.1763,0.2039,0.2727,0.2321,0.2676,0.2934,0.3295,0.491,0.5402,0.6257,0.6826,0.7527,0.8504,0.8938,0.9928,0.9134,0.708,0.6318,0.6126,0.4638,0.2797,0.1721,0.1665,0.2561,0.2735,0.3209,0.2724,0.188,0.1552,0.2522,0.2121,0.1801,0.1473,0.0681,0.1091,0.0919,0.0397,0.0093,0.0076,0.0065,0.0072,0.0108,0.0051,0.0102,0.0041,0.0055,0.005,0.0087,R 56 | 0.0132,0.008,0.0188,0.0141,0.0436,0.0668,0.0609,0.0131,0.0899,0.0922,0.1445,0.1475,0.2087,0.2558,0.2603,0.1985,0.2394,0.3134,0.4077,0.4529,0.4893,0.5666,0.6234,0.6741,0.8282,0.8823,0.9196,0.8965,0.7549,0.6736,0.6463,0.5007,0.3663,0.2298,0.1362,0.2123,0.2395,0.2673,0.2865,0.206,0.1659,0.2633,0.2552,0.1696,0.1467,0.1286,0.0926,0.0716,0.0325,0.0258,0.0136,0.0044,0.0028,0.0021,0.0022,0.0048,0.0138,0.014,0.0028,0.0064,R 57 | 0.0201,0.0116,0.0123,0.0245,0.0547,0.0208,0.0891,0.0836,0.1335,0.1199,0.1742,0.1387,0.2042,0.258,0.2616,0.2097,0.2532,0.3213,0.4327,0.476,0.5328,0.6057,0.6696,0.7476,0.893,0.9405,1.0,0.9785,0.8473,0.7639,0.6701,0.4989,0.3718,0.2196,0.1416,0.268,0.263,0.3104,0.3392,0.2123,0.117,0.2655,0.2203,0.1541,0.1464,0.1044,0.1225,0.0745,0.049,0.0224,0.0032,0.0076,0.0045,0.0056,0.0075,0.0037,0.0045,0.0029,0.0008,0.0018,R 58 | 0.0152,0.0102,0.0113,0.0263,0.0097,0.0391,0.0857,0.0915,0.0949,0.1504,0.1911,0.2115,0.2249,0.2573,0.1701,0.2023,0.2538,0.3417,0.4026,0.4553,0.5525,0.5991,0.5854,0.7114,0.95,0.9858,1.0,0.9578,0.8642,0.7128,0.5893,0.4323,0.2897,0.1744,0.077,0.2297,0.2459,0.3101,0.3312,0.222,0.0871,0.2064,0.1808,0.1624,0.112,0.0815,0.1117,0.095,0.0412,0.012,0.0048,0.0049,0.0041,0.0036,0.0013,0.0046,0.0037,0.0011,0.0034,0.0033,R 59 | 0.0216,0.0124,0.0174,0.0152,0.0608,0.1026,0.1139,0.0877,0.116,0.0866,0.1564,0.078,0.0997,0.0915,0.0662,0.1134,0.174,0.2573,0.3294,0.391,0.5438,0.6115,0.7022,0.761,0.7973,0.9105,0.8807,0.7949,0.799,0.718,0.6407,0.6312,0.5929,0.6168,0.6498,0.6764,0.6253,0.5117,0.389,0.3273,0.2509,0.153,0.1323,0.1657,0.1215,0.0978,0.0452,0.0273,0.0179,0.0092,0.0018,0.0052,0.0049,0.0096,0.0134,0.0122,0.0047,0.0018,0.0006,0.0023,R 60 | 0.0225,0.0019,0.0075,0.0097,0.0445,0.0906,0.0889,0.0655,0.1624,0.1452,0.1442,0.0948,0.0618,0.1641,0.0708,0.0844,0.259,0.2679,0.3094,0.4678,0.5958,0.7245,0.8773,0.9214,0.9282,0.9942,1.0,0.9071,0.8545,0.7293,0.6499,0.6071,0.5588,0.5967,0.6275,0.5459,0.4786,0.3965,0.2087,0.1651,0.1836,0.0652,0.0758,0.0486,0.0353,0.0297,0.0241,0.0379,0.0119,0.0073,0.0051,0.0034,0.0129,0.01,0.0044,0.0057,0.003,0.0035,0.0021,0.0027,R 61 | 0.0125,0.0152,0.0218,0.0175,0.0362,0.0696,0.0873,0.0616,0.1252,0.1302,0.0888,0.05,0.0628,0.1274,0.0801,0.0742,0.2048,0.295,0.3193,0.4567,0.5959,0.7101,0.8225,0.8425,0.9065,0.9802,1.0,0.8752,0.7583,0.6616,0.5786,0.5128,0.4776,0.4994,0.5197,0.5071,0.4577,0.3505,0.1845,0.189,0.1967,0.1041,0.055,0.0492,0.0622,0.0505,0.0247,0.0219,0.0102,0.0047,0.0019,0.0041,0.0074,0.003,0.005,0.0048,0.0017,0.0041,0.0086,0.0058,R 62 | 0.013,0.0006,0.0088,0.0456,0.0525,0.0778,0.0931,0.0941,0.1711,0.1483,0.1532,0.11,0.089,0.1236,0.1197,0.1145,0.2137,0.2838,0.364,0.543,0.6673,0.7979,0.9273,0.9027,0.9192,1.0,0.9821,0.9092,0.8184,0.6962,0.59,0.5447,0.5142,0.5389,0.5531,0.5318,0.4826,0.379,0.1831,0.175,0.1679,0.0674,0.0609,0.0375,0.0533,0.0278,0.0179,0.0114,0.0073,0.0116,0.0092,0.0078,0.0041,0.0013,0.0011,0.0045,0.0039,0.0022,0.0023,0.0016,R 63 | 0.0135,0.0045,0.0051,0.0289,0.0561,0.0929,0.1031,0.0883,0.1596,0.1908,0.1576,0.1112,0.1197,0.1174,0.1415,0.2215,0.2658,0.2713,0.3862,0.5717,0.6797,0.8747,1.0,0.8948,0.842,0.9174,0.9307,0.905,0.8228,0.6986,0.5831,0.4924,0.4563,0.5159,0.567,0.5284,0.5144,0.3742,0.2282,0.1193,0.1088,0.0431,0.107,0.0583,0.0046,0.0473,0.0408,0.029,0.0192,0.0094,0.0025,0.0037,0.0084,0.0102,0.0096,0.0024,0.0037,0.0028,0.003,0.003,R 64 | 0.0086,0.0215,0.0242,0.0445,0.0667,0.0771,0.0499,0.0906,0.1229,0.1185,0.0775,0.1101,0.1042,0.0853,0.0456,0.1304,0.269,0.2947,0.3669,0.4948,0.6275,0.8162,0.9237,0.871,0.8052,0.8756,1.0,0.9858,0.9427,0.8114,0.6987,0.681,0.6591,0.6954,0.729,0.668,0.5917,0.4899,0.3439,0.2366,0.1716,0.1013,0.0766,0.0845,0.026,0.0333,0.0205,0.0309,0.0101,0.0095,0.0047,0.0072,0.0054,0.0022,0.0016,0.0029,0.0058,0.005,0.0024,0.003,R 65 | 0.0067,0.0096,0.0024,0.0058,0.0197,0.0618,0.0432,0.0951,0.0836,0.118,0.0978,0.0909,0.0656,0.0593,0.0832,0.1297,0.2038,0.3811,0.4451,0.5224,0.5911,0.6566,0.6308,0.5998,0.4958,0.5647,0.6906,0.8513,1.0,0.9166,0.7676,0.6177,0.5468,0.5516,0.5463,0.5515,0.4561,0.3466,0.3384,0.2853,0.2502,0.1641,0.1605,0.1491,0.1326,0.0687,0.0602,0.0561,0.0306,0.0154,0.0029,0.0048,0.0023,0.002,0.004,0.0019,0.0034,0.0034,0.0051,0.0031,R 66 | 0.0071,0.0103,0.0135,0.0494,0.0253,0.0806,0.0701,0.0738,0.0117,0.0898,0.0289,0.1554,0.1437,0.1035,0.1424,0.1227,0.0892,0.2047,0.0827,0.1524,0.3031,0.1608,0.0667,0.1426,0.0395,0.1653,0.3399,0.4855,0.5206,0.5508,0.6102,0.5989,0.6764,0.8897,1.0,0.9517,0.8459,0.7073,0.6697,0.6326,0.5102,0.4161,0.2816,0.1705,0.1421,0.0971,0.0879,0.0863,0.0355,0.0233,0.0252,0.0043,0.0048,0.0076,0.0124,0.0105,0.0054,0.0032,0.0073,0.0063,R 67 | 0.0176,0.0172,0.0501,0.0285,0.0262,0.0351,0.0362,0.0535,0.0258,0.0474,0.0526,0.1854,0.104,0.0948,0.0912,0.1688,0.1568,0.0375,0.1316,0.2086,0.1976,0.0946,0.1965,0.1242,0.0616,0.2141,0.4642,0.6471,0.634,0.6107,0.7046,0.5376,0.5934,0.8443,0.9481,0.9705,0.7766,0.6313,0.576,0.6148,0.545,0.4813,0.3406,0.1916,0.1134,0.064,0.0911,0.098,0.0563,0.0187,0.0088,0.0042,0.0175,0.0171,0.0079,0.005,0.0112,0.0179,0.0294,0.0063,R 68 | 0.0265,0.044,0.0137,0.0084,0.0305,0.0438,0.0341,0.078,0.0844,0.0779,0.0327,0.206,0.1908,0.1065,0.1457,0.2232,0.207,0.1105,0.1078,0.1165,0.2224,0.0689,0.206,0.2384,0.0904,0.2278,0.5872,0.8457,0.8467,0.7679,0.8055,0.626,0.6545,0.8747,0.9885,0.9348,0.696,0.5733,0.5872,0.6663,0.5651,0.5247,0.3684,0.1997,0.1512,0.0508,0.0931,0.0982,0.0524,0.0188,0.01,0.0038,0.0187,0.0156,0.0068,0.0097,0.0073,0.0081,0.0086,0.0095,R 69 | 0.0368,0.0403,0.0317,0.0293,0.082,0.1342,0.1161,0.0663,0.0155,0.0506,0.0906,0.2545,0.1464,0.1272,0.1223,0.1669,0.1424,0.1285,0.1857,0.1136,0.2069,0.0219,0.24,0.2547,0.024,0.1923,0.4753,0.7003,0.6825,0.6443,0.7063,0.5373,0.6601,0.8708,0.9518,0.9605,0.7712,0.6772,0.6431,0.672,0.6035,0.5155,0.3802,0.2278,0.1522,0.0801,0.0804,0.0752,0.0566,0.0175,0.0058,0.0091,0.016,0.016,0.0081,0.007,0.0135,0.0067,0.0078,0.0068,R 70 | 0.0195,0.0142,0.0181,0.0406,0.0391,0.0249,0.0892,0.0973,0.084,0.1191,0.1522,0.1322,0.1434,0.1244,0.0653,0.089,0.1226,0.1846,0.388,0.3658,0.2297,0.261,0.4193,0.5848,0.5643,0.5448,0.4772,0.6897,0.9797,1.0,0.9546,0.8835,0.7662,0.6547,0.5447,0.4593,0.4679,0.1987,0.0699,0.1493,0.1713,0.1654,0.26,0.3846,0.3754,0.2414,0.1077,0.0224,0.0155,0.0187,0.0125,0.0028,0.0067,0.012,0.0012,0.0022,0.0058,0.0042,0.0067,0.0012,R 71 | 0.0216,0.0215,0.0273,0.0139,0.0357,0.0785,0.0906,0.0908,0.1151,0.0973,0.1203,0.1102,0.1192,0.1762,0.239,0.2138,0.1929,0.1765,0.0746,0.1265,0.2005,0.1571,0.2605,0.5386,0.844,1.0,0.8684,0.6742,0.5537,0.4638,0.3609,0.2055,0.162,0.2092,0.31,0.2344,0.1058,0.0383,0.0528,0.1291,0.2241,0.1915,0.1587,0.0942,0.084,0.067,0.0342,0.0469,0.0357,0.0136,0.0082,0.014,0.0044,0.0052,0.0073,0.0021,0.0047,0.0024,0.0009,0.0017,R 72 | 0.0065,0.0122,0.0068,0.0108,0.0217,0.0284,0.0527,0.0575,0.1054,0.1109,0.0937,0.0827,0.092,0.0911,0.1487,0.1666,0.1268,0.1374,0.1095,0.1286,0.2146,0.2889,0.4238,0.6168,0.8167,0.9622,0.828,0.5816,0.4667,0.3539,0.2727,0.141,0.1863,0.2176,0.236,0.1725,0.0589,0.0621,0.1847,0.2452,0.2984,0.3041,0.2275,0.148,0.1102,0.1178,0.0608,0.0333,0.0276,0.01,0.0023,0.0069,0.0025,0.0027,0.0052,0.0036,0.0026,0.0036,0.0006,0.0035,R 73 | 0.0036,0.0078,0.0092,0.0387,0.053,0.1197,0.1243,0.1026,0.1239,0.0888,0.0937,0.1245,0.1599,0.1542,0.1846,0.1732,0.1477,0.1748,0.1455,0.1579,0.2257,0.1975,0.3368,0.5828,0.8505,1.0,0.8457,0.6624,0.5564,0.3925,0.3233,0.2054,0.192,0.2227,0.3147,0.2268,0.0795,0.0748,0.1166,0.1969,0.2619,0.2507,0.1983,0.0948,0.0931,0.0965,0.0381,0.0435,0.0336,0.0055,0.0079,0.0119,0.0055,0.0035,0.0036,0.0004,0.0018,0.0049,0.0024,0.0016,R 74 | 0.0208,0.0186,0.0131,0.0211,0.061,0.0613,0.0612,0.0506,0.0989,0.1093,0.1063,0.1179,0.1291,0.1591,0.168,0.1918,0.1615,0.1647,0.1397,0.1426,0.2429,0.2816,0.429,0.6443,0.9061,1.0,0.8087,0.6119,0.526,0.3677,0.2746,0.102,0.1339,0.1582,0.1952,0.1787,0.0429,0.1096,0.1762,0.2481,0.315,0.292,0.1902,0.0696,0.0758,0.091,0.0441,0.0244,0.0265,0.0095,0.014,0.0074,0.0063,0.0081,0.0087,0.0044,0.0028,0.0019,0.0049,0.0023,R 75 | 0.0139,0.0222,0.0089,0.0108,0.0215,0.0136,0.0659,0.0954,0.0786,0.1015,0.1261,0.0828,0.0493,0.0848,0.1514,0.1396,0.1066,0.1923,0.2991,0.3247,0.3797,0.5658,0.7483,0.8757,0.9048,0.7511,0.6858,0.7043,0.5864,0.3773,0.2206,0.2628,0.2672,0.2907,0.1982,0.2288,0.3186,0.2871,0.2921,0.2806,0.2682,0.2112,0.1513,0.1789,0.185,0.1717,0.0898,0.0656,0.0445,0.011,0.0024,0.0062,0.0072,0.0113,0.0012,0.0022,0.0025,0.0059,0.0039,0.0048,R 76 | 0.0109,0.0093,0.0121,0.0378,0.0679,0.0863,0.1004,0.0664,0.0941,0.1036,0.0972,0.0501,0.1546,0.3404,0.4804,0.657,0.7738,0.7827,0.8152,0.8129,0.8297,0.8535,0.887,0.8894,0.898,0.9667,1.0,0.9134,0.6762,0.4659,0.2895,0.2959,0.1746,0.2112,0.2569,0.2276,0.2149,0.1601,0.0371,0.0117,0.0488,0.0288,0.0597,0.0431,0.0369,0.0025,0.0327,0.0257,0.0182,0.0108,0.0124,0.0077,0.0023,0.0117,0.0053,0.0077,0.0076,0.0056,0.0055,0.0039,R 77 | 0.0202,0.0104,0.0325,0.0239,0.0807,0.1529,0.1154,0.0608,0.1317,0.137,0.0843,0.0269,0.1254,0.3046,0.5584,0.7973,0.8341,0.8057,0.8616,0.8769,0.9413,0.9403,0.9409,1.0,0.9725,0.9309,0.9351,0.7317,0.4421,0.3244,0.4161,0.4611,0.4031,0.3,0.2459,0.1348,0.2541,0.2255,0.1598,0.1485,0.0845,0.0569,0.0855,0.1262,0.1153,0.057,0.0426,0.0425,0.0235,0.0006,0.0188,0.0127,0.0081,0.0067,0.0043,0.0065,0.0049,0.0054,0.0073,0.0054,R 78 | 0.0239,0.0189,0.0466,0.044,0.0657,0.0742,0.138,0.1099,0.1384,0.1376,0.0938,0.0259,0.1499,0.2851,0.5743,0.8278,0.8669,0.8131,0.9045,0.9046,1.0,0.9976,0.9872,0.9761,0.9009,0.9724,0.9675,0.7633,0.4434,0.3822,0.4727,0.4007,0.3381,0.3172,0.2222,0.0733,0.2692,0.1888,0.0712,0.1062,0.0694,0.03,0.0893,0.1459,0.1348,0.0391,0.0546,0.0469,0.0201,0.0095,0.0155,0.0091,0.0151,0.008,0.0018,0.0078,0.0045,0.0026,0.0036,0.0024,R 79 | 0.0336,0.0294,0.0476,0.0539,0.0794,0.0804,0.1136,0.1228,0.1235,0.0842,0.0357,0.0689,0.1705,0.3257,0.4602,0.6225,0.7327,0.7843,0.7988,0.8261,1.0,0.9814,0.962,0.9601,0.9118,0.9086,0.7931,0.5877,0.3474,0.4235,0.4633,0.341,0.2849,0.2847,0.1742,0.0549,0.1192,0.1154,0.0855,0.1811,0.1264,0.0799,0.0378,0.1268,0.1125,0.0505,0.0949,0.0677,0.0259,0.017,0.0033,0.015,0.0111,0.0032,0.0035,0.0169,0.0137,0.0015,0.0069,0.0051,R 80 | 0.0231,0.0351,0.003,0.0304,0.0339,0.086,0.1738,0.1351,0.1063,0.0347,0.0575,0.1382,0.2274,0.4038,0.5223,0.6847,0.7521,0.776,0.7708,0.8627,1.0,0.8873,0.8057,0.876,0.9066,0.943,0.8846,0.65,0.297,0.2423,0.2992,0.2285,0.2277,0.1529,0.1037,0.0352,0.1073,0.1373,0.1331,0.1454,0.1115,0.044,0.0762,0.1381,0.0831,0.0654,0.0844,0.0595,0.0497,0.0313,0.0154,0.0106,0.0097,0.0022,0.0052,0.0072,0.0056,0.0038,0.0043,0.003,R 81 | 0.0108,0.0086,0.0058,0.046,0.0752,0.0887,0.1015,0.0494,0.0472,0.0393,0.1106,0.1412,0.2202,0.2976,0.4116,0.4754,0.539,0.6279,0.706,0.7918,0.9493,1.0,0.9645,0.9432,0.8658,0.7895,0.6501,0.4492,0.4739,0.6153,0.4929,0.3195,0.3735,0.3336,0.1052,0.0671,0.0379,0.0461,0.1694,0.2169,0.1677,0.0644,0.0159,0.0778,0.0653,0.021,0.0509,0.0387,0.0262,0.0101,0.0161,0.0029,0.0078,0.0114,0.0083,0.0058,0.0003,0.0023,0.0026,0.0027,R 82 | 0.0229,0.0369,0.004,0.0375,0.0455,0.1452,0.2211,0.1188,0.075,0.1631,0.2709,0.3358,0.4091,0.44,0.5485,0.7213,0.8137,0.9185,1.0,0.9418,0.9116,0.9349,0.7484,0.5146,0.4106,0.3443,0.6981,0.8713,0.9013,0.8014,0.438,0.1319,0.1709,0.2484,0.3044,0.2312,0.1338,0.2056,0.2474,0.279,0.161,0.0056,0.0351,0.1148,0.1331,0.0276,0.0763,0.0631,0.0309,0.024,0.0115,0.0064,0.0022,0.0122,0.0151,0.0056,0.0026,0.0029,0.0104,0.0163,R 83 | 0.01,0.0194,0.0155,0.0489,0.0839,0.1009,0.1627,0.2071,0.2696,0.299,0.3242,0.3565,0.3951,0.5201,0.6953,0.8468,1.0,0.9278,0.851,0.801,0.8142,0.8825,0.7302,0.6107,0.7159,0.8458,0.6319,0.4808,0.6291,0.7152,0.6005,0.4235,0.4106,0.3992,0.173,0.1975,0.237,0.1339,0.1583,0.3151,0.1968,0.2054,0.1272,0.1129,0.1946,0.2195,0.193,0.1498,0.0773,0.0196,0.0122,0.013,0.0073,0.0077,0.0075,0.006,0.008,0.0019,0.0053,0.0019,R 84 | 0.0409,0.0421,0.0573,0.013,0.0183,0.1019,0.1054,0.107,0.2302,0.2259,0.2373,0.3323,0.3827,0.484,0.6812,0.7555,0.9522,0.9826,0.8871,0.8268,0.7561,0.8217,0.6967,0.6444,0.6948,0.8014,0.6053,0.6084,0.8877,0.8557,0.5563,0.2897,0.3638,0.4786,0.2908,0.0899,0.2043,0.1707,0.0407,0.1286,0.1581,0.2191,0.1701,0.0971,0.2217,0.2732,0.1874,0.1062,0.0665,0.0405,0.0113,0.0028,0.0036,0.0105,0.012,0.0087,0.0061,0.0061,0.003,0.0078,R 85 | 0.0217,0.034,0.0392,0.0236,0.1081,0.1164,0.1398,0.1009,0.1147,0.1777,0.4079,0.4113,0.3973,0.5078,0.6509,0.8073,0.9819,1.0,0.9407,0.8452,0.8106,0.846,0.6212,0.5815,0.7745,0.8204,0.5601,0.2989,0.5009,0.6628,0.5753,0.4055,0.3746,0.3481,0.158,0.1422,0.213,0.1866,0.1003,0.2396,0.2241,0.2029,0.071,0.1606,0.1669,0.17,0.1829,0.1403,0.0506,0.0224,0.0095,0.0031,0.0103,0.0078,0.0077,0.0094,0.0031,0.003,0.0013,0.0069,R 86 | 0.0378,0.0318,0.0423,0.035,0.1787,0.1635,0.0887,0.0817,0.1779,0.2053,0.3135,0.3118,0.3686,0.3885,0.585,0.7868,0.9739,1.0,0.9843,0.861,0.8443,0.9061,0.5847,0.4033,0.5946,0.6793,0.6389,0.5002,0.5578,0.4831,0.4729,0.3318,0.3969,0.3894,0.2314,0.1036,0.1312,0.0864,0.2569,0.3179,0.2649,0.2714,0.1713,0.0584,0.123,0.22,0.2198,0.1074,0.0423,0.0162,0.0093,0.0046,0.0044,0.0078,0.0102,0.0065,0.0061,0.0062,0.0043,0.0053,R 87 | 0.0365,0.1632,0.1636,0.1421,0.113,0.1306,0.2112,0.2268,0.2992,0.3735,0.3042,0.0387,0.2679,0.5397,0.6204,0.7257,0.835,0.6888,0.445,0.3921,0.5605,0.7545,0.8311,1.0,0.8762,0.7092,0.7009,0.5014,0.3942,0.4456,0.4072,0.0773,0.1423,0.0401,0.3597,0.6847,0.7076,0.3597,0.0612,0.3027,0.3966,0.3868,0.238,0.2059,0.2288,0.1704,0.1587,0.1792,0.1022,0.0151,0.0223,0.011,0.0071,0.0205,0.0164,0.0063,0.0078,0.0094,0.011,0.0068,R 88 | 0.0188,0.037,0.0953,0.0824,0.0249,0.0488,0.1424,0.1972,0.1873,0.1806,0.2139,0.1523,0.1975,0.4844,0.7298,0.7807,0.7906,0.6122,0.42,0.2807,0.5148,0.7569,0.8596,1.0,0.8457,0.6797,0.6971,0.5843,0.4772,0.5201,0.4241,0.1592,0.1668,0.0588,0.3967,0.7147,0.7319,0.3509,0.0589,0.269,0.42,0.3874,0.244,0.2,0.2307,0.1886,0.196,0.1701,0.1366,0.0398,0.0143,0.0093,0.0033,0.0113,0.003,0.0057,0.009,0.0057,0.0068,0.0024,R 89 | 0.0856,0.0454,0.0382,0.0203,0.0385,0.0534,0.214,0.311,0.2837,0.2751,0.2707,0.0946,0.102,0.4519,0.6737,0.6699,0.7066,0.5632,0.3785,0.2721,0.5297,0.7697,0.8643,0.9304,0.9372,0.6247,0.6024,0.681,0.5047,0.5775,0.4754,0.24,0.2779,0.1997,0.5305,0.7409,0.7775,0.4424,0.1416,0.3508,0.4482,0.4208,0.3054,0.2235,0.2611,0.2798,0.2392,0.2021,0.1326,0.0358,0.0128,0.0172,0.0138,0.0079,0.0037,0.0051,0.0258,0.0102,0.0037,0.0037,R 90 | 0.0274,0.0242,0.0621,0.056,0.1129,0.0973,0.1823,0.1745,0.144,0.1808,0.2366,0.0906,0.1749,0.4012,0.5187,0.7312,0.9062,0.926,0.7434,0.4463,0.5103,0.6952,0.7755,0.8364,0.7283,0.6399,0.5759,0.4146,0.3495,0.4437,0.2665,0.2024,0.1942,0.0765,0.3725,0.5843,0.4827,0.2347,0.0999,0.3244,0.399,0.2975,0.1684,0.1761,0.1683,0.0729,0.119,0.1297,0.0748,0.0067,0.0255,0.0113,0.0108,0.0085,0.0047,0.0074,0.0104,0.0161,0.022,0.0173,R 91 | 0.0235,0.0291,0.0749,0.0519,0.0227,0.0834,0.0677,0.2002,0.2876,0.3674,0.2974,0.0837,0.1912,0.504,0.6352,0.6804,0.7505,0.6595,0.4509,0.2964,0.4019,0.6794,0.8297,1.0,0.824,0.7115,0.7726,0.6124,0.4936,0.5648,0.4906,0.182,0.1811,0.1107,0.4603,0.665,0.6423,0.2166,0.1951,0.4947,0.4925,0.4041,0.2402,0.1392,0.1779,0.1946,0.1723,0.1522,0.0929,0.0179,0.0242,0.0083,0.0037,0.0095,0.0105,0.003,0.0132,0.0068,0.0108,0.009,R 92 | 0.0126,0.0519,0.0621,0.0518,0.1072,0.2587,0.2304,0.2067,0.3416,0.4284,0.3015,0.1207,0.3299,0.5707,0.6962,0.9751,1.0,0.9293,0.621,0.4586,0.5001,0.5032,0.7082,0.842,0.8109,0.769,0.8105,0.6203,0.2356,0.2595,0.6299,0.6762,0.2903,0.4393,0.8529,0.718,0.4801,0.5856,0.4993,0.2866,0.0601,0.1167,0.2737,0.2812,0.2078,0.066,0.0491,0.0345,0.0172,0.0287,0.0027,0.0208,0.0048,0.0199,0.0126,0.0022,0.0037,0.0034,0.0114,0.0077,R 93 | 0.0253,0.0808,0.0507,0.0244,0.1724,0.3823,0.3729,0.3583,0.3429,0.2197,0.2653,0.3223,0.5582,0.6916,0.7943,0.7152,0.3512,0.2008,0.2676,0.4299,0.528,0.3489,0.143,0.5453,0.6338,0.7712,0.6838,0.8015,0.8073,0.831,0.7792,0.5049,0.1413,0.2767,0.5084,0.4787,0.1356,0.2299,0.2789,0.3833,0.2933,0.1155,0.1705,0.1294,0.0909,0.08,0.0567,0.0198,0.0114,0.0151,0.0085,0.0178,0.0073,0.0079,0.0038,0.0116,0.0033,0.0039,0.0081,0.0053,R 94 | 0.026,0.0192,0.0254,0.0061,0.0352,0.0701,0.1263,0.108,0.1523,0.163,0.103,0.2187,0.1542,0.263,0.294,0.2978,0.0699,0.1401,0.299,0.3915,0.3598,0.2403,0.4208,0.5675,0.6094,0.6323,0.6549,0.7673,1.0,0.8463,0.5509,0.4444,0.5169,0.4268,0.1802,0.0791,0.0535,0.1906,0.2561,0.2153,0.2769,0.2841,0.1733,0.0815,0.0335,0.0933,0.1018,0.0309,0.0208,0.0318,0.0132,0.0118,0.012,0.0051,0.007,0.0015,0.0035,0.0008,0.0044,0.0077,R 95 | 0.0459,0.0437,0.0347,0.0456,0.0067,0.089,0.1798,0.1741,0.1598,0.1408,0.2693,0.3259,0.4545,0.5785,0.4471,0.2231,0.2164,0.3201,0.2915,0.4235,0.446,0.238,0.6415,0.8966,0.8918,0.7529,0.6838,0.839,1.0,0.8362,0.5427,0.4577,0.8067,0.6973,0.3915,0.1558,0.1598,0.2161,0.5178,0.4782,0.2344,0.3599,0.2785,0.1807,0.0352,0.0473,0.0322,0.0408,0.0163,0.0088,0.0121,0.0067,0.0032,0.0109,0.0164,0.0151,0.007,0.0085,0.0117,0.0056,R 96 | 0.0025,0.0309,0.0171,0.0228,0.0434,0.1224,0.1947,0.1661,0.1368,0.143,0.0994,0.225,0.2444,0.3239,0.3039,0.241,0.0367,0.1672,0.3038,0.4069,0.3613,0.1994,0.4611,0.6849,0.7272,0.7152,0.7102,0.8516,1.0,0.769,0.4841,0.3717,0.6096,0.511,0.2586,0.0916,0.0947,0.2287,0.348,0.2095,0.1901,0.2941,0.2211,0.1524,0.0746,0.0606,0.0692,0.0446,0.0344,0.0082,0.0108,0.0149,0.0077,0.0036,0.0114,0.0085,0.0101,0.0016,0.0028,0.0014,R 97 | 0.0291,0.04,0.0771,0.0809,0.0521,0.1051,0.0145,0.0674,0.1294,0.1146,0.0942,0.0794,0.0252,0.1191,0.1045,0.205,0.1556,0.269,0.3784,0.4024,0.347,0.1395,0.1208,0.2827,0.15,0.2626,0.4468,0.752,0.9036,0.7812,0.4766,0.2483,0.5372,0.6279,0.3647,0.4572,0.6359,0.6474,0.552,0.3253,0.2292,0.0653,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0056,0.0237,0.0204,0.005,0.0137,0.0164,0.0081,0.0139,0.0111,R 98 | 0.0181,0.0146,0.0026,0.0141,0.0421,0.0473,0.0361,0.0741,0.1398,0.1045,0.0904,0.0671,0.0997,0.1056,0.0346,0.1231,0.1626,0.3652,0.3262,0.2995,0.2109,0.2104,0.2085,0.2282,0.0747,0.1969,0.4086,0.6385,0.797,0.7508,0.5517,0.2214,0.4672,0.4479,0.2297,0.3235,0.448,0.5581,0.652,0.5354,0.2478,0.2268,0.1788,0.0898,0.0536,0.0374,0.099,0.0956,0.0317,0.0142,0.0076,0.0223,0.0255,0.0145,0.0233,0.0041,0.0018,0.0048,0.0089,0.0085,R 99 | 0.0491,0.0279,0.0592,0.127,0.1772,0.1908,0.2217,0.0768,0.1246,0.2028,0.0947,0.2497,0.2209,0.3195,0.334,0.3323,0.278,0.2975,0.2948,0.1729,0.3264,0.3834,0.3523,0.541,0.5228,0.4475,0.534,0.5323,0.3907,0.3456,0.4091,0.4639,0.558,0.5727,0.6355,0.7563,0.6903,0.6176,0.5379,0.5622,0.6508,0.4797,0.3736,0.2804,0.1982,0.2438,0.1789,0.1706,0.0762,0.0238,0.0268,0.0081,0.0129,0.0161,0.0063,0.0119,0.0194,0.014,0.0332,0.0439,M 100 | 0.1313,0.2339,0.3059,0.4264,0.401,0.1791,0.1853,0.0055,0.1929,0.2231,0.2907,0.2259,0.3136,0.3302,0.366,0.3956,0.4386,0.467,0.5255,0.3735,0.2243,0.1973,0.4337,0.6532,0.507,0.2796,0.4163,0.595,0.5242,0.4178,0.3714,0.2375,0.0863,0.1437,0.2896,0.4577,0.3725,0.3372,0.3803,0.4181,0.3603,0.2711,0.1653,0.1951,0.2811,0.2246,0.1921,0.15,0.0665,0.0193,0.0156,0.0362,0.021,0.0154,0.018,0.0013,0.0106,0.0127,0.0178,0.0231,M 101 | 0.0201,0.0423,0.0554,0.0783,0.062,0.0871,0.1201,0.2707,0.1206,0.0279,0.2251,0.2615,0.177,0.3709,0.4533,0.5553,0.4616,0.3797,0.345,0.2665,0.2395,0.1127,0.2556,0.5169,0.3779,0.4082,0.5353,0.5116,0.4544,0.4258,0.3869,0.3939,0.4661,0.3974,0.2194,0.1816,0.1023,0.2108,0.3253,0.3697,0.2912,0.301,0.2563,0.1927,0.2062,0.1751,0.0841,0.1035,0.0641,0.0153,0.0081,0.0191,0.0182,0.016,0.029,0.009,0.0242,0.0224,0.019,0.0096,M 102 | 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-------------------------------------------------------------------------------- 1 | # Makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | PAPER = 8 | BUILDDIR = _build 9 | 10 | # Internal variables. 11 | PAPEROPT_a4 = -D latex_paper_size=a4 12 | PAPEROPT_letter = -D latex_paper_size=letter 13 | ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . 14 | # the i18n builder cannot share the environment and doctrees with the others 15 | I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . 16 | 17 | .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext 18 | 19 | help: 20 | @echo "Please use \`make ' where is one of" 21 | @echo " html to make standalone HTML files" 22 | @echo " dirhtml to make HTML files named index.html in directories" 23 | @echo " singlehtml to make a single large HTML file" 24 | @echo " pickle to make pickle files" 25 | @echo " json to make JSON files" 26 | @echo " htmlhelp to make HTML files and a HTML help project" 27 | @echo " qthelp to make HTML files and a qthelp project" 28 | @echo " devhelp to make HTML files and a Devhelp project" 29 | @echo " epub to make an epub" 30 | @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" 31 | @echo " latexpdf to make LaTeX files and run them through pdflatex" 32 | @echo " text to make text files" 33 | @echo " man to make manual pages" 34 | @echo " texinfo to make Texinfo files" 35 | @echo " info to make Texinfo files and run them through makeinfo" 36 | @echo " gettext to make PO message catalogs" 37 | @echo " changes to make an overview of all changed/added/deprecated items" 38 | @echo " linkcheck to check all external links for integrity" 39 | @echo " doctest to run all doctests embedded in the documentation (if enabled)" 40 | 41 | clean: 42 | -rm -rf $(BUILDDIR)/* 43 | 44 | html: 45 | $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html 46 | @echo 47 | @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." 48 | 49 | dirhtml: 50 | $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml 51 | @echo 52 | @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." 53 | 54 | singlehtml: 55 | $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml 56 | @echo 57 | @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." 58 | 59 | pickle: 60 | $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle 61 | @echo 62 | @echo "Build finished; now you can process the pickle files." 63 | 64 | json: 65 | $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json 66 | @echo 67 | @echo "Build finished; now you can process the JSON files." 68 | 69 | htmlhelp: 70 | $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp 71 | @echo 72 | @echo "Build finished; now you can run HTML Help Workshop with the" \ 73 | ".hhp project file in $(BUILDDIR)/htmlhelp." 74 | 75 | qthelp: 76 | $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp 77 | @echo 78 | @echo "Build finished; now you can run "qcollectiongenerator" with the" \ 79 | ".qhcp project file in $(BUILDDIR)/qthelp, like this:" 80 | @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/normalization_vs_standartization.qhcp" 81 | @echo "To view the help file:" 82 | @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/normalization_vs_standartization.qhc" 83 | 84 | devhelp: 85 | $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp 86 | @echo 87 | @echo "Build finished." 88 | @echo "To view the help file:" 89 | @echo "# mkdir -p $$HOME/.local/share/devhelp/normalization_vs_standartization" 90 | @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/normalization_vs_standartization" 91 | @echo "# devhelp" 92 | 93 | epub: 94 | $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub 95 | @echo 96 | @echo "Build finished. The epub file is in $(BUILDDIR)/epub." 97 | 98 | latex: 99 | $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex 100 | @echo 101 | @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." 102 | @echo "Run \`make' in that directory to run these through (pdf)latex" \ 103 | "(use \`make latexpdf' here to do that automatically)." 104 | 105 | latexpdf: 106 | $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex 107 | @echo "Running LaTeX files through pdflatex..." 108 | $(MAKE) -C $(BUILDDIR)/latex all-pdf 109 | @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." 110 | 111 | text: 112 | $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text 113 | @echo 114 | @echo "Build finished. The text files are in $(BUILDDIR)/text." 115 | 116 | man: 117 | $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man 118 | @echo 119 | @echo "Build finished. The manual pages are in $(BUILDDIR)/man." 120 | 121 | texinfo: 122 | $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo 123 | @echo 124 | @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." 125 | @echo "Run \`make' in that directory to run these through makeinfo" \ 126 | "(use \`make info' here to do that automatically)." 127 | 128 | info: 129 | $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo 130 | @echo "Running Texinfo files through makeinfo..." 131 | make -C $(BUILDDIR)/texinfo info 132 | @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." 133 | 134 | gettext: 135 | $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale 136 | @echo 137 | @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." 138 | 139 | changes: 140 | $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes 141 | @echo 142 | @echo "The overview file is in $(BUILDDIR)/changes." 143 | 144 | linkcheck: 145 | $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck 146 | @echo 147 | @echo "Link check complete; look for any errors in the above output " \ 148 | "or in $(BUILDDIR)/linkcheck/output.txt." 149 | 150 | doctest: 151 | $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest 152 | @echo "Testing of doctests in the sources finished, look at the " \ 153 | "results in $(BUILDDIR)/doctest/output.txt." 154 | -------------------------------------------------------------------------------- /docs/commands.rst: -------------------------------------------------------------------------------- 1 | Commands 2 | ======== 3 | 4 | The Makefile contains the central entry points for common tasks related to this project. 5 | 6 | Syncing data to S3 7 | ^^^^^^^^^^^^^^^^^^ 8 | 9 | * `make sync_data_to_s3` will use `aws s3 sync` to recursively sync files in `data/` up to `s3://[OPTIONAL] your-bucket-for-syncing-data (do not include 's3://')/data/`. 10 | * `make sync_data_from_s3` will use `aws s3 sync` to recursively sync files from `s3://[OPTIONAL] your-bucket-for-syncing-data (do not include 's3://')/data/` to `data/`. 11 | -------------------------------------------------------------------------------- /docs/conf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # 3 | # Normalization_vs_Standartization documentation build configuration file, created by 4 | # sphinx-quickstart. 5 | # 6 | # This file is execfile()d with the current directory set to its containing dir. 7 | # 8 | # Note that not all possible configuration values are present in this 9 | # autogenerated file. 10 | # 11 | # All configuration values have a default; values that are commented out 12 | # serve to show the default. 13 | 14 | import os 15 | import sys 16 | 17 | # If extensions (or modules to document with autodoc) are in another directory, 18 | # add these directories to sys.path here. If the directory is relative to the 19 | # documentation root, use os.path.abspath to make it absolute, like shown here. 20 | # sys.path.insert(0, os.path.abspath('.')) 21 | 22 | # -- General configuration ----------------------------------------------------- 23 | 24 | # If your documentation needs a minimal Sphinx version, state it here. 25 | # needs_sphinx = '1.0' 26 | 27 | # Add any Sphinx extension module names here, as strings. They can be extensions 28 | # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. 29 | extensions = [] 30 | 31 | # Add any paths that contain templates here, relative to this directory. 32 | templates_path = ['_templates'] 33 | 34 | # The suffix of source filenames. 35 | source_suffix = '.rst' 36 | 37 | # The encoding of source files. 38 | # source_encoding = 'utf-8-sig' 39 | 40 | # The master toctree document. 41 | master_doc = 'index' 42 | 43 | # General information about the project. 44 | project = u'Normalization_vs_Standartization' 45 | 46 | # The version info for the project you're documenting, acts as replacement for 47 | # |version| and |release|, also used in various other places throughout the 48 | # built documents. 49 | # 50 | # The short X.Y version. 51 | version = '0.1' 52 | # The full version, including alpha/beta/rc tags. 53 | release = '0.1' 54 | 55 | # The language for content autogenerated by Sphinx. Refer to documentation 56 | # for a list of supported languages. 57 | # language = None 58 | 59 | # There are two options for replacing |today|: either, you set today to some 60 | # non-false value, then it is used: 61 | # today = '' 62 | # Else, today_fmt is used as the format for a strftime call. 63 | # today_fmt = '%B %d, %Y' 64 | 65 | # List of patterns, relative to source directory, that match files and 66 | # directories to ignore when looking for source files. 67 | exclude_patterns = ['_build'] 68 | 69 | # The reST default role (used for this markup: `text`) to use for all documents. 70 | # default_role = None 71 | 72 | # If true, '()' will be appended to :func: etc. cross-reference text. 73 | # add_function_parentheses = True 74 | 75 | # If true, the current module name will be prepended to all description 76 | # unit titles (such as .. function::). 77 | # add_module_names = True 78 | 79 | # If true, sectionauthor and moduleauthor directives will be shown in the 80 | # output. They are ignored by default. 81 | # show_authors = False 82 | 83 | # The name of the Pygments (syntax highlighting) style to use. 84 | pygments_style = 'sphinx' 85 | 86 | # A list of ignored prefixes for module index sorting. 87 | # modindex_common_prefix = [] 88 | 89 | 90 | # -- Options for HTML output --------------------------------------------------- 91 | 92 | # The theme to use for HTML and HTML Help pages. See the documentation for 93 | # a list of builtin themes. 94 | html_theme = 'default' 95 | 96 | # Theme options are theme-specific and customize the look and feel of a theme 97 | # further. For a list of options available for each theme, see the 98 | # documentation. 99 | # html_theme_options = {} 100 | 101 | # Add any paths that contain custom themes here, relative to this directory. 102 | # html_theme_path = [] 103 | 104 | # The name for this set of Sphinx documents. If None, it defaults to 105 | # " v documentation". 106 | # html_title = None 107 | 108 | # A shorter title for the navigation bar. Default is the same as html_title. 109 | # html_short_title = None 110 | 111 | # The name of an image file (relative to this directory) to place at the top 112 | # of the sidebar. 113 | # html_logo = None 114 | 115 | # The name of an image file (within the static path) to use as favicon of the 116 | # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 117 | # pixels large. 118 | # html_favicon = None 119 | 120 | # Add any paths that contain custom static files (such as style sheets) here, 121 | # relative to this directory. They are copied after the builtin static files, 122 | # so a file named "default.css" will overwrite the builtin "default.css". 123 | html_static_path = ['_static'] 124 | 125 | # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, 126 | # using the given strftime format. 127 | # html_last_updated_fmt = '%b %d, %Y' 128 | 129 | # If true, SmartyPants will be used to convert quotes and dashes to 130 | # typographically correct entities. 131 | # html_use_smartypants = True 132 | 133 | # Custom sidebar templates, maps document names to template names. 134 | # html_sidebars = {} 135 | 136 | # Additional templates that should be rendered to pages, maps page names to 137 | # template names. 138 | # html_additional_pages = {} 139 | 140 | # If false, no module index is generated. 141 | # html_domain_indices = True 142 | 143 | # If false, no index is generated. 144 | # html_use_index = True 145 | 146 | # If true, the index is split into individual pages for each letter. 147 | # html_split_index = False 148 | 149 | # If true, links to the reST sources are added to the pages. 150 | # html_show_sourcelink = True 151 | 152 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. 153 | # html_show_sphinx = True 154 | 155 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. 156 | # html_show_copyright = True 157 | 158 | # If true, an OpenSearch description file will be output, and all pages will 159 | # contain a tag referring to it. The value of this option must be the 160 | # base URL from which the finished HTML is served. 161 | # html_use_opensearch = '' 162 | 163 | # This is the file name suffix for HTML files (e.g. ".xhtml"). 164 | # html_file_suffix = None 165 | 166 | # Output file base name for HTML help builder. 167 | htmlhelp_basename = 'normalization_vs_standartizationdoc' 168 | 169 | 170 | # -- Options for LaTeX output -------------------------------------------------- 171 | 172 | latex_elements = { 173 | # The paper size ('letterpaper' or 'a4paper'). 174 | # 'papersize': 'letterpaper', 175 | 176 | # The font size ('10pt', '11pt' or '12pt'). 177 | # 'pointsize': '10pt', 178 | 179 | # Additional stuff for the LaTeX preamble. 180 | # 'preamble': '', 181 | } 182 | 183 | # Grouping the document tree into LaTeX files. List of tuples 184 | # (source start file, target name, title, author, documentclass [howto/manual]). 185 | latex_documents = [ 186 | ('index', 187 | 'normalization_vs_standartization.tex', 188 | u'Normalization_vs_Standartization Documentation', 189 | u"Shay Geller", 'manual'), 190 | ] 191 | 192 | # The name of an image file (relative to this directory) to place at the top of 193 | # the title page. 194 | # latex_logo = None 195 | 196 | # For "manual" documents, if this is true, then toplevel headings are parts, 197 | # not chapters. 198 | # latex_use_parts = False 199 | 200 | # If true, show page references after internal links. 201 | # latex_show_pagerefs = False 202 | 203 | # If true, show URL addresses after external links. 204 | # latex_show_urls = False 205 | 206 | # Documents to append as an appendix to all manuals. 207 | # latex_appendices = [] 208 | 209 | # If false, no module index is generated. 210 | # latex_domain_indices = True 211 | 212 | 213 | # -- Options for manual page output -------------------------------------------- 214 | 215 | # One entry per manual page. List of tuples 216 | # (source start file, name, description, authors, manual section). 217 | man_pages = [ 218 | ('index', 'normalization_vs_standartization', u'Normalization_vs_Standartization Documentation', 219 | [u"Shay Geller"], 1) 220 | ] 221 | 222 | # If true, show URL addresses after external links. 223 | # man_show_urls = False 224 | 225 | 226 | # -- Options for Texinfo output ------------------------------------------------ 227 | 228 | # Grouping the document tree into Texinfo files. List of tuples 229 | # (source start file, target name, title, author, 230 | # dir menu entry, description, category) 231 | texinfo_documents = [ 232 | ('index', 'normalization_vs_standartization', u'Normalization_vs_Standartization Documentation', 233 | u"Shay Geller", 'Normalization_vs_Standartization', 234 | 'Check the difference between Normalization and Standartization methods', 'Miscellaneous'), 235 | ] 236 | 237 | # Documents to append as an appendix to all manuals. 238 | # texinfo_appendices = [] 239 | 240 | # If false, no module index is generated. 241 | # texinfo_domain_indices = True 242 | 243 | # How to display URL addresses: 'footnote', 'no', or 'inline'. 244 | # texinfo_show_urls = 'footnote' 245 | -------------------------------------------------------------------------------- /docs/getting-started.rst: -------------------------------------------------------------------------------- 1 | Getting started 2 | =============== 3 | 4 | This is where you describe how to get set up on a clean install, including the 5 | commands necessary to get the raw data (using the `sync_data_from_s3` command, 6 | for example), and then how to make the cleaned, final data sets. 7 | -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | .. Normalization_vs_Standartization documentation master file, created by 2 | sphinx-quickstart. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Normalization_vs_Standartization documentation! 7 | ============================================== 8 | 9 | Contents: 10 | 11 | .. toctree:: 12 | :maxdepth: 2 13 | 14 | getting-started 15 | commands 16 | 17 | 18 | 19 | Indices and tables 20 | ================== 21 | 22 | * :ref:`genindex` 23 | * :ref:`modindex` 24 | * :ref:`search` 25 | -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | REM Command file for Sphinx documentation 4 | 5 | if "%SPHINXBUILD%" == "" ( 6 | set SPHINXBUILD=sphinx-build 7 | ) 8 | set BUILDDIR=_build 9 | set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . 10 | set I18NSPHINXOPTS=%SPHINXOPTS% . 11 | if NOT "%PAPER%" == "" ( 12 | set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% 13 | set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% 14 | ) 15 | 16 | if "%1" == "" goto help 17 | 18 | if "%1" == "help" ( 19 | :help 20 | echo.Please use `make ^` where ^ is one of 21 | echo. html to make standalone HTML files 22 | echo. dirhtml to make HTML files named index.html in directories 23 | echo. singlehtml to make a single large HTML file 24 | echo. pickle to make pickle files 25 | echo. json to make JSON files 26 | echo. htmlhelp to make HTML files and a HTML help project 27 | echo. qthelp to make HTML files and a qthelp project 28 | echo. devhelp to make HTML files and a Devhelp project 29 | echo. epub to make an epub 30 | echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter 31 | echo. text to make text files 32 | echo. man to make manual pages 33 | echo. texinfo to make Texinfo files 34 | echo. gettext to make PO message catalogs 35 | echo. changes to make an overview over all changed/added/deprecated items 36 | echo. linkcheck to check all external links for integrity 37 | echo. doctest to run all doctests embedded in the documentation if enabled 38 | goto end 39 | ) 40 | 41 | if "%1" == "clean" ( 42 | for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i 43 | del /q /s %BUILDDIR%\* 44 | goto end 45 | ) 46 | 47 | if "%1" == "html" ( 48 | %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html 49 | if errorlevel 1 exit /b 1 50 | echo. 51 | echo.Build finished. The HTML pages are in %BUILDDIR%/html. 52 | goto end 53 | ) 54 | 55 | if "%1" == "dirhtml" ( 56 | %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml 57 | if errorlevel 1 exit /b 1 58 | echo. 59 | echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. 60 | goto end 61 | ) 62 | 63 | if "%1" == "singlehtml" ( 64 | %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml 65 | if errorlevel 1 exit /b 1 66 | echo. 67 | echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. 68 | goto end 69 | ) 70 | 71 | if "%1" == "pickle" ( 72 | %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle 73 | if errorlevel 1 exit /b 1 74 | echo. 75 | echo.Build finished; now you can process the pickle files. 76 | goto end 77 | ) 78 | 79 | if "%1" == "json" ( 80 | %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json 81 | if errorlevel 1 exit /b 1 82 | echo. 83 | echo.Build finished; now you can process the JSON files. 84 | goto end 85 | ) 86 | 87 | if "%1" == "htmlhelp" ( 88 | %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp 89 | if errorlevel 1 exit /b 1 90 | echo. 91 | echo.Build finished; now you can run HTML Help Workshop with the ^ 92 | .hhp project file in %BUILDDIR%/htmlhelp. 93 | goto end 94 | ) 95 | 96 | if "%1" == "qthelp" ( 97 | %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp 98 | if errorlevel 1 exit /b 1 99 | echo. 100 | echo.Build finished; now you can run "qcollectiongenerator" with the ^ 101 | .qhcp project file in %BUILDDIR%/qthelp, like this: 102 | echo.^> qcollectiongenerator %BUILDDIR%\qthelp\normalization_vs_standartization.qhcp 103 | echo.To view the help file: 104 | echo.^> assistant -collectionFile %BUILDDIR%\qthelp\normalization_vs_standartization.ghc 105 | goto end 106 | ) 107 | 108 | if "%1" == "devhelp" ( 109 | %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp 110 | if errorlevel 1 exit /b 1 111 | echo. 112 | echo.Build finished. 113 | goto end 114 | ) 115 | 116 | if "%1" == "epub" ( 117 | %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub 118 | if errorlevel 1 exit /b 1 119 | echo. 120 | echo.Build finished. The epub file is in %BUILDDIR%/epub. 121 | goto end 122 | ) 123 | 124 | if "%1" == "latex" ( 125 | %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex 126 | if errorlevel 1 exit /b 1 127 | echo. 128 | echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. 129 | goto end 130 | ) 131 | 132 | if "%1" == "text" ( 133 | %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text 134 | if errorlevel 1 exit /b 1 135 | echo. 136 | echo.Build finished. The text files are in %BUILDDIR%/text. 137 | goto end 138 | ) 139 | 140 | if "%1" == "man" ( 141 | %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man 142 | if errorlevel 1 exit /b 1 143 | echo. 144 | echo.Build finished. The manual pages are in %BUILDDIR%/man. 145 | goto end 146 | ) 147 | 148 | if "%1" == "texinfo" ( 149 | %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo 150 | if errorlevel 1 exit /b 1 151 | echo. 152 | echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. 153 | goto end 154 | ) 155 | 156 | if "%1" == "gettext" ( 157 | %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale 158 | if errorlevel 1 exit /b 1 159 | echo. 160 | echo.Build finished. The message catalogs are in %BUILDDIR%/locale. 161 | goto end 162 | ) 163 | 164 | if "%1" == "changes" ( 165 | %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes 166 | if errorlevel 1 exit /b 1 167 | echo. 168 | echo.The overview file is in %BUILDDIR%/changes. 169 | goto end 170 | ) 171 | 172 | if "%1" == "linkcheck" ( 173 | %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck 174 | if errorlevel 1 exit /b 1 175 | echo. 176 | echo.Link check complete; look for any errors in the above output ^ 177 | or in %BUILDDIR%/linkcheck/output.txt. 178 | goto end 179 | ) 180 | 181 | if "%1" == "doctest" ( 182 | %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest 183 | if errorlevel 1 exit /b 1 184 | echo. 185 | echo.Testing of doctests in the sources finished, look at the ^ 186 | results in %BUILDDIR%/doctest/output.txt. 187 | goto end 188 | ) 189 | 190 | :end 191 | -------------------------------------------------------------------------------- /models/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/models/.gitkeep -------------------------------------------------------------------------------- /notebooks/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/notebooks/.gitkeep -------------------------------------------------------------------------------- /references/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/references/.gitkeep -------------------------------------------------------------------------------- /reports/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/reports/.gitkeep -------------------------------------------------------------------------------- /reports/figures/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/reports/figures/.gitkeep -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | scikit-learn 3 | numpy 4 | # local package 5 | -e . 6 | 7 | # external requirements 8 | click 9 | Sphinx 10 | coverage 11 | awscli 12 | flake8 13 | python-dotenv>=0.5.1 14 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import find_packages, setup 2 | 3 | setup( 4 | name='src', 5 | packages=find_packages(), 6 | version='0.1.0', 7 | description='Check the difference between Normalization and Standartization methods', 8 | author='Shay Geller', 9 | license='MIT', 10 | ) 11 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/__init__.py -------------------------------------------------------------------------------- /src/data/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/data/.gitkeep -------------------------------------------------------------------------------- /src/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/data/__init__.py -------------------------------------------------------------------------------- /src/data/bank_additional.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | from sklearn import preprocessing 5 | 6 | 7 | class BandAdditionalParser(object): 8 | def __init__(self): 9 | 10 | self.URL = "https://www.kaggle.com/henriqueyamahata/bank-marketing" 11 | self.name = "bank-additional" 12 | self.file_name = 'bank-additional-full.csv' 13 | self.file_path = os.path.join("..", "..", "data", "raw", self.file_name) 14 | self.label_col = "y" 15 | self.X, self.y = self._parse_file() 16 | self.all = pd.concat([self.X, self.y], axis=1) 17 | self.metric = "roc_auc" 18 | self._print_stats() 19 | self._convert_y_to_categorical() 20 | 21 | def _parse_file(self,): 22 | """ 23 | -Read csv data 24 | -Drop nan values 25 | -Keep only numeric columns 26 | -Split to X for features and y for labels 27 | """ 28 | data = pd.read_csv(self.file_path) 29 | 30 | data_cleaned = data.dropna() 31 | 32 | X, y = data_cleaned.drop(columns=[self.label_col]), data_cleaned[self.label_col] 33 | 34 | # keep only numeric features 35 | X = X.loc[:, (X.dtypes == np.int64) | (X.dtypes == np.float64)].dropna() 36 | 37 | return X, y 38 | 39 | def save_to_csv(self): 40 | save_path = os.path.join("..", "..", "data", "interim", self.file_name) 41 | self.all.to_csv(save_path, index=False) 42 | 43 | def _convert_y_to_categorical(self): 44 | encoder = preprocessing.LabelEncoder() 45 | encoder.fit(self.y) 46 | transformed = encoder.transform(self.y) 47 | self.y = pd.Series(transformed) 48 | 49 | def _print_stats(self): 50 | print("#"*30 + " Start Dataset - " + self.name + " Stats " + "#"*30) 51 | print("Dataset shape:", self.all.shape) 52 | print("Counts for each class:") 53 | print(self.y.value_counts()) 54 | print("Sample of first 5 rows:") 55 | print(self.all.head(5)) 56 | print("#"*30 + " End Dataset Stats " + "#"*30) 57 | 58 | 59 | if __name__ == '__main__': 60 | 61 | parser = BandAdditionalParser() 62 | X, y = parser.X, parser.y 63 | parser.save_to_csv() 64 | -------------------------------------------------------------------------------- /src/data/income_evaluation.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | from sklearn import preprocessing 5 | 6 | 7 | class IncomeEvaluationParser(object): 8 | def __init__(self): 9 | 10 | self.URL = "https://www.kaggle.com/lodetomasi1995/income-classification" 11 | self.name = "income_evaluation" 12 | self.file_name = 'income_evaluation.csv' 13 | self.file_path = os.path.join("..", "..", "data", "raw", self.file_name) 14 | self.label_col = " income" 15 | self.X, self.y = self._parse_file() 16 | self.all = pd.concat([self.X, self.y], axis=1) 17 | self.metric = "roc_auc" 18 | self._print_stats() 19 | self._convert_y_to_categorical() 20 | 21 | 22 | def _parse_file(self,): 23 | """ 24 | -Read csv data 25 | -Drop nan values 26 | -Keep only numeric columns 27 | -Split to X for features and y for labels 28 | """ 29 | data = pd.read_csv(self.file_path) 30 | 31 | data_cleaned = data.dropna() 32 | 33 | X, y = data_cleaned.drop(columns=[self.label_col]), data_cleaned[self.label_col] 34 | 35 | # keep only numeric features 36 | X = X.loc[:, X.dtypes == np.int64].dropna() 37 | 38 | return X, y 39 | 40 | def save_to_csv(self): 41 | save_path = os.path.join("..", "..", "data", "interim", self.file_name) 42 | self.all.to_csv(save_path, index=False) 43 | 44 | def _convert_y_to_categorical(self): 45 | encoder = preprocessing.LabelEncoder() 46 | encoder.fit(self.y) 47 | transformed = encoder.transform(self.y) 48 | self.y = pd.Series(transformed) 49 | 50 | def _print_stats(self): 51 | print("#"*30 + " Start Dataset - " + self.name + " Stats " + "#"*30) 52 | print("Dataset shape:", self.all.shape) 53 | print("Counts for each class:") 54 | print(self.y.value_counts()) 55 | print("Sample of first 5 rows:") 56 | print(self.all.head(5)) 57 | print("#"*30 + " End Dataset Stats " + "#"*30) 58 | 59 | 60 | if __name__ == '__main__': 61 | 62 | parser = IncomeEvaluationParser() 63 | X, y = parser.X, parser.y 64 | parser.save_to_csv() 65 | -------------------------------------------------------------------------------- /src/data/skyserver.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | 5 | 6 | class SkyserverParser(object): 7 | def __init__(self): 8 | 9 | self.URL = "https://www.kaggle.com/lucidlenn/sloan-digital-sky-survey" 10 | self.name = "Skyserver" 11 | self.file_name = 'Skyserver.csv' 12 | self.file_path = os.path.join("..", "..", "data", "raw", self.file_name) 13 | self.label_col = "class" 14 | self.X, self.y = self._parse_file() 15 | self.all = pd.concat([self.X, self.y], axis=1) 16 | self.metric = "accuracy" 17 | self._print_stats() 18 | 19 | def _parse_file(self,): 20 | """ 21 | -Read csv data 22 | -Drop nan values 23 | -Keep only numeric columns 24 | -Split to X for features and y for labels 25 | """ 26 | data = pd.read_csv(self.file_path) 27 | 28 | data_cleaned = data.dropna() 29 | 30 | X, y = data_cleaned.drop(columns=[self.label_col]), data_cleaned[self.label_col] 31 | 32 | # keep only numeric features 33 | # X = X.loc[:, X.dtypes == np.float64].dropna() 34 | 35 | return X, y 36 | 37 | def save_to_csv(self): 38 | save_path = os.path.join("..", "..", "data", "interim", self.file_name) 39 | self.all.to_csv(save_path, index=False) 40 | 41 | def _print_stats(self): 42 | print("#"*30 + " Start Dataset - " + self.name + " Stats " + "#"*30) 43 | print("Dataset shape:", self.all.shape) 44 | print("Counts for each class:") 45 | print(self.y.value_counts()) 46 | print("Sample of first 5 rows:") 47 | print(self.all.head(5)) 48 | print("#"*30 + " End Dataset Stats " + "#"*30) 49 | 50 | 51 | if __name__ == '__main__': 52 | parser = SkyserverParser() 53 | X, y = parser.X, parser.y 54 | # parser.save_to_csv() 55 | -------------------------------------------------------------------------------- /src/data/sonar.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | 5 | 6 | class SonarParser(object): 7 | def __init__(self): 8 | 9 | self.URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data" 10 | self.name = "sonar" 11 | self.file_name = 'sonar.csv' 12 | self.file_path = os.path.join("..", "..", "data", "raw", self.file_name) 13 | self.label_col = "60" 14 | self.X, self.y = self._parse_file() 15 | self.all = pd.concat([self.X, self.y], axis=1) 16 | self.metric = "accuracy" 17 | self._print_stats() 18 | 19 | def _parse_file(self,): 20 | """ 21 | -Read csv data 22 | -Drop nan values 23 | -Keep only numeric columns 24 | -Split to X for features and y for labels 25 | """ 26 | data = pd.read_csv(self.file_path) 27 | 28 | data_cleaned = data.dropna() 29 | 30 | X, y = data_cleaned.drop(columns=[self.label_col]), data_cleaned[self.label_col] 31 | 32 | # keep only numeric features 33 | X = X.loc[:, X.dtypes == np.float64].dropna() 34 | 35 | return X, y 36 | 37 | def save_to_csv(self): 38 | save_path = os.path.join("..", "..", "data", "interim", self.file_name) 39 | self.all.to_csv(save_path, index=False) 40 | 41 | def _print_stats(self): 42 | print("#"*30 + " Start Dataset - " + self.name + " Stats " + "#"*30) 43 | print("Dataset shape:", self.all.shape) 44 | print("Counts for each class:") 45 | print(self.y.value_counts()) 46 | print("Sample of first 5 rows:") 47 | print(self.all.head(5)) 48 | print("#"*30 + " End Dataset Stats " + "#"*30) 49 | 50 | 51 | if __name__ == '__main__': 52 | parser = SonarParser() 53 | X, y = parser.X, parser.y 54 | # parser.save_to_csv() 55 | -------------------------------------------------------------------------------- /src/data/weather_aus.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | 5 | 6 | class WeatherAUSParser(object): 7 | def __init__(self): 8 | 9 | self.URL = "https://www.kaggle.com/jsphyg/weather-dataset-rattle-package#weatherAUS.csv" 10 | self.name = "weatherAUS" 11 | self.file_name = 'weatherAUS.csv' 12 | self.file_path = os.path.join("..", "..", "data", "raw", self.file_name) 13 | self.label_col = "RainTomorrow" 14 | self.X, self.y = self._parse_file() 15 | self.all = pd.concat([self.X, self.y], axis=1) 16 | self.metric = "accuracy" 17 | self._print_stats() 18 | 19 | def _parse_file(self,): 20 | """ 21 | -Read csv data 22 | -Drop nan values 23 | -Keep only numeric columns 24 | -Split to X for features and y for labels 25 | """ 26 | data = pd.read_csv(self.file_path) 27 | 28 | data_cleaned = data.dropna() 29 | 30 | X, y = data_cleaned.drop(columns=[self.label_col]), data_cleaned[self.label_col] 31 | 32 | # keep only numeric features 33 | X = X.loc[:, X.dtypes == np.float64].dropna() 34 | 35 | return X, y 36 | 37 | def save_to_csv(self): 38 | save_path = os.path.join("..", "..", "data", "interim", self.file_name) 39 | self.all.to_csv(save_path, index=False) 40 | 41 | def _print_stats(self): 42 | print("#"*30 + " Start Dataset - " + self.name + " Stats " + "#"*30) 43 | print("Dataset shape:", self.all.shape) 44 | print("Counts for each class:") 45 | print(self.y.value_counts()) 46 | print("Sample of first 5 rows:") 47 | print(self.all.head(5)) 48 | print("#"*30 + " End Dataset Stats " + "#"*30) 49 | 50 | 51 | if __name__ == '__main__': 52 | 53 | parser = WeatherAUSParser() 54 | X, y = parser.X, parser.y 55 | parser.save_to_csv() 56 | -------------------------------------------------------------------------------- /src/features/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/features/.gitkeep -------------------------------------------------------------------------------- /src/features/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/features/__init__.py -------------------------------------------------------------------------------- /src/models/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/models/.gitkeep -------------------------------------------------------------------------------- /src/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/models/__init__.py -------------------------------------------------------------------------------- /src/models/expirament.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | import itertools 4 | import os 5 | import pandas as pd 6 | from sklearn.model_selection import train_test_split 7 | from sklearn.utils.validation import column_or_1d 8 | import numpy as np 9 | 10 | from src.data.bank_additional import BandAdditionalParser 11 | from src.data.income_evaluation import IncomeEvaluationParser 12 | from src.data.skyserver import SkyserverParser 13 | from src.data.sonar import SonarParser 14 | from src.data.weather_aus import WeatherAUSParser 15 | from src.models.expirament_utils import create_pipelines, run_cv_and_test, get_hypertune_params, \ 16 | run_cv_and_test_hypertuned_params 17 | 18 | # Global_vars 19 | seed = 1234 20 | num_folds = 10 21 | n_jobs = -1 22 | hypertuned_experiment = False 23 | is_save_results = False 24 | 25 | 26 | if __name__ == '__main__': 27 | 28 | parsers = [SonarParser, WeatherAUSParser, BandAdditionalParser, IncomeEvaluationParser, SkyserverParser] 29 | 30 | for parser_class in parsers: 31 | 32 | # Read datasets 33 | parser = parser_class() 34 | scoring = parser.metric 35 | print("Working on " + parser.name + " dataset") 36 | print("Metric: " + parser.metric) 37 | 38 | X, y = parser.X, parser.y 39 | 40 | y_sonar = column_or_1d(y, warn=False) 41 | X_train, X_test, y_train, y_test = train_test_split(X, y_sonar, test_size=0.20, random_state=seed) 42 | 43 | # Create pipelines 44 | pipelines = create_pipelines(seed) 45 | 46 | # Run cv experiment without hyper_param_tuning 47 | results_df = run_cv_and_test(X_train, y_train, X_test, y_test, pipelines, scoring, seed, num_folds, 48 | dataset_name=parser.name, n_jobs=n_jobs) 49 | 50 | # Save cv experiment to csv 51 | if is_save_results: 52 | dataset_results_name = parser.name + "_results.csv" 53 | results_path = os.path.join("..", "..", "data", "processed", dataset_results_name) 54 | results_df.to_csv(results_path, index=False) 55 | 56 | 57 | if hypertuned_experiment: 58 | # Run same experiment with hypertuned parameters 59 | print("#"*30 + "Hyper tuning parameters" "#"*30) 60 | hypertuned_params = get_hypertune_params() 61 | 62 | hypertune_results_df = run_cv_and_test_hypertuned_params(X_train, y_train, X_test, y_test, pipelines, scoring, seed, 63 | num_folds, dataset_name=parser.name, n_jobs=n_jobs, 64 | hypertuned_params=hypertuned_params,) 65 | 66 | if is_save_results: 67 | dataset_results_name = parser.name + "_results_hypertuned.csv" 68 | results_path = os.path.join("..", "..", "data", "processed", dataset_results_name) 69 | hypertune_results_df.to_csv(results_path, index=False) 70 | -------------------------------------------------------------------------------- /src/models/expirament_utils.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | import itertools 4 | import numpy as np 5 | import pandas as pd 6 | from sklearn import model_selection 7 | from sklearn.decomposition import PCA 8 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis 9 | from sklearn.ensemble import RandomForestClassifier 10 | from sklearn.linear_model import LogisticRegression 11 | from sklearn.metrics import accuracy_score, roc_auc_score 12 | from sklearn.model_selection import GridSearchCV, KFold 13 | from sklearn.naive_bayes import GaussianNB 14 | from sklearn.neighbors import KNeighborsClassifier 15 | from sklearn.neural_network import MLPClassifier 16 | from sklearn.pipeline import Pipeline 17 | from sklearn.preprocessing import Normalizer 18 | from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler, QuantileTransformer, \ 19 | PowerTransformer 20 | from sklearn.svm import SVC 21 | from sklearn.tree import DecisionTreeClassifier 22 | 23 | 24 | def print_results(names, results, test_scores): 25 | print() 26 | print("#" * 30 + "Results" + "#" * 30) 27 | counter = 0 28 | 29 | class Color: 30 | PURPLE = '\033[95m' 31 | CYAN = '\033[96m' 32 | DARKCYAN = '\033[36m' 33 | BLUE = '\033[94m' 34 | GREEN = '\033[92m' 35 | YELLOW = '\033[93m' 36 | RED = '\033[91m' 37 | BOLD = '\033[1m' 38 | UNDERLINE = '\033[4m' 39 | END = '\033[0m' 40 | 41 | # Get max row 42 | clf_names = set([name.split("_")[1] for name in names]) 43 | max_mean = {name: 0 for name in clf_names} 44 | max_mean_counter = {name: 0 for name in clf_names} 45 | for name, result in zip(names, results): 46 | counter += 1 47 | clf_name = name.split("_")[1] 48 | if result.mean() > max_mean[clf_name]: 49 | max_mean_counter[clf_name] = counter 50 | max_mean[clf_name] = result.mean() 51 | 52 | # print max row in BOLD 53 | counter = 0 54 | prev_clf_name = names[0].split("_")[1] 55 | for name, result, score in zip(names, results, test_scores): 56 | counter += 1 57 | clf_name = name.split("_")[1] 58 | if prev_clf_name != clf_name: 59 | print() 60 | prev_clf_name = clf_name 61 | msg = "%s: %f (%f) [test_score:%.3f]" % (name, result.mean(), result.std(), score) 62 | if counter == max_mean_counter[clf_name]: 63 | print(Color.BOLD + msg) 64 | else: 65 | print(Color.END + msg) 66 | 67 | 68 | def create_pipelines(seed, verbose=1): 69 | """ 70 | Creates a list of pipelines with preprocessing(PCA), models and scalers. 71 | 72 | :param seed: Random seed for models who needs it 73 | :return: 74 | """ 75 | 76 | models = [ 77 | ('LR', LogisticRegression()), 78 | ('LDA', LinearDiscriminantAnalysis()), 79 | ('KNN', KNeighborsClassifier()), 80 | ('CART', DecisionTreeClassifier(random_state=seed)), 81 | ('NB', GaussianNB()), 82 | ('SVM', SVC(random_state=seed, probability=True)), 83 | ('RF', RandomForestClassifier(max_depth=3, random_state=seed)), 84 | ('MLP', MLPClassifier(random_state=seed)) 85 | ] 86 | scalers = [('StandardScaler', StandardScaler()), 87 | ('MinMaxScaler', MinMaxScaler()), 88 | ('MaxAbsScaler', MaxAbsScaler()), 89 | ('RobustScaler', RobustScaler()), 90 | ('QuantileTransformer-Normal', QuantileTransformer(output_distribution='normal')), 91 | ('QuantileTransformer-Uniform', QuantileTransformer(output_distribution='uniform')), 92 | ('PowerTransformer-Yeo-Johnson', PowerTransformer(method='yeo-johnson')), 93 | ('Normalizer', Normalizer()) 94 | ] 95 | additions = [('PCA', PCA(n_components=4)), 96 | ] 97 | # Create pipelines 98 | pipelines = [] 99 | for model in models: 100 | # Append only model 101 | model_name = "_" + model[0] 102 | pipelines.append((model_name, Pipeline([model]))) 103 | 104 | # Append model+scaler 105 | for scalar in scalers: 106 | model_name = scalar[0] + "_" + model[0] 107 | pipelines.append((model_name, Pipeline([scalar, model]))) 108 | 109 | # To easier distinguish between with and without Additions (i.e: PCA) 110 | # Append model+addition 111 | for addition in additions: 112 | model_name = "_" + model[0] + "-" + addition[0] 113 | pipelines.append((model_name, Pipeline([addition, model]))) 114 | 115 | # Append model+scaler+addition 116 | for scalar in scalers: 117 | for addition in additions: 118 | model_name = scalar[0] + "_" + model[0] + "-" + addition[0] 119 | pipelines.append((model_name, Pipeline([scalar, addition, model]))) 120 | 121 | if verbose: 122 | print("Created these pipelines:") 123 | for pipe in pipelines: 124 | print(pipe[0]) 125 | 126 | return pipelines 127 | 128 | 129 | def run_cv_and_test(X_train, y_train, X_test, y_test, pipelines, scoring, seed, num_folds, 130 | dataset_name, n_jobs): 131 | """ 132 | 133 | Iterate over the pipelines, calculate CV mean and std scores, fit on train and predict on test. 134 | Return the results in a dataframe 135 | 136 | """ 137 | 138 | # List that contains the rows for a dataframe 139 | rows_list = [] 140 | 141 | # Lists for the pipeline results 142 | results = [] 143 | names = [] 144 | test_scores = [] 145 | prev_clf_name = pipelines[0][0].split("_")[1] 146 | print("First name is : ", prev_clf_name) 147 | 148 | for name, model in pipelines: 149 | kfold = model_selection.KFold(n_splits=num_folds, random_state=seed) 150 | cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, n_jobs=n_jobs, scoring=scoring) 151 | results.append(cv_results) 152 | names.append(name) 153 | 154 | # Print CV results of the best CV classier 155 | msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) 156 | print(msg) 157 | 158 | # fit on train and predict on test 159 | model.fit(X_train, y_train) 160 | if scoring == "accuracy": 161 | curr_test_score = model.score(X_test, y_test) 162 | elif scoring == "roc_auc": 163 | y_pred = model.predict_proba(X_test)[:, 1] 164 | curr_test_score = roc_auc_score(y_test, y_pred) 165 | 166 | test_scores.append(curr_test_score) 167 | 168 | # Add separation line if different classifier applied 169 | rows_list, prev_clf_name = check_seperation_line(name, prev_clf_name, rows_list) 170 | 171 | # Add for final dataframe 172 | results_dict = {"Dataset": dataset_name, 173 | "Classifier_Name": name, 174 | "CV_mean": cv_results.mean(), 175 | "CV_std": cv_results.std(), 176 | "Test_score": curr_test_score 177 | } 178 | rows_list.append(results_dict) 179 | 180 | print_results(names, results, test_scores) 181 | 182 | df = pd.DataFrame(rows_list) 183 | return df[["Dataset", "Classifier_Name", "CV_mean", "CV_std", "Test_score"]] 184 | 185 | 186 | def run_cv_and_test_hypertuned_params(X_train, y_train, X_test, y_test, pipelines, scoring, seed, num_folds, 187 | dataset_name, hypertuned_params, n_jobs): 188 | """ 189 | 190 | Iterate over the pipelines, calculate CV mean and std scores, fit on train and predict on test. 191 | Return the results in a dataframe 192 | 193 | :param X_train: 194 | :param y_train: 195 | :param X_test: 196 | :param y_test: 197 | :param scoring: 198 | :param seed: 199 | :param num_folds: 200 | :param dataset_name: 201 | :return: 202 | """ 203 | 204 | # List that contains the rows for a dataframe 205 | rows_list = [] 206 | 207 | # Lists for the pipeline results 208 | results = [] 209 | names = [] 210 | test_scores = [] 211 | prev_clf_name = pipelines[0][0].split("_")[1] 212 | print("First name is : ", prev_clf_name) 213 | 214 | # To be used within GridSearch (5 in your case) 215 | inner_cv = KFold(n_splits=5, shuffle=True, random_state=seed) 216 | # To be used in outer CV (you asked for num_folds) 217 | outer_cv = KFold(n_splits=num_folds, shuffle=True, random_state=seed) 218 | for name, model in pipelines: 219 | 220 | # Get model's hyper parameters 221 | model_name = name.split("_")[1] 222 | if "-" in model_name: 223 | model_name = model_name.split("-")[0] 224 | 225 | if model_name in hypertuned_params.keys(): 226 | random_grid = hypertuned_params[model_name] 227 | else: 228 | continue 229 | 230 | # Train nested-CV 231 | clf = GridSearchCV(estimator=model, param_grid=random_grid, cv=inner_cv, scoring=scoring, 232 | verbose=2, n_jobs=n_jobs, refit=True) 233 | cv_results = model_selection.cross_val_score(clf, X_train, y_train, cv=outer_cv, n_jobs=n_jobs, scoring=scoring) 234 | results.append(cv_results) 235 | names.append(name) 236 | 237 | # Print CV results of the best CV classier 238 | msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) 239 | print(msg) 240 | 241 | # fit on train and predict on test 242 | model.fit(X_train, y_train) 243 | if scoring is "accuracy": 244 | curr_test_score = model.score(X_test, y_test) 245 | elif scoring is "roc_auc": 246 | y_pred = model.predict(X_test) 247 | curr_test_score = accuracy_score(y_test, y_pred) 248 | 249 | test_scores.append(curr_test_score) 250 | 251 | # Add separation line if different classifier applied 252 | rows_list, prev_clf_name = check_seperation_line(name, prev_clf_name, rows_list) 253 | 254 | # Add for final dataframe 255 | results_dict = {"Dataset": dataset_name, 256 | "Classifier_Name": name, 257 | "CV_mean": cv_results.mean(), 258 | "CV_std": cv_results.std(), 259 | "Test_score": curr_test_score 260 | } 261 | rows_list.append(results_dict) 262 | 263 | print_results(names, results, test_scores) 264 | 265 | df = pd.DataFrame(rows_list) 266 | return df[["Dataset", "Classifier_Name", "CV_mean", "CV_std", "Test_score"]] 267 | 268 | 269 | def check_seperation_line(name, prev_clf_name, rows_list): 270 | """ 271 | Add empty row if different classifier ending 272 | 273 | """ 274 | 275 | clf_name = name.split("_")[1] 276 | if prev_clf_name != clf_name: 277 | empty_dict = {"Dataset": "", 278 | "Classifier_Name": "", 279 | "CV_mean": "", 280 | "CV_std": "", 281 | "Test_acc": "" 282 | } 283 | rows_list.append(empty_dict) 284 | prev_clf_name = clf_name 285 | return rows_list, prev_clf_name 286 | 287 | 288 | def get_hypertune_params(): 289 | """ 290 | 291 | Create a dictionary with classifier name as a key and it's hyper parameters options as a value 292 | 293 | :return: 294 | """ 295 | # RF PARAMS 296 | n_estimators = [int(x) for x in np.linspace(start=3, stop=20, num=3)] 297 | max_features = ['auto', 'sqrt'] 298 | max_depth = [int(x) for x in np.linspace(10, 110, num=11)] 299 | max_depth.append(None) 300 | min_samples_split = [2, 5, 10] 301 | min_samples_leaf = [1, 2, 4] 302 | rf_params = {'RF__n_estimators': n_estimators, 303 | 'RF__max_features': max_features, 304 | 'RF__max_depth': max_depth, 305 | 'RF__min_samples_split': min_samples_split, 306 | 'RF__min_samples_leaf': min_samples_leaf, 307 | } 308 | 309 | # KNN PARAMS 310 | n_neighbors = [int(x) for x in np.linspace(start=1, stop=20, num=2)] 311 | weights = ["uniform", "distance"] 312 | algorithm = ["auto", "ball_tree", "kd_tree", "brute"] 313 | leaf_size = [int(x) for x in np.linspace(start=5, stop=50, num=2)] 314 | p = [int(x) for x in np.linspace(start=1, stop=4, num=1)] 315 | knn_params = {'KNN__n_neighbors': n_neighbors, 316 | 'KNN__weights': weights, 317 | 'KNN__algorithm': algorithm, 318 | 'KNN__leaf_size': leaf_size, 319 | 'KNN__p': p, 320 | } 321 | 322 | # SVM PARAMS 323 | C = [x for x in np.arange(0.1, 2, 0.2)] 324 | kernel = ["linear", "poly", "rbf", "sigmoid"] 325 | svm_params = {'SVM__C': C, 326 | 'SVM__kernel': kernel, 327 | } 328 | 329 | # Logistic Regression Params 330 | C = [x for x in np.arange(0.1, 3, 0.2)] 331 | penalty = ["l1", "l2"] 332 | fit_intercept = [True, False] 333 | lr_params = {'LR__C': C, 334 | 'LR__penalty': penalty, 335 | 'LR__fit_intercept': fit_intercept 336 | } 337 | 338 | # LDA PARAMS 339 | solver = ["lsqr", "eigen"] 340 | shrinkage = ["auto", None, 0.1, 0.3, 0.5, 0.7, 0.9] 341 | lda_params = {'LDA__solver': solver, 342 | 'LDA__shrinkage': shrinkage 343 | } 344 | 345 | # MLP PARAMS 346 | hidden_layer_sizes = [(x, y) for x, y in itertools.product([x for x in range(1, 3)], [x for x in range(5, 120, 5)])] 347 | activation = ["tanh", "relu"] 348 | solver = ["lbfgs", "sgd", "adam"] 349 | alpha = [0.1, 0.001, 0.0001] 350 | learning_rate = ["constant", "invscaling", "adaptive"] 351 | mlp_params = {'MLP__hidden_layer_sizes': hidden_layer_sizes, 352 | 'MLP__activation': activation, 353 | 'MLP__solver': solver, 354 | 'MLP__alpha': alpha, 355 | 'MLP__learning_rate': learning_rate, 356 | } 357 | 358 | # DecisionTreeClassifier PARAMS 359 | criterion = ['gini', 'entropy'] 360 | splitter = ['best', 'random'] 361 | max_depth = [int(x) for x in np.linspace(10, 110, num=11)] 362 | max_depth.append(None) 363 | min_samples_split = [2, 5, 10] 364 | min_samples_leaf = [1, 2, 4] 365 | max_features = ["auto", "sqrt", "log2"] 366 | cart_params = {'CART__criterion': criterion, 367 | 'CART__splitter': splitter, 368 | 'CART__max_depth': max_depth, 369 | 'CART__min_samples_split': min_samples_split, 370 | 'CART__min_samples_leaf': min_samples_leaf, 371 | 'CART__max_features': max_features 372 | } 373 | 374 | hypertuned_params = {"RF": rf_params, 375 | "LDA": lda_params, 376 | "MLP": mlp_params, 377 | "SVM": svm_params, 378 | "LR": lr_params, 379 | "KNN": knn_params, 380 | "CART": cart_params, 381 | } 382 | 383 | return hypertuned_params 384 | 385 | -------------------------------------------------------------------------------- /src/visualization/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/visualization/.gitkeep -------------------------------------------------------------------------------- /src/visualization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shaygeller/Normalization_vs_Standardization/81207cb86ef5869b68f4bb0899cebda578dbb74c/src/visualization/__init__.py -------------------------------------------------------------------------------- /test_environment.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | REQUIRED_PYTHON = "python3" 4 | 5 | 6 | def main(): 7 | system_major = sys.version_info.major 8 | if REQUIRED_PYTHON == "python": 9 | required_major = 2 10 | elif REQUIRED_PYTHON == "python3": 11 | required_major = 3 12 | else: 13 | raise ValueError("Unrecognized python interpreter: {}".format( 14 | REQUIRED_PYTHON)) 15 | 16 | if system_major != required_major: 17 | raise TypeError( 18 | "This project requires Python {}. Found: Python {}".format( 19 | required_major, sys.version)) 20 | else: 21 | print(">>> Development environment passes all tests!") 22 | 23 | 24 | if __name__ == '__main__': 25 | main() 26 | -------------------------------------------------------------------------------- /tox.ini: -------------------------------------------------------------------------------- 1 | [flake8] 2 | max-line-length = 79 3 | max-complexity = 10 4 | --------------------------------------------------------------------------------