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        "description": "# ValueBasedPrioritization\n\n## Article\n\nAcademic article: https://github.com/freeradical13/ValueBasedPrioritization/raw/master/value_based_prioritization.pdf\n\n## Installation\n\nhttps://pypi.org/project/vbp/\n\n    pip3 install vbp\n\n## vbp\n\nValue Based Prioritization (`vbp`) uses value theory to quantitatively\nprioritize potential actions to accomplish a goal.\n\nThe `vbp.run` module may be used from the command line to perform\ndifferent VBP actions such as listing actions (`list`), counting\nactions (`count`), predicting values (`predict`), running Modeled VBP\n(`modeled_value_based_prioritization`), and more. For usage, run:\n\n    python3 -m vbp.run\n\nAny non-terminal output goes to the `vbpoutput` sub-folder.\n\nHere is a simple example counting the number of groupings of\nunderlying causes of death for the United States for the default data\ntype:\n\n    python3 -m vbp.run count UCODUnitedStates\n\nAlternatively, if installed through `pip`, a `vbp` script wrapper may\nbe used:\n\n    vbp count UCODUnitedStates\n\nThe academic article above includes footnotes with details on how to\nrun `vbp` to produce the output of each step.\n\nThis package provides abstract classes and utility methods to run\nVBP, mostly focused on Modeled VBP which uses time series data to\npredict future values and prioritize actions based on the relative\npredicted values.\n\nThe `vbp.DataSource` class is the base abstract class for VBP.\n\nThe `vbp.TimeSeriesDataSource` abstract class inherits from\n`vbp.DataSource` and may be used for Modeled VBP. The\n`vbp.ExampleDataSource` class demonstrates a simple data source based\non `vbp.TimeSeriesDataSource`.\n\nBuilt-in Modeled VBPs include Underlying Cause of Death models for\nthe United States (`vbp.ucod.united_states.UCODUnitedStates`) and the\nWorld (`vbp.ucod.world.UCODWorld`). These\ndata sources both inherit from `vbp.ucod.icd.ICDDataSource` which\ninherits from `vbp.TimeSeriesDataSource`.\n\n## Running\n\nThe model type is specified with `--ets`, `--ols`, and/or `--prophet`.\nThese are not mutually exclusive; if combined during\n`modeled_value_based_prioritization`, an average is taken of the\nresults. The default is `--ets`.\n\nBy default, action names are obfuscated to reduce bias during model\nbuilding and testing. Specify `--do-not-obfuscate` to show actual names.\n\nSome data sources have different data types (e.g. mutually exclusive\ngroupings of data). Add the `-a` argument before the data source\nname to run for all data types. Add the `--data-type X` argument after\nthe data source name to specify a specific data type.\n\nIn general, a list of actions may be specified to run for just that\nlist; otherwise, without such a list, all actions are processed.\n\nExamples:\n\n    python3 -m vbp.run modeled_value_based_prioritization UCODUnitedStates\n\n    python3 -m vbp.run modeled_value_based_prioritization UCODUnitedStates --do-not-obfuscate \"Heart disease\" Cancer\n\n### Exponential Smoothing\n\nUsing [exponential smoothing](https://otexts.com/fpp2/expsmooth.html):\n\n    python3 -m vbp.run modeled_value_based_prioritization ${DATA_SOURCE} --ets\n\nSpecify `--ets-no-multiplicative-models` to only use additive models.\n\nSpecify `--ets-no-additive-models` to only use multiplicative models.\n\n### Linear Regression\n\nUsing [linear regression](https://otexts.com/fpp2/regression.html).\n\n    python3 -m vbp.run modeled_value_based_prioritization ${DATA_SOURCE} --ols\n\nSpecify `--ols-max-degrees X` to model higher degrees.\n\n### Prophet\n\nUsing [Facebook Prophet](https://facebook.github.io/prophet/).\n\n    python3 -m vbp.run modeled_value_based_prioritization ${DATA_SOURCE} --prophet\n\n### United States\n\nAs of 2019-03-01, the unzipped U.S. mortality data consumes ~36GB of\ndisk. It will be downloaded and unzipped automatically when a function\nis used that needs it.\n\n#### Long-term, comparable, leading causes of death\n\nGenerate `data/ucod/united_states/comparable_data_since_1959.xlsx` for\nall long-term, comparable, leading causes of death in\nhttps://www.cdc.gov/nchs/data/dvs/lead1900_98.pdf:\n\n    python3 -m vbp.run prepare_data UCODUnitedStates\n\nRows 1900:1957 and the sheet `Comparability Ratios` in\n`data/ucod/united_states/comparable_ucod_estimates.xlsx` were manually\ninput from https://www.cdc.gov/nchs/data/dvs/lead1900_98.pdf\n\nOpen `comparable_data_since_1959.xlsx` and copy rows 1959:Present.\n\nOpen `comparable_ucod_estimates.xlsx` and paste on top starting\nat 1959.\n\nProcess `comparable_ucod_estimates.xlsx` with its\n`Comparability Ratios` sheet to generate\n`comparable_ucod_estimates_ratios_applied.xlsx`:\n\n    python3 -m vbp.run prepare_data UCODUnitedStates --comparable-ratios\n\nFinal output:\n\n    python3 -m vbp.run modeled_value_based_prioritization UCODUnitedStates --data-type UCOD_LONGTERM_COMPARABLE_LEADING\n\n### World\n\nAs of 2019-03-01, the unzipped World mortality data consumes ~320MB of\ndisk. 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