<img align="left" src="PySiMMMulator_logo.png" alt="logo" width="150"/>
# PySiMMMulator
[](https://www.codefactor.io/repository/github/ryanaugust/pysimmmulator)
[](
https://pypi.org/project/pysimmmulator/)
PySiMMMulator is an open source Python adaption of the R-package [siMMMulator](https://github.com/facebookexperimental/siMMMulator), which allows users to generate simulated data to use in testing Marketing Mix Models (MMMs).
While this package cointains a full pipeline for data generation (configurable via .conf file) it can also be utilized in parts to generate select portions of MMM input data (ie campaign/channel spend)
## Installation
Accessable via PyPI
```bash
pip install pysimmmulator
```
## Usage
PySiMMMulator's simulator can either be run on a step-by-step basis, or can be run single-shot by passing a config file.
### Run via config
Run using this method, you'll be returned both a dataframe of for MMM input as well as the "True ROI" values for each of your channels. These true values are critical to validating your MMM model.
```python
cfg = load_config(config_path="./my_config.yaml")
sim = simmm()
mmm_input_df, channel_roi = sim.run_with_config(config=cfg)
```
### Run by stages
Alternatively you may run each of the stages independently, which allows for easier debugging and in-run adjustments based on the results of each stage. The order of the stages is reflected below **(without their inputs)**. Once you've run through every stage, results are available by calling the `sim.final_df` object (channel ROI results are stored as `sim.channel_roi`).
```python
sim = simmm()
sim.simulate_baseline()
sim.simulate_ad_spend()
sim.simulate_media()
sim.simulate_cvr()
sim.simulate_decay_returns()
sim.calculate_conversions()
sim.consolidate_dataframe()
sim.calculate_channel_roi()
sim.finalize_output()
```
### Run via CLI
A configuration file is required as input for this and should be passed as seen below. An output path can also be passed via `-o`, however when not passed the current working directory will be used.
```bash
pysimmm -i example_config.yaml -o .
```
## Development
Setting up a dev environment
```bash
conda create --prefix=./env python=3.11
conda activate ./env
pip install -e '.[dev]'
```
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"description": "<img align=\"left\" src=\"PySiMMMulator_logo.png\" alt=\"logo\" width=\"150\"/>\n\n# PySiMMMulator\n\n[](https://www.codefactor.io/repository/github/ryanaugust/pysimmmulator)\n[](\nhttps://pypi.org/project/pysimmmulator/)\n\nPySiMMMulator is an open source Python adaption of the R-package [siMMMulator](https://github.com/facebookexperimental/siMMMulator), which allows users to generate simulated data to use in testing Marketing Mix Models (MMMs).\nWhile this package cointains a full pipeline for data generation (configurable via .conf file) it can also be utilized in parts to generate select portions of MMM input data (ie campaign/channel spend)\n\n## Installation\n\nAccessable via PyPI\n\n```bash\npip install pysimmmulator\n```\n\n## Usage\n\nPySiMMMulator's simulator can either be run on a step-by-step basis, or can be run single-shot by passing a config file.\n\n### Run via config\n\nRun using this method, you'll be returned both a dataframe of for MMM input as well as the \"True ROI\" values for each of your channels. These true values are critical to validating your MMM model.\n\n```python\ncfg = load_config(config_path=\"./my_config.yaml\")\nsim = simmm()\nmmm_input_df, channel_roi = sim.run_with_config(config=cfg)\n```\n\n### Run by stages\n\nAlternatively you may run each of the stages independently, which allows for easier debugging and in-run adjustments based on the results of each stage. The order of the stages is reflected below **(without their inputs)**. Once you've run through every stage, results are available by calling the `sim.final_df` object (channel ROI results are stored as `sim.channel_roi`).\n\n```python\nsim = simmm()\nsim.simulate_baseline()\nsim.simulate_ad_spend()\nsim.simulate_media()\nsim.simulate_cvr()\nsim.simulate_decay_returns()\nsim.calculate_conversions()\nsim.consolidate_dataframe()\nsim.calculate_channel_roi()\nsim.finalize_output()\n```\n\n### Run via CLI\n\nA configuration file is required as input for this and should be passed as seen below. An output path can also be passed via `-o`, however when not passed the current working directory will be used.\n\n```bash\npysimmm -i example_config.yaml -o .\n```\n\n## Development\n\nSetting up a dev environment\n\n```bash\nconda create --prefix=./env python=3.11\nconda activate ./env\npip install -e '.[dev]'\n```\n",
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