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        "author": "mdmpy Authors",
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        "description": "# MDM Py\n\n[![Documentation Status](https://readthedocs.org/projects/mdmpy/badge/?version=latest)](https://mdmpy.readthedocs.io/en/latest/?badge=latest)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/c13f603535364a7ba5a6a18ea6756a64)](https://app.codacy.com/app/justanothergithubber/mdmpy?utm_source=github.com&utm_medium=referral&utm_content=justanothergithubber/mdmpy&utm_campaign=Badge_Grade_Dashboard)\n[![PyPI version](https://badge.fury.io/py/mdmpy.svg)](https://badge.fury.io/py/mdmpy)\n[![Project Status: WIP \u00e2\u20ac\u201c Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)\n\nThis package is a `Python` implementation of [Marginal Distribution Models](https://pubsonline.informs.org/doi/10.1287/mnsc.2014.1906) (MDMs), which can be used in Discrete Choice Modelling.\n\n## Documentation\nDocumentation is kindly hosted by [Read The Docs](https://mdmpy.readthedocs.io/ \"mdmpy Documentation\").\n\n## Install\nThis package is uploaded to [PyPI](https://pypi.org/ \"Python Package Index\"). Hence, \n\n```bat\npip install mdmpy\n```\n\nshould work.\n\n## How to use\n### Simplest Case\n#### Gradient Descent\nIn the simplest case, we will use the Multinomial Logit (MNL) model, which is used as a default. Assuming `numpy`, `scipy` and `pandas` are installed, we generate choice data assuming a random utility model:\n\n```python\nfrom string import ascii_uppercase as letters\nimport pandas as pd\nimport scipy.stats as stats\nimport numpy as np\n\nNUM_INDIV   = 57\nNUM_CHOICES = 3\nNUM_ATTR    = 4\n\nnp.random.seed(2019)\nX = np.random.random((NUM_ATTR, NUM_INDIV * NUM_CHOICES))\ntrue_beta = np.random.random(NUM_ATTR)\nV = np.dot(true_beta.T, X)\nV = np.reshape(V, (NUM_INDIV,NUM_CHOICES))\neps = stats.gumbel_r.rvs(size=NUM_INDIV * NUM_CHOICES)\neps = np.reshape(eps, (NUM_INDIV, NUM_CHOICES))\nU = V + eps\nhighest_util = np.argmax(U, 1)\n\ndf = pd.DataFrame(X.T)\ndf['choice'] = [1 if idx == x else 0 for idx in highest_util for x in range(NUM_CHOICES)]\ndf['individual'] = [indiv for indiv in range(NUM_INDIV) for _ in range(NUM_CHOICES)]\ndf['altvar'] = [altlvl for _ in range(NUM_INDIV) for altlvl in letters[:NUM_CHOICES]]\n```\n\nWith this package, we will assume that `df` is the dataframe which is simply given to us. Instead of having the code itself find out how many individuals, choices and coefficients or attributes there are, we will simply feed them into the class. To perform a gradient descent with this class, we will use the `grad_desc` method, using the `df` from above as input,\n\n```python\nimport mdmpy\n\n# In a typical case one would load df before this line\nmdm = mdmpy.MDM(df, 4, 3, [0, 1, 2, 3])\nnp.random.seed(4)\ninit_beta = np.random.random(4)\ngrad_beta = mdm.grad_desc(init_beta)\nprint(grad_beta)\n# expected output [0.30238122 0.07955214 0.86779824 0.50951981]\n```\n\n#### Solver\nThe `MDM` class acts as a wrapper and adds the necessary `pyomo` variables and sets to model the problem, but requires a solver. [IPOPT](https://projects.coin-or.org/Ipopt \"Ipopt home page\"), an interior point solver, is recommended. If you have such a solver, it can be called. Assuming IPOPT is being used:\n\n```python\nimport mdmpy\n\nipopt_exec_path = /path/to/ipopt # Replace with proper path\nmdm = mdmpy.MDM(df, 4, 3, [0, 1, 2, 3])\nmdm.model_init()\nmdm.model_solve(\"ipopt\",ipopt_exec_path)\nprint([mdm.m.beta[idx].value for idx in mdm.m.beta])\n# expected output [0.30238834989235025, 0.07953888508425154, 0.8678050334295714, 0.5095096796373667]\n```\n\n## Todo\n\n1.  Add documentation and more meaningful comments\n    *   Add more type hints, especially those involving Python builtins\n\n2.  Add tests.\n\n3.  Put `pandas` into `extras_require` of `setup.py`, and remove the dependency. \n    *   Input of `MDM` class will become a `NumPy` array rather than a dataframe.\n\n    *   Dataframe conversion will be turned into a utility function,\n    likely using try-except block for imports\n\n\n",
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