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pdpiechpdpiech
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Merge branch 'emm_iters_exps'
- Publish final implementations used for ICML camera ready
2 parents 78e51ee + db77fe6 commit 648c3b0

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‎.gitignore‎

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# ignore compilation files
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*,cover
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.hypothesis/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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#Ipython Notebook
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.ipynb_checkpoints

‎README.md‎

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PrefPy
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======
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Rank aggregation algorithms in the computer science field of computational social choice
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What's New
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==========
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- Generalized method of moments algorithm for mixtures of Plackett-Luce models
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- Implementation of EMM algorithm for mixtures of Plackett-Luce by Gormley & Murphy
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Work In Progress
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================
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- This is an initial version of the Python package form, further structural changes will be coming
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- Module naming conventions will be changed; currently the algorithm files take the initials of the names of the papers from which they originate (e.g. "gmmra" for Generalized Method of Moments for Rank Aggregation)
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- Mixture Model for Plackett-Luce EMM algorithm by Gormley & Murphy is forthcoming pending verification and testing of the method
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- Random utility model algorithms (verification of the implentation needs to be completed)
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Installation
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============
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- Use of MATLAB optimization in this package requires Python 3.4 due to lack of support yet for Python 3.5 by the MATLAB Engine
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Install by running setup.py with Python 3.4 (or greater) with the command
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python3 setup.py install
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Symlink install while developing to keep changes in the code instead with the command
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python3 setup.py develop
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PrefPy
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======
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Rank aggregation algorithms in the computer science field of computational social choice
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6+
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What's New
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==========
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- Experiments and tests have been factored out of the repository (now located at https://github.com/pdpiech/prefpy-experiments)
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- Generalized method of moments algorithm for mixtures of Plackett-Luce models
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- Implementation of EMM algorithm for mixtures of Plackett-Luce by Gormley & Murphy
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14+
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Work In Progress
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================
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- This is an initial version of the Python package form, further structural changes will be coming
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- Module naming conventions will be changed; currently the algorithm files take the initials of the names of the papers from which they originate (e.g. "gmmra" for Generalized Method of Moments for Rank Aggregation)
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- Mixture Model for Plackett-Luce EMM algorithm by Gormley & Murphy is forthcoming pending verification and testing of the method
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- Random utility model algorithms (verification of the implentation needs to be completed)
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Installation
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============
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- Use of MATLAB optimization in this package requires Python 3.4 due to lack of support yet for Python 3.5 by the MATLAB Engine
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Install by running setup.py with Python 3.4 (or greater) with the command
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python3 setup.py install
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Symlink install while developing to keep changes in the code instead with the command
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python3 setup.py develop

‎prefpy/__init__.py‎

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from . import aggregate
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from . import distributions
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from . import evbwie
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from . import gmm_mixpl_moments
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from . import gmm_mixpl_objectives
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from . import gmm_mixpl
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from . import gmmra
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from . import mmgbtl
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from . import plackettluce
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from . import stats
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from . import util
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__all__ = ["aggregate", "distributions", "evbwie", "gmm_mixpl_moments",
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"gmm_mixpl_objectives", "gmm_mixpl", "gmmra", "mmgbtl",
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"plackettluce", "stats", "util"]

‎prefpy/evbwie.py‎

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# A Mixture Modeling Approach by Gormley and Murphy, 2008
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import numpy as np
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import aggregate
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import plackettluce as pl
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import stats
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from . import aggregate
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from . import plackettluce as pl
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from . import stats
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class EMMMixPLResult:
@@ -107,11 +107,12 @@ def aggregate(self, rankings, K, epsilon, max_iters, epsilon_mm, max_iters_mm):
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z_h1[i][k] = (pi_h[k] * EMMMixPLAggregator.f(x[i], p_h[k])) / denom_sum
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# M-Step:
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#for l in range(max_iters_mm):
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for l in range(int(g/50) + 5):
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for l in range(max_iters_mm):
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#for l in range(int(g/50) + 5):
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for k in range(K):
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normconst = 0
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pi_h1[k] = np.sum(z_h1.T[k]) / len(z_h1)
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if l == 0: # only need to compute pi at first MM iteration
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pi_h1[k] = np.sum(z_h1.T[k]) / len(z_h1)
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for j in range(self.m):
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omega_k_j = EMMMixPLAggregator.omega(k, j, z_h1, x) # numerator
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denom_sum = 0
@@ -132,15 +133,18 @@ def aggregate(self, rankings, K, epsilon, max_iters, epsilon_mm, max_iters_mm):
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p_h1[k][j] /= normconst
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if (epsilon_mm != None and
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np.all(np.absolute(p_h1 - p_h) < epsilon_mm) and
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np.all(np.absolute(pi_h1 - pi_h) < epsilon_mm)):
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np.all(np.absolute(p_h1 - p_h) < epsilon_mm)):
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break
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p_h = np.copy(p_h1) # deep copy p for next MM iteration
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# pi does not change across MM iterations, no copy needed
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if (epsilon != None and
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np.all(np.absolute(p_h1 - p_h) < epsilon) and
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np.all(np.absolute(pi_h1 - pi_h) < epsilon)):
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break
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# remember that assignments below are references only, not copies
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p_h = p_h1
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pi_h = pi_h1
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‎prefpy/evbwie1.py‎

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# Implementation of algorithm (2) from
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# Exploring Voting Blocs Within the Irish Electorate:
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# A Mixture Modeling Approach by Gormley and Murphy, 2008
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import numpy as np
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from . import aggregate
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from . import plackettluce as pl
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from . import stats
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class EMMMixPLResult:
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def __init__(self, num_alts, num_votes, num_mix, true_params, epsilon, max_iters, epsilon_mm, max_iters_mm, init_guess, soln_params, runtime):
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self.num_alts = num_alts
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self.num_votes = num_votes
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self.num_mix = num_mix
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self.true_params = true_params
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self.epsilon = epsilon
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self.max_iters = max_iters
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self.epsilon_mm = epsilon_mm
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self.max_iters_mm = max_iters_mm
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self.init_guess = init_guess
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self.soln_params = soln_params
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self.runtime = runtime
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class EMMMixPLAggregator(aggregate.RankAggregator):
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def c(x_i, j):
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try:
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return x_i[j]
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except IndexError:
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return -1
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def f(x_i, p):
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prod = 1
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for t in range(len(x_i)):
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denom_sum = 0
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for s in range(t, len(p)):
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denom_sum += p[EMMMixPLAggregator.c(x_i, s)]
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prod *= p[EMMMixPLAggregator.c(x_i, t)] / denom_sum
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return prod
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def indic(j, x_i, s):
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flag = j == EMMMixPLAggregator.c(x_i, s)
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if flag:
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return 1
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else:
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return 0
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def delta(x_i, j, s, N):
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""" delta_i_j_s """
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flag = j == EMMMixPLAggregator.c(x_i, s)
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if flag and s < len(x_i):
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return 1
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elif s == N:
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found_equal = False
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for l in range(len(x_i)):
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if j == EMMMixPLAggregator.c(x_i, l):
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found_equal = True
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break
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if not found_equal:
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return 1
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return 0
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def omega(k, j, z, x):
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""" omega_k_j """
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sum_out = 0
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for i in range(len(x)):
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sum_in = 0
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for t in range(len(x[i])):
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sum_in += z[i][k] * EMMMixPLAggregator.indic(j, x[i], t)
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sum_out += sum_in
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return sum_out
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def aggregate(self, rankings, K, epsilon, tot_iters, epsilon_mm, max_iters_em):
75+
x = rankings # shorter pseudonym for voting data
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self.n = len(rankings) # number of votes
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# pre-compute the delta values
79+
delta_i_j_s = np.empty((self.n, self.m, self.m + 1))
80+
for i in range(self.n):
81+
for j in range(self.m):
82+
for s in range(self.m + 1):
83+
delta_i_j_s[i][j][s] = EMMMixPLAggregator.delta(x[i], j, s, self.m)
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# generate initial values for p and pi:
86+
p_h0 = np.random.rand(K, self.m)
87+
p_h0 /= np.sum(p_h0, axis=1, keepdims=True)
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pi_h0 = np.random.rand(K)
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pi_h0 /= np.sum(pi_h0)
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p_h = np.copy(p_h0)
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pi_h = np.copy(pi_h0)
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inner = tot_iters // max_iters_em
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for g in range(max_iters_em):
98+
#for g in range(max_iters):
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p_h1 = np.empty((K, self.m))
101+
pi_h1 = np.empty(K)
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z_h1 = np.empty((self.n, K))
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# E-Step:
105+
for i in range(self.n):
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for k in range(K):
107+
denom_sum = 0
108+
for k2 in range(K):
109+
denom_sum += pi_h[k2] * EMMMixPLAggregator.f(x[i], p_h[k2])
110+
z_h1[i][k] = (pi_h[k] * EMMMixPLAggregator.f(x[i], p_h[k])) / denom_sum
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112+
# M-Step:
113+
test = (g + 1) * inner + (max_iters_em - g - 1) * (inner + 1)
114+
if test < tot_iters:
115+
inner += 1
116+
for l in range(inner):
117+
#for l in range(max_iters_mm):
118+
#for l in range(int(g/50) + 5):
119+
for k in range(K):
120+
normconst = 0
121+
if l == 0: # only need to compute pi at first MM iteration
122+
pi_h1[k] = np.sum(z_h1.T[k]) / len(z_h1)
123+
for j in range(self.m):
124+
omega_k_j = EMMMixPLAggregator.omega(k, j, z_h1, x) # numerator
125+
denom_sum = 0
126+
for i in range(self.n):
127+
sum1 = 0
128+
for t in range(len(x[i])):
129+
sum2 = 0
130+
sum3 = 0
131+
for s in range(t, self.m):
132+
sum2 += p_h[k][EMMMixPLAggregator.c(x[i], s)]
133+
for s in range(t, self.m + 1):
134+
sum3 += delta_i_j_s[i][j][s]
135+
sum1 += z_h1[i][k] * (sum2 ** -1) * sum3
136+
denom_sum += sum1
137+
p_h1[k][j] = omega_k_j / denom_sum
138+
normconst += p_h1[k][j]
139+
for j in range(self.m):
140+
p_h1[k][j] /= normconst
141+
142+
if (epsilon_mm != None and
143+
np.all(np.absolute(p_h1 - p_h) < epsilon_mm)):
144+
break
145+
146+
p_h = np.copy(p_h1) # deep copy p for next MM iteration
147+
# pi does not change across MM iterations, no copy needed
148+
149+
if (epsilon != None and
150+
np.all(np.absolute(p_h1 - p_h) < epsilon) and
151+
np.all(np.absolute(pi_h1 - pi_h) < epsilon)):
152+
break
153+
154+
# remember that assignments below are references only, not copies
155+
p_h = p_h1
156+
pi_h = pi_h1
157+
158+
return (pi_h1, p_h1, pi_h0, p_h0)
159+
160+
def main():
161+
n = 100
162+
m = 4
163+
k = 2
164+
cand_set = np.arange(m)
165+
#np.random.seed(0)
166+
params, votes = pl.generate_mix2pl_dataset(n, m, useDirichlet=True)
167+
print("Ground-Truth Parameters:\n" + str(params))
168+
print("EMM Algorithm:")
169+
170+
emmagg = EMMMixPLAggregator(cand_set)
171+
pi, p = emmagg.aggregate(votes, K=2, epsilon=1e-8, max_iters=1000, epsilon_mm=1e-8, max_iters_mm=10)
172+
173+
sol_params = np.empty(2*m+1)
174+
sol_params[0] = pi[0]
175+
sol_params[1:m+1] = p[0]
176+
sol_params[m+1:] = p[1]
177+
178+
print("Ground-Truth Parameters:\n" + str(params))
179+
print("Final Solution:\n" + str(sol_params))
180+
print("\t\"1 - alpha\" = " + str(pi[1]))
181+
print("WSSE:\n" + str(stats.mix2PL_wsse(params, sol_params, m)))
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183+
if __name__ == "__main__":
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main()

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