diff --git a/.gitignore b/.gitignore index 0dad58e0..44216d61 100644 --- a/.gitignore +++ b/.gitignore @@ -1,24 +1,28 @@ benchmark_data.csv -data analysis/keys/keytemp.json -data analysis/__pycache__/analysis.cpython-37.pyc +data-analysis/keys/keytemp.json +data-analysis/__pycache__/analysis.cpython-37.pyc apps/android/source/app/src/main/res/drawable-v24/uuh.png apps/android/source/app/src/main/java/com/example/titanscouting/tits.java -data analysis/analysis.cp37-win_amd64.pyd -data analysis/analysis/analysis.c -data analysis/analysis/analysis.cp37-win_amd64.pyd -data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.exp -data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.lib -data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.obj -data analysis/test.ipynb -data analysis/.ipynb_checkpoints/test-checkpoint.ipynb +data-analysis/analysis.cp37-win_amd64.pyd +data-analysis/analysis/analysis.c +data-analysis/analysis/analysis.cp37-win_amd64.pyd +data-analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.exp +data-analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.lib +data-analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.obj +data-analysis/test.ipynb +data-analysis/.ipynb_checkpoints/test-checkpoint.ipynb .vscode/settings.json .vscode -data analysis/arthur_pull.ipynb -data analysis/keys.txt -data analysis/check_for_new_matches.ipynb -data analysis/test.ipynb -data analysis/visualize_pit.ipynb -data analysis/config/keys.config +data-analysis/arthur_pull.ipynb +data-analysis/keys.txt +data-analysis/check_for_new_matches.ipynb +data-analysis/test.ipynb +data-analysis/visualize_pit.ipynb +data-analysis/config/keys.config analysis-master/analysis/__pycache__/ -data analysis/__pycache__/ \ No newline at end of file +data-analysis/__pycache__/ +analysis-master/analysis.egg-info/ +analysis-master/build/ +analysis-master/metrics/ +data-analysis/config-pop.json \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 86ec58be..357ccc48 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1 +1,66 @@ -These sets of code is more unstable than an antimatter bear taunted with a barrel of fish. Add at your own risk. +# Contributing Guidelines + +This project accept contributions via GitHub pull requests. +This document outlines some of the +conventions on development workflow, commit message formatting, contact points, +and other resources to make it easier to get your contribution accepted. + +## Certificate of Origin + +By contributing to this project, you agree to the [Developer Certificate of +Origin (DCO)](https://developercertificate.org/). This document was created by the Linux Kernel community and is a +simple statement that you, as a contributor, have the legal right to make the +contribution. + +In order to show your agreement with the DCO you should include at the end of the commit message, +the following line: `Signed-off-by: John Doe `, using your real name. + +This can be done easily using the [`-s`](https://github.com/git/git/blob/b2c150d3aa82f6583b9aadfecc5f8fa1c74aca09/Documentation/git-commit.txt#L154-L161) flag on the `git commit`. + +Visual Studio code also has a flag to enable signoff on commits + +If you find yourself pushed a few commits without `Signed-off-by`, you can still add it afterwards. Read this for help: [fix-DCO.md](https://github.com/src-d/guide/blob/master/developer-community/fix-DCO.md). + +## Support Channels + +The official support channel, for both users and contributors, is: + +- GitHub issues: each repository has its own list of issues. + +*Before opening a new issue or submitting a new pull request, it's helpful to +search the project - it's likely that another user has already reported the +issue you're facing, or it's a known issue that we're already aware of. + + +## How to Contribute +In general, please use conventional approaches to development and contribution such as: +* Create branches for additions or deletions, and or side projects +* Do not commit to master! +* Use Pull Requests (PRs) to indicate that an addition is ready to merge. +PRs are the main and exclusive way to contribute code to source{d} projects. +In order for a PR to be accepted it needs to pass this list of requirements: + +- The contribution must be correctly explained with natural language and providing a minimum working example that reproduces it. +- All PRs must be written idiomaticly: + - for Node: formatted according to [AirBnB standards](https://github.com/airbnb/javascript), and no warnings from `eslint` using the AirBnB style guide + - for other languages, similar constraints apply. +- They should in general include tests, and those shall pass. + - In any case, all the PRs have to pass the personal evaluation of at least one of the [maintainers](MAINTAINERS) of the project. + + +### Format of the commit message + +Every commit message should describe what was changed, under which context and, if applicable, the issue it relates to (mentioning a GitHub issue number when applicable): + +For small changes, or changes to a testing or personal branch, the commit message should be a short changelog entry + +For larger changes or for changes on branches that are more widely used, the commit message should simply reference an entry to some other changelog system. It is encouraged to use some sort of versioning system to log changes. Example commit messages: +``` +superscript.py v 2.0.5.006 +``` + +The format can be described more formally as follows: + +``` + v +``` \ No newline at end of file diff --git a/LICENSE b/LICENSE index f288702d..87c521aa 100644 --- a/LICENSE +++ b/LICENSE @@ -1,674 +1,29 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If the program does terminal interaction, make it output a short -notice like this when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type `show c' for details. - -The hypothetical commands `show w' and `show c' should show the appropriate -parts of the General Public License. Of course, your program's commands -might be different; for a GUI interface, you would use an "about box". - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU GPL, see -. - - The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. +BSD 3-Clause License + +Copyright (c) 2020, Titan Robotics FRC 2022 +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/MAINTAINERS b/MAINTAINERS new file mode 100644 index 00000000..97461781 --- /dev/null +++ b/MAINTAINERS @@ -0,0 +1,3 @@ +Arthur Lu +Jacob Levine +Dev Singh diff --git a/analysis-master/analysis.egg-info/PKG-INFO b/analysis-master/analysis.egg-info/PKG-INFO deleted file mode 100644 index 83058193..00000000 --- a/analysis-master/analysis.egg-info/PKG-INFO +++ /dev/null @@ -1,14 +0,0 @@ -Metadata-Version: 2.1 -Name: analysis -Version: 1.0.0.12 -Summary: analysis package developed by Titan Scouting for The Red Alliance -Home-page: https://github.com/titanscout2022/tr2022-strategy -Author: The Titan Scouting Team -Author-email: titanscout2022@gmail.com -License: GNU General Public License v3.0 -Description: UNKNOWN -Platform: UNKNOWN -Classifier: Programming Language :: Python :: 3 -Classifier: Operating System :: OS Independent -Requires-Python: >=3.6 -Description-Content-Type: text/markdown diff --git a/analysis-master/analysis.egg-info/SOURCES.txt b/analysis-master/analysis.egg-info/SOURCES.txt deleted file mode 100644 index 2d8be231..00000000 --- a/analysis-master/analysis.egg-info/SOURCES.txt +++ /dev/null @@ -1,15 +0,0 @@ -setup.py -analysis/__init__.py -analysis/analysis.py -analysis/regression.py -analysis/titanlearn.py -analysis/visualization.py -analysis.egg-info/PKG-INFO -analysis.egg-info/SOURCES.txt -analysis.egg-info/dependency_links.txt -analysis.egg-info/requires.txt -analysis.egg-info/top_level.txt -analysis/metrics/__init__.py -analysis/metrics/elo.py -analysis/metrics/glicko2.py -analysis/metrics/trueskill.py \ No newline at end of file diff --git a/analysis-master/analysis.egg-info/dependency_links.txt b/analysis-master/analysis.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/analysis-master/analysis.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/analysis-master/analysis.egg-info/requires.txt b/analysis-master/analysis.egg-info/requires.txt deleted file mode 100644 index 6868226f..00000000 --- a/analysis-master/analysis.egg-info/requires.txt +++ /dev/null @@ -1,6 +0,0 @@ -numba -numpy -scipy -scikit-learn -six -matplotlib diff --git a/analysis-master/analysis.egg-info/top_level.txt b/analysis-master/analysis.egg-info/top_level.txt deleted file mode 100644 index 09ad3be3..00000000 --- a/analysis-master/analysis.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -analysis diff --git a/analysis-master/analysis/analysis.py b/analysis-master/analysis/analysis.py index e938cc4f..c4cf49de 100644 --- a/analysis-master/analysis/analysis.py +++ b/analysis-master/analysis/analysis.py @@ -7,10 +7,23 @@ # current benchmark of optimization: 1.33 times faster # setup: -__version__ = "1.2.0.005" +__version__ = "1.2.1.002" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: + 1.2.1.002: + - renamed ArrayTest class to Array + 1.2.1.001: + - added add, mul, neg, and inv functions to ArrayTest class + - added normalize function to ArrayTest class + - added dot and cross functions to ArrayTest class + 1.2.1.000: + - added ArrayTest class + - added elementwise mean, median, standard deviation, variance, min, max functions to ArrayTest class + - added elementwise_stats to ArrayTest which encapsulates elementwise statistics + - appended to __all__ to reflect changes + 1.2.0.006: + - renamed func functions in regression to lin, log, exp, and sig 1.2.0.005: - moved random_forrest_regressor and random_forrest_classifier to RandomForrest class - renamed Metrics to Metric @@ -302,6 +315,7 @@ __all__ = [ 'RandomForrest', 'CorrelationTest', 'StatisticalTest', + 'ArrayTest', # all statistics functions left out due to integration in other functions ] @@ -357,11 +371,11 @@ def z_score(point, mean, stdev): @jit(forceobj=True) def z_normalize(array, *args): - array = np.array(array) - for arg in args: - array = sklearn.preprocessing.normalize(array, axis = arg) + array = np.array(array) + for arg in args: + array = sklearn.preprocessing.normalize(array, axis = arg) - return array + return array @jit(forceobj=True) # expects 2d array of [x,y] @@ -392,11 +406,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array try: - def func(x, a, b): + def lin(x, a, b): return a * x + b - popt, pcov = scipy.optimize.curve_fit(func, X, y) + popt, pcov = scipy.optimize.curve_fit(lin, X, y) regressions.append((popt.flatten().tolist(), None)) @@ -408,11 +422,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array try: - def func(x, a, b, c, d): + def log(x, a, b, c, d): return a * np.log(b*(x + c)) + d - popt, pcov = scipy.optimize.curve_fit(func, X, y) + popt, pcov = scipy.optimize.curve_fit(log, X, y) regressions.append((popt.flatten().tolist(), None)) @@ -424,11 +438,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array try: - def func(x, a, b, c, d): + def exp(x, a, b, c, d): return a * np.exp(b*(x + c)) + d - popt, pcov = scipy.optimize.curve_fit(func, X, y) + popt, pcov = scipy.optimize.curve_fit(exp, X, y) regressions.append((popt.flatten().tolist(), None)) @@ -463,11 +477,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array try: - def func(x, a, b, c, d): + def sig(x, a, b, c, d): return a * np.tanh(b*(x + c)) + d - popt, pcov = scipy.optimize.curve_fit(func, X, y) + popt, pcov = scipy.optimize.curve_fit(sig, X, y) regressions.append((popt.flatten().tolist(), None)) @@ -929,4 +943,81 @@ class StatisticalTest: def normaltest(self, a, axis = 0, nan_policy = 'propogate'): results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} \ No newline at end of file + return {"z-score": results[0], "p-value": results[1]} + +class Array(): # tests on nd arrays independent of basic_stats + + def elementwise_mean(self, *args): # expects arrays that are size normalized + + return np.mean([*args], axis = 0) + + def elementwise_median(self, *args): + + return np.median([*args], axis = 0) + + def elementwise_stdev(self, *args): + + return np.std([*args], axis = 0) + + def elementwise_variance(self, *args): + + return np.var([*args], axis = 0) + + def elementwise_npmin(self, *args): + + return np.amin([*args], axis = 0) + + def elementwise_npmax(self, *args): + + return np.amax([*args], axis = 0) + + def elementwise_stats(self, *args): + + _mean = self.elementwise_mean(*args) + _median = self.elementwise_median(*args) + _stdev = self.elementwise_stdev(*args) + _variance = self.elementwise_variance(*args) + _min = self.elementwise_npmin(*args) + _max = self.elementwise_npmax(*args) + + return _mean, _median, _stdev, _variance, _min, _max + + def normalize(self, array): + + a = np.atleast_1d(np.linalg.norm(array)) + a[a==0] = 1 + return array / np.expand_dims(a, -1) + + def add(self, *args): + + temp = np.array([]) + + for a in args: + temp += a + + return temp + + def mul(self, *args): + + temp = np.array([]) + + for a in args: + temp *= a + + return temp + + def neg(self, array): + + return -array + + def inv(self, array): + + return 1/array + + def dot(self, a, b): + + return np.dot(a, b) + + def cross(self, a, b): + + return np.cross(a, b) \ No newline at end of file diff --git a/analysis-master/analysis/metrics/__pycache__/__init__.cpython-37.pyc b/analysis-master/analysis/metrics/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 00000000..2e4375f0 Binary files /dev/null and b/analysis-master/analysis/metrics/__pycache__/__init__.cpython-37.pyc differ diff --git a/analysis-master/analysis/metrics/__pycache__/elo.cpython-37.pyc b/analysis-master/analysis/metrics/__pycache__/elo.cpython-37.pyc new file mode 100644 index 00000000..7b83047f Binary files /dev/null and b/analysis-master/analysis/metrics/__pycache__/elo.cpython-37.pyc differ diff --git a/analysis-master/analysis/metrics/__pycache__/glicko2.cpython-37.pyc b/analysis-master/analysis/metrics/__pycache__/glicko2.cpython-37.pyc new file mode 100644 index 00000000..10f44375 Binary files /dev/null and b/analysis-master/analysis/metrics/__pycache__/glicko2.cpython-37.pyc differ diff --git a/analysis-master/analysis/metrics/__pycache__/trueskill.cpython-37.pyc b/analysis-master/analysis/metrics/__pycache__/trueskill.cpython-37.pyc new file mode 100644 index 00000000..c151e0ba Binary files /dev/null and b/analysis-master/analysis/metrics/__pycache__/trueskill.cpython-37.pyc differ diff --git a/analysis-master/analysis/visualization.py b/analysis-master/analysis/visualization.py index 0d52c0f5..231a3a05 100644 --- a/analysis-master/analysis/visualization.py +++ b/analysis-master/analysis/visualization.py @@ -6,10 +6,12 @@ # fancy # setup: -__version__ = "1.0.0.000" +__version__ = "1.0.0.001" #changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: + 1.0.0.001: + - added graphhistogram function as a fragment of visualize_pit.py 1.0.0.000: - created visualization.py - added graphloss() @@ -26,9 +28,31 @@ __all__ = [ ] import matplotlib.pyplot as plt +import numpy as np def graphloss(losses): x = range(0, len(losses)) plt.plot(x, losses) + plt.show() + +def graphhistogram(data, figsize, sharey = True): # expects library with key as variable and contents as occurances + + fig, ax = plt.subplots(1, len(data), sharey=sharey, figsize=figsize) + + i = 0 + + for variable in data: + + ax[i].hist(data[variable]) + ax[i].invert_xaxis() + + ax[i].set_xlabel('Variable') + ax[i].set_ylabel('Frequency') + ax[i].set_title(variable) + + plt.yticks(np.arange(len(data[variable]))) + + i+=1 + plt.show() \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/__init__.py b/analysis-master/build/lib/analysis/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/analysis-master/build/lib/analysis/analysis.py b/analysis-master/build/lib/analysis/analysis.py deleted file mode 100644 index c13aef90..00000000 --- a/analysis-master/build/lib/analysis/analysis.py +++ /dev/null @@ -1,923 +0,0 @@ -# Titan Robotics Team 2022: Data Analysis Module -# Written by Arthur Lu & Jacob Levine -# Notes: -# this should be imported as a python module using 'from analysis import analysis' -# this should be included in the local directory or environment variable -# this module has been optimized for multhreaded computing -# current benchmark of optimization: 1.33 times faster -# setup: - -__version__ = "1.2.0.004" - -# changelog should be viewed using print(analysis.__changelog__) -__changelog__ = """changelog: - 1.2.0.004: - - fixed __all__ to reflected the correct functions and classes - - fixed CorrelationTests and StatisticalTests class functions to require self invocation - - added missing math import - - fixed KNN class functions to require self invocation - - fixed Metrics class functions to require self invocation - - various spelling fixes in CorrelationTests and StatisticalTests - 1.2.0.003: - - bug fixes with CorrelationTests and StatisticalTests - - moved glicko2 and trueskill to the metrics subpackage - - moved elo to a new metrics subpackage - 1.2.0.002: - - fixed docs - 1.2.0.001: - - fixed docs - 1.2.0.000: - - cleaned up wild card imports with scipy and sklearn - - added CorrelationTests class - - added StatisticalTests class - - added several correlation tests to CorrelationTests - - added several statistical tests to StatisticalTests - 1.1.13.009: - - moved elo, glicko2, trueskill functions under class Metrics - 1.1.13.008: - - moved Glicko2 to a seperate package - 1.1.13.007: - - fixed bug with trueskill - 1.1.13.006: - - cleaned up imports - 1.1.13.005: - - cleaned up package - 1.1.13.004: - - small fixes to regression to improve performance - 1.1.13.003: - - filtered nans from regression - 1.1.13.002: - - removed torch requirement, and moved Regression back to regression.py - 1.1.13.001: - - bug fix with linear regression not returning a proper value - - cleaned up regression - - fixed bug with polynomial regressions - 1.1.13.000: - - fixed all regressions to now properly work - 1.1.12.006: - - fixed bg with a division by zero in histo_analysis - 1.1.12.005: - - fixed numba issues by removing numba from elo, glicko2 and trueskill - 1.1.12.004: - - renamed gliko to glicko - 1.1.12.003: - - removed depreciated code - 1.1.12.002: - - removed team first time trueskill instantiation in favor of integration in superscript.py - 1.1.12.001: - - improved readibility of regression outputs by stripping tensor data - - used map with lambda to acheive the improved readibility - - lost numba jit support with regression, and generated_jit hangs at execution - - TODO: reimplement correct numba integration in regression - 1.1.12.000: - - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures - 1.1.11.010: - - alphabeticaly ordered import lists - 1.1.11.009: - - bug fixes - 1.1.11.008: - - bug fixes - 1.1.11.007: - - bug fixes - 1.1.11.006: - - tested min and max - - bug fixes - 1.1.11.005: - - added min and max in basic_stats - 1.1.11.004: - - bug fixes - 1.1.11.003: - - bug fixes - 1.1.11.002: - - consolidated metrics - - fixed __all__ - 1.1.11.001: - - added test/train split to RandomForestClassifier and RandomForestRegressor - 1.1.11.000: - - added RandomForestClassifier and RandomForestRegressor - - note: untested - 1.1.10.000: - - added numba.jit to remaining functions - 1.1.9.002: - - kernelized PCA and KNN - 1.1.9.001: - - fixed bugs with SVM and NaiveBayes - 1.1.9.000: - - added SVM class, subclasses, and functions - - note: untested - 1.1.8.000: - - added NaiveBayes classification engine - - note: untested - 1.1.7.000: - - added knn() - - added confusion matrix to decisiontree() - 1.1.6.002: - - changed layout of __changelog to be vscode friendly - 1.1.6.001: - - added additional hyperparameters to decisiontree() - 1.1.6.000: - - fixed __version__ - - fixed __all__ order - - added decisiontree() - 1.1.5.003: - - added pca - 1.1.5.002: - - reduced import list - - added kmeans clustering engine - 1.1.5.001: - - simplified regression by using .to(device) - 1.1.5.000: - - added polynomial regression to regression(); untested - 1.1.4.000: - - added trueskill() - 1.1.3.002: - - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2 - 1.1.3.001: - - changed glicko2() to return tuple instead of array - 1.1.3.000: - - added glicko2_engine class and glicko() - - verified glicko2() accuracy - 1.1.2.003: - - fixed elo() - 1.1.2.002: - - added elo() - - elo() has bugs to be fixed - 1.1.2.001: - - readded regrression import - 1.1.2.000: - - integrated regression.py as regression class - - removed regression import - - fixed metadata for regression class - - fixed metadata for analysis class - 1.1.1.001: - - regression_engine() bug fixes, now actaully regresses - 1.1.1.000: - - added regression_engine() - - added all regressions except polynomial - 1.1.0.007: - - updated _init_device() - 1.1.0.006: - - removed useless try statements - 1.1.0.005: - - removed impossible outcomes - 1.1.0.004: - - added performance metrics (r^2, mse, rms) - 1.1.0.003: - - resolved nopython mode for mean, median, stdev, variance - 1.1.0.002: - - snapped (removed) majority of uneeded imports - - forced object mode (bad) on all jit - - TODO: stop numba complaining about not being able to compile in nopython mode - 1.1.0.001: - - removed from sklearn import * to resolve uneeded wildcard imports - 1.1.0.000: - - removed c_entities,nc_entities,obstacles,objectives from __all__ - - applied numba.jit to all functions - - depreciated and removed stdev_z_split - - cleaned up histo_analysis to include numpy and numba.jit optimizations - - depreciated and removed all regression functions in favor of future pytorch optimizer - - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data) - - optimized z_normalize using sklearn.preprocessing.normalize - - TODO: implement kernel/function based pytorch regression optimizer - 1.0.9.000: - - refactored - - numpyed everything - - removed stats in favor of numpy functions - 1.0.8.005: - - minor fixes - 1.0.8.004: - - removed a few unused dependencies - 1.0.8.003: - - added p_value function - 1.0.8.002: - - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001 - 1.0.8.001: - - refactors - - bugfixes - 1.0.8.000: - - depreciated histo_analysis_old - - depreciated debug - - altered basic_analysis to take array data instead of filepath - - refactor - - optimization - 1.0.7.002: - - bug fixes - 1.0.7.001: - - bug fixes - 1.0.7.000: - - added tanh_regression (logistical regression) - - bug fixes - 1.0.6.005: - - added z_normalize function to normalize dataset - - bug fixes - 1.0.6.004: - - bug fixes - 1.0.6.003: - - bug fixes - 1.0.6.002: - - bug fixes - 1.0.6.001: - - corrected __all__ to contain all of the functions - 1.0.6.000: - - added calc_overfit, which calculates two measures of overfit, error and performance - - added calculating overfit to optimize_regression - 1.0.5.000: - - added optimize_regression function, which is a sample function to find the optimal regressions - - optimize_regression function filters out some overfit funtions (functions with r^2 = 1) - - planned addition: overfit detection in the optimize_regression function - 1.0.4.002: - - added __changelog__ - - updated debug function with log and exponential regressions - 1.0.4.001: - - added log regressions - - added exponential regressions - - added log_regression and exp_regression to __all__ - 1.0.3.008: - - added debug function to further consolidate functions - 1.0.3.007: - - added builtin benchmark function - - added builtin random (linear) data generation function - - added device initialization (_init_device) - 1.0.3.006: - - reorganized the imports list to be in alphabetical order - - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives - 1.0.3.005: - - major bug fixes - - updated historical analysis - - depreciated old historical analysis - 1.0.3.004: - - added __version__, __author__, __all__ - - added polynomial regression - - added root mean squared function - - added r squared function - 1.0.3.003: - - bug fixes - - added c_entities - 1.0.3.002: - - bug fixes - - added nc_entities, obstacles, objectives - - consolidated statistics.py to analysis.py - 1.0.3.001: - - compiled 1d, column, and row basic stats into basic stats function - 1.0.3.000: - - added historical analysis function - 1.0.2.xxx: - - added z score test - 1.0.1.xxx: - - major bug fixes - 1.0.0.xxx: - - added loading csv - - added 1d, column, row basic stats -""" - -__author__ = ( - "Arthur Lu ", - "Jacob Levine ", -) - -__all__ = [ - 'load_csv', - 'basic_stats', - 'z_score', - 'z_normalize', - 'histo_analysis', - 'regression', - 'Metrics', - 'RegressionMetrics', - 'ClassificationMetrics', - 'kmeans', - 'pca', - 'decisiontree', - 'KNN', - 'NaiveBayes', - 'SVM', - 'random_forest_classifier', - 'random_forest_regressor', - 'CorrelationTests', - 'StatisticalTests', - # all statistics functions left out due to integration in other functions -] - -# now back to your regularly scheduled programming: - -# imports (now in alphabetical order! v 1.0.3.006): - -import csv -from analysis.metrics import elo as Elo -from analysis.metrics import glicko2 as Glicko2 -import math -import numba -from numba import jit -import numpy as np -import scipy -from scipy import optimize, stats -import sklearn -from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble -from analysis.metrics import trueskill as Trueskill - -class error(ValueError): - pass - -def load_csv(filepath): - with open(filepath, newline='') as csvfile: - file_array = np.array(list(csv.reader(csvfile))) - csvfile.close() - return file_array - -# expects 1d array -@jit(forceobj=True) -def basic_stats(data): - - data_t = np.array(data).astype(float) - - _mean = mean(data_t) - _median = median(data_t) - _stdev = stdev(data_t) - _variance = variance(data_t) - _min = npmin(data_t) - _max = npmax(data_t) - - return _mean, _median, _stdev, _variance, _min, _max - -# returns z score with inputs of point, mean and standard deviation of spread -@jit(forceobj=True) -def z_score(point, mean, stdev): - score = (point - mean) / stdev - - return score - -# expects 2d array, normalizes across all axes -@jit(forceobj=True) -def z_normalize(array, *args): - - array = np.array(array) - for arg in args: - array = sklearn.preprocessing.normalize(array, axis = arg) - - return array - -@jit(forceobj=True) -# expects 2d array of [x,y] -def histo_analysis(hist_data): - - if(len(hist_data[0]) > 2): - - hist_data = np.array(hist_data) - derivative = np.array(len(hist_data) - 1, dtype = float) - t = np.diff(hist_data) - derivative = t[1] / t[0] - np.sort(derivative) - - return basic_stats(derivative)[0], basic_stats(derivative)[3] - - else: - - return None - -def regression(inputs, outputs, args): # inputs, outputs expects N-D array - - X = np.array(inputs) - y = np.array(outputs) - - regressions = [] - - if 'lin' in args: # formula: ax + b - - try: - - def func(x, a, b): - - return a * x + b - - popt, pcov = scipy.optimize.curve_fit(func, X, y) - - regressions.append((popt.flatten().tolist(), None)) - - except Exception as e: - - pass - - if 'log' in args: # formula: a log (b(x + c)) + d - - try: - - def func(x, a, b, c, d): - - return a * np.log(b*(x + c)) + d - - popt, pcov = scipy.optimize.curve_fit(func, X, y) - - regressions.append((popt.flatten().tolist(), None)) - - except Exception as e: - - pass - - if 'exp' in args: # formula: a e ^ (b(x + c)) + d - - try: - - def func(x, a, b, c, d): - - return a * np.exp(b*(x + c)) + d - - popt, pcov = scipy.optimize.curve_fit(func, X, y) - - regressions.append((popt.flatten().tolist(), None)) - - except Exception as e: - - pass - - if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ... - - inputs = np.array([inputs]) - outputs = np.array([outputs]) - - plys = [] - limit = len(outputs[0]) - - for i in range(2, limit): - - model = sklearn.preprocessing.PolynomialFeatures(degree = i) - model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression()) - model = model.fit(np.rot90(inputs), np.rot90(outputs)) - - params = model.steps[1][1].intercept_.tolist() - params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::]) - params.flatten() - params = params.tolist() - - plys.append(params) - - regressions.append(plys) - - if 'sig' in args: # formula: a tanh (b(x + c)) + d - - try: - - def func(x, a, b, c, d): - - return a * np.tanh(b*(x + c)) + d - - popt, pcov = scipy.optimize.curve_fit(func, X, y) - - regressions.append((popt.flatten().tolist(), None)) - - except Exception as e: - - pass - - return regressions - -class Metrics: - - def elo(self, starting_score, opposing_score, observed, N, K): - - return Elo.calculate(starting_score, opposing_score, observed, N, K) - - def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations): - - player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol) - - player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations) - - return (player.rating, player.rd, player.vol) - - def trueskill(self, teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]] - - team_ratings = [] - - for team in teams_data: - team_temp = () - for player in team: - player = Trueskill.Rating(player[0], player[1]) - team_temp = team_temp + (player,) - team_ratings.append(team_temp) - - return Trueskill.rate(team_ratings, ranks=observations) - -class RegressionMetrics(): - - def __new__(cls, predictions, targets): - - return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets) - - def r_squared(self, predictions, targets): # assumes equal size inputs - - return sklearn.metrics.r2_score(targets, predictions) - - def mse(self, predictions, targets): - - return sklearn.metrics.mean_squared_error(targets, predictions) - - def rms(self, predictions, targets): - - return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) - -class ClassificationMetrics(): - - def __new__(cls, predictions, targets): - - return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets) - - def cm(self, predictions, targets): - - return sklearn.metrics.confusion_matrix(targets, predictions) - - def cr(self, predictions, targets): - - return sklearn.metrics.classification_report(targets, predictions) - -@jit(nopython=True) -def mean(data): - - return np.mean(data) - -@jit(nopython=True) -def median(data): - - return np.median(data) - -@jit(nopython=True) -def stdev(data): - - return np.std(data) - -@jit(nopython=True) -def variance(data): - - return np.var(data) - -@jit(nopython=True) -def npmin(data): - - return np.amin(data) - -@jit(nopython=True) -def npmax(data): - - return np.amax(data) - -@jit(forceobj=True) -def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): - - kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) - kernel.fit(data) - predictions = kernel.predict(data) - centers = kernel.cluster_centers_ - - return centers, predictions - -@jit(forceobj=True) -def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): - - kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state) - - return kernel.fit_transform(data) - -@jit(forceobj=True) -def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth) - model = model.fit(data_train,labels_train) - predictions = model.predict(data_test) - metrics = ClassificationMetrics(predictions, labels_test) - - return model, metrics - -class KNN: - - def knn_classifier(self, data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.neighbors.KNeighborsClassifier() - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetrics(predictions, labels_test) - - def knn_regressor(self, data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): - - data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) - model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) - model.fit(data_train, outputs_train) - predictions = model.predict(data_test) - - return model, RegressionMetrics(predictions, outputs_test) - -class NaiveBayes: - - def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetrics(predictions, labels_test) - - def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetrics(predictions, labels_test) - - def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetrics(predictions, labels_test) - - def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetrics(predictions, labels_test) - -class SVM: - - class CustomKernel: - - def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): - - return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) - - class StandardKernel: - - def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): - - return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) - - class PrebuiltKernel: - - class Linear: - - def __new__(cls): - - return sklearn.svm.SVC(kernel = 'linear') - - class Polynomial: - - def __new__(cls, power, r_bias): - - return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) - - class RBF: - - def __new__(cls, gamma): - - return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) - - class Sigmoid: - - def __new__(cls, r_bias): - - return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) - - def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs - - return kernel.fit(train_data, train_outputs) - - def eval_classification(self, kernel, test_data, test_outputs): - - predictions = kernel.predict(test_data) - - return ClassificationMetrics(predictions, test_outputs) - - def eval_regression(self, kernel, test_data, test_outputs): - - predictions = kernel.predict(test_data) - - return RegressionMetrics(predictions, test_outputs) - -def random_forest_classifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) - kernel.fit(data_train, labels_train) - predictions = kernel.predict(data_test) - - return kernel, ClassificationMetrics(predictions, labels_test) - -def random_forest_regressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): - - data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) - kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) - kernel.fit(data_train, outputs_train) - predictions = kernel.predict(data_test) - - return kernel, RegressionMetrics(predictions, outputs_test) - -class CorrelationTests: - - def anova_oneway(self, *args): #expects arrays of samples - - results = scipy.stats.f_oneway(*args) - return {"F-value": results[0], "p-value": results[1]} - - def pearson(self, x, y): - - results = scipy.stats.pearsonr(x, y) - return {"r-value": results[0], "p-value": results[1]} - - def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'): - - results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) - return {"r-value": results[0], "p-value": results[1]} - - def point_biserial(self, x,y): - - results = scipy.stats.pointbiserialr(x, y) - return {"r-value": results[0], "p-value": results[1]} - - def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): - - results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method) - return {"tau": results[0], "p-value": results[1]} - - def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True): - - results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) - return {"tau": results[0], "p-value": results[1]} - - def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): - - results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state) - return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value - -class StatisticalTests: - - def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'): - - results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy) - return {"t-value": results[0], "p-value": results[1]} - - def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'): - - results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy) - return {"t-value": results[0], "p-value": results[1]} - - def ttest_statistic(self, o1, o2, equal = True): - - results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal) - return {"t-value": results[0], "p-value": results[1]} - - def ttest_related(self, a, b, axis = 0, nan_policy='propagate'): - - results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy) - return {"t-value": results[0], "p-value": results[1]} - - def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'): - - results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode) - return {"ks-value": results[0], "p-value": results[1]} - - def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0): - - results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis) - return {"chisquared-value": results[0], "p-value": results[1]} - - def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None): - - results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_) - return {"powerdivergence-value": results[0], "p-value": results[1]} - - def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'): - - results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode) - return {"ks-value": results[0], "p-value": results[1]} - - def es_twosample(self, x, y, t = (0.4, 0.8)): - - results = scipy.stats.epps_singleton_2samp(x, y, t = t) - return {"es-value": results[0], "p-value": results[1]} - - def mw_rank(self, x, y, use_continuity = True, alternative = None): - - results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative) - return {"u-value": results[0], "p-value": results[1]} - - def mw_tiecorrection(self, rank_values): - - results = scipy.stats.tiecorrect(rank_values) - return {"correction-factor": results} - - def rankdata(self, a, method = 'average'): - - results = scipy.stats.rankdata(a, method = method) - return results - - def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test - - results = scipy.stats.ranksums(a, b) - return {"u-value": results[0], "p-value": results[1]} - - def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'): - - results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative) - return {"t-value": results[0], "p-value": results[1]} - - def kw_htest(self, *args, nan_policy = 'propagate'): - - results = scipy.stats.kruskal(*args, nan_policy = nan_policy) - return {"h-value": results[0], "p-value": results[1]} - - def friedman_chisquare(self, *args): - - results = scipy.stats.friedmanchisquare(*args) - return {"chisquared-value": results[0], "p-value": results[1]} - - def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'): - - results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy) - return {"w-value": results[0], "p-value": results[1]} - - def combine_pvalues(self, pvalues, method = 'fisher', weights = None): - - results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights) - return {"combined-statistic": results[0], "p-value": results[1]} - - def jb_fitness(self, x): - - results = scipy.stats.jarque_bera(x) - return {"jb-value": results[0], "p-value": results[1]} - - def ab_equality(self, x, y): - - results = scipy.stats.ansari(x, y) - return {"ab-value": results[0], "p-value": results[1]} - - def bartlett_variance(self, *args): - - results = scipy.stats.bartlett(*args) - return {"t-value": results[0], "p-value": results[1]} - - def levene_variance(self, *args, center = 'median', proportiontocut = 0.05): - - results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut) - return {"w-value": results[0], "p-value": results[1]} - - def sw_normality(self, x): - - results = scipy.stats.shapiro(x) - return {"w-value": results[0], "p-value": results[1]} - - def shapiro(self, x): - - return "destroyed by facts and logic" - - def ad_onesample(self, x, dist = 'norm'): - - results = scipy.stats.anderson(x, dist = dist) - return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} - - def ad_ksample(self, samples, midrank = True): - - results = scipy.stats.anderson_ksamp(samples, midrank = midrank) - return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} - - def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'): - - results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative) - return {"p-value": results} - - def fk_variance(self, *args, center = 'median', proportiontocut = 0.05): - - results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut) - return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value - - def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'): - - results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy) - return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]} - - def mood_equalscale(self, x, y, axis = 0): - - results = scipy.stats.mood(x, y, axis = axis) - return {"z-score": results[0], "p-value": results[1]} - - def skewtest(self, a, axis = 0, nan_policy = 'propogate'): - - results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} - - def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'): - - results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} - - def normaltest(self, a, axis = 0, nan_policy = 'propogate'): - - results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/glicko2.py b/analysis-master/build/lib/analysis/glicko2.py deleted file mode 100644 index 66c0df94..00000000 --- a/analysis-master/build/lib/analysis/glicko2.py +++ /dev/null @@ -1,99 +0,0 @@ -import math - -class Glicko2: - _tau = 0.5 - - def getRating(self): - return (self.__rating * 173.7178) + 1500 - - def setRating(self, rating): - self.__rating = (rating - 1500) / 173.7178 - - rating = property(getRating, setRating) - - def getRd(self): - return self.__rd * 173.7178 - - def setRd(self, rd): - self.__rd = rd / 173.7178 - - rd = property(getRd, setRd) - - def __init__(self, rating = 1500, rd = 350, vol = 0.06): - - self.setRating(rating) - self.setRd(rd) - self.vol = vol - - def _preRatingRD(self): - - self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2)) - - def update_player(self, rating_list, RD_list, outcome_list): - - rating_list = [(x - 1500) / 173.7178 for x in rating_list] - RD_list = [x / 173.7178 for x in RD_list] - - v = self._v(rating_list, RD_list) - self.vol = self._newVol(rating_list, RD_list, outcome_list, v) - self._preRatingRD() - - self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v)) - - tempSum = 0 - for i in range(len(rating_list)): - tempSum += self._g(RD_list[i]) * \ - (outcome_list[i] - self._E(rating_list[i], RD_list[i])) - self.__rating += math.pow(self.__rd, 2) * tempSum - - - def _newVol(self, rating_list, RD_list, outcome_list, v): - - i = 0 - delta = self._delta(rating_list, RD_list, outcome_list, v) - a = math.log(math.pow(self.vol, 2)) - tau = self._tau - x0 = a - x1 = 0 - - while x0 != x1: - # New iteration, so x(i) becomes x(i-1) - x0 = x1 - d = math.pow(self.__rating, 2) + v + math.exp(x0) - h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \ - / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2) - h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \ - (math.pow(self.__rating, 2) + v) \ - / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \ - * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3) - x1 = x0 - (h1 / h2) - - return math.exp(x1 / 2) - - def _delta(self, rating_list, RD_list, outcome_list, v): - - tempSum = 0 - for i in range(len(rating_list)): - tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i])) - return v * tempSum - - def _v(self, rating_list, RD_list): - - tempSum = 0 - for i in range(len(rating_list)): - tempE = self._E(rating_list[i], RD_list[i]) - tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE) - return 1 / tempSum - - def _E(self, p2rating, p2RD): - - return 1 / (1 + math.exp(-1 * self._g(p2RD) * \ - (self.__rating - p2rating))) - - def _g(self, RD): - - return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2)) - - def did_not_compete(self): - - self._preRatingRD() \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/metrics/__init__.py b/analysis-master/build/lib/analysis/metrics/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/analysis-master/build/lib/analysis/metrics/elo.py b/analysis-master/build/lib/analysis/metrics/elo.py deleted file mode 100644 index 3c8ef2e0..00000000 --- a/analysis-master/build/lib/analysis/metrics/elo.py +++ /dev/null @@ -1,7 +0,0 @@ -import numpy as np - -def calculate(starting_score, opposing_score, observed, N, K): - - expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N)) - - return starting_score + K*(np.sum(observed) - np.sum(expected)) \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/metrics/glicko2.py b/analysis-master/build/lib/analysis/metrics/glicko2.py deleted file mode 100644 index 66c0df94..00000000 --- a/analysis-master/build/lib/analysis/metrics/glicko2.py +++ /dev/null @@ -1,99 +0,0 @@ -import math - -class Glicko2: - _tau = 0.5 - - def getRating(self): - return (self.__rating * 173.7178) + 1500 - - def setRating(self, rating): - self.__rating = (rating - 1500) / 173.7178 - - rating = property(getRating, setRating) - - def getRd(self): - return self.__rd * 173.7178 - - def setRd(self, rd): - self.__rd = rd / 173.7178 - - rd = property(getRd, setRd) - - def __init__(self, rating = 1500, rd = 350, vol = 0.06): - - self.setRating(rating) - self.setRd(rd) - self.vol = vol - - def _preRatingRD(self): - - self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2)) - - def update_player(self, rating_list, RD_list, outcome_list): - - rating_list = [(x - 1500) / 173.7178 for x in rating_list] - RD_list = [x / 173.7178 for x in RD_list] - - v = self._v(rating_list, RD_list) - self.vol = self._newVol(rating_list, RD_list, outcome_list, v) - self._preRatingRD() - - self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v)) - - tempSum = 0 - for i in range(len(rating_list)): - tempSum += self._g(RD_list[i]) * \ - (outcome_list[i] - self._E(rating_list[i], RD_list[i])) - self.__rating += math.pow(self.__rd, 2) * tempSum - - - def _newVol(self, rating_list, RD_list, outcome_list, v): - - i = 0 - delta = self._delta(rating_list, RD_list, outcome_list, v) - a = math.log(math.pow(self.vol, 2)) - tau = self._tau - x0 = a - x1 = 0 - - while x0 != x1: - # New iteration, so x(i) becomes x(i-1) - x0 = x1 - d = math.pow(self.__rating, 2) + v + math.exp(x0) - h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \ - / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2) - h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \ - (math.pow(self.__rating, 2) + v) \ - / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \ - * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3) - x1 = x0 - (h1 / h2) - - return math.exp(x1 / 2) - - def _delta(self, rating_list, RD_list, outcome_list, v): - - tempSum = 0 - for i in range(len(rating_list)): - tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i])) - return v * tempSum - - def _v(self, rating_list, RD_list): - - tempSum = 0 - for i in range(len(rating_list)): - tempE = self._E(rating_list[i], RD_list[i]) - tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE) - return 1 / tempSum - - def _E(self, p2rating, p2RD): - - return 1 / (1 + math.exp(-1 * self._g(p2RD) * \ - (self.__rating - p2rating))) - - def _g(self, RD): - - return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2)) - - def did_not_compete(self): - - self._preRatingRD() \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/metrics/trueskill.py b/analysis-master/build/lib/analysis/metrics/trueskill.py deleted file mode 100644 index 116357df..00000000 --- a/analysis-master/build/lib/analysis/metrics/trueskill.py +++ /dev/null @@ -1,907 +0,0 @@ -from __future__ import absolute_import - -from itertools import chain -import math - -from six import iteritems -from six.moves import map, range, zip -from six import iterkeys - -import copy -try: - from numbers import Number -except ImportError: - Number = (int, long, float, complex) - -inf = float('inf') - -class Gaussian(object): - #: Precision, the inverse of the variance. - pi = 0 - #: Precision adjusted mean, the precision multiplied by the mean. - tau = 0 - - def __init__(self, mu=None, sigma=None, pi=0, tau=0): - if mu is not None: - if sigma is None: - raise TypeError('sigma argument is needed') - elif sigma == 0: - raise ValueError('sigma**2 should be greater than 0') - pi = sigma ** -2 - tau = pi * mu - self.pi = pi - self.tau = tau - - @property - def mu(self): - return self.pi and self.tau / self.pi - - @property - def sigma(self): - return math.sqrt(1 / self.pi) if self.pi else inf - - def __mul__(self, other): - pi, tau = self.pi + other.pi, self.tau + other.tau - return Gaussian(pi=pi, tau=tau) - - def __truediv__(self, other): - pi, tau = self.pi - other.pi, self.tau - other.tau - return Gaussian(pi=pi, tau=tau) - - __div__ = __truediv__ # for Python 2 - - def __eq__(self, other): - return self.pi == other.pi and self.tau == other.tau - - def __lt__(self, other): - return self.mu < other.mu - - def __le__(self, other): - return self.mu <= other.mu - - def __gt__(self, other): - return self.mu > other.mu - - def __ge__(self, other): - return self.mu >= other.mu - - def __repr__(self): - return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma) - - def _repr_latex_(self): - latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma) - return '$%s$' % latex - -class Matrix(list): - def __init__(self, src, height=None, width=None): - if callable(src): - f, src = src, {} - size = [height, width] - if not height: - def set_height(height): - size[0] = height - size[0] = set_height - if not width: - def set_width(width): - size[1] = width - size[1] = set_width - try: - for (r, c), val in f(*size): - src[r, c] = val - except TypeError: - raise TypeError('A callable src must return an interable ' - 'which generates a tuple containing ' - 'coordinate and value') - height, width = tuple(size) - if height is None or width is None: - raise TypeError('A callable src must call set_height and ' - 'set_width if the size is non-deterministic') - if isinstance(src, list): - is_number = lambda x: isinstance(x, Number) - unique_col_sizes = set(map(len, src)) - everything_are_number = filter(is_number, sum(src, [])) - if len(unique_col_sizes) != 1 or not everything_are_number: - raise ValueError('src must be a rectangular array of numbers') - two_dimensional_array = src - elif isinstance(src, dict): - if not height or not width: - w = h = 0 - for r, c in iterkeys(src): - if not height: - h = max(h, r + 1) - if not width: - w = max(w, c + 1) - if not height: - height = h - if not width: - width = w - two_dimensional_array = [] - for r in range(height): - row = [] - two_dimensional_array.append(row) - for c in range(width): - row.append(src.get((r, c), 0)) - else: - raise TypeError('src must be a list or dict or callable') - super(Matrix, self).__init__(two_dimensional_array) - - @property - def height(self): - return len(self) - - @property - def width(self): - return len(self[0]) - - def transpose(self): - height, width = self.height, self.width - src = {} - for c in range(width): - for r in range(height): - src[c, r] = self[r][c] - return type(self)(src, height=width, width=height) - - def minor(self, row_n, col_n): - height, width = self.height, self.width - if not (0 <= row_n < height): - raise ValueError('row_n should be between 0 and %d' % height) - elif not (0 <= col_n < width): - raise ValueError('col_n should be between 0 and %d' % width) - two_dimensional_array = [] - for r in range(height): - if r == row_n: - continue - row = [] - two_dimensional_array.append(row) - for c in range(width): - if c == col_n: - continue - row.append(self[r][c]) - return type(self)(two_dimensional_array) - - def determinant(self): - height, width = self.height, self.width - if height != width: - raise ValueError('Only square matrix can calculate a determinant') - tmp, rv = copy.deepcopy(self), 1. - for c in range(width - 1, 0, -1): - pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1)) - pivot = tmp[r][c] - if not pivot: - return 0. - tmp[r], tmp[c] = tmp[c], tmp[r] - if r != c: - rv = -rv - rv *= pivot - fact = -1. / pivot - for r in range(c): - f = fact * tmp[r][c] - for x in range(c): - tmp[r][x] += f * tmp[c][x] - return rv * tmp[0][0] - - def adjugate(self): - height, width = self.height, self.width - if height != width: - raise ValueError('Only square matrix can be adjugated') - if height == 2: - a, b = self[0][0], self[0][1] - c, d = self[1][0], self[1][1] - return type(self)([[d, -b], [-c, a]]) - src = {} - for r in range(height): - for c in range(width): - sign = -1 if (r + c) % 2 else 1 - src[r, c] = self.minor(r, c).determinant() * sign - return type(self)(src, height, width) - - def inverse(self): - if self.height == self.width == 1: - return type(self)([[1. / self[0][0]]]) - return (1. / self.determinant()) * self.adjugate() - - def __add__(self, other): - height, width = self.height, self.width - if (height, width) != (other.height, other.width): - raise ValueError('Must be same size') - src = {} - for r in range(height): - for c in range(width): - src[r, c] = self[r][c] + other[r][c] - return type(self)(src, height, width) - - def __mul__(self, other): - if self.width != other.height: - raise ValueError('Bad size') - height, width = self.height, other.width - src = {} - for r in range(height): - for c in range(width): - src[r, c] = sum(self[r][x] * other[x][c] - for x in range(self.width)) - return type(self)(src, height, width) - - def __rmul__(self, other): - if not isinstance(other, Number): - raise TypeError('The operand should be a number') - height, width = self.height, self.width - src = {} - for r in range(height): - for c in range(width): - src[r, c] = other * self[r][c] - return type(self)(src, height, width) - - def __repr__(self): - return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__()) - - def _repr_latex_(self): - rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self] - latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows) - return '$%s$' % latex - -def _gen_erfcinv(erfc, math=math): - def erfcinv(y): - """The inverse function of erfc.""" - if y >= 2: - return -100. - elif y <= 0: - return 100. - zero_point = y < 1 - if not zero_point: - y = 2 - y - t = math.sqrt(-2 * math.log(y / 2.)) - x = -0.70711 * \ - ((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t) - for i in range(2): - err = erfc(x) - y - x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err) - return x if zero_point else -x - return erfcinv - -def _gen_ppf(erfc, math=math): - erfcinv = _gen_erfcinv(erfc, math) - def ppf(x, mu=0, sigma=1): - return mu - sigma * math.sqrt(2) * erfcinv(2 * x) - return ppf - -def erfc(x): - z = abs(x) - t = 1. / (1. + z / 2.) - r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * ( - 0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * ( - 0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * ( - -0.82215223 + t * 0.17087277 - ))) - ))) - ))) - return 2. - r if x < 0 else r - -def cdf(x, mu=0, sigma=1): - return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2))) - - -def pdf(x, mu=0, sigma=1): - return (1 / math.sqrt(2 * math.pi) * abs(sigma) * - math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2))) - -ppf = _gen_ppf(erfc) - -def choose_backend(backend): - if backend is None: # fallback - return cdf, pdf, ppf - elif backend == 'mpmath': - try: - import mpmath - except ImportError: - raise ImportError('Install "mpmath" to use this backend') - return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath) - elif backend == 'scipy': - try: - from scipy.stats import norm - except ImportError: - raise ImportError('Install "scipy" to use this backend') - return norm.cdf, norm.pdf, norm.ppf - raise ValueError('%r backend is not defined' % backend) - -def available_backends(): - backends = [None] - for backend in ['mpmath', 'scipy']: - try: - __import__(backend) - except ImportError: - continue - backends.append(backend) - return backends - -class Node(object): - - pass - -class Variable(Node, Gaussian): - - def __init__(self): - self.messages = {} - super(Variable, self).__init__() - - def set(self, val): - delta = self.delta(val) - self.pi, self.tau = val.pi, val.tau - return delta - - def delta(self, other): - pi_delta = abs(self.pi - other.pi) - if pi_delta == inf: - return 0. - return max(abs(self.tau - other.tau), math.sqrt(pi_delta)) - - def update_message(self, factor, pi=0, tau=0, message=None): - message = message or Gaussian(pi=pi, tau=tau) - old_message, self[factor] = self[factor], message - return self.set(self / old_message * message) - - def update_value(self, factor, pi=0, tau=0, value=None): - value = value or Gaussian(pi=pi, tau=tau) - old_message = self[factor] - self[factor] = value * old_message / self - return self.set(value) - - def __getitem__(self, factor): - return self.messages[factor] - - def __setitem__(self, factor, message): - self.messages[factor] = message - - def __repr__(self): - args = (type(self).__name__, super(Variable, self).__repr__(), - len(self.messages), '' if len(self.messages) == 1 else 's') - return '<%s %s with %d connection%s>' % args - - -class Factor(Node): - - def __init__(self, variables): - self.vars = variables - for var in variables: - var[self] = Gaussian() - - def down(self): - return 0 - - def up(self): - return 0 - - @property - def var(self): - assert len(self.vars) == 1 - return self.vars[0] - - def __repr__(self): - args = (type(self).__name__, len(self.vars), - '' if len(self.vars) == 1 else 's') - return '<%s with %d connection%s>' % args - - -class PriorFactor(Factor): - - def __init__(self, var, val, dynamic=0): - super(PriorFactor, self).__init__([var]) - self.val = val - self.dynamic = dynamic - - def down(self): - sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2) - value = Gaussian(self.val.mu, sigma) - return self.var.update_value(self, value=value) - - -class LikelihoodFactor(Factor): - - def __init__(self, mean_var, value_var, variance): - super(LikelihoodFactor, self).__init__([mean_var, value_var]) - self.mean = mean_var - self.value = value_var - self.variance = variance - - def calc_a(self, var): - return 1. / (1. + self.variance * var.pi) - - def down(self): - # update value. - msg = self.mean / self.mean[self] - a = self.calc_a(msg) - return self.value.update_message(self, a * msg.pi, a * msg.tau) - - def up(self): - # update mean. - msg = self.value / self.value[self] - a = self.calc_a(msg) - return self.mean.update_message(self, a * msg.pi, a * msg.tau) - - -class SumFactor(Factor): - - def __init__(self, sum_var, term_vars, coeffs): - super(SumFactor, self).__init__([sum_var] + term_vars) - self.sum = sum_var - self.terms = term_vars - self.coeffs = coeffs - - def down(self): - vals = self.terms - msgs = [var[self] for var in vals] - return self.update(self.sum, vals, msgs, self.coeffs) - - def up(self, index=0): - coeff = self.coeffs[index] - coeffs = [] - for x, c in enumerate(self.coeffs): - try: - if x == index: - coeffs.append(1. / coeff) - else: - coeffs.append(-c / coeff) - except ZeroDivisionError: - coeffs.append(0.) - vals = self.terms[:] - vals[index] = self.sum - msgs = [var[self] for var in vals] - return self.update(self.terms[index], vals, msgs, coeffs) - - def update(self, var, vals, msgs, coeffs): - pi_inv = 0 - mu = 0 - for val, msg, coeff in zip(vals, msgs, coeffs): - div = val / msg - mu += coeff * div.mu - if pi_inv == inf: - continue - try: - # numpy.float64 handles floating-point error by different way. - # For example, it can just warn RuntimeWarning on n/0 problem - # instead of throwing ZeroDivisionError. So div.pi, the - # denominator has to be a built-in float. - pi_inv += coeff ** 2 / float(div.pi) - except ZeroDivisionError: - pi_inv = inf - pi = 1. / pi_inv - tau = pi * mu - return var.update_message(self, pi, tau) - - -class TruncateFactor(Factor): - - def __init__(self, var, v_func, w_func, draw_margin): - super(TruncateFactor, self).__init__([var]) - self.v_func = v_func - self.w_func = w_func - self.draw_margin = draw_margin - - def up(self): - val = self.var - msg = self.var[self] - div = val / msg - sqrt_pi = math.sqrt(div.pi) - args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi) - v = self.v_func(*args) - w = self.w_func(*args) - denom = (1. - w) - pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom - return val.update_value(self, pi, tau) - -#: Default initial mean of ratings. -MU = 25. -#: Default initial standard deviation of ratings. -SIGMA = MU / 3 -#: Default distance that guarantees about 76% chance of winning. -BETA = SIGMA / 2 -#: Default dynamic factor. -TAU = SIGMA / 100 -#: Default draw probability of the game. -DRAW_PROBABILITY = .10 -#: A basis to check reliability of the result. -DELTA = 0.0001 - - -def calc_draw_probability(draw_margin, size, env=None): - if env is None: - env = global_env() - return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1 - - -def calc_draw_margin(draw_probability, size, env=None): - if env is None: - env = global_env() - return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta - - -def _team_sizes(rating_groups): - team_sizes = [0] - for group in rating_groups: - team_sizes.append(len(group) + team_sizes[-1]) - del team_sizes[0] - return team_sizes - - -def _floating_point_error(env): - if env.backend == 'mpmath': - msg = 'Set "mpmath.mp.dps" to higher' - else: - msg = 'Cannot calculate correctly, set backend to "mpmath"' - return FloatingPointError(msg) - - -class Rating(Gaussian): - def __init__(self, mu=None, sigma=None): - if isinstance(mu, tuple): - mu, sigma = mu - elif isinstance(mu, Gaussian): - mu, sigma = mu.mu, mu.sigma - if mu is None: - mu = global_env().mu - if sigma is None: - sigma = global_env().sigma - super(Rating, self).__init__(mu, sigma) - - def __int__(self): - return int(self.mu) - - def __long__(self): - return long(self.mu) - - def __float__(self): - return float(self.mu) - - def __iter__(self): - return iter((self.mu, self.sigma)) - - def __repr__(self): - c = type(self) - args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma) - return '%s(mu=%.3f, sigma=%.3f)' % args - - -class TrueSkill(object): - def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU, - draw_probability=DRAW_PROBABILITY, backend=None): - self.mu = mu - self.sigma = sigma - self.beta = beta - self.tau = tau - self.draw_probability = draw_probability - self.backend = backend - if isinstance(backend, tuple): - self.cdf, self.pdf, self.ppf = backend - else: - self.cdf, self.pdf, self.ppf = choose_backend(backend) - - def create_rating(self, mu=None, sigma=None): - if mu is None: - mu = self.mu - if sigma is None: - sigma = self.sigma - return Rating(mu, sigma) - - def v_win(self, diff, draw_margin): - x = diff - draw_margin - denom = self.cdf(x) - return (self.pdf(x) / denom) if denom else -x - - def v_draw(self, diff, draw_margin): - abs_diff = abs(diff) - a, b = draw_margin - abs_diff, -draw_margin - abs_diff - denom = self.cdf(a) - self.cdf(b) - numer = self.pdf(b) - self.pdf(a) - return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1) - - def w_win(self, diff, draw_margin): - x = diff - draw_margin - v = self.v_win(diff, draw_margin) - w = v * (v + x) - if 0 < w < 1: - return w - raise _floating_point_error(self) - - def w_draw(self, diff, draw_margin): - abs_diff = abs(diff) - a, b = draw_margin - abs_diff, -draw_margin - abs_diff - denom = self.cdf(a) - self.cdf(b) - if not denom: - raise _floating_point_error(self) - v = self.v_draw(abs_diff, draw_margin) - return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom - - def validate_rating_groups(self, rating_groups): - # check group sizes - if len(rating_groups) < 2: - raise ValueError('Need multiple rating groups') - elif not all(rating_groups): - raise ValueError('Each group must contain multiple ratings') - # check group types - group_types = set(map(type, rating_groups)) - if len(group_types) != 1: - raise TypeError('All groups should be same type') - elif group_types.pop() is Rating: - raise TypeError('Rating cannot be a rating group') - # normalize rating_groups - if isinstance(rating_groups[0], dict): - dict_rating_groups = rating_groups - rating_groups = [] - keys = [] - for dict_rating_group in dict_rating_groups: - rating_group, key_group = [], [] - for key, rating in iteritems(dict_rating_group): - rating_group.append(rating) - key_group.append(key) - rating_groups.append(tuple(rating_group)) - keys.append(tuple(key_group)) - else: - rating_groups = list(rating_groups) - keys = None - return rating_groups, keys - - def validate_weights(self, weights, rating_groups, keys=None): - if weights is None: - weights = [(1,) * len(g) for g in rating_groups] - elif isinstance(weights, dict): - weights_dict, weights = weights, [] - for x, group in enumerate(rating_groups): - w = [] - weights.append(w) - for y, rating in enumerate(group): - if keys is not None: - y = keys[x][y] - w.append(weights_dict.get((x, y), 1)) - return weights - - def factor_graph_builders(self, rating_groups, ranks, weights): - flatten_ratings = sum(map(tuple, rating_groups), ()) - flatten_weights = sum(map(tuple, weights), ()) - size = len(flatten_ratings) - group_size = len(rating_groups) - # create variables - rating_vars = [Variable() for x in range(size)] - perf_vars = [Variable() for x in range(size)] - team_perf_vars = [Variable() for x in range(group_size)] - team_diff_vars = [Variable() for x in range(group_size - 1)] - team_sizes = _team_sizes(rating_groups) - # layer builders - def build_rating_layer(): - for rating_var, rating in zip(rating_vars, flatten_ratings): - yield PriorFactor(rating_var, rating, self.tau) - def build_perf_layer(): - for rating_var, perf_var in zip(rating_vars, perf_vars): - yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2) - def build_team_perf_layer(): - for team, team_perf_var in enumerate(team_perf_vars): - if team > 0: - start = team_sizes[team - 1] - else: - start = 0 - end = team_sizes[team] - child_perf_vars = perf_vars[start:end] - coeffs = flatten_weights[start:end] - yield SumFactor(team_perf_var, child_perf_vars, coeffs) - def build_team_diff_layer(): - for team, team_diff_var in enumerate(team_diff_vars): - yield SumFactor(team_diff_var, - team_perf_vars[team:team + 2], [+1, -1]) - def build_trunc_layer(): - for x, team_diff_var in enumerate(team_diff_vars): - if callable(self.draw_probability): - # dynamic draw probability - team_perf1, team_perf2 = team_perf_vars[x:x + 2] - args = (Rating(team_perf1), Rating(team_perf2), self) - draw_probability = self.draw_probability(*args) - else: - # static draw probability - draw_probability = self.draw_probability - size = sum(map(len, rating_groups[x:x + 2])) - draw_margin = calc_draw_margin(draw_probability, size, self) - if ranks[x] == ranks[x + 1]: # is a tie? - v_func, w_func = self.v_draw, self.w_draw - else: - v_func, w_func = self.v_win, self.w_win - yield TruncateFactor(team_diff_var, - v_func, w_func, draw_margin) - # build layers - return (build_rating_layer, build_perf_layer, build_team_perf_layer, - build_team_diff_layer, build_trunc_layer) - - def run_schedule(self, build_rating_layer, build_perf_layer, - build_team_perf_layer, build_team_diff_layer, - build_trunc_layer, min_delta=DELTA): - if min_delta <= 0: - raise ValueError('min_delta must be greater than 0') - layers = [] - def build(builders): - layers_built = [list(build()) for build in builders] - layers.extend(layers_built) - return layers_built - # gray arrows - layers_built = build([build_rating_layer, - build_perf_layer, - build_team_perf_layer]) - rating_layer, perf_layer, team_perf_layer = layers_built - for f in chain(*layers_built): - f.down() - # arrow #1, #2, #3 - team_diff_layer, trunc_layer = build([build_team_diff_layer, - build_trunc_layer]) - team_diff_len = len(team_diff_layer) - for x in range(10): - if team_diff_len == 1: - # only two teams - team_diff_layer[0].down() - delta = trunc_layer[0].up() - else: - # multiple teams - delta = 0 - for x in range(team_diff_len - 1): - team_diff_layer[x].down() - delta = max(delta, trunc_layer[x].up()) - team_diff_layer[x].up(1) # up to right variable - for x in range(team_diff_len - 1, 0, -1): - team_diff_layer[x].down() - delta = max(delta, trunc_layer[x].up()) - team_diff_layer[x].up(0) # up to left variable - # repeat until to small update - if delta <= min_delta: - break - # up both ends - team_diff_layer[0].up(0) - team_diff_layer[team_diff_len - 1].up(1) - # up the remainder of the black arrows - for f in team_perf_layer: - for x in range(len(f.vars) - 1): - f.up(x) - for f in perf_layer: - f.up() - return layers - - def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA): - rating_groups, keys = self.validate_rating_groups(rating_groups) - weights = self.validate_weights(weights, rating_groups, keys) - group_size = len(rating_groups) - if ranks is None: - ranks = range(group_size) - elif len(ranks) != group_size: - raise ValueError('Wrong ranks') - # sort rating groups by rank - by_rank = lambda x: x[1][1] - sorting = sorted(enumerate(zip(rating_groups, ranks, weights)), - key=by_rank) - sorted_rating_groups, sorted_ranks, sorted_weights = [], [], [] - for x, (g, r, w) in sorting: - sorted_rating_groups.append(g) - sorted_ranks.append(r) - # make weights to be greater than 0 - sorted_weights.append(max(min_delta, w_) for w_ in w) - # build factor graph - args = (sorted_rating_groups, sorted_ranks, sorted_weights) - builders = self.factor_graph_builders(*args) - args = builders + (min_delta,) - layers = self.run_schedule(*args) - # make result - rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups) - transformed_groups = [] - for start, end in zip([0] + team_sizes[:-1], team_sizes): - group = [] - for f in rating_layer[start:end]: - group.append(Rating(float(f.var.mu), float(f.var.sigma))) - transformed_groups.append(tuple(group)) - by_hint = lambda x: x[0] - unsorting = sorted(zip((x for x, __ in sorting), transformed_groups), - key=by_hint) - if keys is None: - return [g for x, g in unsorting] - # restore the structure with input dictionary keys - return [dict(zip(keys[x], g)) for x, g in unsorting] - - def quality(self, rating_groups, weights=None): - rating_groups, keys = self.validate_rating_groups(rating_groups) - weights = self.validate_weights(weights, rating_groups, keys) - flatten_ratings = sum(map(tuple, rating_groups), ()) - flatten_weights = sum(map(tuple, weights), ()) - length = len(flatten_ratings) - # a vector of all of the skill means - mean_matrix = Matrix([[r.mu] for r in flatten_ratings]) - # a matrix whose diagonal values are the variances (sigma ** 2) of each - # of the players. - def variance_matrix(height, width): - variances = (r.sigma ** 2 for r in flatten_ratings) - for x, variance in enumerate(variances): - yield (x, x), variance - variance_matrix = Matrix(variance_matrix, length, length) - # the player-team assignment and comparison matrix - def rotated_a_matrix(set_height, set_width): - t = 0 - for r, (cur, _next) in enumerate(zip(rating_groups[:-1], - rating_groups[1:])): - for x in range(t, t + len(cur)): - yield (r, x), flatten_weights[x] - t += 1 - x += 1 - for x in range(x, x + len(_next)): - yield (r, x), -flatten_weights[x] - set_height(r + 1) - set_width(x + 1) - rotated_a_matrix = Matrix(rotated_a_matrix) - a_matrix = rotated_a_matrix.transpose() - # match quality further derivation - _ata = (self.beta ** 2) * rotated_a_matrix * a_matrix - _atsa = rotated_a_matrix * variance_matrix * a_matrix - start = mean_matrix.transpose() * a_matrix - middle = _ata + _atsa - end = rotated_a_matrix * mean_matrix - # make result - e_arg = (-0.5 * start * middle.inverse() * end).determinant() - s_arg = _ata.determinant() / middle.determinant() - return math.exp(e_arg) * math.sqrt(s_arg) - - def expose(self, rating): - k = self.mu / self.sigma - return rating.mu - k * rating.sigma - - def make_as_global(self): - return setup(env=self) - - def __repr__(self): - c = type(self) - if callable(self.draw_probability): - f = self.draw_probability - draw_probability = '.'.join([f.__module__, f.__name__]) - else: - draw_probability = '%.1f%%' % (self.draw_probability * 100) - if self.backend is None: - backend = '' - elif isinstance(self.backend, tuple): - backend = ', backend=...' - else: - backend = ', backend=%r' % self.backend - args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma, - self.beta, self.tau, draw_probability, backend) - return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, ' - 'draw_probability=%s%s)' % args) - - -def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None): - if env is None: - env = global_env() - ranks = [0, 0 if drawn else 1] - teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta) - return teams[0][0], teams[1][0] - - -def quality_1vs1(rating1, rating2, env=None): - if env is None: - env = global_env() - return env.quality([(rating1,), (rating2,)]) - - -def global_env(): - try: - global_env.__trueskill__ - except AttributeError: - # setup the default environment - setup() - return global_env.__trueskill__ - - -def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU, - draw_probability=DRAW_PROBABILITY, backend=None, env=None): - if env is None: - env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend) - global_env.__trueskill__ = env - return env - - -def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA): - return global_env().rate(rating_groups, ranks, weights, min_delta) - - -def quality(rating_groups, weights=None): - return global_env().quality(rating_groups, weights) - - -def expose(rating): - return global_env().expose(rating) \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/regression.py b/analysis-master/build/lib/analysis/regression.py deleted file mode 100644 index e899e9ff..00000000 --- a/analysis-master/build/lib/analysis/regression.py +++ /dev/null @@ -1,220 +0,0 @@ -# Titan Robotics Team 2022: CUDA-based Regressions Module -# Written by Arthur Lu & Jacob Levine -# Notes: -# this module has been automatically inegrated into analysis.py, and should be callable as a class from the package -# this module is cuda-optimized and vectorized (except for one small part) -# setup: - -__version__ = "1.0.0.004" - -# changelog should be viewed using print(analysis.regression.__changelog__) -__changelog__ = """ - 1.0.0.004: - - bug fixes - - fixed changelog - 1.0.0.003: - - bug fixes - 1.0.0.002: - -Added more parameters to log, exponential, polynomial - -Added SigmoidalRegKernelArthur, because Arthur apparently needs - to train the scaling and shifting of sigmoids - 1.0.0.001: - -initial release, with linear, log, exponential, polynomial, and sigmoid kernels - -already vectorized (except for polynomial generation) and CUDA-optimized -""" - -__author__ = ( - "Jacob Levine ", - "Arthur Lu " -) - -__all__ = [ - 'factorial', - 'take_all_pwrs', - 'num_poly_terms', - 'set_device', - 'LinearRegKernel', - 'SigmoidalRegKernel', - 'LogRegKernel', - 'PolyRegKernel', - 'ExpRegKernel', - 'SigmoidalRegKernelArthur', - 'SGDTrain', - 'CustomTrain' -] - -import torch - -global device - -device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" - -#todo: document completely - -def set_device(self, new_device): - device=new_device - -class LinearRegKernel(): - parameters= [] - weights=None - bias=None - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.bias] - def forward(self,mtx): - long_bias=self.bias.repeat([1,mtx.size()[1]]) - return torch.matmul(self.weights,mtx)+long_bias - -class SigmoidalRegKernel(): - parameters= [] - weights=None - bias=None - sigmoid=torch.nn.Sigmoid() - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.bias] - def forward(self,mtx): - long_bias=self.bias.repeat([1,mtx.size()[1]]) - return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias) - -class SigmoidalRegKernelArthur(): - parameters= [] - weights=None - in_bias=None - scal_mult=None - out_bias=None - sigmoid=torch.nn.Sigmoid() - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.in_bias=torch.rand(1, requires_grad=True, device=device) - self.scal_mult=torch.rand(1, requires_grad=True, device=device) - self.out_bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] - def forward(self,mtx): - long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) - long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) - return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias - -class LogRegKernel(): - parameters= [] - weights=None - in_bias=None - scal_mult=None - out_bias=None - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.in_bias=torch.rand(1, requires_grad=True, device=device) - self.scal_mult=torch.rand(1, requires_grad=True, device=device) - self.out_bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] - def forward(self,mtx): - long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) - long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) - return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias - -class ExpRegKernel(): - parameters= [] - weights=None - in_bias=None - scal_mult=None - out_bias=None - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.in_bias=torch.rand(1, requires_grad=True, device=device) - self.scal_mult=torch.rand(1, requires_grad=True, device=device) - self.out_bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] - def forward(self,mtx): - long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) - long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) - return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias - -class PolyRegKernel(): - parameters= [] - weights=None - bias=None - power=None - def __init__(self, num_vars, power): - self.power=power - num_terms=self.num_poly_terms(num_vars, power) - self.weights=torch.rand(num_terms, requires_grad=True, device=device) - self.bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.bias] - def num_poly_terms(self,num_vars, power): - if power == 0: - return 0 - return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1) - def factorial(self,n): - if n==0: - return 1 - else: - return n*self.factorial(n-1) - def take_all_pwrs(self, vec, pwr): - #todo: vectorize (kinda) - combins=torch.combinations(vec, r=pwr, with_replacement=True) - out=torch.ones(combins.size()[0]).to(device).to(torch.float) - for i in torch.t(combins).to(device).to(torch.float): - out *= i - if pwr == 1: - return out - else: - return torch.cat((out,self.take_all_pwrs(vec, pwr-1))) - def forward(self,mtx): - #TODO: Vectorize the last part - cols=[] - for i in torch.t(mtx): - cols.append(self.take_all_pwrs(i,self.power)) - new_mtx=torch.t(torch.stack(cols)) - long_bias=self.bias.repeat([1,mtx.size()[1]]) - return torch.matmul(self.weights,new_mtx)+long_bias - -def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False): - optim=torch.optim.SGD(kernel.parameters, lr=learning_rate) - data_cuda=data.to(device) - ground_cuda=ground.to(device) - if (return_losses): - losses=[] - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data_cuda) - ls=loss(pred,ground_cuda) - losses.append(ls.item()) - ls.backward() - optim.step() - return [kernel,losses] - else: - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data_cuda) - ls=loss(pred,ground_cuda) - ls.backward() - optim.step() - return kernel - -def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False): - data_cuda=data.to(device) - ground_cuda=ground.to(device) - if (return_losses): - losses=[] - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data) - ls=loss(pred,ground) - losses.append(ls.item()) - ls.backward() - optim.step() - return [kernel,losses] - else: - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data_cuda) - ls=loss(pred,ground_cuda) - ls.backward() - optim.step() - return kernel \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/titanlearn.py b/analysis-master/build/lib/analysis/titanlearn.py deleted file mode 100644 index b69d36e3..00000000 --- a/analysis-master/build/lib/analysis/titanlearn.py +++ /dev/null @@ -1,122 +0,0 @@ -# Titan Robotics Team 2022: ML Module -# Written by Arthur Lu & Jacob Levine -# Notes: -# this should be imported as a python module using 'import titanlearn' -# this should be included in the local directory or environment variable -# this module is optimized for multhreaded computing -# this module learns from its mistakes far faster than 2022's captains -# setup: - -__version__ = "2.0.1.001" - -#changelog should be viewed using print(analysis.__changelog__) -__changelog__ = """changelog: - 2.0.1.001: - - removed matplotlib import - - removed graphloss() - 2.0.1.000: - - added net, dataset, dataloader, and stdtrain template definitions - - added graphloss function - 2.0.0.001: - - added clear functions - 2.0.0.000: - - complete rewrite planned - - depreciated 1.0.0.xxx versions - - added simple training loop - 1.0.0.xxx: - -added generation of ANNS, basic SGD training -""" - -__author__ = ( - "Arthur Lu ," - "Jacob Levine ," - ) - -__all__ = [ - 'clear', - 'net', - 'dataset', - 'dataloader', - 'train', - 'stdtrainer', - ] - -import torch -from os import system, name -import numpy as np - -def clear(): - if name == 'nt': - _ = system('cls') - else: - _ = system('clear') - -class net(torch.nn.Module): #template for standard neural net - def __init__(self): - super(Net, self).__init__() - - def forward(self, input): - pass - -class dataset(torch.utils.data.Dataset): #template for standard dataset - - def __init__(self): - super(torch.utils.data.Dataset).__init__() - - def __getitem__(self, index): - pass - - def __len__(self): - pass - -def dataloader(dataset, batch_size, num_workers, shuffle = True): - - return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) - -def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels) - - dataset_len = trainloader.dataset.__len__() - iter_count = 0 - running_loss = 0 - running_loss_list = [] - - for epoch in range(epochs): # loop over the dataset multiple times - - for i, data in enumerate(trainloader, 0): - - inputs = data[0].to(device) - labels = data[1].to(device) - - optimizer.zero_grad() - - outputs = net(inputs) - loss = criterion(outputs, labels.to(torch.float)) - - loss.backward() - optimizer.step() - - # monitoring steps below - - iter_count += 1 - running_loss += loss.item() - running_loss_list.append(running_loss) - clear() - - print("training on: " + device) - print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs)) - print("current batch loss: " + str(loss.item)) - print("running loss: " + str(running_loss / iter_count)) - - return net, running_loss_list - print("finished training") - -def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size): - - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - net = net.to(device) - criterion = criterion.to(device) - optimizer = optimizer.to(device) - trainloader = dataloader - - return train(device, net, epochs, trainloader, optimizer, criterion) \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/trueskill.py b/analysis-master/build/lib/analysis/trueskill.py deleted file mode 100644 index 116357df..00000000 --- a/analysis-master/build/lib/analysis/trueskill.py +++ /dev/null @@ -1,907 +0,0 @@ -from __future__ import absolute_import - -from itertools import chain -import math - -from six import iteritems -from six.moves import map, range, zip -from six import iterkeys - -import copy -try: - from numbers import Number -except ImportError: - Number = (int, long, float, complex) - -inf = float('inf') - -class Gaussian(object): - #: Precision, the inverse of the variance. - pi = 0 - #: Precision adjusted mean, the precision multiplied by the mean. - tau = 0 - - def __init__(self, mu=None, sigma=None, pi=0, tau=0): - if mu is not None: - if sigma is None: - raise TypeError('sigma argument is needed') - elif sigma == 0: - raise ValueError('sigma**2 should be greater than 0') - pi = sigma ** -2 - tau = pi * mu - self.pi = pi - self.tau = tau - - @property - def mu(self): - return self.pi and self.tau / self.pi - - @property - def sigma(self): - return math.sqrt(1 / self.pi) if self.pi else inf - - def __mul__(self, other): - pi, tau = self.pi + other.pi, self.tau + other.tau - return Gaussian(pi=pi, tau=tau) - - def __truediv__(self, other): - pi, tau = self.pi - other.pi, self.tau - other.tau - return Gaussian(pi=pi, tau=tau) - - __div__ = __truediv__ # for Python 2 - - def __eq__(self, other): - return self.pi == other.pi and self.tau == other.tau - - def __lt__(self, other): - return self.mu < other.mu - - def __le__(self, other): - return self.mu <= other.mu - - def __gt__(self, other): - return self.mu > other.mu - - def __ge__(self, other): - return self.mu >= other.mu - - def __repr__(self): - return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma) - - def _repr_latex_(self): - latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma) - return '$%s$' % latex - -class Matrix(list): - def __init__(self, src, height=None, width=None): - if callable(src): - f, src = src, {} - size = [height, width] - if not height: - def set_height(height): - size[0] = height - size[0] = set_height - if not width: - def set_width(width): - size[1] = width - size[1] = set_width - try: - for (r, c), val in f(*size): - src[r, c] = val - except TypeError: - raise TypeError('A callable src must return an interable ' - 'which generates a tuple containing ' - 'coordinate and value') - height, width = tuple(size) - if height is None or width is None: - raise TypeError('A callable src must call set_height and ' - 'set_width if the size is non-deterministic') - if isinstance(src, list): - is_number = lambda x: isinstance(x, Number) - unique_col_sizes = set(map(len, src)) - everything_are_number = filter(is_number, sum(src, [])) - if len(unique_col_sizes) != 1 or not everything_are_number: - raise ValueError('src must be a rectangular array of numbers') - two_dimensional_array = src - elif isinstance(src, dict): - if not height or not width: - w = h = 0 - for r, c in iterkeys(src): - if not height: - h = max(h, r + 1) - if not width: - w = max(w, c + 1) - if not height: - height = h - if not width: - width = w - two_dimensional_array = [] - for r in range(height): - row = [] - two_dimensional_array.append(row) - for c in range(width): - row.append(src.get((r, c), 0)) - else: - raise TypeError('src must be a list or dict or callable') - super(Matrix, self).__init__(two_dimensional_array) - - @property - def height(self): - return len(self) - - @property - def width(self): - return len(self[0]) - - def transpose(self): - height, width = self.height, self.width - src = {} - for c in range(width): - for r in range(height): - src[c, r] = self[r][c] - return type(self)(src, height=width, width=height) - - def minor(self, row_n, col_n): - height, width = self.height, self.width - if not (0 <= row_n < height): - raise ValueError('row_n should be between 0 and %d' % height) - elif not (0 <= col_n < width): - raise ValueError('col_n should be between 0 and %d' % width) - two_dimensional_array = [] - for r in range(height): - if r == row_n: - continue - row = [] - two_dimensional_array.append(row) - for c in range(width): - if c == col_n: - continue - row.append(self[r][c]) - return type(self)(two_dimensional_array) - - def determinant(self): - height, width = self.height, self.width - if height != width: - raise ValueError('Only square matrix can calculate a determinant') - tmp, rv = copy.deepcopy(self), 1. - for c in range(width - 1, 0, -1): - pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1)) - pivot = tmp[r][c] - if not pivot: - return 0. - tmp[r], tmp[c] = tmp[c], tmp[r] - if r != c: - rv = -rv - rv *= pivot - fact = -1. / pivot - for r in range(c): - f = fact * tmp[r][c] - for x in range(c): - tmp[r][x] += f * tmp[c][x] - return rv * tmp[0][0] - - def adjugate(self): - height, width = self.height, self.width - if height != width: - raise ValueError('Only square matrix can be adjugated') - if height == 2: - a, b = self[0][0], self[0][1] - c, d = self[1][0], self[1][1] - return type(self)([[d, -b], [-c, a]]) - src = {} - for r in range(height): - for c in range(width): - sign = -1 if (r + c) % 2 else 1 - src[r, c] = self.minor(r, c).determinant() * sign - return type(self)(src, height, width) - - def inverse(self): - if self.height == self.width == 1: - return type(self)([[1. / self[0][0]]]) - return (1. / self.determinant()) * self.adjugate() - - def __add__(self, other): - height, width = self.height, self.width - if (height, width) != (other.height, other.width): - raise ValueError('Must be same size') - src = {} - for r in range(height): - for c in range(width): - src[r, c] = self[r][c] + other[r][c] - return type(self)(src, height, width) - - def __mul__(self, other): - if self.width != other.height: - raise ValueError('Bad size') - height, width = self.height, other.width - src = {} - for r in range(height): - for c in range(width): - src[r, c] = sum(self[r][x] * other[x][c] - for x in range(self.width)) - return type(self)(src, height, width) - - def __rmul__(self, other): - if not isinstance(other, Number): - raise TypeError('The operand should be a number') - height, width = self.height, self.width - src = {} - for r in range(height): - for c in range(width): - src[r, c] = other * self[r][c] - return type(self)(src, height, width) - - def __repr__(self): - return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__()) - - def _repr_latex_(self): - rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self] - latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows) - return '$%s$' % latex - -def _gen_erfcinv(erfc, math=math): - def erfcinv(y): - """The inverse function of erfc.""" - if y >= 2: - return -100. - elif y <= 0: - return 100. - zero_point = y < 1 - if not zero_point: - y = 2 - y - t = math.sqrt(-2 * math.log(y / 2.)) - x = -0.70711 * \ - ((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t) - for i in range(2): - err = erfc(x) - y - x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err) - return x if zero_point else -x - return erfcinv - -def _gen_ppf(erfc, math=math): - erfcinv = _gen_erfcinv(erfc, math) - def ppf(x, mu=0, sigma=1): - return mu - sigma * math.sqrt(2) * erfcinv(2 * x) - return ppf - -def erfc(x): - z = abs(x) - t = 1. / (1. + z / 2.) - r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * ( - 0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * ( - 0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * ( - -0.82215223 + t * 0.17087277 - ))) - ))) - ))) - return 2. - r if x < 0 else r - -def cdf(x, mu=0, sigma=1): - return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2))) - - -def pdf(x, mu=0, sigma=1): - return (1 / math.sqrt(2 * math.pi) * abs(sigma) * - math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2))) - -ppf = _gen_ppf(erfc) - -def choose_backend(backend): - if backend is None: # fallback - return cdf, pdf, ppf - elif backend == 'mpmath': - try: - import mpmath - except ImportError: - raise ImportError('Install "mpmath" to use this backend') - return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath) - elif backend == 'scipy': - try: - from scipy.stats import norm - except ImportError: - raise ImportError('Install "scipy" to use this backend') - return norm.cdf, norm.pdf, norm.ppf - raise ValueError('%r backend is not defined' % backend) - -def available_backends(): - backends = [None] - for backend in ['mpmath', 'scipy']: - try: - __import__(backend) - except ImportError: - continue - backends.append(backend) - return backends - -class Node(object): - - pass - -class Variable(Node, Gaussian): - - def __init__(self): - self.messages = {} - super(Variable, self).__init__() - - def set(self, val): - delta = self.delta(val) - self.pi, self.tau = val.pi, val.tau - return delta - - def delta(self, other): - pi_delta = abs(self.pi - other.pi) - if pi_delta == inf: - return 0. - return max(abs(self.tau - other.tau), math.sqrt(pi_delta)) - - def update_message(self, factor, pi=0, tau=0, message=None): - message = message or Gaussian(pi=pi, tau=tau) - old_message, self[factor] = self[factor], message - return self.set(self / old_message * message) - - def update_value(self, factor, pi=0, tau=0, value=None): - value = value or Gaussian(pi=pi, tau=tau) - old_message = self[factor] - self[factor] = value * old_message / self - return self.set(value) - - def __getitem__(self, factor): - return self.messages[factor] - - def __setitem__(self, factor, message): - self.messages[factor] = message - - def __repr__(self): - args = (type(self).__name__, super(Variable, self).__repr__(), - len(self.messages), '' if len(self.messages) == 1 else 's') - return '<%s %s with %d connection%s>' % args - - -class Factor(Node): - - def __init__(self, variables): - self.vars = variables - for var in variables: - var[self] = Gaussian() - - def down(self): - return 0 - - def up(self): - return 0 - - @property - def var(self): - assert len(self.vars) == 1 - return self.vars[0] - - def __repr__(self): - args = (type(self).__name__, len(self.vars), - '' if len(self.vars) == 1 else 's') - return '<%s with %d connection%s>' % args - - -class PriorFactor(Factor): - - def __init__(self, var, val, dynamic=0): - super(PriorFactor, self).__init__([var]) - self.val = val - self.dynamic = dynamic - - def down(self): - sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2) - value = Gaussian(self.val.mu, sigma) - return self.var.update_value(self, value=value) - - -class LikelihoodFactor(Factor): - - def __init__(self, mean_var, value_var, variance): - super(LikelihoodFactor, self).__init__([mean_var, value_var]) - self.mean = mean_var - self.value = value_var - self.variance = variance - - def calc_a(self, var): - return 1. / (1. + self.variance * var.pi) - - def down(self): - # update value. - msg = self.mean / self.mean[self] - a = self.calc_a(msg) - return self.value.update_message(self, a * msg.pi, a * msg.tau) - - def up(self): - # update mean. - msg = self.value / self.value[self] - a = self.calc_a(msg) - return self.mean.update_message(self, a * msg.pi, a * msg.tau) - - -class SumFactor(Factor): - - def __init__(self, sum_var, term_vars, coeffs): - super(SumFactor, self).__init__([sum_var] + term_vars) - self.sum = sum_var - self.terms = term_vars - self.coeffs = coeffs - - def down(self): - vals = self.terms - msgs = [var[self] for var in vals] - return self.update(self.sum, vals, msgs, self.coeffs) - - def up(self, index=0): - coeff = self.coeffs[index] - coeffs = [] - for x, c in enumerate(self.coeffs): - try: - if x == index: - coeffs.append(1. / coeff) - else: - coeffs.append(-c / coeff) - except ZeroDivisionError: - coeffs.append(0.) - vals = self.terms[:] - vals[index] = self.sum - msgs = [var[self] for var in vals] - return self.update(self.terms[index], vals, msgs, coeffs) - - def update(self, var, vals, msgs, coeffs): - pi_inv = 0 - mu = 0 - for val, msg, coeff in zip(vals, msgs, coeffs): - div = val / msg - mu += coeff * div.mu - if pi_inv == inf: - continue - try: - # numpy.float64 handles floating-point error by different way. - # For example, it can just warn RuntimeWarning on n/0 problem - # instead of throwing ZeroDivisionError. So div.pi, the - # denominator has to be a built-in float. - pi_inv += coeff ** 2 / float(div.pi) - except ZeroDivisionError: - pi_inv = inf - pi = 1. / pi_inv - tau = pi * mu - return var.update_message(self, pi, tau) - - -class TruncateFactor(Factor): - - def __init__(self, var, v_func, w_func, draw_margin): - super(TruncateFactor, self).__init__([var]) - self.v_func = v_func - self.w_func = w_func - self.draw_margin = draw_margin - - def up(self): - val = self.var - msg = self.var[self] - div = val / msg - sqrt_pi = math.sqrt(div.pi) - args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi) - v = self.v_func(*args) - w = self.w_func(*args) - denom = (1. - w) - pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom - return val.update_value(self, pi, tau) - -#: Default initial mean of ratings. -MU = 25. -#: Default initial standard deviation of ratings. -SIGMA = MU / 3 -#: Default distance that guarantees about 76% chance of winning. -BETA = SIGMA / 2 -#: Default dynamic factor. -TAU = SIGMA / 100 -#: Default draw probability of the game. -DRAW_PROBABILITY = .10 -#: A basis to check reliability of the result. -DELTA = 0.0001 - - -def calc_draw_probability(draw_margin, size, env=None): - if env is None: - env = global_env() - return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1 - - -def calc_draw_margin(draw_probability, size, env=None): - if env is None: - env = global_env() - return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta - - -def _team_sizes(rating_groups): - team_sizes = [0] - for group in rating_groups: - team_sizes.append(len(group) + team_sizes[-1]) - del team_sizes[0] - return team_sizes - - -def _floating_point_error(env): - if env.backend == 'mpmath': - msg = 'Set "mpmath.mp.dps" to higher' - else: - msg = 'Cannot calculate correctly, set backend to "mpmath"' - return FloatingPointError(msg) - - -class Rating(Gaussian): - def __init__(self, mu=None, sigma=None): - if isinstance(mu, tuple): - mu, sigma = mu - elif isinstance(mu, Gaussian): - mu, sigma = mu.mu, mu.sigma - if mu is None: - mu = global_env().mu - if sigma is None: - sigma = global_env().sigma - super(Rating, self).__init__(mu, sigma) - - def __int__(self): - return int(self.mu) - - def __long__(self): - return long(self.mu) - - def __float__(self): - return float(self.mu) - - def __iter__(self): - return iter((self.mu, self.sigma)) - - def __repr__(self): - c = type(self) - args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma) - return '%s(mu=%.3f, sigma=%.3f)' % args - - -class TrueSkill(object): - def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU, - draw_probability=DRAW_PROBABILITY, backend=None): - self.mu = mu - self.sigma = sigma - self.beta = beta - self.tau = tau - self.draw_probability = draw_probability - self.backend = backend - if isinstance(backend, tuple): - self.cdf, self.pdf, self.ppf = backend - else: - self.cdf, self.pdf, self.ppf = choose_backend(backend) - - def create_rating(self, mu=None, sigma=None): - if mu is None: - mu = self.mu - if sigma is None: - sigma = self.sigma - return Rating(mu, sigma) - - def v_win(self, diff, draw_margin): - x = diff - draw_margin - denom = self.cdf(x) - return (self.pdf(x) / denom) if denom else -x - - def v_draw(self, diff, draw_margin): - abs_diff = abs(diff) - a, b = draw_margin - abs_diff, -draw_margin - abs_diff - denom = self.cdf(a) - self.cdf(b) - numer = self.pdf(b) - self.pdf(a) - return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1) - - def w_win(self, diff, draw_margin): - x = diff - draw_margin - v = self.v_win(diff, draw_margin) - w = v * (v + x) - if 0 < w < 1: - return w - raise _floating_point_error(self) - - def w_draw(self, diff, draw_margin): - abs_diff = abs(diff) - a, b = draw_margin - abs_diff, -draw_margin - abs_diff - denom = self.cdf(a) - self.cdf(b) - if not denom: - raise _floating_point_error(self) - v = self.v_draw(abs_diff, draw_margin) - return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom - - def validate_rating_groups(self, rating_groups): - # check group sizes - if len(rating_groups) < 2: - raise ValueError('Need multiple rating groups') - elif not all(rating_groups): - raise ValueError('Each group must contain multiple ratings') - # check group types - group_types = set(map(type, rating_groups)) - if len(group_types) != 1: - raise TypeError('All groups should be same type') - elif group_types.pop() is Rating: - raise TypeError('Rating cannot be a rating group') - # normalize rating_groups - if isinstance(rating_groups[0], dict): - dict_rating_groups = rating_groups - rating_groups = [] - keys = [] - for dict_rating_group in dict_rating_groups: - rating_group, key_group = [], [] - for key, rating in iteritems(dict_rating_group): - rating_group.append(rating) - key_group.append(key) - rating_groups.append(tuple(rating_group)) - keys.append(tuple(key_group)) - else: - rating_groups = list(rating_groups) - keys = None - return rating_groups, keys - - def validate_weights(self, weights, rating_groups, keys=None): - if weights is None: - weights = [(1,) * len(g) for g in rating_groups] - elif isinstance(weights, dict): - weights_dict, weights = weights, [] - for x, group in enumerate(rating_groups): - w = [] - weights.append(w) - for y, rating in enumerate(group): - if keys is not None: - y = keys[x][y] - w.append(weights_dict.get((x, y), 1)) - return weights - - def factor_graph_builders(self, rating_groups, ranks, weights): - flatten_ratings = sum(map(tuple, rating_groups), ()) - flatten_weights = sum(map(tuple, weights), ()) - size = len(flatten_ratings) - group_size = len(rating_groups) - # create variables - rating_vars = [Variable() for x in range(size)] - perf_vars = [Variable() for x in range(size)] - team_perf_vars = [Variable() for x in range(group_size)] - team_diff_vars = [Variable() for x in range(group_size - 1)] - team_sizes = _team_sizes(rating_groups) - # layer builders - def build_rating_layer(): - for rating_var, rating in zip(rating_vars, flatten_ratings): - yield PriorFactor(rating_var, rating, self.tau) - def build_perf_layer(): - for rating_var, perf_var in zip(rating_vars, perf_vars): - yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2) - def build_team_perf_layer(): - for team, team_perf_var in enumerate(team_perf_vars): - if team > 0: - start = team_sizes[team - 1] - else: - start = 0 - end = team_sizes[team] - child_perf_vars = perf_vars[start:end] - coeffs = flatten_weights[start:end] - yield SumFactor(team_perf_var, child_perf_vars, coeffs) - def build_team_diff_layer(): - for team, team_diff_var in enumerate(team_diff_vars): - yield SumFactor(team_diff_var, - team_perf_vars[team:team + 2], [+1, -1]) - def build_trunc_layer(): - for x, team_diff_var in enumerate(team_diff_vars): - if callable(self.draw_probability): - # dynamic draw probability - team_perf1, team_perf2 = team_perf_vars[x:x + 2] - args = (Rating(team_perf1), Rating(team_perf2), self) - draw_probability = self.draw_probability(*args) - else: - # static draw probability - draw_probability = self.draw_probability - size = sum(map(len, rating_groups[x:x + 2])) - draw_margin = calc_draw_margin(draw_probability, size, self) - if ranks[x] == ranks[x + 1]: # is a tie? - v_func, w_func = self.v_draw, self.w_draw - else: - v_func, w_func = self.v_win, self.w_win - yield TruncateFactor(team_diff_var, - v_func, w_func, draw_margin) - # build layers - return (build_rating_layer, build_perf_layer, build_team_perf_layer, - build_team_diff_layer, build_trunc_layer) - - def run_schedule(self, build_rating_layer, build_perf_layer, - build_team_perf_layer, build_team_diff_layer, - build_trunc_layer, min_delta=DELTA): - if min_delta <= 0: - raise ValueError('min_delta must be greater than 0') - layers = [] - def build(builders): - layers_built = [list(build()) for build in builders] - layers.extend(layers_built) - return layers_built - # gray arrows - layers_built = build([build_rating_layer, - build_perf_layer, - build_team_perf_layer]) - rating_layer, perf_layer, team_perf_layer = layers_built - for f in chain(*layers_built): - f.down() - # arrow #1, #2, #3 - team_diff_layer, trunc_layer = build([build_team_diff_layer, - build_trunc_layer]) - team_diff_len = len(team_diff_layer) - for x in range(10): - if team_diff_len == 1: - # only two teams - team_diff_layer[0].down() - delta = trunc_layer[0].up() - else: - # multiple teams - delta = 0 - for x in range(team_diff_len - 1): - team_diff_layer[x].down() - delta = max(delta, trunc_layer[x].up()) - team_diff_layer[x].up(1) # up to right variable - for x in range(team_diff_len - 1, 0, -1): - team_diff_layer[x].down() - delta = max(delta, trunc_layer[x].up()) - team_diff_layer[x].up(0) # up to left variable - # repeat until to small update - if delta <= min_delta: - break - # up both ends - team_diff_layer[0].up(0) - team_diff_layer[team_diff_len - 1].up(1) - # up the remainder of the black arrows - for f in team_perf_layer: - for x in range(len(f.vars) - 1): - f.up(x) - for f in perf_layer: - f.up() - return layers - - def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA): - rating_groups, keys = self.validate_rating_groups(rating_groups) - weights = self.validate_weights(weights, rating_groups, keys) - group_size = len(rating_groups) - if ranks is None: - ranks = range(group_size) - elif len(ranks) != group_size: - raise ValueError('Wrong ranks') - # sort rating groups by rank - by_rank = lambda x: x[1][1] - sorting = sorted(enumerate(zip(rating_groups, ranks, weights)), - key=by_rank) - sorted_rating_groups, sorted_ranks, sorted_weights = [], [], [] - for x, (g, r, w) in sorting: - sorted_rating_groups.append(g) - sorted_ranks.append(r) - # make weights to be greater than 0 - sorted_weights.append(max(min_delta, w_) for w_ in w) - # build factor graph - args = (sorted_rating_groups, sorted_ranks, sorted_weights) - builders = self.factor_graph_builders(*args) - args = builders + (min_delta,) - layers = self.run_schedule(*args) - # make result - rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups) - transformed_groups = [] - for start, end in zip([0] + team_sizes[:-1], team_sizes): - group = [] - for f in rating_layer[start:end]: - group.append(Rating(float(f.var.mu), float(f.var.sigma))) - transformed_groups.append(tuple(group)) - by_hint = lambda x: x[0] - unsorting = sorted(zip((x for x, __ in sorting), transformed_groups), - key=by_hint) - if keys is None: - return [g for x, g in unsorting] - # restore the structure with input dictionary keys - return [dict(zip(keys[x], g)) for x, g in unsorting] - - def quality(self, rating_groups, weights=None): - rating_groups, keys = self.validate_rating_groups(rating_groups) - weights = self.validate_weights(weights, rating_groups, keys) - flatten_ratings = sum(map(tuple, rating_groups), ()) - flatten_weights = sum(map(tuple, weights), ()) - length = len(flatten_ratings) - # a vector of all of the skill means - mean_matrix = Matrix([[r.mu] for r in flatten_ratings]) - # a matrix whose diagonal values are the variances (sigma ** 2) of each - # of the players. - def variance_matrix(height, width): - variances = (r.sigma ** 2 for r in flatten_ratings) - for x, variance in enumerate(variances): - yield (x, x), variance - variance_matrix = Matrix(variance_matrix, length, length) - # the player-team assignment and comparison matrix - def rotated_a_matrix(set_height, set_width): - t = 0 - for r, (cur, _next) in enumerate(zip(rating_groups[:-1], - rating_groups[1:])): - for x in range(t, t + len(cur)): - yield (r, x), flatten_weights[x] - t += 1 - x += 1 - for x in range(x, x + len(_next)): - yield (r, x), -flatten_weights[x] - set_height(r + 1) - set_width(x + 1) - rotated_a_matrix = Matrix(rotated_a_matrix) - a_matrix = rotated_a_matrix.transpose() - # match quality further derivation - _ata = (self.beta ** 2) * rotated_a_matrix * a_matrix - _atsa = rotated_a_matrix * variance_matrix * a_matrix - start = mean_matrix.transpose() * a_matrix - middle = _ata + _atsa - end = rotated_a_matrix * mean_matrix - # make result - e_arg = (-0.5 * start * middle.inverse() * end).determinant() - s_arg = _ata.determinant() / middle.determinant() - return math.exp(e_arg) * math.sqrt(s_arg) - - def expose(self, rating): - k = self.mu / self.sigma - return rating.mu - k * rating.sigma - - def make_as_global(self): - return setup(env=self) - - def __repr__(self): - c = type(self) - if callable(self.draw_probability): - f = self.draw_probability - draw_probability = '.'.join([f.__module__, f.__name__]) - else: - draw_probability = '%.1f%%' % (self.draw_probability * 100) - if self.backend is None: - backend = '' - elif isinstance(self.backend, tuple): - backend = ', backend=...' - else: - backend = ', backend=%r' % self.backend - args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma, - self.beta, self.tau, draw_probability, backend) - return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, ' - 'draw_probability=%s%s)' % args) - - -def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None): - if env is None: - env = global_env() - ranks = [0, 0 if drawn else 1] - teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta) - return teams[0][0], teams[1][0] - - -def quality_1vs1(rating1, rating2, env=None): - if env is None: - env = global_env() - return env.quality([(rating1,), (rating2,)]) - - -def global_env(): - try: - global_env.__trueskill__ - except AttributeError: - # setup the default environment - setup() - return global_env.__trueskill__ - - -def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU, - draw_probability=DRAW_PROBABILITY, backend=None, env=None): - if env is None: - env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend) - global_env.__trueskill__ = env - return env - - -def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA): - return global_env().rate(rating_groups, ranks, weights, min_delta) - - -def quality(rating_groups, weights=None): - return global_env().quality(rating_groups, weights) - - -def expose(rating): - return global_env().expose(rating) \ No newline at end of file diff --git a/analysis-master/build/lib/analysis/visualization.py b/analysis-master/build/lib/analysis/visualization.py deleted file mode 100644 index 72358662..00000000 --- a/analysis-master/build/lib/analysis/visualization.py +++ /dev/null @@ -1,34 +0,0 @@ -# Titan Robotics Team 2022: Visualization Module -# Written by Arthur Lu & Jacob Levine -# Notes: -# this should be imported as a python module using 'import visualization' -# this should be included in the local directory or environment variable -# fancy -# setup: - -__version__ = "1.0.0.000" - -#changelog should be viewed using print(analysis.__changelog__) -__changelog__ = """changelog: - 1.0.0.000: - - created visualization.py - - added graphloss() - - added imports -""" - -__author__ = ( - "Arthur Lu ," - "Jacob Levine ," - ) - -__all__ = [ - 'graphloss', - ] - -import matplotlib.pyplot as plt - -def graphloss(losses): - - x = range(0, len(losses)) - plt.plot(x, losses) - plt.show() \ No newline at end of file diff --git a/data-analysis/__pycache__/data.cpython-37.pyc b/data-analysis/__pycache__/data.cpython-37.pyc new file mode 100644 index 00000000..9c1a4a46 Binary files /dev/null and b/data-analysis/__pycache__/data.cpython-37.pyc differ diff --git a/data analysis/config/competition.config b/data-analysis/config/competition.config similarity index 100% rename from data analysis/config/competition.config rename to data-analysis/config/competition.config diff --git a/data analysis/config/database.config b/data-analysis/config/database.config similarity index 100% rename from data analysis/config/database.config rename to data-analysis/config/database.config diff --git a/data-analysis/config/keys.config b/data-analysis/config/keys.config new file mode 100644 index 00000000..77a53a68 --- /dev/null +++ b/data-analysis/config/keys.config @@ -0,0 +1,2 @@ +mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true +UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5 \ No newline at end of file diff --git a/data analysis/config/stats.config b/data-analysis/config/stats.config similarity index 100% rename from data analysis/config/stats.config rename to data-analysis/config/stats.config diff --git a/data analysis/data.py b/data-analysis/data.py similarity index 100% rename from data analysis/data.py rename to data-analysis/data.py diff --git a/data analysis/get_team_rankings.py b/data-analysis/get_team_rankings.py similarity index 100% rename from data analysis/get_team_rankings.py rename to data-analysis/get_team_rankings.py diff --git a/data analysis/requirements.txt b/data-analysis/requirements.txt similarity index 100% rename from data analysis/requirements.txt rename to data-analysis/requirements.txt diff --git a/data analysis/superscript.py b/data-analysis/superscript.py similarity index 100% rename from data analysis/superscript.py rename to data-analysis/superscript.py diff --git a/data analysis/visualize_pit.py b/data-analysis/visualize_pit.py similarity index 100% rename from data analysis/visualize_pit.py rename to data-analysis/visualize_pit.py