mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-25 09:09:09 +00:00
Arthur Lu
GitHub
commit
12e0a263bd
@ -1,7 +1,6 @@
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FROM ubuntu:20.04
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FROM python:slim
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WORKDIR /
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RUN apt-get -y update
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RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends tzdata
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RUN apt-get install -y python3 python3-dev git python3-pip python3-kivy python-is-python3 libgl1-mesa-dev build-essential
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RUN ln -s $(which pip3) /usr/bin/pip
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RUN pip install pymongo pandas numpy scipy scikit-learn matplotlib pylint kivy
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RUN apt-get -y update; apt-get -y upgrade
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RUN apt-get -y install git
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COPY requirements.txt .
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RUN pip install -r requirements.txt
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@ -1,2 +0,0 @@
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FROM titanscout2022/tra-analysis-base:latest
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WORKDIR /
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@ -1,28 +1,22 @@
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{
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"name": "TRA Analysis Development Environment",
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"build": {
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"dockerfile": "dev-dockerfile",
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"dockerfile": "Dockerfile",
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},
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"settings": {
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"terminal.integrated.shell.linux": "/bin/bash",
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||||
"python.pythonPath": "/usr/local/bin/python",
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||||
"python.pythonPath": "",
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||||
"python.linting.enabled": true,
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"python.linting.pylintEnabled": true,
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||||
"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
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||||
"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
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||||
"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
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||||
"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
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||||
"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
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||||
"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
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||||
"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
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"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
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"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
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"python.testing.pytestPath": "/usr/local/py-utils/bin/pytest"
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"python.linting.pylintPath": "",
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"python.testing.pytestPath": "",
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"editor.tabSize": 4,
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"editor.insertSpaces": false
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},
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"extensions": [
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"mhutchie.git-graph",
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"ms-python.python",
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"waderyan.gitblame"
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],
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"postCreateCommand": "/usr/bin/pip3 install -r ${containerWorkspaceFolder}/analysis-master/requirements.txt && /usr/bin/pip3 install --no-cache-dir pylint && /usr/bin/pip3 install pytest"
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"postCreateCommand": ""
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}
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8
.devcontainer/requirements.txt
Normal file
8
.devcontainer/requirements.txt
Normal file
@ -0,0 +1,8 @@
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numpy
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scipy
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scikit-learn
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six
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pyparsing
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pylint
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pytest
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4
.github/workflows/ut-analysis.yml
vendored
4
.github/workflows/ut-analysis.yml
vendored
@ -10,12 +10,12 @@ on:
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branches: [ master ]
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jobs:
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build:
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unittest:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.7, 3.8]
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python-version: ["3.7", "3.8", "3.9", "3.10"]
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env:
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working-directory: ./analysis-master/
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@ -2,5 +2,7 @@ numpy
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scipy
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scikit-learn
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six
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matplotlib
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pyparsing
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pylint
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pytest
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@ -5,9 +5,11 @@ from sklearn import metrics
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from tra_analysis import Analysis as an
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from tra_analysis import Array
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from tra_analysis import ClassificationMetric
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from tra_analysis import Clustering
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from tra_analysis import CorrelationTest
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from tra_analysis import Fit
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from tra_analysis import KNN
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from tra_analysis import metrics as m
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from tra_analysis import NaiveBayes
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from tra_analysis import RandomForest
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from tra_analysis import RegressionMetric
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@ -26,7 +28,7 @@ x_data_circular = []
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y_data_circular = []
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y_data_ccu = [1, 3, 7, 14, 21]
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y_data_ccd = [1, 5, 7, 8.5, 8.66]
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y_data_ccd = [8.66, 8.5, 7, 5, 1]
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test_data_scrambled = [-32, 34, 19, 72, -65, -11, -43, 6, 85, -17, -98, -26, 12, 20, 9, -92, -40, 98, -78, 17, -20, 49, 93, -27, -24, -66, 40, 84, 1, -64, -68, -25, -42, -46, -76, 43, -3, 30, -14, -34, -55, -13, 41, -30, 0, -61, 48, 23, 60, 87, 80, 77, 53, 73, 79, 24, -52, 82, 8, -44, 65, 47, -77, 94, 7, 37, -79, 36, -94, 91, 59, 10, 97, -38, -67, 83, 54, 31, -95, -63, 16, -45, 21, -12, 66, -48, -18, -96, -90, -21, -83, -74, 39, 64, 69, -97, 13, 55, 27, -39]
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test_data_sorted = [-98, -97, -96, -95, -94, -92, -90, -83, -79, -78, -77, -76, -74, -68, -67, -66, -65, -64, -63, -61, -55, -52, -48, -46, -45, -44, -43, -42, -40, -39, -38, -34, -32, -30, -27, -26, -25, -24, -21, -20, -18, -17, -14, -13, -12, -11, -3, 0, 1, 6, 7, 8, 9, 10, 12, 13, 16, 17, 19, 20, 21, 23, 24, 27, 30, 31, 34, 36, 37, 39, 40, 41, 43, 47, 48, 49, 53, 54, 55, 59, 60, 64, 65, 66, 69, 72, 73, 77, 79, 80, 82, 83, 84, 85, 87, 91, 93, 94, 97, 98]
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@ -47,16 +49,25 @@ def test_basicstats():
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def test_regression():
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["lin"])) == True
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
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def test_metrics():
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assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
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assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
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#assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))]
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e = [[(21.346, 7.875), (20.415, 7.808), (29.037, 7.170)], [(28.654, 7.875), (28.654, 7.875), (23.225, 6.287)]]
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r = an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0])
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i = 0
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for group in r:
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j = 0
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for team in group:
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assert abs(team.mu - e[i][j][0]) < 0.001
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assert abs(team.sigma - e[i][j][1]) < 0.001
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j+=1
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i+=1
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def test_array():
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@ -143,13 +154,8 @@ def test_sort():
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def test_statisticaltest():
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#print(StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]))
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assert StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]) == \
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{'group 1 and group 2': [0.32571517201527916, False], 'group 1 and group 3': [0.977145516045838, False], 'group 2 and group 3': [0.6514303440305589, False]}
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#assert all(np.isclose([i[0] for i in list(StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]).values],
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# [0.32571517201527916, 0.977145516045838, 0.6514303440305589]))
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#assert [i[1] for i in StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]).values] == \
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# [False, False, False]
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def test_svm():
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@ -231,3 +237,17 @@ def test_equation():
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}
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for key in list(correctParse.keys()):
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assert parser.eval(key) == correctParse[key]
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def test_clustering():
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normalizer = sklearn.preprocessing.Normalizer()
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data = X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]])
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assert Clustering.dbscan(data, eps=3, min_samples=2).tolist() == [0, 0, 0, 1, 1, -1]
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assert Clustering.dbscan(data, normalizer=normalizer, eps=3, min_samples=2).tolist() == [0, 0, 0, 0, 0, 0]
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data = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]])
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assert Clustering.spectral(data, n_clusters=2, assign_labels='discretize', random_state=0).tolist() == [1, 1, 1, 0, 0, 0]
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assert Clustering.spectral(data, normalizer=normalizer, n_clusters=2, assign_labels='discretize', random_state=0).tolist() == [0, 1, 1, 0, 0, 0]
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@ -7,10 +7,19 @@
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "3.0.2"
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__version__ = "3.0.6"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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3.0.6:
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- added docstrings
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3.0.5:
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- removed extra submodule imports
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- fixed/optimized header
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3.0.4:
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- removed -_obj imports
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3.0.3:
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- fixed spelling of deprecate
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3.0.2:
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- fixed __all__
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3.0.1:
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@ -58,7 +67,7 @@ __changelog__ = """changelog:
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- cycle sort
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- cocktail sort
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- tested all sorting algorithms with both lists and numpy arrays
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- depreciated sort function from Array class
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- deprecated sort function from Array class
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- added warnings as an import
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2.1.4:
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- added sort and search functions to Array class
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@ -136,7 +145,7 @@ __changelog__ = """changelog:
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1.12.4:
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- renamed gliko to glicko
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1.12.3:
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- removed depreciated code
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- removed deprecated code
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1.12.2:
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- removed team first time trueskill instantiation in favor of integration in superscript.py
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1.12.1:
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@ -248,10 +257,10 @@ __changelog__ = """changelog:
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1.0.0:
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- removed c_entities,nc_entities,obstacles,objectives from __all__
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- applied numba.jit to all functions
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- depreciated and removed stdev_z_split
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- deprecated and removed stdev_z_split
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- cleaned up histo_analysis to include numpy and numba.jit optimizations
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- depreciated and removed all regression functions in favor of future pytorch optimizer
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- depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
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- deprecated and removed all regression functions in favor of future pytorch optimizer
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- deprecated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
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- optimized z_normalize using sklearn.preprocessing.normalize
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- TODO: implement kernel/function based pytorch regression optimizer
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0.9.0:
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@ -270,8 +279,8 @@ __changelog__ = """changelog:
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- refactors
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- bugfixes
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0.8.0:
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- depreciated histo_analysis_old
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- depreciated debug
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- deprecated histo_analysis_old
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- deprecated debug
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- altered basic_analysis to take array data instead of filepath
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- refactor
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- optimization
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@ -319,7 +328,7 @@ __changelog__ = """changelog:
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0.3.5:
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- major bug fixes
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- updated historical analysis
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- depreciated old historical analysis
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- deprecated old historical analysis
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0.3.4:
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- added __version__, __author__, __all__
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- added polynomial regression
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@ -357,7 +366,6 @@ __all__ = [
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'histo_analysis',
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'regression',
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'Metric',
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'kmeans',
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'pca',
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'decisiontree',
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# all statistics functions left out due to integration in other functions
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@ -370,40 +378,39 @@ __all__ = [
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import csv
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from tra_analysis.metrics import elo as Elo
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from tra_analysis.metrics import glicko2 as Glicko2
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import math
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import numpy as np
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import scipy
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from scipy import optimize, stats
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import sklearn
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from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
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import sklearn, sklearn.cluster, sklearn.pipeline
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from tra_analysis.metrics import trueskill as Trueskill
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import warnings
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# import submodules
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from .Array import Array
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from .ClassificationMetric import ClassificationMetric
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from .CorrelationTest_obj import CorrelationTest
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from .KNN_obj import KNN
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from .NaiveBayes_obj import NaiveBayes
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from .RandomForest_obj import RandomForest
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from .RegressionMetric import RegressionMetric
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from .Sort_obj import Sort
|
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from .StatisticalTest_obj import StatisticalTest
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from . import SVM
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|
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class error(ValueError):
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pass
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def load_csv(filepath):
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"""
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Loads csv file into 2D numpy array. Does not check csv file validity.
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parameters:
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filepath: String path to the csv file
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return:
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2D numpy array of values stored in csv file
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"""
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with open(filepath, newline='') as csvfile:
|
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file_array = np.array(list(csv.reader(csvfile)))
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csvfile.close()
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return file_array
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|
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# expects 1d array
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def basic_stats(data):
|
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|
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"""
|
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Calculates mean, median, standard deviation, variance, minimum, maximum of a simple set of elements.
|
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parameters:
|
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data: List representing set of unordered elements
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return:
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Dictionary with (mean, median, standard-deviation, variance, minimum, maximum) as keys and corresponding values
|
||||
"""
|
||||
data_t = np.array(data).astype(float)
|
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|
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_mean = mean(data_t)
|
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@ -415,24 +422,43 @@ def basic_stats(data):
|
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|
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return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
|
||||
|
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# returns z score with inputs of point, mean and standard deviation of spread
|
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def z_score(point, mean, stdev):
|
||||
"""
|
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Calculates z score of a specific point given mean and standard deviation of data.
|
||||
parameters:
|
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point: Real value corresponding to a single point of data
|
||||
mean: Real value corresponding to the mean of the dataset
|
||||
stdev: Real value corresponding to the standard deviation of the dataset
|
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return:
|
||||
Real value that is the point's z score
|
||||
"""
|
||||
score = (point - mean) / stdev
|
||||
|
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return score
|
||||
|
||||
# expects 2d array, normalizes across all axes
|
||||
def z_normalize(array, *args):
|
||||
|
||||
"""
|
||||
Applies sklearn.normalize(array, axis = args) on any arraylike parseable by numpy.
|
||||
parameters:
|
||||
array: array like structure of reals aka nested indexables
|
||||
*args: arguments relating to axis normalized against
|
||||
return:
|
||||
numpy array of normalized values from ArrayLike input
|
||||
"""
|
||||
array = np.array(array)
|
||||
for arg in args:
|
||||
array = sklearn.preprocessing.normalize(array, axis = arg)
|
||||
|
||||
return array
|
||||
|
||||
# expects 2d array of [x,y]
|
||||
def histo_analysis(hist_data):
|
||||
|
||||
"""
|
||||
Calculates the mean and standard deviation of derivatives of (x,y) points. Requires at least 2 points to compute.
|
||||
parameters:
|
||||
hist_data: list of real coordinate point data (x, y)
|
||||
return:
|
||||
Dictionary with (mean, deviation) as keys to corresponding values
|
||||
"""
|
||||
if len(hist_data[0]) > 2:
|
||||
|
||||
hist_data = np.array(hist_data)
|
||||
@ -448,7 +474,15 @@ def histo_analysis(hist_data):
|
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return None
|
||||
|
||||
def regression(inputs, outputs, args): # inputs, outputs expects N-D array
|
||||
|
||||
"""
|
||||
Applies specified regression kernels onto input, output data pairs.
|
||||
parameters:
|
||||
inputs: List of Reals representing independent variable values of each point
|
||||
outputs: List of Reals representing dependent variable values of each point
|
||||
args: List of Strings from values (lin, log, exp, ply, sig)
|
||||
return:
|
||||
Dictionary with keys (lin, log, exp, ply, sig) as keys to correspondiong regression models
|
||||
"""
|
||||
X = np.array(inputs)
|
||||
y = np.array(outputs)
|
||||
|
||||
@ -552,13 +586,39 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
|
||||
return regressions
|
||||
|
||||
class Metric:
|
||||
|
||||
"""
|
||||
The metric class wraps the metrics models. Call without instantiation as Metric.<method>(...)
|
||||
"""
|
||||
def elo(self, starting_score, opposing_score, observed, N, K):
|
||||
|
||||
"""
|
||||
Calculates an elo adjusted ELO score given a player's current score, opponent's score, and outcome of match.
|
||||
reference: https://en.wikipedia.org/wiki/Elo_rating_system
|
||||
parameters:
|
||||
starting_score: Real value representing player's ELO score before a match
|
||||
opposing_score: Real value representing opponent's score before the match
|
||||
observed: Array of Real values representing multiple sequential match outcomes against the same opponent. 1 for match win, 0.5 for tie, 0 for loss.
|
||||
N: Real value representing the normal or mean score expected (usually 1200)
|
||||
K: R eal value representing a system constant, determines how quickly players will change scores (usually 24)
|
||||
return:
|
||||
Real value representing the player's new ELO score
|
||||
"""
|
||||
return Elo.calculate(starting_score, opposing_score, observed, N, K)
|
||||
|
||||
def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
|
||||
|
||||
"""
|
||||
Calculates an adjusted Glicko-2 score given a player's current score, multiple opponent's score, and outcome of several matches.
|
||||
reference: http://www.glicko.net/glicko/glicko2.pdf
|
||||
parameters:
|
||||
starting_score: Real value representing the player's Glicko-2 score
|
||||
starting_rd: Real value representing the player's RD
|
||||
starting_vol: Real value representing the player's volatility
|
||||
opposing_score: List of Real values representing multiple opponent's Glicko-2 scores
|
||||
opposing_rd: List of Real values representing multiple opponent's RD
|
||||
opposing_vol: List of Real values representing multiple opponent's volatility
|
||||
observations: List of Real values representing the outcome of several matches, where each match's opponent corresponds with the opposing_score, opposing_rd, opposing_vol values of the same indesx. Outcomes can be a score, presuming greater score is better.
|
||||
return:
|
||||
Tuple of 3 Real values representing the player's new score, rd, and vol
|
||||
"""
|
||||
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)
|
||||
@ -566,7 +626,15 @@ class Metric:
|
||||
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)]]
|
||||
|
||||
"""
|
||||
Calculates the score changes for multiple teams playing in a single match accoding to the trueskill algorithm.
|
||||
reference: https://trueskill.org/
|
||||
parameters:
|
||||
teams_data: List of List of Tuples of 2 Real values representing multiple player ratings. List of teams, which is a List of players. Each player rating is a Tuple of 2 Real values (mu, sigma).
|
||||
observations: List of Real values representing the match outcome. Each value in the List is the score corresponding to the team at the same index in teams_data.
|
||||
return:
|
||||
List of List of Tuples of 2 Real values representing new player ratings. Same structure as teams_data.
|
||||
"""
|
||||
team_ratings = []
|
||||
|
||||
for team in teams_data:
|
||||
@ -602,23 +670,31 @@ def npmax(data):
|
||||
|
||||
return np.amax(data)
|
||||
|
||||
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
|
||||
|
||||
def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
|
||||
|
||||
"""
|
||||
Performs a principle component analysis on the input data.
|
||||
reference: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
|
||||
parameters:
|
||||
data: Arraylike of Reals representing the set of data to perform PCA on
|
||||
* : refer to reference for usage, parameters follow same usage
|
||||
return:
|
||||
Arraylike of Reals representing the set of data that has had PCA performed. The dimensionality of the Arraylike may be smaller or equal.
|
||||
"""
|
||||
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)
|
||||
|
||||
def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
|
||||
|
||||
"""
|
||||
Generates a decision tree classifier fitted to the given data.
|
||||
reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
|
||||
parameters:
|
||||
data: List of values representing each data point of multiple axes
|
||||
labels: List of values represeing the labels corresponding to the same index at data
|
||||
* : refer to reference for usage, parameters follow same usage
|
||||
return:
|
||||
DecisionTreeClassifier model and corresponding classification accuracy metrics
|
||||
"""
|
||||
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)
|
||||
|
||||
@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import Array'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.3"
|
||||
__version__ = "1.0.4"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.4:
|
||||
- fixed spelling of deprecate
|
||||
1.0.3:
|
||||
- fixed __all__
|
||||
1.0.2:
|
||||
@ -135,8 +137,8 @@ class Array(): # tests on nd arrays independent of basic_stats
|
||||
|
||||
return Array(np.transpose(self.array))
|
||||
|
||||
def sort(self, array): # depreciated
|
||||
warnings.warn("Array.sort has been depreciated in favor of Sort")
|
||||
def sort(self, array): # deprecated
|
||||
warnings.warn("Array.sort has been deprecated in favor of Sort")
|
||||
array_length = len(array)
|
||||
if array_length <= 1:
|
||||
return array
|
||||
|
||||
@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import ClassificationMetric'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.1"
|
||||
__version__ = "1.0.2"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.2:
|
||||
- optimized imports
|
||||
1.0.1:
|
||||
- fixed __all__
|
||||
1.0.0:
|
||||
@ -22,7 +24,6 @@ __all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import metrics
|
||||
|
||||
class ClassificationMetric():
|
||||
|
||||
|
||||
63
analysis-master/tra_analysis/Clustering.py
Normal file
63
analysis-master/tra_analysis/Clustering.py
Normal file
@ -0,0 +1,63 @@
|
||||
# Titan Robotics Team 2022: Clustering submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import Clustering'
|
||||
# setup:
|
||||
|
||||
__version__ = "2.0.2"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
2.0.2:
|
||||
- generalized optional args to **kwargs
|
||||
2.0.1:
|
||||
- added normalization preprocessing to clustering, expects instance of sklearn.preprocessing.Normalizer()
|
||||
2.0.0:
|
||||
- added dbscan clustering algo
|
||||
- added spectral clustering algo
|
||||
1.0.0:
|
||||
- created this submodule
|
||||
- copied kmeans clustering from Analysis
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"kmeans",
|
||||
"dbscan",
|
||||
"spectral",
|
||||
]
|
||||
|
||||
import sklearn
|
||||
|
||||
def kmeans(data, normalizer = None, **kwargs):
|
||||
|
||||
if normalizer != None:
|
||||
data = normalizer.transform(data)
|
||||
|
||||
kernel = sklearn.cluster.KMeans(**kwargs)
|
||||
kernel.fit(data)
|
||||
predictions = kernel.predict(data)
|
||||
centers = kernel.cluster_centers_
|
||||
|
||||
return centers, predictions
|
||||
|
||||
def dbscan(data, normalizer=None, **kwargs):
|
||||
|
||||
if normalizer != None:
|
||||
data = normalizer.transform(data)
|
||||
|
||||
model = sklearn.cluster.DBSCAN(**kwargs).fit(data)
|
||||
|
||||
return model.labels_
|
||||
|
||||
def spectral(data, normalizer=None, **kwargs):
|
||||
|
||||
if normalizer != None:
|
||||
data = normalizer.transform(data)
|
||||
|
||||
model = sklearn.cluster.SpectralClustering(**kwargs).fit(data)
|
||||
|
||||
return model.labels_
|
||||
@ -4,9 +4,13 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import CorrelationTest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.1"
|
||||
__version__ = "1.0.3"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3:
|
||||
- generalized optional args to **kwargs
|
||||
1.0.2:
|
||||
- optimized imports
|
||||
1.0.1:
|
||||
- fixed __all__
|
||||
1.0.0:
|
||||
@ -29,7 +33,6 @@ __all__ = [
|
||||
]
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
def anova_oneway(*args): #expects arrays of samples
|
||||
|
||||
@ -41,9 +44,9 @@ def pearson(x, y):
|
||||
results = scipy.stats.pearsonr(x, y)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def spearman(a, b = None, axis = 0, nan_policy = 'propagate'):
|
||||
def spearman(a, b = None, **kwargs):
|
||||
|
||||
results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
|
||||
results = scipy.stats.spearmanr(a, b = b, **kwargs)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def point_biserial(x, y):
|
||||
@ -51,17 +54,17 @@ def point_biserial(x, y):
|
||||
results = scipy.stats.pointbiserialr(x, y)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
|
||||
def kendall(x, y, **kwargs):
|
||||
|
||||
results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
|
||||
results = scipy.stats.kendalltau(x, y, **kwargs)
|
||||
return {"tau": results[0], "p-value": results[1]}
|
||||
|
||||
def kendall_weighted(x, y, rank = True, weigher = None, additive = True):
|
||||
def kendall_weighted(x, y, **kwargs):
|
||||
|
||||
results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
|
||||
results = scipy.stats.weightedtau(x, y, **kwargs)
|
||||
return {"tau": results[0], "p-value": results[1]}
|
||||
|
||||
def mgc(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
|
||||
def mgc(x, y, **kwargs):
|
||||
|
||||
results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
|
||||
results = scipy.stats.multiscale_graphcorr(x, y, **kwargs)
|
||||
return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
|
||||
@ -1,41 +0,0 @@
|
||||
# Only included for backwards compatibility! Do not update, CorrelationTest is preferred and supported.
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
class CorrelationTest:
|
||||
|
||||
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
|
||||
@ -4,9 +4,13 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import KNN'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__version__ = "1.0.2"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.2:
|
||||
- generalized optional args to **kwargs
|
||||
1.0.1:
|
||||
- optimized imports
|
||||
1.0.0:
|
||||
- ported analysis.KNN() here
|
||||
- removed classness
|
||||
@ -23,22 +27,21 @@ __all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, neighbors
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
|
||||
def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, **kwargs): #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(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 = sklearn.neighbors.KNeighborsClassifier(n_neighbors = n_neighbors, **kwargs)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
|
||||
def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, **kwargs):
|
||||
|
||||
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 = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, **kwargs)
|
||||
model.fit(data_train, outputs_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, neighbors
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class KNN:
|
||||
|
||||
def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, 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, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, 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, RegressionMetric(predictions, outputs_test)
|
||||
@ -4,9 +4,13 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import NaiveBayes'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__version__ = "1.0.2"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.2:
|
||||
- generalized optional args to **kwargs
|
||||
1.0.1:
|
||||
- optimized imports
|
||||
1.0.0:
|
||||
- ported analysis.NaiveBayes() here
|
||||
- removed classness
|
||||
@ -18,46 +22,45 @@ __author__ = (
|
||||
|
||||
__all__ = [
|
||||
'gaussian',
|
||||
'multinomial'
|
||||
'multinomial',
|
||||
'bernoulli',
|
||||
'complement'
|
||||
'complement',
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, naive_bayes
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
from . import ClassificationMetric
|
||||
|
||||
def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
|
||||
def gaussian(data, labels, test_size = 0.3, **kwargs):
|
||||
|
||||
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 = sklearn.naive_bayes.GaussianNB(**kwargs)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def multinomial(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
|
||||
def multinomial(data, labels, test_size = 0.3, **kwargs):
|
||||
|
||||
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 = sklearn.naive_bayes.MultinomialNB(**kwargs)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def bernoulli(data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
|
||||
def bernoulli(data, labels, test_size = 0.3, **kwargs):
|
||||
|
||||
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 = sklearn.naive_bayes.BernoulliNB(**kwargs)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def complement(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
|
||||
def complement(data, labels, test_size = 0.3, **kwargs):
|
||||
|
||||
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 = sklearn.naive_bayes.ComplementNB(**kwargs)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
|
||||
@ -1,43 +0,0 @@
|
||||
# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, naive_bayes
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
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, ClassificationMetric(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, ClassificationMetric(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, ClassificationMetric(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, ClassificationMetric(predictions, labels_test)
|
||||
@ -4,9 +4,14 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import RandomForest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.1"
|
||||
__version__ = "1.0.3"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3:
|
||||
- updated RandomForestClassifier and RandomForestRegressor parameters to match sklearn v 1.0.2
|
||||
- changed default values for kwargs to rely on sklearn
|
||||
1.0.2:
|
||||
- optimized imports
|
||||
1.0.1:
|
||||
- fixed __all__
|
||||
1.0.0:
|
||||
@ -23,23 +28,22 @@ __all__ = [
|
||||
"random_forest_regressor",
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import ensemble, model_selection
|
||||
import sklearn, sklearn.ensemble, sklearn.naive_bayes
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
def random_forest_classifier(data, labels, test_size, n_estimators, 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):
|
||||
def random_forest_classifier(data, labels, test_size, n_estimators, **kwargs):
|
||||
|
||||
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 = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, **kwargs)
|
||||
kernel.fit(data_train, labels_train)
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def random_forest_regressor(data, outputs, test_size, n_estimators, 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):
|
||||
def random_forest_regressor(data, outputs, test_size, n_estimators, **kwargs):
|
||||
|
||||
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 = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, **kwargs)
|
||||
kernel.fit(data_train, outputs_train)
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
# Only included for backwards compatibility! Do not update, RandomForest is preferred and supported.
|
||||
|
||||
import sklearn
|
||||
from sklearn import ensemble, model_selection
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class RandomForest:
|
||||
|
||||
def random_forest_classifier(self, data, labels, test_size, n_estimators, 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, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def random_forest_regressor(self, data, outputs, test_size, n_estimators, 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, RegressionMetric(predictions, outputs_test)
|
||||
@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import RegressionMetric'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__version__ = "1.0.1"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.1:
|
||||
- optimized imports
|
||||
1.0.0:
|
||||
- ported analysis.RegressionMetric() here
|
||||
"""
|
||||
@ -21,7 +23,6 @@ __all__ = [
|
||||
|
||||
import numpy as np
|
||||
import sklearn
|
||||
from sklearn import metrics
|
||||
|
||||
class RegressionMetric():
|
||||
|
||||
|
||||
@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import SVM'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.2"
|
||||
__version__ = "1.0.3"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3:
|
||||
- optimized imports
|
||||
1.0.2:
|
||||
- fixed __all__
|
||||
1.0.1:
|
||||
@ -30,7 +32,6 @@ __all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import svm
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class CustomKernel:
|
||||
|
||||
@ -16,7 +16,7 @@ __changelog__ = """changelog:
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"James Pan <zpan@imsa.edu>"
|
||||
"James Pan <zpan@imsa.edu>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -1,391 +0,0 @@
|
||||
# Only included for backwards compatibility! Do not update, Sort is preferred and supported.
|
||||
|
||||
class Sort: # if you haven't used a sort, then you've never lived
|
||||
|
||||
def quicksort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
less = []
|
||||
equal = []
|
||||
greater = []
|
||||
|
||||
if len(array) > 1:
|
||||
pivot = array[0]
|
||||
for x in array:
|
||||
if x < pivot:
|
||||
less.append(x)
|
||||
elif x == pivot:
|
||||
equal.append(x)
|
||||
elif x > pivot:
|
||||
greater.append(x)
|
||||
return sort(less)+equal+sort(greater)
|
||||
else:
|
||||
return array
|
||||
|
||||
return np.array(sort(a))
|
||||
|
||||
def mergesort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
if len(array) >1:
|
||||
middle = len(array) // 2
|
||||
L = array[:middle]
|
||||
R = array[middle:]
|
||||
|
||||
sort(L)
|
||||
sort(R)
|
||||
|
||||
i = j = k = 0
|
||||
|
||||
while i < len(L) and j < len(R):
|
||||
if L[i] < R[j]:
|
||||
array[k] = L[i]
|
||||
i+= 1
|
||||
else:
|
||||
array[k] = R[j]
|
||||
j+= 1
|
||||
k+= 1
|
||||
|
||||
while i < len(L):
|
||||
array[k] = L[i]
|
||||
i+= 1
|
||||
k+= 1
|
||||
|
||||
while j < len(R):
|
||||
array[k] = R[j]
|
||||
j+= 1
|
||||
k+= 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def introsort(self, a):
|
||||
|
||||
def sort(array, start, end, maxdepth):
|
||||
|
||||
array = array
|
||||
|
||||
if end - start <= 1:
|
||||
return
|
||||
elif maxdepth == 0:
|
||||
heapsort(array, start, end)
|
||||
else:
|
||||
p = partition(array, start, end)
|
||||
sort(array, start, p + 1, maxdepth - 1)
|
||||
sort(array, p + 1, end, maxdepth - 1)
|
||||
|
||||
return array
|
||||
|
||||
def partition(array, start, end):
|
||||
pivot = array[start]
|
||||
i = start - 1
|
||||
j = end
|
||||
|
||||
while True:
|
||||
i = i + 1
|
||||
while array[i] < pivot:
|
||||
i = i + 1
|
||||
j = j - 1
|
||||
while array[j] > pivot:
|
||||
j = j - 1
|
||||
|
||||
if i >= j:
|
||||
return j
|
||||
|
||||
swap(array, i, j)
|
||||
|
||||
def swap(array, i, j):
|
||||
array[i], array[j] = array[j], array[i]
|
||||
|
||||
def heapsort(array, start, end):
|
||||
build_max_heap(array, start, end)
|
||||
for i in range(end - 1, start, -1):
|
||||
swap(array, start, i)
|
||||
max_heapify(array, index=0, start=start, end=i)
|
||||
|
||||
def build_max_heap(array, start, end):
|
||||
def parent(i):
|
||||
return (i - 1)//2
|
||||
length = end - start
|
||||
index = parent(length - 1)
|
||||
while index >= 0:
|
||||
max_heapify(array, index, start, end)
|
||||
index = index - 1
|
||||
|
||||
def max_heapify(array, index, start, end):
|
||||
def left(i):
|
||||
return 2*i + 1
|
||||
def right(i):
|
||||
return 2*i + 2
|
||||
|
||||
size = end - start
|
||||
l = left(index)
|
||||
r = right(index)
|
||||
if (l < size and array[start + l] > array[start + index]):
|
||||
largest = l
|
||||
else:
|
||||
largest = index
|
||||
if (r < size and array[start + r] > array[start + largest]):
|
||||
largest = r
|
||||
if largest != index:
|
||||
swap(array, start + largest, start + index)
|
||||
max_heapify(array, largest, start, end)
|
||||
|
||||
maxdepth = (len(a).bit_length() - 1)*2
|
||||
|
||||
return sort(a, 0, len(a), maxdepth)
|
||||
|
||||
def heapsort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
n = len(array)
|
||||
|
||||
for i in range(n//2 - 1, -1, -1):
|
||||
heapify(array, n, i)
|
||||
|
||||
for i in range(n-1, 0, -1):
|
||||
array[i], array[0] = array[0], array[i]
|
||||
heapify(array, i, 0)
|
||||
|
||||
return array
|
||||
|
||||
def heapify(array, n, i):
|
||||
|
||||
array = array
|
||||
|
||||
largest = i
|
||||
l = 2 * i + 1
|
||||
r = 2 * i + 2
|
||||
|
||||
if l < n and array[i] < array[l]:
|
||||
largest = l
|
||||
|
||||
if r < n and array[largest] < array[r]:
|
||||
largest = r
|
||||
|
||||
if largest != i:
|
||||
array[i],array[largest] = array[largest],array[i]
|
||||
heapify(array, n, largest)
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def insertionsort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(1, len(array)):
|
||||
|
||||
key = array[i]
|
||||
|
||||
j = i-1
|
||||
while j >=0 and key < array[j] :
|
||||
array[j+1] = array[j]
|
||||
j -= 1
|
||||
array[j+1] = key
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def timsort(self, a, block = 32):
|
||||
|
||||
BLOCK = block
|
||||
|
||||
def sort(array, n):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(0, n, BLOCK):
|
||||
insertionsort(array, i, min((i+31), (n-1)))
|
||||
|
||||
size = BLOCK
|
||||
while size < n:
|
||||
|
||||
for left in range(0, n, 2*size):
|
||||
|
||||
mid = left + size - 1
|
||||
right = min((left + 2*size - 1), (n-1))
|
||||
merge(array, left, mid, right)
|
||||
|
||||
size = 2*size
|
||||
|
||||
return array
|
||||
|
||||
def insertionsort(array, left, right):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(left + 1, right+1):
|
||||
|
||||
temp = array[i]
|
||||
j = i - 1
|
||||
while j >= left and array[j] > temp :
|
||||
|
||||
array[j+1] = array[j]
|
||||
j -= 1
|
||||
|
||||
array[j+1] = temp
|
||||
|
||||
return array
|
||||
|
||||
|
||||
def merge(array, l, m, r):
|
||||
|
||||
len1, len2 = m - l + 1, r - m
|
||||
left, right = [], []
|
||||
for i in range(0, len1):
|
||||
left.append(array[l + i])
|
||||
for i in range(0, len2):
|
||||
right.append(array[m + 1 + i])
|
||||
|
||||
i, j, k = 0, 0, l
|
||||
|
||||
while i < len1 and j < len2:
|
||||
|
||||
if left[i] <= right[j]:
|
||||
array[k] = left[i]
|
||||
i += 1
|
||||
|
||||
else:
|
||||
array[k] = right[j]
|
||||
j += 1
|
||||
|
||||
k += 1
|
||||
|
||||
while i < len1:
|
||||
|
||||
array[k] = left[i]
|
||||
k += 1
|
||||
i += 1
|
||||
|
||||
while j < len2:
|
||||
array[k] = right[j]
|
||||
k += 1
|
||||
j += 1
|
||||
|
||||
return sort(a, len(a))
|
||||
|
||||
def selectionsort(self, a):
|
||||
array = a
|
||||
for i in range(len(array)):
|
||||
min_idx = i
|
||||
for j in range(i+1, len(array)):
|
||||
if array[min_idx] > array[j]:
|
||||
min_idx = j
|
||||
array[i], array[min_idx] = array[min_idx], array[i]
|
||||
return array
|
||||
|
||||
def shellsort(self, a):
|
||||
array = a
|
||||
n = len(array)
|
||||
gap = n//2
|
||||
|
||||
while gap > 0:
|
||||
|
||||
for i in range(gap,n):
|
||||
|
||||
temp = array[i]
|
||||
j = i
|
||||
while j >= gap and array[j-gap] >temp:
|
||||
array[j] = array[j-gap]
|
||||
j -= gap
|
||||
array[j] = temp
|
||||
gap //= 2
|
||||
|
||||
return array
|
||||
|
||||
def bubblesort(self, a):
|
||||
|
||||
def sort(array):
|
||||
for i, num in enumerate(array):
|
||||
try:
|
||||
if array[i+1] < num:
|
||||
array[i] = array[i+1]
|
||||
array[i+1] = num
|
||||
sort(array)
|
||||
except IndexError:
|
||||
pass
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def cyclesort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
writes = 0
|
||||
|
||||
for cycleStart in range(0, len(array) - 1):
|
||||
item = array[cycleStart]
|
||||
|
||||
pos = cycleStart
|
||||
for i in range(cycleStart + 1, len(array)):
|
||||
if array[i] < item:
|
||||
pos += 1
|
||||
|
||||
if pos == cycleStart:
|
||||
continue
|
||||
|
||||
while item == array[pos]:
|
||||
pos += 1
|
||||
array[pos], item = item, array[pos]
|
||||
writes += 1
|
||||
|
||||
while pos != cycleStart:
|
||||
|
||||
pos = cycleStart
|
||||
for i in range(cycleStart + 1, len(array)):
|
||||
if array[i] < item:
|
||||
pos += 1
|
||||
|
||||
while item == array[pos]:
|
||||
pos += 1
|
||||
array[pos], item = item, array[pos]
|
||||
writes += 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def cocktailsort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
n = len(array)
|
||||
swapped = True
|
||||
start = 0
|
||||
end = n-1
|
||||
while (swapped == True):
|
||||
swapped = False
|
||||
for i in range (start, end):
|
||||
if (array[i] > array[i + 1]) :
|
||||
array[i], array[i + 1]= array[i + 1], array[i]
|
||||
swapped = True
|
||||
if (swapped == False):
|
||||
break
|
||||
swapped = False
|
||||
end = end-1
|
||||
for i in range(end-1, start-1, -1):
|
||||
if (array[i] > array[i + 1]):
|
||||
array[i], array[i + 1] = array[i + 1], array[i]
|
||||
swapped = True
|
||||
start = start + 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.2"
|
||||
__version__ = "1.0.3"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3:
|
||||
- optimized imports
|
||||
1.0.2:
|
||||
- added tukey_multicomparison
|
||||
- fixed styling
|
||||
@ -61,7 +63,6 @@ __all__ = [
|
||||
|
||||
import numpy as np
|
||||
import scipy
|
||||
from scipy import stats, interpolate
|
||||
|
||||
def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
|
||||
|
||||
@ -279,9 +280,9 @@ def get_tukeyQcrit(k, df, alpha=0.05):
|
||||
cv001 = c[:, 2::2]
|
||||
|
||||
if alpha == 0.05:
|
||||
intp = interpolate.interp1d(crows, cv005[:,k-2])
|
||||
intp = scipy.interpolate.interp1d(crows, cv005[:,k-2])
|
||||
elif alpha == 0.01:
|
||||
intp = interpolate.interp1d(crows, cv001[:,k-2])
|
||||
intp = scipy.interpolate.interp1d(crows, cv001[:,k-2])
|
||||
else:
|
||||
raise ValueError('only implemented for alpha equal to 0.01 and 0.05')
|
||||
return intp(df)
|
||||
|
||||
@ -1,170 +0,0 @@
|
||||
# Only included for backwards compatibility! Do not update, StatisticalTest is preferred and supported.
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
class StatisticalTest:
|
||||
|
||||
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]}
|
||||
@ -7,10 +7,17 @@
|
||||
# current benchmark of optimization: 1.33 times faster
|
||||
# setup:
|
||||
|
||||
__version__ = "3.0.0"
|
||||
__version__ = "4.0.0-dev"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
4.0.0:
|
||||
- deprecated all *_obj.py compatibility modules
|
||||
- deprecated titanlearn.py
|
||||
- deprecated visualization.py
|
||||
- removed matplotlib from requirements
|
||||
- removed extra submodule imports in Analysis
|
||||
- added typehinting, docstrings for each function
|
||||
3.0.0:
|
||||
- incremented version to release 3.0.0
|
||||
3.0.0-rc2:
|
||||
@ -40,6 +47,7 @@ __all__ = [
|
||||
"Analysis",
|
||||
"Array",
|
||||
"ClassificationMetric",
|
||||
"Clustering",
|
||||
"CorrelationTest",
|
||||
"Expression",
|
||||
"Fit",
|
||||
@ -53,9 +61,9 @@ __all__ = [
|
||||
]
|
||||
|
||||
from . import Analysis as Analysis
|
||||
from . import Analysis as analysis
|
||||
from .Array import Array
|
||||
from .ClassificationMetric import ClassificationMetric
|
||||
from . import Clustering
|
||||
from . import CorrelationTest
|
||||
from .equation import Expression
|
||||
from . import Fit
|
||||
|
||||
@ -0,0 +1,24 @@
|
||||
# Titan Robotics Team 2022: Metrics submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import metrics'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- implemented elo, glicko2, trueskill
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = {
|
||||
"Expression"
|
||||
}
|
||||
|
||||
from . import elo
|
||||
from . import glicko2
|
||||
from . import trueskill
|
||||
@ -1,222 +0,0 @@
|
||||
# Titan Robotics Team 2022: CUDA-based Regressions Module
|
||||
# Not actively maintained, may be removed in future release
|
||||
# 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 (as appropriate) and vectorized (except for one small part)
|
||||
# setup:
|
||||
|
||||
__version__ = "0.0.4"
|
||||
|
||||
# changelog should be viewed using print(analysis.regression.__changelog__)
|
||||
__changelog__ = """
|
||||
0.0.4:
|
||||
- bug fixes
|
||||
- fixed changelog
|
||||
0.0.3:
|
||||
- bug fixes
|
||||
0.0.2:
|
||||
-Added more parameters to log, exponential, polynomial
|
||||
-Added SigmoidalRegKernelArthur, because Arthur apparently needs
|
||||
to train the scaling and shifting of sigmoids
|
||||
0.0.1:
|
||||
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
|
||||
-already vectorized (except for polynomial generation) and CUDA-optimized
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'factorial',
|
||||
'take_all_pwrs',
|
||||
'num_poly_terms',
|
||||
'set_device',
|
||||
'LinearRegKernel',
|
||||
'SigmoidalRegKernel',
|
||||
'LogRegKernel',
|
||||
'PolyRegKernel',
|
||||
'ExpRegKernel',
|
||||
'SigmoidalRegKernelArthur',
|
||||
'SGDTrain',
|
||||
'CustomTrain',
|
||||
'CircleFit'
|
||||
]
|
||||
|
||||
import torch
|
||||
|
||||
global device
|
||||
|
||||
device = "cuda:0" if 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
|
||||
@ -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__ = "1.1.1"
|
||||
|
||||
#changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
1.1.1:
|
||||
- removed matplotlib import
|
||||
- removed graphloss()
|
||||
1.1.0:
|
||||
- added net, dataset, dataloader, and stdtrain template definitions
|
||||
- added graphloss function
|
||||
1.0.1:
|
||||
- added clear functions
|
||||
1.0.0:
|
||||
- complete rewrite planned
|
||||
- depreciated 1.0.0.xxx versions
|
||||
- added simple training loop
|
||||
0.0.x:
|
||||
-added generation of ANNS, basic SGD training
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <arthurlu@ttic.edu>,"
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
|
||||
__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)
|
||||
@ -1,58 +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__ = "0.0.1"
|
||||
|
||||
#changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.0.1:
|
||||
- added graphhistogram function as a fragment of visualize_pit.py
|
||||
0.0.0:
|
||||
- created visualization.py
|
||||
- added graphloss()
|
||||
- added imports
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <arthurlu@ttic.edu>,"
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'graphloss',
|
||||
]
|
||||
|
||||
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()
|
||||
Loading…
Reference in New Issue
Block a user