incremented version to 4.0.0

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

.devcontainer/Dockerfile

@@ -1,6 +1,7 @@
-FROM python:slim
+FROM ubuntu:20.04
 WORKDIR /
-RUN apt-get -y update; apt-get -y upgrade
-RUN apt-get -y install git
-COPY requirements.txt .
-RUN pip install -r requirements.txt
+RUN apt-get -y update
+RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends tzdata
+RUN apt-get install -y python3 python3-dev git python3-pip python3-kivy python-is-python3 libgl1-mesa-dev build-essential
+RUN ln -s $(which pip3) /usr/bin/pip
+RUN pip install pymongo pandas numpy scipy scikit-learn matplotlib pylint kivy

.devcontainer/dev-dockerfile

@@ -0,0 +1,2 @@
+FROM titanscout2022/tra-analysis-base:latest
+WORKDIR /

.devcontainer/devcontainer.json

@@ -1,22 +1,28 @@
 {
     "name": "TRA Analysis Development Environment",
     "build": {
-		"dockerfile": "Dockerfile",
+		"dockerfile": "dev-dockerfile",
     },
     "settings": { 
 		"terminal.integrated.shell.linux": "/bin/bash",
-		"python.pythonPath": "",
+		"python.pythonPath": "/usr/local/bin/python",
 		"python.linting.enabled": true,
 		"python.linting.pylintEnabled": true,
-		"python.linting.pylintPath": "",
-		"python.testing.pytestPath": "",
-    "editor.tabSize": 4,
-    "editor.insertSpaces": false
+		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
+		"python.testing.pytestPath": "/usr/local/py-utils/bin/pytest"
 	},
 	"extensions": [
 		"mhutchie.git-graph",
 		"ms-python.python",
 		"waderyan.gitblame"
 	],
-		"postCreateCommand": ""
+		"postCreateCommand": "/usr/bin/pip3 install -r ${containerWorkspaceFolder}/analysis-master/requirements.txt && /usr/bin/pip3 install --no-cache-dir pylint && /usr/bin/pip3 install pytest"
 	}

.devcontainer/requirements.txt

@@ -1,8 +0,0 @@
-numpy
-scipy
-scikit-learn
-six
-pyparsing
-
-pylint
-pytest

.github/workflows/ut-analysis.yml

@@ -10,12 +10,12 @@ on:
     branches: [ master ]
 
 jobs:
-  unittest:
+  build:
 
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: [3.7, 3.8]
 
     env:
       working-directory: ./analysis-master/

analysis-master/requirements.txt

@@ -2,7 +2,4 @@ numpy
 scipy
 scikit-learn
 six
-pyparsing
-
-pylint
-pytest
+pyparsing

analysis-master/test_analysis.py

@@ -9,7 +9,6 @@ from tra_analysis import Clustering
 from tra_analysis import CorrelationTest
 from tra_analysis import Fit
 from tra_analysis import KNN
-from tra_analysis import metrics as m
 from tra_analysis import NaiveBayes
 from tra_analysis import RandomForest
 from tra_analysis import RegressionMetric
@@ -28,7 +27,7 @@ x_data_circular = []
 y_data_circular = []
 
 y_data_ccu = [1, 3, 7, 14, 21]
-y_data_ccd = [8.66, 8.5, 7, 5, 1]
+y_data_ccd = [1, 5, 7, 8.5, 8.66]
 
 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]
 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]
@@ -49,25 +48,16 @@ def test_basicstats():
 def test_regression():
 
 	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["lin"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
 
 def test_metrics():
 
 	assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
 	assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
-	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)]]
-	r = an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0])
-	i = 0
-	for group in r:
-		j = 0
-		for team in group:
-			assert abs(team.mu - e[i][j][0]) < 0.001
-			assert abs(team.sigma - e[i][j][1]) < 0.001
-			j+=1
-		i+=1
+	#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))]
 
 def test_array():
 
@@ -153,9 +143,14 @@ def test_sort():
 		assert all(a == b for a, b in zip(sort(test_data_scrambled), test_data_sorted))
 
 def test_statisticaltest():
-
+	
+	#print(StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]))
 	assert StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]) == \
 		{'group 1 and group 2': [0.32571517201527916, False], 'group 1 and group 3': [0.977145516045838, False], 'group 2 and group 3': [0.6514303440305589, False]}
+	#assert all(np.isclose([i[0] for i in list(StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]).values],
+	#	[0.32571517201527916, 0.977145516045838, 0.6514303440305589]))
+	#assert [i[1] for i in StatisticalTest.tukey_multicomparison([test_data_linear, test_data_linear2, test_data_linear3]).values] == \
+	#	[False, False, False]
 
 def test_svm():
 

analysis-master/tra_analysis/Analysis.py

@@ -380,7 +380,7 @@ from tra_analysis.metrics import elo as Elo
 from tra_analysis.metrics import glicko2 as Glicko2
 import numpy as np
 import scipy
-import sklearn, sklearn.cluster, sklearn.pipeline
+import sklearn, sklearn.cluster
 from tra_analysis.metrics import trueskill as Trueskill
 
 # import submodules

analysis-master/tra_analysis/Clustering.py

@@ -4,12 +4,10 @@
 #    this should be imported as a python module using 'from tra_analysis import Clustering'
 # setup:
 
-__version__ = "2.0.2"
+__version__ = "2.0.1"
 
 # 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:
@@ -32,32 +30,32 @@ __all__ = [
 
 import sklearn
 
-def kmeans(data, normalizer = None, **kwargs):
+def kmeans(data, normalizer = None, 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"):
 
 	if  normalizer != None:
 		data = normalizer.transform(data)
 
-	kernel = sklearn.cluster.KMeans(**kwargs)
+	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 dbscan(data, normalizer=None, **kwargs):
+def dbscan(data, normalizer=None, eps=0.5, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None):
 
 	if  normalizer != None:
 		data = normalizer.transform(data)
 
-	model = sklearn.cluster.DBSCAN(**kwargs).fit(data)
+	model = sklearn.cluster.DBSCAN(eps = eps, min_samples = min_samples, metric = metric, metric_params = metric_params, algorithm = algorithm, leaf_size = leaf_size, p = p, n_jobs = n_jobs).fit(data)
 
 	return model.labels_
 
-def spectral(data, normalizer=None, **kwargs):
+def spectral(data, normalizer=None, n_clusters=8, eigen_solver=None, n_components=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, verbose=False):
 
 	if  normalizer != None:
 		data = normalizer.transform(data)
 
-	model = sklearn.cluster.SpectralClustering(**kwargs).fit(data)
+	model = sklearn.cluster.SpectralClustering(n_clusters = n_clusters, eigen_solver = eigen_solver, n_components = n_components, random_state = random_state, n_init = n_init, gamma = gamma, affinity = affinity, n_neighbors = n_neighbors, eigen_tol = eigen_tol, assign_labels = assign_labels, degree = degree, coef0 = coef0, kernel_params = kernel_params, n_jobs = n_jobs).fit(data)
 
 	return model.labels_

analysis-master/tra_analysis/CorrelationTest.py

@@ -4,11 +4,9 @@
 #    this should be imported as a python module using 'from tra_analysis import CorrelationTest'
 # setup:
 
-__version__ = "1.0.3"
+__version__ = "1.0.2"
 
 __changelog__ = """changelog:
-	1.0.3:
-		- generalized optional args to **kwargs
 	1.0.2:
 		- optimized imports
 	1.0.1:
@@ -44,9 +42,9 @@ def pearson(x, y):
 	results = scipy.stats.pearsonr(x, y)
 	return {"r-value": results[0], "p-value": results[1]}
 
-def spearman(a, b = None, **kwargs):
+def spearman(a, b = None, axis = 0, nan_policy = 'propagate'):
 
-	results = scipy.stats.spearmanr(a, b = b, **kwargs)
+	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(x, y):
@@ -54,17 +52,17 @@ def point_biserial(x, y):
 	results = scipy.stats.pointbiserialr(x, y)
 	return {"r-value": results[0], "p-value": results[1]}
 
-def kendall(x, y, **kwargs):
+def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
 
-	results = scipy.stats.kendalltau(x, y, **kwargs)
+	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(x, y, **kwargs):
+def kendall_weighted(x, y, rank = True, weigher = None, additive = True):
 
-	results = scipy.stats.weightedtau(x, y, **kwargs)
+	results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
 	return {"tau": results[0], "p-value": results[1]}
 
-def mgc(x, y, **kwargs):
+def mgc(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
 
-	results = scipy.stats.multiscale_graphcorr(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)
 	return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value

analysis-master/tra_analysis/KNN.py

@@ -4,11 +4,9 @@
 #    this should be imported as a python module using 'from tra_analysis import KNN'
 # setup:
 
-__version__ = "1.0.2"
+__version__ = "1.0.1"
 
 __changelog__ = """changelog:
-	1.0.2:
-		- generalized optional args to **kwargs
 	1.0.1:
 		- optimized imports
 	1.0.0:
@@ -29,19 +27,19 @@ __all__ = [
 import sklearn
 from . import ClassificationMetric, RegressionMetric
 
-def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, **kwargs): #expects *2d data and 1d labels post-scaling
+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
 
 	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, **kwargs)
+	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.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, **kwargs):
+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):
 
 	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, **kwargs)
+	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)
 

analysis-master/tra_analysis/NaiveBayes.py

@@ -4,11 +4,9 @@
 #    this should be imported as a python module using 'from tra_analysis import NaiveBayes'
 # setup:
 
-__version__ = "1.0.2"
+__version__ = "1.0.1"
 
 __changelog__ = """changelog:
-	1.0.2:
-		- generalized optional args to **kwargs
 	1.0.1:
 		- optimized imports
 	1.0.0:
@@ -22,45 +20,45 @@ __author__ = (
 
 __all__ = [
 	'gaussian',
-	'multinomial',
+	'multinomial'
 	'bernoulli',
-	'complement',
+	'complement'
 ]
 
 import sklearn
 from . import ClassificationMetric
 
-def gaussian(data, labels, test_size = 0.3, **kwargs):
+def gaussian(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(**kwargs)
+	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(data, labels, test_size = 0.3, **kwargs):
+def multinomial(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(**kwargs)
+	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(data, labels, test_size = 0.3, **kwargs):
+def bernoulli(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(**kwargs)
+	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(data, labels, test_size = 0.3, **kwargs):
+def complement(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(**kwargs)
+	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)
 

analysis-master/tra_analysis/RandomForest.py

@@ -4,12 +4,9 @@
 #    this should be imported as a python module using 'from tra_analysis import RandomForest'
 # setup:
 
-__version__ = "1.0.3"
+__version__ = "1.0.2"
 
 __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:
@@ -31,19 +28,19 @@ __all__ = [
 import sklearn, sklearn.ensemble, sklearn.naive_bayes
 from . import ClassificationMetric, RegressionMetric
 
-def random_forest_classifier(data, labels, test_size, n_estimators, **kwargs):
+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):
 
 	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, **kwargs)
+	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(data, outputs, test_size, n_estimators, **kwargs):
+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):
 
 	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, **kwargs)
+	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)
 

analysis-master/tra_analysis/metrics/__init__.py

@@ -1,24 +0,0 @@
-# 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