tests: New unit tests for submoduling (#66)

* feat: created kivy gui boilerplate

* migrated docker base image to debian

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

* migrated to ubuntu

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

* fixed issues

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

* fix: docker build?

* fix: use ubuntu bionic

* fix: get kivy installed

* @ltcptgeneral can't spell

* optim dockerfile for not installing unused packages

* install basic stuff while building the container

* use prebuilt image for development

* install pylint on base image

* rename and use new kivy

* tests: added tests for Array and CorrelationTest

Both are not working due to errors

* fix: Array no longer has *args and CorrelationTest functions no longer have self in the arguments

* use new thing

* use 20.04 base

* symlink pip3 to pip

* use pip instead of pip3

* tra_analysis v 2.1.0-alpha.2
SVM v 1.0.1
added unvalidated SVM unit tests

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

* fixed version number

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

* tests: added tests for ClassificationMetric

* partially fixed and commented out svm unit tests

* fixed some SVM unit tests

* added installing pytest to devcontainer.json

* fix: small fixes to KNN

Namely, removing self from parameters and passing correct arguments to KNeighborsClassifier constructor

* fix, test: Added tests for KNN and NaiveBayes.

Also made some small fixes in KNN, NaiveBayes, and RegressionMetric

* test: finished unit tests except for StatisticalTest

Also made various small fixes and style changes

* StatisticalTest v 1.0.1

* fixed RegressionMetric unit test
temporarily disabled CorrelationTest unit tests

* tra_analysis v 2.1.0-alpha.3

* readded __all__

* fix: floating point issues in unit tests for CorrelationTest

Co-authored-by: AGawde05 <agawde05@gmail.com>
Co-authored-by: ltcptgeneral <learthurgo@gmail.com>
Co-authored-by: Dev Singh <dev@devksingh.com>
Co-authored-by: jzpan1 <panzhenyu2014@gmail.com>
This commit is contained in:
zpan1 2021-01-26 21:46:29 -06:00 committed by GitHub
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commit f72d8457a7
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16 changed files with 322 additions and 96 deletions

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@ -1,2 +1,7 @@
FROM python:3.8 FROM ubuntu:20.04
WORKDIR ~/ WORKDIR /
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

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

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@ -1,7 +1,7 @@
{ {
"name": "TRA Analysis Development Environment", "name": "TRA Analysis Development Environment",
"build": { "build": {
"dockerfile": "Dockerfile", "dockerfile": "dev-dockerfile",
}, },
"settings": { "settings": {
"terminal.integrated.shell.linux": "/bin/bash", "terminal.integrated.shell.linux": "/bin/bash",
@ -24,5 +24,5 @@
"ms-python.python", "ms-python.python",
"waderyan.gitblame" "waderyan.gitblame"
], ],
"postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install --no-cache-dir pylint ; pip install --no-cache-dir tra-analysis" "postCreateCommand": "/usr/bin/pip3 install -r /workspaces/red-alliance-analysis/data-analysis/requirements.txt && /usr/bin/pip3 install -r /workspaces/red-alliance-analysis/analysis-master/requirements.txt && /usr/bin/pip3 install --no-cache-dir pylint && pip3 install pytest"
} }

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@ -1,3 +1,7 @@
import numpy as np
import sklearn
from sklearn import metrics
from tra_analysis import Analysis as an from tra_analysis import Analysis as an
from tra_analysis import Array from tra_analysis import Array
from tra_analysis import ClassificationMetric from tra_analysis import ClassificationMetric
@ -12,13 +16,27 @@ from tra_analysis import StatisticalTest
from tra_analysis import SVM from tra_analysis import SVM
def test_(): def test_():
test_data_linear = [1, 3, 6, 7, 9] test_data_linear = [1, 3, 6, 7, 9]
test_data_linear2 = [2, 2, 5, 7, 13]
test_data_array = Array(test_data_linear)
x_data_circular = [] x_data_circular = []
y_data_circular = [] y_data_circular = []
y_data_ccu = [1, 3, 7, 14, 21] y_data_ccu = [1, 3, 7, 14, 21]
y_data_ccd = [1, 5, 7, 8.5, 8.66] 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_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] 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]
test_data_2D_pairs = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
test_data_2D_positive = np.array([[23, 51], [21, 32], [15, 25], [17, 31]])
test_output = np.array([1, 3, 4, 5])
test_labels_2D_pairs = np.array([1, 1, 2, 2])
validation_data_2D_pairs = np.array([[-0.8, -1], [0.8, 1.2]])
validation_labels_2D_pairs = np.array([1, 2])
assert an.basic_stats(test_data_linear) == {"mean": 5.2, "median": 6.0, "standard-deviation": 2.85657137141714, "variance": 8.16, "minimum": 1.0, "maximum": 9.0} assert an.basic_stats(test_data_linear) == {"mean": 5.2, "median": 6.0, "standard-deviation": 2.85657137141714, "variance": 8.16, "minimum": 1.0, "maximum": 9.0}
assert an.z_score(3.2, 6, 1.5) == -1.8666666666666665 assert an.z_score(3.2, 6, 1.5) == -1.8666666666666665
assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]] assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
@ -30,6 +48,58 @@ def test_():
assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0 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) assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
#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))] #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))]
assert test_data_array.elementwise_mean() == 5.2
assert test_data_array.elementwise_median() == 6.0
assert test_data_array.elementwise_stdev() == 2.85657137141714
assert test_data_array.elementwise_variance() == 8.16
assert test_data_array.elementwise_npmin() == 1
assert test_data_array.elementwise_npmax() == 9
assert test_data_array.elementwise_stats() == (5.2, 6.0, 2.85657137141714, 8.16, 1, 9)
classif_metric = ClassificationMetric(test_data_linear2, test_data_linear)
assert classif_metric[0].all() == metrics.confusion_matrix(test_data_linear, test_data_linear2).all()
assert classif_metric[1] == metrics.classification_report(test_data_linear, test_data_linear2)
assert all(np.isclose(list(CorrelationTest.anova_oneway(test_data_linear, test_data_linear2).values()), [0.05825242718446602, 0.8153507906592907], rtol=1e-10))
assert all(np.isclose(list(CorrelationTest.pearson(test_data_linear, test_data_linear2).values()), [0.9153061540753287, 0.02920895440940868], rtol=1e-10))
assert all(np.isclose(list(CorrelationTest.spearman(test_data_linear, test_data_linear2).values()), [0.9746794344808964, 0.004818230468198537], rtol=1e-10))
assert all(np.isclose(list(CorrelationTest.point_biserial(test_data_linear, test_data_linear2).values()), [0.9153061540753287, 0.02920895440940868], rtol=1e-10))
assert all(np.isclose(list(CorrelationTest.kendall(test_data_linear, test_data_linear2).values()), [0.9486832980505137, 0.022977401503206086], rtol=1e-10))
assert all(np.isclose(list(CorrelationTest.kendall_weighted(test_data_linear, test_data_linear2).values()), [0.9750538072369643, np.nan], rtol=1e-10, equal_nan=True))
assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
model, metric = KNN.knn_classifier(test_data_2D_pairs, test_labels_2D_pairs, 2)
assert isinstance(model, sklearn.neighbors.KNeighborsClassifier)
assert np.array([[0,0], [2,0]]).all() == metric[0].all()
assert ' precision recall f1-score support\n\n 1 0.00 0.00 0.00 0.0\n 2 0.00 0.00 0.00 2.0\n\n accuracy 0.00 2.0\n macro avg 0.00 0.00 0.00 2.0\nweighted avg 0.00 0.00 0.00 2.0\n' == metric[1]
model, metric = KNN.knn_regressor(test_data_2D_pairs, test_output, 2)
assert isinstance(model, sklearn.neighbors.KNeighborsRegressor)
assert (-25.0, 6.5, 2.5495097567963922) == metric
model, metric = NaiveBayes.gaussian(test_data_2D_pairs, test_labels_2D_pairs)
assert isinstance(model, sklearn.naive_bayes.GaussianNB)
assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
model, metric = NaiveBayes.multinomial(test_data_2D_positive, test_labels_2D_pairs)
assert isinstance(model, sklearn.naive_bayes.MultinomialNB)
assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
model, metric = NaiveBayes.bernoulli(test_data_2D_pairs, test_labels_2D_pairs)
assert isinstance(model, sklearn.naive_bayes.BernoulliNB)
assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
model, metric = NaiveBayes.complement(test_data_2D_positive, test_labels_2D_pairs)
assert isinstance(model, sklearn.naive_bayes.ComplementNB)
assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
model, metric = RandomForest.random_forest_classifier(test_data_2D_pairs, test_labels_2D_pairs, 0.3, 2)
assert isinstance(model, sklearn.ensemble.RandomForestClassifier)
assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
model, metric = RandomForest.random_forest_regressor(test_data_2D_pairs, test_labels_2D_pairs, 0.3, 2)
assert isinstance(model, sklearn.ensemble.RandomForestRegressor)
assert metric == (0.0, 1.0, 1.0)
assert RegressionMetric(test_data_linear, test_data_linear2)== (0.7705314009661837, 3.8, 1.9493588689617927)
assert all(a == b for a, b in zip(Sort.quicksort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.quicksort(test_data_scrambled), test_data_sorted))
assert all(a == b for a, b in zip(Sort.mergesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.mergesort(test_data_scrambled), test_data_sorted))
assert all(a == b for a, b in zip(Sort.heapsort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.heapsort(test_data_scrambled), test_data_sorted))
@ -41,4 +111,35 @@ def test_():
assert all(a == b for a, b in zip(Sort.bubblesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.bubblesort(test_data_scrambled), test_data_sorted))
assert all(a == b for a, b in zip(Sort.cyclesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.cyclesort(test_data_scrambled), test_data_sorted))
assert all(a == b for a, b in zip(Sort.cocktailsort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.cocktailsort(test_data_scrambled), test_data_sorted))
assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
svm(test_data_2D_pairs, test_labels_2D_pairs, validation_data_2D_pairs, validation_labels_2D_pairs)
def svm(data, labels, test_data, test_labels):
lin_kernel = SVM.PrebuiltKernel.Linear()
#ply_kernel = SVM.PrebuiltKernel.Polynomial(3, 0)
rbf_kernel = SVM.PrebuiltKernel.RBF('scale')
sig_kernel = SVM.PrebuiltKernel.Sigmoid(0)
lin_kernel = SVM.fit(lin_kernel, data, labels)
#ply_kernel = SVM.fit(ply_kernel, data, labels)
rbf_kernel = SVM.fit(rbf_kernel, data, labels)
sig_kernel = SVM.fit(sig_kernel, data, labels)
for i in range(len(test_data)):
assert lin_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
#for i in range(len(test_data)):
# assert ply_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
for i in range(len(test_data)):
assert rbf_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
for i in range(len(test_data)):
assert sig_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]

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@ -397,7 +397,7 @@ from .RandomForest_obj import RandomForest
from .RegressionMetric import RegressionMetric from .RegressionMetric import RegressionMetric
from .Sort_obj import Sort from .Sort_obj import Sort
from .StatisticalTest_obj import StatisticalTest from .StatisticalTest_obj import StatisticalTest
from .SVM import SVM from . import SVM
class error(ValueError): class error(ValueError):
pass pass

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@ -27,55 +27,37 @@ class Array(): # tests on nd arrays independent of basic_stats
return str(self.array) return str(self.array)
def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized def elementwise_mean(self, axis = 0): # expects arrays that are size normalized
if len(*args) == 0:
return np.mean(self.array, axis = axis)
else:
return np.mean([*args], axis = axis)
def elementwise_median(self, *args, axis = 0): return np.mean(self.array, axis = axis)
if len(*args) == 0: def elementwise_median(self, axis = 0):
return np.median(self.array, axis = axis)
else:
return np.median([*args], axis = axis)
def elementwise_stdev(self, *args, axis = 0): return np.median(self.array, axis = axis)
if len(*args) == 0: def elementwise_stdev(self, axis = 0):
return np.std(self.array, axis = axis)
else:
return np.std([*args], axis = axis)
def elementwise_variance(self, *args, axis = 0): return np.std(self.array, axis = axis)
if len(*args) == 0: def elementwise_variance(self, axis = 0):
return np.var(self.array, axis = axis)
else:
return np.var([*args], axis = axis)
def elementwise_npmin(self, *args, axis = 0): return np.var(self.array, axis = axis)
if len(*args) == 0: def elementwise_npmin(self, axis = 0):
return np.amin(self.array, axis = axis) return np.amin(self.array, axis = axis)
else:
return np.amin([*args], axis = axis)
def elementwise_npmax(self, *args, axis = 0):
if len(*args) == 0: def elementwise_npmax(self, axis = 0):
return np.amax(self.array, axis = axis) return np.amax(self.array, axis = axis)
else:
return np.amax([*args], axis = axis)
def elementwise_stats(self, *args, axis = 0): def elementwise_stats(self, axis = 0):
_mean = self.elementwise_mean(*args, axis = axis) _mean = self.elementwise_mean(axis = axis)
_median = self.elementwise_median(*args, axis = axis) _median = self.elementwise_median(axis = axis)
_stdev = self.elementwise_stdev(*args, axis = axis) _stdev = self.elementwise_stdev(axis = axis)
_variance = self.elementwise_variance(*args, axis = axis) _variance = self.elementwise_variance(axis = axis)
_min = self.elementwise_npmin(*args, axis = axis) _min = self.elementwise_npmin(axis = axis)
_max = self.elementwise_npmax(*args, axis = axis) _max = self.elementwise_npmax(axis = axis)
return _mean, _median, _stdev, _variance, _min, _max return _mean, _median, _stdev, _variance, _min, _max

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@ -22,37 +22,37 @@ __all__ = [
import scipy import scipy
from scipy import stats from scipy import stats
def anova_oneway(self, *args): #expects arrays of samples def anova_oneway(*args): #expects arrays of samples
results = scipy.stats.f_oneway(*args) results = scipy.stats.f_oneway(*args)
return {"f-value": results[0], "p-value": results[1]} return {"f-value": results[0], "p-value": results[1]}
def pearson(self, x, y): def pearson(x, y):
results = scipy.stats.pearsonr(x, y) results = scipy.stats.pearsonr(x, y)
return {"r-value": results[0], "p-value": results[1]} return {"r-value": results[0], "p-value": results[1]}
def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'): def spearman(a, b = None, axis = 0, nan_policy = 'propagate'):
results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
return {"r-value": results[0], "p-value": results[1]} return {"r-value": results[0], "p-value": results[1]}
def point_biserial(self, x,y): def point_biserial(x, y):
results = scipy.stats.pointbiserialr(x, y) results = scipy.stats.pointbiserialr(x, y)
return {"r-value": results[0], "p-value": results[1]} return {"r-value": results[0], "p-value": results[1]}
def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): def kendall(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) results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
return {"tau": results[0], "p-value": results[1]} return {"tau": results[0], "p-value": results[1]}
def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True): def kendall_weighted(x, y, rank = True, weigher = None, additive = True):
results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
return {"tau": results[0], "p-value": results[1]} 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): def mgc(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) 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 return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value

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@ -14,29 +14,32 @@ __changelog__ = """changelog:
__author__ = ( __author__ = (
"Arthur Lu <learthurgo@gmail.com>", "Arthur Lu <learthurgo@gmail.com>",
"James Pan <zpan@imsa.edu>"
) )
__all__ = [ __all__ = [
'knn_classifier',
'knn_regressor'
] ]
import sklearn import sklearn
from sklearn import model_selection, neighbors from sklearn import model_selection, neighbors
from . import ClassificationMetric, RegressionMetric from . import ClassificationMetric, RegressionMetric
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 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) 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 = 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) model.fit(data_train, labels_train)
predictions = model.predict(data_test) predictions = model.predict(data_test)
return model, ClassificationMetric(predictions, labels_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): 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) 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, 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) model.fit(data_train, outputs_train)
predictions = model.predict(data_test) predictions = model.predict(data_test)
return model, RegressionMetric(predictions, outputs_test) return model, RegressionMetric.RegressionMetric(predictions, outputs_test)

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@ -16,13 +16,17 @@ __author__ = (
) )
__all__ = [ __all__ = [
'gaussian',
'multinomial'
'bernoulli',
'complement'
] ]
import sklearn import sklearn
from sklearn import model_selection, naive_bayes from sklearn import model_selection, naive_bayes
from . import ClassificationMetric, RegressionMetric from . import ClassificationMetric, RegressionMetric
def guassian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): 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) 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(priors = priors, var_smoothing = var_smoothing)

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@ -39,4 +39,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="m
kernel.fit(data_train, outputs_train) kernel.fit(data_train, outputs_train)
predictions = kernel.predict(data_test) predictions = kernel.predict(data_test)
return kernel, RegressionMetric(predictions, outputs_test) return kernel, RegressionMetric.RegressionMetric(predictions, outputs_test)

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@ -16,8 +16,10 @@ __author__ = (
) )
__all__ = [ __all__ = [
'RegressionMetric'
] ]
import numpy as np
import sklearn import sklearn
from sklearn import metrics from sklearn import metrics
@ -37,4 +39,4 @@ class RegressionMetric():
def rms(self, predictions, targets): def rms(self, predictions, targets):
return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) return np.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))

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@ -4,9 +4,12 @@
# this should be imported as a python module using 'from tra_analysis import SVM' # this should be imported as a python module using 'from tra_analysis import SVM'
# setup: # setup:
__version__ = "1.0.0" __version__ = "1.0.1"
__changelog__ = """changelog: __changelog__ = """changelog:
1.0.1:
- removed unessasary self calls
- removed classness
1.0.0: 1.0.0:
- ported analysis.SVM() here - ported analysis.SVM() here
""" """
@ -22,58 +25,56 @@ import sklearn
from sklearn import svm from sklearn import svm
from . import ClassificationMetric, RegressionMetric from . import ClassificationMetric, RegressionMetric
class SVM: class CustomKernel:
class CustomKernel: def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) class StandardKernel:
class StandardKernel: def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) class PrebuiltKernel:
class PrebuiltKernel: class Linear:
class Linear: def __new__(cls):
def __new__(cls): return sklearn.svm.SVC(kernel = 'linear')
return sklearn.svm.SVC(kernel = 'linear') class Polynomial:
class Polynomial: def __new__(cls, power, r_bias):
def __new__(cls, power, r_bias): return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) class RBF:
class RBF: def __new__(cls, gamma):
def __new__(cls, gamma): return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) class Sigmoid:
class Sigmoid: def __new__(cls, r_bias):
def __new__(cls, r_bias): return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) def fit(kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs return kernel.fit(train_data, train_outputs)
return kernel.fit(train_data, train_outputs) def eval_classification(kernel, test_data, test_outputs):
def eval_classification(self, kernel, test_data, test_outputs): predictions = kernel.predict(test_data)
predictions = kernel.predict(test_data) return ClassificationMetric(predictions, test_outputs)
return ClassificationMetric(predictions, test_outputs) def eval_regression(kernel, test_data, test_outputs):
def eval_regression(self, kernel, test_data, test_outputs): predictions = kernel.predict(test_data)
predictions = kernel.predict(test_data) return RegressionMetric(predictions, test_outputs)
return RegressionMetric(predictions, test_outputs)

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@ -4,9 +4,11 @@
# this should be imported as a python module using 'from tra_analysis import StatisticalTest' # this should be imported as a python module using 'from tra_analysis import StatisticalTest'
# setup: # setup:
__version__ = "1.0.0" __version__ = "1.0.1"
__changelog__ = """changelog: __changelog__ = """changelog:
1.0.1:
- fixed typo in __all__
1.0.0: 1.0.0:
- ported analysis.StatisticalTest() here - ported analysis.StatisticalTest() here
- removed classness - removed classness
@ -17,6 +19,39 @@ __author__ = (
) )
__all__ = [ __all__ = [
'ttest_onesample',
'ttest_independent',
'ttest_statistic',
'ttest_related',
'ks_fitness',
'chisquare',
'powerdivergence'
'ks_twosample',
'es_twosample',
'mw_rank',
'mw_tiecorrection',
'rankdata',
'wilcoxon_ranksum',
'wilcoxon_signedrank',
'kw_htest',
'friedman_chisquare',
'bm_wtest',
'combine_pvalues',
'jb_fitness',
'ab_equality',
'bartlett_variance',
'levene_variance',
'sw_normality',
'shapiro',
'ad_onesample',
'ad_ksample',
'binomial',
'fk_variance',
'mood_mediantest',
'mood_equalscale',
'skewtest',
'kurtosistest',
'normaltest'
] ]
import scipy import scipy

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@ -7,20 +7,24 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "2.1.0-alpha.1" __version__ = "2.1.0-alpha.3"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
2.1.0-alpha.1: 2.1.0-alpha.3:
- moved multiple submodules under analysis to their own modules/files - fixed indentation in meta data
- added header, __version__, __changelog__, __author__, __all__ (unpopulated) 2.1.0-alpha.2:
- updated SVM import
2.1.0-alpha.1:
- moved multiple submodules under analysis to their own modules/files
- added header, __version__, __changelog__, __author__, __all__ (unpopulated)
""" """
__author__ = ( __author__ = (
"Arthur Lu <learthurgo@gmail.com>", "Arthur Lu <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>", "Jacob Levine <jlevine@imsa.edu>",
"Dev Singh <dev@devksingh.com>", "Dev Singh <dev@devksingh.com>",
"James Pan <zpan@imsa.edu>" "James Pan <zpan@imsa.edu>"
) )
__all__ = [ __all__ = [
@ -37,4 +41,4 @@ from . import RandomForest
from .RegressionMetric import RegressionMetric from .RegressionMetric import RegressionMetric
from . import Sort from . import Sort
from . import StatisticalTest from . import StatisticalTest
from .SVM import SVM from . import SVM

46
data-analysis/design.kv Normal file
View File

@ -0,0 +1,46 @@
<HomeScreen>:
GridLayout:
cols: 1
GridLayout:
cols: 1
padding: 15, 15
spacing: 20, 20
Label:
text: "User Login"
font_size: "20sp"
TextInput:
id: username
hint_text: "Username"
TextInput:
id: password
password: True
hint_text: "Password"
RelativeLayout:
Button:
text: "Login"
on_press: root.login(root.ids.username.text, root.ids.password.text)
size_hint: 0.3, 0.5
pos_hint: {"center_x": 0.5, "center_y": 0.6}
Label:
id: login_wrong
text: ""
GridLayout:
cols: 2
size_hint: 0.2, 0.2
padding: 10, 10
spacing: 10, 0
Button:
text: "Forgot Password?"
background_color: 1, 1, 1, 0
opacity: 1 if self.state == "normal" else 0.5
color: 0.1, 0.7, 1, 1
Button:
text: "Sign Up"
on_press: root.sign_up()
background_color: 1, 1, 1 , 0
opacity: 1 if self.state == "normal" else 0.5
color: 0.1, 0.7, 1, 1
<RootWidget>:
HomeScreen:
name: "home_screen"

41
data-analysis/main.py Normal file
View File

@ -0,0 +1,41 @@
from kivy.app import App
from kivy.lang import Builder
from kivy.uix.screenmanager import ScreenManager , Screen
from kivy.animation import Animation
from hoverable import HoverBehavior
from kivy.uix.image import Image
from kivy.uix.behaviors import ButtonBehavior
import json
from datetime import datetime
import glob
from pathlib import Path
import random
import superscript as ss
Builder.load_file('design.kv')
class HomeScreen(Screen):
# def sign_up(self):
# self.manager.transition.direction = "left"
# self.manager.current = "sign_up_screen"
# def login(self, uname, pword):
# with open ("users.json") as file:
# users = json.load(file)
# if uname in users and users[uname]["password"] == pword:
# self.manager.transition.direction = "left"
# self.manager.current = "login_screen_success"
# else:
# self.ids.login_wrong.text = "Incorrect Username or Password"
class RootWidget(ScreenManager):
pass
class MainApp(App):
def build(self):
return RootWidget()
if __name__ == "__main__":
MainApp().run()