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>

.devcontainer/Dockerfile

@@ -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

.devcontainer/dev-dockerfile

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

.devcontainer/devcontainer.json

@@ -1,7 +1,7 @@
 {
     "name": "TRA Analysis Development Environment",
     "build": {
-		"dockerfile": "Dockerfile",
+		"dockerfile": "dev-dockerfile",
     },
     "settings": { 
 		"terminal.integrated.shell.linux": "/bin/bash",
@@ -24,5 +24,5 @@
 		"ms-python.python",
 		"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"
 }

analysis-master/test_analysis.py

@@ -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 Array
 from tra_analysis import ClassificationMetric
@@ -12,13 +16,27 @@ from tra_analysis import StatisticalTest
 from tra_analysis import SVM
 
 def test_():
+
 	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 = []
 	y_data_circular = []
+
 	y_data_ccu = [1, 3, 7, 14, 21]
 	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]
+
+	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.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]]
@@ -30,6 +48,58 @@ def test_():
 	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().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.mergesort(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.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 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]]

analysis-master/tra_analysis/Analysis.py

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

analysis-master/tra_analysis/Array.py

@@ -27,55 +27,37 @@ class Array(): # tests on nd arrays independent of basic_stats
 
 		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
 

analysis-master/tra_analysis/CorrelationTest.py

@@ -22,37 +22,37 @@ __all__ = [
 import scipy
 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)
 	return {"f-value": results[0], "p-value": results[1]}
 
-def pearson(self, x, y):
+def pearson(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'):
+def spearman(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):
+def point_biserial(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'):
+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)
 	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)
 	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)
 	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

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

analysis-master/tra_analysis/NaiveBayes.py

@@ -16,13 +16,17 @@ __author__ = (
 )
 
 __all__ = [
+	'gaussian',
+	'multinomial'
+	'bernoulli',
+	'complement'
 ]
 
 import sklearn
 from sklearn import model_selection, naive_bayes
 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)
 	model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)

analysis-master/tra_analysis/RandomForest.py

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

analysis-master/tra_analysis/RegressionMetric.py

@@ -16,8 +16,10 @@ __author__ = (
 )
 
 __all__ = [
+	'RegressionMetric'
 ]
 
+import numpy as np
 import sklearn
 from sklearn import metrics
 
@@ -37,4 +39,4 @@ class RegressionMetric():
 
 	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))

analysis-master/tra_analysis/SVM.py

@@ -4,9 +4,12 @@
 #    this should be imported as a python module using 'from tra_analysis import SVM'
 # setup:
 
-__version__ = "1.0.0"
+__version__ = "1.0.1"
 
 __changelog__ = """changelog:
+	1.0.1:
+		- removed unessasary self calls
+		- removed classness
 	1.0.0:
 		- ported analysis.SVM() here
 """
@@ -22,58 +25,56 @@ import sklearn
 from sklearn import svm
 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)

analysis-master/tra_analysis/StatisticalTest.py

@@ -4,9 +4,11 @@
 #    this should be imported as a python module using 'from tra_analysis import StatisticalTest'
 # setup:
 
-__version__ = "1.0.0"
+__version__ = "1.0.1"
 
 __changelog__ = """changelog:
+	1.0.1:
+		- fixed typo in __all__
 	1.0.0:
 		- ported analysis.StatisticalTest() here
 		- removed classness
@@ -17,6 +19,39 @@ __author__ = (
 )
 
 __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

analysis-master/tra_analysis/__init__.py

@@ -7,20 +7,24 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "2.1.0-alpha.1"
+__version__ = "2.1.0-alpha.3"
 
 # changelog should be viewed using print(analysis.__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__ = (
 	"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__ = [
@@ -37,4 +41,4 @@ from . import RandomForest
 from .RegressionMetric import RegressionMetric
 from . import Sort
 from . import StatisticalTest
-from .SVM import SVM
+from . import SVM

data-analysis/design.kv

@@ -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"

data-analysis/main.py

@@ -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()