mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-25 17:19:09 +00:00
started branch for analysis v4 dev
deprecated old modules
This commit is contained in:
Arthur Lu
parent
8a365b784e
commit
23329da2b5
@ -7,10 +7,12 @@
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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__ = "3.0.3"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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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 +60,7 @@ __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 +138,7 @@ __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 +250,10 @@ __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 +272,8 @@ __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 +321,7 @@ __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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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import Array'
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# setup:
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__version__ = "1.0.3"
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__version__ = "1.0.4"
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__changelog__ = """changelog:
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1.0.4:
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- fixed spelling of deprecate
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1.0.3:
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- fixed __all__
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1.0.2:
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@ -135,8 +137,8 @@ class Array(): # tests on nd arrays independent of basic_stats
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return Array(np.transpose(self.array))
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def sort(self, array): # depreciated
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warnings.warn("Array.sort has been depreciated in favor of Sort")
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def sort(self, array): # deprecated
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warnings.warn("Array.sort has been deprecated in favor of Sort")
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array_length = len(array)
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if array_length <= 1:
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return array
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@ -1,41 +0,0 @@
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# Only included for backwards compatibility! Do not update, CorrelationTest is preferred and supported.
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import scipy
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from scipy import stats
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class CorrelationTest:
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def anova_oneway(self, *args): #expects arrays of samples
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results = scipy.stats.f_oneway(*args)
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return {"f-value": results[0], "p-value": results[1]}
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def pearson(self, x, y):
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results = scipy.stats.pearsonr(x, y)
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return {"r-value": results[0], "p-value": results[1]}
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def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
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results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
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return {"r-value": results[0], "p-value": results[1]}
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def point_biserial(self, x,y):
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results = scipy.stats.pointbiserialr(x, y)
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return {"r-value": results[0], "p-value": results[1]}
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def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
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results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
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return {"tau": results[0], "p-value": results[1]}
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def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
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results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
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return {"tau": results[0], "p-value": results[1]}
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def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
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results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
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return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
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@ -1,25 +0,0 @@
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# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
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import sklearn
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from sklearn import model_selection, neighbors
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from . import ClassificationMetric, RegressionMetric
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class KNN:
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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
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.neighbors.KNeighborsClassifier()
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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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):
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data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
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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)
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model.fit(data_train, outputs_train)
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predictions = model.predict(data_test)
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return model, RegressionMetric(predictions, outputs_test)
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@ -1,43 +0,0 @@
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# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
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import sklearn
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from sklearn import model_selection, naive_bayes
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from . import ClassificationMetric, RegressionMetric
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class NaiveBayes:
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def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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@ -1,25 +0,0 @@
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# Only included for backwards compatibility! Do not update, RandomForest is preferred and supported.
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import sklearn
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from sklearn import ensemble, model_selection
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from . import ClassificationMetric, RegressionMetric
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class RandomForest:
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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):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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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)
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kernel.fit(data_train, labels_train)
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predictions = kernel.predict(data_test)
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return kernel, ClassificationMetric(predictions, labels_test)
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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):
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data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
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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)
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kernel.fit(data_train, outputs_train)
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predictions = kernel.predict(data_test)
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return kernel, RegressionMetric(predictions, outputs_test)
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@ -1,391 +0,0 @@
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# Only included for backwards compatibility! Do not update, Sort is preferred and supported.
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class Sort: # if you haven't used a sort, then you've never lived
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def quicksort(self, a):
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def sort(array):
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less = []
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equal = []
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greater = []
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if len(array) > 1:
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pivot = array[0]
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for x in array:
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if x < pivot:
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less.append(x)
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elif x == pivot:
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equal.append(x)
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elif x > pivot:
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greater.append(x)
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return sort(less)+equal+sort(greater)
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else:
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return array
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return np.array(sort(a))
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def mergesort(self, a):
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def sort(array):
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array = array
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if len(array) >1:
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middle = len(array) // 2
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L = array[:middle]
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R = array[middle:]
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sort(L)
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sort(R)
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i = j = k = 0
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while i < len(L) and j < len(R):
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if L[i] < R[j]:
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array[k] = L[i]
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i+= 1
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else:
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array[k] = R[j]
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j+= 1
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k+= 1
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while i < len(L):
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array[k] = L[i]
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i+= 1
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k+= 1
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while j < len(R):
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array[k] = R[j]
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j+= 1
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k+= 1
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return array
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return sort(a)
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def introsort(self, a):
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def sort(array, start, end, maxdepth):
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array = array
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if end - start <= 1:
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return
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elif maxdepth == 0:
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heapsort(array, start, end)
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else:
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p = partition(array, start, end)
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sort(array, start, p + 1, maxdepth - 1)
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sort(array, p + 1, end, maxdepth - 1)
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return array
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def partition(array, start, end):
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pivot = array[start]
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i = start - 1
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j = end
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while True:
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i = i + 1
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while array[i] < pivot:
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i = i + 1
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j = j - 1
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while array[j] > pivot:
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j = j - 1
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if i >= j:
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return j
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swap(array, i, j)
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def swap(array, i, j):
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array[i], array[j] = array[j], array[i]
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def heapsort(array, start, end):
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build_max_heap(array, start, end)
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for i in range(end - 1, start, -1):
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swap(array, start, i)
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max_heapify(array, index=0, start=start, end=i)
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def build_max_heap(array, start, end):
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def parent(i):
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return (i - 1)//2
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length = end - start
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index = parent(length - 1)
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while index >= 0:
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max_heapify(array, index, start, end)
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index = index - 1
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def max_heapify(array, index, start, end):
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def left(i):
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return 2*i + 1
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def right(i):
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return 2*i + 2
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size = end - start
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l = left(index)
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r = right(index)
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if (l < size and array[start + l] > array[start + index]):
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largest = l
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else:
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largest = index
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if (r < size and array[start + r] > array[start + largest]):
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largest = r
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if largest != index:
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swap(array, start + largest, start + index)
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max_heapify(array, largest, start, end)
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maxdepth = (len(a).bit_length() - 1)*2
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return sort(a, 0, len(a), maxdepth)
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def heapsort(self, a):
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def sort(array):
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array = array
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n = len(array)
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for i in range(n//2 - 1, -1, -1):
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heapify(array, n, i)
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for i in range(n-1, 0, -1):
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array[i], array[0] = array[0], array[i]
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heapify(array, i, 0)
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return array
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def heapify(array, n, i):
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array = array
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largest = i
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l = 2 * i + 1
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r = 2 * i + 2
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if l < n and array[i] < array[l]:
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largest = l
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if r < n and array[largest] < array[r]:
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largest = r
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if largest != i:
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array[i],array[largest] = array[largest],array[i]
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heapify(array, n, largest)
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return array
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return sort(a)
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def insertionsort(self, a):
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def sort(array):
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array = array
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for i in range(1, len(array)):
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key = array[i]
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j = i-1
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while j >=0 and key < array[j] :
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array[j+1] = array[j]
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j -= 1
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array[j+1] = key
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return array
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return sort(a)
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def timsort(self, a, block = 32):
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BLOCK = block
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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)
|
||||
@ -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 +7,14 @@
|
||||
# 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
|
||||
3.0.0:
|
||||
- incremented version to release 3.0.0
|
||||
3.0.0-rc2:
|
||||
@ -53,7 +57,6 @@ __all__ = [
|
||||
]
|
||||
|
||||
from . import Analysis as Analysis
|
||||
from . import Analysis as analysis
|
||||
from .Array import Array
|
||||
from .ClassificationMetric import ClassificationMetric
|
||||
from . import CorrelationTest
|
||||
|
||||
@ -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