mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2025-09-08 07:47:21 +00:00
analysis pkg v 1.0.0.12
analysis.py v 1.2.0.004
This commit is contained in:
ltcptgeneral
@@ -7,10 +7,17 @@
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.2.0.003"
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__version__ = "1.2.0.004"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.2.0.004:
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- fixed __all__ to reflected the correct functions and classes
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- fixed CorrelationTests and StatisticalTests class functions to require self invocation
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- added missing math import
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- fixed KNN class functions to require self invocation
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- fixed Metrics class functions to require self invocation
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- various spelling fixes in CorrelationTests and StatisticalTests
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1.2.0.003:
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- bug fixes with CorrelationTests and StatisticalTests
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- moved glicko2 and trueskill to the metrics subpackage
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@@ -275,22 +282,19 @@ __all__ = [
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'z_normalize',
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'histo_analysis',
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'regression',
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'elo',
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'glicko2',
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'trueskill',
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'Metrics',
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'RegressionMetrics',
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'ClassificationMetrics',
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'kmeans',
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'pca',
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'decisiontree',
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'knn_classifier',
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'knn_regressor',
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'KNN',
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'NaiveBayes',
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'SVM',
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'random_forest_classifier',
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'random_forest_regressor',
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'CorrelationTests',
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'RegressionTests',
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'StatisticalTests',
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# all statistics functions left out due to integration in other functions
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]
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@@ -301,6 +305,7 @@ __all__ = [
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import csv
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from analysis.metrics import elo as Elo
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from analysis.metrics import glicko2 as Glicko2
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import math
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import numba
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from numba import jit
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import numpy as np
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@@ -467,11 +472,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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class Metrics:
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def elo(starting_score, opposing_score, observed, N, K):
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def elo(self, starting_score, opposing_score, observed, N, K):
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return Elo.calculate(starting_score, opposing_score, observed, N, K)
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def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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@@ -479,7 +484,7 @@ class Metrics:
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return (player.rating, player.rd, player.vol)
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def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
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def trueskill(self, teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
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team_ratings = []
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@@ -584,7 +589,7 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
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class KNN:
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def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
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def knn_classifier(self, data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, 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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@@ -593,7 +598,7 @@ class KNN:
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return model, ClassificationMetrics(predictions, labels_test)
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def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
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def knn_regressor(self, data, outputs, test_size, n_neighbors = 5, 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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@@ -716,203 +721,203 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
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class CorrelationTests:
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def anova_oneway(*args): #expects arrays of samples
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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(x, y):
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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(a, b = None, axis = 0, nan_policy = 'propagate'):
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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(x,y):
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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(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
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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(x, y, rank = True, weigher = None, additive = True):
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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(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
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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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class StatisticalTests:
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def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
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def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'):
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results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
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return {"t-value": results[0], "p-value": results[1]}
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def ttest_independent(a, b, equal = True, nan_policy = 'propagate'):
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def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'):
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results = scipt.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
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results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
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return {"t-value": results[0], "p-value": results[1]}
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def ttest_statistic(o1, o2, equal = True):
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def ttest_statistic(self, o1, o2, equal = True):
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results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
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return {"t-value": results[0], "p-value": results[1]}
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def ttest_related(a, b, axis = 0, nan_policy='propagate'):
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def ttest_related(self, a, b, axis = 0, nan_policy='propagate'):
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results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
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return {"t-value": results[0], "p-value": results[1]}
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def ks_fitness(rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
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def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
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results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
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return {"ks-value": results[0], "p-value": results[1]}
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def chisquare(f_obs, f_exp = None, ddof = None, axis = 0):
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def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0):
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results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
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return {"chisquared-value": results[0], "p-value": results[1]}
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def powerdivergence(f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
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def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
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results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
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return {"powerdivergence-value": results[0], "p-value": results[1]}
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def ks_twosample(x, y, alternative = 'two_sided', mode = 'auto'):
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def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'):
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results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
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return {"ks-value": results[0], "p-value": results[1]}
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def es_twosample(x, y, t = (0.4, 0.8)):
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def es_twosample(self, x, y, t = (0.4, 0.8)):
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results = scipy.stats.epps_singleton_2samp(x, y, t = t)
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return {"es-value": results[0], "p-value": results[1]}
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def mw_rank(x, y, use_continuity = True, alternative = None):
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def mw_rank(self, x, y, use_continuity = True, alternative = None):
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results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
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return {"u-value": results[0], "p-value": results[1]}
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def mw_tiecorrection(rank_values):
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def mw_tiecorrection(self, rank_values):
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results = scipy.stats.tiecorrect(rank_values)
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return {"correction-factor": results}
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def rankdata(a, method = 'average'):
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def rankdata(self, a, method = 'average'):
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results = scipy.stats.rankdata(a, method = method)
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return results
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def wilcoxon_ranksum(a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
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def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
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results = scipy.stats.ranksums(a, b)
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return {"u-value": results[0], "p-value": results[1]}
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def wilcoxon_signedrank(x, y = None, method = 'wilcox', correction = False, alternative = 'two-sided'):
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def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
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results = scipy.stats.wilcoxon(x, y = y, method = method, correction = correction, alternative = alternative)
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results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
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return {"t-value": results[0], "p-value": results[1]}
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def kw_htest(*args, nan_policy = 'propagate'):
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def kw_htest(self, *args, nan_policy = 'propagate'):
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results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
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return {"h-value": results[0], "p-value": results[1]}
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def friedman_chisquare(*args):
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def friedman_chisquare(self, *args):
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results = scipy.stats.friedmanchisquare(*args)
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return {"chisquared-value": results[0], "p-value": results[1]}
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def bm_wtest(x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
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def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
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results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
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return {"w-value": results[0], "p-value": results[1]}
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def combine_pvalues(pvalues, method = 'fisher', weights = None):
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def combine_pvalues(self, pvalues, method = 'fisher', weights = None):
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results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
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return {"combined-statistic": results[0], "p-value": results[1]}
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def jb_fitness(x):
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def jb_fitness(self, x):
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results = scipy.stats.jarque_bera(x)
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return {"jb-value": results[0], "p-value": results[1]}
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def ab_equality(x, y):
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def ab_equality(self, x, y):
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results = scipy.stats.ansari(x, y)
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return {"ab-value": results[0], "p-value": results[1]}
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def bartlett_variance(*args):
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def bartlett_variance(self, *args):
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results = scipy.stats.bartlett(*args)
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return {"t-value": results[0], "p-value": results[1]}
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def levene_variance(*args, center = 'median', proportiontocut = 0.05):
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def levene_variance(self, *args, center = 'median', proportiontocut = 0.05):
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results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
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return {"w-value": results[0], "p-value": results[1]}
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def sw_normality(x):
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def sw_normality(self, x):
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results = scipy.stats.shapiro(x)
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return {"w-value": results[0], "p-value": results[1]}
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def shapiro(x):
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def shapiro(self, x):
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return "destroyed by facts and logic"
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def ad_onesample(x, dist = 'norm'):
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def ad_onesample(self, x, dist = 'norm'):
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results = scipy.stats.anderson(x, dist = dist)
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return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
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def ad_ksample(samples, midrank = True):
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def ad_ksample(self, samples, midrank = True):
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results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
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return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
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def binomial(x, n = None, p = 0.5, alternative = 'two-sided'):
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def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'):
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results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
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return {"p-value": results}
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def fk_variance(*args, center = 'median', proportiontocut = 0.05):
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def fk_variance(self, *args, center = 'median', proportiontocut = 0.05):
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results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
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return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
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def mood_mediantest(*args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
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def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
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results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
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return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
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def mood_equalscale(x, y, axis = 0):
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def mood_equalscale(self, x, y, axis = 0):
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results = scipy.stats.mood(x, y, axis = axis)
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return {"z-score": results[0], "p-value": results[1]}
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def skewtest(a, axis = 0, nan_policy = 'propogate'):
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def skewtest(self, a, axis = 0, nan_policy = 'propogate'):
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results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
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return {"z-score": results[0], "p-value": results[1]}
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def kurtosistest(a, axis = 0, nan_policy = 'propogate'):
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def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'):
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results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
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return {"z-score": results[0], "p-value": results[1]}
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def normaltest(a, axis = 0, nan_policy = 'propogate'):
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def normaltest(self, a, axis = 0, nan_policy = 'propogate'):
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results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
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return {"z-score": results[0], "p-value": results[1]}
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