Merge pull request #9 from titanscout2022/fix

testing release 1.2 of analysis.py

analysis-master/analysis-amd64/analysis/analysis.py

@@ -7,10 +7,16 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.009"
+__version__ = "1.2.0.000"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.2.0.000:
+        - cleaned up wild card imports with scipy and sklearn
+        - added CorrelationTests class
+        - added StatisticalTests class
+        - added several correlation tests to CorrelationTests
+        - added several statistical tests to StatisticalTests
     1.1.13.009:
         - moved elo, glicko2, trueskill functions under class Metrics
     1.1.13.008:
@@ -288,9 +294,9 @@ import numba
 from numba import jit
 import numpy as np
 import scipy
-from scipy import *
+from scipy import optimize, stats
 import sklearn
-from sklearn import *
+from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
 from analysis import trueskill as Trueskill
 
 class error(ValueError):
@@ -698,3 +704,206 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
     predictions = kernel.predict(data_test)
 
     return kernel, RegressionMetrics(predictions, outputs_test)
+
+class CorrelationTests:
+
+    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(x, y):
+
+        results = scipy.stats.pearsonr(x, y)
+        return {"r-value": results[0], "p-value": results[1]}
+
+    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(x,y):
+
+        results = scipy.stats.pointbiserialr(x, y)
+        return {"r-value": results[0], "p-value": results[1]}
+
+    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(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(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
+
+class StatisticalTests:
+
+    def ttest_onesample(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(a, b, equal = True, nan_policy = 'propagate'):
+
+        results = scipt.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
+        return {"t-value": results[0], "p-value": results[1]}
+
+    def ttest_statistic(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(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(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(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(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(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(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(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(rank_values):
+
+        results = scipy.stats.tiecorrect(rank_values)
+        return {"correction-factor": results}
+
+    def rankdata(a, method = 'average'):
+
+        results = scipy.stats.rankdata(a, method = method)
+        return results
+
+    def wilcoxon_ranksum(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(x, y = None, method = 'wilcox', correction = False, alternative = 'two-sided'):
+
+        results = scipy.stats.wilcoxon(x, y = y, method = method, correction = correction, alternative = alternative)
+        return {"t-value": results[0], "p-value": results[1]}
+
+    def kw_htest(*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(*args):
+
+        results = scipy.stats.friedmanchisquare(*args)
+        return {"chisquared-value": results[0], "p-value": results[1]}
+
+    def bm-wtest(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(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(x):
+
+        results = scipy.stats.jarque_bera(x)
+        return {"jb-value": results[0], "p-value": results[1]}
+
+    def ab_equality(x, y):
+
+        results = scipy.stats.ansari(x, y)
+        return {"ab-value": results[0], "p-value": results[1]}
+
+    def bartlett_variance(*args):
+
+        results = scipy.stats.bartlett(*args)
+        return {"t-value": results[0], "p-value": results[1]}
+
+    def levene_variance(*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(x):
+
+        results = scipy.stats.shapiro(x)
+        return {"w-value": results[0], "p-value": results[1]}
+
+    def shapiro(x):
+
+        return "destroyed by facts and logic"
+
+    def ad_onesample(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(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(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(*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(*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(x, y, axis = 0):
+
+        results = scipy.stats.mood(x, y, axis = axis)
+        return {"z-score": results[0], "p-value": results[1]}
+
+    def skewtest(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(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(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]}