Iteratively increase sample size for good/bad classification.

scripts/slave/recipe_modules/auto_bisect/resources/significantly_different_inner.py

+"""This file is meant to be run in an environment where scipy is available."""
+import json
+import logging
+import sys
+
+try:
+  from scipy import stats
+except ImportError:
+  def main():
+    # scipy required, see module docstring.
+    logging.warning(sys.modules[__name__].__doc__)
+    return 1
+else:
+
+  def main():
+    if len(sys.argv) < 4:
+      return 1
+    _, list_a, list_b, significance = sys.argv[:4]
+    list_a = json.loads(list_a)
+    list_b = json.loads(list_b)
+    significance = float(significance)
+
+    shapiro_p_value = stats.shapiro(list_a)[1], stats.shapiro(list_b)[1]
+    mann_whitney_p_value = stats.mannwhitneyu(list_a, list_b).pvalue
+    anderson_p_value = stats.anderson_ksamp([list_a, list_b]).significance_level
+    welch_p_value = stats.ttest_ind(list_a, list_b, equal_var=False)[1]
+
+    results = {
+        'first_sample': list_a,
+        'second_sample': list_b,
+        'shapiro_p_value': shapiro_p_value,
+        'mann_p_value': mann_whitney_p_value,
+        'anderson_p_value': anderson_p_value,
+        'welch_p_value': welch_p_value,
+    }
+
+    if (results['shapiro_p_value'][0] < significance and
+        results['shapiro_p_value'][1] < significance):
+      results['normal-y'] = True
+    else:
+      results['normal-y'] = False
+    results['significantly_different'] = bool(
+        float(results['mann_p_value']) < float(significance))
+
+    print json.dumps(results)
+    return 0
+
+if __name__ == '__main__':
+  sys.exit(main())