[EXTREME ESTIMATOR] fix quantile function. add test.

extreme_estimator/gev_params.py

@@ -15,6 +15,7 @@ class GevParams(object):
     GEV_QUANTILE_100 = 'quantile 100'
     GEV_QUANTILE_1000 = 'quantile 1000'
     GEV_QUANTILE_NAMES = [GEV_QUANTILE_10, GEV_QUANTILE_100, GEV_QUANTILE_1000]
+    GEV_QUANTILE_P_VALUES = [0.9, 0.99, 0.999]
     # GEV values
     GEV_VALUE_NAMES = GEV_PARAM_NAMES + GEV_QUANTILE_NAMES[:-1]
 
@@ -51,11 +52,7 @@ class GevParams(object):
 
     @property
     def quantile_name_to_p(self) -> dict:
-        return {
-            self.GEV_QUANTILE_10: 0.1,
-            self.GEV_QUANTILE_100: 0.01,
-            self.GEV_QUANTILE_1000: 0.001,
-        }
+        return dict(zip(self.GEV_QUANTILE_NAMES, self.GEV_QUANTILE_P_VALUES))
 
     @property
     def quantile_dict(self) -> dict:

test/test_extreme_estimator/test_gev_params.py

+import unittest
+
+import numpy as np
+
+from extreme_estimator.extreme_models.utils import r, set_seed_r
+from extreme_estimator.gev.gevmle_fit import GevMleFit
+from extreme_estimator.gev_params import GevParams
+
+
+class TestGevParams(unittest.TestCase):
+
+    def test_quantile(self):
+        # For GEV(1,1,1), the repartition function is exp(-y^-1) the formula for the quantile p is -1/log(p)
+        gev_params = GevParams(loc=1.0, shape=1.0, scale=1.0)
+        quantile_dict = gev_params.quantile_dict
+        for quantile_name, p in gev_params.quantile_name_to_p.items():
+            self.assertAlmostEqual(- 1 / np.log(p), quantile_dict[quantile_name])
+
+
+if __name__ == '__main__':
+    unittest.main()