Adding unit tests for PitMetric wrapper.

tests/qp/test_pit.py

@@ -1,9 +1,13 @@
+# pylint: disable=no-member
+# pylint: disable=protected-access
+
 import unittest
 import numpy as np
 import qp
 from qp import interp_gen
 from qp.ensemble import Ensemble
 from qp.metrics.pit import PIT
+from qp.metrics.concrete_metric_classes import PITMetric
 
 
 # constants for tests
@@ -23,15 +27,16 @@
 # BIAS = -0.00001576
 # SIGMAD = 0.0046489
 
+
 class PitTestCase(unittest.TestCase):
-    """ Test cases for PIT metric. """
+    """Test cases for PIT metric."""
 
     def setUp(self):
         np.random.seed(87)
         self.true_zs = np.random.uniform(high=NMAX, size=NPDF)
 
         locs = np.expand_dims(self.true_zs + np.random.normal(0.0, 0.01, NPDF), -1)
-        scales = np.ones((NPDF, 1)) * 0.1 + np.random.uniform(size=(NPDF, 1)) * .05
+        scales = np.ones((NPDF, 1)) * 0.1 + np.random.uniform(size=(NPDF, 1)) * 0.05
         self.n_ens = Ensemble(qp.stats.norm, data=dict(loc=locs, scale=scales))
 
         self.grid_ens = self.n_ens.convert_to(interp_gen, xvals=ZGRID)
@@ -49,36 +54,38 @@ def test_pit_metrics(self):
 
         meta_metrics = pit_obj.calculate_pit_meta_metrics()
 
-        ad_stat = meta_metrics['ad'].statistic
+        ad_stat = meta_metrics["ad"].statistic
         assert np.isclose(ad_stat, ADVAL_ALL)
 
-        cut_ad_stat = pit_obj.evaluate_PIT_anderson_ksamp(pit_min=0.6, pit_max=0.9).statistic
+        cut_ad_stat = pit_obj.evaluate_PIT_anderson_ksamp(
+            pit_min=0.6, pit_max=0.9
+        ).statistic
         assert np.isclose(cut_ad_stat, ADVAL_CUT)
 
-        cvm_stat = meta_metrics['cvm'].statistic
+        cvm_stat = meta_metrics["cvm"].statistic
         assert np.isclose(cvm_stat, CVMVAL)
 
-        ks_stat = meta_metrics['ks'].statistic
+        ks_stat = meta_metrics["ks"].statistic
         assert np.isclose(ks_stat, KSVAL)
 
-        assert np.isclose(meta_metrics['outlier_rate'], OUTRATE)
+        assert np.isclose(meta_metrics["outlier_rate"], OUTRATE)
 
     def test_pit_metric_small_eval_grid(self):
         """Test PIT metric warning message when number of pit samples is smaller than the evaluation grid"""
-        with self.assertLogs(level='WARNING') as log:
+        with self.assertLogs(level="WARNING") as log:
             quant_grid = np.linspace(0, 1, 1000)
             _ = PIT(self.grid_ens, self.true_zs, quant_grid)
-            self.assertIn('Number of pit samples is smaller', log.output[0])
+            self.assertIn("Number of pit samples is smaller", log.output[0])
 
     def test_pit_metric_masking(self):
         """The normal distributions created in this test will produce a quantile
         array in PIT that have multiple values of 1.0. This test will confirm
-        that the some quants have been removed. 
+        that the some quants have been removed.
 
         If no quants had been removed, then the final length would be 101.
         """
 
-        true_zs = np.random.uniform(low=NMAX-0.1, high=NMAX, size=NPDF)
+        true_zs = np.random.uniform(low=NMAX - 0.1, high=NMAX, size=NPDF)
 
         locs = np.expand_dims(true_zs + np.random.normal(0.0, 0.01, NPDF), -1)
         scales = np.ones((NPDF, 1)) * 0.001
@@ -93,13 +100,27 @@ def test_pit_metric_masking(self):
 
     def test_pit_create_quant_mask(self):
         """Basic test where all values should be returned"""
-        input = np.linspace(0.1, 0.9, 10)
-        mask = PIT._create_quant_mask(input)
-        assert np.all(input[mask] == input)
+        input_grid = np.linspace(0.1, 0.9, 10)
+        mask = PIT._create_quant_mask(input_grid)
+        assert np.all(input_grid[mask] == input_grid)
 
     def test_pit_create_quant_mask_with_exclusions(self):
         """Test with values that should be excluded"""
-        input = np.linspace(-0.1, 1.1, 10)
-        mask = PIT._create_quant_mask(input)
-        assert np.all(input[mask] > 0)
-        assert np.all(input[mask] < 1)
+        input_grid = np.linspace(-0.1, 1.1, 10)
+        mask = PIT._create_quant_mask(input_grid)
+        assert np.all(input_grid[mask] > 0)
+        assert np.all(input_grid[mask] < 1)
+
+    def test_pit_metric_class_matches_original_pit_class(self):
+        """Compare base test of PIT metric generation with the metric class wrapped
+        version of PIT. We compare the values of the PDFs directly."""
+        quant_grid = np.linspace(0, 1, 101)
+        pit_obj = PIT(self.grid_ens, self.true_zs, quant_grid)
+
+        pit_metric = PITMetric(eval_grid=quant_grid)
+        pit_metric.initialize()
+        class_result = pit_metric.evaluate(self.grid_ens, self.true_zs)
+        pit_metric.finalize()
+
+        eval_grid = np.linspace(0, 3, 100)
+        assert np.all(class_result.pdf(eval_grid) == pit_obj.pit.pdf(eval_grid))