Add unit tests for cross calibration

Many tests still missing.

Author: peanutfun

climada/util/calibrate/test/test_cross_calibrate.py

@@ -0,0 +1,109 @@
+"""
+This file is part of CLIMADA.
+
+Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
+
+CLIMADA is free software: you can redistribute it and/or modify it under the
+terms of the GNU General Public License as published by the Free
+Software Foundation, version 3.
+
+CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License along
+with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
+
+---
+Tests for cross calibration module
+"""
+import unittest
+from tempfile import TemporaryDirectory
+from pathlib import Path
+
+import pandas as pd
+import pandas.testing as pdt
+import numpy as np
+import numpy.testing as npt
+
+from climada.util.calibrate.cross_calibrate import (
+    EnsembleOptimizerOutput,
+    SingleEnsembleOptimizerOutput,
+)
+
+
+class TestEnsembleOptimizerOutput(unittest.TestCase):
+    """Test the EnsembleOptimizerOutput"""
+
+    def setUp(self):
+        """Initialize single outputs"""
+        self.output1 = SingleEnsembleOptimizerOutput(
+            params={"param1": 1.0, "param2": 2.0},
+            target=1,
+            event_info={
+                "event_id": np.array([1, 2]),
+                "region_id": np.array([1, 2]),
+                "event_name": ["a", "b"],
+            },
+        )
+        self.output2 = SingleEnsembleOptimizerOutput(
+            params={"param1": 1.1, "param2": 2.1},
+            target=2,
+            event_info={
+                "event_name": [1],
+                "event_id": np.array([3]),
+                "region_id": np.array([4]),
+            },
+        )
+
+    def test_from_outputs(self):
+        """Test 'from_outputs' initialization"""
+        out = EnsembleOptimizerOutput.from_outputs([self.output1, self.output2])
+        data = out.data
+
+        # Test MultiIndex columns
+        npt.assert_array_equal(data["Parameters"].columns, ["param1", "param2"])
+        npt.assert_array_equal(
+            data["Event"].columns, ["event_id", "region_id", "event_name"]
+        )
+
+        # Test parameters
+        npt.assert_array_equal(data[("Parameters", "param1")], [1.0, 1.1])
+        npt.assert_array_equal(data[("Parameters", "param2")], [2.0, 2.1])
+
+        # Test event info
+        pdt.assert_series_equal(
+            data[("Event", "event_id")],
+            pd.Series([np.array([1, 2]), np.array([3])], name=("Event", "event_id")),
+        )
+        pdt.assert_series_equal(
+            data[("Event", "region_id")],
+            pd.Series([np.array([1, 2]), np.array([4])], name=("Event", "region_id")),
+        )
+        pdt.assert_series_equal(
+            data[("Event", "event_name")],
+            pd.Series([["a", "b"], [1]], name=("Event", "event_name")),
+        )
+
+    def test_cycling(self):
+        """Test correct cycling to files"""
+        with TemporaryDirectory() as tmp:
+            filepath = Path(tmp, "file.h5")
+
+            out = EnsembleOptimizerOutput.from_outputs([self.output1, self.output2])
+            out.to_hdf(filepath)
+
+            out_new = EnsembleOptimizerOutput.from_hdf(filepath)
+            pdt.assert_frame_equal(out.data, out_new.data)
+
+    @unittest.skip("Cycling with CSV does not preserve data types")
+    def test_cycling_csv(self):
+        """Test correct cycling with CSV"""
+        with TemporaryDirectory() as tmp:
+            filepath = Path(tmp, "file.csv")
+
+            out = EnsembleOptimizerOutput.from_outputs([self.output1, self.output2])
+            out.to_csv(filepath)
+
+            out_new = EnsembleOptimizerOutput.from_csv(filepath)
+            pdt.assert_frame_equal(out.data, out_new.data)