Expression: test evaluate method

mesmer/mesmer_x/train_utils_mesmerx.py

@@ -325,7 +325,8 @@ def evaluate(self, coefficients_values, inputs_values, forced_shape=None):
         # - use broadcasting
         # - can we avoid using exec & eval?
         # - only parse the values once? (to avoid doing it repeatedly)
-        # - don't allow list of coefficients_values
+        # - require list of coefficients_values (similar to minimize)?
+        # - convert dataset to numpy arrays?
 
         # Check 1: are all the coefficients provided?
         if isinstance(coefficients_values, dict | xr.Dataset):
@@ -351,7 +352,7 @@ def evaluate(self, coefficients_values, inputs_values, forced_shape=None):
         # Check 3: do the inputs have the same shape
         shapes = {inputs_values[i].shape for i in self.inputs_list}
         if len(shapes) > 1:
-            raise ValueError("Different shapes of inputs detected.")
+            raise ValueError("shapes of inputs must be equal")
 
         # Evaluation 1: coefficients
         for c in coefficients_values:

tests/unit/test_mesmer_x_expression.py

@@ -1,6 +1,9 @@
+import numpy as np
 import pytest
 import scipy as sp
+import xarray as xr
 
+import mesmer
 from mesmer.mesmer_x import Expression
 
 inf = float("inf")
@@ -213,3 +216,135 @@ def test_expression_covariate_substring():
 
     coeffs_per_param = {"loc": ["c1", "c2", "c3"], "scale": ["c4"]}
     assert expr.coefficients_dict == coeffs_per_param
+
+
+def test_evaluate_missing_coefficient_dict():
+
+    expr = Expression("norm(loc=c1, scale=c2)", expr_name="name")
+
+    with pytest.raises(
+        ValueError, match="Missing information for the coefficient: 'c1'"
+    ):
+        expr.evaluate({}, {})
+
+    with pytest.raises(
+        ValueError, match="Missing information for the coefficient: 'c2'"
+    ):
+        expr.evaluate({"c1": 1}, {})
+
+
+def test_evaluate_missing_coefficient_dataset():
+
+    expr = Expression("norm(loc=c1, scale=c2)", expr_name="name")
+
+    with pytest.raises(
+        ValueError, match="Missing information for the coefficient: 'c1'"
+    ):
+        expr.evaluate(xr.Dataset(), {})
+
+    with pytest.raises(
+        ValueError, match="Missing information for the coefficient: 'c2'"
+    ):
+        expr.evaluate(xr.Dataset(data_vars={"c1": 1}), {})
+
+
+def test_evaluate_missing_coefficient_list():
+
+    expr = Expression("norm(loc=c1, scale=c2)", expr_name="name")
+
+    with pytest.raises(
+        ValueError, match="Inconsistent information for the coefficients_values"
+    ):
+        expr.evaluate([], {})
+
+    with pytest.raises(
+        ValueError, match="Inconsistent information for the coefficients_values"
+    ):
+        expr.evaluate([1], {})
+
+
+def test_evaluate_missing_covariates_dict():
+
+    expr = Expression("norm(loc=c1 * __T__, scale=c2 * __F__)", expr_name="name")
+
+    with pytest.raises(ValueError, match="Missing information for the input: 'T'"):
+        expr.evaluate([1, 1], {})
+
+    with pytest.raises(ValueError, match="Missing information for the input: 'F'"):
+        expr.evaluate([1, 1], {"T": 1})
+
+
+def test_evaluate_missing_covariates_ds():
+
+    expr = Expression("norm(loc=c1 * __T__, scale=c2 * __F__)", expr_name="name")
+
+    with pytest.raises(ValueError, match="Missing information for the input: 'T'"):
+        expr.evaluate([1, 1], xr.Dataset())
+
+    with pytest.raises(ValueError, match="Missing information for the input: 'F'"):
+        expr.evaluate([1, 1], xr.Dataset(data_vars={"T": 1}))
+
+
+def test_evaluate_covariates_wrong_shape():
+
+    expr = Expression("norm(loc=c1 * __T__, scale=c2 * __F__)", expr_name="name")
+
+    T = np.array([1])
+    F = np.array([1, 1])
+    data_vars = {"T": T, "F": F}
+
+    with pytest.raises(ValueError, match="shapes of inputs must be equal"):
+        expr.evaluate([1, 1], data_vars)
+
+    with pytest.raises(ValueError, match="shapes of inputs must be equal"):
+        expr.evaluate([1, 1], xr.Dataset(data_vars=data_vars))
+
+
+def test_evaluate_norm():
+
+    expr = Expression("norm(loc=c1 * __T__, scale=c2)", expr_name="name")
+    dist = expr.evaluate([1, 2], {"T": np.array([1, 2])})
+
+    assert isinstance(dist.dist, type(sp.stats.norm))
+
+    expected = {"loc": np.array([1, 2]), "scale": np.array([2.0, 2.0])}
+
+    # assert frozen params are equal
+    mesmer.testing.assert_dict_allclose(dist.kwds, expected)
+
+    # NOTE: will write own function to return param values
+    mesmer.testing.assert_dict_allclose(dist.kwds, expr.parameters_values)
+
+    # a second set of values
+    dist = expr.evaluate([2, 1], {"T": np.array([2, 5])})
+
+    expected = {"loc": np.array([4, 10]), "scale": np.array([1.0, 1.0])}
+
+    # assert frozen params are equal
+    mesmer.testing.assert_dict_allclose(dist.kwds, expected)
+
+    mesmer.testing.assert_dict_allclose(dist.kwds, expr.parameters_values)
+
+
+def test_evaluate_norm_dataset():
+    # NOTE: not sure if passing DataArray to scipy distribution is a good idea
+
+    expr = Expression("norm(loc=c1 * __T__, scale=c2)", expr_name="name")
+
+    coefficients_values = xr.Dataset(data_vars={"c1": 1, "c2": 2})
+    inputs_values = xr.Dataset(data_vars={"T": ("x", np.array([1, 2]))})
+
+    dist = expr.evaluate(coefficients_values, inputs_values)
+
+    assert isinstance(dist.dist, type(sp.stats.norm))
+
+    loc = xr.DataArray([1, 2], dims="x")
+    scale = xr.DataArray([2, 2], dims="x")
+
+    expected = {"loc": loc, "scale": scale}
+
+    # assert frozen params are equal
+    mesmer.testing.assert_dict_allclose(dist.kwds, expected)
+
+    # NOTE: will write own function to return param values
+    mesmer.testing.assert_dict_allclose(dist.kwds, expr.parameters_values)