Merge pull request #180 from ChristopherMayes/bugfixes

implement bugfix for adding constants + update tests

tests/test_xopt.py

@@ -199,6 +199,24 @@ def test_evaluate(self):
         out = xopt.evaluate({"x1": 0.4, "x2": 0.3})
         assert isinstance(out, dict)
 
+        # test with vocs that uses "x1" as a constant
+        test_vocs = deepcopy(TEST_VOCS_BASE)
+
+        evaluator = Evaluator(function=xtest_callable)
+        generator = RandomGenerator(vocs=test_vocs)
+
+        xopt = Xopt(generator=generator, evaluator=evaluator, vocs=test_vocs)
+
+        test_vocs.variables = {"x2": [0, 1]}
+        test_vocs.constants = {"x1": 2.0}
+
+        out = xopt.evaluate({"x2": 0.2})
+        assert isinstance(out, dict)
+
+        xopt.evaluate_data({"x2": 0.2})
+        assert len(xopt.data) == 1
+        assert xopt.data["x1"].iloc[0] == 2.0
+
     def test_evaluate_data(self):
         evaluator = Evaluator(function=xtest_callable)
         generator = RandomGenerator(vocs=deepcopy(TEST_VOCS_BASE))

xopt/base.py

@@ -1,5 +1,6 @@
 import json
 import logging
+from copy import deepcopy
 from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
@@ -275,7 +276,13 @@ def evaluate(self, input_dict: Dict):
             The result of the evaluation.
 
         """
-        self.vocs.validate_input_data(DataFrame(input_dict, index=[0]))
+        inputs = deepcopy(input_dict)
+
+        # add constants to input data
+        for name, value in self.vocs.constants.items():
+            inputs[name] = value
+
+        self.vocs.validate_input_data(DataFrame(inputs, index=[0]))
         return self.evaluator.evaluate(input_dict)
 
     def evaluate_data(
@@ -309,12 +316,17 @@ def evaluate_data(
         # translate input data into pandas dataframes
         if not isinstance(input_data, DataFrame):
             try:
-                input_data = DataFrame(input_data)
+                input_data = DataFrame(deepcopy(input_data))
             except ValueError:
-                input_data = DataFrame(input_data, index=[0])
+                input_data = DataFrame(deepcopy(input_data), index=[0])
 
         logger.debug(f"Evaluating {len(input_data)} inputs")
         self.vocs.validate_input_data(input_data)
+
+        # add constants to input data
+        for name, value in self.vocs.constants.items():
+            input_data[name] = value
+
         output_data = self.evaluator.evaluate_data(input_data)
 
         if self.strict:

xopt/vocs.py

@@ -4,6 +4,7 @@
 import numpy as np
 import pandas as pd
 import yaml
+from pandas import DataFrame
 from pydantic import ConfigDict, conlist, Field
 
 from xopt.pydantic import XoptBaseModel
@@ -502,14 +503,14 @@ def select_best(self, data: pd.DataFrame, n=1):
 OBJECTIVE_WEIGHT = {"MINIMIZE": 1.0, "MAXIMIZE": -1.0}
 
 
-def form_variable_data(variables: Dict, data, prefix="variable_"):
+def form_variable_data(variables: Union[Dict, DataFrame], data, prefix="variable_"):
     """
     Use variables dict to form a dataframe.
     """
     if not variables:
         return pd.DataFrame([])
 
-    if not isinstance(data, pd.DataFrame):
+    if not isinstance(data, DataFrame):
         data = pd.DataFrame(data)
 
     # Pick out columns in right order