allow parameter values to be adjusted before modeling

(e.g. to ensure they're positive)

2 files changed, 49 insertions, 0 deletions

diff --git a/lib/cli.py b/lib/cli.py
index a1e4a58..a176e88 100644
--- a/lib/cli.py
+++ b/lib/cli.py
@@ -115,3 +115,31 @@ def add_standard_arguments(parser):
         action="store_true",
         help="Perform parameter-aware cross-validation: ensure that parameter values (and not just observations) are mutually exclusive between training and validation sets.",
     )
+    parser.add_argument(
+        "--param-shift",
+        metavar="<key>=<+|-|*|/><value>;...",
+        type=str,
+        help="Adjust parameter values before passing them to model generation",
+    )
+
+
+def parse_param_shift(raw_param_shift):
+    shift_list = list()
+    for shift_pair in raw_param_shift.split(";"):
+        param_name, param_shift = shift_pair.split("=")
+        if param_shift.startswith("+"):
+            param_shift_value = float(param_shift[1:])
+            param_shift_function = lambda p: p + param_shift_value
+        elif param_shift.startswith("-"):
+            param_shift_value = float(param_shift[1:])
+            param_shift_function = lambda p: p - param_shift_value
+        elif param_shift.startswith("*"):
+            param_shift_value = float(param_shift[1:])
+            param_shift_function = lambda p: p * param_shift_value
+        elif param_shift.startswith("/"):
+            param_shift_value = float(param_shift[1:])
+            param_shift_function = lambda p: p / param_shift_value
+        else:
+            raise ValueError(f"Unsupported shift operation {param_name}={param_shift}")
+        shift_list.append((param_name, param_shift_function))
+    return shift_list
diff --git a/lib/utils.py b/lib/utils.py
index 10f0172..7372995 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -314,6 +314,27 @@ def by_param_to_by_name(by_param: dict) -> dict:
     return by_name
 
 
+def shift_param_in_aggregate(aggregate, parameters, parameter_shift):
+    """
+    Remove entries which do not have certain parameter values from `aggregate`.
+
+    :param aggregate: aggregated measurement data, must be a dict conforming to
+        aggregate[state or transition name]['param'] = (first parameter value, second parameter value, ...)
+        and
+        aggregate[state or transition name]['attributes'] = [list of keys with measurement data, e.g. 'power' or 'duration']
+    :param parameters: list of parameters, used to map parameter index to parameter name. parameters=['foo', ...] means 'foo' is the first parameter
+    :param parameter_filter: [[name, value], [name, value], ...] list of parameter values to keep, all others are removed. Values refer to normalizad parameter data.
+    """
+    for param_name, param_shift_function in parameter_shift:
+        param_index = parameters.index(param_name)
+        for name in aggregate.keys():
+            for param_list in aggregate[name]["param"]:
+                if param_list[param_index] is not None:
+                    param_list[param_index] = param_shift_function(
+                        param_list[param_index]
+                    )
+
+
 def filter_aggregate_by_param(aggregate, parameters, parameter_filter):
     """
     Remove entries which do not have certain parameter values from `aggregate`.