analyze-log: add --information-gain

2 files changed, 40 insertions, 0 deletions

diff --git a/lib/cli.py b/lib/cli.py
index d2c9840..d59c997 100644
--- a/lib/cli.py
+++ b/lib/cli.py
@@ -92,6 +92,15 @@ def print_info_by_name(model, by_name):
             )
 
 
+def print_information_gain_by_name(model, by_name):
+    for name in model.names:
+        for attr in model.attributes(name):
+            print(f"{name} {attr}:")
+            mutual_information = model.mutual_information(name, attr)
+            for i, param in enumerate(model.parameters):
+                print(f"    Parameter {param} : {mutual_information[i]:5.2f}")
+
+
 def print_analyticinfo(prefix, info):
     model_function = info.model_function.removeprefix("0 + ")
     for i in range(len(info.model_args)):
@@ -576,6 +585,11 @@ def add_standard_arguments(parser):
         help="Show benchmark information (number of measurements, parameter values, ...)",
     )
     parser.add_argument(
+        "--information-gain",
+        action="store_true",
+        help="Show information gain of parameters",
+    )
+    parser.add_argument(
         "--log-level",
         metavar="LEVEL",
         choices=["debug", "info", "warning", "error"],
diff --git a/lib/parameters.py b/lib/parameters.py
index 4047c10..352e7c7 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -604,6 +604,9 @@ class ModelAttribute:
         # The best model we have. May be Static, Split, or Param (and later perhaps Substate)
         self.model_function = None
 
+        # Information gain cache. Used for statistical analysis
+        self.mutual_information_cache = None
+
         self._check_codependent_param()
 
         # There must be at least 3 distinct data values (≠ None) if an analytic model
@@ -699,6 +702,29 @@ class ModelAttribute:
     def webconf_function_map(self):
         return self.model_function.webconf_function_map()
 
+    def mutual_information(self):
+        if self.mutual_information_cache is not None:
+            return self.mutual_information_cache
+
+        from sklearn.feature_selection import mutual_info_regression
+
+        fit_parameters, _, ignore_index = param_to_ndarray(
+            self.param_values, with_nan=False, categorical_to_scalar=True
+        )
+
+        param_to_fit_param = dict()
+        j = 0
+        for i in range(len(self.param_names)):
+            if not ignore_index[i]:
+                param_to_fit_param[i] = j
+                j += 1
+
+        self.mutual_information_cache = mutual_info_regression(
+            fit_parameters, self.data
+        )
+
+        return self.mutual_information_cache
+
     @classmethod
     def from_json(cls, name, attr, data):
         param_names = data["paramNames"]