move build_dtree method back to ModelAttribute class

1 files changed, 130 insertions, 0 deletions

diff --git a/lib/parameters.py b/lib/parameters.py
index e516926..238f496 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -7,6 +7,7 @@ from collections import OrderedDict
 from copy import deepcopy
 from multiprocessing import Pool
 import dfatool.functions as df
+from .paramfit import ParamFit
 from .utils import remove_index_from_tuple, is_numeric
 from .utils import filter_aggregate_by_param, partition_by_param
 
@@ -723,3 +724,132 @@ class ModelAttribute:
 
             if x.fit_success:
                 self.model_function = x
+
+    def build_dtree(self, parameters, data, with_function_leaves=False, threshold=100):
+        """
+        Build a Decision Tree on `param` / `data` for kconfig models.
+
+        :param this_symbols: parameter names
+        :param this_data: list of measurements. Each entry is a (param vector, mearusements vector) tuple.
+            param vector holds parameter values (same order as parameter names). mearuserements vector holds measurements.
+        :param data_index: Index in measurements vector to use for model generation. Default 0.
+        :param threshold: Return a StaticFunction leaf node if std(data[data_index]) < threshold. Default 100.
+
+        :returns: SplitFunction or StaticFunction
+        """
+        self.model_function = self._build_dtree(
+            parameters, data, with_function_leaves, threshold
+        )
+
+    def _build_dtree(
+        self, parameters, data, with_function_leaves=False, threshold=100, level=0
+    ):
+        """
+        Build a Decision Tree on `param` / `data` for kconfig models.
+
+        :param this_symbols: parameter names
+        :param this_data: list of measurements. Each entry is a (param vector, mearusements vector) tuple.
+            param vector holds parameter values (same order as parameter names). mearuserements vector holds measurements.
+        :param data_index: Index in measurements vector to use for model generation. Default 0.
+        :param threshold: Return a StaticFunction leaf node if std(data[data_index]) < threshold. Default 100.
+
+        :returns: SplitFunction or StaticFunction
+        """
+
+        # TODO remove data entries which are None (and remove corresponding parameters, too!)
+
+        parameter_names = self.param_names
+        if len(parameter_names) == 0 or np.std(data) < threshold:
+            return df.StaticFunction(np.mean(data))
+            # sf.value_error["std"] = np.std(data)
+
+        mean_stds = list()
+        for param_index, param in enumerate(parameter_names):
+
+            unique_values = list(set(map(lambda p: p[param_index], parameters)))
+
+            if None in unique_values:
+                # param is a choice and undefined in some configs. Do not split on it.
+                mean_stds.append(np.inf)
+                continue
+
+            if (
+                with_function_leaves
+                and len(unique_values) > 3
+                and all(map(lambda x: type(x) is int, unique_values))
+            ):
+                # param can be modeled as a function. Do not split on it.
+                mean_stds.append(np.inf)
+                continue
+
+            child_indexes = list()
+            for value in unique_values:
+                child_indexes.append(
+                    list(
+                        filter(
+                            lambda i: parameters[i][param_index] == value,
+                            range(len(parameters)),
+                        )
+                    )
+                )
+
+            if len(list(filter(len, child_indexes))) < 2:
+                # this param only has a single value. there's no point in splitting.
+                mean_stds.append(np.inf)
+                continue
+
+            children = list()
+            for child in child_indexes:
+                children.append(np.std(list(map(lambda i: data[i], child))))
+
+            if np.any(np.isnan(children)):
+                mean_stds.append(np.inf)
+            else:
+                mean_stds.append(np.mean(children))
+
+        if np.all(np.isinf(mean_stds)):
+            # all children have the same configuration. We shouldn't get here due to the threshold check above...
+            if with_function_leaves:
+                # try generating a function. if it fails, model_function is a StaticFunction.
+                ma = ModelAttribute("tmp", "tmp", data, parameters, self.param_names, 0)
+                ParamStats.compute_for_attr(ma)
+                paramfit = ParamFit(parallel=False)
+                for key, param, args, kwargs in ma.get_data_for_paramfit():
+                    paramfit.enqueue(key, param, args, kwargs)
+                paramfit.fit()
+                ma.set_data_from_paramfit(paramfit)
+                return ma.model_function
+            else:
+                logging.warning(
+                    f"While building DTree for configurations {parameters}: Children have identical configuration, but high stddev ({np.std(data)}). Falling back to Staticfunction"
+                )
+            return df.StaticFunction(np.mean(data))
+
+        symbol_index = np.argmin(mean_stds)
+        symbol = parameter_names[symbol_index]
+
+        unique_values = list(set(map(lambda p: p[symbol_index], parameters)))
+
+        child = dict()
+
+        for value in unique_values:
+            indexes = list(
+                filter(
+                    lambda i: parameters[i][symbol_index] == value,
+                    range(len(parameters)),
+                )
+            )
+            child_parameters = list(map(lambda i: parameters[i], indexes))
+            child_data = list(map(lambda i: data[i], indexes))
+            if len(child_data):
+                child[value] = self._build_dtree(
+                    child_parameters,
+                    child_data,
+                    with_function_leaves,
+                    threshold,
+                    level + 1,
+                )
+
+        assert len(child.values()) >= 2
+
+        return df.SplitFunction(np.mean(data), symbol_index, child)