New: print out trees in compact form

Author: andreas-zeller

notebooks/Alhazen.ipynb

@@ -715,7 +715,8 @@
     "           e.g., 'num(<integer>)'\n",
     "    rule : The production rule.\n",
     "    '''\n",
-    "    def __init__(self, name: str, rule: str, friendly_name: str = None) -> None:\n",
+    "    def __init__(self, name: str, rule: str, /, \n",
+    "                 friendly_name: str = None) -> None:\n",
     "        super().__init__(name, rule, rule, friendly_name=friendly_name)\n",
     "\n",
     "    def name_rep(self) -> str:\n",
@@ -2213,7 +2214,7 @@
    "source": [
     "class InputSpecification:\n",
     "    '''\n",
-    "    This class represents a complet input specification of a new input. A input specification\n",
+    "    This class represents a complete input specification of a new input. A input specification\n",
     "    consists of one or more requirements.\n",
     "    requirements  : Is a list of all requirements that must be used.\n",
     "    '''\n",
@@ -2669,7 +2670,8 @@
     "\n",
     "        self._all_features = extract_existence(self._grammar) + extract_numeric(self._grammar)\n",
     "        self._feature_names = [f.name for f in self._all_features]\n",
-    "        print(f\"Features: {self._feature_names}\")\n",
+    "        print(\"Features:\", \", \".join(f.friendly_name() \n",
+    "                                     for f in self._all_features))\n",
     "\n",
     "    def _add_new_data(self, exec_data, feature_data):\n",
     "        joined_data = exec_data.join(feature_data.drop(['sample'], axis=1))\n",
@@ -2821,6 +2823,53 @@
     "show_decision_tree(remove_unequal_decisions(final_tree), all_feature_names)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "def friendly_decision_tree(clf, feature_names, class_names = ['NO_BUG', 'BUG']):\n",
+    "    def _tree(index, indent=0):\n",
+    "        s = \"\"\n",
+    "        feature = clf.tree_.feature[index]\n",
+    "        feature_name = feature_names[feature]\n",
+    "        threshold = clf.tree_.threshold[index]\n",
+    "        value = clf.tree_.value[index]\n",
+    "        class_ = int(value[0][0])\n",
+    "        class_name = class_names[class_]\n",
+    "        left = clf.tree_.children_left[index]\n",
+    "        right = clf.tree_.children_right[index]\n",
+    "        if left == right:\n",
+    "            # Leaf node\n",
+    "            s += \" \" * indent + class_name + \"\\n\"\n",
+    "        else:\n",
+    "            if math.isclose(threshold, 0.5):\n",
+    "                s += \" \" * indent + f\"if {feature_name}:\\n\"\n",
+    "                s += _tree(right, indent + 2)\n",
+    "                s += \" \" * indent + f\"else:\\n\"\n",
+    "                s += _tree(left, indent + 2)\n",
+    "            else:\n",
+    "                s += \" \" * indent + f\"if {feature_name} <= {threshold:.4f}:\\n\"\n",
+    "                s += _tree(left, indent + 2)\n",
+    "                s += \" \" * indent + f\"else:\\n\"\n",
+    "                s += _tree(right, indent + 2)\n",
+    "        return s\n",
+    "\n",
+    "    return _tree(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(friendly_decision_tree(final_tree, all_feature_names))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},