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   "cell_type": "markdown",
   "id": "animal-spencer",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2021-03-25T06:28:01.094646Z",
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     "shell.execute_reply": "2021-03-25T06:28:01.098635Z"
    }
   },
   "source": [
    "## Data / Model Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "exposed-provincial",
   "metadata": {
    "execution": {
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   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import pandas as pd\n",
    "from pyexplainer.pyexplainer_pyexplainer import PyExplainer\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "\n",
    "cwd = os.getcwd()\n",
    "parent_dir = os.path.dirname(cwd)\n",
    "path_train = parent_dir + \"/tests/pyexplainer_test_data/activemq-5.0.0.zip\"\n",
    "training_data = pd.read_csv(path_train, index_col = 'File')\n",
    "\n",
    "dep = training_data.columns[-4]\n",
    "selected_features = [\"ADEV\", \"AvgCyclomaticModified\", \"AvgEssential\", \"AvgLineBlank\", \"AvgLineComment\",\n",
    "                     \"CountClassBase\", \"CountClassCoupled\", \"CountClassDerived\", \"CountDeclClass\",\n",
    "                     \"CountDeclClassMethod\", \"CountDeclClassVariable\", \"CountDeclInstanceVariable\",\n",
    "                     \"CountDeclMethodDefault\", \"CountDeclMethodPrivate\", \"CountDeclMethodProtected\",\n",
    "                     \"CountDeclMethodPublic\", \"CountInput_Mean\", \"CountInput_Min\", \"CountOutput_Min\", \"MAJOR_LINE\",\n",
    "                     \"MaxInheritanceTree\", \"MaxNesting_Min\", \"MINOR_COMMIT\", \"OWN_COMMIT\", \"OWN_LINE\",\n",
    "                     \"PercentLackOfCohesion\", \"RatioCommentToCode\"]\n",
    "all_cols = training_data.columns\n",
    "for col in all_cols:\n",
    "    if col not in selected_features:\n",
    "        all_cols = all_cols.drop(col)\n",
    "indep = all_cols\n",
    "\n",
    "X_train = training_data.loc[:, indep]\n",
    "y_train = training_data.loc[:, dep]\n",
    "\n",
    "blackbox_model = RandomForestClassifier(max_depth=3, random_state=0)\n",
    "blackbox_model.fit(X_train, y_train)\n",
    "\n",
    "class_label = ['Clean', 'Defect']\n",
    "\n",
    "path_test = parent_dir + \"/tests/pyexplainer_test_data/activemq-5.1.0.zip\"\n",
    "testing_data = pd.read_csv(path_test, index_col = 'File')\n",
    "X_test = testing_data.loc[:, indep]\n",
    "y_test = testing_data.loc[:, dep]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invisible-storage",
   "metadata": {},
   "source": [
    "## python unittest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dressed-increase",
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     "shell.execute_reply": "2021-03-26T04:55:13.673592Z"
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     "text": [
      "C:\\Users\\micha\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
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      "C:\\Users\\micha\\anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\widget.py:412: DeprecationWarning: Passing unrecognized arguments to super(SliderStyle).__init__(widget_width='60%').\n",
      "object.__init__() takes exactly one argument (the instance to initialize)\n",
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      "."
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      "C:\\Users\\micha\\anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\widget.py:412: DeprecationWarning: Passing unrecognized arguments to super(SliderStyle).__init__(widget_width='60%').\n",
      "object.__init__() takes exactly one argument (the instance to initialize)\n",
      "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n",
      "  super(Widget, self).__init__(**kwargs)\n"
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     "text": [
      ".\n",
      "----------------------------------------------------------------------\n",
      "Ran 2 tests in 5.005s\n",
      "\n",
      "OK\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import pandas as pd\n",
    "from pyexplainer.pyexplainer_pyexplainer import PyExplainer\n",
    "import pickle\n",
    "import os\n",
    "import unittest\n",
    "\n",
    "\n",
    "class TestVisualisation(unittest.TestCase):\n",
    "    \"\"\"unittest the visualisation stuff in pyexplainer\"\"\"\n",
    "    \n",
    "    def test_visualise_pyobject(self):\n",
    "        def load_object(filename):\n",
    "            with open(filename, 'rb') as file:\n",
    "                object_o = pickle.load(file)\n",
    "            return (object_o)\n",
    "        # load rule obj\n",
    "        if os.path.isfile('../tests/rule_objects/rule_object.pyobject'):\n",
    "            loaded_rule_obj = load_object('../tests/rule_objects/rule_object.pyobject')\n",
    "        py_explainer = PyExplainer(X_train,\n",
    "                                    y_train,\n",
    "                                    indep,\n",
    "                                    dep,\n",
    "                                    blackbox_model,\n",
    "                                    class_label=class_label)\n",
    "        py_explainer.visualise(loaded_rule_obj)\n",
    "    \n",
    "    def test_visualise_manually_created(self):\n",
    "        for explain_index in range(14, 15):\n",
    "            X_explain = X_test.iloc[[explain_index]]\n",
    "            y_explain = y_test.iloc[[explain_index]]\n",
    "            py_explainer = PyExplainer(X_train,\n",
    "                                        y_train,\n",
    "                                        indep,\n",
    "                                        dep,\n",
    "                                        blackbox_model,\n",
    "                                        class_label=class_label)\n",
    "            rule_object = py_explainer.explain(X_explain, y_explain)\n",
    "            py_explainer.visualise(rule_object)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    unittest.main(argv=['first-arg-is-ignored'], exit=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "prostate-wholesale",
   "metadata": {},
   "outputs": [],
   "source": []
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