evaluation/2_modelling_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling Results - Reference Predictions\n",
    "\n",
    "In this notebook, the predictions from the reference model are utilized. For detailed theoretical information, please refer to the report.\n",
    "\n",
    "\n",
    "After training a Graph Neural Network (GNN), additional features were evaluated using a Random Forest (RF) model. Key features included GNN prediction scores, cosine similarity, and various node metrics like in-degree, out-degree, and cluster coefficients. Permutation Importance was applied to identify the most influential features. \n",
    "\n",
    "Permutation importance is a model-agnostic technique to evaluate feature importance by assessing the change in a model's performance after shuffling the values of a specific feature. A significant drop in performance suggests the feature is crucial for accurate predictions. This process is iterated to obtain a stable importance score.\n",
    "\n",
    "\n",
    "To ensure robustness, a 5-fold cross-validation was performed on the validation set, followed by testing on the test set to verify generalization. This approach ensures that the model's performance and the selected features are reliable and effective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "current_dir = os.getcwd()\n",
    "if os.path.basename(current_dir) != 'link-prediction-in-graphs':\n",
    "    parent_dir = os.path.dirname(current_dir)\n",
    "    os.chdir(parent_dir)\n",
    "print(f\"current working directory: {os.getcwd()}\")\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from ogb.nodeproppred import NodePropPredDataset\n",
    "import seaborn as sns\n",
    "import dask.dataframe as dd\n",
    "from dask.multiprocessing import get\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.ensemble import RandomForestClassifier,HistGradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from ogb.linkproppred import Evaluator\n",
    "from joblib import dump,load\n",
    "# set default plt figsize to (12,6)\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "pd.set_option('display.colheader_justify', 'center')\n",
    "# Set default font sizes\n",
    "plt.rcParams['axes.titlesize'] = 20\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "plt.rcParams['axes.labelsize'] = 18 \n",
    "plt.rcParams['legend.fontsize'] = 16\n",
    "plt.rcParams['legend.title_fontsize'] = 18\n",
    "dataset = \"ogbn-arxiv\"\n",
    "\n",
    "data_path = \"./dataset/sample_data_for_evaluation_build/gcn_reference/valid/\"\n",
    "data = NodePropPredDataset(name=dataset)\n",
    "from modelling.dataset_split.dataset_splitter import Dataset_Splitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Splits for dataset ogbn-arxiv\n",
      "Returning Splits for dataset ogbn-arxiv\n"
     ]
    }
   ],
   "source": [
    "ds_split = Dataset_Splitter()\n",
    "dataset_ = ds_split.load_dataset(dataset)\n",
    "split_edge, edge_index = ds_split.get_edges_split(dataset_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = np.load(f\"{data_path}source.npy\")\n",
    "source_multiplied = np.array(torch.tensor(source).view(-1, 1).repeat(1, 1000).view(-1))\n",
    "target_neg = np.load(f\"{data_path}target_neg.npy\")\n",
    "target_pos = np.load(f\"{data_path}target.npy\")\n",
    "neg_preds = np.load(f\"{data_path}neg_preds.npy\")\n",
    "pos_preds = np.load(f\"{data_path}pos_preds.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_edges = torch.load(f\"./temp/split_edge-{dataset}.pt\")[\"train\"]\n",
    "train_edges = np.array([train_edges[\"source_node\"],train_edges[\"target_node\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = set(np.hstack((np.unique(train_edges),source,np.unique(target_neg),target_pos)))\n",
    "unique_validation_nodes = set(np.hstack((source,np.unique(target_neg),target_pos)))\n",
    "\n",
    "nx_graph = nx.DiGraph()\n",
    "nx_graph.add_nodes_from(nodes)\n",
    "nx_graph.add_edges_from(train_edges.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Node Feature Dataframe\n",
    "\n",
    "Here the node feature dataframe is build. This are all the features, which can be calculated for each node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "clustering_source = nx.clustering(nx_graph,unique_validation_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_in_strong_connected_components = set.union(*(set(s) for s in nx.strongly_connected_components(nx_graph) if len(s)!=1))\n",
    "nodes_in_strong = {node: node in nodes_in_strong_connected_components for node in unique_validation_nodes}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_degrees = dict(nx_graph.in_degree())\n",
    "in_degrees_source_nodes = {key:in_degrees.get(key) for key in unique_validation_nodes}\n",
    "out_degrees = dict(nx_graph.out_degree())\n",
    "out_degrees_source_nodes = {key:out_degrees.get(key) for key in unique_validation_nodes}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_nodes = data[0][0][\"node_year\"]\n",
    "year_of_nodes = {i:years_nodes[i][0] for i in range(len(years_nodes))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\n",
       "      <th></th>\n",
       "      <th>Node Index</th>\n",
       "      <th>clustering_coeffficient</th>\n",
       "      <th>in_degrees</th>\n",
       "      <th>out_degrees</th>\n",
       "      <th>node_in_strong</th>\n",
       "      <th>node_year</th>\n",
       "      <th>node_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2013</td>\n",
       "      <td>4</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>2014</td>\n",
       "      <td>28</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>2014</td>\n",
       "      <td>27</td>\n",
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       "      <th>169338</th>\n",
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       "      <td>0.121505</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169340</th>\n",
       "      <td>169340</td>\n",
       "      <td>0.140523</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169341</th>\n",
       "      <td>169341</td>\n",
       "      <td>0.134211</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
       "<p>169343 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Node Index  clustering_coeffficient  in_degrees  out_degrees  \\\n",
       "0              0           0.015654              283          2        \n",
       "1              1           0.000000                0          1        \n",
       "2              2           0.142857                5          2        \n",
       "3              3           0.000000                1          0        \n",
       "4              4           0.400000                5          1        \n",
       "...            ...                      ...         ...          ...   \n",
       "169338    169338           0.121505                0         31        \n",
       "169339    169339           0.166667                1          2        \n",
       "169340    169340           0.140523                1         17        \n",
       "169341    169341           0.134211                3         17        \n",
       "169342    169342           0.000000                0          2        \n",
       "\n",
       "        node_in_strong  node_year  node_label  \n",
       "0            False        2013          4      \n",
       "1            False        2015          5      \n",
       "2             True        2014         28      \n",
       "3            False        2014          8      \n",
       "4            False        2014         27      \n",
       "...                ...        ...         ...  \n",
       "169338       False        2020          4      \n",
       "169339       False        2020         24      \n",
       "169340       False        2020         10      \n",
       "169341       False        2020          4      \n",
       "169342       False        2020          1      \n",
       "\n",
       "[169343 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame([clustering_source]).T.rename(columns={0:\"clustering_coeffficient\"})\n",
    "df2 = pd.DataFrame([in_degrees_source_nodes]).T.rename(columns={0:\"in_degrees\"})\n",
    "df3 = pd.DataFrame([out_degrees_source_nodes]).T.rename(columns={0:\"out_degrees\"})\n",
    "df4 = pd.DataFrame([nodes_in_strong]).T.rename(columns={0:\"node_in_strong\"})\n",
    "df5 = pd.DataFrame([year_of_nodes]).T.rename(columns={0:\"node_year\"})\n",
    "df6 = pd.read_csv(\"./dataset/ogbn_arxiv/raw/node-label.csv.gz\",header=None).rename(columns={0:\"node_label\"})\n",
    "\n",
    "# Concatenate transposed DataFrames along columns\n",
    "feature_df = pd.concat([df1, df2, df3,df4,df5,df6], axis=1).reset_index(names=\"Node Index\")\n",
    "feature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature_df.to_csv(\"./dataset/modelling_results_calc_saved/feature_per_node_test.csv\")\n",
    "feature_df.to_csv(\"./dataset/modelling_results_calc_saved/feature_per_node.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Prediction Dataframe\n",
    "\n",
    "Here the link feature dataframe is build. This are all the features, which can be calculated for each link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_common_neighbours(nx_graph,u,v):\n",
    "    common = len(set(nx_graph.successors(u)) & set(nx_graph.successors(v)))\n",
    "    return common\n",
    "\n",
    "def calc_common_neighbours_preds_and_succ(nx_graph,u,v):\n",
    "    common = len(set.union(set(nx_graph.predecessors(u)), set(nx_graph.successors(u))) & set.union(set(nx_graph.predecessors(v)), set(nx_graph.successors(v))))\n",
    "    return common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pos_df = pd.DataFrame((source,target_pos)).T.rename(columns={0:\"source\",1:\"target\"})\n",
    "pred_pos_df['common_neighbours_succ'] = pred_pos_df.apply(lambda row: calc_common_neighbours(nx_graph, row['source'], row['target']), axis=1)\n",
    "pred_pos_df['common_neighbours_all'] = pred_pos_df.apply(lambda row: calc_common_neighbours_preds_and_succ(nx_graph, row['source'], row['target']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_cosine(source, target, node_features):\n",
    "    return cosine_similarity(node_features[source].reshape(1, -1), node_features[target].reshape(1, -1))[0][0]\n",
    "\n",
    "\n",
    "node_features = data[0][0][\"node_feat\"]\n",
    "pred_pos_df['cosine_similarity'] = pred_pos_df.apply(lambda row: calc_cosine(row['source'], row['target'], node_features), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pos_df_featured = pd.merge(pred_pos_df,feature_df,left_on=\"source\",right_on=\"Node Index\").rename(\n",
    "    columns={\"clustering_coeffficient\":\"clustering_coeffficient_source\",\"in_degrees\":\"in_degrees_source\",\"out_degrees\":\"out_degrees_source\",\"node_in_strong\":\"node_in_strong_source\",\"node_label\": \"arxiv_category_source\"}).drop(columns=[\"Node Index\",\"node_year\"])\n",
    "pred_pos_df_featured = pd.merge(pred_pos_df_featured,feature_df,left_on=\"target\",right_on=\"Node Index\").rename(\n",
    "    columns={\"clustering_coeffficient\":\"clustering_coeffficient_target\",\"in_degrees\":\"in_degrees_target\",\"out_degrees\":\"out_degrees_target\",\"node_in_strong\":\"node_in_strong_target\",\"node_year\":\"node_year_target\",\"node_label\":\"arxiv_category_target\"}).drop(columns=\"Node Index\")\n",
    "pred_pos_df_featured[\"output\"] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_neg_df = pd.DataFrame({'source': source_multiplied, 'target': target_neg})\n",
    "pred_neg_df['common_neighbours_succ'] = pred_neg_df.apply(lambda row: calc_common_neighbours(nx_graph, row['source'], row['target']), axis=1)\n",
    "pred_neg_df['common_neighbours_all'] = pred_neg_df.apply(lambda row: calc_common_neighbours_preds_and_succ(nx_graph, row['source'], row['target']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/dask/dataframe/core.py:7364: FutureWarning: Meta is not valid, `map_partitions` and `map_overlap` expects output to be a pandas object. Try passing a pandas object as meta or a dict or tuple representing the (name, dtype) of the columns. In the future the meta you passed will not work.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# calc cosine similarity\n",
    "ddf = dd.from_pandas(pred_neg_df, npartitions=64)\n",
    "def calc_cosine_row(row):\n",
    "    return calc_cosine(row['source'], row['target'], node_features)\n",
    "ddf['cosine_similarity'] = ddf.apply(calc_cosine_row, axis=1, meta=('float'))\n",
    "pred_neg_df = ddf.compute(scheduler=get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_neg_df_featured = pd.merge(pred_neg_df,feature_df,left_on=\"source\",right_on=\"Node Index\").rename(\n",
    "    columns={\"clustering_coeffficient\":\"clustering_coeffficient_source\",\"in_degrees\":\"in_degrees_source\",\"out_degrees\":\"out_degrees_source\",\"node_in_strong\":\"node_in_strong_source\",\"node_label\": \"arxiv_category_source\"}).drop(columns=[\"Node Index\",\"node_year\"])\n",
    "pred_neg_df_featured = pd.merge(pred_neg_df_featured,feature_df,left_on=\"target\",right_on=\"Node Index\").rename(\n",
    "    columns={\"clustering_coeffficient\":\"clustering_coeffficient_target\",\"in_degrees\":\"in_degrees_target\",\"out_degrees\":\"out_degrees_target\",\"node_in_strong\":\"node_in_strong_target\",\"node_year\":\"node_year_target\",\"node_label\":\"arxiv_category_target\"}).drop(columns=\"Node Index\")\n",
    "pred_neg_df_featured[\"output\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred_pos_df_featured.to_csv(\"./dataset/modelling_results_calc_saved/pred_pos_df_featured_test.csv\")\n",
    "#pred_neg_df_featured.to_csv(\"./dataset/modelling_results_calc_saved/pred_neg_df_featured_test.csv\")\n",
    "pred_pos_df_featured.to_csv(\"./dataset/modelling_results_calc_saved/pred_pos_df_featured.csv\")\n",
    "pred_neg_df_featured.to_csv(\"./dataset/modelling_results_calc_saved/pred_neg_df_featured.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load precalculated_data\n",
    "\n",
    "If the dataset is already calculated it can be loaded here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pos_df_featured = pd.read_csv(\"./dataset/modelling_results_calc_saved/pred_pos_df_featured.csv\", index_col=0)\n",
    "pred_neg_df_featured = pd.read_csv(\"./dataset/modelling_results_calc_saved/pred_neg_df_featured.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_preds = np.load(f\"{data_path}pos_preds.npy\")\n",
    "neg_preds = np.load(f\"{data_path}neg_preds.npy\")\n",
    "pred_pos_df_featured[\"prediction_model\"] = pos_preds\n",
    "pred_neg_df_featured[\"prediction_model\"] = neg_preds.reshape(-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "comined_pred_df_featured = pd.concat([pred_neg_df_featured,pred_pos_df_featured])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GCN Reference\n",
    "\n",
    "### Overall Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.9250)\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "model_to_select = \"Random Forest\"\n",
    "features_to_drop = [\"source\",\"target\"]\n",
    "mrr_list = []\n",
    "n=0\n",
    "for train_index, test_index in kf.split(source):\n",
    "    n+=1\n",
    "    X_train = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[train_index])].copy()\n",
    "    y_train = X_train.pop(\"output\").values\n",
    "    X_test = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[test_index])].copy()\n",
    "    y_test = X_test.pop(\"output\").values\n",
    "    if model_to_select == \"Random Forest\":\n",
    "        model = RandomForestClassifier(n_estimators=120, random_state=42,n_jobs = -1)\n",
    "    elif model_to_select == \"Logistic Regression\":\n",
    "        model = LogisticRegression()\n",
    "    model.fit(X_train.copy().drop(columns=features_to_drop), y_train)\n",
    "\n",
    "    X_test_pos = pred_pos_df_featured[pred_pos_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "    y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "    X_test_neg = pred_neg_df_featured[pred_neg_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "    y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "    evaluator = Evaluator(name='ogbl-citation2')\n",
    "    eval_dict = evaluator.eval({\n",
    "                'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "                'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "            })\n",
    "    print(eval_dict[\"mrr_list\"].mean())\n",
    "    mrr_list.append(eval_dict[\"mrr_list\"].mean())\n",
    "    dump(model, f'./models_saved/random_forest_model_{n}.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.9252)\n",
      "tensor(0.9300)\n",
      "tensor(0.9293)\n",
      "tensor(0.9271)\n",
      "tensor(0.9250)\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "model_to_select = \"Random Forest\"\n",
    "features_to_drop = [\"source\",\"target\"]\n",
    "mrr_list = []\n",
    "n=0\n",
    "for train_index, test_index in kf.split(source):\n",
    "    n+=1\n",
    "\n",
    "    X_test = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[test_index])].copy()\n",
    "    y_test = X_test.pop(\"output\").values\n",
    "    if model_to_select == \"Random Forest\":\n",
    "        model = load(f'./models_saved/random_forest_model_{n}.joblib')\n",
    "    elif model_to_select == \"Logistic Regression\":\n",
    "        model = LogisticRegression()\n",
    "        \n",
    "    X_test_pos = pred_pos_df_featured[pred_pos_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "    y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "    X_test_neg = pred_neg_df_featured[pred_neg_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "    y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "    evaluator = Evaluator(name='ogbl-citation2')\n",
    "    eval_dict = evaluator.eval({\n",
    "                'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "                'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "            })\n",
    "    print(eval_dict[\"mrr_list\"].mean())\n",
    "    mrr_list.append(eval_dict[\"mrr_list\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance = []\n",
    "n_times = 2\n",
    "for col in X_test_pos.columns:\n",
    "    if col not in features_to_drop:\n",
    "        kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "        saved_per_model = []\n",
    "        model_n = 0\n",
    "        for train_index, test_index in kf.split(source):\n",
    "            model_n +=1\n",
    "            model = load(f'./models_saved/random_forest_model_{model_n}.joblib')\n",
    "            X_test = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[test_index])].copy()\n",
    "            y_test = X_test.pop(\"output\").values\n",
    "\n",
    "            X_test_pos = pred_pos_df_featured[pred_pos_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "            y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "            X_test_neg = pred_neg_df_featured[pred_neg_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "            y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "            \n",
    "            saved_per_feature = []\n",
    "            for x in range(n_times):\n",
    "                X_test_neg_permuted = X_test_neg.copy()\n",
    "                X_test_pos_permuted = X_test_pos.copy()\n",
    "\n",
    "                permuted_columns = np.random.permutation(np.hstack((X_test_pos_permuted[col].values,X_test_neg_permuted[col].values)))\n",
    "                X_test_neg_permuted[col] = permuted_columns[:X_test_neg_permuted.shape[0]]\n",
    "                X_test_pos_permuted[col] = permuted_columns[X_test_neg_permuted.shape[0]:]\n",
    "\n",
    "                y_pred_pos = model.predict_proba(X_test_pos_permuted.copy().drop(columns=features_to_drop))[:,1]\n",
    "                y_pred_neg = model.predict_proba(X_test_neg_permuted.copy().drop(columns=features_to_drop))[:,1]\n",
    "                evaluator = Evaluator(name='ogbl-citation2')\n",
    "                eval_dict = evaluator.eval({\n",
    "                            'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "                            'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "                        })\n",
    "                importance = mrr_list[model_n-1] -  eval_dict[\"mrr_list\"].mean()\n",
    "                saved_per_feature.append(importance)\n",
    "            saved_per_model.append((np.mean(saved_per_feature)))\n",
    "        perm_importance.append((col, np.mean(saved_per_model),np.std(saved_per_model)))\n",
    "perm_importance.sort(key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, values, errors = zip(*perm_importance)\n",
    "\n",
    "# Creating the bar plot with error bars\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(labels, values, xerr=errors, color='skyblue')\n",
    "plt.xlabel('Permutation Importance MRR difference')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importance with Standard Deviation')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the highest value at the top\n",
    "plt.savefig(\"gcn_reference_permutation_importnace_all_features.pdf\", format='pdf',dpi=5000,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.8914)\n",
      "tensor(0.8949)\n",
      "tensor(0.8902)\n",
      "tensor(0.8994)\n",
      "tensor(0.8936)\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "model_to_select = \"Random Forest\"\n",
    "features_to_drop = comined_pred_df_featured.columns[~comined_pred_df_featured.columns.isin([\"output\",\"prediction_model\",\"node_year_target\",\"common_neighbours_all\",\"in_degrees_target\"])]\n",
    "mrr_list = []\n",
    "n=0\n",
    "for train_index, test_index in kf.split(source):\n",
    "    n+=1\n",
    "   \n",
    "    X_train = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[train_index])].copy()\n",
    "    y_train = X_train.pop(\"output\").values\n",
    "    X_test = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[test_index])].copy()\n",
    "    y_test = X_test.pop(\"output\").values\n",
    "    if model_to_select == \"Random Forest\":\n",
    "        model = RandomForestClassifier(n_estimators=120, random_state=42,n_jobs = -1)\n",
    "    elif model_to_select == \"Logistic Regression\":\n",
    "        model = LogisticRegression()\n",
    "    model.fit(X_train.copy().drop(columns=features_to_drop), y_train)\n",
    "    \n",
    "    X_test_pos = pred_pos_df_featured[pred_pos_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "    y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "    X_test_neg = pred_neg_df_featured[pred_neg_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "    y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "    evaluator = Evaluator(name='ogbl-citation2')\n",
    "    eval_dict = evaluator.eval({\n",
    "                'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "                'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "            })\n",
    "    print(eval_dict[\"mrr_list\"].mean())\n",
    "    mrr_list.append(eval_dict[\"mrr_list\"].mean())\n",
    "    dump(model, f'./models_saved/random_forest_model_further_trained{n}.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrr_list = [0.8914,0.8949,0.8902,0.8994,0.8936]\n",
    "features_to_drop = comined_pred_df_featured.columns[~comined_pred_df_featured.columns.isin([\"output\",\"prediction_model\",\"node_year_target\",\"common_neighbours_all\",\"in_degrees_target\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance = []\n",
    "n_times = 2\n",
    "for col in X_test_pos.columns:\n",
    "    if col not in features_to_drop:\n",
    "        kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "        saved_per_model = []\n",
    "        model_n = 0\n",
    "        for train_index, test_index in kf.split(source):\n",
    "            model_n +=1\n",
    "            model = load(f'./models_saved/random_forest_model_further_trained{model_n}.joblib')\n",
    "            \n",
    "            X_test = comined_pred_df_featured[comined_pred_df_featured[\"source\"].isin(source[test_index])].copy()\n",
    "            y_test = X_test.pop(\"output\").values\n",
    "\n",
    "            X_test_pos = pred_pos_df_featured[pred_pos_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "            y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "            X_test_neg = pred_neg_df_featured[pred_neg_df_featured[\"source\"].isin(source[test_index])].copy().drop(columns=\"output\")\n",
    "            y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "            \n",
    "            saved_per_feature = []\n",
    "            for x in range(n_times):\n",
    "                X_test_neg_permuted = X_test_neg.copy()\n",
    "                X_test_pos_permuted = X_test_pos.copy()\n",
    "\n",
    "                permuted_columns = np.random.permutation(np.hstack((X_test_pos_permuted[col].values,X_test_neg_permuted[col].values)))\n",
    "                X_test_neg_permuted[col] = permuted_columns[:X_test_neg_permuted.shape[0]]\n",
    "                X_test_pos_permuted[col] = permuted_columns[X_test_neg_permuted.shape[0]:]\n",
    "\n",
    "                y_pred_pos = model.predict_proba(X_test_pos_permuted.copy().drop(columns=features_to_drop))[:,1]\n",
    "                y_pred_neg = model.predict_proba(X_test_neg_permuted.copy().drop(columns=features_to_drop))[:,1]\n",
    "                evaluator = Evaluator(name='ogbl-citation2')\n",
    "                eval_dict = evaluator.eval({\n",
    "                            'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "                            'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "                        })\n",
    "                mrr_mean = eval_dict[\"mrr_list\"].mean()\n",
    "                importance = mrr_list[model_n-1] - mrr_mean\n",
    "                saved_per_feature.append(importance)\n",
    "            saved_per_model.append((np.mean(saved_per_feature)))\n",
    "        perm_importance.append((col, np.mean(saved_per_model),np.std(saved_per_model)))\n",
    "perm_importance.sort(key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, values, errors = zip(*perm_importance)\n",
    "\n",
    "# Creating the bar plot with error bars\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(labels, values, xerr=errors, color='skyblue')\n",
    "plt.xlabel('Permutation Importance MRR difference')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importance with Standard Deviation')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the highest value at the top\n",
    "plt.savefig(\"gcn_reference_permutation_importnace_four_features.pdf\", format='pdf',dpi=5000,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test on testset\n",
    "\n",
    "Here the random forest model is trained on the validation set and tested on the testset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path_test = \"./dataset/sample_data_for_evaluation_build/gcn_reference/test/\"\n",
    "pos_preds_test = np.load(f\"{data_path_test}pos_preds.npy\")\n",
    "neg_preds_test = np.load(f\"{data_path_test}neg_preds.npy\")\n",
    "pred_pos_df_featured_test= pd.read_csv(\"./dataset/modelling_results_calc_saved/pred_pos_df_featured_test.csv\", index_col=0)\n",
    "pred_neg_df_featured_test = pd.read_csv(\"./dataset/modelling_results_calc_saved/pred_neg_df_featured_test.csv\", index_col=0)\n",
    "pred_pos_df_featured_test[\"prediction_model\"] = pos_preds_test\n",
    "pred_neg_df_featured_test[\"prediction_model\"] = neg_preds_test.reshape(-1)\n",
    "comined_pred_df_featured_test = pd.concat([pred_neg_df_featured_test,pred_pos_df_featured_test])\n",
    "\n",
    "features_to_drop = comined_pred_df_featured_test.columns[~comined_pred_df_featured_test.columns.isin([\"output\",\"prediction_model\",\"node_year_target\",\"common_neighbours_all\",\"in_degrees_target\"])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_to_select = \"Random Forest\"\n",
    "features_to_drop = comined_pred_df_featured.columns[~comined_pred_df_featured.columns.isin([\"output\",\"prediction_model\",\"node_year_target\",\"common_neighbours_all\",\"in_degrees_target\"])]\n",
    "   \n",
    "X_train = comined_pred_df_featured.copy()\n",
    "y_train = X_train.pop(\"output\").values\n",
    "X_test = comined_pred_df_featured_test.copy()\n",
    "y_test = X_test.pop(\"output\").values\n",
    "if model_to_select == \"Random Forest\":\n",
    "    model = RandomForestClassifier(n_estimators=120, random_state=42,n_jobs = -1)\n",
    "elif model_to_select == \"Logistic Regression\":\n",
    "    model = LogisticRegression()\n",
    "model.fit(X_train.copy().drop(columns=features_to_drop), y_train)\n",
    "\n",
    "X_test_pos = pred_pos_df_featured_test.copy().drop(columns=\"output\")\n",
    "y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "X_test_neg = pred_neg_df_featured_test.copy().drop(columns=\"output\")\n",
    "y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "evaluator = Evaluator(name='ogbl-citation2')\n",
    "eval_dict = evaluator.eval({\n",
    "            'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "            'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "        })\n",
    "print(eval_dict[\"mrr_list\"])\n",
    "dump(model, f'./models_saved/random_forest_model_further_trained_full_data.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load(f'./models_saved/random_forest_model_further_trained_full_data.joblib')\n",
    "\n",
    "X_test_pos = pred_pos_df_featured_test.copy().drop(columns=\"output\")\n",
    "y_pred_pos = model.predict_proba(X_test_pos.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "X_test_neg = pred_neg_df_featured_test.copy().drop(columns=\"output\")\n",
    "y_pred_neg = model.predict_proba(X_test_neg.copy().drop(columns=features_to_drop))[:,1]\n",
    "\n",
    "evaluator = Evaluator(name='ogbl-citation2')\n",
    "eval_dict = evaluator.eval({\n",
    "            'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "            'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance = []\n",
    "result = eval_dict[\"mrr_list\"].mean()\n",
    "n_times = 5\n",
    "model = load(f'./models_saved/random_forest_model_further_trained_full_data.joblib')\n",
    "for col in X_test_pos.columns:\n",
    "    if col not in features_to_drop:            \n",
    "\n",
    "        saved_per_feature = []\n",
    "        for x in range(n_times):\n",
    "            X_test_neg_permuted = X_test_neg.copy()\n",
    "            X_test_pos_permuted = X_test_pos.copy()\n",
    "\n",
    "            permuted_columns = np.random.permutation(np.hstack((X_test_pos_permuted[col].values,X_test_neg_permuted[col].values)))\n",
    "            X_test_neg_permuted[col] = permuted_columns[:X_test_neg_permuted.shape[0]]\n",
    "            X_test_pos_permuted[col] = permuted_columns[X_test_neg_permuted.shape[0]:]\n",
    "\n",
    "            y_pred_pos = model.predict_proba(X_test_pos_permuted.copy().drop(columns=features_to_drop))[:,1]\n",
    "            y_pred_neg = model.predict_proba(X_test_neg_permuted.copy().drop(columns=features_to_drop))[:,1]\n",
    "            evaluator = Evaluator(name='ogbl-citation2')\n",
    "            eval_dict = evaluator.eval({\n",
    "                        'y_pred_pos': torch.tensor(y_pred_pos),\n",
    "                        'y_pred_neg': torch.tensor(y_pred_neg).view(-1,1000),\n",
    "                    })\n",
    "            mrr_mean = eval_dict[\"mrr_list\"].mean()\n",
    "            importance = result - mrr_mean\n",
    "            saved_per_feature.append(importance)\n",
    "        perm_importance.append((col, np.mean(saved_per_feature),np.std(saved_per_feature)))\n",
    "perm_importance.sort(key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, values, errors = zip(*perm_importance)\n",
    "\n",
    "# Creating the bar plot with error bars\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(labels, values, xerr=errors, color='skyblue')\n",
    "plt.xlabel('Permutation Importance MRR difference')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importance with Standard Deviation')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the highest value at the top\n",
    "plt.savefig(\"gcn_reference_permutation_importnace_four_features_test.pdf\", format='pdf',dpi=5000,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Correlation Matrix\n",
    "\n",
    "Correlation matrices to check for correlation between the features. In the first plot the correlation between node specific features are tested. In the second plot the link specific features are added aswell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix =feature_df.drop(columns = [\"Node Index\",\"node_label\"]).corr()\n",
    "# Visualize the correlation matrix using a heatmap\n",
    "plt.figure(figsize=(16, 16))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Matrix Heatmap of features')\n",
    "plt.savefig(\"feature_df.pdf\", format='pdf',dpi=2000,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix =comined_pred_df_featured.drop(columns = [\"source\",\"target\",\"output\"]).corr()\n",
    "plt.figure(figsize=(16, 16))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Matrix Heatmap of link features')\n",
    "plt.savefig(\"gcn_transfer_Correlation Matrix Heatmap.pdf\", format='pdf',dpi=2000,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}