casual_model.ipynb

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   "cell_type": "markdown",
   "id": "c9ebf88d-8d36-44b1-8e2c-de286d58f0ad",
   "metadata": {},
   "source": [
    "# TODO List\n",
    "- Graph based causal modellerin implementasyonu\n",
    "- NLP based causal modellerin implementasyonu\n",
    "- Text özelliklerinin embeddingini oluşturdukdan sonra, tree based model eğitip shap vs. ile model interpret raporları\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b0b6c5c-3d39-4026-9912-37a1d9e5d123",
   "metadata": {},
   "outputs": [
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   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from causalgraphicalmodels import CausalGraphicalModel\n",
    "from dowhy import CausalModel\n",
    "import networkx as nx\n",
    "import torch\n",
    "from torch_geometric.data import Data, DataLoader\n",
    "from torch_geometric.utils import from_networkx\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv, GATConv, global_mean_pool\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from category_encoders import TargetEncoder\n",
    "\n",
    "%load_ext jupyter_black\n",
    "pd.options.display.max_columns = 300\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da48f942-1790-49b3-a602-211749598582",
   "metadata": {},
   "outputs": [],
   "source": [
    "alaka = pd.read_csv(\"alaka_skorları.csv\")\n",
    "ilan = pd.read_csv(\"ilan_bilgileri.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd562946-05ca-46d4-9596-26c5429f50f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "alaka[\"item_id\"] = alaka[\"item_id\"].astype(str)\n",
    "ilan[\"id\"] = ilan[\"id\"].astype(str)\n",
    "df = pd.merge(ilan, alaka, left_on=\"id\", right_on=\"item_id\")\n",
    "df = df.sample(n=20000)  # modeller uzun sürmesin diye sample aldım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7adbc9dd-fe9e-4a50-9cca-1915b157ab2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats = df.select_dtypes(\"object\").columns.tolist()\n",
    "nums = df.select_dtypes([\"int\", \"float\"]).columns.tolist()\n",
    "\n",
    "df[cats] = df[cats].fillna(\"unk\")\n",
    "df[nums] = df[nums].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed835fd2-7feb-4354-9af1-93e6d2347e7f",
   "metadata": {},
   "source": [
    "# causal tabular model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b462db3c-8864-4a2b-81d0-c08dcd197db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_causal(df, treatment, outcome):\n",
    "    \"\"\"\n",
    "    Runs a causal analysis on the provided dataframe.\n",
    "\n",
    "    Parameters:\n",
    "    df (pd.DataFrame): The input dataframe containing the data for analysis.\n",
    "    treatment (str): The treatment variable for the causal analysis.\n",
    "    outcome (str): The outcome variable for the causal analysis.\n",
    "\n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "\n",
    "    causal_graph = CausalGraphicalModel(\n",
    "        nodes=[\n",
    "            \"kategori_1\",\n",
    "            \"kategori_2\",\n",
    "            \"kategori_3\",\n",
    "            \"mahalle\",\n",
    "            \"ilce\",\n",
    "            \"il\",\n",
    "            \"ilan_baslik\",\n",
    "            \"ilan_fiyat\",\n",
    "            \"ilan_sahip_adi\",\n",
    "            \"site_icinde_mi\",\n",
    "            \"m2_gross\",\n",
    "            \"m2_net\",\n",
    "            \"bina_kat_sayisi\",\n",
    "            \"cephe\",\n",
    "            \"sosyal_imkanlar\",\n",
    "            \"bina_ozellikleri\",\n",
    "            \"ulasim\",\n",
    "            \"oda_sayisi\",\n",
    "            \"ilan_kat_sayisi\",\n",
    "            \"bina_yasi\",\n",
    "            \"ilab_query\",\n",
    "            outcome,\n",
    "        ],\n",
    "        edges=[\n",
    "            (\"ilab_query\", outcome),\n",
    "            (\"kategori_1\", outcome),\n",
    "            (\"kategori_2\", outcome),\n",
    "            (\"kategori_3\", outcome),\n",
    "            (\"mahalle\", outcome),\n",
    "            (\"ilce\", outcome),\n",
    "            (\"il\", outcome),\n",
    "            (\"ilan_baslik\", outcome),\n",
    "            (\"ilan_fiyat\", outcome),\n",
    "            (\"ilan_sahip_adi\", outcome),\n",
    "            (\"site_icinde_mi\", outcome),\n",
    "            (\"m2_gross\", outcome),\n",
    "            (\"m2_net\", outcome),\n",
    "            (\"bina_kat_sayisi\", outcome),\n",
    "            (\"cephe\", outcome),\n",
    "            (\"sosyal_imkanlar\", outcome),\n",
    "            (\"bina_ozellikleri\", outcome),\n",
    "            (\"ulasim\", outcome),\n",
    "            (\"oda_sayisi\", outcome),\n",
    "            (\"ilan_kat_sayisi\", outcome),\n",
    "            (\"bina_yasi\", outcome),\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    display(causal_graph.draw())\n",
    "\n",
    "    y = df[outcome]\n",
    "    X = df.drop(\n",
    "        columns=[\n",
    "            \"query_id\",\n",
    "            \"item_id\",\n",
    "            \"attr\",\n",
    "            \"sat_click\",\n",
    "            \"sat_pur\",\n",
    "            \"pur\",\n",
    "            \"cont\",\n",
    "            \"relevance\",\n",
    "            \"date\",\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.2, random_state=42\n",
    "    )\n",
    "\n",
    "    encoder = TargetEncoder()\n",
    "    X_train_encoded = encoder.fit_transform(X_train, y_train)\n",
    "    X_test_encoded = encoder.transform(X_test)\n",
    "\n",
    "    df_encoded = pd.concat([X_train_encoded, y_train], axis=1)\n",
    "\n",
    "    common_causes = [\n",
    "        col for col in df_encoded.columns if col not in [outcome, treatment]\n",
    "    ]\n",
    "\n",
    "    model = CausalModel(\n",
    "        data=df_encoded,\n",
    "        treatment=treatment,\n",
    "        outcome=outcome,\n",
    "        common_causes=common_causes,\n",
    "    )\n",
    "\n",
    "    identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "    print(identified_estimand)\n",
    "\n",
    "    linear_estimate = model.estimate_effect(\n",
    "        identified_estimand, method_name=\"backdoor.linear_regression\"\n",
    "    )\n",
    "    print(linear_estimate)\n",
    "\n",
    "    refute_placebo = model.refute_estimate(\n",
    "        identified_estimand, linear_estimate, method_name=\"placebo_treatment_refuter\"\n",
    "    )\n",
    "    print(refute_placebo)\n",
    "\n",
    "    refute_subset = model.refute_estimate(\n",
    "        identified_estimand, linear_estimate, method_name=\"data_subset_refuter\"\n",
    "    )\n",
    "    print(refute_subset)"
   ]
  },
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   "cell_type": "code",
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   "id": "751ce4ff-e4fe-4616-8e78-8e937729cbcb",
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       "<!-- sosyal_imkanlar&#45;&gt;relevance -->\n",
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       "<!-- ilce&#45;&gt;relevance -->\n",
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       "<title>ilce&#45;&gt;relevance</title>\n",
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       "<title>ilan_sahip_adi</title>\n",
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       "<!-- ilan_sahip_adi&#45;&gt;relevance -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>ilan_sahip_adi&#45;&gt;relevance</title>\n",
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       "<title>m2_net&#45;&gt;relevance</title>\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
      "WARNING:dowhy.causal_identifier.auto_identifier:Max number of iterations 100000 reached. Could not find a valid backdoor set.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "      d                                                                       \n",
      "─────────────(E[relevance|bina_ozellikleri,bina_yasi,kategori_2,updated_at,ila\n",
      "d[ilab_query]                                                                 \n",
      "\n",
      "                                                                              \n",
      "n_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi\n",
      "                                                                              \n",
      "\n",
      "                                                                              \n",
      ",kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,\n",
      "                                                                              \n",
      "\n",
      "                                                                \n",
      "sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net])\n",
      "                                                                \n",
      "Estimand assumption 1, Unconfoundedness: If U→{ilab_query} and U→relevance then P(relevance|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net,U) = P(relevance|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable(s) found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n",
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "      d                                                                       \n",
      "─────────────(E[relevance|bina_ozellikleri,bina_yasi,kategori_2,updated_at,ila\n",
      "d[ilab_query]                                                                 \n",
      "\n",
      "                                                                              \n",
      "n_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi\n",
      "                                                                              \n",
      "\n",
      "                                                                              \n",
      ",kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,\n",
      "                                                                              \n",
      "\n",
      "                                                                \n",
      "sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net])\n",
      "                                                                \n",
      "Estimand assumption 1, Unconfoundedness: If U→{ilab_query} and U→relevance then P(relevance|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net,U) = P(relevance|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net)\n",
      "\n",
      "## Realized estimand\n",
      "b: relevance~ilab_query+bina_ozellikleri+bina_yasi+kategori_2+updated_at+ilan_baslik+ilan_kat_sayisi+m2_gross+cephe+category_id+row_num+il+bina_kat_sayisi+kategori_3+ulasim+created_at+mahalle+id+site_icinde_mi+ilan_fiyat+kategori_1+sosyal_imkanlar+ilce+ilan_sahip_adi+etl_date+oda_sayisi+m2_net\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.2446972659828323\n",
      "\n",
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:0.2446972659828323\n",
      "New effect:-4.884140825806016e-07\n",
      "p value:0.0\n",
      "\n",
      "Refute: Use a subset of data\n",
      "Estimated effect:0.2446972659828323\n",
      "New effect:0.24443347482798103\n",
      "p value:0.98\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run_causal(df, \"ilab_query\", \"relevance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58fd5c75-d0b3-4462-b5b3-8eba28e63b02",
   "metadata": {},
   "outputs": [
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       "<!-- il -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>il</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"878.75\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"878.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">il</text>\n",
       "</g>\n",
       "<!-- il&#45;&gt;attr -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>il&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M899.3,-78.11C904.26,-75.82 909.61,-73.61 914.75,-72 1012.91,-41.3 1133.99,-27.36 1194.52,-21.95\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1194.62,-25.45 1204.28,-21.1 1194.02,-18.48 1194.62,-25.45\"/>\n",
       "</g>\n",
       "<!-- bina_kat_sayisi -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>bina_kat_sayisi</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"991.75\" cy=\"-90\" rx=\"68.24\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"991.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">bina_kat_sayisi</text>\n",
       "</g>\n",
       "<!-- bina_kat_sayisi&#45;&gt;attr -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>bina_kat_sayisi&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1036.52,-76C1082.9,-62.52 1154.47,-41.74 1197.15,-29.34\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1198.04,-32.73 1206.67,-26.58 1196.09,-26.01 1198.04,-32.73\"/>\n",
       "</g>\n",
       "<!-- kategori_3 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>kategori_3</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1128.75\" cy=\"-90\" rx=\"50.33\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1128.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">kategori_3</text>\n",
       "</g>\n",
       "<!-- kategori_3&#45;&gt;attr -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>kategori_3&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1151.58,-73.64C1167.27,-63.07 1188.21,-48.98 1204.89,-37.75\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1206.55,-40.86 1212.89,-32.37 1202.64,-35.05 1206.55,-40.86\"/>\n",
       "</g>\n",
       "<!-- ulasim -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>ulasim</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1232.75\" cy=\"-90\" rx=\"36\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1232.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">ulasim</text>\n",
       "</g>\n",
       "<!-- ulasim&#45;&gt;attr -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>ulasim&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1232.75,-71.7C1232.75,-64.41 1232.75,-55.73 1232.75,-47.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1236.25,-47.62 1232.75,-37.62 1229.25,-47.62 1236.25,-47.62\"/>\n",
       "</g>\n",
       "<!-- ilab_query -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>ilab_query</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1336.75\" cy=\"-90\" rx=\"50.33\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1336.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">ilab_query</text>\n",
       "</g>\n",
       "<!-- ilab_query&#45;&gt;attr -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>ilab_query&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1313.93,-73.64C1298.23,-63.07 1277.3,-48.98 1260.62,-37.75\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1262.87,-35.05 1252.62,-32.37 1258.96,-40.86 1262.87,-35.05\"/>\n",
       "</g>\n",
       "<!-- mahalle -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>mahalle</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1445.75\" cy=\"-90\" rx=\"40.6\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1445.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">mahalle</text>\n",
       "</g>\n",
       "<!-- mahalle&#45;&gt;attr -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>mahalle&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1414.3,-78.23C1408.16,-76.15 1401.76,-74 1395.75,-72 1351.68,-57.34 1300.75,-40.85 1267.71,-30.22\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1268.85,-26.91 1258.25,-27.18 1266.7,-33.57 1268.85,-26.91\"/>\n",
       "</g>\n",
       "<!-- site_icinde_mi -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>site_icinde_mi</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1569.75\" cy=\"-90\" rx=\"65.68\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1569.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">site_icinde_mi</text>\n",
       "</g>\n",
       "<!-- site_icinde_mi&#45;&gt;attr -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>site_icinde_mi&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1520.83,-77.57C1512.48,-75.67 1503.87,-73.75 1495.75,-72 1415.28,-54.65 1320.57,-36 1269.64,-26.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1270.53,-22.72 1260.05,-24.25 1269.2,-29.59 1270.53,-22.72\"/>\n",
       "</g>\n",
       "<!-- ilan_fiyat -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>ilan_fiyat</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1699.75\" cy=\"-90\" rx=\"46.23\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1699.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">ilan_fiyat</text>\n",
       "</g>\n",
       "<!-- ilan_fiyat&#45;&gt;attr -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>ilan_fiyat&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1665.67,-77.47C1658.8,-75.43 1651.59,-73.48 1644.75,-72 1508.9,-42.5 1344.03,-27.37 1270.88,-21.7\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.28,-18.22 1261.04,-20.96 1270.75,-25.2 1271.28,-18.22\"/>\n",
       "</g>\n",
       "<!-- kategori_1 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>kategori_1</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1814.75\" cy=\"-90\" rx=\"50.33\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1814.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">kategori_1</text>\n",
       "</g>\n",
       "<!-- kategori_1&#45;&gt;attr -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>kategori_1&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1777.87,-77.46C1770.28,-75.39 1762.3,-73.44 1754.75,-72 1575.94,-37.86 1358.19,-24.72 1271.27,-20.6\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.55,-17.11 1261.4,-20.15 1271.23,-24.11 1271.55,-17.11\"/>\n",
       "</g>\n",
       "<!-- sosyal_imkanlar -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>sosyal_imkanlar</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1954.75\" cy=\"-90\" rx=\"71.82\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1954.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">sosyal_imkanlar</text>\n",
       "</g>\n",
       "<!-- sosyal_imkanlar&#45;&gt;attr -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>sosyal_imkanlar&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1903.07,-77.15C1893.37,-75.21 1883.28,-73.39 1873.75,-72 1646.79,-38.93 1371.38,-24.81 1271.38,-20.52\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.72,-17.03 1261.58,-20.11 1271.42,-24.02 1271.72,-17.03\"/>\n",
       "</g>\n",
       "<!-- ilce -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>ilce</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2071.75\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2071.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">ilce</text>\n",
       "</g>\n",
       "<!-- ilce&#45;&gt;attr -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>ilce&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2051.31,-77.73C2046.35,-75.46 2040.97,-73.35 2035.75,-72 1888.57,-33.86 1411.19,-22.26 1271.49,-19.64\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.73,-16.15 1261.67,-19.47 1271.6,-23.15 1271.73,-16.15\"/>\n",
       "</g>\n",
       "<!-- ilan_sahip_adi -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>ilan_sahip_adi</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2181.75\" cy=\"-90\" rx=\"64.66\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2181.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">ilan_sahip_adi</text>\n",
       "</g>\n",
       "<!-- ilan_sahip_adi&#45;&gt;attr -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>ilan_sahip_adi&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2135.39,-77.12C2126.28,-75.13 2116.76,-73.3 2107.75,-72 1787.04,-25.77 1394.56,-19.78 1271.52,-19.07\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.67,-15.57 1261.65,-19.02 1271.63,-22.57 1271.67,-15.57\"/>\n",
       "</g>\n",
       "<!-- oda_sayisi -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>oda_sayisi</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2313.75\" cy=\"-90\" rx=\"49.82\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2313.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">oda_sayisi</text>\n",
       "</g>\n",
       "<!-- oda_sayisi&#45;&gt;attr -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>oda_sayisi&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2277.86,-77.07C2270.61,-75.06 2262.99,-73.23 2255.75,-72 2060.98,-38.88 1433.68,-23.33 1271.38,-19.8\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.67,-16.3 1261.6,-19.59 1271.52,-23.3 1271.67,-16.3\"/>\n",
       "</g>\n",
       "<!-- m2_net -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>m2_net</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2420.75\" cy=\"-90\" rx=\"39.07\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2420.75\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">m2_net</text>\n",
       "</g>\n",
       "<!-- m2_net&#45;&gt;attr -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>m2_net&#45;&gt;attr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2392.2,-77.35C2385.89,-75.21 2379.17,-73.26 2372.75,-72 2155,-29.35 1445.57,-20.76 1271.56,-19.28\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.73,-15.78 1261.71,-19.2 1271.68,-22.78 1271.73,-15.78\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7fe398f73fa0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
      "WARNING:dowhy.causal_identifier.auto_identifier:Max number of iterations 100000 reached. Could not find a valid backdoor set.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "      d                                                                       \n",
      "─────────────(E[attr|bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_bas\n",
      "d[ilab_query]                                                                 \n",
      "\n",
      "                                                                              \n",
      "lik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kate\n",
      "                                                                              \n",
      "\n",
      "                                                                              \n",
      "gori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosya\n",
      "                                                                              \n",
      "\n",
      "                                                           \n",
      "l_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net])\n",
      "                                                           \n",
      "Estimand assumption 1, Unconfoundedness: If U→{ilab_query} and U→attr then P(attr|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net,U) = P(attr|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable(s) found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n",
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "      d                                                                       \n",
      "─────────────(E[attr|bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_bas\n",
      "d[ilab_query]                                                                 \n",
      "\n",
      "                                                                              \n",
      "lik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kate\n",
      "                                                                              \n",
      "\n",
      "                                                                              \n",
      "gori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosya\n",
      "                                                                              \n",
      "\n",
      "                                                           \n",
      "l_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net])\n",
      "                                                           \n",
      "Estimand assumption 1, Unconfoundedness: If U→{ilab_query} and U→attr then P(attr|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net,U) = P(attr|ilab_query,bina_ozellikleri,bina_yasi,kategori_2,updated_at,ilan_baslik,ilan_kat_sayisi,m2_gross,cephe,category_id,row_num,il,bina_kat_sayisi,kategori_3,ulasim,created_at,mahalle,id,site_icinde_mi,ilan_fiyat,kategori_1,sosyal_imkanlar,ilce,ilan_sahip_adi,etl_date,oda_sayisi,m2_net)\n",
      "\n",
      "## Realized estimand\n",
      "b: attr~ilab_query+bina_ozellikleri+bina_yasi+kategori_2+updated_at+ilan_baslik+ilan_kat_sayisi+m2_gross+cephe+category_id+row_num+il+bina_kat_sayisi+kategori_3+ulasim+created_at+mahalle+id+site_icinde_mi+ilan_fiyat+kategori_1+sosyal_imkanlar+ilce+ilan_sahip_adi+etl_date+oda_sayisi+m2_net\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.23261438934325857\n",
      "\n",
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:0.23261438934325857\n",
      "New effect:-4.438893603520011e-07\n",
      "p value:0.0\n",
      "\n",
      "Refute: Use a subset of data\n",
      "Estimated effect:0.23261438934325857\n",
      "New effect:0.23344524916883116\n",
      "p value:0.8799999999999999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run_causal(df, \"ilab_query\", \"attr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f349145-1767-43da-b6ea-1193db043d98",
   "metadata": {},
   "source": [
    "# graph based causality - feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f586e883-ea31-4fd2-b16e-0e9178195080",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    \"\"\"\n",
    "    A base class representing a node in the graph.\n",
    "\n",
    "    Attributes:\n",
    "    node_id (str): The unique identifier for the node.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, node_id):\n",
    "        self.node_id = node_id\n",
    "\n",
    "\n",
    "class AdNode(Node):\n",
    "    \"\"\"\n",
    "    A class representing an advertisement node in the graph.\n",
    "\n",
    "    Attributes:\n",
    "    node_id (str): The unique identifier for the ad node.\n",
    "    kategori_1 (str): The first category of the ad.\n",
    "    kategori_2 (str): The second category of the ad.\n",
    "    kategori_3 (str): The third category of the ad.\n",
    "    mahalle (str): The neighborhood of the ad.\n",
    "    ilce (str): The district of the ad.\n",
    "    il (str): The city of the ad.\n",
    "    ilan_baslik (str): The title of the ad.\n",
    "    ilan_fiyat (float): The price of the ad.\n",
    "    ilan_sahip_adi (str): The name of the ad owner.\n",
    "    site_icinde_mi (bool): Indicates if the ad is within a site.\n",
    "    m2_gross (float): The gross square meter area of the property.\n",
    "    m2_net (float): The net square meter area of the property.\n",
    "    bina_kat_sayisi (int): The number of floors in the building.\n",
    "    cephe (str): The facade direction of the property.\n",
    "    sosyal_imkanlar (str): The social facilities available.\n",
    "    bina_ozellikleri (str): The characteristics of the building.\n",
    "    ulasim (str): The transportation options available.\n",
    "    oda_sayisi (int): The number of rooms in the property.\n",
    "    ilan_kat_sayisi (int): The number of floors listed in the ad.\n",
    "    bina_yasi (int): The age of the building.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        node_id,\n",
    "        kategori_1,\n",
    "        kategori_2,\n",
    "        kategori_3,\n",
    "        mahalle,\n",
    "        ilce,\n",
    "        il,\n",
    "        ilan_baslik,\n",
    "        ilan_fiyat,\n",
    "        ilan_sahip_adi,\n",
    "        site_icinde_mi,\n",
    "        m2_gross,\n",
    "        m2_net,\n",
    "        bina_kat_sayisi,\n",
    "        cephe,\n",
    "        sosyal_imkanlar,\n",
    "        bina_ozellikleri,\n",
    "        ulasim,\n",
    "        oda_sayisi,\n",
    "        ilan_kat_sayisi,\n",
    "        bina_yasi,\n",
    "    ):\n",
    "        super().__init__(node_id)\n",
    "        self.kategori_1 = kategori_1\n",
    "        self.kategori_2 = kategori_2\n",
    "        self.kategori_3 = kategori_3\n",
    "        self.mahalle = mahalle\n",
    "        self.ilce = ilce\n",
    "        self.il = il\n",
    "        self.ilan_baslik = ilan_baslik\n",
    "        self.ilan_fiyat = ilan_fiyat\n",
    "        self.ilan_sahip_adi = ilan_sahip_adi\n",
    "        self.site_icinde_mi = site_icinde_mi\n",
    "        self.m2_gross = m2_gross\n",
    "        self.m2_net = m2_net\n",
    "        self.bina_kat_sayisi = bina_kat_sayisi\n",
    "        self.cephe = cephe\n",
    "        self.sosyal_imkanlar = sosyal_imkanlar\n",
    "        self.bina_ozellikleri = bina_ozellikleri\n",
    "        self.ulasim = ulasim\n",
    "        self.oda_sayisi = oda_sayisi\n",
    "        self.ilan_kat_sayisi = ilan_kat_sayisi\n",
    "        self.bina_yasi = bina_yasi\n",
    "\n",
    "\n",
    "class QueryNode(Node):\n",
    "    \"\"\"\n",
    "    A class representing a query node in the graph.\n",
    "\n",
    "    Attributes:\n",
    "    node_id (str): The unique identifier for the query node.\n",
    "    ilab_query (str): The query string.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, node_id, ilab_query):\n",
    "        super().__init__(node_id)\n",
    "        self.ilab_query = ilab_query\n",
    "\n",
    "\n",
    "graph = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b335a332-9dfb-42d0-a0f3-718db788d83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add ad nodes\n",
    "ad_features = [\n",
    "    \"kategori_1\",\n",
    "    \"kategori_2\",\n",
    "    \"kategori_3\",\n",
    "    \"mahalle\",\n",
    "    \"ilce\",\n",
    "    \"il\",\n",
    "    \"ilan_baslik\",\n",
    "    \"ilan_fiyat\",\n",
    "    \"ilan_sahip_adi\",\n",
    "    \"site_icinde_mi\",\n",
    "    \"m2_gross\",\n",
    "    \"m2_net\",\n",
    "    \"bina_kat_sayisi\",\n",
    "    \"cephe\",\n",
    "    \"sosyal_imkanlar\",\n",
    "    \"bina_ozellikleri\",\n",
    "    \"ulasim\",\n",
    "    \"oda_sayisi\",\n",
    "    \"ilan_kat_sayisi\",\n",
    "    \"bina_yasi\",\n",
    "]\n",
    "\n",
    "for _, row in df.iterrows():\n",
    "    ad_node = AdNode(\n",
    "        node_id=row[\"id\"],\n",
    "        kategori_1=row[\"kategori_1\"],\n",
    "        kategori_2=row[\"kategori_2\"],\n",
    "        kategori_3=row[\"kategori_3\"],\n",
    "        mahalle=row[\"mahalle\"],\n",
    "        ilce=row[\"ilce\"],\n",
    "        il=row[\"il\"],\n",
    "        ilan_baslik=row[\"ilan_baslik\"],\n",
    "        ilan_fiyat=row[\"ilan_fiyat\"],\n",
    "        ilan_sahip_adi=row[\"ilan_sahip_adi\"],\n",
    "        site_icinde_mi=row[\"site_icinde_mi\"],\n",
    "        m2_gross=row[\"m2_gross\"],\n",
    "        m2_net=row[\"m2_net\"],\n",
    "        bina_kat_sayisi=row[\"bina_kat_sayisi\"],\n",
    "        cephe=row[\"cephe\"],\n",
    "        sosyal_imkanlar=row[\"sosyal_imkanlar\"],\n",
    "        bina_ozellikleri=row[\"bina_ozellikleri\"],\n",
    "        ulasim=row[\"ulasim\"],\n",
    "        oda_sayisi=row[\"oda_sayisi\"],\n",
    "        ilan_kat_sayisi=row[\"ilan_kat_sayisi\"],\n",
    "        bina_yasi=row[\"bina_yasi\"],\n",
    "    )\n",
    "    graph.add_node(ad_node.node_id, **vars(ad_node))\n",
    "\n",
    "# Add query nodes\n",
    "for _, row in df.iterrows():\n",
    "    query_node = QueryNode(node_id=row[\"query_id\"], ilab_query=row[\"ilab_query\"])\n",
    "    graph.add_node(query_node.node_id, **vars(query_node))\n",
    "\n",
    "# Add edges\n",
    "edge_attrs = [\"attr\", \"sat_click\", \"sat_pur\", \"pur\", \"cont\", \"relevance\"]\n",
    "for _, row in df.iterrows():\n",
    "    edge_attr = {attr: row[attr] for attr in edge_attrs}\n",
    "    graph.add_edge(row[\"id\"], row[\"query_id\"], **edge_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9210f9f8-e1d1-4cb4-9288-d46be4470907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 27656\n",
      "Number of edges: 20000\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of nodes: {graph.number_of_nodes()}\")\n",
    "print(f\"Number of edges: {graph.number_of_edges()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69bd3601-dbc5-4e30-97b6-23ac36693bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_node_id = df[\"id\"].iloc[0]\n",
    "query_node_id = df[\"query_id\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91630680-1cc7-40f6-8324-823d7b631f18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ad Node ID: 13109529\n",
      "kategori_1: rent\n",
      "kategori_2: konut\n",
      "kategori_3: daire\n",
      "mahalle: Boyalık Mahallesi\n",
      "ilce: Çeşme\n",
      "il: İzmir\n",
      "ilan_baslik:  ALAÇATI GAYRİMENKULDEN SEZONLUK KİRALIK SIFIR DAİRELER\n",
      "ilan_fiyat: 200000.0\n",
      "ilan_sahip_adi: Alaçatı Gayrimenkul\n",
      "site_icinde_mi: False\n",
      "m2_gross: 75.0\n",
      "m2_net: 60.0\n",
      "bina_kat_sayisi: 4.0\n",
      "cephe: Güney Cepheli&Kuzey Cepheli\n",
      "sosyal_imkanlar: Engelliye Uygun&Açık Otopark&Çocuk Parkı&Yürüyüş Parkuru\n",
      "bina_ozellikleri: Hırsız Alarmı&Yangın Merdiveni&Asansör&Ses Yalıtımı&Yangın Alarmı&Güvenlik&Hidrofor&Isıcam&Alüminyum Doğrama&Isı Yalıtımı&Apartman Görevlisi\n",
      "ulasim: Anayol&Denize Yakın&Minibüs&Otoban\n",
      "oda_sayisi: 1+1\n",
      "ilan_kat_sayisi: 4\n",
      "bina_yasi: 0 (Yeni)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>132894</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>13109529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kategori_1</th>\n",
       "      <td>rent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kategori_2</th>\n",
       "      <td>konut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kategori_3</th>\n",
       "      <td>daire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mahalle</th>\n",
       "      <td>Boyalık Mahallesi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilce</th>\n",
       "      <td>Çeşme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>il</th>\n",
       "      <td>İzmir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilan_baslik</th>\n",
       "      <td>ALAÇATI GAYRİMENKULDEN SEZONLUK KİRALIK SIFIR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilan_fiyat</th>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilan_sahip_adi</th>\n",
       "      <td>Alaçatı Gayrimenkul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_icinde_mi</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m2_gross</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m2_net</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bina_kat_sayisi</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cephe</th>\n",
       "      <td>Güney Cepheli&amp;Kuzey Cepheli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sosyal_imkanlar</th>\n",
       "      <td>Engelliye Uygun&amp;Açık Otopark&amp;Çocuk Parkı&amp;Yürüy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bina_ozellikleri</th>\n",
       "      <td>Hırsız Alarmı&amp;Yangın Merdiveni&amp;Asansör&amp;Ses Yal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ulasim</th>\n",
       "      <td>Anayol&amp;Denize Yakın&amp;Minibüs&amp;Otoban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oda_sayisi</th>\n",
       "      <td>1+1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilan_kat_sayisi</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bina_yasi</th>\n",
       "      <td>0 (Yeni)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             132894\n",
       "id                                                         13109529\n",
       "kategori_1                                                     rent\n",
       "kategori_2                                                    konut\n",
       "kategori_3                                                    daire\n",
       "mahalle                                           Boyalık Mahallesi\n",
       "ilce                                                          Çeşme\n",
       "il                                                            İzmir\n",
       "ilan_baslik        ALAÇATI GAYRİMENKULDEN SEZONLUK KİRALIK SIFIR...\n",
       "ilan_fiyat                                                 200000.0\n",
       "ilan_sahip_adi                                  Alaçatı Gayrimenkul\n",
       "site_icinde_mi                                                False\n",
       "m2_gross                                                       75.0\n",
       "m2_net                                                         60.0\n",
       "bina_kat_sayisi                                                 4.0\n",
       "cephe                                   Güney Cepheli&Kuzey Cepheli\n",
       "sosyal_imkanlar   Engelliye Uygun&Açık Otopark&Çocuk Parkı&Yürüy...\n",
       "bina_ozellikleri  Hırsız Alarmı&Yangın Merdiveni&Asansör&Ses Yal...\n",
       "ulasim                           Anayol&Denize Yakın&Minibüs&Otoban\n",
       "oda_sayisi                                                      1+1\n",
       "ilan_kat_sayisi                                                   4\n",
       "bina_yasi                                                  0 (Yeni)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print all features of the specific ad node from the graph\n",
    "ad_features = [\n",
    "    \"kategori_1\",\n",
    "    \"kategori_2\",\n",
    "    \"kategori_3\",\n",
    "    \"mahalle\",\n",
    "    \"ilce\",\n",
    "    \"il\",\n",
    "    \"ilan_baslik\",\n",
    "    \"ilan_fiyat\",\n",
    "    \"ilan_sahip_adi\",\n",
    "    \"site_icinde_mi\",\n",
    "    \"m2_gross\",\n",
    "    \"m2_net\",\n",
    "    \"bina_kat_sayisi\",\n",
    "    \"cephe\",\n",
    "    \"sosyal_imkanlar\",\n",
    "    \"bina_ozellikleri\",\n",
    "    \"ulasim\",\n",
    "    \"oda_sayisi\",\n",
    "    \"ilan_kat_sayisi\",\n",
    "    \"bina_yasi\",\n",
    "]\n",
    "\n",
    "\n",
    "ad_node = graph.nodes[ad_node_id]\n",
    "print(f\"Ad Node ID: {ad_node_id}\")\n",
    "for feature in ad_features:\n",
    "    print(f\"{feature}: {ad_node[feature]}\")\n",
    "\n",
    "# Display the ad node features from the datafram\n",
    "ad_node_details = df.query(f\"id=='{ad_node_id}'\")[[\"id\"] + ad_features].T\n",
    "ad_node_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c5f4a5b-27e7-446e-87e4-a8fd792a65a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ad Node ID: 39468\n",
      "ilab_query: izmir-cesme-boyalik-mahallesi-kiralik-konut-daire\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>132894</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>query_id</th>\n",
       "      <td>39468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilab_query</th>\n",
       "      <td>izmir-cesme-boyalik-mahallesi-kiralik-konut-daire</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       132894\n",
       "query_id                                                39468\n",
       "ilab_query  izmir-cesme-boyalik-mahallesi-kiralik-konut-daire"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print all features of the specific ad node from the graph\n",
    "query_features = [\"ilab_query\"]\n",
    "\n",
    "query_node = graph.nodes[query_node_id]\n",
    "print(f\"Ad Node ID: {query_node_id}\")\n",
    "for feature in query_features:\n",
    "    print(f\"{feature}: {query_node[feature]}\")\n",
    "\n",
    "# Display the ad node features from the dataframe\n",
    "query_node_details = df.query(f\"query_id=={query_node_id}\")[\n",
    "    [\"query_id\"] + query_features\n",
    "].T\n",
    "query_node_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85279128-b1d0-4bc9-b04d-b0cd38b9ebc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge Ad Node ID: 13109529\n",
      "Edge Query Node ID: 39468\n",
      "attr 0.1904761904761904\n",
      "sat_click 0.2\n",
      "sat_pur 0.5\n",
      "pur 0.2\n",
      "cont 0.95\n",
      "relevance 0.0495238095238095\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attr</th>\n",
       "      <th>sat_click</th>\n",
       "      <th>sat_pur</th>\n",
       "      <th>pur</th>\n",
       "      <th>cont</th>\n",
       "      <th>relevance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132894</th>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.049524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            attr  sat_click  sat_pur  pur  cont  relevance\n",
       "132894  0.190476        0.2      0.5  0.2  0.95   0.049524"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_features = [\"attr\", \"sat_click\", \"sat_pur\", \"pur\", \"cont\", \"relevance\"]\n",
    "\n",
    "edge = list(graph.edges(data=True))[0]\n",
    "print(f\"Edge Ad Node ID: {edge[0]}\")\n",
    "print(f\"Edge Query Node ID: {edge[1]}\")\n",
    "\n",
    "for feature in edge_features:\n",
    "    print(f\"{feature} {edge[2][feature]}\")\n",
    "\n",
    "\n",
    "df.query(f\"id=='{edge[0]}' & query_id == {edge[1]}\")[edge_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d86402d-d893-402f-b15c-b50ba679259b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for node in graph.nodes:\n",
    "    for feature in ad_features:\n",
    "        if feature not in graph.nodes[node]:\n",
    "            graph.nodes[node][feature] = (\n",
    "                0\n",
    "                if \"fiyat\" in feature\n",
    "                or \"m2\" in feature\n",
    "                or \"kat_sayisi\" in feature\n",
    "                or \"yasi\" in feature\n",
    "                else \"\"\n",
    "            )\n",
    "\n",
    "    if \"ilab_query\" not in graph.nodes[node]:\n",
    "        graph.nodes[node][\"ilab_query\"] = \"\"\n",
    "\n",
    "\n",
    "def convert_to_numeric(value):\n",
    "    if isinstance(value, str):\n",
    "        return hash(value) % 10000  # hash function to convert string to int\n",
    "    return value\n",
    "\n",
    "\n",
    "node_features = []\n",
    "for node in graph.nodes:\n",
    "    node_features.append(\n",
    "        [\n",
    "            convert_to_numeric(graph.nodes[node][feature])\n",
    "            for feature in ad_features + [\"ilab_query\"]\n",
    "        ]\n",
    "    )\n",
    "\n",
    "graph_data = from_networkx(graph)\n",
    "graph_data.x = torch.tensor(node_features, dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d11a1d94-0638-4289-b0be-fe65787a3ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_nan_in_edge_attr(value):\n",
    "    return 0 if pd.isna(value) else value\n",
    "\n",
    "\n",
    "graph_data.edge_attr = torch.tensor(\n",
    "    [\n",
    "        [\n",
    "            handle_nan_in_edge_attr(graph[u][v][\"attr\"]),\n",
    "            handle_nan_in_edge_attr(graph[u][v][\"sat_click\"]),\n",
    "            handle_nan_in_edge_attr(graph[u][v][\"sat_pur\"]),\n",
    "            handle_nan_in_edge_attr(graph[u][v][\"pur\"]),\n",
    "            handle_nan_in_edge_attr(graph[u][v][\"cont\"]),\n",
    "            handle_nan_in_edge_attr(graph[u][v][\"relevance\"]),\n",
    "        ]\n",
    "        for u, v in graph.edges\n",
    "    ],\n",
    "    dtype=torch.float,\n",
    ")\n",
    "\n",
    "# Ensure edge_attr is 2D\n",
    "if graph_data.edge_attr.dim() == 1:\n",
    "    graph_data.edge_attr = graph_data.edge_attr.unsqueeze(1)\n",
    "\n",
    "\n",
    "graph_data.edge_attr = graph_data.edge_attr.clone().detach().requires_grad_(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe84dcca-1803-465a-b39b-2d4e0f8f06a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GNN(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    A Graph Neural Network (GNN) model that uses Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).\n",
    "\n",
    "    Attributes:\n",
    "    in_channels (int): Number of input features per node.\n",
    "    hidden_channels (int): Number of hidden units in the GNN layers.\n",
    "    out_channels (int): Number of output features per node.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GNN, self).__init__()\n",
    "        self.conv1 = GCNConv(in_channels, hidden_channels)\n",
    "        self.conv2 = GATConv(hidden_channels, hidden_channels)\n",
    "        self.lin = torch.nn.Linear(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        \"\"\"\n",
    "        Forward pass through the GNN.\n",
    "\n",
    "        Parameters:\n",
    "        x (torch.Tensor): Node feature matrix.\n",
    "        edge_index (torch.Tensor): Graph edge indices.\n",
    "\n",
    "        Returns:\n",
    "        torch.Tensor: Output feature matrix.\n",
    "        \"\"\"\n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = F.relu(x)\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = F.relu(x)\n",
    "        x = global_mean_pool(x, torch.arange(x.size(0), device=x.device))  # Pooling\n",
    "        x = self.lin(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def train(model, data, optimizer, criterion):\n",
    "    \"\"\"\n",
    "    Trains the GNN model for one epoch.\n",
    "\n",
    "    Parameters:\n",
    "    model (torch.nn.Module): The GNN model.\n",
    "    data (torch_geometric.data.Data): The input data containing node features and edge indices.\n",
    "    optimizer (torch.optim.Optimizer): The optimizer for training.\n",
    "    criterion (torch.nn.Module): The loss function.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Training loss and feature importance.\n",
    "    \"\"\"\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    out = model(data.x, data.edge_index)\n",
    "    out = out[data.edge_index[1]]  # Ensure correct output size\n",
    "    loss = criterion(out, data.edge_attr)\n",
    "    loss.backward(retain_graph=True)  # Retain graph for gradient computation\n",
    "\n",
    "    # Calculate feature importance from gradients\n",
    "    feature_importance = data.x.grad.abs().mean(dim=0).cpu().numpy()\n",
    "\n",
    "    optimizer.step()\n",
    "    return loss.item(), feature_importance\n",
    "\n",
    "\n",
    "def test(model, data, criterion):\n",
    "    \"\"\"\n",
    "    Evaluates the GNN model on the test data.\n",
    "\n",
    "    Parameters:\n",
    "    model (torch.nn.Module): The GNN model.\n",
    "    data (torch_geometric.data.Data): The input data containing node features and edge indices.\n",
    "    criterion (torch.nn.Module): The loss function.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Test loss and model output.\n",
    "    \"\"\"\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        out = model(data.x, data.edge_index)\n",
    "        out = out[data.edge_index[1]]  # Ensure correct output size\n",
    "        loss = criterion(out, data.edge_attr)\n",
    "    return loss.item(), out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7267679b-0008-4b12-b127-18522cb8f5ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "in_channels = graph_data.num_node_features\n",
    "hidden_channels = 64\n",
    "out_channels = graph_data.num_edge_features\n",
    "model = GNN(in_channels, hidden_channels, out_channels)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "criterion = torch.nn.MSELoss()\n",
    "\n",
    "\n",
    "kf = KFold(n_splits=5)\n",
    "losses = []\n",
    "mae_scores = []\n",
    "node_feature_importances = np.zeros((graph_data.x.size(1),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "51550cfc-f210-4811-80cd-537366d6e26c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "normalized_node_features = scaler.fit_transform(graph_data.x.numpy())\n",
    "graph_data.x = torch.tensor(normalized_node_features, dtype=torch.float).requires_grad_(\n",
    "    True\n",
    ")\n",
    "\n",
    "edge_scaler = StandardScaler()\n",
    "normalized_edge_attributes = edge_scaler.fit_transform(\n",
    "    graph_data.edge_attr.detach().numpy()\n",
    ")\n",
    "graph_data.edge_attr = torch.tensor(\n",
    "    normalized_edge_attributes, dtype=torch.float\n",
    ").requires_grad_(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "856dc9a9-cdf1-4586-ac54-309270eee2f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 0.8558059930801392\n",
      "Epoch 2, Loss: 0.8393093943595886\n",
      "Epoch 3, Loss: 0.8371469974517822\n",
      "Epoch 4, Loss: 0.8304164409637451\n",
      "Epoch 5, Loss: 0.8259166479110718\n",
      "Epoch 6, Loss: 0.8243261575698853\n",
      "Epoch 7, Loss: 0.8240921497344971\n",
      "Epoch 8, Loss: 0.8232749104499817\n",
      "Epoch 9, Loss: 0.8217213749885559\n",
      "Epoch 10, Loss: 0.8200227618217468\n",
      "Epoch 11, Loss: 0.8184000849723816\n",
      "Epoch 12, Loss: 0.8171130418777466\n",
      "Epoch 13, Loss: 0.8163050413131714\n",
      "Epoch 14, Loss: 0.8154780268669128\n",
      "Epoch 15, Loss: 0.8143885731697083\n",
      "Epoch 16, Loss: 0.8131905794143677\n",
      "Epoch 17, Loss: 0.8118491172790527\n",
      "Epoch 18, Loss: 0.8103981018066406\n",
      "Epoch 19, Loss: 0.8090041279792786\n",
      "Epoch 20, Loss: 0.807505190372467\n",
      "Epoch 21, Loss: 0.8059923648834229\n",
      "Epoch 22, Loss: 0.8044798970222473\n",
      "Epoch 23, Loss: 0.8030529022216797\n",
      "Epoch 24, Loss: 0.8016608953475952\n",
      "Epoch 25, Loss: 0.8004289269447327\n",
      "Epoch 26, Loss: 0.7992312908172607\n",
      "Epoch 27, Loss: 0.7980259656906128\n",
      "Epoch 28, Loss: 0.7967669367790222\n",
      "Epoch 29, Loss: 0.7953426241874695\n",
      "Epoch 30, Loss: 0.7937991619110107\n",
      "Epoch 31, Loss: 0.7923488616943359\n",
      "Epoch 32, Loss: 0.7909246683120728\n",
      "Epoch 33, Loss: 0.7895309925079346\n",
      "Epoch 34, Loss: 0.7881426811218262\n",
      "Epoch 35, Loss: 0.7867205142974854\n",
      "Epoch 36, Loss: 0.785347580909729\n",
      "Epoch 37, Loss: 0.783917248249054\n",
      "Epoch 38, Loss: 0.7827112078666687\n",
      "Epoch 39, Loss: 0.7825289964675903\n",
      "Epoch 40, Loss: 0.7860152125358582\n",
      "Epoch 41, Loss: 0.7815302014350891\n",
      "Epoch 42, Loss: 0.7795524001121521\n",
      "Epoch 43, Loss: 0.7802935242652893\n",
      "Epoch 44, Loss: 0.7768566012382507\n",
      "Epoch 45, Loss: 0.7783882021903992\n",
      "Epoch 46, Loss: 0.7747617959976196\n",
      "Epoch 47, Loss: 0.776303231716156\n",
      "Epoch 48, Loss: 0.7729969024658203\n",
      "Epoch 49, Loss: 0.7744238376617432\n",
      "Epoch 50, Loss: 0.7713476419448853\n",
      "Epoch 51, Loss: 0.7724752426147461\n",
      "Epoch 52, Loss: 0.7697489261627197\n",
      "Epoch 53, Loss: 0.7706161141395569\n",
      "Epoch 54, Loss: 0.7683084607124329\n",
      "Epoch 55, Loss: 0.7686154842376709\n",
      "Epoch 56, Loss: 0.7669369578361511\n",
      "Epoch 57, Loss: 0.7664287090301514\n",
      "Epoch 58, Loss: 0.7656922340393066\n",
      "Epoch 59, Loss: 0.7642096281051636\n",
      "Epoch 60, Loss: 0.7642259001731873\n",
      "Epoch 61, Loss: 0.7623927593231201\n",
      "Epoch 62, Loss: 0.7620276808738708\n",
      "Epoch 63, Loss: 0.7612542510032654\n",
      "Epoch 64, Loss: 0.7597907185554504\n",
      "Epoch 65, Loss: 0.7594626545906067\n",
      "Epoch 66, Loss: 0.7586193084716797\n",
      "Epoch 67, Loss: 0.7571951746940613\n",
      "Epoch 68, Loss: 0.756471574306488\n",
      "Epoch 69, Loss: 0.7559435963630676\n",
      "Epoch 70, Loss: 0.7549362182617188\n",
      "Epoch 71, Loss: 0.7535954713821411\n",
      "Epoch 72, Loss: 0.7526354789733887\n",
      "Epoch 73, Loss: 0.7520847916603088\n",
      "Epoch 74, Loss: 0.7516908645629883\n",
      "Epoch 75, Loss: 0.751246988773346\n",
      "Epoch 76, Loss: 0.7505508065223694\n",
      "Epoch 77, Loss: 0.7497915625572205\n",
      "Epoch 78, Loss: 0.7483396530151367\n",
      "Epoch 79, Loss: 0.7469475865364075\n",
      "Epoch 80, Loss: 0.7455731630325317\n",
      "Epoch 81, Loss: 0.7445772886276245\n",
      "Epoch 82, Loss: 0.7441072463989258\n",
      "Epoch 83, Loss: 0.743859052658081\n",
      "Epoch 84, Loss: 0.744874119758606\n",
      "Epoch 85, Loss: 0.7475475072860718\n",
      "Epoch 86, Loss: 0.7501775622367859\n",
      "Epoch 87, Loss: 0.7436347603797913\n",
      "Epoch 88, Loss: 0.7395123839378357\n",
      "Epoch 89, Loss: 0.743588387966156\n",
      "Epoch 90, Loss: 0.7407246232032776\n",
      "Epoch 91, Loss: 0.7371708154678345\n",
      "Epoch 92, Loss: 0.7401764392852783\n",
      "Epoch 93, Loss: 0.7375009059906006\n",
      "Epoch 94, Loss: 0.7350592613220215\n",
      "Epoch 95, Loss: 0.7366636395454407\n",
      "Epoch 96, Loss: 0.7351841926574707\n",
      "Epoch 97, Loss: 0.7331409454345703\n",
      "Epoch 98, Loss: 0.7333529591560364\n",
      "Epoch 99, Loss: 0.733346700668335\n",
      "Epoch 100, Loss: 0.7310432195663452\n",
      "Epoch 101, Loss: 0.7302014827728271\n",
      "Epoch 102, Loss: 0.7302297949790955\n",
      "Epoch 103, Loss: 0.7300838828086853\n",
      "Epoch 104, Loss: 0.7283945083618164\n",
      "Epoch 105, Loss: 0.7270605564117432\n",
      "Epoch 106, Loss: 0.7270022630691528\n",
      "Epoch 107, Loss: 0.7266137599945068\n",
      "Epoch 108, Loss: 0.726098895072937\n",
      "Epoch 109, Loss: 0.7247425317764282\n",
      "Epoch 110, Loss: 0.7236599922180176\n",
      "Epoch 111, Loss: 0.7227787375450134\n",
      "Epoch 112, Loss: 0.7223610877990723\n",
      "Epoch 113, Loss: 0.7222031354904175\n",
      "Epoch 114, Loss: 0.7221345901489258\n",
      "Epoch 115, Loss: 0.7226247787475586\n",
      "Epoch 116, Loss: 0.7241194844245911\n",
      "Epoch 117, Loss: 0.7273925542831421\n",
      "Epoch 118, Loss: 0.7256580591201782\n",
      "Epoch 119, Loss: 0.7201305627822876\n",
      "Epoch 120, Loss: 0.718049943447113\n",
      "Epoch 121, Loss: 0.7201297879219055\n",
      "Epoch 122, Loss: 0.7197525501251221\n",
      "Epoch 123, Loss: 0.7165258526802063\n",
      "Epoch 124, Loss: 0.7148667573928833\n",
      "Epoch 125, Loss: 0.7163328528404236\n",
      "Epoch 126, Loss: 0.7160361409187317\n",
      "Epoch 127, Loss: 0.7145045399665833\n",
      "Epoch 128, Loss: 0.7122628688812256\n",
      "Epoch 129, Loss: 0.7119022011756897\n",
      "Epoch 130, Loss: 0.7119209170341492\n",
      "Epoch 131, Loss: 0.7127565145492554\n",
      "Epoch 132, Loss: 0.7126235365867615\n",
      "Epoch 133, Loss: 0.7111142873764038\n",
      "Epoch 134, Loss: 0.7092447876930237\n",
      "Epoch 135, Loss: 0.7077796459197998\n",
      "Epoch 136, Loss: 0.7074167728424072\n",
      "Epoch 137, Loss: 0.7075983881950378\n",
      "Epoch 138, Loss: 0.7078704237937927\n",
      "Epoch 139, Loss: 0.7080675363540649\n",
      "Epoch 140, Loss: 0.7078577280044556\n",
      "Epoch 141, Loss: 0.7081243395805359\n",
      "Epoch 142, Loss: 0.7061814069747925\n",
      "Epoch 143, Loss: 0.7047483921051025\n",
      "Epoch 144, Loss: 0.703201174736023\n",
      "Epoch 145, Loss: 0.7017384171485901\n",
      "Epoch 146, Loss: 0.7015931010246277\n",
      "Epoch 147, Loss: 0.7011954188346863\n",
      "Epoch 148, Loss: 0.7014558911323547\n",
      "Epoch 149, Loss: 0.7019643783569336\n",
      "Epoch 150, Loss: 0.704470694065094\n",
      "Epoch 151, Loss: 0.7062979936599731\n",
      "Epoch 152, Loss: 0.7078806757926941\n",
      "Epoch 153, Loss: 0.7035641074180603\n",
      "Epoch 154, Loss: 0.6979114413261414\n",
      "Epoch 155, Loss: 0.6974270939826965\n",
      "Epoch 156, Loss: 0.70029217004776\n",
      "Epoch 157, Loss: 0.7004815936088562\n",
      "Epoch 158, Loss: 0.6971808075904846\n",
      "Epoch 159, Loss: 0.6949127316474915\n",
      "Epoch 160, Loss: 0.6953119039535522\n",
      "Epoch 161, Loss: 0.6962599158287048\n",
      "Epoch 162, Loss: 0.6971898674964905\n",
      "Epoch 163, Loss: 0.6945886015892029\n",
      "Epoch 164, Loss: 0.6922656297683716\n",
      "Epoch 165, Loss: 0.6920728087425232\n",
      "Epoch 166, Loss: 0.6918601989746094\n",
      "Epoch 167, Loss: 0.6926254034042358\n",
      "Epoch 168, Loss: 0.6924550533294678\n",
      "Epoch 169, Loss: 0.6918312907218933\n",
      "Epoch 170, Loss: 0.6902728080749512\n",
      "Epoch 171, Loss: 0.6892816424369812\n",
      "Epoch 172, Loss: 0.6883727312088013\n",
      "Epoch 173, Loss: 0.6877993941307068\n",
      "Epoch 174, Loss: 0.6874449849128723\n",
      "Epoch 175, Loss: 0.6872898936271667\n",
      "Epoch 176, Loss: 0.6878280639648438\n",
      "Epoch 177, Loss: 0.6896927356719971\n",
      "Epoch 178, Loss: 0.6951590776443481\n",
      "Epoch 179, Loss: 0.700467586517334\n",
      "Epoch 180, Loss: 0.7039057016372681\n",
      "Epoch 181, Loss: 0.6945487260818481\n",
      "Epoch 182, Loss: 0.6862770318984985\n",
      "Epoch 183, Loss: 0.694699764251709\n",
      "Epoch 184, Loss: 0.6918648481369019\n",
      "Epoch 185, Loss: 0.6853047609329224\n",
      "Epoch 186, Loss: 0.6889731287956238\n",
      "Epoch 187, Loss: 0.6896360516548157\n",
      "Epoch 188, Loss: 0.6841375231742859\n",
      "Epoch 189, Loss: 0.6842190027236938\n",
      "Epoch 190, Loss: 0.6890836358070374\n",
      "Epoch 191, Loss: 0.6870837211608887\n",
      "Epoch 192, Loss: 0.68363356590271\n",
      "Epoch 193, Loss: 0.6846471428871155\n",
      "Epoch 194, Loss: 0.6865705847740173\n",
      "Epoch 195, Loss: 0.6829664707183838\n",
      "Epoch 196, Loss: 0.6815730929374695\n",
      "Epoch 197, Loss: 0.6832139492034912\n",
      "Epoch 198, Loss: 0.6824625134468079\n",
      "Epoch 199, Loss: 0.6801974177360535\n",
      "Epoch 200, Loss: 0.6795347929000854\n",
      "Test Loss: 0.8788255453109741, MAE: 0.5978545546531677\n",
      "Epoch 1, Loss: 0.7493336796760559\n",
      "Epoch 2, Loss: 0.7448736429214478\n",
      "Epoch 3, Loss: 0.7402991652488708\n",
      "Epoch 4, Loss: 0.737702488899231\n",
      "Epoch 5, Loss: 0.7354219555854797\n",
      "Epoch 6, Loss: 0.7325255274772644\n",
      "Epoch 7, Loss: 0.7299825549125671\n",
      "Epoch 8, Loss: 0.7285327315330505\n",
      "Epoch 9, Loss: 0.7261718511581421\n",
      "Epoch 10, Loss: 0.7243776917457581\n",
      "Epoch 11, Loss: 0.723316490650177\n",
      "Epoch 12, Loss: 0.7210052013397217\n",
      "Epoch 13, Loss: 0.7201536297798157\n",
      "Epoch 14, Loss: 0.7187411189079285\n",
      "Epoch 15, Loss: 0.7170342803001404\n",
      "Epoch 16, Loss: 0.7162367105484009\n",
      "Epoch 17, Loss: 0.7148733139038086\n",
      "Epoch 18, Loss: 0.713639497756958\n",
      "Epoch 19, Loss: 0.7124860882759094\n",
      "Epoch 20, Loss: 0.7114635705947876\n",
      "Epoch 21, Loss: 0.7102962136268616\n",
      "Epoch 22, Loss: 0.7091689705848694\n",
      "Epoch 23, Loss: 0.7082672715187073\n",
      "Epoch 24, Loss: 0.7072407007217407\n",
      "Epoch 25, Loss: 0.7061595320701599\n",
      "Epoch 26, Loss: 0.7052805423736572\n",
      "Epoch 27, Loss: 0.7044448256492615\n",
      "Epoch 28, Loss: 0.7035350799560547\n",
      "Epoch 29, Loss: 0.702633798122406\n",
      "Epoch 30, Loss: 0.701834499835968\n",
      "Epoch 31, Loss: 0.7010354995727539\n",
      "Epoch 32, Loss: 0.7001875638961792\n",
      "Epoch 33, Loss: 0.6993837356567383\n",
      "Epoch 34, Loss: 0.6985818147659302\n",
      "Epoch 35, Loss: 0.6978539824485779\n",
      "Epoch 36, Loss: 0.6971814632415771\n",
      "Epoch 37, Loss: 0.6964170932769775\n",
      "Epoch 38, Loss: 0.6957104206085205\n",
      "Epoch 39, Loss: 0.6949213147163391\n",
      "Epoch 40, Loss: 0.6942189335823059\n",
      "Epoch 41, Loss: 0.6935859322547913\n",
      "Epoch 42, Loss: 0.692886233329773\n",
      "Epoch 43, Loss: 0.6922576427459717\n",
      "Epoch 44, Loss: 0.6916490793228149\n",
      "Epoch 45, Loss: 0.6910089254379272\n",
      "Epoch 46, Loss: 0.6905022859573364\n",
      "Epoch 47, Loss: 0.6900789141654968\n",
      "Epoch 48, Loss: 0.6899559497833252\n",
      "Epoch 49, Loss: 0.6905148029327393\n",
      "Epoch 50, Loss: 0.6920039653778076\n",
      "Epoch 51, Loss: 0.6953635811805725\n",
      "Epoch 52, Loss: 0.697064995765686\n",
      "Epoch 53, Loss: 0.6966010928153992\n",
      "Epoch 54, Loss: 0.6903927326202393\n",
      "Epoch 55, Loss: 0.6859191060066223\n",
      "Epoch 56, Loss: 0.6874895691871643\n",
      "Epoch 57, Loss: 0.6902167201042175\n",
      "Epoch 58, Loss: 0.6899557113647461\n",
      "Epoch 59, Loss: 0.6850243210792542\n",
      "Epoch 60, Loss: 0.6834380030632019\n",
      "Epoch 61, Loss: 0.6858078837394714\n",
      "Epoch 62, Loss: 0.6869099736213684\n",
      "Epoch 63, Loss: 0.6854206323623657\n",
      "Epoch 64, Loss: 0.6819313764572144\n",
      "Epoch 65, Loss: 0.6811429262161255\n",
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      "Epoch 200, Loss: 0.6180741190910339\n",
      "Test Loss: 0.9048617482185364, MAE: 0.6005526185035706\n",
      "Epoch 1, Loss: 0.7148041725158691\n",
      "Epoch 2, Loss: 0.7060413360595703\n",
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      "Epoch 159, Loss: 0.6202130317687988\n",
      "Epoch 160, Loss: 0.6208629012107849\n",
      "Epoch 161, Loss: 0.622614324092865\n",
      "Epoch 162, Loss: 0.6221786141395569\n",
      "Epoch 163, Loss: 0.6207623481750488\n",
      "Epoch 164, Loss: 0.6193758845329285\n",
      "Epoch 165, Loss: 0.6183919310569763\n",
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      "Epoch 167, Loss: 0.619184136390686\n",
      "Epoch 168, Loss: 0.6178073883056641\n",
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      "Epoch 170, Loss: 0.6190420985221863\n",
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      "Epoch 172, Loss: 0.6182895302772522\n",
      "Epoch 173, Loss: 0.6176286935806274\n",
      "Epoch 174, Loss: 0.6175389885902405\n",
      "Epoch 175, Loss: 0.6160255670547485\n",
      "Epoch 176, Loss: 0.6166970133781433\n",
      "Epoch 177, Loss: 0.6156012415885925\n",
      "Epoch 178, Loss: 0.615466296672821\n",
      "Epoch 179, Loss: 0.6152234673500061\n",
      "Epoch 180, Loss: 0.6152539849281311\n",
      "Epoch 181, Loss: 0.6145603656768799\n",
      "Epoch 182, Loss: 0.6145808100700378\n",
      "Epoch 183, Loss: 0.6148650050163269\n",
      "Epoch 184, Loss: 0.6143636703491211\n",
      "Epoch 185, Loss: 0.6142882704734802\n",
      "Epoch 186, Loss: 0.6142975091934204\n",
      "Epoch 187, Loss: 0.6150378584861755\n",
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      "Epoch 189, Loss: 0.6181196570396423\n",
      "Epoch 190, Loss: 0.6222937703132629\n",
      "Epoch 191, Loss: 0.6303341388702393\n",
      "Epoch 192, Loss: 0.6404908299446106\n",
      "Epoch 193, Loss: 0.6489911079406738\n",
      "Epoch 194, Loss: 0.6374160051345825\n",
      "Epoch 195, Loss: 0.6180667877197266\n",
      "Epoch 196, Loss: 0.6188192963600159\n",
      "Epoch 197, Loss: 0.6307047605514526\n",
      "Epoch 198, Loss: 0.6362743973731995\n",
      "Epoch 199, Loss: 0.6252291798591614\n",
      "Epoch 200, Loss: 0.6147458553314209\n",
      "Test Loss: 0.8898637890815735, MAE: 0.5935608744621277\n",
      "Mean Squared Error: 0.8992092490196228\n",
      "Mean Absolute Error: 0.59993947\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in kf.split(graph_data.edge_index.t().numpy()):\n",
    "    train_data = Data(\n",
    "        x=graph_data.x.clone().detach().requires_grad_(True),\n",
    "        edge_index=graph_data.edge_index[:, train_index],\n",
    "        edge_attr=graph_data.edge_attr[train_index],\n",
    "    )\n",
    "    test_data = Data(\n",
    "        x=graph_data.x.clone().detach(),\n",
    "        edge_index=graph_data.edge_index[:, test_index],\n",
    "        edge_attr=graph_data.edge_attr[test_index],\n",
    "    )\n",
    "\n",
    "    for epoch in range(1, 201):\n",
    "        loss, feature_importance = train(model, train_data, optimizer, criterion)\n",
    "        print(f\"Epoch {epoch}, Loss: {loss}\")\n",
    "\n",
    "    test_loss, predictions = test(model, test_data, criterion)\n",
    "    losses.append(test_loss)\n",
    "    mae = mean_absolute_error(\n",
    "        test_data.edge_attr.cpu().detach().numpy(), predictions.cpu().detach().numpy()\n",
    "    )\n",
    "\n",
    "    mae_scores.append(mae)\n",
    "    print(f\"Test Loss: {test_loss}, MAE: {mae}\")\n",
    "\n",
    "    node_feature_importances += feature_importance\n",
    "\n",
    "print(\"Mean Squared Error:\", np.mean(losses))\n",
    "print(\"Mean Absolute Error:\", np.mean(mae_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00d76331-a96c-4195-8349-7e4bff4ae52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_feature_importances /= kf.get_n_splits()\n",
    "node_feature_names = [\n",
    "    \"kategori_1\",\n",
    "    \"kategori_2\",\n",
    "    \"kategori_3\",\n",
    "    \"mahalle\",\n",
    "    \"ilce\",\n",
    "    \"il\",\n",
    "    \"ilan_baslik\",\n",
    "    \"ilan_fiyat\",\n",
    "    \"ilan_sahip_adi\",\n",
    "    \"site_icinde_mi\",\n",
    "    \"m2_gross\",\n",
    "    \"m2_net\",\n",
    "    \"bina_kat_sayisi\",\n",
    "    \"cephe\",\n",
    "    \"sosyal_imkanlar\",\n",
    "    \"bina_ozellikleri\",\n",
    "    \"ulasim\",\n",
    "    \"oda_sayisi\",\n",
    "    \"ilan_kat_sayisi\",\n",
    "    \"bina_yasi\",\n",
    "    \"ilab_query\",\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(range(len(node_feature_importances)), node_feature_importances)\n",
    "plt.xlabel(\"Node Feature Index\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.title(\"Node Feature Importances\")\n",
    "plt.xticks(range(len(node_feature_names)), node_feature_names, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5d4099a-3bef-4901-8913-5b5a384f1abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27656, 21])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_data.x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a72215e2-98dd-4597-aaac-f9643dbd0162",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a4699c-2a09-4511-a07c-63d5f48d9929",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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