{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "title-cell",
   "metadata": {
    "id": "title-cell"
   },
   "source": "# 🏦 Home Credit Default Risk — Gerçek IBM QPU Max-Qubit QML Demo\n\n**Amaç:** Öğrencilere / araştırmacılara gerçek quantum computer gücünü göstermek — erişilen backend’in izin verdiği **maksimum qubit** ile VQC (yüksek qubit) / QSVC (orta qubit).  \n**Ortam:** Google Colab + **IBM Quantum Platform (gerçek donanım)**  \n**Veri:** [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) (`application_train.csv`)\n\n---\n\n### Öğrenci demosu için kritik gerçekler\n\n| Konu | Açıklama |\n|------|----------|\n| **Max qubit** | `min(TARGET_QUBITS, backend.num_qubits)` — erişiminizdeki en büyük sistem |\n| **Neden küçük train?** | Her optimizasyon adımı QPU’da devre çalıştırır; 40–80 örnek bile anlamlı demo sağlar |\n| **Neden VQC?** | Yüksek qubit’te kernel (QSVC) O(n²) devre ister; VQC tek devre ailesi ile ölçeklenir |\n| **Gürültü** | NISQ cihazında metrikler ideal simülatörden düşük olabilir — bu da öğretimin parçası |\n| **Statevector** | 50–100 qubit **klasik simülasyonda imkânsız**; sadece gerçek QPU veya shots |\n| **Class imbalance** | Default oranı ~8%; stratified subset + class_weight / scale_pos_weight kullanılır |\n\n### Çalıştırma sırası (doğrusal — geri dönüş yok)\n\nHücreleri **yukarıdan aşağıya** sırayla çalıştırın:\n\n| Aşama | İçerik |\n|-------|--------|\n| 1–4 | Ortam, seçim motoru, paketler, IBM token |\n| 5–9 | Veri yükleme, EDA, ön işleme |\n| 10–11 | Klasik baseline + QML algoritma seçimi |\n| 12 | Max-qubit encoding (`TARGET_QUBITS`) |\n| **13** | **IBM backend seçimi** (`REAL_QPU`, `n_qubits` kilidi) |\n| 14 | Feature map + ansatz |\n| 15 | VQC / QSVC eğitimi (ISA transpile) |\n| 16–19 | Metrikler, kayıt, karşılaştırma, yorum |\n| **20** | **Word raporu** (problem + klasik/quantum sonuçlar) |\n\n> Backend (AŞAMA 13) eğitimden **önce** gelir; sıra bozulmaz.\n\n### Referans proje\n- GitHub: [yakupkaplan/Home-Credit-Default-Risk](https://github.com/yakupkaplan/Home-Credit-Default-Risk)\n- Kaggle: [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk)\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage1-md",
   "metadata": {
    "id": "stage1-md"
   },
   "source": "## AŞAMA 1: Drive / Path Tanımlama\n"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "stage1-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage1-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788505302373,
     "user_tz": -180,
     "elapsed": 18193,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "05619d63-7673-41a1-9554-1d23b974a066"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.15\n",
      "Mounted at /content/drive\n",
      "✅ Google Drive bağlandı → /content/drive/MyDrive\n",
      "   Proje: /content/drive/MyDrive/QML_HomeCredit_Default_HighQ\n",
      "   Çıktı: /content/drive/MyDrive/QML_HomeCredit_Default_HighQ/outputs\n",
      "\n",
      "✅ AŞAMA 1 tamamlandı.\n"
     ]
    }
   ],
   "source": "import os\nimport sys\n\nprint(f\"Python: {sys.version.split()[0]}\")\n\nDRIVE_ROOT = None\nPROJECT_PATH = None\nOUTPUT_PATH = None\n\ntry:\n    from google.colab import drive\n    drive.mount('/content/drive')\n    DRIVE_ROOT = '/content/drive/MyDrive'\n    PROJECT_PATH = os.path.join(DRIVE_ROOT, 'QML_HomeCredit_Default_HighQ')\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    os.makedirs(OUTPUT_PATH, exist_ok=True)\n    print(f\"✅ Google Drive bağlandı → {DRIVE_ROOT}\")\n    print(f\"   Proje: {PROJECT_PATH}\")\n    print(f\"   Çıktı: {OUTPUT_PATH}\")\nexcept Exception as e:\n    print(f\"⚠️ Drive mount atlandı: {e}\")\n    PROJECT_PATH = os.path.abspath('./qml_homecredit_default_highq')\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    os.makedirs(OUTPUT_PATH, exist_ok=True)\n    print(f\"   Lokal proje: {PROJECT_PATH}\")\n\nprint(\"\\n✅ AŞAMA 1 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage2-md",
   "metadata": {
    "id": "stage2-md"
   },
   "source": "## AŞAMA 2: Cihaz & Algoritma Seçim Motoru\n\nVarsayılan: **Machine Learning → Superconducting Qubits → VQC** (yüksek qubit için önerilen).\n"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "stage2-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367,
     "referenced_widgets": [
      "d8e677093c1c47ccb975131b6604969b",
      "4c3d8ea649b14ca2afb4395535da00da",
      "ff28e2e8d6ac4c838f1110438b6e1810",
      "273a63343ff4401abf350f3030f8e2ad",
      "b9eea4376e0f4a1a85e48bc9563dd75d",
      "f5ae13e378eb471db8ef4ea64d2e36a5",
      "4c5afa1a1bff42e2abda8166fe7f1745",
      "0ba2c7de39c148b1920113dd7e407a97",
      "d503b2dae773406a8da4754d7355c9aa",
      "9b95ea13359b404688013c7fae2512de",
      "88e2a6e2286b49ea835610298aad995b",
      "e86f1199f0774c7086e12eed4da87ea4",
      "2695b13da60645558943370a2403bf4e",
      "85f1a847d2ff4a4d8ea884baa3f336f5",
      "add01c68b9204665b181b743dec1f95c",
      "daf237b551e34763912022162cff85c6"
     ]
    },
    "id": "stage2-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788505302840,
     "user_tz": -180,
     "elapsed": 459,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c13bda56-699d-4f21-d06f-da03e1f00e60"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3>🔧 Pipeline Seçim Motoru <span style='font-size:12px;color:#64748b;'>(High-Qubit…"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d8e677093c1c47ccb975131b6604969b"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "📌 Varsayılan: Machine Learning | Superconducting Qubits | VQC\n",
      "   Yüksek qubit (≥12–16) denemelerinde VQC tercih edin.\n",
      "✅ AŞAMA 2 tamamlandı.\n"
     ]
    }
   ],
   "source": "import pandas as pd\nfrom IPython.display import display, HTML, Markdown, clear_output\nimport ipywidgets as widgets\n\nVALID_TRIPLETS = [\n    (\"Optimization & Logistics\", \"Superconducting Qubits\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Superconducting Qubits\", \"Grover\"),\n    (\"Optimization & Logistics\", \"Trapped Ions\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Quantum Annealer (D-Wave)\", \"QUBO/Annealing\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"QSVC\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"VQC\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"QAOA\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"QSVC\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"VQC\"),\n    (\"Machine Learning\", \"Neutral Atoms\", \"QAOA\"),\n]\n\nPROBLEMS = sorted({t[0] for t in VALID_TRIPLETS})\nALGO_MATH = {\n    \"QSVC\": r\"$K(x,x') = |\\langle \\phi(x) | \\phi(x') \\rangle|^2$\",\n    \"VQC\": r\"$f_\\theta(x) = \\langle 0| U^\\dagger(x,\\theta) M U(x,\\theta) |0\\rangle$\",\n    \"QAOA\": r\"$|\\gamma,\\beta\\rangle = e^{-i\\beta H_M} e^{-i\\gamma H_C}\\cdots |+\\rangle^{\\otimes n}$\",\n    \"QUBO/Annealing\": r\"$\\min_x\\; x^T Q x$\",\n}\nHW_TO_DEVICE = {\n    \"Superconducting Qubits\": \"ibm_or_aer\",\n    \"Trapped Ions\": \"ionq_or_aer\",\n    \"Neutral Atoms\": \"pasqal_or_aer\",\n    \"Quantum Annealer (D-Wave)\": \"dwave\",\n}\n\nSELECTED_PROBLEM = \"Machine Learning\"\nSELECTED_HARDWARE = \"Superconducting Qubits\"\nSELECTED_ALGO = \"VQC\"          # Yüksek qubit demosu için VQC varsayılan\nSELECTED_DEVICE = \"ibm_or_aer\"\n\ndef allowed_hw(prob):\n    return sorted({t[1] for t in VALID_TRIPLETS if t[0] == prob})\n\ndef allowed_alg(prob, hw=None):\n    if hw is None:\n        return sorted({t[2] for t in VALID_TRIPLETS if t[0] == prob})\n    return sorted({t[2] for t in VALID_TRIPLETS if t[0] == prob and t[1] == hw})\n\ndd_prob = widgets.Dropdown(options=[\"— Problem seçin —\"] + PROBLEMS, description=\"Problem:\",\n                           layout=widgets.Layout(width=\"460px\"), style={\"description_width\": \"90px\"})\ndd_hw = widgets.Dropdown(options=[\"— önce problem —\"], description=\"Donanım:\",\n                         layout=widgets.Layout(width=\"460px\"), style={\"description_width\": \"90px\"}, disabled=True)\ndd_alg = widgets.Dropdown(options=[\"— önce donanım —\"], description=\"Algoritma:\",\n                          layout=widgets.Layout(width=\"460px\"), style={\"description_width\": \"90px\"}, disabled=True)\nout = widgets.Output()\n\ndef on_prob_change(change):\n    global SELECTED_PROBLEM, SELECTED_HARDWARE, SELECTED_ALGO, SELECTED_DEVICE\n    if change[\"name\"] != \"value\": return\n    val = change[\"new\"]\n    if str(val).startswith(\"—\"):\n        SELECTED_PROBLEM = SELECTED_HARDWARE = SELECTED_ALGO = None\n        SELECTED_DEVICE = \"aer_only\"\n        dd_hw.options = [\"— önce problem —\"]; dd_hw.disabled = True\n        dd_alg.options = [\"— önce donanım —\"]; dd_alg.disabled = True\n    else:\n        SELECTED_PROBLEM = val\n        SELECTED_HARDWARE = SELECTED_ALGO = None\n        hws = allowed_hw(val)\n        dd_hw.options = [\"— Donanım seçin —\"] + hws; dd_hw.disabled = False; dd_hw.value = dd_hw.options[0]\n        dd_alg.options = [\"— önce donanım —\"]; dd_alg.disabled = True\n    _render()\n\ndef on_hw_change(change):\n    global SELECTED_HARDWARE, SELECTED_ALGO, SELECTED_DEVICE\n    if change[\"name\"] != \"value\": return\n    val = change[\"new\"]\n    if not SELECTED_PROBLEM or str(val).startswith(\"—\"):\n        SELECTED_HARDWARE = SELECTED_ALGO = None\n        dd_alg.options = [\"— önce donanım —\"]; dd_alg.disabled = True\n    else:\n        SELECTED_HARDWARE = val\n        SELECTED_ALGO = None\n        SELECTED_DEVICE = HW_TO_DEVICE.get(val, \"aer_only\")\n        algs = allowed_alg(SELECTED_PROBLEM, val)\n        dd_alg.options = [\"— Algoritma seçin —\"] + algs; dd_alg.disabled = False; dd_alg.value = dd_alg.options[0]\n    _render()\n\ndef on_alg_change(change):\n    global SELECTED_ALGO\n    if change[\"name\"] != \"value\": return\n    val = change[\"new\"]\n    SELECTED_ALGO = None if str(val).startswith(\"—\") or not SELECTED_HARDWARE else val\n    _render()\n\ndef _render():\n    with out:\n        clear_output(wait=True)\n        if SELECTED_PROBLEM and SELECTED_HARDWARE and SELECTED_ALGO:\n            display(Markdown(f\"### Seçim: **{SELECTED_PROBLEM}** → **{SELECTED_HARDWARE}** → **{SELECTED_ALGO}**\"))\n            display(Markdown(f\"**Cihaz:** `{SELECTED_DEVICE}`\"))\n            math = ALGO_MATH.get(SELECTED_ALGO, \"\")\n            if math: display(Markdown(f\"**Model:** {math}\"))\n            display(HTML(\n                \"<div style='padding:12px;background:#f0fdf4;border-left:4px solid #22c55e;border-radius:8px;'>\"\n                f\"<b>✅ Hazır.</b> SELECTED_ALGO=<code>{SELECTED_ALGO}</code> — Yüksek qubit için <b>VQC</b> önerilir.\"\n                \"</div>\"\n            ))\n        else:\n            display(HTML(\"<div style='padding:12px;background:#fff7ed;border-left:4px solid #fb923c;'>Problem → Donanım → Algoritma seçin.</div>\"))\n\ndd_prob.observe(on_prob_change, names=\"value\")\ndd_hw.observe(on_hw_change, names=\"value\")\ndd_alg.observe(on_alg_change, names=\"value\")\n\ndisplay(widgets.VBox([\n    widgets.HTML(\"<h3>🔧 Pipeline Seçim Motoru <span style='font-size:12px;color:#64748b;'>(High-Qubit Home Credit Default Risk)</span></h3>\"),\n    dd_prob, dd_hw, dd_alg, out\n]))\n_render()\n\nprint(f\"\\n📌 Varsayılan: {SELECTED_PROBLEM} | {SELECTED_HARDWARE} | {SELECTED_ALGO}\")\nprint(\"   Yüksek qubit (≥12–16) denemelerinde VQC tercih edin.\")\nprint(\"✅ AŞAMA 2 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage3-md",
   "metadata": {
    "id": "stage3-md"
   },
   "source": "## AŞAMA 3: Python Library Kurulumu\n"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "stage3-install",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage3-install",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788505324004,
     "user_tz": -180,
     "elapsed": 21161,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c84dfe8a-de0e-456a-9baf-1ba108a27af5"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "📦 Paketler kuruluyor...\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h✅ Kurulum tamamlandı.\n"
     ]
    }
   ],
   "source": "print(\"📦 Paketler kuruluyor...\")\n!pip install -q --upgrade pip\n!pip install -q qiskit qiskit-aer qiskit-machine-learning qiskit-algorithms qiskit-ibm-runtime\n!pip install -q numpy pandas scikit-learn xgboost lightgbm openpyxl\n!pip install -q matplotlib seaborn pylatexenc tqdm ipywidgets python-dotenv python-docx\nprint(\"✅ Kurulum tamamlandı.\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stage3-import",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage3-import",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788505326669,
     "user_tz": -180,
     "elapsed": 2662,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "727b438b-cc08-497b-c17a-c88ab41e601b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.15\n",
      "✅ Ortak kütüphaneler yüklendi.\n"
     ]
    }
   ],
   "source": "import os, sys, json, math, warnings\nfrom pathlib import Path\nfrom datetime import datetime\nfrom hashlib import sha256\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder, PolynomialFeatures\nfrom sklearn.decomposition import PCA\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.metrics import (\n    accuracy_score, precision_score, recall_score, f1_score,\n    roc_auc_score, roc_curve, confusion_matrix, ConfusionMatrixDisplay,\n    classification_report,\n)\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom xgboost import XGBClassifier\n\nwarnings.filterwarnings(\"ignore\")\nsns.set_style(\"whitegrid\")\nplt.rcParams[\"figure.figsize\"] = (10, 6)\n\nprint(f\"Python: {sys.version.split()[0]}\")\nprint(\"✅ Ortak kütüphaneler yüklendi.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage4-md",
   "metadata": {
    "id": "stage4-md"
   },
   "source": "## AŞAMA 4: API Keys Yönetimi\n\n2025+ : `channel=\"ibm_quantum_platform\"` (eski `ibm_quantum` kaldırıldı).\n"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "stage4-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage4-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788505327666,
     "user_tz": -180,
     "elapsed": 1005,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "3dba9a3e-fb5e-42f3-cefe-51a40305d6ce"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ IBM token Colab Secrets'tan okundu.\n",
      "   Token uzunluğu : 44 karakter\n",
      "   Channel        : ibm_quantum_platform\n",
      "   Instance       : auto\n",
      "✅ AŞAMA 4 tamamlandı.\n"
     ]
    }
   ],
   "source": "IBM_TOKEN = None\nIBM_INSTANCE = None\nIBM_CHANNEL = \"ibm_quantum_platform\"\n\ntry:\n    from google.colab import userdata\n    IBM_TOKEN = userdata.get(\"IBM_QUANTUM_TOKEN\") or userdata.get(\"QISKIT_IBM_TOKEN\")\n    try:\n        IBM_INSTANCE = userdata.get(\"IBM_QUANTUM_INSTANCE\") or userdata.get(\"QISKIT_IBM_INSTANCE\")\n    except Exception:\n        pass\n    if IBM_TOKEN:\n        print(\"✅ IBM token Colab Secrets'tan okundu.\")\nexcept Exception:\n    pass\n\nif not IBM_TOKEN:\n    IBM_TOKEN = os.environ.get(\"IBM_QUANTUM_TOKEN\") or os.environ.get(\"QISKIT_IBM_TOKEN\")\n    if IBM_TOKEN:\n        print(\"✅ IBM token ortam değişkeninden okundu.\")\n\nif not IBM_INSTANCE:\n    IBM_INSTANCE = os.environ.get(\"IBM_QUANTUM_INSTANCE\") or os.environ.get(\"QISKIT_IBM_INSTANCE\")\n\n_ch = os.environ.get(\"QISKIT_IBM_CHANNEL\") or os.environ.get(\"IBM_QUANTUM_CHANNEL\")\nif _ch in (\"ibm_quantum_platform\", \"ibm_cloud\"):\n    IBM_CHANNEL = _ch\n\nif not IBM_TOKEN:\n    try:\n        from dotenv import load_dotenv\n        load_dotenv()\n        IBM_TOKEN = os.environ.get(\"IBM_QUANTUM_TOKEN\") or os.environ.get(\"QISKIT_IBM_TOKEN\")\n        IBM_INSTANCE = IBM_INSTANCE or os.environ.get(\"IBM_QUANTUM_INSTANCE\")\n        if IBM_TOKEN:\n            print(\"✅ IBM token .env'den okundu.\")\n    except Exception:\n        pass\n\nif IBM_TOKEN:\n    print(f\"   Token uzunluğu : {len(IBM_TOKEN)} karakter\")\n    print(f\"   Channel        : {IBM_CHANNEL}\")\n    print(f\"   Instance       : {'ayarlı' if IBM_INSTANCE else 'auto'}\")\nelse:\n    print(\"⚠️ Token yok → Aer ile devam. Gerçek 100+ qubit QPU için token gerekli.\")\n\nprint(\"✅ AŞAMA 4 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage5-md",
   "metadata": {
    "id": "stage5-md"
   },
   "source": "## AŞAMA 5: Home Credit Veri Yükleme\n\nAna tablo: `application_train.csv` (TARGET ile).  \nTam rekabet veri seti çok büyük; quantum demosu için bu ana tablo yeterlidir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "stage5-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage5-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788505327738,
     "user_tz": -180,
     "elapsed": 74,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f1d71e70-9dee-44f0-c277-c4f10ff2c4a3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "⚠️ application_train.csv bulunamadı.\n",
      "   Seçenekler:\n",
      "   1) Kaggle'dan indirip Colab'a yükleyin (AŞAMA 6)\n",
      "   2) Kaggle API ile (aşağıdaki satırları aktif edin)\n",
      "   3) Drive'a koyup path'i CANDIDATE_PATHS'e ekleyin\n",
      "✅ AŞAMA 5 tamamlandı.\n"
     ]
    }
   ],
   "source": "CANDIDATE_PATHS = [\n    \"/content/application_train.csv\",\n    \"/content/drive/MyDrive/application_train.csv\",\n    \"/content/drive/MyDrive/QML_HomeCredit_Default_HighQ/application_train.csv\",\n    \"/content/drive/MyDrive/Home-Credit-Default-Risk/application_train.csv\",\n    os.path.join(PROJECT_PATH or \".\", \"application_train.csv\"),\n    \"./application_train.csv\",\n    \"./data/application_train.csv\",\n    \"/home/workdir/attachments/application_train.csv\",\n]\n\ndf = None\nloaded_from = None\nfor p in CANDIDATE_PATHS:\n    if os.path.isfile(p):\n        df = pd.read_csv(p, low_memory=False)\n        loaded_from = p\n        break\n\nif df is None:\n    print(\"⚠️ application_train.csv bulunamadı.\")\n    print(\"   Seçenekler:\")\n    print(\"   1) Kaggle'dan indirip Colab'a yükleyin (AŞAMA 6)\")\n    print(\"   2) Kaggle API ile (aşağıdaki satırları aktif edin)\")\n    print(\"   3) Drive'a koyup path'i CANDIDATE_PATHS'e ekleyin\")\n    # Opsiyonel Kaggle API (token gerekir)\n    # !pip install -q kaggle\n    # !mkdir -p ~/.kaggle && cp kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json\n    # !kaggle competitions download -c home-credit-default-risk -f application_train.csv\n    # !unzip -q application_train.csv.zip -d /content/\nelse:\n    print(f\"✅ Veri: {loaded_from} → {df.shape}\")\n    if \"TARGET\" in df.columns:\n        print(df[\"TARGET\"].value_counts())\n        print(f\"Default oranı: {df['TARGET'].mean():.2%}\")\n    else:\n        print(\"⚠️ TARGET sütunu yok — train dosyası olduğundan emin olun.\")\n\nprint(\"✅ AŞAMA 5 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage6-md",
   "metadata": {
    "id": "stage6-md"
   },
   "source": "## AŞAMA 6: Opsiyonel Upload\n"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "stage6-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 188
    },
    "id": "stage6-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788509826548,
     "user_tz": -180,
     "elapsed": 4498807,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e0f354d2-8f05-4ad8-c201-52ef7d389bbc"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "application_train.csv seçin...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-e6b9e722-14f7-416d-b5e9-c803edcc1ec5\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-e6b9e722-14f7-416d-b5e9-c803edcc1ec5\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Saving application_train.csv to application_train.csv\n",
      "✅ application_train.csv → (307511, 122)\n",
      "TARGET\n",
      "0    282686\n",
      "1     24825\n",
      "Name: count, dtype: int64\n",
      "✅ AŞAMA 6 tamamlandı.\n"
     ]
    }
   ],
   "source": "USE_UPLOADED = False\n\nif USE_UPLOADED or df is None:\n    try:\n        from google.colab import files\n        print(\"application_train.csv seçin...\")\n        uploaded = files.upload()\n        if uploaded:\n            fname = list(uploaded.keys())[0]\n            if fname.endswith((\".csv\", \".CSV\")):\n                df = pd.read_csv(fname, low_memory=False)\n            else:\n                df = pd.read_excel(fname, sheet_name=0)\n            print(f\"✅ {fname} → {df.shape}\")\n            if \"TARGET\" in df.columns:\n                print(df[\"TARGET\"].value_counts())\n    except Exception as e:\n        print(f\"Upload atlandı: {e}\")\nelse:\n    print(f\"Veri zaten yüklü: {df.shape if df is not None else None}\")\n\nprint(\"✅ AŞAMA 6 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage7-md",
   "metadata": {
    "id": "stage7-md"
   },
   "source": "## AŞAMA 7: EDA (Exploratory Data Analysis)\n"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "stage7-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "stage7-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788509881312,
     "user_tz": -180,
     "elapsed": 54811,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e0d03bd3-603c-4e85-e809-09606c7fa272"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== Genel Bilgi ===\n",
      "Şekil: (307511, 122)\n",
      "Sütun sayısı: 122\n",
      "Eksik değer oranı (ortalama): 24.40%\n",
      "\n",
      "Sınıf dağılımı (TARGET):\n",
      "TARGET\n",
      "0    0.919271\n",
      "1    0.080729\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Mevcut ana sayısal özellikler (14): ['AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'REGION_POPULATION_RELATIVE', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "                               count           mean            std  \\\n",
       "AMT_INCOME_TOTAL            307511.0  168797.919297  237123.146279   \n",
       "AMT_CREDIT                  307511.0  599025.999706  402490.776996   \n",
       "AMT_ANNUITY                 307499.0   27108.573909   14493.737315   \n",
       "AMT_GOODS_PRICE             307233.0  538396.207429  369446.460540   \n",
       "DAYS_BIRTH                  307511.0  -16036.995067    4363.988632   \n",
       "DAYS_EMPLOYED               307511.0   63815.045904  141275.766519   \n",
       "DAYS_REGISTRATION           307511.0   -4986.120328    3522.886321   \n",
       "DAYS_ID_PUBLISH             307511.0   -2994.202373    1509.450419   \n",
       "CNT_CHILDREN                307511.0       0.417052       0.722121   \n",
       "CNT_FAM_MEMBERS             307509.0       2.152665       0.910682   \n",
       "REGION_POPULATION_RELATIVE  307511.0       0.020868       0.013831   \n",
       "EXT_SOURCE_1                134133.0       0.502130       0.211062   \n",
       "EXT_SOURCE_2                306851.0       0.514393       0.191060   \n",
       "EXT_SOURCE_3                246546.0       0.510853       0.194844   \n",
       "TARGET                      307511.0       0.080729       0.272419   \n",
       "\n",
       "                                     min            25%            50%  \\\n",
       "AMT_INCOME_TOTAL            2.565000e+04  112500.000000  147150.000000   \n",
       "AMT_CREDIT                  4.500000e+04  270000.000000  513531.000000   \n",
       "AMT_ANNUITY                 1.615500e+03   16524.000000   24903.000000   \n",
       "AMT_GOODS_PRICE             4.050000e+04  238500.000000  450000.000000   \n",
       "DAYS_BIRTH                 -2.522900e+04  -19682.000000  -15750.000000   \n",
       "DAYS_EMPLOYED              -1.791200e+04   -2760.000000   -1213.000000   \n",
       "DAYS_REGISTRATION          -2.467200e+04   -7479.500000   -4504.000000   \n",
       "DAYS_ID_PUBLISH            -7.197000e+03   -4299.000000   -3254.000000   \n",
       "CNT_CHILDREN                0.000000e+00       0.000000       0.000000   \n",
       "CNT_FAM_MEMBERS             1.000000e+00       2.000000       2.000000   \n",
       "REGION_POPULATION_RELATIVE  2.900000e-04       0.010006       0.018850   \n",
       "EXT_SOURCE_1                1.456813e-02       0.334007       0.505998   \n",
       "EXT_SOURCE_2                8.173617e-08       0.392457       0.565961   \n",
       "EXT_SOURCE_3                5.272652e-04       0.370650       0.535276   \n",
       "TARGET                      0.000000e+00       0.000000       0.000000   \n",
       "\n",
       "                                      75%           max  \n",
       "AMT_INCOME_TOTAL            202500.000000  1.170000e+08  \n",
       "AMT_CREDIT                  808650.000000  4.050000e+06  \n",
       "AMT_ANNUITY                  34596.000000  2.580255e+05  \n",
       "AMT_GOODS_PRICE             679500.000000  4.050000e+06  \n",
       "DAYS_BIRTH                  -12413.000000 -7.489000e+03  \n",
       "DAYS_EMPLOYED                 -289.000000  3.652430e+05  \n",
       "DAYS_REGISTRATION            -2010.000000  0.000000e+00  \n",
       "DAYS_ID_PUBLISH              -1720.000000  0.000000e+00  \n",
       "CNT_CHILDREN                     1.000000  1.900000e+01  \n",
       "CNT_FAM_MEMBERS                  3.000000  2.000000e+01  \n",
       "REGION_POPULATION_RELATIVE       0.028663  7.250800e-02  \n",
       "EXT_SOURCE_1                     0.675053  9.626928e-01  \n",
       "EXT_SOURCE_2                     0.663617  8.549997e-01  \n",
       "EXT_SOURCE_3                     0.669057  8.960095e-01  \n",
       "TARGET                           0.000000  1.000000e+00  "
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-06014530-fe7d-4c3d-8d1b-12349b8a5373\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>168797.919297</td>\n",
       "      <td>237123.146279</td>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>112500.000000</td>\n",
       "      <td>147150.000000</td>\n",
       "      <td>202500.000000</td>\n",
       "      <td>1.170000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>599025.999706</td>\n",
       "      <td>402490.776996</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>270000.000000</td>\n",
       "      <td>513531.000000</td>\n",
       "      <td>808650.000000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <td>307499.0</td>\n",
       "      <td>27108.573909</td>\n",
       "      <td>14493.737315</td>\n",
       "      <td>1.615500e+03</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>2.580255e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <td>307233.0</td>\n",
       "      <td>538396.207429</td>\n",
       "      <td>369446.460540</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>238500.000000</td>\n",
       "      <td>450000.000000</td>\n",
       "      <td>679500.000000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>-16036.995067</td>\n",
       "      <td>4363.988632</td>\n",
       "      <td>-2.522900e+04</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-7.489000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>63815.045904</td>\n",
       "      <td>141275.766519</td>\n",
       "      <td>-1.791200e+04</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>3.652430e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>-4986.120328</td>\n",
       "      <td>3522.886321</td>\n",
       "      <td>-2.467200e+04</td>\n",
       "      <td>-7479.500000</td>\n",
       "      <td>-4504.000000</td>\n",
       "      <td>-2010.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>-2994.202373</td>\n",
       "      <td>1509.450419</td>\n",
       "      <td>-7.197000e+03</td>\n",
       "      <td>-4299.000000</td>\n",
       "      <td>-3254.000000</td>\n",
       "      <td>-1720.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>0.417052</td>\n",
       "      <td>0.722121</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <td>307509.0</td>\n",
       "      <td>2.152665</td>\n",
       "      <td>0.910682</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>2.900000e-04</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>7.250800e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>134133.0</td>\n",
       "      <td>0.502130</td>\n",
       "      <td>0.211062</td>\n",
       "      <td>1.456813e-02</td>\n",
       "      <td>0.334007</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>0.675053</td>\n",
       "      <td>9.626928e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>306851.0</td>\n",
       "      <td>0.514393</td>\n",
       "      <td>0.191060</td>\n",
       "      <td>8.173617e-08</td>\n",
       "      <td>0.392457</td>\n",
       "      <td>0.565961</td>\n",
       "      <td>0.663617</td>\n",
       "      <td>8.549997e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>246546.0</td>\n",
       "      <td>0.510853</td>\n",
       "      <td>0.194844</td>\n",
       "      <td>5.272652e-04</td>\n",
       "      <td>0.370650</td>\n",
       "      <td>0.535276</td>\n",
       "      <td>0.669057</td>\n",
       "      <td>8.960095e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>307511.0</td>\n",
       "      <td>0.080729</td>\n",
       "      <td>0.272419</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "        document.querySelector('#df-06014530-fe7d-4c3d-8d1b-12349b8a5373 button.colab-df-convert');\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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     "data": {
      "text/plain": [
       "<Figure size 1500x900 with 6 Axes>"
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ AŞAMA 7 tamamlandı.\n"
     ]
    }
   ],
   "source": "if df is None:\n    raise ValueError(\"Veri yok. AŞAMA 5/6 çalıştırın.\")\n\nprint(\"=== Genel Bilgi ===\")\nprint(f\"Şekil: {df.shape}\")\nprint(f\"Sütun sayısı: {df.shape[1]}\")\nprint(f\"Eksik değer oranı (ortalama): {df.isnull().mean().mean():.2%}\")\n\nif \"TARGET\" in df.columns:\n    print(\"\\nSınıf dağılımı (TARGET):\")\n    print(df[\"TARGET\"].value_counts(normalize=True))\n\n# Sayısal özelliklerden bir örnek set\nnum_candidates = [\n    \"AMT_INCOME_TOTAL\", \"AMT_CREDIT\", \"AMT_ANNUITY\", \"AMT_GOODS_PRICE\",\n    \"DAYS_BIRTH\", \"DAYS_EMPLOYED\", \"DAYS_REGISTRATION\", \"DAYS_ID_PUBLISH\",\n    \"CNT_CHILDREN\", \"CNT_FAM_MEMBERS\", \"REGION_POPULATION_RELATIVE\",\n    \"EXT_SOURCE_1\", \"EXT_SOURCE_2\", \"EXT_SOURCE_3\",\n]\nnum_exist = [c for c in num_candidates if c in df.columns]\nprint(f\"\\nMevcut ana sayısal özellikler ({len(num_exist)}): {num_exist}\")\n\nif num_exist and \"TARGET\" in df.columns:\n    display(df[num_exist + [\"TARGET\"]].describe().T)\n\n    fig, axes = plt.subplots(2, 3, figsize=(15, 9))\n    axes = axes.ravel()\n    for i, col in enumerate(num_exist[:6]):\n        sns.histplot(df[col].dropna(), kde=True, ax=axes[i], color=\"#3498db\")\n        axes[i].set_title(col)\n    for j in range(len(num_exist[:6]), 6):\n        axes[j].axis(\"off\")\n    plt.tight_layout()\n    plt.show()\n\n    # Korelasyon (TARGET ile)\n    corr_cols = num_exist + [\"TARGET\"]\n    corr = df[corr_cols].corr()[\"TARGET\"].drop(\"TARGET\").sort_values()\n    plt.figure(figsize=(8, 5))\n    corr.plot(kind=\"barh\", color=\"#e74c3c\")\n    plt.title(\"TARGET ile Korelasyon (seçili özellikler)\")\n    plt.tight_layout()\n    plt.show()\n\nprint(\"✅ AŞAMA 7 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage8-md",
   "metadata": {
    "id": "stage8-md"
   },
   "source": "## AŞAMA 8: Anonimleştirme (Privacy)\n"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "stage8-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage8-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788509881741,
     "user_tz": -180,
     "elapsed": 436,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8ff53a9c-7de9-43aa-cd4a-75a4837b129d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Örnek maskeli ID:\n",
      "   SK_ID_CURR SK_ID_CURR_masked\n",
      "0      100002      d0307bbec9e4\n",
      "1      100003      0095c8f449a7\n",
      "2      100004      ba8846ba88d7\n",
      "✅ AŞAMA 8 tamamlandı.\n"
     ]
    }
   ],
   "source": "def mask_id(val, salt=\"homecredit_highq_salt\"):\n    return sha256(f\"{salt}{val}\".encode()).hexdigest()[:12]\n\ndf_anon = df.copy()\nif \"SK_ID_CURR\" in df_anon.columns:\n    df_anon[\"SK_ID_CURR_masked\"] = df_anon[\"SK_ID_CURR\"].astype(str).apply(mask_id)\n    print(\"Örnek maskeli ID:\")\n    print(df_anon[[\"SK_ID_CURR\", \"SK_ID_CURR_masked\"]].head(3))\nelse:\n    print(\"SK_ID_CURR yok — anonimleştirme atlandı.\")\n\nprint(\"✅ AŞAMA 8 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage9-md",
   "metadata": {
    "id": "stage9-md"
   },
   "source": "## AŞAMA 9: Ön İşleme + Feature Engineering (Temel)\n\nAna tablo üzerinden:\n- Eksik değer imputasyonu\n- Basit türetilmiş özellikler (oranlar, yaş, istihdam süresi)\n- Label encoding (kategorikler için sınırlı)\n- Train/Test split (stratified)\n\nYüksek boyut genişletme **AŞAMA 12**’de yapılacak (poly + tile/PCA → `TARGET_QUBITS`).\n"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "stage9-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage9-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788509882714,
     "user_tz": -180,
     "elapsed": 972,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "6290b2b8-7165-482e-ce6d-bfe4d8cfd7cf"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Kullanılan özellik sayısı: 21\n",
      "['AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'REGION_POPULATION_RELATIVE', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'HOUR_APPR_PROCESS_START', 'OWN_CAR_AGE', 'CREDIT_INCOME_RATIO', 'ANNUITY_INCOME_RATIO', 'CREDIT_GOODS_RATIO', 'AGE_YEARS', 'EMPLOYED_YEARS']\n",
      "Train: (230633, 21) | Test: (76878, 21)\n",
      "Train default: 8.07% | Test: 8.07%\n",
      "✅ AŞAMA 9 (temel) tamamlandı. Yüksek boyut genişletme AŞAMA 12'de yapılacak.\n"
     ]
    }
   ],
   "source": "TARGET = \"TARGET\"\nif TARGET not in df.columns:\n    raise ValueError(\"TARGET sütunu bulunamadı. application_train.csv kullandığınızdan emin olun.\")\n\n# --- Temel sayısal özellik seti ---\nBASE_NUM = [\n    \"AMT_INCOME_TOTAL\", \"AMT_CREDIT\", \"AMT_ANNUITY\", \"AMT_GOODS_PRICE\",\n    \"DAYS_BIRTH\", \"DAYS_EMPLOYED\", \"DAYS_REGISTRATION\", \"DAYS_ID_PUBLISH\",\n    \"CNT_CHILDREN\", \"CNT_FAM_MEMBERS\", \"REGION_POPULATION_RELATIVE\",\n    \"EXT_SOURCE_1\", \"EXT_SOURCE_2\", \"EXT_SOURCE_3\",\n    \"HOUR_APPR_PROCESS_START\", \"OWN_CAR_AGE\",\n]\nBASE_NUM = [c for c in BASE_NUM if c in df.columns]\n\nwork = df[BASE_NUM + [TARGET]].copy()\n\n# Türetilmiş özellikler\nif \"AMT_CREDIT\" in work.columns and \"AMT_INCOME_TOTAL\" in work.columns:\n    work[\"CREDIT_INCOME_RATIO\"] = work[\"AMT_CREDIT\"] / (work[\"AMT_INCOME_TOTAL\"] + 1)\nif \"AMT_ANNUITY\" in work.columns and \"AMT_INCOME_TOTAL\" in work.columns:\n    work[\"ANNUITY_INCOME_RATIO\"] = work[\"AMT_ANNUITY\"] / (work[\"AMT_INCOME_TOTAL\"] + 1)\nif \"AMT_CREDIT\" in work.columns and \"AMT_GOODS_PRICE\" in work.columns:\n    work[\"CREDIT_GOODS_RATIO\"] = work[\"AMT_CREDIT\"] / (work[\"AMT_GOODS_PRICE\"] + 1)\nif \"DAYS_BIRTH\" in work.columns:\n    work[\"AGE_YEARS\"] = (-work[\"DAYS_BIRTH\"] / 365.25).clip(0, 100)\nif \"DAYS_EMPLOYED\" in work.columns:\n    # Anomalileri (pozitif değerler) NaN yap\n    work.loc[work[\"DAYS_EMPLOYED\"] > 0, \"DAYS_EMPLOYED\"] = np.nan\n    work[\"EMPLOYED_YEARS\"] = (-work[\"DAYS_EMPLOYED\"] / 365.25).clip(0, 60)\n\nFEATURE_COLS = [c for c in work.columns if c != TARGET]\nprint(f\"Kullanılan özellik sayısı: {len(FEATURE_COLS)}\")\nprint(FEATURE_COLS)\n\nX = work[FEATURE_COLS]\ny = work[TARGET].astype(int)\n\n# Impute + scale (klasik modeller için)\nimputer = SimpleImputer(strategy=\"median\")\nX_imp = imputer.fit_transform(X)\n\nX_train_base, X_test_base, y_train, y_test = train_test_split(\n    X_imp, y, test_size=0.25, random_state=42, stratify=y\n)\n\nscaler_cls = StandardScaler()\nX_train_sc = scaler_cls.fit_transform(X_train_base)\nX_test_sc = scaler_cls.transform(X_test_base)\n\nprint(f\"Train: {X_train_base.shape} | Test: {X_test_base.shape}\")\nprint(f\"Train default: {y_train.mean():.2%} | Test: {y_test.mean():.2%}\")\nprint(\"✅ AŞAMA 9 (temel) tamamlandı. Yüksek boyut genişletme AŞAMA 12'de yapılacak.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage10-md",
   "metadata": {
    "id": "stage10-md"
   },
   "source": "## AŞAMA 10: Klasik Baseline\n"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "stage10-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage10-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788510028358,
     "user_tz": -180,
     "elapsed": 145652,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "11cf58b1-772a-4290-da97-dd5bc8b08212"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Class balance → neg=212014, pos=18619, scale_pos_weight≈11.4\n",
      "Logistic Regression    | AUC=0.7329  F1=0.2501  Recall=0.6608\n",
      "Random Forest          | AUC=0.7388  F1=0.2640  Recall=0.5994\n",
      "SVM (RBF)              | AUC=0.6983  F1=0.2390  Recall=0.5537\n",
      "XGBoost                | AUC=0.7506  F1=0.2664  Recall=0.6666\n",
      "\n",
      "✅ AŞAMA 10 tamamlandı.\n"
     ]
    }
   ],
   "source": "neg, pos = (y_train == 0).sum(), (y_train == 1).sum()\nscale_pos = neg / max(pos, 1)\nprint(f\"Class balance → neg={neg}, pos={pos}, scale_pos_weight≈{scale_pos:.1f}\")\n\nmodels = {\n    \"Logistic Regression\": LogisticRegression(\n        max_iter=1000, class_weight=\"balanced\", random_state=42\n    ),\n    \"Random Forest\": RandomForestClassifier(\n        n_estimators=200, max_depth=10, class_weight=\"balanced\", random_state=42, n_jobs=-1\n    ),\n    \"SVM (RBF)\": SVC(\n        kernel=\"rbf\", probability=True, class_weight=\"balanced\", random_state=42\n    ),\n    \"XGBoost\": XGBClassifier(\n        n_estimators=200, max_depth=5, learning_rate=0.08,\n        scale_pos_weight=scale_pos, eval_metric=\"logloss\",\n        random_state=42, verbosity=0, n_jobs=-1\n    ),\n}\n\n# SVM büyük veri setinde yavaş olabilir → küçük alt örnek ile eğit\nresults = {}\nfor name, model in models.items():\n    if name == \"SVM (RBF)\" and len(y_train) > 8000:\n        rng = np.random.RandomState(42)\n        idx = rng.choice(len(y_train), 8000, replace=False)\n        model.fit(X_train_sc[idx], y_train.iloc[idx] if hasattr(y_train, \"iloc\") else y_train[idx])\n    else:\n        model.fit(X_train_sc, y_train)\n\n    y_pred = model.predict(X_test_sc)\n    y_prob = model.predict_proba(X_test_sc)[:, 1] if hasattr(model, \"predict_proba\") else y_pred.astype(float)\n    results[name] = {\n        \"model\": model,\n        \"accuracy\": accuracy_score(y_test, y_pred),\n        \"precision\": precision_score(y_test, y_pred, zero_division=0),\n        \"recall\": recall_score(y_test, y_pred, zero_division=0),\n        \"f1\": f1_score(y_test, y_pred, zero_division=0),\n        \"roc_auc\": roc_auc_score(y_test, y_prob),\n        \"y_pred\": y_pred,\n        \"y_prob\": y_prob,\n    }\n    print(f\"{name:22s} | AUC={results[name]['roc_auc']:.4f}  F1={results[name]['f1']:.4f}  Recall={results[name]['recall']:.4f}\")\n\nprint(\"\\n✅ AŞAMA 10 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage11-md",
   "metadata": {
    "id": "stage11-md"
   },
   "source": "## AŞAMA 11: QML Algoritma Seçimi\n\n- **QSVC**: Kernel tabanlı; yüksek qubit’te O(n²) maliyeti çok artar → orta subset + orta qubit.\n- **VQC**: Parametrik devre; daha fazla qubit ile daha zengin ansatz denenebilir (**önerilen yüksek-qubit yolu**).\n"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "stage11-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage11-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788510028445,
     "user_tz": -180,
     "elapsed": 78,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "7b7c2d41-9424-4c7a-b056-21aa6e3fc159"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "SELECTED_PROBLEM  = Machine Learning\n",
      "SELECTED_HARDWARE = Superconducting Qubits\n",
      "SELECTED_ALGO     = VQC\n",
      "SELECTED_DEVICE   = ibm_or_aer\n",
      "\n",
      "💡 İpucu: TARGET_QUBITS ≥ 12 ise VQC tercih edin (AŞAMA 2'den değiştirilebilir).\n",
      "✅ AŞAMA 11 tamamlandı.\n"
     ]
    }
   ],
   "source": "print(f\"SELECTED_PROBLEM  = {SELECTED_PROBLEM}\")\nprint(f\"SELECTED_HARDWARE = {SELECTED_HARDWARE}\")\nprint(f\"SELECTED_ALGO     = {SELECTED_ALGO}\")\nprint(f\"SELECTED_DEVICE   = {SELECTED_DEVICE}\")\n\nif SELECTED_ALGO not in (\"QSVC\", \"VQC\"):\n    print(\"⚠️ Detaylı hat QSVC/VQC için. VQC ile devam.\")\n    SELECTED_ALGO = \"VQC\"\n\nprint(\"\\n💡 İpucu: TARGET_QUBITS ≥ 12 ise VQC tercih edin (AŞAMA 2'den değiştirilebilir).\")\nprint(\"✅ AŞAMA 11 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage12-md",
   "metadata": {
    "id": "stage12-md"
   },
   "source": "## AŞAMA 12: Max-Qubit Encoding & Kaynak Planlama\n\n### Kritik parametre: `TARGET_QUBITS`\n\n| Ortam | Önerilen TARGET_QUBITS | Not |\n|-------|------------------------|-----|\n| **Colab / laptop (Statevector)** | **6–10** | 2^n bellek; 12 üstü patlar |\n| Aer shot-based | 12–16 (max ~20) | Yaklaşık, yavaş |\n| Gerçek IBM QPU | 16–50+ (deneysel) | `use_real_hardware=True` şart |\n| 50–100 qubit klasik simülasyon | **İMKÂNSIZ** | 2^50 ≈ 16 PiB RAM |\n\n### Neden 50 qubit Colab’da çöküyor?\n\n`StatevectorSampler` tüm 2^n genliği belleğe alır.  \nn=50 → 2^50 ≈ 1.13×10^15 complex128 ≈ **16 PiB** → `MemoryError`.\n\nBu hücre otomatik olarak:\n1. `TARGET_QUBITS > 20` ise 20’ye düşürür (klasik simülasyon tavanı)\n2. `> 12` ise Statevector yerine **AerSampler (shots)** seçer\n3. Gerçek 50–100+ qubit için AŞAMA 13’te IBM backend ister\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "stage12-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage12-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788510029754,
     "user_tz": -180,
     "elapsed": 1305,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e12d27c2-a9c5-4216-b6c6-369426f1c05d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "============================================================\n",
      "KAYNAK PLANI — GERÇEK QPU ÖNCELİKLİ\n",
      "============================================================\n",
      "İstenen TARGET_QUBITS     = 8\n",
      "MAX_TRAIN_SAMPLES_Q       = 24  (yüksek qubit → küçük subset)\n",
      "MAX_TEST_SAMPLES_Q        = 12\n",
      "\n",
      "Not: Gerçek backend bağlanınca (AŞAMA 13) n_qubits backend kapasitesine göre\n",
      "     min(TARGET_QUBITS, backend.num_qubits) olarak kilitlenecek.\n",
      "Şimdilik planlanan n_qubits = 8\n",
      "USE_STATEVECTOR (yalnızca Aer için) = True\n",
      "Polynomial sonrası boyut: 252\n",
      "Feature resize (PCA): → 8\n",
      "Planlanan n_qubits = 8\n",
      "\n",
      "Quantum train subset: (24, 8) | default: 50.00%\n",
      "Quantum test  subset: (12, 8) | default: 33.33%\n",
      "\n",
      "💡 AŞAMA 13'da gerçek backend seçilince n_qubits backend'e göre güncellenebilir.\n",
      "✅ AŞAMA 12 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 12: MAX-QUBIT ENCODING (GERÇEK IBM QPU ODAKLI)\n# ============================================================\n#\n# Öğrenci demosu stratejisi:\n#   - Gerçek IBM backend varsa → backend.num_qubits kadar (veya TARGET) kullan\n#   - Yoksa → güvenli simülasyon tavanı (≤12 statevector / ≤20 shots)\n#   - Yüksek qubit = daha zengin Hilbert uzayı; örnek sayısı yine küçük tutulur\n#     (QPU kuyruk + gürültü + süre). Bu \"max qubit gücü\"nü göstermek için doğru yoldur.\n#\n\n# ========== KULLANICI AYARI ==========\nTARGET_QUBITS = 8          # Free/Aer: 6–10 | QPU denemesi: 8–12          # İstek: mümkün olan en yüksek. Backend daha azsa otomatik düşer.\nUSE_POLY_FEATURES = True\nPOLY_DEGREE = 2\nMAX_TRAIN_SAMPLES_Q = 24   # QPU kotası için küçük tutun    # Yüksek qubit demosunda KÜÇÜK tutun (QPU süresi)\nMAX_TEST_SAMPLES_Q = 12\n# =====================================\n\n# Simülasyon güvenlik tavanları (sadece Aer yolu için)\nSTATEVECTOR_SAFE_MAX = 12\nSHOT_BASED_SAFE_MAX = 20\n\nprint(\"=\" * 60)\nprint(\"KAYNAK PLANI — GERÇEK QPU ÖNCELİKLİ\")\nprint(\"=\" * 60)\nprint(f\"İstenen TARGET_QUBITS     = {TARGET_QUBITS}\")\nprint(f\"MAX_TRAIN_SAMPLES_Q       = {MAX_TRAIN_SAMPLES_Q}  (yüksek qubit → küçük subset)\")\nprint(f\"MAX_TEST_SAMPLES_Q        = {MAX_TEST_SAMPLES_Q}\")\n\nUSE_STATEVECTOR = TARGET_QUBITS <= STATEVECTOR_SAFE_MAX\nprint(f\"\\nNot: Gerçek backend bağlanınca (AŞAMA 13) n_qubits backend kapasitesine göre\")\nprint(\"     min(TARGET_QUBITS, backend.num_qubits) olarak kilitlenecek.\")\nprint(f\"Şimdilik planlanan n_qubits = {TARGET_QUBITS}\")\nprint(f\"USE_STATEVECTOR (yalnızca Aer için) = {USE_STATEVECTOR}\")\n\n# --- Feature expansion ---\nX_tr = X_train_base.copy()\nX_te = X_test_base.copy()\n\nif USE_POLY_FEATURES:\n    poly = PolynomialFeatures(degree=POLY_DEGREE, include_bias=False, interaction_only=False)\n    X_tr = poly.fit_transform(X_tr)\n    X_te = poly.transform(X_te)\n    print(f\"Polynomial sonrası boyut: {X_tr.shape[1]}\")\n\ndef _resize_features(Xtr, Xte, n_target):\n    n_feat = Xtr.shape[1]\n    if n_feat > n_target:\n        pca = PCA(n_components=n_target, random_state=42)\n        return pca.fit_transform(Xtr), pca.transform(Xte), \"PCA\"\n    if n_feat < n_target:\n        reps = int(np.ceil(n_target / n_feat))\n        return (\n            np.tile(Xtr, (1, reps))[:, :n_target],\n            np.tile(Xte, (1, reps))[:, :n_target],\n            \"tile\",\n        )\n    return Xtr, Xte, \"exact\"\n\nX_tr, X_te, how = _resize_features(X_tr, X_te, TARGET_QUBITS)\nprint(f\"Feature resize ({how}): → {TARGET_QUBITS}\")\n\nn_qubits = TARGET_QUBITS\nprint(f\"Planlanan n_qubits = {n_qubits}\")\n\nscaler_q = MinMaxScaler(feature_range=(0, np.pi))\nX_train_q = scaler_q.fit_transform(X_tr)\nX_test_q = scaler_q.transform(X_te)\n\n# Dengeli küçük subset — QPU demosu için şart (stratified)\nnp.random.seed(42)\ny_train_arr = np.asarray(y_train)\nidx_pos = np.where(y_train_arr == 1)[0]\nidx_neg = np.where(y_train_arr == 0)[0]\nn_pos = min(MAX_TRAIN_SAMPLES_Q // 2, len(idx_pos))\nn_neg = min(MAX_TRAIN_SAMPLES_Q - n_pos, len(idx_neg))\nsel = np.concatenate([\n    np.random.choice(idx_pos, n_pos, replace=False),\n    np.random.choice(idx_neg, n_neg, replace=False),\n])\nnp.random.shuffle(sel)\nX_train_q_sub = X_train_q[sel]\ny_train_q = y_train_arr[sel]\n\ny_test_arr = np.asarray(y_test)\nidx_t_pos = np.where(y_test_arr == 1)[0]\nidx_t_neg = np.where(y_test_arr == 0)[0]\nn_t_pos = min(max(MAX_TEST_SAMPLES_Q // 3, 1), len(idx_t_pos))\nn_t_neg = min(MAX_TEST_SAMPLES_Q - n_t_pos, len(idx_t_neg))\nsel_t = np.concatenate([\n    np.random.choice(idx_t_pos, n_t_pos, replace=False),\n    np.random.choice(idx_t_neg, n_t_neg, replace=False),\n])\nnp.random.shuffle(sel_t)\nX_test_q_sub = X_test_q[sel_t]\ny_test_q = y_test_arr[sel_t]\n\nprint(f\"\\nQuantum train subset: {X_train_q_sub.shape} | default: {y_train_q.mean():.2%}\")\nprint(f\"Quantum test  subset: {X_test_q_sub.shape} | default: {y_test_q.mean():.2%}\")\nprint(\"\\n💡 AŞAMA 13'da gerçek backend seçilince n_qubits backend'e göre güncellenebilir.\")\nprint(\"✅ AŞAMA 12 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage16-md",
   "metadata": {
    "id": "stage16-md"
   },
   "source": "## AŞAMA 13: IBM Backend Seçimi (Hızlı / Free-Plan Dostu)\n\nFree IBM süreniz (~10 dk) **kuyruk + eğitim** ile biter. Bu hücre kasıtlı olarak hızlıdır:\n\n| Mod | `use_real_hardware` | Ne olur |\n|-----|---------------------|---------|\n| **Geliştirme (önerilen)** | `False` | Anında Aer simülatör — süre harcanmaz |\n| **Gerçek QPU** | `True` | Tek hızlı seçim; uzun backend listesi / status yok |\n\n> İpucu: Önce Aer ile AŞAMA 14–15’i doğrulayın. QPU’yu yalnızca kısa bir final koşuda açın (`TARGET_QUBITS` ve `maxiter` düşük tutun).\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "stage16-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage16-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513321569,
     "user_tz": -180,
     "elapsed": 47,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "5a5fb73f-ea51-4c7c-9436-cff6b41ed54d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Aer simülatör yolu (use_real_hardware=False).\n",
      "   IBM süresi harcanmaz. Gerçek QPU için: use_real_hardware=True\n",
      "\n",
      "Aktif backend : aer_simulator\n",
      "REAL_QPU      : False\n",
      "n_qubits      : 8\n",
      "Süre          : 0.0s\n",
      "✅ AŞAMA 13 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 13: Backend seçimi — HIZLI / Free-plan dostu\n# ============================================================\n#\n# Free 10 dk süresi: backend tarama + kuyruk + VQC fit birlikte biter.\n# Bu yüzden varsayılan Aer'dir. Gerçek QPU için bayrağı True yapın.\n#\n\n# ========== KULLANICI AYARI ==========\nuse_real_hardware = False   # True = IBM QPU (süre harcar) | False = Aer (anında)\nFORCE_BACKEND_NAME = None   # örn. \"ibm_brisbane\" — biliyorsanız yazın, arama atlanır\nFORCE_INSTANCE = None       # instance CRN / adı (opsiyonel)\nMIN_QUBITS_PREFERRED = 7    # free/open için düşük tutun (127 aramak kuyruk + ret riski)\nCONNECT_TIMEOUT_SEC = 45    # servis/backend için üst süre (takılmayı keser)\n# =====================================\n\nimport time\n\nbackend_name = \"aer_simulator\"\nsampler = None\nservice = None\nbackend = None\nREAL_QPU = False\n\nSHOT_BASED_SAFE_MAX = globals().get(\"SHOT_BASED_SAFE_MAX\", 20)\nSTATEVECTOR_SAFE_MAX = globals().get(\"STATEVECTOR_SAFE_MAX\", 12)\n\ndef _fit_to_nq(X, n_target):\n    if X is None:\n        return X\n    if X.shape[1] == n_target:\n        return X\n    if X.shape[1] > n_target:\n        return X[:, :n_target]\n    reps = int(np.ceil(n_target / max(X.shape[1], 1)))\n    return np.tile(X, (1, reps))[:, :n_target]\n\n\ndef _apply_n_qubits(final_nq):\n    \"\"\"n_qubits ve quantum feature matrislerini güncelle.\"\"\"\n    global n_qubits, X_train_q, X_test_q, X_train_q_sub, X_test_q_sub\n    if final_nq == n_qubits:\n        print(f\"   n_qubits = {n_qubits} (değişmedi)\")\n        return\n    print(f\"🔧 n_qubits: {n_qubits} → {final_nq}\")\n    if \"X_train_q\" in globals():\n        X_train_q = _fit_to_nq(X_train_q, final_nq)\n    if \"X_test_q\" in globals():\n        X_test_q = _fit_to_nq(X_test_q, final_nq)\n    if \"X_train_q_sub\" in globals():\n        X_train_q_sub = _fit_to_nq(X_train_q_sub, final_nq)\n    if \"X_test_q_sub\" in globals():\n        X_test_q_sub = _fit_to_nq(X_test_q_sub, final_nq)\n    n_qubits = final_nq\n    print(f\"   train subset shape = {globals().get('X_train_q_sub', np.array([])).shape}\")\n\n\ndef _pick_backend_fast(service, min_q, preferred_name=None):\n    \"\"\"Uzun status taraması YOK. İsim veya tek least_busy denemesi.\"\"\"\n    # 1) Kullanıcı backend adı verdiyse doğrudan al\n    if preferred_name:\n        try:\n            b = service.backend(preferred_name)\n            return b, f\"forced:{preferred_name}\"\n        except Exception as e:\n            print(f\"   FORCE_BACKEND_NAME başarısız ({preferred_name}): {e}\")\n\n    # 2) backends() listesi — status() ÇAĞIRMA (yavaş + kota)\n    try:\n        cands = service.backends(simulator=False, operational=True)\n    except Exception:\n        try:\n            cands = service.backends(simulator=False)\n        except Exception as e:\n            print(f\"   backends() hata: {e}\")\n            cands = []\n\n    if cands:\n        def nq(b):\n            return int(getattr(b, \"num_qubits\", 0) or 0)\n\n        # min_q karşılayan en küçük kuyruk adayı yerine: yeterli qubit + isim sırası\n        ok = [b for b in cands if nq(b) >= min_q] or list(cands)\n        # Büyük sistemler free planda genelde kuyrukta — orta boy tercih\n        ok = sorted(ok, key=lambda b: (abs(nq(b) - max(min_q, 7)), nq(b)))\n        # İlk 5 isim göster (status yok → hızlı)\n        print(\"   Adaylar (status sorgulanmadı):\")\n        for b in ok[:5]:\n            print(f\"      · {b.name:28s} qubits={nq(b)}\")\n        return ok[0], \"backends_fast\"\n\n    # 3) Son çare least_busy (tek deneme)\n    try:\n        b = service.least_busy(operational=True, simulator=False, min_qubits=min(min_q, 5))\n        if b is not None:\n            return b, \"least_busy_once\"\n    except Exception as e:\n        print(f\"   least_busy: {e}\")\n    return None, \"yok\"\n\n\nt0 = time.time()\n\nif use_real_hardware and IBM_TOKEN:\n    try:\n        from qiskit_ibm_runtime import QiskitRuntimeService\n        from qiskit_ibm_runtime import SamplerV2 as Sampler\n\n        channel = IBM_CHANNEL if IBM_CHANNEL in (\"ibm_quantum_platform\", \"ibm_cloud\") else \"ibm_quantum_platform\"\n        instance = FORCE_INSTANCE or IBM_INSTANCE\n        svc_kwargs = {\"channel\": channel, \"token\": IBM_TOKEN}\n        if instance:\n            svc_kwargs[\"instance\"] = instance\n        else:\n            svc_kwargs[\"instance\"] = \"auto\"\n\n        print(f\"IBM bağlanıyor (channel={channel}, timeout≈{CONNECT_TIMEOUT_SEC}s)...\")\n        service = QiskitRuntimeService(**svc_kwargs)\n        print(f\"   Servis OK ({time.time() - t0:.1f}s)\")\n\n        backend, how = _pick_backend_fast(service, MIN_QUBITS_PREFERRED, FORCE_BACKEND_NAME)\n        if backend is None:\n            raise RuntimeError(\"Uygun backend yok — Aer'e düşülüyor.\")\n\n        backend_name = backend.name\n        backend_nq = int(getattr(backend, \"num_qubits\", 0) or 0)\n        sampler = Sampler(mode=backend)\n        REAL_QPU = True\n\n        print(f\"✅ QPU: {backend_name} | qubits={backend_nq} | seçim={how} | {time.time() - t0:.1f}s\")\n        print(\"⚠️ Free plan: kuyruk + fit süresi 10 dk kotasını yer. maxiter düşük tutun (AŞAMA 15).\")\n\n        planned = int(TARGET_QUBITS) if \"TARGET_QUBITS\" in globals() else n_qubits\n        final_nq = min(planned, backend_nq) if backend_nq > 0 else planned\n        # Free planda çok yüksek qubit fit'i kotayı yakar — soft cap\n        FREE_QPU_SOFT_CAP = 16\n        if final_nq > FREE_QPU_SOFT_CAP:\n            print(f\"   Free-plan soft cap: n_qubits {final_nq} → {FREE_QPU_SOFT_CAP} (isteğe bağlı)\")\n            # Yorum satırı: soft cap'i kapatmak için aşağıdaki satırı silin\n            final_nq = min(final_nq, FREE_QPU_SOFT_CAP)\n        _apply_n_qubits(final_nq)\n\n    except Exception as e:\n        print(f\"⚠️ QPU bağlantısı başarısız ({time.time() - t0:.1f}s): {e}\")\n        print(\"   Aer simülatöre düşülüyor.\")\n        use_real_hardware = False\n        REAL_QPU = False\n        backend_name = \"aer_simulator\"\n        sampler = None\n        backend = None\n        if n_qubits > SHOT_BASED_SAFE_MAX:\n            _apply_n_qubits(SHOT_BASED_SAFE_MAX)\nelse:\n    reason = []\n    if not use_real_hardware:\n        reason.append(\"use_real_hardware=False\")\n    if not IBM_TOKEN:\n        reason.append(\"token yok\")\n    print(f\"Aer simülatör yolu ({', '.join(reason) or 'varsayılan'}).\")\n    print(\"   IBM süresi harcanmaz. Gerçek QPU için: use_real_hardware=True\")\n    REAL_QPU = False\n    backend_name = \"aer_simulator\"\n    sampler = None\n    backend = None\n    if n_qubits > SHOT_BASED_SAFE_MAX:\n        print(f\"   Simülasyon tavanı: n_qubits {n_qubits} → {SHOT_BASED_SAFE_MAX}\")\n        _apply_n_qubits(SHOT_BASED_SAFE_MAX)\n\nprint(f\"\\nAktif backend : {backend_name}\")\nprint(f\"REAL_QPU      : {REAL_QPU}\")\nprint(f\"n_qubits      : {n_qubits}\")\nprint(f\"Süre          : {time.time() - t0:.1f}s\")\nprint(\"✅ AŞAMA 13 tamamlandı.\")\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage13-md",
   "metadata": {
    "id": "stage13-md"
   },
   "source": "## AŞAMA 14: Yüksek Boyutlu Feature Map + Kernel / Ansatz\n\n- **ZZFeatureMap** veya **PauliFeatureMap** (yüksek boyut için)\n- QSVC → FidelityQuantumKernel\n- VQC → EfficientSU2 ansatz (qubit sayısına göre)\n\nBloch küresi sadece `n_qubits ≤ 4` iken çizilir (görselleştirme sınırı).\n\n> `n_qubits` AŞAMA 12 + 13 sonrası nihai değerdir.\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "stage13-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage13-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513328692,
     "user_tz": -180,
     "elapsed": 15,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "227bcda7-2448-46d0-bcbe-1a89f20ca171"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Feature map: ZZFeatureMap (reps=2, linear)\n",
      "n_qubits = 8 | feature_map.num_qubits = 8\n",
      "(Devre çizimi n_qubits>6 için atlandı — çok geniş)\n",
      "✅ EfficientSU2 ansatz hazır (VQC için). parametre sayısı ≈ 32\n",
      "\n",
      "Bloch atlandı (n_qubits=8 > 4). Yüksek boyutta statevector görselleştirmesi pratik değil.\n",
      "✅ AŞAMA 14 tamamlandı.\n"
     ]
    }
   ],
   "source": "from qiskit.circuit.library import ZZFeatureMap, PauliFeatureMap, RealAmplitudes, EfficientSU2\nfrom qiskit_machine_learning.kernels import FidelityQuantumKernel\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.visualization import plot_bloch_multivector\nfrom IPython.display import display\n\n# Feature map seçimi: yüksek qubit'te PauliFeatureMap bazen daha esnek\nif n_qubits <= 8:\n    feature_map = ZZFeatureMap(feature_dimension=n_qubits, reps=2, entanglement=\"linear\")\n    print(\"Feature map: ZZFeatureMap (reps=2, linear)\")\nelse:\n    # Yüksek boyutta daha sığ tutmak için reps=1\n    feature_map = PauliFeatureMap(\n        feature_dimension=n_qubits, reps=1, paulis=[\"Z\", \"ZZ\"], entanglement=\"linear\"\n    )\n    print(\"Feature map: PauliFeatureMap (reps=1, Z+ZZ) — yüksek qubit için sığ tutularak depth kontrolü\")\n\nprint(f\"n_qubits = {n_qubits} | feature_map.num_qubits = {feature_map.num_qubits}\")\n\n# Devre çizimi sadece küçükse\nif n_qubits <= 6:\n    display(feature_map.decompose().draw(output=\"mpl\", style=\"clifford\", fold=80))\nelse:\n    print(\"(Devre çizimi n_qubits>6 için atlandı — çok geniş)\")\n\nquantum_kernel = None\nif SELECTED_ALGO == \"QSVC\":\n    quantum_kernel = FidelityQuantumKernel(feature_map=feature_map)\n    print(\"✅ FidelityQuantumKernel oluşturuldu.\")\n\n# VQC için ansatz (yüksek qubit)\nansatz = None\nif SELECTED_ALGO == \"VQC\" or n_qubits >= 12:\n    ansatz = EfficientSU2(num_qubits=n_qubits, reps=1, entanglement=\"linear\")\n    print(f\"✅ EfficientSU2 ansatz hazır (VQC için). parametre sayısı ≈ {ansatz.num_parameters}\")\n\n# Bloch: sadece çok küçük sistemlerde\nif n_qubits <= 4 and len(X_train_q_sub) > 0:\n    print(\"\\nBloch örneği (n_qubits≤4):\")\n    x_vec = X_train_q_sub[0]\n    qc = feature_map.assign_parameters(x_vec)\n    sv = Statevector.from_instruction(qc)\n    fig = plot_bloch_multivector(sv, title=f\"Örnek state | n_qubits={n_qubits}\")\n    display(fig)\n    plt.close(fig)\nelse:\n    print(f\"\\nBloch atlandı (n_qubits={n_qubits} > 4). Yüksek boyutta statevector görselleştirmesi pratik değil.\")\n\nprint(\"✅ AŞAMA 14 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage14-md",
   "metadata": {
    "id": "stage14-md"
   },
   "source": "## AŞAMA 15: QSVC / VQC Eğitimi (ISA Transpile + QPU/Sim)\n\n### Neden ISA transpile?\nMart 2024 sonrası IBM Runtime, soyut kapılı devreleri (`PauliFeatureMap` vb.) **reddeder**.  \nDevre, seçilen backend’in basis gate’leri ve bağlantı haritasına uydurulmalıdır.\n\nBu hücre `generate_preset_pass_manager` ile feature map + ansatz’ı **ISA** hale getirir, sonra VQC/QSVC’yi eğitir.\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "stage14-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage14-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513340566,
     "user_tz": -180,
     "elapsed": 8054,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "216072d4-e818-4d4a-e827-b927a7699384"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ℹ️ REAL_QPU=False → simülatör yolu (AŞAMA 13 Aer veya token yok).\n",
      "Soyut feature map qubits=8 | ansatz params≈32\n",
      "Simülatör yolu: ISA transpile gerekmez.\n",
      "Sampler: StatevectorSampler (sim, n_qubits=8)\n",
      "VQC | n_qubits=8 | train=24 | REAL_QPU=False\n",
      "   COBYLA maxiter=40\n",
      "   fit başlıyor (gerçek QPU'da her iterasyon kuyruğa girer; sabır)...\n",
      "=== Quantum VQC (sim) | n_qubits=8 | backend=aer_simulator ===\n",
      "  accuracy    : 0.5833\n",
      "  precision   : 0.4286\n",
      "  recall      : 0.7500\n",
      "  f1          : 0.5455\n",
      "  roc_auc     : 0.6250\n",
      "✅ AŞAMA 15 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 15: QSVC / VQC — ISA transpile + gerçek IBM QPU\n# ============================================================\n#\n# 2024+ kuralı: Runtime, backend target'ına uymayan (non-ISA) devreleri reddeder.\n# PauliFeatureMap / ZZFeatureMap soyut kapılar içerir → mutlaka transpile.\n#\n\nfrom qiskit_machine_learning.algorithms import QSVC, VQC\nfrom qiskit_machine_learning.optimizers import COBYLA\nfrom qiskit.circuit.library import EfficientSU2, ZZFeatureMap, PauliFeatureMap\nfrom qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n\n# --- 0) AŞAMA 13 henüz çalışmadıysa güvenli varsayılanlar ---\n# Önerilen sıra: AŞAMA 13 → 13 → 14. Aksi halde simülatör yolu.\nif \"REAL_QPU\" not in globals():\n    REAL_QPU = False\nif \"backend\" not in globals():\n    backend = None\nif \"backend_name\" not in globals():\n    backend_name = \"aer_simulator\"\nif \"sampler\" not in globals():\n    sampler = None\n\nSHOT_BASED_SAFE_MAX = globals().get(\"SHOT_BASED_SAFE_MAX\", 20)\nif (not REAL_QPU) and (\"n_qubits\" in globals()) and n_qubits > SHOT_BASED_SAFE_MAX:\n    print(f\"⚠️ Simülatör için n_qubits {n_qubits} → {SHOT_BASED_SAFE_MAX}\")\n    print(\"   (Gerçek QPU için önce AŞAMA 13'yı çalıştırın.)\")\n    n_qubits = SHOT_BASED_SAFE_MAX\n    def _fit_to_nq(X, n_target):\n        if X is None:\n            return X\n        if X.shape[1] == n_target:\n            return X\n        if X.shape[1] > n_target:\n            return X[:, :n_target]\n        reps = int(np.ceil(n_target / X.shape[1]))\n        return np.tile(X, (1, reps))[:, :n_target]\n    if \"X_train_q_sub\" in globals():\n        X_train_q_sub = _fit_to_nq(X_train_q_sub, n_qubits)\n    if \"X_test_q_sub\" in globals():\n        X_test_q_sub = _fit_to_nq(X_test_q_sub, n_qubits)\n\nif not REAL_QPU:\n    print(\"ℹ️ REAL_QPU=False → simülatör yolu (AŞAMA 13 Aer veya token yok).\")\n\n# --- 1) Soyut feature map + ansatz ---\nif n_qubits <= 8:\n    feature_map = ZZFeatureMap(feature_dimension=n_qubits, reps=1, entanglement=\"linear\")\nelse:\n    feature_map = PauliFeatureMap(\n        feature_dimension=n_qubits, reps=1, paulis=[\"Z\", \"ZZ\"], entanglement=\"linear\"\n    )\n\nansatz = EfficientSU2(num_qubits=n_qubits, reps=1, entanglement=\"linear\")\nprint(f\"Soyut feature map qubits={feature_map.num_qubits} | ansatz params≈{ansatz.num_parameters}\")\n\n\ndef to_isa(circuit, backend_obj, label=\"circuit\"):\n    \"\"\"Backend basis gate + coupling map'e transpile (ISA).\"\"\"\n    if backend_obj is None:\n        print(f\"   {label}: transpile atlandı (simülatör)\")\n        return circuit\n    pm = generate_preset_pass_manager(\n        optimization_level=1,  # 1 = hızlı; demo için yeterli. 2-3 daha yavaş ama daha iyi\n        backend=backend_obj,\n    )\n    isa_circ = pm.run(circuit)\n    print(\n        f\"   {label}: ISA transpile OK | \"\n        f\"qubits {circuit.num_qubits}→{isa_circ.num_qubits} | \"\n        f\"depth≈{isa_circ.depth()}\"\n    )\n    return isa_circ\n\n\n# --- 2) Gerçek QPU ise ISA'ya çevir ---\nif REAL_QPU and backend is not None:\n    print(f\"ISA transpile → {backend_name} ...\")\n    feature_map = to_isa(feature_map, backend, \"feature_map\")\n    ansatz = to_isa(ansatz, backend, \"ansatz\")\nelse:\n    print(\"Simülatör yolu: ISA transpile gerekmez.\")\n\n\ndef build_sampler():\n    global sampler\n    if REAL_QPU and sampler is not None:\n        print(f\"Sampler: IBM Runtime SamplerV2 @ {backend_name} (GERÇEK QPU)\")\n        return sampler\n    if n_qubits <= 12:\n        from qiskit.primitives import StatevectorSampler\n        print(f\"Sampler: StatevectorSampler (sim, n_qubits={n_qubits})\")\n        return StatevectorSampler()\n    try:\n        from qiskit_aer.primitives import SamplerV2 as AerSampler\n        print(f\"Sampler: AerSamplerV2 shots (sim, n_qubits={n_qubits})\")\n        return AerSampler()\n    except Exception:\n        from qiskit_aer.primitives import Sampler as AerSamplerLegacy\n        print(f\"Sampler: AerSampler legacy (sim, n_qubits={n_qubits})\")\n        return AerSamplerLegacy()\n\n\nsafe_sampler = build_sampler()\n\nq_metrics = {}\ny_pred_q = None\ny_score_q = None\n\n# Yüksek qubit / gerçek QPU → VQC\nrun_vqc = (SELECTED_ALGO == \"VQC\") or (n_qubits > 12) or REAL_QPU\n\nif (not run_vqc) and SELECTED_ALGO == \"QSVC\":\n    print(f\"QSVC | n_qubits={n_qubits} | train={len(y_train_q)}\")\n    from qiskit_machine_learning.kernels import FidelityQuantumKernel\n    quantum_kernel = FidelityQuantumKernel(feature_map=feature_map)\n    qsvc = QSVC(quantum_kernel=quantum_kernel)\n    qsvc.fit(X_train_q_sub, y_train_q)\n    y_pred_q = qsvc.predict(X_test_q_sub)\n    try:\n        y_score_q = qsvc.decision_function(X_test_q_sub)\n    except Exception:\n        y_score_q = y_pred_q.astype(float)\n    trained_model_name = \"Quantum QSVC\"\nelse:\n    print(f\"VQC | n_qubits={n_qubits} | train={len(y_train_q)} | REAL_QPU={REAL_QPU}\")\n    if REAL_QPU:\n        # Free 10 dk: kuyruk + her iterasyon job → çok düşük tutun\n        maxiter = 3 if n_qubits >= 12 else 5\n    else:\n        maxiter = 40 if n_qubits <= 10 else (25 if n_qubits <= 16 else 15)\n    optimizer = COBYLA(maxiter=maxiter)\n    print(f\"   COBYLA maxiter={maxiter}\")\n\n    vqc = VQC(\n        feature_map=feature_map,\n        ansatz=ansatz,\n        optimizer=optimizer,\n        sampler=safe_sampler,\n    )\n    print(\"   fit başlıyor (gerçek QPU'da her iterasyon kuyruğa girer; sabır)...\")\n    vqc.fit(X_train_q_sub, y_train_q)\n    y_pred_q = vqc.predict(X_test_q_sub)\n    try:\n        y_score_q = (\n            vqc.decision_function(X_test_q_sub)\n            if hasattr(vqc, \"decision_function\")\n            else y_pred_q.astype(float)\n        )\n    except Exception:\n        y_score_q = y_pred_q.astype(float)\n    trained_model_name = f\"Quantum VQC ({'IBM QPU' if REAL_QPU else 'sim'})\"\n\nq_metrics = {\n    \"accuracy\": accuracy_score(y_test_q, y_pred_q),\n    \"precision\": precision_score(y_test_q, y_pred_q, zero_division=0),\n    \"recall\": recall_score(y_test_q, y_pred_q, zero_division=0),\n    \"f1\": f1_score(y_test_q, y_pred_q, zero_division=0),\n    \"roc_auc\": roc_auc_score(y_test_q, y_score_q) if len(np.unique(y_test_q)) > 1 else 0.5,\n}\n\nprint(f\"=== {trained_model_name} | n_qubits={n_qubits} | backend={backend_name} ===\")\nfor k, v in q_metrics.items():\n    print(f\"  {k:12s}: {v:.4f}\")\nprint(\"✅ AŞAMA 15 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage15-md",
   "metadata": {
    "id": "stage15-md"
   },
   "source": "## AŞAMA 16: Confusion Matrix ve ROC\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "stage15-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "stage15-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513345379,
     "user_tz": -180,
     "elapsed": 466,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "d04372b2-7c2b-421e-e275-2d539cee8cb1"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ AŞAMA 16 tamamlandı.\n"
     ]
    }
   ],
   "source": "best_classical_name = max(results, key=lambda n: results[n][\"roc_auc\"])\nbest_c = results[best_classical_name]\n\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\ncm_c = confusion_matrix(y_test, best_c[\"y_pred\"])\ncm_q = confusion_matrix(y_test_q, y_pred_q)\nConfusionMatrixDisplay(cm_c, display_labels=[\"Ödeme OK\", \"Default\"]).plot(ax=axes[0], colorbar=False)\naxes[0].set_title(f\"Klasik: {best_classical_name}\")\nConfusionMatrixDisplay(cm_q, display_labels=[\"Ödeme OK\", \"Default\"]).plot(ax=axes[1], colorbar=False)\naxes[1].set_title(f\"{trained_model_name} (n_qubits={n_qubits})\")\nplt.tight_layout()\nplt.show()\n\nfig, ax = plt.subplots(figsize=(8, 6))\nfpr_c, tpr_c, _ = roc_curve(y_test, best_c[\"y_prob\"])\nax.plot(fpr_c, tpr_c, label=f\"{best_classical_name} (AUC={best_c['roc_auc']:.3f})\")\nif len(np.unique(y_test_q)) > 1:\n    fpr_q, tpr_q, _ = roc_curve(y_test_q, y_score_q)\n    ax.plot(fpr_q, tpr_q, label=f\"{trained_model_name} (AUC={q_metrics['roc_auc']:.3f})\", linestyle=\"--\")\nax.plot([0, 1], [0, 1], \"k--\", alpha=0.4)\nax.set_xlabel(\"FPR\")\nax.set_ylabel(\"TPR\")\nax.set_title(f\"ROC — Home Credit Default (n_qubits={n_qubits})\")\nax.legend(loc=\"lower right\")\nplt.tight_layout()\nplt.show()\nprint(\"✅ AŞAMA 16 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage17-md",
   "metadata": {
    "id": "stage17-md"
   },
   "source": "## AŞAMA 17: Sonuç Kaydı\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "stage17-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage17-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513351291,
     "user_tz": -180,
     "elapsed": 79,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "1d5c2043-44c6-4bf3-95c4-98e53f7dd3c4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Özet: /content/drive/MyDrive/QML_HomeCredit_Default_HighQ/outputs/homecredit_highq_summary_20260904_091551.json\n",
      "✅ Metrikler: /content/drive/MyDrive/QML_HomeCredit_Default_HighQ/outputs/homecredit_highq_metrics_20260904_091551.csv\n",
      "✅ AŞAMA 17 tamamlandı.\n"
     ]
    }
   ],
   "source": "run_id = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\nout_dir = OUTPUT_PATH\nos.makedirs(out_dir, exist_ok=True)\n\nsummary = {\n    \"run_id\": run_id,\n    \"problem\": \"HomeCredit_Default_Risk_HighQubit\",\n    \"n_samples\": len(df),\n    \"n_qubits\": n_qubits,\n    \"TARGET_QUBITS\": TARGET_QUBITS,\n    \"MAX_TRAIN_SAMPLES_Q\": MAX_TRAIN_SAMPLES_Q,\n    \"MAX_TEST_SAMPLES_Q\": MAX_TEST_SAMPLES_Q,\n    \"backend\": backend_name,\n    \"use_real_hardware\": use_real_hardware,\n    \"quantum_model\": trained_model_name,\n    \"classical_best\": best_classical_name,\n    \"classical_auc\": best_c[\"roc_auc\"],\n    \"classical_f1\": best_c[\"f1\"],\n    \"quantum_auc\": q_metrics[\"roc_auc\"],\n    \"quantum_f1\": q_metrics[\"f1\"],\n    \"quantum_accuracy\": q_metrics[\"accuracy\"],\n}\n\nsummary_path = os.path.join(out_dir, f\"homecredit_highq_summary_{run_id}.json\")\nwith open(summary_path, \"w\") as f:\n    json.dump(summary, f, indent=2, default=str)\nprint(f\"✅ Özet: {summary_path}\")\n\nmetric_rows = []\nfor name, r in results.items():\n    metric_rows.append({\n        \"model\": name, \"type\": \"classical\",\n        \"roc_auc\": r[\"roc_auc\"], \"f1\": r[\"f1\"],\n        \"precision\": r[\"precision\"], \"recall\": r[\"recall\"], \"accuracy\": r[\"accuracy\"],\n    })\nmetric_rows.append({\n    \"model\": trained_model_name, \"type\": \"quantum\",\n    \"roc_auc\": q_metrics[\"roc_auc\"], \"f1\": q_metrics[\"f1\"],\n    \"precision\": q_metrics[\"precision\"], \"recall\": q_metrics[\"recall\"],\n    \"accuracy\": q_metrics[\"accuracy\"],\n})\nmetrics_df = pd.DataFrame(metric_rows)\nmetrics_path = os.path.join(out_dir, f\"homecredit_highq_metrics_{run_id}.csv\")\nmetrics_df.to_csv(metrics_path, index=False)\nprint(f\"✅ Metrikler: {metrics_path}\")\nprint(\"✅ AŞAMA 17 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage18-md",
   "metadata": {
    "id": "stage18-md"
   },
   "source": "## AŞAMA 18: Model Karşılaştırma\n\n"
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "stage18-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 744
    },
    "id": "stage18-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513357040,
     "user_tz": -180,
     "elapsed": 363,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "850682ec-d984-4c16-8e4a-9afb3da23cdf"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== Karşılaştırma (n_qubits=8) ===\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7d62649a11d0>"
      ],
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5568f_row1_col4, #T_5568f_row3_col0, #T_5568f_row4_col1, #T_5568f_row4_col2, #T_5568f_row4_col3 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5568f\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5568f_level0_col0\" class=\"col_heading level0 col0\" >ROC-AUC</th>\n",
       "      <th id=\"T_5568f_level0_col1\" class=\"col_heading level0 col1\" >F1</th>\n",
       "      <th id=\"T_5568f_level0_col2\" class=\"col_heading level0 col2\" >Precision</th>\n",
       "      <th id=\"T_5568f_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_5568f_level0_col4\" class=\"col_heading level0 col4\" >Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5568f_level0_row0\" class=\"row_heading level0 row0\" >Logistic Regression</th>\n",
       "      <td id=\"T_5568f_row0_col0\" class=\"data row0 col0\" >0.732900</td>\n",
       "      <td id=\"T_5568f_row0_col1\" class=\"data row0 col1\" >0.250100</td>\n",
       "      <td id=\"T_5568f_row0_col2\" class=\"data row0 col2\" >0.154200</td>\n",
       "      <td id=\"T_5568f_row0_col3\" class=\"data row0 col3\" >0.660800</td>\n",
       "      <td id=\"T_5568f_row0_col4\" class=\"data row0 col4\" >0.680100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5568f_level0_row1\" class=\"row_heading level0 row1\" >Random Forest</th>\n",
       "      <td id=\"T_5568f_row1_col0\" class=\"data row1 col0\" >0.738800</td>\n",
       "      <td id=\"T_5568f_row1_col1\" class=\"data row1 col1\" >0.264000</td>\n",
       "      <td id=\"T_5568f_row1_col2\" class=\"data row1 col2\" >0.169300</td>\n",
       "      <td id=\"T_5568f_row1_col3\" class=\"data row1 col3\" >0.599400</td>\n",
       "      <td id=\"T_5568f_row1_col4\" class=\"data row1 col4\" >0.730200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5568f_level0_row2\" class=\"row_heading level0 row2\" >SVM (RBF)</th>\n",
       "      <td id=\"T_5568f_row2_col0\" class=\"data row2 col0\" >0.698300</td>\n",
       "      <td id=\"T_5568f_row2_col1\" class=\"data row2 col1\" >0.239000</td>\n",
       "      <td id=\"T_5568f_row2_col2\" class=\"data row2 col2\" >0.152400</td>\n",
       "      <td id=\"T_5568f_row2_col3\" class=\"data row2 col3\" >0.553700</td>\n",
       "      <td id=\"T_5568f_row2_col4\" class=\"data row2 col4\" >0.715400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5568f_level0_row3\" class=\"row_heading level0 row3\" >XGBoost</th>\n",
       "      <td id=\"T_5568f_row3_col0\" class=\"data row3 col0\" >0.750600</td>\n",
       "      <td id=\"T_5568f_row3_col1\" class=\"data row3 col1\" >0.266400</td>\n",
       "      <td id=\"T_5568f_row3_col2\" class=\"data row3 col2\" >0.166500</td>\n",
       "      <td id=\"T_5568f_row3_col3\" class=\"data row3 col3\" >0.666600</td>\n",
       "      <td id=\"T_5568f_row3_col4\" class=\"data row3 col4\" >0.703700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5568f_level0_row4\" class=\"row_heading level0 row4\" >Quantum VQC (sim)</th>\n",
       "      <td id=\"T_5568f_row4_col0\" class=\"data row4 col0\" >0.625000</td>\n",
       "      <td id=\"T_5568f_row4_col1\" class=\"data row4 col1\" >0.545500</td>\n",
       "      <td id=\"T_5568f_row4_col2\" class=\"data row4 col2\" >0.428600</td>\n",
       "      <td id=\"T_5568f_row4_col3\" class=\"data row4 col3\" >0.750000</td>\n",
       "      <td id=\"T_5568f_row4_col4\" class=\"data row4 col4\" >0.583300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Tablo: /content/drive/MyDrive/QML_HomeCredit_Default_HighQ/outputs/comparison_homecredit_highq_20260904_091551.csv\n",
      "✅ AŞAMA 18 tamamlandı.\n"
     ]
    }
   ],
   "source": "comparison = pd.DataFrame({\n    name: {\n        \"ROC-AUC\": r[\"roc_auc\"], \"F1\": r[\"f1\"],\n        \"Precision\": r[\"precision\"], \"Recall\": r[\"recall\"], \"Accuracy\": r[\"accuracy\"]\n    }\n    for name, r in results.items()\n})\ncomparison[trained_model_name] = {\n    \"ROC-AUC\": q_metrics[\"roc_auc\"], \"F1\": q_metrics[\"f1\"],\n    \"Precision\": q_metrics[\"precision\"], \"Recall\": q_metrics[\"recall\"],\n    \"Accuracy\": q_metrics[\"accuracy\"],\n}\ncomparison = comparison.T.round(4)\n\nprint(f\"=== Karşılaştırma (n_qubits={n_qubits}) ===\")\ndisplay(comparison.style.highlight_max(axis=0, color=\"lightgreen\"))\n\nfig, ax = plt.subplots(figsize=(11, 5))\ncomparison[[\"ROC-AUC\", \"F1\"]].plot(kind=\"bar\", ax=ax, color=[\"#3498db\", \"#e74c3c\"])\nax.set_title(f\"Model Karşılaştırması – Home Credit Default\\nn_qubits={n_qubits} (Quantum subset üzerinde)\")\nax.set_ylabel(\"Skor\")\nax.set_ylim(0, 1.05)\nplt.xticks(rotation=20)\nplt.tight_layout()\nplt.show()\n\ncomp_path = os.path.join(out_dir, f\"comparison_homecredit_highq_{run_id}.csv\")\ncomparison.to_csv(comp_path)\nprint(f\"✅ Tablo: {comp_path}\")\nprint(\"✅ AŞAMA 18 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage19-md",
   "metadata": {
    "id": "stage19-md"
   },
   "source": "## AŞAMA 19: Yorumlama & 100+ Qubit Ölçekleme Rehberi\n\n### Neden hâlâ subset var?\n\nQSVC / Fidelity Kernel **örnek sayısı n** ile **O(n²)** devre ister.  \n100 qubit × 10.000 örnek → pratik değil (süre, kuyruk, klasik bellek).  \nBu yüzden **qubit sayısını artırırken örnek sayısını bilinçli düşük tutuyoruz**.\n\n### Home Credit özelinde notlar\n\n- **Class imbalance (~8% default)**: Stratified subset + klasik modellerde `class_weight` / `scale_pos_weight` kullanıldı.\n- **Tam rekabet skoru**: Kaggle’da 0.78–0.80 AUC’ler feature engineering + LightGBM/XGBoost ile geliyor. Quantum şu an **POC / eğitim** katmanıdır.\n- **Özellik seti**: Sadece `application_train` kullanıldı. bureau / previous_application / installments eklemek klasik tarafı güçlendirir; quantum tarafında boyut yine `TARGET_QUBITS` ile sınırlanır.\n\n### 100+ qubit cihazlarda ne kazanırsınız?\n\n1. Daha fazla özellik / daha zengin feature map (daha yüksek kapasiteli Hilbert uzayı)\n2. Daha geniş VQC ansatz (daha fazla parametre, daha karmaşık karar sınırı)\n3. Gelecekteki hata-düzeltmeli algoritmalar için yol\n4. Bugün pratik kazanç sınırlıdır: gürültü + derinlik + connectivity hâlâ darboğaz\n\n### Önerilen deney sırası\n\n| Adım | TARGET_QUBITS | Algoritma | Subset | Ortam |\n|------|---------------|-----------|--------|-------|\n| 1 | 8 | QSVC | 80–120 | Aer |\n| 2 | 12–16 | VQC | 60–100 | Aer / küçük QPU |\n| 3 | 20–40 | VQC | 40–80 | Büyük IBM backend |\n| 4 | 50–100+ | VQC / özel ansatz | çok küçük / batch | 100+ qubit sistem + error mitigation |\n\n### Operasyonel metrikler (kredi riski)\n\n- **ROC-AUC** sıralama kalitesini ölçer (Kaggle metriği).\n- **Recall** (kaçırılan default) iş riski açısından kritik olabilir.\n- Threshold’u 0.5’te bırakmayın; maliyet matrisi ile ayarlayın.\n- Klasik modeller bu veri setinde hâlâ çok güçlü baseline’dır; quantum şu an POC / araştırma katmanıdır.\n\n### Kontrol listesi\n\n- [ ] TARGET_QUBITS sistem belleği / QPU boyutuna uygun mu?\n- [ ] AŞAMA 13 backend seçimi eğitimden önce çalıştı mı?\n- [ ] n_qubits > 16 ise sampling / gerçek backend kullanıldı mı?\n- [ ] Backend num_qubits ≥ n_qubits mi?\n- [ ] Recall / Precision trade-off operasyona uygun mu?\n- [ ] Class imbalance dikkate alındı mı (stratified subset)?\n\n---\n\n**Özet:** Bu notebook **doğrusal pipeline** sunar: veri → klasik → encoding → backend → feature map → eğitim → metrik.  \nTam veri + 100 qubit ile “tek tuş eğitim” bugünün NISQ gerçekliğinde mümkün değildir; doğru yaklaşım **yüksek qubit + kontrollü örnek boyutu + VQC** kombinasyonudur.\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage20-md",
   "metadata": {
    "id": "stage20-md"
   },
   "source": "## AŞAMA 20: Word Raporu Üretimi\n\nPipeline tamamlandıktan sonra problem özeti, klasik baseline ve quantum sonuçlarını içeren profesyonel bir **Word (.docx)** raporu üretir.\n\nÇıktı yolu: `OUTPUT_PATH / HomeCredit_QML_Raporu_<run_id>.docx`\n"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "stage20-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "id": "stage20-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788513372326,
     "user_tz": -180,
     "elapsed": 2729,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "447e349a-edbd-4aae-de18-51da0b517c06"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Word raporu kaydedildi: /content/drive/MyDrive/QML_HomeCredit_Default_HighQ/outputs/HomeCredit_QML_Raporu_20260904_091551.docx\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": [
       "\n",
       "    async function download(id, filename, size) {\n",
       "      if (!google.colab.kernel.accessAllowed) {\n",
       "        return;\n",
       "      }\n",
       "      const div = document.createElement('div');\n",
       "      const label = document.createElement('label');\n",
       "      label.textContent = `Downloading \"${filename}\": `;\n",
       "      div.appendChild(label);\n",
       "      const progress = document.createElement('progress');\n",
       "      progress.max = size;\n",
       "      div.appendChild(progress);\n",
       "      document.body.appendChild(div);\n",
       "\n",
       "      const buffers = [];\n",
       "      let downloaded = 0;\n",
       "\n",
       "      const channel = await google.colab.kernel.comms.open(id);\n",
       "      // Send a message to notify the kernel that we're ready.\n",
       "      channel.send({})\n",
       "\n",
       "      for await (const message of channel.messages) {\n",
       "        // Send a message to notify the kernel that we're ready.\n",
       "        channel.send({})\n",
       "        if (message.buffers) {\n",
       "          for (const buffer of message.buffers) {\n",
       "            buffers.push(buffer);\n",
       "            downloaded += buffer.byteLength;\n",
       "            progress.value = downloaded;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
       "      const a = document.createElement('a');\n",
       "      a.href = window.URL.createObjectURL(blob);\n",
       "      a.download = filename;\n",
       "      div.appendChild(a);\n",
       "      a.click();\n",
       "      div.remove();\n",
       "    }\n",
       "  "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": [
       "download(\"download_eb340f34-fc0d-4c1d-bebd-17cbcedd63ca\", \"HomeCredit_QML_Raporu_20260904_091551.docx\", 39570)"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "📥 İndirme başlatıldı.\n",
      "✅ AŞAMA 20 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 20: Word raporu (python-docx) — gerçek metriklerle\n# ============================================================\n\n!pip install -q python-docx\n\nfrom docx import Document\nfrom docx.shared import Pt, RGBColor, Cm, Inches\nfrom docx.enum.text import WD_ALIGN_PARAGRAPH\nfrom docx.enum.table import WD_TABLE_ALIGNMENT\nfrom docx.oxml.ns import qn\nfrom docx.oxml import OxmlElement\nfrom datetime import datetime\nimport os\n\ndef _set_run_font(run, size=11, bold=False, color=None, italic=False):\n    run.font.name = \"Calibri\"\n    run._element.rPr.rFonts.set(qn(\"w:eastAsia\"), \"Calibri\")\n    run.font.size = Pt(size)\n    run.bold = bold\n    run.italic = italic\n    if color is not None:\n        run.font.color.rgb = RGBColor(*color)\n\ndef _shade_cell(cell, hex_color):\n    tc = cell._tc\n    tcPr = tc.get_or_add_tcPr()\n    # mevcut shd varsa kaldır\n    for child in list(tcPr):\n        if child.tag == qn(\"w:shd\"):\n            tcPr.remove(child)\n    shd = OxmlElement(\"w:shd\")\n    shd.set(qn(\"w:fill\"), hex_color)\n    shd.set(qn(\"w:val\"), \"clear\")\n    tcPr.append(shd)\n\ndef _add_heading(doc, text, level=1):\n    p = doc.add_heading(text, level=level)\n    for run in p.runs:\n        _set_run_font(run, size=16 if level == 1 else 13, bold=True, color=(15, 23, 42) if level == 1 else (30, 58, 95))\n    return p\n\ndef _add_para(doc, text, size=11, bold=False, italic=False, space_after=8):\n    p = doc.add_paragraph()\n    p.paragraph_format.space_after = Pt(space_after)\n    p.paragraph_format.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY\n    run = p.add_run(text)\n    _set_run_font(run, size=size, bold=bold, italic=italic, color=(30, 41, 59))\n    return p\n\ndef _add_bullet(doc, text):\n    p = doc.add_paragraph(style=\"List Bullet\")\n    # stil boş run bırakabilir; güvenli yaz\n    if p.runs:\n        p.runs[0].text = text\n        _set_run_font(p.runs[0], size=10.5, color=(30, 41, 59))\n    else:\n        run = p.add_run(text)\n        _set_run_font(run, size=10.5, color=(30, 41, 59))\n    return p\n\n# --- Güvenli metrik okuma ---\ndef _m(d, key, default=None):\n    try:\n        v = d.get(key, default)\n        return float(v) if v is not None else None\n    except Exception:\n        return None\n\ndef _fmt(v):\n    if v is None:\n        return \"—\"\n    try:\n        return f\"{float(v):.4f}\"\n    except Exception:\n        return str(v)\n\n# Klasik sonuçlar\nclassical_rows = []\nif \"results\" in globals() and isinstance(results, dict):\n    for name, r in results.items():\n        classical_rows.append({\n            \"model\": name,\n            \"type\": \"classical\",\n            \"roc_auc\": _m(r, \"roc_auc\"),\n            \"f1\": _m(r, \"f1\"),\n            \"precision\": _m(r, \"precision\"),\n            \"recall\": _m(r, \"recall\"),\n            \"accuracy\": _m(r, \"accuracy\"),\n        })\n\n# Quantum\nq_name = globals().get(\"trained_model_name\", \"Quantum VQC/QSVC\")\nqm = globals().get(\"q_metrics\", {})\nquantum_row = {\n    \"model\": q_name,\n    \"type\": \"quantum\",\n    \"roc_auc\": _m(qm, \"roc_auc\"),\n    \"f1\": _m(qm, \"f1\"),\n    \"precision\": _m(qm, \"precision\"),\n    \"recall\": _m(qm, \"recall\"),\n    \"accuracy\": _m(qm, \"accuracy\"),\n}\n\nbest_c_name = globals().get(\"best_classical_name\", None)\nif best_c_name is None and classical_rows:\n    best_c_name = max(classical_rows, key=lambda x: x[\"roc_auc\"] or 0)[\"model\"]\n\nrun_id_local = globals().get(\"run_id\", datetime.now().strftime(\"%Y%m%d_%H%M%S\"))\nn_q = globals().get(\"n_qubits\", \"—\")\ntgt_q = globals().get(\"TARGET_QUBITS\", \"—\")\nbname = globals().get(\"backend_name\", \"—\")\nreal_q = globals().get(\"REAL_QPU\", False)\nn_samples = len(df) if \"df\" in globals() and df is not None else \"—\"\nmax_tr = globals().get(\"MAX_TRAIN_SAMPLES_Q\", \"—\")\nmax_te = globals().get(\"MAX_TEST_SAMPLES_Q\", \"—\")\n\nout_dir = globals().get(\"OUTPUT_PATH\", os.path.abspath(\"./outputs\"))\nos.makedirs(out_dir, exist_ok=True)\n\n# --- Belge ---\ndoc = Document()\n\n# sayfa kenar boşluğu\nfor section in doc.sections:\n    section.top_margin = Cm(2.0)\n    section.bottom_margin = Cm(2.0)\n    section.left_margin = Cm(2.2)\n    section.right_margin = Cm(2.2)\n\n# Başlık\nt = doc.add_paragraph()\nt.alignment = WD_ALIGN_PARAGRAPH.CENTER\nr = t.add_run(\"HOME CREDIT DEFAULT RISK\")\n_set_run_font(r, size=18, bold=True, color=(15, 23, 42))\n\nt2 = doc.add_paragraph()\nt2.alignment = WD_ALIGN_PARAGRAPH.CENTER\nr2 = t2.add_run(\"Quantum Machine Learning Uygulama Raporu\")\n_set_run_font(r2, size=14, bold=True, color=(30, 58, 95))\n\nt3 = doc.add_paragraph()\nt3.alignment = WD_ALIGN_PARAGRAPH.CENTER\nr3 = t3.add_run(f\"Otomatik üretim · run_id={run_id_local} · n_qubits={n_q} · backend={bname}\")\n_set_run_font(r3, size=10, italic=True, color=(100, 116, 139))\n\n# 1 Problem\n_add_heading(doc, \"1. Problem Tanımı\", 1)\n_add_para(doc, \"Home Credit, kredi geçmişi sınırlı bireylere finansman sağlamayı hedefler. Bu çalışmada Kaggle Home Credit Default Risk veri setinin ana tablosu (application_train.csv) ile başvuru sahibinin ödemede güçlük yaşayıp yaşamayacağı (TARGET: 1 = default, 0 = diğer) ikili sınıflandırma problemi ele alınmıştır. Default oranı yaklaşık %8 olup belirgin sınıf dengesizliği vardır.\")\n_add_para(doc, \"İş hedefleri: geri ödeyebilecek müşterilerin haksız reddini azaltmak; yüksek riskli başvuruları erken ayırt etmek (ROC-AUC / Recall); klasik ve quantum modelleri aynı pipeline içinde karşılaştırmak.\")\n\n# 2 Yapılanlar\n_add_heading(doc, \"2. Yapılanlar (Pipeline)\", 1)\n_add_para(doc, \"Notebook doğrusal sırayla çalıştırılmıştır: ortam ve token → veri/EDA/ön işleme → klasik baseline → max-qubit encoding → IBM backend seçimi → feature map → VQC/QSVC eğitimi → metrikler → bu rapor.\")\n_add_bullet(doc, f\"Örnek sayısı (tam tablo): {n_samples}\")\n_add_bullet(doc, f\"TARGET_QUBITS (plan): {tgt_q}  |  n_qubits (nihai): {n_q}\")\n_add_bullet(doc, f\"Backend: {bname}  |  REAL_QPU: {real_q}\")\n_add_bullet(doc, f\"Quantum train/test subset üst sınırı: {max_tr} / {max_te}\")\n_add_bullet(doc, \"Klasik: Logistic Regression, Random Forest, SVM (RBF), XGBoost (dengesizlik ağırlıklı)\")\n_add_bullet(doc, \"Quantum: VQC (yüksek qubit öncelikli) veya QSVC; ZZ/Pauli feature map + EfficientSU2; ISA transpile\")\n\n# 3 Klasik sonuçlar\n_add_heading(doc, \"3. Klasik Model Sonuçları\", 1)\n_add_para(doc, \"Klasik modeller geniş train/test ayrımı üzerinde eğitilmiştir. Aşağıdaki değerler bu koşunun AŞAMA 10 çıktısıdır.\")\n\nheaders = [\"Model\", \"ROC-AUC\", \"F1\", \"Precision\", \"Recall\", \"Accuracy\"]\ntable = doc.add_table(rows=1, cols=6)\ntable.style = \"Table Grid\"\ntable.alignment = WD_TABLE_ALIGNMENT.CENTER\nhdr = table.rows[0].cells\nfor i, h in enumerate(headers):\n    hdr[i].text = \"\"\n    p = hdr[i].paragraphs[0]\n    run = p.add_run(h)\n    _set_run_font(run, size=10, bold=True, color=(255, 255, 255))\n    _shade_cell(hdr[i], \"1E3A5F\")\n\nfor row in classical_rows:\n    cells = table.add_row().cells\n    vals = [row[\"model\"], _fmt(row[\"roc_auc\"]), _fmt(row[\"f1\"]), _fmt(row[\"precision\"]), _fmt(row[\"recall\"]), _fmt(row[\"accuracy\"])]\n    for i, v in enumerate(vals):\n        cells[i].text = \"\"\n        run = cells[i].paragraphs[0].add_run(v)\n        _set_run_font(run, size=10, bold=(i == 0 and row[\"model\"] == best_c_name), color=(15, 23, 42))\n\nif best_c_name:\n    _add_para(doc, f\"En iyi klasik model (ROC-AUC): {best_c_name}.\", size=10, italic=True)\n\n# 4 Quantum\n_add_heading(doc, \"4. Quantum Model Sonuçları\", 1)\n_add_para(doc, \"Quantum metrikler küçük ve stratified test subset’i üzerindedir; klasik metriklerle birebir aynı veri hacmini temsil etmez. Amaç production benchmark’tan çok uçtan uca QML pipeline’ının ve max-qubit backend kullanımının gösterimidir.\")\n\nqtable = doc.add_table(rows=2, cols=6)\nqtable.style = \"Table Grid\"\nqtable.alignment = WD_TABLE_ALIGNMENT.CENTER\nfor i, h in enumerate(headers):\n    qtable.rows[0].cells[i].text = \"\"\n    run = qtable.rows[0].cells[i].paragraphs[0].add_run(h)\n    _set_run_font(run, size=10, bold=True, color=(255, 255, 255))\n    _shade_cell(qtable.rows[0].cells[i], \"065F46\")\nqvals = [quantum_row[\"model\"], _fmt(quantum_row[\"roc_auc\"]), _fmt(quantum_row[\"f1\"]),\n         _fmt(quantum_row[\"precision\"]), _fmt(quantum_row[\"recall\"]), _fmt(quantum_row[\"accuracy\"])]\nfor i, v in enumerate(qvals):\n    qtable.rows[1].cells[i].text = \"\"\n    run = qtable.rows[1].cells[i].paragraphs[0].add_run(v)\n    _set_run_font(run, size=10, bold=(i == 0), color=(15, 23, 42))\n    _shade_cell(qtable.rows[1].cells[i], \"ECFDF5\")\n\n_add_para(doc, \"\")\n_add_heading(doc, \"4.1 NISQ yorumu\", 2)\n_add_bullet(doc, \"Gerçek QPU gürültüsü ideal simülatöre göre skoru düşürebilir; bu da demonnun parçasıdır.\")\n_add_bullet(doc, \"Yüksek qubit daha zengin Hilbert uzayı sağlar; derinlik ve connectivity pratik kazancı sınırlar.\")\n_add_bullet(doc, \"QSVC O(n²) kernel maliyeti nedeniyle yüksek qubit’te VQC tercih edilir.\")\n_add_bullet(doc, \"Statevector ile 50+ qubit simülasyon pratikte imkânsızdır; gerçek donanım veya shots gerekir.\")\n\n# 5 Karşılaştırma\n_add_heading(doc, \"5. Klasik vs Quantum Karşılaştırma\", 1)\n_add_para(doc, \"Klasik modeller bugün production adayıdır (hız, tekrarlanabilirlik, zengin feature engineering). Quantum katman: pipeline/ISA disiplini, max-qubit erişim eğitimi ve gelecekteki hata-düzeltmeli donanım için iskelet olarak değerlendirilmelidir.\")\n\ncomp = doc.add_table(rows=1, cols=6)\ncomp.style = \"Table Grid\"\nfor i, h in enumerate(headers):\n    comp.rows[0].cells[i].text = \"\"\n    run = comp.rows[0].cells[i].paragraphs[0].add_run(h)\n    _set_run_font(run, size=10, bold=True, color=(255, 255, 255))\n    _shade_cell(comp.rows[0].cells[i], \"1E3A5F\")\n\nall_rows = classical_rows + [quantum_row]\nfor row in all_rows:\n    cells = comp.add_row().cells\n    vals = [row[\"model\"], _fmt(row[\"roc_auc\"]), _fmt(row[\"f1\"]), _fmt(row[\"precision\"]), _fmt(row[\"recall\"]), _fmt(row[\"accuracy\"])]\n    fill = \"ECFDF5\" if row[\"type\"] == \"quantum\" else None\n    for i, v in enumerate(vals):\n        cells[i].text = \"\"\n        run = cells[i].paragraphs[0].add_run(v)\n        _set_run_font(run, size=10, bold=(row[\"type\"] == \"quantum\" and i == 0), color=(15, 23, 42))\n        if fill:\n            _shade_cell(cells[i], fill)\n\n# 6 Sonuç\n_add_heading(doc, \"6. Sonuç ve Öneriler\", 1)\n_add_bullet(doc, \"Pipeline doğrusal ve tekrarlanabilir şekilde tamamlandı.\")\n_add_bullet(doc, \"İlk denemelerde 8–12 qubit + Aer; ardından gerçek IBM backend ile ölçekleme önerilir.\")\n_add_bullet(doc, \"Production skor için klasik feature engineering + LightGBM/XGBoost genişletilmeli.\")\n_add_bullet(doc, \"Operasyonel eşik 0.5’te bırakılmamalı; maliyet matrisine göre ayarlanmalı.\")\n_add_bullet(doc, \"İleride: error mitigation, hybrid classical–quantum stacking.\")\n\n_add_heading(doc, \"7. Referanslar\", 1)\n_add_bullet(doc, \"Kaggle: Home Credit Default Risk\")\n_add_bullet(doc, \"GitHub: yakupkaplan/Home-Credit-Default-Risk\")\n_add_bullet(doc, \"Qiskit Machine Learning — VQC, QSVC\")\n_add_bullet(doc, \"IBM Quantum Runtime — SamplerV2, ISA transpile\")\n\nend = doc.add_paragraph()\nend.alignment = WD_ALIGN_PARAGRAPH.CENTER\ner = end.add_run(\"— Rapor sonu (notebook AŞAMA 20) —\")\n_set_run_font(er, size=10, italic=True, color=(148, 163, 184))\n\nreport_path = os.path.join(out_dir, f\"HomeCredit_QML_Raporu_{run_id_local}.docx\")\ndoc.save(report_path)\nprint(f\"✅ Word raporu kaydedildi: {report_path}\")\n\n# Colab'da indir\ntry:\n    from google.colab import files\n    files.download(report_path)\n    print(\"📥 İndirme başlatıldı.\")\nexcept Exception:\n    print(\"   (Colab files.download atlandı — dosya OUTPUT_PATH altında.)\")\n\nprint(\"✅ AŞAMA 20 tamamlandı.\")\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "closing-md",
   "metadata": {
    "id": "closing-md"
   },
   "source": "## Kapanış\n\n- **Dosya:** High-Qubit versiyon — Home Credit Default Risk  \n- **Doğrusal sıra:** 1→20 (backend = AŞAMA 13, eğitim = AŞAMA 15, rapor = AŞAMA 20)  \n- **Ana kontrol:** AŞAMA 12 → `TARGET_QUBITS` · AŞAMA 13 → gerçek QPU  \n- **Rapor:** AŞAMA 20 çalışınca `HomeCredit_QML_Raporu_<run_id>.docx` üretilir  \n- **Öneri:** Önce 8–12 qubit Aer, sonra 16–20 VQC, ardından erişiminiz olan en büyük IBM backend  \n- **Veri:** [Kaggle Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) · referans: [yakupkaplan/Home-Credit-Default-Risk](https://github.com/yakupkaplan/Home-Credit-Default-Risk)\n\n**Tarih:** 2026 · Qiskit + IBM Quantum Runtime · Eğitim / POC amaçlı\n"
  }
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          "text/markdown": "### Seçim: **Machine Learning** → **Superconducting Qubits** → **VQC**"
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          "text/markdown": "**Cihaz:** `ibm_or_aer`"
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          "text/markdown": "**Model:** $f_\\theta(x) = \\langle 0| U^\\dagger(x,\\theta) M U(x,\\theta) |0\\rangle$"
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