{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1beff0a4",
   "metadata": {
    "id": "1beff0a4"
   },
   "source": "# 🚀 Quantum Computing Uygulama Geliştirme Şablonu (QML POC/Pilot)\n\n**Tasarlayan ve Geliştiren:** Joseph Volkan Özcan  \n\n**Hedef Kitle:** İş dünyasına çözümler (POC, Pilot) proje yapmak isteyen herkes  \n\n**Çalışma Ortamı:** Google Colab (ortam değişkenleri, hata önerileri, GPU/TPU, cloud erişim)\n\n---\n\n### ML ve QML Arasındaki Temel Fark\n\n| | Klasik ML | Quantum ML (QML) |\n|---|---|---|\n| **Süreç** | Veri → Özellikler → Model → Tahmin | Veri → Scaling → Quantum Encoding → Quantum Circuit → QML Model → Tahmin |\n| **Bu projede** | Tüm değişkenler → Random Forest → Default Var/Yok | 3–4 kritik özellik → N Qubit → Feature Map → Quantum Kernel / QSVC → Default Var/Yok |\n| **Amaç** | Baseline performans | Aynı problemi kuantum yaklaşımıyla karşılaştırmak |\n\n---\n\n### İçindekiler (Aşamalar)\n\n1. Drive / Path Tanımlama  \n2. Cihaz Seçimi Tablosu  \n3. Python Kütüphane Kurulumu  \n4. API Keys Yönetimi  \n5. Sentetik Veri Üretimi  \n6. CSV Sürükle-Bırak Yükleme  \n7. Keşifsel Veri Analizi (EDA)  \n8. GDPR / KVKK Anonimleştirme  \n9. Veri Hazırlama ve Ön İşleme  \n10. Klasik Makine Öğrenmesi Modelleri  \n11. QML Algoritma Seçimi  \n12. Veri Encoding ve Qubit Maliyeti  \n13. Quantum Feature Map + Fidelity Kernel  \n14. Quantum Kernel Eğitimleri (QSVC)  \n15. Confusion Matrix ve ROC  \n16. Gerçek Quantum Donanım Seçimi  \n17. QML Gerçek Donanımda Çalıştırma ve Sonuç Kaydı  \n18. Model Karşılaştırma  \n19. Sonuçların Yorumlanması  \n\n> **Not:** Gerçek IBM Quantum donanımı kullanıldığında quantum kısmı küçük subset (ör. 60 train / 30 test) ile çalışır. Tam veri seti için simülatör veya daha fazla QPU süresi gerekir.\n\n\n---\n\n### Bu notebook nasıl kullanılır? (Öğrenciler için)\n\n1. **Dosyayı indirin** ve [Google Colab](https://colab.research.google.com/)’a yükleyin (*File → Upload notebook*).\n2. Aşamaları **sırayla** çalıştırın (yukarıdan aşağıya). Bir hücre hata verirse bir sonrakine geçmeyin.\n3. Her aşamanın markdown bölümünde **“Öğrenci notu”** tablosu vardır: kodun ne yaptığı ve neden yazıldığı anlatılır.\n4. Kod hücrelerindeki yorum satırlarını okuyun; parametreleri değiştirip (ör. `n_samples`, `n_qubits`, `N_TRAIN_Q`) deneyi tekrarlayın.\n5. AŞAMA 2’de farklı problem–donanım–algoritma seçerek pipeline iskeletinin nasıl değiştiğini gözlemleyin.\n6. Gerçek QPU için AŞAMA 4’te kendi IBM Quantum token’ınızı ekleyin (ücretsiz Open Plan yeterli başlangıçtır).\n\n**Öğrenme çıktıları:** Sentetik veri → EDA → klasik baseline → özellik seçimi → quantum feature map → QSVC → metrik karşılaştırma → yorumlama zincirini kendi başınıza kurabilmek.\n\n\n> **Hata düzeltmeleri (19 aşama / uzun metin aynı):** `#!pip` · `pylatexenc` · Statevector `n_qubits≤8` · ISA transpile · QSVC.fit fallback · QPU varsayılan kapalı.\n"
  },
  {
   "cell_type": "markdown",
   "id": "0bbc662a",
   "metadata": {
    "id": "0bbc662a"
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   "source": "## AŞAMA 1: Drive / Path Tanımlama\n\nColab notebook içerisinde dosyaların erişileceği path tanımlanır:\n\n- **Google Drive**\n- **iCloud Drive** (lokal / macOS senaryoları)\n- **Any given address** — erişim kimlik bilgileri olan cloud disk provider\n\nAşağıdaki hücre önce Colab Drive’ı dener; lokal ortamda proje klasörüne düşer.\n\n\n---\n### Öğrenci notu — Bu kod ne işe yarar?\n\n| Satır / blok | Ne yapıyor? | Neden önemli? |\n|--------------|-------------|---------------|\n| `drive.mount(...)` | Colab’da Google Drive’ı `/content/drive` altına bağlar | Veri ve çıktıları kalıcı saklamak için |\n| `PROJECT_PATH` / `OUTPUT_PATH` | Proje ve sonuç klasör yollarını sabitler | Sonraki aşamalarda CSV/PNG yazarken aynı path kullanılır |\n| `os.makedirs(..., exist_ok=True)` | Klasör yoksa oluşturur | İlk çalıştırmada hata almamak için |\n\n**Öğrenme hedefi:** QML pipeline’ında “veri nereden gelir, sonuç nereye gider?” sorusunu netleştirmek. Cloud notebook’ta path yönetimi klasik lokal Python’dan farklıdır.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1bbf3a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f1bbf3a7",
    "outputId": "d31b5329-b931-424f-f7d4-4efb72eb6305",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731492159,
     "user_tz": -180,
     "elapsed": 89222,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.18\n",
      "Mounted at /content/drive\n",
      "✅ Google Drive bağlandı → /content/drive/MyDrive\n",
      "   Proje klasörü: /content/drive/MyDrive/QML_Projects\n",
      "   Çıktı klasörü: /content/drive/MyDrive/QML_Projects/outputs\n",
      "\n",
      "✅ AŞAMA 1 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 1: DRIVE / PATH TANIMLAMA\n# Amaç: Veri ve çıktı dosyalarının yazılacağı klasör yolunu sabitlemek\n# ============================================================\n\nimport os   # İşletim sistemi yolları, klasör oluşturma (makedirs) için\nimport sys  # Python sürümü ve ortam bilgisi için\n\n# Kullanılan Python sürümünün major.minor bilgisini yazdır\nprint(f\"Python: {sys.version.split()[0]}\")\n\n# Başlangıçta path değişkenlerini boş (None) bırakıyoruz; aşağıda doldurulacak\nDRIVE_ROOT = None      # Google Drive kök dizini (Colab'da /content/drive/MyDrive)\nPROJECT_PATH = None    # Proje klasörü yolu\nOUTPUT_PATH = None     # CSV/PNG sonuçlarının yazılacağı klasör\n\ntry:\n    # Colab ortamında Google Drive'ı bağlayan özel modül\n    from google.colab import drive\n    # Drive'ı /content/drive altına mount et (tarayıcıda izin ister)\n    drive.mount('/content/drive')\n    # Mount sonrası standart MyDrive yolu\n    DRIVE_ROOT = '/content/drive/MyDrive'\n    # Proje dosyaları için alt klasör yolu birleştir\n    PROJECT_PATH = os.path.join(DRIVE_ROOT, 'QML_Projects')\n    # Çıktılar (metrik CSV, grafik PNG) bu alt klasöre yazılacak\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    # Klasör yoksa oluştur; varsa hata verme (exist_ok=True)\n    os.makedirs(OUTPUT_PATH, exist_ok=True)\n    print(f\"✅ Google Drive bağlandı → {DRIVE_ROOT}\")\n    print(f\"   Proje klasörü: {PROJECT_PATH}\")\n    print(f\"   Çıktı klasörü: {OUTPUT_PATH}\")\nexcept Exception as e:\n    # Colab değilse veya mount reddedilirse buraya düşer\n    print(f\"⚠️ Drive mount atlandı (lokal ortam olabilir): {e}\")\n    # Lokal diskte göreli proje klasörü (notebook'un çalıştığı yer)\n    PROJECT_PATH = os.path.abspath('./qml_project')\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    os.makedirs(OUTPUT_PATH, exist_ok=True)  # Lokal outputs klasörünü oluştur\n    print(f\"   Lokal proje klasörü: {PROJECT_PATH}\")\n    print(f\"   Çıktı klasörü: {OUTPUT_PATH}\")\n\nprint(\"\\n✅ AŞAMA 1 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "abzLILhsre4g"
   },
   "source": "## AŞAMA 2: Cihaz & Algoritma Seçim Motoru (Etkileşimli Engine)\n\nHTML pipeline designer (`abc.html`) ile aynı mantık: **Problem → Donanım → Algoritma** hiyerarşik seçim.\n\n1. Önce **Problem** seçin  \n2. O probleme uygun **Donanım**lar aktif kalır; uyumsuzlar listeden çıkar  \n3. Seçilen problem+donanıma uygun **Algoritma**lar görünür  \n4. Üçlü tamamlanınca matematiksel model + uçtan uca pipeline kod iskeleti üretilir  \n\nSeçimler sonraki aşamalara aktarılır:\n- `SELECTED_PROBLEM`\n- `SELECTED_HARDWARE`\n- `SELECTED_ALGO`\n- `SELECTED_DEVICE` (ibm_or_aer / ionq_or_aer / dwave / …)\n\n> Colab / Jupyter'de kart tıklama yerine **dropdown** kullanılır; geçerli üçlü filtresi HTML'deki `validTriplets` + `dimmed` davranışının birebir karşılığıdır.\n\n\n---\n### Öğrenci notu — Bu motor ne işe yarar?\n\n| Kavram | Anlamı |\n|--------|--------|\n| **validTriplets** | Hangi problem + donanım + algoritma birlikte anlamlı? (ör. Chemistry + VQE; ML + QSVC) |\n| **Dropdown hiyerarşisi** | Önce problem → sonra donanım → sonra algoritma; uyumsuz seçenekler listelenmez |\n| **SELECTED_*** | Sonraki hücrelerin okuduğu global değişkenler (`SELECTED_ALGO`, `SELECTED_DEVICE`) |\n| **Pipeline iskeleti** | Seçime göre minimal Qiskit kod şablonu üretir |\n\n**Öğrenme hedefi:** QML’de “algoritmayı rastgele seçmek” yerine problem–donanım–algoritma üçlüsünü bilinçli kurmak. NISQ döneminde her algoritma her cihazda aynı verimle çalışmaz.\n",
   "id": "abzLILhsre4g"
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   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    "outputId": "922f25b0-83b7-447d-9a4d-e9086bc0063a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731509892,
     "user_tz": -180,
     "elapsed": 1022,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='margin:0 0 8px 0;'>🔧 Pipeline Seçim Motoru <span style='font-size:12px;c…"
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     "text": [
      "\n",
      "📌 Varsayılan seçim: Machine Learning | Superconducting Qubits | QSVC\n",
      "   SELECTED_DEVICE = ibm_or_aer\n",
      "   (Dropdown'dan değiştirirseniz SELECTED_* anında güncellenir.)\n",
      "\n",
      "✅ AŞAMA 2 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 2: ETKİLEŞİMLİ CİHAZ & ALGORİTMA SEÇİM MOTORU\n# Öğrenci rehberi (bu hücredeki ana kavramlar):\n#   VALID_TRIPLETS  → hangi problem+donanım+algoritma birlikte geçerli?\n#   dd_prob/hw/alg  → dropdown; seçim sırası zorunlu (önce problem)\n#   SELECTED_*      → sonraki aşamaların okuduğu global değişkenler\n#   build_pipeline_skeleton → seçime göre minimal Qiskit kod şablonu\n#   allowed_hw / allowed_alg → validTriplets'ten filtre (HTML dimmed mantığı)\n# Satır satır: aşağıdaki her fonksiyon/atama kendi docstring veya yorumuyla açıklanır.\n# ============================================================\n# ============================================================\n# AŞAMA 2: ETKİLEŞİMLİ CİHAZ & ALGORİTMA SEÇİM MOTORU\n# HTML \"Kuantum Ekosistemi Pipeline Designer\" (abc.html) ile aynı hiyerarşi\n# ============================================================\nimport pandas as pd\nfrom IPython.display import display, HTML, Markdown, clear_output\nimport ipywidgets as widgets\n\n# --- Geçerli üçlüler (Problem × Hardware × Algorithm) ---\n# Kaynak: kurumsal pipeline designer validTriplets + QML genişletmesi\n# Geçerli (Problem, Donanım, Algoritma) üçlüleri — UI filtre kaynağı\nVALID_TRIPLETS = [\n    # Optimization\n    (\"Optimization & Logistics\", \"Superconducting Qubits\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Superconducting Qubits\", \"Grover\"),\n    (\"Optimization & Logistics\", \"Trapped Ions\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Trapped Ions\", \"Grover\"),\n    (\"Optimization & Logistics\", \"Neutral Atoms\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Neutral Atoms\", \"Grover\"),\n    (\"Optimization & Logistics\", \"Quantum Annealer (D-Wave)\", \"QUBO/Annealing\"),\n    # Chemistry\n    (\"Quantum Chemistry\", \"Superconducting Qubits\", \"VQE\"),\n    (\"Quantum Chemistry\", \"Trapped Ions\", \"VQE\"),\n    # Cryptography\n    (\"Cryptography\", \"Superconducting Qubits\", \"Shor\"),\n    (\"Cryptography\", \"Trapped Ions\", \"Shor\"),\n    (\"Cryptography\", \"Photonic Qubits\", \"Shor\"),\n    # Machine Learning (QML)\n    (\"Machine Learning\", \"Superconducting Qubits\", \"QAOA\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"Grover\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"QSVC\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"VQC\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"QAOA\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"Grover\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"QSVC\"),\n    (\"Machine Learning\", \"Photonic Qubits\", \"QAOA\"),\n    (\"Machine Learning\", \"Photonic Qubits\", \"Grover\"),\n    (\"Machine Learning\", \"Neutral Atoms\", \"QAOA\"),\n    (\"Machine Learning\", \"Neutral Atoms\", \"Grover\"),\n]\n\n# Üçlülerden benzersiz problem listesi (dropdown 1)\nPROBLEMS = sorted({t[0] for t in VALID_TRIPLETS})\n# Üçlülerden benzersiz donanım listesi\nHARDWARES = sorted({t[1] for t in VALID_TRIPLETS})\n# Üçlülerden benzersiz algoritma listesi\nALGORITHMS = sorted({t[2] for t in VALID_TRIPLETS})\n\n# Her algoritma için matematiksel formül (Markdown/LaTeX)\nALGO_MATH = {\n    \"VQE\": r\"$E_0 \\le \\langle H \\rangle_{\\theta} = \\dfrac{\\langle \\psi(\\theta) | H | \\psi(\\theta) \\rangle}{\\langle \\psi(\\theta) | \\psi(\\theta) \\rangle}$\",\n    \"QAOA\": r\"$|\\gamma,\\beta\\rangle = e^{-i\\beta_p H_M} e^{-i\\gamma_p H_C}\\cdots e^{-i\\beta_1 H_M} e^{-i\\gamma_1 H_C}|+\\rangle^{\\otimes n}$\",\n    \"Grover\": r\"$U_s = 2|s\\rangle\\langle s| - I \\quad \\rightarrow \\quad \\mathcal{O}(\\sqrt{N})$\",\n    \"Shor\": r\"$\\mathrm{QFT}|x\\rangle = \\dfrac{1}{\\sqrt{2^n}}\\sum_{y=0}^{2^n-1} e^{2\\pi i x y / 2^n}|y\\rangle$\",\n    \"QSVC\": r\"$K(x,x') = |\\langle \\phi(x) | \\phi(x') \\rangle|^2$  (Fidelity Quantum Kernel)\",\n    \"VQC\": r\"$f_\\theta(x) = \\langle 0| U^\\dagger(x,\\theta) M U(x,\\theta) |0\\rangle$\",\n    \"QUBO/Annealing\": r\"$\\min_x\\; x^T Q x$  $\\leftrightarrow$  Ising $H = \\sum_i h_i \\sigma_i^z + \\sum_{ij} J_{ij}\\sigma_i^z\\sigma_j^z$\",\n}\n\n# Donanım → pratik cihaz profili (AŞAMA 16 backend seçimi için)\n# Donanım adı → sonraki aşamada kullanılacak cihaz profili kodu\nHW_TO_DEVICE = {\n    \"Superconducting Qubits\": \"ibm_or_aer\",\n    \"Trapped Ions\": \"ionq_or_aer\",\n    \"Photonic Qubits\": \"xanadu_or_aer\",\n    \"Neutral Atoms\": \"pasqal_or_aer\",\n    \"Quantum Annealer (D-Wave)\": \"dwave\",\n}\n\n# --- Global seçim state (sonraki hücreler okur) ---\n# Global seçim durumu (AŞAMA 11–16 bu değişkenleri okur)\nSELECTED_PROBLEM = None\nSELECTED_HARDWARE = None\nSELECTED_ALGO = None\nSELECTED_DEVICE = \"aer_only\"\n\ndef allowed_hw(prob):\n    \"\"\"Seçilen probleme uygun donanım listesini döndür.\"\"\"\n    return sorted({t[1] for t in VALID_TRIPLETS if t[0] == prob})\n\ndef allowed_alg(prob, hw=None):\n    \"\"\"Problem (+isteğe bağlı donanım) için uygun algoritmaları döndür.\"\"\"\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\ndef quantum_core_snippet(alg, hw):\n    \"\"\"Seçilen algoritma/donanıma göre minimal kuantum kod parçası üretir.\"\"\"\n    \"\"\"Seçime göre minimal kuantum çekirdek kodu (pipeline iskeletine enjekte).\"\"\"\n    if alg == \"VQE\":\n        if hw == \"Trapped Ions\":\n            return (\n                \"ansatz = TwoLocal(num_qubits=2, rotation_blocks='ry',\\n\"\n                \"                  entanglement_blocks='rxx', entanglement='full')\\n\"\n                \"estimator = Estimator()\\n\"\n                \"job = estimator.run([(ansatz, hamiltonian, [0.1]*ansatz.num_parameters)])\\n\"\n                \"quantum_result = float(job.result()[0].data.evs)\"\n            )\n        return (\n            \"ansatz = TwoLocal(num_qubits=2, rotation_blocks='ry',\\n\"\n            \"                  entanglement_blocks='cx', entanglement='linear')\\n\"\n            \"estimator = Estimator()\\n\"\n            \"job = estimator.run([(ansatz, hamiltonian, [0.1]*ansatz.num_parameters)])\\n\"\n            \"quantum_result = float(job.result()[0].data.evs)\"\n        )\n    if alg == \"QAOA\":\n        if hw == \"Neutral Atoms\":\n            return (\n                \"# Pasqal-style / analog-inspired QAOA placeholder\\n\"\n                \"ansatz = QAOAAnsatz(cost_operator=hamiltonian, reps=2)\\n\"\n                \"quantum_result = [1.2, -0.4]  # örnek parametre\"\n            )\n        return (\n            \"ansatz = QAOAAnsatz(cost_operator=hamiltonian, reps=1)\\n\"\n            \"sampler = Sampler()\\n\"\n            \"quantum_result = [0.5, 0.5]\"\n        )\n    if alg == \"Grover\":\n        if hw == \"Trapped Ions\":\n            return (\n                \"oracle = QuantumCircuit(3)\\n\"\n                \"oracle.ccx(0, 1, 2)\\n\"\n                \"grover_op = GroverOperator(oracle)\"\n            )\n        return (\n            \"oracle = QuantumCircuit(2)\\n\"\n            \"oracle.cz(0, 1)\\n\"\n            \"grover_op = GroverOperator(oracle)\"\n        )\n    if alg == \"Shor\":\n        return (\n            \"qc = QuantumCircuit(3)\\n\"\n            \"for j in range(3):\\n\"\n            \"    qc.h(j)\\n\"\n            \"    for k in range(j+1, 3):\\n\"\n            \"        qc.cp(np.pi / (2 ** (k - j)), k, j)\"\n        )\n    if alg in (\"QSVC\", \"VQC\"):\n        return (\n            \"from qiskit.circuit.library import ZZFeatureMap\\n\"\n            \"from qiskit_machine_learning.kernels import FidelityQuantumKernel\\n\"\n            \"from qiskit_machine_learning.algorithms import QSVC\\n\"\n            \"feature_map = ZZFeatureMap(feature_dimension=n_qubits, reps=2)\\n\"\n            \"quantum_kernel = FidelityQuantumKernel(feature_map=feature_map)\\n\"\n            \"model = QSVC(quantum_kernel=quantum_kernel)\\n\"\n            \"# model.fit(X_train_q, y_train_q)\"\n        )\n    if alg == \"QUBO/Annealing\":\n        return (\n            \"# dimod + D-Wave Ocean (pip install dimod dwave-ocean-sdk)\\n\"\n            \"import dimod\\n\"\n            \"bqm = dimod.BinaryQuadraticModel.from_qubo({(0,0): -1, (1,1): -1, (0,1): 2})\\n\"\n            \"# sampler = EmbeddingComposite(DWaveSampler())\\n\"\n            \"# sampleset = sampler.sample(bqm, num_reads=100)\\n\"\n            \"quantum_result = 'qubo_placeholder'\"\n        )\n    return \"# (çekirdek şablonu tanımsız)\"\n\ndef build_pipeline_skeleton(prob, hw, alg):\n    \"\"\"Uçtan uca pipeline iskelet metnini birleştirir (öğrenci kopyalayabilir).\"\"\"\n    core = quantum_core_snippet(alg, hw)\n    return f'''# -*- coding: utf-8 -*-\n\"\"\"\nKURUMSAL PIPELINE İSKELETİ (AŞAMA 2 Engine çıktısı)\nProblem : {prob}\nDonanım : {hw}\nAlgoritma: {alg}\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import SparsePauliOp\nfrom qiskit.circuit.library import TwoLocal, QAOAAnsatz, GroverOperator\nfrom qiskit.primitives import StatevectorEstimator as Estimator\nfrom qiskit.primitives import StatevectorSampler as Sampler\n\n# --- Veri girişi (AŞAMA 5/6 ile doldurulur) ---\n# df_main = ...\n\n# --- Kuantum çekirdek ---\nhamiltonian = SparsePauliOp.from_list([(\"ZZ\", 1.0)])\nn_qubits = 2  # AŞAMA 12'de güncellenir\n\n{core}\n\n# --- Sonuç (AŞAMA 17 ile kaydedilir) ---\n# results → CSV / API / SQL\n'''\n\n# --- Widgets (HTML kart tıklama yerine dropdown; Colab/Jupyter uyumlu) ---\n# Dropdown 1: problem seçimi\ndd_prob = widgets.Dropdown(\n    options=[\"— Problem seçin —\"] + PROBLEMS,\n    description=\"Problem:\",\n    layout=widgets.Layout(width=\"460px\"),\n    style={\"description_width\": \"90px\"},\n)\n# Dropdown 2: donanım (problem seçilmeden disabled)\ndd_hw = widgets.Dropdown(\n    options=[\"— önce problem —\"],\n    description=\"Donanım:\",\n    layout=widgets.Layout(width=\"460px\"),\n    style={\"description_width\": \"90px\"},\n    disabled=True,\n)\n# Dropdown 3: algoritma (donanım seçilmeden disabled)\ndd_alg = widgets.Dropdown(\n    options=[\"— önce donanım —\"],\n    description=\"Algoritma:\",\n    layout=widgets.Layout(width=\"460px\"),\n    style={\"description_width\": \"90px\"},\n    disabled=True,\n)\nout = widgets.Output()  # Sonuçların basılacağı dinamik çıktı alanı\n\ndef on_prob_change(change):\n    \"\"\"Problem değişince donanım listesini yenile, alt seçimleri sıfırla.\"\"\"\n    global SELECTED_PROBLEM, SELECTED_HARDWARE, SELECTED_ALGO, SELECTED_DEVICE\n    if change[\"name\"] != \"value\":\n        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 —\"]\n        dd_hw.disabled = True\n        dd_alg.options = [\"— önce donanım —\"]\n        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\n        dd_hw.disabled = False\n        dd_hw.value = dd_hw.options[0]\n        dd_alg.options = [\"— önce donanım —\"]\n        dd_alg.disabled = True\n    _render()\n\ndef on_hw_change(change):\n    \"\"\"Donanım değişince algoritma listesini ve SELECTED_DEVICE'ı güncelle.\"\"\"\n    global SELECTED_HARDWARE, SELECTED_ALGO, SELECTED_DEVICE\n    if change[\"name\"] != \"value\":\n        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 —\"]\n        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\n        dd_alg.disabled = False\n        dd_alg.value = dd_alg.options[0]\n        if len(algs) == 1:\n            dd_alg.value = algs[0]\n    _render()\n\ndef on_alg_change(change):\n    \"\"\"Algoritma seçimini SELECTED_ALGO'ya yaz ve paneli yenile.\"\"\"\n    global SELECTED_ALGO\n    if change[\"name\"] != \"value\":\n        return\n    val = change[\"new\"]\n    if str(val).startswith(\"—\") or not SELECTED_HARDWARE:\n        SELECTED_ALGO = None\n    else:\n        SELECTED_ALGO = val\n    _render()\n\ndef _render():\n    \"\"\"Üçlü tablo + formül + pipeline iskeletini Output widget'a bas.\"\"\"\n    with out:\n        clear_output(wait=True)\n        rows = []\n        for p, h, a in VALID_TRIPLETS:\n            mark = \"\"\n            if SELECTED_PROBLEM == p and SELECTED_HARDWARE == h and SELECTED_ALGO == a:\n                mark = \"✅ SEÇİLİ\"\n            elif SELECTED_PROBLEM == p and (SELECTED_HARDWARE is None or SELECTED_HARDWARE == h):\n                mark = \"· uygun\"\n            rows.append({\"Problem\": p, \"Donanım\": h, \"Algoritma\": a, \"Durum\": mark})\n        display(Markdown(\"### Geçerli üçlü matrisi (HTML validTriplets)\"))\n        display(pd.DataFrame(rows))\n\n        if SELECTED_PROBLEM and SELECTED_HARDWARE and SELECTED_ALGO:\n            display(Markdown(\n                f\"### Seçim: **{SELECTED_PROBLEM}** → **{SELECTED_HARDWARE}** → **{SELECTED_ALGO}**\"\n            ))\n            display(Markdown(f\"**Cihaz profili (`SELECTED_DEVICE`):** `{SELECTED_DEVICE}`\"))\n            math = ALGO_MATH.get(SELECTED_ALGO, \"\")\n            if math:\n                display(Markdown(f\"**Matematiksel model:** {math}\"))\n            display(Markdown(\"**Üretilen pipeline iskeleti:**\"))\n            print(build_pipeline_skeleton(SELECTED_PROBLEM, SELECTED_HARDWARE, SELECTED_ALGO))\n            display(HTML(\n                \"<div style='padding:12px;background:#f0fdf4;border-left:4px solid #22c55e;\"\n                \"border-radius:8px;margin-top:8px;'>\"\n                f\"<b>✅ Engine hazır.</b> SELECTED_ALGO=<code>{SELECTED_ALGO}</code>, \"\n                f\"SELECTED_DEVICE=<code>{SELECTED_DEVICE}</code> — AŞAMA 11–16 bu değerleri kullanır.\"\n                \"</div>\"\n            ))\n        else:\n            display(HTML(\n                \"<div style='padding:12px;background:#fff7ed;border-left:4px solid #fb923c;\"\n                \"border-radius:8px;'>\"\n                \"Soldan sağa seçin: <b>Problem → Donanım → Algoritma</b>. \"\n                \"Uyumsuz seçenekler dropdown'da listelenmez \"\n                \"(HTML arayüzündeki <code>dimmed</code> kart mantığının Jupyter karşılığı).\"\n                \"</div>\"\n            ))\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\nui = widgets.VBox([\n    widgets.HTML(\"<h3 style='margin:0 0 8px 0;'>🔧 Pipeline Seçim Motoru \"\n                 \"<span style='font-size:12px;color:#64748b;'>(abc.html engine)</span></h3>\"),\n    dd_prob, dd_hw, dd_alg, out\n])\ndisplay(ui)\n_render()\n\n# Varsayılan POC: kredi riski → ML + Superconducting + QSVC\nif SELECTED_ALGO is None:\n    SELECTED_PROBLEM = \"Machine Learning\"\n    SELECTED_HARDWARE = \"Superconducting Qubits\"\n    SELECTED_ALGO = \"QSVC\"\n    SELECTED_DEVICE = \"ibm_or_aer\"\n    print(f\"\\n📌 Varsayılan seçim: {SELECTED_PROBLEM} | {SELECTED_HARDWARE} | {SELECTED_ALGO}\")\n    print(f\"   SELECTED_DEVICE = {SELECTED_DEVICE}\")\n    print(\"   (Dropdown'dan değiştirirseniz SELECTED_* anında güncellenir.)\")\n\nprint(\"\\n✅ AŞAMA 2 tamamlandı.\")\n",
   "id": "isiLXJRore4h"
  },
  {
   "cell_type": "markdown",
   "id": "6ccdbcaa",
   "metadata": {
    "id": "6ccdbcaa"
   },
   "source": "## AŞAMA 3: Python Library Kurulumu\n\nKuantum Makine Öğrenimi (QML), kuantum kimyası, yönlendirici (prescriptive) optimizasyon ve post-kuantum kriptografi odaklı çalışmalarımızda altyapıyı oluşturan temel Python kütüphaneleri ve bu kütüphanelerin ekosistemdeki rolleri aşağıdaki gibidir:\n\n| **Python Kütüphanesi** | **Kategori** | **Projelerimizdeki İşlevi ve Ne İşe Yaradığı** |\n| --- | --- | --- |\n| **Qiskit** | Kuantum Hesaplama & QML | Kuantum devre mimarilerinin tasarlanması, klasik verilerin kuantum durumlarına kodlanması (data encoding) ve VQC, QSVC gibi hibrit kuantum-klasik algoritmaların inşasında ana geliştirme ortamını sağlar. |\n| **Qiskit Aer** | Simülasyon | Geliştirilen algoritmaların ve özellik haritalarının (feature maps) gerçek IBM kuantum donanımlarına gönderilmeden önce, gürültülü (noisy) veya ideal ortamlarda yüksek performanslı simülasyon testlerinin yapılmasını sağlar. |\n| **PySCF** | Kuantum Kimyası | GLP-1R aktif bölge simülasyonları ve havacılık kompozit materyallerinin radyo frekansı emilim analizleri gibi projelerde moleküler elektronik yapı hesaplamalarını ve Hamiltonian çıkarımlarını yapar. |\n| **dimod** | Optimizasyon | Yönlendirici analitik ve karar destek motorları geliştirirken, karmaşık yapıdaki endüstriyel planlama problemlerini kuantum bilgisayarların çözebileceği QUBO (Quadratic Unconstrained Binary Optimization) ve Ising modellerine dönüştürür. |\n| **D-Wave Ocean SDK** | Kuantum Tavlama (Annealing) | Oluşturulan QUBO ve Ising formülasyonlarının D-Wave kuantum sistemlerine veya hibrit çözücülere iletilerek hızla optimize edilmesini yönetir. |\n| **Scikit-learn** | Klasik ML & Ön İşleme | Kuantum makine öğrenimi modellerine (örneğin QML classification) veriler beslenmeden önce verilerin standartlaştırılması, ön işleme adımlarının yapılması ve hibrit yapıların klasik referans (baseline) modellerle karşılaştırılması için kullanılır. |\n| **NumPy & Pandas** | Veri İşleme & Analiz | Jupyter ortamında veri setlerinin yapılandırılması, karar destek motorlarındaki matris operasyonlarının vektörel yönetimi ve KVKK/GDPR uyumlu veri maskeleme boru hatlarının (pipeline) tasarlanmasını sağlar. |\n| **Matplotlib / Seaborn** | Görselleştirme | Eğitim materyallerinde, araştırma raporlarında (LaTeX entegrasyonu öncesi) ve projelerde devre topolojilerinin, QML model performans ölçümlerinin ve Bloch küresi gösterimlerinin görselleştirilmesi için kullanılır. |\n| **OQS-Python / PyCryptodome** | Kriptografi | BB84, BBM92 kuantum anahtar dağıtımı (QKD) protokolleri simülasyonları ile ML-KEM ve Kyber gibi post-kuantum kriptografi standartlarının Python üzerinde test edilmesini ve uygulanmasını sağlar. |\n\nColab notebook içerisinde bunların tümünün kurulması için aşağıdaki code hücresini çalıştırın. **Kurulmasını istemediğiniz paketleri yorum satırı (`#`) yaparak kapatabilirsiniz.**\n\n> Bu notebook’un varsayılan POC hattı (QSVC + klasik baseline) için **zorunlu** paketler: Qiskit, Qiskit Aer, qiskit-machine-learning, qiskit-ibm-runtime, scikit-learn, pandas, numpy, matplotlib, seaborn, xgboost.\n>\n> PySCF, dimod, D-Wave Ocean, OQS/PyCryptodome isteğe bağlıdır (kimya / QUBO / kriptografi senaryoları).\n\n\n---\n### Öğrenci notu — Kurulum ve import ne işe yarar?\n\n1. **`!pip install ...`** — Colab oturumu her seferinde temiz başlar; paketleri yeniden kurmak gerekir. `#` ile yorumladığınız paket kurulmaz (kota / süre tasarrufu).\n2. **İkinci hücre (import)** — Kurulan paketleri Python ad alanına alır. Qiskit import’ları bilinçli olarak **quantum aşamalarına** bırakılmıştır; böylece token/backend olmadan da EDA ve klasik ML çalışır.\n\n**Alıştırma:** `dimod` satırının `#` işaretini kaldırıp kurun; `import dimod` ile doğrulayın. QUBO senaryosuna geçerken bu adım zorunlu olur.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d4b9bf2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2d4b9bf2",
    "outputId": "a0d0a125-8a51-4287-eafc-c974ff9aef4f",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731589228,
     "user_tz": -180,
     "elapsed": 37773,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "📦 Paketler kuruluyor (1–3 dk sürebilir)...\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\u001b[0m\n",
      "\u001b[31mibis-framework 9.5.0 requires python-dateutil<3,>=2.8.2, but you have python-dateutil 3.0.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mgoogle-cloud-bigquery 3.46.2 requires python-dateutil<3.0.0,>=2.8.2, but you have python-dateutil 3.0.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mbetterproto 2.0.0b7 requires python-dateutil<3.0,>=2.8, but you have python-dateutil 3.0.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mbigframes 2.51.0 requires python-dateutil<3,>=2.8.2, but you have python-dateutil 3.0.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mholidays 0.107 requires python-dateutil<3,>=2.9.0.post0, but you have python-dateutil 3.0.0 which is incompatible.\u001b[0m\n",
      "✅ Kurulum tamamlandı.\n",
      "   Zorunlu: qiskit, qiskit-aer, qiskit-machine-learning, qiskit-algorithms,\n",
      "            qiskit-ibm-runtime, numpy, pandas, scikit-learn, xgboost,\n",
      "            matplotlib, seaborn, pylatexenc, tqdm, ipywidgets, python-dotenv\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 3: PYTHON LIBRARY KURULUMU (tüm notebook bağımlılıkları)\n# Bu hücre notebook'un başında BİR KEZ çalıştırılmalıdır.\n# Opsiyonel paket: satır başındaki # karakterini silin (boşluk bırakmadan).\n# ============================================================\n\n# Kullanıcıya kurulumun başladığını bildir\nprint(\"📦 Paketler kuruluyor (1–3 dk sürebilir)...\")\n\n# pip'i güncelle (eski pip bazı paketleri kuramayabilir)\n!pip install -q --upgrade pip\n\n# Qiskit çekirdeği: devre, algoritma, QML, IBM bulut erişimi\n!pip install -q qiskit qiskit-aer qiskit-machine-learning qiskit-algorithms qiskit-ibm-runtime\n\n# Klasik veri bilimi ve ML yığını\n!pip install -q numpy pandas scikit-learn xgboost\n\n# Grafik çizimi + Qiskit mpl drawer için pylatexenc\n!pip install -q matplotlib seaborn pylatexenc\n\n# Notebook widget, ilerleme çubuğu, .env dosyasından token okuma\n!pip install -q tqdm ipywidgets python-dotenv\n\n# Opsiyonel — QUBO / D-Wave (AŞAMA 2'de Annealing seçilirse açın)\n#!pip install -q dimod dwave-ocean-sdk\n\n# Opsiyonel — moleküler VQE / PySCF\n#!pip install -q pyscf qiskit-nature\n\n# Opsiyonel — kriptografi denemeleri\n#!pip install -q pycryptodome\n\nprint(\"✅ Kurulum tamamlandı.\")\nprint(\"   Zorunlu: qiskit, qiskit-aer, qiskit-machine-learning, qiskit-algorithms,\")\nprint(\"            qiskit-ibm-runtime, numpy, pandas, scikit-learn, xgboost,\")\nprint(\"            matplotlib, seaborn, pylatexenc, tqdm, ipywidgets, python-dotenv\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a7f8353",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2a7f8353",
    "outputId": "654513cb-c0a9-4ea0-ad44-f0aa0d051c4d",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731602546,
     "user_tz": -180,
     "elapsed": 2579,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.18\n",
      "✅ Ortak kütüphaneler yüklendi (numpy, pandas, sklearn, matplotlib, seaborn, xgboost).\n",
      "   Qiskit import'ları quantum aşamalarında (13–16) yapılır.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 3b: Ortak import'lar (sonraki tüm aşamalar bunları kullanır)\n# ============================================================\n\nimport os          # Dosya yolu ve klasör işlemleri\nimport sys         # Python sürümü\nimport json        # api.json okuma (AŞAMA 4)\nimport math        # log2, ceil (qubit maliyeti hesabı)\nimport warnings    # sklearn uyarılarını susturmak için\nfrom pathlib import Path              # Modern dosya yolu nesnesi\nfrom datetime import datetime         # run_id zaman damgası\nfrom hashlib import sha256            # KVKK maskeleme hash'i\n\nimport numpy as np                    # Sayısal dizi ve rastgele üretim\nimport pandas as pd                   # Tablo (DataFrame) işlemleri\nimport matplotlib.pyplot as plt       # Genel grafik\nimport seaborn as sns                 # İstatistiksel görselleştirme\n\n# Veriyi train/test ayırma ve çapraz doğrulama\nfrom sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n# Ölçekleme ve kategorik → sayı dönüşümü\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler\n# Yüksek boyutta boyut indirgeme (gerekirse)\nfrom sklearn.decomposition import PCA\n# Sınıflandırma metrikleri\nfrom sklearn.metrics import (\n    accuracy_score, precision_score, recall_score, f1_score,\n    roc_auc_score, roc_curve, precision_recall_curve, average_precision_score,\n    confusion_matrix, classification_report, ConfusionMatrixDisplay,\n)\n# Klasik baseline modeller\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom xgboost import XGBClassifier\n\nwarnings.filterwarnings(\"ignore\")           # Konsolu gereksiz uyarıyla doldurma\nsns.set_style(\"whitegrid\")                  # Seaborn grafik stili\nplt.rcParams[\"figure.figsize\"] = (10, 6)    # Varsayılan figür boyutu\n\nprint(f\"Python: {sys.version.split()[0]}\")\nprint(\"✅ Ortak kütüphaneler yüklendi (numpy, pandas, sklearn, matplotlib, seaborn, xgboost).\")\nprint(\"   Qiskit import'ları quantum aşamalarında (13–16) yapılır.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "b8c4539e",
   "metadata": {
    "id": "b8c4539e"
   },
   "source": "## AŞAMA 4: API Keys Yönetimi\n\nColab notebook içerisinde API key / token’ların kullanılması:\n\n- `.env` dosyasından okuyarak\n- `api.json` dosyasından okuyarak\n- **Colab secret key**’den okuyarak\n- `api.txt` dosyasından okuyarak\n\nÖncelik sırası: Colab Secrets → `.env` → `api.json` → `api.txt`. Production’da token’ı notebook’a gömmeyin.\n\n\n---\n### Öğrenci notu — API key yönetimi\n\n| Yöntem | Ne zaman? |\n|--------|-----------|\n| Colab Secrets | En güvenlisi; notebook’a token yazılmaz |\n| `.env` / `api.json` / `api.txt` | Lokal veya paylaşılan proje iskeleti |\n| `QiskitRuntimeService.save_account` | Token’ı disk’e kaydeder; sonraki `QiskitRuntimeService()` otomatik bulur |\n\n**Güvenlik kuralı:** Token’ı GitHub’a, ekran görüntüsüne veya public notebook’a koymayın. Sızdırılan token ile başkasının kotası tüketilebilir.\n\n**Öğrenme hedefi:** Bulut QPU erişiminin kimlik doğrulama modelini anlamak (API token = anahtar).\n"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd24927b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fd24927b",
    "outputId": "3952edef-a551-495d-f862-50dfe23856c4",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731641218,
     "user_tz": -180,
     "elapsed": 818,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Token Colab Secrets'ten okundu.\n",
      "Token uzunluğu: 44 karakter (güvenlik için tam değer yazdırılmaz).\n",
      "✅ AŞAMA 4 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 4: API KEYS — token'ı güvenli kaynaklardan oku\n# Öncelik: Colab Secrets → .env → api.json → api.txt\n# ============================================================\n\nimport json                 # api.json dosyasını sözlüğe çevirmek için\nfrom pathlib import Path    # Dosyanın varlığını kontrol etmek için\n\nIBM_TOKEN = None            # Bulunan token burada tutulacak (başta yok)\n\n# 1) Colab Secrets: notebook metnine yazılmadan saklanan gizli değer\ntry:\n    from google.colab import userdata\n    IBM_TOKEN = userdata.get('IBM_QUANTUM_TOKEN')  # Secret adı birebir bu olmalı\n    if IBM_TOKEN:\n        print(\"✅ Token Colab Secrets'ten okundu.\")\nexcept Exception:\n    pass  # Colab değilse veya secret yoksa sessizce geç\n\n# 2) .env dosyası: IBM_QUANTUM_TOKEN=... satırı\nif not IBM_TOKEN:\n    try:\n        from dotenv import load_dotenv\n        load_dotenv()  # .env içeriğini ortam değişkenlerine yükle\n        IBM_TOKEN = os.getenv('IBM_QUANTUM_TOKEN')\n        if IBM_TOKEN:\n            print(\"✅ Token .env dosyasından okundu.\")\n    except Exception:\n        pass\n\n# 3) api.json: {\"IBM_QUANTUM_TOKEN\": \"...\"} veya {\"token\": \"...\"}\nif not IBM_TOKEN:\n    api_json = Path('api.json')\n    if api_json.exists():\n        with open(api_json) as f:\n            data = json.load(f)  # JSON → Python dict\n        IBM_TOKEN = data.get('IBM_QUANTUM_TOKEN') or data.get('token')\n        if IBM_TOKEN:\n            print(\"✅ Token api.json'dan okundu.\")\n\n# 4) api.txt: dosyanın tamamı token metni\nif not IBM_TOKEN:\n    api_txt = Path('api.txt')\n    if api_txt.exists():\n        IBM_TOKEN = api_txt.read_text().strip()  # Boşluk/satır sonu temizle\n        if IBM_TOKEN:\n            print(\"✅ Token api.txt'den okundu.\")\n\nif not IBM_TOKEN:\n    print(\"⚠️ Token bulunamadı. Simülatör ile devam edilebilir; gerçek QPU için token şart.\")\nelse:\n    # Güvenlik: token'ın kendisini yazdırma, sadece uzunluğunu göster\n    print(f\"Token uzunluğu: {len(IBM_TOKEN)} karakter (güvenlik için tam değer yazdırılmaz).\")\n\nprint(\"✅ AŞAMA 4 tamamlandı.\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58023770",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "58023770",
    "outputId": "ac8723da-070b-48de-eb46-af719747db7e",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731707990,
     "user_tz": -180,
     "elapsed": 42988,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "qiskit_runtime_service.__init__:WARNING:2026-11-03 18:48:26,064: Instance was not set at service instantiation. Free and trial plan instances will be prioritized. Based on the following filters: (tags: None, region: us-east, eu-de), and available plans: (open), the available account instances are: student-ahmeteminselvi, student-cesur.bagci, student-gamzekevser.temur, student-ahmet.bilgen, student-gumusemirhan, student-selimserdartafrali, student-onur.gungor2, student-serdalkorkutavci, student-ozlemdundar_sau, student-ozden.ayberk, student-2533101003, student-asyrigel, student-atilla.bcn, student-emre62722, student-yekta.sokelen, student-serhatsevim01, student-ozdemir, student-mehmetbozdemir21, student-hie, student-suleymanemir94041, student-ubilecen, student-pinar.tan.ozcan, student-stglysn, student-h.emredemirel, student-yuce.nursultan, student-tsinanergun, student-cagri20049, student-gulsugun02, student-k.iremkaynar, student-yasinntosun, student-tevfik.sen, student-2533101003, student-denizguzelay, student-muh.oguz.sezgin, open-instance, student-mervekaragoz7, student-cempeker1985, student-cuneytsargullar, student-acarsefa621, student-betul.altinok, student-mustafamurathazneci, student-seo.fates, student-eyup.erkaya, student-onur.gungor2, student-sina.kusoglu. If you need a specific instance set it explicitly either by using a saved account with a saved default instance or passing it in directly to QiskitRuntimeService(). Alternatively, pass instance='auto' or save it to your account for auto-selection without warning.\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:26,066: Loading instance: student-ahmeteminselvi, plan: open\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ IBM Quantum Runtime Service başarıyla bağlandı.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,686: Loading instance: student-cesur.bagci, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,687: Loading instance: student-gamzekevser.temur, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,688: Loading instance: student-ahmet.bilgen, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,689: Loading instance: student-gumusemirhan, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,691: Loading instance: student-selimserdartafrali, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,692: Loading instance: student-onur.gungor2, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,694: Loading instance: student-serdalkorkutavci, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,695: Loading instance: student-ozlemdundar_sau, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,695: Loading instance: student-ozden.ayberk, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,697: Loading instance: student-2533101003, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,697: Loading instance: student-asyrigel, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,698: Loading instance: student-atilla.bcn, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,699: Loading instance: student-emre62722, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,699: Loading instance: student-yekta.sokelen, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,702: Loading instance: student-serhatsevim01, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,702: Loading instance: student-ozdemir, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,703: Loading instance: student-mehmetbozdemir21, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,704: Loading instance: student-hie, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,707: Loading instance: student-suleymanemir94041, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,708: Loading instance: student-ubilecen, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,709: Loading instance: student-pinar.tan.ozcan, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,710: Loading instance: student-stglysn, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,712: Loading instance: student-h.emredemirel, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,712: Loading instance: student-yuce.nursultan, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,713: Loading instance: student-tsinanergun, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,713: Loading instance: student-cagri20049, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,715: Loading instance: student-gulsugun02, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,715: Loading instance: student-k.iremkaynar, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,717: Loading instance: student-yasinntosun, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,718: Loading instance: student-tevfik.sen, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,718: Loading instance: student-2533101003, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,719: Loading instance: student-denizguzelay, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,720: Loading instance: student-muh.oguz.sezgin, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,720: Loading instance: open-instance, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,721: Loading instance: student-mervekaragoz7, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,721: Loading instance: student-cempeker1985, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,722: Loading instance: student-cuneytsargullar, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,723: Loading instance: student-acarsefa621, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,723: Loading instance: student-betul.altinok, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,724: Loading instance: student-mustafamurathazneci, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,726: Loading instance: student-seo.fates, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,726: Loading instance: student-eyup.erkaya, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,727: Loading instance: student-onur.gungor2, plan: open\n",
      "qiskit_runtime_service.backends:WARNING:2026-11-03 18:48:27,727: Loading instance: student-sina.kusoglu, plan: open\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Kullanılabilir backend sayısı: 3\n"
     ]
    }
   ],
   "source": "# Token bulunduysa IBM hesabına kaydet ve Runtime servisini aç\nfrom qiskit_ibm_runtime import QiskitRuntimeService  # IBM Quantum bulut API istemcisi\n\nservice = None  # Başarısız olursa None kalır; sonraki aşamalar bunu kontrol eder\n\nif IBM_TOKEN:\n    try:\n        # Token'ı diske kaydet; sonraki oturumlarda tekrar yapıştırmaya gerek kalmaz\n        QiskitRuntimeService.save_account(\n            channel=\"ibm_quantum_platform\",  # IBM Quantum Platform kanalı\n            token=IBM_TOKEN,                 # Az önce okunan API anahtarı\n            overwrite=True,                  # Eski kayıt varsa üzerine yaz\n        )\n        service = QiskitRuntimeService()     # Kaydedilmiş hesapla servis nesnesi\n        print(\"✅ IBM Quantum Runtime Service başarıyla bağlandı.\")\n        print(f\"Kullanılabilir backend sayısı: {len(service.backends())}\")\n    except Exception as e:\n        print(f\"⚠️ Servis bağlantı hatası: {e}\")\n        service = None\nelse:\n    try:\n        # Token bu oturumda yok ama daha önce save_account yapılmış olabilir\n        service = QiskitRuntimeService()\n        print(\"✅ Kaydedilmiş hesap ile servis bağlandı.\")\n        print(f\"Kullanılabilir backend sayısı: {len(service.backends())}\")\n    except Exception as e:\n        print(f\"⚠️ Hesap yok veya token hatalı: {e}\")\n        print(\"   Quantum kısmı simülatör ile devam edebilir.\")\n        service = None\n"
  },
  {
   "cell_type": "markdown",
   "id": "33cfe759",
   "metadata": {
    "id": "33cfe759"
   },
   "source": "## AŞAMA 5: Sentetik Veri Üretimi\n\nSeçtiğimiz problemdeki ihtiyaç duyulan feature’ları tek tek verdiğimizde, istediğimiz sayı ve özellikte sentetik veri üretilir.\n\nBu POC’ta kredi risk senaryosu için gerçekçi dağılımlarla **30000 satır** üretilir (`age`, `income`, `debt_to_income`, `credit_score`, … ve hedef `default`).\n\n\n---\n### Öğrenci notu — Sentetik veri kodu satır satır\n\n| Blok | Ne yapıyor? |\n|------|-------------|\n| `np.random.seed(42)` | Aynı “rastgele” veriyi tekrar üretir → deneyler yeniden üretilebilir |\n| `age`, `income`, … | Her feature için gerçekçi dağılım (uniform, lognormal, beta, choice) |\n| `logit = -4.5 + ...` | Risk skorunu özelliklerin doğrusal birleşimiyle üretir (lojistik regresyon mantığı) |\n| `prob = 1/(1+exp(-logit))` | Logit’i olasılığa çevirir |\n| `default = (rand < prob)` | Olasılığa göre 0/1 etiket atar |\n| `pd.DataFrame({...})` | Tüm sütunları tek tabloda birleştirir |\n\n**Öğrenme hedefi:** Etiketli (supervised) QML için “X özellikler, y hedef” yapısını kurmak. Gerçek banka verisi yoksa sentetik veri ile pipeline’ı uçtan uca test edersiniz.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d259c49",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "id": "5d259c49",
    "outputId": "c2dcd6e0-60e8-4b89-db1e-94fc3f68665b",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731736164,
     "user_tz": -180,
     "elapsed": 102,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Veri seti şekli: (30000, 11)\n",
      "\n",
      "Default oranı: 1.84%\n",
      "\n",
      "İlk 5 satır:\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "   age  income  employment_length  debt_to_income  credit_score  loan_amount  \\\n",
       "0   59   52286                  8           0.167           693        24570   \n",
       "1   49   74356                  7           0.197           676        22915   \n",
       "2   35   22810                 15           0.255           593        32731   \n",
       "3   63   55438                 24           0.150           630        17424   \n",
       "4   28   55119                  9           0.323           673        10179   \n",
       "\n",
       "   loan_term home_ownership             purpose  previous_defaults  default  \n",
       "0         24       MORTGAGE                 car                  0        0  \n",
       "1         36           RENT    home_improvement                  0        0  \n",
       "2         36           RENT                 car                  0        0  \n",
       "3         12       MORTGAGE  debt_consolidation                  0        0  \n",
       "4         36            OWN                 car                  0        0  "
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       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    <div class=\"colab-df-buttons\">\n",
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       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-413dbebc-7223-424a-b2d9-bb459c4f8312')\"\n",
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       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-413dbebc-7223-424a-b2d9-bb459c4f8312 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-413dbebc-7223-424a-b2d9-bb459c4f8312');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\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",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"print(\\\"\\\\n\\u2705 A\\u015eAMA 5 tamamland\\u0131\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 28,\n        \"max\": 63,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          49,\n          28,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18530,\n        \"min\": 22810,\n        \"max\": 74356,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          74356,\n          55119,\n          22810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employment_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 7,\n        \"max\": 24,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7,\n          9,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"debt_to_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07081525259433874,\n        \"min\": 0.15,\n        \"max\": 0.323,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.197,\n          0.323,\n          0.255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"credit_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 593,\n        \"max\": 693,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          676,\n          673,\n          593\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8401,\n        \"min\": 10179,\n        \"max\": 32731,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          22915,\n          10179,\n          32731\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan_term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 12,\n        \"max\": 36,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          24,\n          36,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"home_ownership\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"MORTGAGE\",\n          \"RENT\",\n          \"OWN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"car\",\n          \"home_improvement\",\n          \"debt_consolidation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_defaults\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "✅ AŞAMA 5 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 5: SENTETİK BANKACILIK VERİ SETİ (30000 satır)\n# Amaç: Etiketli (X, y) supervised öğrenme tablosu üretmek\n# ============================================================\n\nnp.random.seed(42)          # Aynı \"rastgele\" sonuçlar için sabitle (reproducibility)\nn_samples = 30000           # Üretilecek müşteri / satır sayısı\n\n# --- Bağımsız özellikler (X adayları) ---\nage = np.random.randint(21, 70, n_samples)  # Yaş: 21–69 arası tam sayı\n# Gelir: log-normal (sağa çarpık, gerçek gelir dağılımına benzer)\nincome = np.random.lognormal(mean=10.8, sigma=0.45, size=n_samples).astype(int)\nemployment_length = np.random.randint(0, 25, n_samples)  # İşte çalışma yılı\n# Borç/gelir oranı: beta dağılımı ile 0–0.55 aralığına sıkıştır\ndebt_to_income = np.clip(np.random.beta(2, 5, n_samples) * 0.6, 0.01, 0.55)\n# Kredi skoru: ortalama 680, std 60; 500–850 dışını kes\ncredit_score = np.clip(np.random.normal(680, 60, n_samples).astype(int), 500, 850)\nloan_amount = np.random.lognormal(mean=9.5, sigma=0.5, size=n_samples).astype(int)\n# Vade ayı: verilen olasılıklarla seç\nloan_term = np.random.choice([12, 24, 36, 48, 60], n_samples, p=[0.1, 0.25, 0.35, 0.2, 0.1])\nhome_ownership = np.random.choice(['RENT', 'MORTGAGE', 'OWN'], n_samples, p=[0.4, 0.45, 0.15])\npurpose = np.random.choice(\n    ['debt_consolidation', 'home_improvement', 'business', 'car', 'other'],\n    n_samples, p=[0.35, 0.2, 0.15, 0.15, 0.15]\n)\nprevious_defaults = np.random.choice([0, 1, 2], n_samples, p=[0.85, 0.12, 0.03])\n\n# --- Hedef etiket y: temerrüt olasılığını logit ile modelle ---\nlogit = (\n    -4.5                                    # Temel (düşük) risk seviyesi\n    + 0.012 * (age - 40)                    # Yaş etkisi\n    - 0.000025 * income                     # Yüksek gelir riski azaltır\n    - 0.04 * employment_length              # Uzun istihdam riski azaltır\n    + 6.0 * debt_to_income                  # Yüksek DTI riski artırır\n    - 0.008 * (credit_score - 650)          # İyi skor riski azaltır\n    + 0.00004 * loan_amount                 # Büyük kredi riski artırır\n    + 0.8 * previous_defaults               # Geçmiş temerrüt güçlü sinyal\n    + np.where(home_ownership == 'RENT', 0.3, 0)  # Kiracıya küçük ek risk\n)\nprob = 1 / (1 + np.exp(-logit))             # Logit → (0,1) olasılık (sigmoid)\ndefault = (np.random.rand(n_samples) < prob).astype(int)  # Bernoulli örnekleme → 0/1\n\n# Tüm sütunları tek DataFrame'de birleştir\ndf = pd.DataFrame({\n    'age': age,\n    'income': income,\n    'employment_length': employment_length,\n    'debt_to_income': debt_to_income.round(3),\n    'credit_score': credit_score,\n    'loan_amount': loan_amount,\n    'loan_term': loan_term,\n    'home_ownership': home_ownership,\n    'purpose': purpose,\n    'previous_defaults': previous_defaults,\n    'default': default,                     # Hedef sütun (y)\n})\n\nprint(f\"Veri seti şekli: {df.shape}\")       # (satır, sütun)\nprint(f\"\\nDefault oranı: {df['default'].mean():.2%}\")  # Sınıf dengesini gör\nprint(\"\\nİlk 5 satır:\")\ndisplay(df.head())                          # Notebook'ta tablo önizleme\nprint(\"\\n✅ AŞAMA 5 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "d51f90eb",
   "metadata": {
    "id": "d51f90eb"
   },
   "source": "## AŞAMA 6: CSV Dosyalarını Sürükle-Bırak ile Alma\n\nKullanıcının bilgisayarından sürükle-bırak ile CSV dosyalarını projeye ekleyebilme kodu. Colab’da `files.upload()`, lokalde path ile okuma.\n\nSentetik veri kullanıyorsanız `USE_UPLOADED_CSV = False` bırakın.\n\n\n---\n### Öğrenci notu — CSV yükleme\n\n- `USE_UPLOADED_CSV = True` → Colab dosya seçici veya lokal path ile `pd.read_csv`\n- `False` → AŞAMA 5’teki sentetik `df` kullanılmaya devam eder\n\n**Öğrenme hedefi:** Aynı pipeline’ı kendi CSV’nizle çalıştırmak. Sütun adları farklıysa AŞAMA 9’daki `feature_cols` listesini güncellemeniz gerekir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f2a21c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "07f2a21c",
    "outputId": "869b8fef-83f2-4824-c2b0-dd2f65c84e34",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843545192,
     "user_tz": -180,
     "elapsed": 94,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sentetik veri kullanılıyor (USE_UPLOADED_CSV=False).\n",
      "df şekli: (30000, 11)\n",
      "✅ AŞAMA 6 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 6: CSV YÜKLEME (sürükle-bırak / path)\n# Sentetik veri kullanıyorsanız USE_UPLOADED_CSV=False bırakın\n# ============================================================\n\nUSE_UPLOADED_CSV = False  # True yaparsanız aşağıdaki yükleme devreye girer\n\nif USE_UPLOADED_CSV:\n    try:\n        from google.colab import files       # Colab dosya seçici UI\n        print(\"CSV dosyanızı seçin...\")\n        uploaded = files.upload()           # Kullanıcı dosya seçer → belleğe alır\n        for fname in uploaded.keys():\n            if fname.lower().endswith('.csv'):\n                df = pd.read_csv(fname)     # CSV → DataFrame; sentetik df'nin yerine geçer\n                print(f\"✅ Yüklendi: {fname} → {df.shape}\")\n                break\n    except Exception:\n        # Colab değilse klavyeden path iste\n        csv_path = input(\"CSV dosya yolu girin: \").strip()\n        if csv_path and Path(csv_path).exists():\n            df = pd.read_csv(csv_path)\n            print(f\"✅ Okundu: {csv_path} → {df.shape}\")\n        else:\n            print(\"⚠️ Dosya bulunamadı; sentetik veri kullanılmaya devam edilecek.\")\nelse:\n    print(\"Sentetik veri kullanılıyor (USE_UPLOADED_CSV=False).\")\n    print(f\"df şekli: {df.shape}\")\n\nprint(\"✅ AŞAMA 6 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "b19b1c1f",
   "metadata": {
    "id": "b19b1c1f"
   },
   "source": "## AŞAMA 7: Keşifsel Veri Analizi (EDA)\n\nMinimumda hedef dağılımı (bar + pie) ve sayısal özelliklerin default’a göre boxplot’ları; ayrıca korelasyon matrisi ve kategorik crosstab eklenmiştir. Olabilecek tüm temel analizler bu hücrede toplanır.\n\n\n---\n### Öğrenci notu — EDA grafikleri ne anlatır?\n\n| Grafik | Soru |\n|--------|------|\n| Bar / pie (`default`) | Sınıflar dengesiz mi? (QML’de dengesizlik kernel ve metrikleri bozar) |\n| Boxplot (sayısal × default) | Hangi özellik temerrüt ile ayrışıyor? → Qubit’e hangi 3–4 feature gidecek? |\n| Korelasyon ısı haritası | Çok yüksek korelasyonlu özellikler gereksiz qubit harcar |\n| Kategorik crosstab | `RENT` vs `OWN` gibi gruplarda default oranı farklı mı? |\n\n**Öğrenme hedefi:** QML’de qubit sayısı kısıtlıdır; EDA, “hangi özellikleri encode edeceğiz?” kararının veriye dayalı gerekçesidir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "248ee1a3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "248ee1a3",
    "outputId": "d30d7f4f-561c-4a27-ac9d-8da274e9dda1",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731778801,
     "user_tz": -180,
     "elapsed": 2791,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1500x900 with 6 Axes>"
      ],
      "image/png": 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auXOnXZybm5sCAgKMf97e3sa2/fv3a/369Zo4caIaNGigpk2bKj4+Xl999ZXdBzSUHNcqohdmOwAAcE5JSUlKSEhQrVq1NGXKFC1btkxTpkxRrVq1lJCQwGd5AKUWhXQ4hMVi0bRp0/KNmTZtmiwWS/EkBAC4aY0aNdKmTZt04MABSdJvv/2mrVu36t5777WL+/nnn3XPPfeoQ4cOGjt2rE6ePGls27Ztm7y8vBQZGWm0RUdHy2QyXVWQh/ObO3dukcYBAADnYLFYlJiYqGbNmik+Pl7h4eGqUKGCwsPDFR8fr2bNmikxMZHP8gBKJaZ2gUPs2LFD586dkyQ1bdpUTZs2lbu7uy5evKgtW7Zoy5YtOnfunHbs2KHGjRs7OFsAQH4GDhyoM2fOqGPHjjKbzbJYLHrllVf08MMPGzEtW7ZUu3btVL16dR06dEjvvPOOnnnmGX322Wcym806fvy4fH197fZbrlw5eXt7KyMjo9A58eHNsa6cE/1GcQMGDLjN2QDAraNfAS5LTk5WWlqahg0bJpvNpp07d9rNkd69e3cNGzZMycnJioqKcnS6AFCkKKTDIb755htJkr+/vw4ePKgtW7YY2wIDA+Xn56fMzEx98803FNIBwMmtWbNGq1at0tSpUxUSEqKUlBQlJCQYi45KUufOnY34sLAwhYWFqW3btsYo9aK2a9euIt8nbo/t27c7OgUAAFBAeVcUHjt2TG+99dZV65316dPHLg4AShMK6XCIvM72+PHjuvvuuzV8+HDVqlVLqampWrp0qX7++We7OACA83rrrbc0cOBAo1geFhamo0ePau7cuUYh/a9q1KghHx8fpaam6p577pG/v79OnDhhF3Pp0iVlZWUpICCg0DlFRkbKbDYX/sGg2DVs2NDRKQDADVksFr6kBST5+PhIkqZMmXLVwvGnTp0yFonPiwOA0oRCOhwiICBAKSkpqlChgkaMGKE9e/bo559/lo+Pj0aMGKHevXvr/PnzN1U8AQAUrwsXLsjFxcWuzWw2y2azXfc+x44d06lTp4y/840aNVJ2drZ2796t+vXrS5I2bdokq9V6U5cFm81mCuklBK8TAAAlR3h4uFxcXGSz2RQVFaXHH3/cGBS3ZMkSbdmyRSaTSeHh4Y5OFQCKHIV0OESdOnX0ww8/6Pz58+rVq5dycnKMbW5ubsbtOnXqOCpFAEABtW7dWnPmzFHVqlWNqV0WLFigbt26SZLOnj2rWbNmqUOHDvL399ehQ4f09ttvq1atWmrZsqUkKTg4WC1bttTo0aM1fvx45ebmasKECercubOCgoIc+fAAAADwf3799VdjsITJZJLNZjP+mUwmSZLVatWvv/7KVWcASh0K6XAIPz8/4/cri+h/vX1lHADAOcXHx2v69OkaP368MjMzFRgYqJ49e+r555+XdHnE8f/+9z+tXLlSp0+fVmBgoFq0aKHBgwfbXRI8ZcoUTZgwQf369ZPJZFL79u0VHx/vqIcFAACAv8ib4uiJJ57QN998o2HDhhnbgoKC9Pjjj2vJkiXatWsXhXQApQ6FdDhEQedLY141AHB+Hh4eGjVqlEaNGnXN7eXLl1diYuIN91O5cmVjXk0AAAA4r4iICD322GNavXq1jh07pipVqqhTp0769ddfHZ0aANw2FNLhEFarVdLl4kqlSpWUmZlpbPP399eZM2d04cIFIw4AAAAAADhWZGSkPvvsM82ePVs5OTlKT083tn355ZfG1YaRkZGOShEAbhuToxNA2ZScnCzp8gJ1VxbRJen48eO6cOGCXRwAAAAAAHCsyMhIVaxYUYcPH1ZOTo5eeOEFLVy4UC+88IJycnJ0+PBhVaxYkUI6gFKJEekAAAAl3LFjx3TmzBlHp2FwdXVVbm5ugeL27dtXDBndmIeHh6pUqeLoNAAAcHqurq6SpHPnzmnWrFlGu7u7uyTZrYEDAKUJhXQ4RL169Yzfvby8dP/996tKlSo6duyYvvnmG2VnZ18VBwAArpaVlaWBAweWyOnQcnNz9fLLLzs6DUmSyWTSokWL5O3t7ehUAABwWsnJycrKylK/fv20evVqZWRkGNu8vb31wAMPaOHChUpOTlZUVJQDMwWAokchHQ5x4MAB4/e6desqOjpatWrVUmpqqg4ePKitW7cacU2bNnVUmgAAOD1vb2+9//77TjUiPScnR8OHD79h3FtvveU0o9Y8PDwoogMAcAMnT56UJAUEBMjFxeWq7QEBAXZxAFCaUEiHQ2zevNn4fdeuXUbhXLK/DGzz5s3q3r17seYGAEBJ44xTkjRv3lw//fRTvtvvuuuuYswIAMoWi8WimTNn6ssvv9Tx48cVGBioRx99VM8995xRALXZbJoxY4aWLVum7OxsNW7cWOPGjdOdd95p7OfUqVOaMGGCvv32W5lMJrVv316jRo1SpUqVjJjffvtNr7/+unbt2iVfX1/17t1bzzzzjF0+a9as0fTp03XkyBHdeeedGjp0qFq1alUszwWKjo+PjyRpypQpuvvuuzV8+HBjUNzSpUs1depUuzgAKE1YbBQOYbPZJElt2rRR5cqV7bb5+PiodevWdnEAAKBkGT16tJo3b37Nbc2bN9fo0aOLOSMAKFvmzZunTz/9VGPGjNHq1as1dOhQzZ8/X4sWLbKLWbRokcaNG6elS5eqQoUKio2N1cWLF42YoUOHat++fVqwYIHmzJmjLVu2aMyYMcb2M2fOKDY2VlWrVtWKFSs0fPhwzZo1S5999pkR88svv2jIkCF67LHHtHLlSt1///16/vnn9b///a94ngwUmfDwcJlMJlWuXFkjRoxQTk6Ofv75Z+Xk5GjEiBGqXLmyzGazwsPDHZ0qABQ5RqTDIZo3b66UlBRt3LhRH330kf7973/r2LFjqlKlitq3b69+/foZcYAjONvCfSURC/cBGD16tM6fP69p06bpxx9/VIsWLfTyyy+rQoUKjk4NAEq9bdu26f7779d9990nSapevbq++uor7dy5U9LlQUsLFy7Us88+q7Zt20q6POVWdHS01q1bp86dO2v//v1av369Pv/8c0VGRkqS4uPjNXDgQA0fPlxBQUH68ssvlZubq0mTJsnNzU1169ZVSkqKFixYoJ49e0qSFi5cqJYtW2rAgAGSpJdffllJSUlavHixXn/99WJ+ZnArfvvtN1mtVp06dUq9evVSTk6Osc3Nzc24/dtvvzFHOoBSh0I6HOLhhx/Whx9+qPPnz6tnz552I88TExON2w8//LCjUkQZVpIX7nMmLNwHQJIqVKig7t2768cff1T37t0pogNAMWnUqJGWLl2qAwcOqHbt2vrtt9+0detWvfbaa5Kkw4cPKyMjQ9HR0cZ9PD091aBBA23btk2dO3fWtm3b5OXlZRTRJSk6Olomk0k7d+5Uu3bttH37djVt2tRuis6YmBjNmzdPWVlZ8vb21vbt2/XUU0/Z5RcTE6N169YV+nFZLJZC3wdFJzMz87rbrpwzPTMzk9cKQIlQmL9VFNLhEG5ubsbcqX+dviXvdvPmzZ1mATKULc64cN9fHTp0SFOnTtWQIUNUo0YNR6dzTSzcBwAA4DgDBw7UmTNn1LFjR5nNZlksFr3yyivGYKWMjAxJkp+fn939/Pz8dPz4cUnS8ePH5evra7e9XLly8vb2Nu5//PhxVa9e3S7G39/f2Obt7a3jx48bbdc6TmHs2rWr0PdB0cl73WvWrKl+/frp0KFDOnPmjDw8PFSjRg199NFHOnjwoDIyMrR9+3bHJgsARcwpCukff/yxEhMTlZGRofDwcI0ePfq6lwCtWLFCcXFxdm1ubm50piWMxWK54Wu2a9cuWSwWmc3mYsoK+P9KypQkNWrUUEhIiKPTAAAAgJNZs2aNVq1apalTpyokJEQpKSlKSEgwFh0tqSIjI/mM6EB5o84rVaqkxo0bq2nTpsY2q9Wqzz//XJIUEhKiBg0aOCRHACiMgtQo8zi8kL569WolJCRo/PjxatCggT766CPFxsZq7dq1V30znsfDw0Nr1641bl95+RBKhh07dujcuXMqX768KlWqZHd5mJ+fn86ePatz585px44daty4sQMzBQAAAICS56233tLAgQPVuXNnSVJYWJiOHj2quXPn6tFHH1VAQICky1NwBAYGGvfLzMw0For09/fXiRMn7PZ76dIlZWVlGff39/e/amR53u28UejXisnMzLxqlHpBmM1mCukOdPr0aUkyvpjp3r27atWqpdTUVC1btkwpKSlGHK8TgNLG5OgEFixYoB49eqhbt24KCQnR+PHjVb58eS1fvvy693FxcVFAQIDx72Y6XzjWt99+K0m6cOGCgoODNWXKFC1btkxTpkxRcHCwLly4YBcHAAAAACi4CxcuXDXozGw2G1NpVq9eXQEBAdq4caOx/cyZM9qxY4caNWok6fI869nZ2dq9e7cRs2nTJlmtVuMq8oYNG2rLli3Kzc01YpKSklS7dm1jmr+GDRtq06ZNdrkkJSWpYcOGRfeAUSx8fHwkSf369VNqaqqGDRumHj16aNiwYUpNTVXfvn3t4gCgNHHoiPScnBwlJydr0KBBRpvJZFJ0dLS2bdt23fudO3dOrVu3ltVq1V133aVXX31VdevWLdSxWfTCsc6ePStJqlu3rgYPHqzp06crLS1NQUFBGjx4sMaOHat9+/bp7NmzvFbANeQthGq1WjlHnAivBQAAcBatW7fWnDlzVLVqVWNqlwULFqhbt26SLg9Q69u3r2bPnq1atWqpevXqmj59ugIDA9W2bVtJUnBwsFq2bKnRo0dr/Pjxys3N1YQJE9S5c2cFBQVJkh566CG99957GjVqlJ555hnt3btXCxcutJuStW/fvurTp48++OADtWrVSqtXr9bu3bv1+uuvF/8Tg1sSERGhoKAgpaSkaM6cOUpJSdHJkyfl4+OjevXqKSEhQUFBQYqIiHB0qoBTsVgsSk5ONs6XiIgIrtoogRxaSD958qQsFss1Fzf5/fffr3mf2rVra9KkSQoLC9Pp06f1wQcf6PHHH9dXX31VqDmNmVPdsfJGQezfv19PPvmk0Z6amqonn3xSJpPJiGOBEuBqR48elSTt2bPHuLwSAAAAyBMfH6/p06dr/PjxxvQtPXv21PPPP2/EPPPMMzp//rzGjBmj7OxsNWnSRPPnz5e7u7sRM2XKFE2YMEH9+vWTyWRS+/btFR8fb2z39PRUYmKiXn/9dXXt2lU+Pj567rnn1LNnTyOmcePGmjJliqZNm6Z33nlHd955p9577z2FhoYWz5OBImM2mxUbG6uEhARjapdmzZopNTVVCQkJ2rx5s+Li4igQAldISkpSYmKi0tLSjLagoCDFxsYqOjragZmhsBw+R3phNWrUyLjMLO92p06dtGTJEr388ssF3g8LlDjWiRMntHnzZmNU7V/ltUdHR3O5H3ANnp6eki7PdRkcHOzgbJCnMIuUAAAA3E4eHh4aNWqURo0add0YFxcXDR48WIMHD75uTOXKlTV16tR8jxUeHq5PPvkk35iOHTuqY8eO+SeNEiE6OlpxcXFKTEzUsGHDjPagoCDFxcVRGASukJSUpISEBDVr1kzDhg0z1hRYunSpEhISOGdKGIcW0n18fGQ2m+0WmpQKt+iIq6ur6tWrp4MHDxbq2CxQ4lh5c+UVJI7XCbha3lUbJpOJcwQAAABAsYqOjlbz5s2ZqgLIh8ViUWJiopo1a6b4+Hjjc3x4eLji4+M1ceJEJSYmqnnz5pw7JYRDFxt1c3NTRESE3eImVqtVGzdutBt1nh+LxaL//e9/xorhKBnWrFlTpHEAAAAAAKD4mM1mRUVFqVWrVoqKiqIQCPxFcnKy0tLS1KNHD6OInsdkMql79+5KS0tTcnKygzJEYTl8apf+/ftrxIgRql+/vqKiovTRRx/p/Pnz6tq1qyRp+PDhCgoK0pAhQyRJs2bNUsOGDVWrVi1lZ2crMTFRR48eVffu3R35MFBIqampRRoHAAAAAAAAOIuTJ09KkmrVqnXN7XnteXFwfg4vpHfq1EknTpzQjBkzlJGRoXr16mn+/PnG1C5//vmn3bc22dnZGj16tDIyMuTt7a2IiAgtWbJEISEhjnoIuAnnz58v0jgAAAAAAFB8LBYLU7sA+fDx8ZF0eZBoeHj4VdvzBo/mxcH5ObyQLkm9e/dW7969r7lt0aJFdrdHjhypkSNHFkdauI0sFkuRxgEAAAAAgOKRlJSkxMREpaWlGW1BQUGKjY1l4UTg/0RERCgoKEhLly61myNdujy19bJlyxQUFKSIiAgHZonCcIpCOsqenJycIo0DAAAACuLYsWM6c+aMo9Mo8Tw8PFSlShVHpwHAAZKSkpSQkKCmTZvq0Ucflbu7uy5evKitW7cqISFBcXFxFNMBXV5HIDY2VgkJCZo4caK6d++uWrVqKTU1VcuWLdPmzZsVFxfHlRwlCIV0OITZbNalS5cKFAcAAAAUhaysLA0cOFBWq9XRqZR4JpNJixYtkre3t6NTAVCMLBaLEhMTFRwcrNTUVG3evNnYFhgYqODgYCUmJqp58+Z8ngckRUdHKy4uTomJiRo2bJjRHhQUxJdOJRCFdDhExYoVdfHixQLFAQAAAEXB29tb77//vlOPSD906JCmTp2qIUOGqEaNGo5O57o8PDwoogNlUHJystLS0uymdMmTnp6u9PR0Iy4qKqq40wOcUnR0tJo3b86aAqUAhXQ4ROXKlQu0KnHlypVvfzIAAAAoM0rKdCQ1atRQSEiIo9MAADuZmZnG797e3urbt6+aNWumzZs3a+HChcrKyroqDsDlGRf4cqnko5AOh8jIyCjSOAAAAAAAcHudOHFCklShQgV99NFHKlfuclmpQ4cOuv/++9WrVy+dP3/eiAOA0oRCOhziwoULRRoHAAAAAABur99//12SFBAQIJvNpp07dxpTVdSrV0/+/v46dOiQEQcApQmFdDhEQRYaLUwcAAAAAAC4vfLWOjt48KB69uypnJwcY5ubm5txuyBrogFASWNydAIAAAAAAABwfnfddZfxu81ms9t25e0r4wCgtKCQDgAAAAAAgBvq2LGj8buLi4vdtitvXxkHAKUFhXQ4xF873FuNAwAAAAAAt9fevXuN36+c1uWvt6+MA4DSgkI6HIJCOgAAAAAAJUtmZqYkKSgo6KptLi4uRnteHACUJiw2Cocwm82yWq0FigMAAAAAAI6XlZUlSerRo4dat26t1atX69ixY6pSpYo6deqkb775Ru+9954RBwClCYV0OERubm6RxgEAAAAAgNvL29tbkpSUlKR27dqpS5cuxjar1apNmzbZxQFAaUIhHQAA3BKLxaKZM2fqyy+/1PHjxxUYGKhHH31Uzz33nDFFl81m04wZM7Rs2TJlZ2ercePGGjdunO68805jP6dOndKECRP07bffymQyqX379ho1apQqVarkoEcGAACAK/n5+UmStm7dqgkTJqhJkyZyd3fXxYsXtXXrVm3dutUuDgBKEwrpAADglsybN0+ffvqp3nzzTYWEhGj37t2Ki4uTp6en+vbta8QsWrRIkydPVvXq1TV9+nTFxsZq9erVcnd3lyQNHTpUGRkZWrBggXJzczVy5EiNGTNGU6dOdeTDAwAAwP+JiIhQUFCQXFxctGXLFm3evNnYZjKZVKVKFdlsNkVERDgwSwC4PVhsFAAA3JJt27bp/vvv13333afq1avrgQceUExMjHbu3Cnp8mj0hQsX6tlnn1Xbtm0VHh6ut956S+np6Vq3bp0kaf/+/Vq/fr0mTpyoBg0aqGnTpoqPj9dXX32ltLQ0Rz48AAAA/B+z2awWLVro2LFj8vb2VpcuXfTss8+qS5cu8vLy0rFjx9SiRQvWOwNQKlFIBwAAt6RRo0batGmTDhw4IEn67bfftHXrVt17772SpMOHDysjI0PR0dHGfTw9PdWgQQNt27ZN0uVivJeXlyIjI42Y6OhomUwmoyAPAAAAx7JYLPrxxx8VEhIiNzc3rVy5UrNnz9bKlSvl7u6ukJAQ/fjjj7JYLI5OFQCKHFO7AACAWzJw4ECdOXNGHTt2lNlslsVi0SuvvKKHH35YkpSRkSHp6rky/fz8dPz4cUnS8ePH5evra7e9XLly8vb2Nu5fGHx4cy5Wq9X4yWsD5I/zxTnxWgCXJScnKy0tTcOGDVOdOnW0evVqHTt2TFWqVFGnTp20f/9+DRs2TMnJyYqKinJ0ugBQpCikAwCAW7JmzRqtWrVKU6dOVUhIiFJSUpSQkGAsOuoIu3btcshxcW1Hjx6VJO3Zs0enT592cDaAc+N8AeDMTp48KUk6duyYMVVfni+//FJ9+vSxiwOA0oRCOgAAuCVvvfWWBg4cqM6dO0uSwsLCdPToUc2dO1ePPvqoAgICJEmZmZkKDAw07peZmanw8HBJkr+/v06cOGG330uXLikrK8u4f2FERkYyN6cT8fT0lHT5/0ZwcLCDswGcG+eLc7JYLHxJC0jy8fGRJE2ZMkVubm52206dOmUsEp8XBwClCYV0AABwSy5cuCAXFxe7NrPZLJvNJkmqXr26AgICtHHjRtWrV0+SdObMGe3YsUO9evWSdHme9ezsbO3evVv169eXJG3atElWq/WmLgs2m80U0p2IyWQyfvK6APnjfAHgzMLDw+Xi4iKbzaYGDRqoZ8+eqlWrllJTU/XZZ59p8+bNMplMxmAJAChNKKQDAIBb0rp1a82ZM0dVq1Y1pnZZsGCBunXrJklycXFR3759NXv2bNWqVUvVq1fX9OnTFRgYqLZt20qSgoOD1bJlS40ePVrjx49Xbm6uJkyYoM6dOysoKMiRDw8AAAD/59dffzUGS9hsNu3bt0+HDh3SxYsXjXar1apff/1VDRs2dGCmgHOxWCxKTk7WyZMn5ePjo4iICL4wL4EopAMAgFsSHx+v6dOna/z48cb0LT179tTzzz9vxDzzzDM6f/68xowZo+zsbDVp0kTz58+Xu7u7ETNlyhRNmDBB/fr1k8lkUvv27RUfH++IhwQAAIBryJviKCYmRklJSdqyZYuxzWQyKSYmRhs2bNCuXbsopAP/JykpSYmJiUpLSzPagoKCFBsbq+joaAdmhsKikA4AAG6Jh4eHRo0apVGjRl03xsXFRYMHD9bgwYOvG1O5cmVjXk0AAAA4rw0bNsjV1VVWq9VoM5vN2rBhgwOzApxPUlKSEhIS1KxZMw0bNsyYCmnp0qVKSEhQXFwcxfQSxOToBAAAAAAAAOD8IiIijN8bNmyoKVOmaNmyZZoyZYrdCPQr44CyymKxKDExUc2aNVN8fLzCw8NVoUIFhYeHKz4+Xs2aNVNiYqIsFoujU0UBUUgHAAAAAABAoVitVu3bt0/r16/Xvn377EanA5CSk5OVlpamHj16GIuJ5zGZTOrevbvS0tKUnJzsoAxRWEztAgAAAAAAgBu6suC3detWbd269bpxjRs3Lq60AKd08uRJSVKtWrWuuT2vPS8Ozo8R6QAAAAAAACgUNze3fG8DZZ2Pj48kKTU19Zrb89rz4uD8GJEOAAAAAACAG8qb+9zDw0MfffSR9uzZo5MnT8rHx0dhYWHq16+fzpw5wxzpgC6fL0FBQVq6dKni4+PtpnexWq1atmyZgoKCOF9KEEakAwAAAAAA4IbyCoFnzpzR5MmT5erqqmbNmsnV1VWTJ0/WmTNn7OKAssxsNis2NlabN2/WxIkTlZKSonPnziklJUUTJ07U5s2bFRsbK7PZ7OhUUUCMSAcAAAAAAMANZWVlGb/v2LFDmzdvNm5fObXLlXFAWRYdHa24uDglJiZq2LBhRntQUJDi4uIUHR3twOxQWBTSAQAAAAAAcEN5czn369dPa9asUXp6ut22Dh06aOHChcz5DFwhOjpazZs3V3JysjEVUkREBCPRSyAK6WXIsWPHjMusSpJ9+/Y5OgWDh4eHqlSp4ug0AAAAAAAodnlzPqekpGju3LlKSUkxCoP16tVTQkICcz4DKLUopJcRWVlZGjhwoKxWq6NTKbSXX37Z0SkYTCaTFi1aJG9vb0enAgAAAABAscqb8zkhIUEJCQnq3r27mjVrptTUVCUkJGjz5s2Ki4tjpC1whaSkJCUmJiotLc1oCwoKUmxsLFO7lDAU0ssIb29vvf/++04zIv2NN95QRkbGDeMCAgI0atSoYsioYDw8PCiiAwAAAADKrLw5n+fPn8+cz8ANJCUlKSEhQc2aNdOwYcNUq1YtpaamaunSpUpISOCcKWEopJchzjQlybvvvqvevXsXKK5y5cq3PyEAAAAAAHDTbDabo1MAnIrFYlFiYqKaNWum+Ph4mUwmSVJ4eLji4+M1ceJEJSYmqnnz5lzFUUKYHJ0AyqbKlSvfsEBekBgAAAAAAFB88kbY1qxZUw8//LAeeOABPfzww6pZs6YSEhKUlJTk6BQBp5CcnKy0tDT16NHDKKLnMZlM6t69u9LS0pScnOygDFFYjEiHwyxevFi9e/fWqVOnrtpWuXJlLV68uPiTAgAAAAAA15Q3wjYoKEhbt27Vli1bjG0uLi4KCgpihC3wf06ePClJqlWr1jW357XnxcH5UUiHQy1evFinTp3SkCFDlJaWpqCgIE2dOpWR6AAAAAAAOJm8EbbS5QFwffr0UbNmzbR582YtWrRIx44dM+KioqIcmSrgcD4+PpKk1NRUhYeHX7U9NTXVLg7Oj6ld4HCVK1dWXFycJCkuLo4iOgAAAAAATigjI0OS5O3trQ8//FAdOnSQr6+vOnTooA8//FDe3t52cUBZFhERoaCgIC1dulRWq9Vum9Vq1bJlyxQUFKSIiAgHZYjCopAOAAAAAACAG9qzZ48kqX379nJxcdHOnTv1/fffa+fOnXJxcVHbtm3t4oCyzGw2KzY2Vps3b9bEiROVkpKic+fOKSUlRRMnTtTmzZsVGxvLNEglCFO7AAAAAAAA4IZcXFwkSVu2bNH333+v9PR0Y1tgYKAqVapkFweUddHR0YqLi1NiYqKGDRtmtAcFBSkuLk7R0dEOzA6FRSEdAAAAAAAAN3THHXdIkg4cOCBvb2/FxMSofPnyunDhgnbt2mUU1vPiAFwupjdv3lzJyck6efKkfHx8FBERwUj0EohCOgAAAAAAAG6oQ4cOmj9/vlxcXJSVlaUNGzbYbXdxcZHNZlOHDh0clCHgnMxmMwvwlgLMkQ4AAAAAAIAb2rt3ryTJZrNJkho2bKh+/fqpYcOGdu15cQBQmjAiHQAAAAAAADeUkZEhSXJzc9OlS5e0fft2bd++XZJkMplUrlw55eTkGHEALrNYLEztUgpQSAcAAAAAAMAN7dmzR5L0yCOPqEePHlqwYIH+/PNP3XHHHerfv7+WLFmi5cuXa8+ePbr//vsdnC3gHJKSkpSYmKi0tDSjLSgoSLGxsSw2WsJQSAcAAAAAAMANubi4SJK2bNmi77//3lhcdNu2bdqyZYsqVapkFweUdUlJSUpISFCzZs00bNgw1apVS6mpqVq6dKkSEhIUFxdHMb0EcYo50j/++GO1adNGkZGR6t69u3bu3Fmg+3311VcKCwvTc889d5szBAAAAAAAKNvuuOMOSdKBAweUk5OjF154QQsXLtQLL7ygnJwcHThwwC4OKMssFosSExPVrFkzxcfHKzw8XBUqVFB4eLji4+PVrFkzJSYmymKxODpVFJDDC+mrV69WQkKCnn/+eX3xxRcKDw9XbGysMjMz873f4cOH9eabb6pp06bFlCkAAAAAAEDZ1aFDB0mX50N3c3PTrFmz1LdvX82aNUvu7u4ymUx2cUBZlpycrLS0NPXo0cM4N/KYTCZ1795daWlpSk5OdlCGKCyHF9IXLFigHj16qFu3bgoJCdH48eNVvnx5LV++/Lr3sVgsGjp0qF588UXVqFGjGLMFAAAAAAAom/bu3StJslqtunjxorp06aK///3v6tKliy5cuCCr1WoXB5RlJ0+elCTVqlXrmtvz2vPi4PwcWkjPyclRcnKy3VxAJpNJ0dHR2rZt23Xv995778nPz0/du3cvjjQBAAAAAADKvLyC38MPP6zTp09r5cqVmjNnjlauXKkzZ87o4YcftosDyjIfHx9JUmpq6jW357XnxcH5OXSx0ZMnT8piscjPz8+u3c/PT7///vs177NlyxZ9/vnnWrly5S0dm/mHnEvet9ZWq5XXBigAzhnnxGsBAACA0iyv4HfvvfeqT58+WrBggf7880/dcccd6t+/v/744w99+eWXFAYBSREREQoKCtLSpUsVHx9vN72L1WrVsmXLFBQUpIiICAdmicJwaCG9sM6cOaPhw4drwoQJ8vX1vaV97dq1q4iyQlE4evSoJGnPnj06ffq0g7MBnB/nDAAAAIDillcYnDt3rk6dOqWMjAxJ0rZt27R582ZVrlyZwiDwf8xms2JjY5WQkKCJEyeqe/fuqlWrllJTU7Vs2TJt3rxZcXFxMpvNjk4VBeTQQrqPj4/MZvNVC4tmZmbK39//qvhDhw7pyJEjevbZZ422vFGZd911l9auXauaNWsW6NiRkZH8R3Uinp6ekqSwsDAFBwc7OBvA+XHOOCeLxcIXtQAAACi1zGazWrRooRUrVly1LSMjQxkZGeratSv1FuD/REdHKy4uTomJiRo2bJjRHhQUpLi4OLvpruH8HFpId3NzU0REhDZu3Ki2bdtKulwY37hxo3r37n1VfJ06dbRq1Sq7tmnTpuns2bMaNWqUqlSpUuBjm81m/rA7kbzLW0wmE68LUACcMwAAAACKm8Vi0TfffJNvzDfffKN+/frxOQX4P9HR0WrevLmSk5N18uRJ+fj4KCIignOkBHL41C79+/fXiBEjVL9+fUVFRemjjz7S+fPn1bVrV0nS8OHDFRQUpCFDhsjd3V2hoaF29/fy8pKkq9oBAAAAAABQdHbt2qWsrCxJUtOmTXXHHXcoNzdXrq6u+vPPP7VlyxZlZWVp165datiwoWOTBZyI2WxWVFSUo9PALXJ4Ib1Tp046ceKEZsyYoYyMDNWrV0/z5883pnb5888/7SbjBwAAAAAAQPHbsWOHJKlatWo6ePCgtmzZYmwLDAxUtWrVdOTIEe3YsYNCOoBSx+GFdEnq3bv3NadykaRFixble9/JkyffjpQAAEAhtGnTRkeOHLmq/YknntDYsWPVp08f/fzzz3bbevbsqddff924ffToUY0bN04//fSTKlasqC5dumjIkCEqV84p3q4AAACUecePH5ckHTlyRE2bNtXf/vY35eTkyM3NTUePHjUK63lxAFCa8MkUAADcss8//1wWi8W4vXfvXvXv318PPPCA0dajRw+99NJLxu0KFSoYv1ssFg0aNEj+/v5asmSJ0tPTNWLECLm6uurVV18tngcBAEApk5aWprffflvr16/X+fPnVatWLU2aNEmRkZGSJJvNphkzZmjZsmXKzs5W48aNNW7cON15553GPk6dOqUJEybo22+/lclkUvv27TVq1ChVqlTJiPntt9/0+uuva9euXfL19VXv3r31zDPP2OWyZs0aTZ8+XUeOHNGdd96poUOHqlWrVsXyPKDo+Pn5SZJcXV21detWuxHpLi4ucnV1VW5urhEHAKUJc6YAAIBb5uvrq4CAAOPft99+q5o1a+ruu+82YsqXL28X4+HhYWzbsGGD9u3bp7ffflv16tVTq1atNHjwYH388cfKyclxxEMCAKBEy8rKUq9eveTq6qp58+bpq6++0ogRI+Tt7W3EzJs3T4sWLdK4ceO0dOlSVahQQbGxsbp48aIRM3ToUO3bt08LFizQnDlztGXLFo0ZM8bYfubMGcXGxqpq1apasWKFhg8frlmzZumzzz4zYn755RcNGTJEjz32mFauXKn7779fzz//vP73v/8Vz5OBIpO3Tl1ubq5sNpvdNpvNptzcXLs4AChNGJEOAACKVE5Ojr788kv1799fLi4uRvuqVav05ZdfKiAgQK1bt9Zzzz1njErfvn27QkNDjTVSJCkmJkbjxo3Tvn37dNdddxUqhytHx8PxrFar8ZPXBsgf54tzKomvxbx581SlShUlJCQYbTVq1DB+t9lsWrhwoZ599lm1bdtWkvTWW28pOjpa69atU+fOnbV//36tX79en3/+uTGKPT4+XgMHDtTw4cMVFBSkL7/8Urm5uZo0aZLc3NxUt25dpaSkaMGCBerZs6ckaeHChWrZsqUGDBggSXr55ZeVlJSkxYsX203zBufn6elZpHEAUJJQSAcAAEVq3bp1On36tB599FGj7cEHH1TVqlUVGBioPXv2aMqUKTpw4IBmzZol6fI8mlcW0SUZtzMyMgqdw65du27hEaCoHT16VJK0Z88enT592sHZAM6N8wVF5b///a9iYmL00ksvafPmzQoKCtITTzyhHj16SJIOHz6sjIwMRUdHG/fx9PRUgwYNtG3bNnXu3Fnbtm2Tl5eXUUSXpOjoaJlMJu3cuVPt2rXT9u3b1bRpU7m5uRkxMTExmjdvnrKysuTt7a3t27frqaeesssvJiZG69atu71PAorcnj17ChzXrl2725wNABQvCukAAKBILV++XPfee6+CgoKMtrwRaZIUFhamgIAAPfXUUzp48KBq1qxZ5DlERkbKbDYX+X5xc/JGpYWFhSk4ONjB2QDOjfPFOVkslhL3Je2hQ4f06aefqn///vr73/+uXbt2aeLEiXJ1ddWjjz5qfFH917ms/fz8jIUijx8/Ll9fX7vt5cqVk7e3t3H/48ePq3r16nYxeV+GHz9+XN7e3tf8wvzK4xRGSbw6oDTZv39/geN4rQCUBIX5W0UhHQAAFJkjR44oKSlJM2fOzDeuQYMGkqTU1FTVrFlT/v7+2rlzp11M3ofrgICAQudhNpsppDsRk8lk/OR1AfLH+YKiYrPZVL9+fWPR7rvuukt79+7VkiVL7K4aK2lK2hcapc2RI0eM300mkzEd1V9vHzlyRNu3by/u9ADgtqKQDgAAisyKFSvk5+en++67L9+4lJQUSf+/SN6wYUPNmTNHmZmZxsi4pKQkeXh4KCQk5LbmDABAaRQQEHDVVQ116tTR119/bWyXpMzMTAUGBhoxmZmZCg8Pl3R5ZPmJEyfs9nHp0iVlZWUZ9/f3979qZHne7bxR6NeKyczMvGqUekFw1ZljXfncX1lE/+tts9mshg0bFldaAHDTCnPVGYV0AABQJKxWq1asWKEuXbqoXLn//xbj4MGDWrVqlVq1aqXKlStrz549SkhIULNmzYwP6jExMQoJCdHw4cM1bNgwZWRkaNq0aXryySft5lwFAAAF07hxYx04cMCu7Y8//lC1atUkSdWrV1dAQIA2btyoevXqSZLOnDmjHTt2qFevXpKkRo0aKTs7W7t371b9+vUlSZs2bZLValVUVJSky1+GT5s2Tbm5uXJ1dZV0+cvw2rVry9vb24jZtGmT3TzpSUlJN1Vo5aozx6pcuXKB1m+oXLkyrxOAUsfk6AQAAEDpkJSUpKNHj6pbt2527a6urtq4caNiY2PVsWNHvfnmm2rfvr3mzJljxJjNZs2ZM0cmk0k9e/bUsGHD1KVLF7300kvF/TAAACgV+vXrpx07dmjOnDlKTU3VqlWrtHTpUj3xxBOSJBcXF/Xt21ezZ8/WN998oz179mj48OEKDAxU27ZtJUnBwcFq2bKlRo8erZ07d2rr1q2aMGGCOnfubKyF8tBDD8nV1VWjRo3S3r17tXr1ai1cuFD9+/c3cunbt6/Wr1+vDz74QPv379fMmTO1e/du9e7du/ifGNySsLCwIo0DygqLxaKdO3fq+++/186dO1lDoIRiRDoAACgSMTEx2rNnz1Xtd9xxhxYvXnzD+1erVk3z5s27HakBAFDmREVFadasWXrnnXf03nvvqXr16ho5cqQefvhhI+aZZ57R+fPnNWbMGGVnZ6tJkyaaP3++3N3djZgpU6ZowoQJ6tevn0wmk9q3b6/4+Hhju6enpxITE/X666+ra9eu8vHx0XPPPWe30Hjjxo01ZcoUTZs2Te+8847uvPNOvffeewoNDS2eJwNF5uLFi0UaB5QFSUlJSkxMVFpamtEWFBSk2NhYRUdHOzAzFBaFdAAAAAAASqHWrVurdevW193u4uKiwYMHa/DgwdeNqVy5sqZOnZrvccLDw/XJJ5/kG9OxY0d17Ngx/4Th9A4ePFikcUBpl5SUZExrOWzYMNWqVUupqalaunSpEhISFBcXRzG9BGFqFwAAAAAAANxQhQoVijQOKM0sFosSExPVrFkzxcfHKzw8XBUqVFB4eLji4+PVrFkzJSYmMs1LCUIhHQAAAAAAADdUs2ZN4/dGjRrpoYceUocOHfTQQw+pUaNG14wDyqrk5GSlpaWpR48eMpnsS7Amk0ndu3dXWlqakpOTHZQhCoupXQAAAAAAAHBDXl5exu/btm3Ttm3bbhgHlFUnT56UJNWqVeua2/Pa8+Lg/BiRDgAAAAAAgBsym81FGgeUZj4+PpKk1NTUa27Pa8+Lg/OjkA4AAAAAAIAbioyMLNI4oDSLiIhQUFCQli5dKqvVarfNarVq2bJlCgoKUkREhIMyRGFRSAcAAAAAAMAN3XXXXUUaB5RmZrNZsbGx2rx5syZOnKiUlBSdO3dOKSkpmjhxojZv3qzY2Fiu4ChBbnqO9C1btmjJkiU6dOiQZsyYoaCgIK1cuVLVq1dX06ZNizJHAAAAAAAAONjOnTsLHEdtCJCio6MVFxenxMREDRs2zGgPCgpSXFycoqOjHZgdCuumCulff/21hg8froceeki//vqrcnJyJElnzpzR3Llz+WMJAAAAAABQyixbtqzAcdSGgMuio6PVvHlzJScn6+TJk/Lx8VFERAQj0Uugmyqkz549W+PHj1eXLl301VdfGe2NGzfW7Nmziyw5AAAAAAAAOIfffvutSOOAssJsNisqKsrRaeAW3dQc6QcOHLjmN4uenp7Kzs6+5aQAAAAAAADgXCwWS5HGAUBJclOFdH9/fx08ePCq9q1bt6pGjRq3nBQAAAAAAAAAAM7ipgrpPXr00BtvvKEdO3bIxcVFaWlp+vLLL/Xmm2+qV69eRZ0jAAAAAAAAHKxcuYLNEFzQOAAoSW7qL9vAgQNltVr11FNP6fz58+rdu7fc3Nz09NNPq0+fPkWdIwAAAAAAABzM3d1dly5dKlAcAJQ2N1VId3Fx0bPPPqvY2FgdPHhQ586dU3BwsCpVqlTU+QEAAAAAAMAJ2Gy2Io0DgJLklq61cXNzU0hISFHlAgAAAAAAACfl5uamc+fOFSgOAEqbmyqkX7x4UYsWLdJPP/2kzMzMq75p/OKLL4okOQAAAAAAADiHghbIKaQDKI1uqpA+cuRI/fjjj+rQoYOioqLk4uJS1HkBAAAAAADAiVit1iKNA4CS5KYK6d99953ef/99NWnSpKjzAQAAAACgTKtXr542bNggPz8/u/aTJ08qOjpaKSkpDsoMZZ3FYinSOAAoSW6qkB4UFMTCogAAoExIT09Xdna2o9Mo0Q4dOmT3EzfHy8tLgYGBjk4DQDG43kKNOTk5cnV1LeZsgP+PEekAyrKbKqSPGDFCU6ZM0fjx41WtWrWizgkAABSjS5cu6eeff9bBgwf14IMPysPDQ2lpafLw8CjzX5ynp6dr0N//rtycHEenUipMnTrV0SmUaK5ubpo7Zw7FdKAUW7hwoSTJxcVFy5YtU8WKFY1tVqtVmzdvVp06dRyVHqCcAr4nKmgcAJQkN1VIj4yM1MWLF9W2bVuVL1/+qm/Ef/755yJJDgAA3F5HjhzRgAED9OeffyonJ0ctWrSQh4eH5s2bp5ycHL3++uuOTtGhsrOzlZuTo5z2VWX1YdEsOI7pZI7076PKzs6mkA6UYh9++KGkyyPSlyxZIpPJZGxzdXVV9erVNX78eAdlB8ju/2RRxAFlhcViUXJysk6ePCkfHx9FRETIbDY7Oi0U0k0V0l999VWlp6frlVdekb+/P4uNAgBQQr3xxhuqX7++/vnPf6p58+ZGe7t27TR69GgHZuZcrD5usgVWcHQaKMO4QB4oG/773/9Kkvr06aNZs2bJ29vbwRkB9qpUqaL9+/cXKA7AZUlJSUpMTFRaWprRFhQUpNjYWEVHRzswMxTWTRXSt23bps8++0zh4eFFnQ8AAChGW7du1aeffio3N/vR1tWqVbN7owcAAIrPokWLHJ0CcE25ublFGgeUdklJSUpISFDTpk316KOPyt3dXRcvXtTWrVuVkJCguLg4iuklyE0V0uvUqaMLFy4UdS4AAKCYWa3Way4GdezYsTI/PzoAAI5isVi0YsUKbdq0SZmZmVf11XlzqQPF7c8//yzSOKA0s1gsSkxMVEhIiFJTU7V582ZjW2BgoEJCQpSYmKjmzZszzUsJcVOF9CFDhmjy5Ml65ZVXFBoaetUc6R4eHkWSHAAAuL1atGihjz76SBMmTDDazp49q5kzZ6pVq1YOzAwAgLLrjTfe0BdffKFWrVqpbt26TKcKp8GIdKDgkpOTlZaWprS0NN19990aPny4atWqpdTUVC1dutRYYzI5OVlRUVEOzhYFcVOF9AEDBkiSnnrqKbt2m80mFxcXpaSk3HJiAADg9hsxYoQGDBigTp06KScnR0OHDtUff/whHx8fvfPOO45ODwCAMumrr77StGnT+FIbAEqwzMxMSVKTJk0UHx9vLMIbHh6u+Ph4jR8/Xlu3bjXi4PxuqpDOZWQAAJQOd9xxh/75z39q9erV+u2333Tu3Dk99thjeuihh1S+fHlHpwcAQJnk6uqqmjVrOjoNAMAtyMrKkiRFR0cbRfQ8JpNJf/vb37R161YjDs7vpgrpd999d1HnAQAAillubq46duyouXPn6uGHH9bDDz/s6JQAAICkp59+WgsXLtSYMWOY1gUASihvb29JlxccbdeunV0x3Wq1atOmTXZxcH43VUiXpOzsbH3++efav3+/JKlu3brq1q2bPD09iyw5AABw+7i6uurixYuOTgMAAPzF1q1b9dNPP+mHH35Q3bp1Va6c/Uf3WbNmOSgzAEBB+fn5SZJ++eUXTZgwQU2aNJG7u7suXryorVu36pdffrGLg/O7qUL6rl27NGDAALm7uxuT4S9YsECzZ8/WBx98oIiIiCJNEgAA3B5PPvmk5s2bp4kTJ171IR0AADiGl5eX2rVr5+g0AAC3ICIiQkFBQTKZTNq6das2b95sbDOZTKpSpYqsVit11BLkpj4xJyQkqE2bNpowYYLxofvSpUuKj4/XpEmT9PHHHxdpkgAA4PbYtWuXNm7cqA0bNigsLEwVKlSw286INwAAil9CQoKjUwAA3CKz2awWLVpoxYoV8vb2VuvWrXXHHXfozz//1Lfffqs///xTXbt2ldlsdnSqKKCbKqTv3r3broguSeXKldOAAQPUrVu3IksOAADcXl5eXurQoYOj0wAAAACAUsVisejHH39USEiIsrOztXLlSmNbUFCQAgIC9OOPP6pfv34U00uImyqke3h46M8//1RwcLBd+59//qlKlSoVSWIAAOD2K6oRb23atNGRI0euan/iiSc0duxYXbx4UZMnT9bq1auVk5OjmJgYjR07Vv7+/kbs0aNHNW7cOP3000+qWLGiunTpoiFDhjDlDACgzGnTpk2+i4x+8803xZgNAOBmJCcnKy0tTcOGDVOdOnW0evVqHTt2TFWqVFGnTp20f/9+DRs2TMnJycbU2XBuN/XJtFOnTho1apRGjBihRo0aSbo8cf5bb72lzp07F2mCAADg9jtx4oR+//13SVKdOnXk6+tbqPt//vnnslgsxu29e/eqf//+euCBByRJkyZN0vfff69p06bJ09NTEyZM0AsvvKAlS5ZIujxaY9CgQfL399eSJUuUnp6uESNGyNXVVa+++moRPUoAAEqGfv362d2+dOnS/2vvzuOqLPP/j785RxaVTdncSAsSSMAly+SLmaYtNs5oplZq5thopbmRG2qKkWhKmeaouJCaM2pZlo1a01Qz+hPNXHIZM5dUHBcWF9wSOPD7wy/n2xkUWQ7cB3g9Hw8fcK77Otf9uT3nPhfnc1/3denf//63tmzZooEDBxoUFQCgJC5cuCBJOnv2rGbOnKlz585Zt61fv159+/a1qQfHV6pE+pgxY6w/C74016hRQ88995xef/11+0UHAADK1bVr1/Tmm2/qs88+U15enqSbc/n94Q9/0KRJkwrNmX47/514T0pK0l133aUHH3xQly9f1tq1azVr1iy1bdtW0s3EepcuXbRnzx61aNFCW7Zs0ZEjR5ScnCxfX1+FhYVp+PDhmjVrloYOHSoXFxf7HjgAAA7svxPpBVauXKn9+/dXcDQAgNKoU6eOJCkxMVEPPPCARo8ercaNG+vEiRNas2aN3nnnHZt6cHylSqS7uLho4sSJiomJ0cmTJyVJd911V7G/bAOo3tLS0pSVlWV0GJVaamqqzU+Ujqenp/z9/Y0Ow1DTp0/Xjh07NH/+fN1///2SpJ07dyo+Pl7Tp09XXFxcidvMzs7W559/rgEDBsjJyUn79+9XTk6OoqKirHWCgoLUoEEDayJ9z549atq0qc1UL9HR0ZoyZYqOHDmi++67r+wHCwBAJffwww8rMTGRxUgBoBIIDQ2VyWSSp6enYmNjrVNWhoaGKjY2Vi+++KIuX76s0NBQgyNFcZUqkX758mVZLBZ5e3srJCTEWn7x4kXVqFFD7u7udgsQQNWSlpamVwYP1o2cHKNDqRISExONDqFSc3V21vyFC6t1Mv3LL7/UnDlz1KZNG2tZ+/bt5erqqhEjRpQqkf7111/r8uXL6t69uyQpIyNDzs7O8vT0tKnn4+Oj9PR0a53fJtElWR8X1CmJ304zUxYFo/QBR5GXl2e39zccU8HnDq+1Y3GU12LTpk3y9vY2OgxUY05OTsrPzy9WPaC6++mnn5SXl6eLFy9q2rRp6tmzp3VE+kcffaSLFy9a6zFHeuVQqkT6yJEj1aFDB/Xp08emfOPGjfrmm2+0aNEiuwQHoOrJysrSjZwc9aptlr+ZP65gnDRLvtZczVFWVla1TqT/+uuvhRLY0s0k96+//lqqNteuXauHH35YAQEBZQ2v1Pbt22eXdk6fPm2XdgB7OXTokC5fvmx0GChHBZ87vNbVW7du3WwSkfn5+crIyND58+c1efJkAyNDdVejRg3lFGNQFIvFA/839/nrr7+u5cuXa/To0dZt/v7+iomJUWJiInOkVyKl+mTbu3evxo8fX6j8wQcf1OzZs0vc3sqVK7VkyRKlp6crNDRUkyZNuu2VmK+++koLFizQyZMnlZubq8aNG2vAgAHq1q1bifcLwDj+Zic1rGEyOgxUa4w0lqQWLVpozpw5evvtt+Xq6irpZnL9/fffV4sWLUrc3n/+8x9t3bpVc+fOtZb5+voqJ+fmRYvfjkrPzMyUn5+ftc7evXtt2srIyJAka52SiIiIkNlsLvHz/puHh0eZ2wDsKSQkREFBQUaHgXJU8LnDa+1YLBaL3S7SFkenTp1sHjs5Oalu3bp68MEHeV/AUMVJopekHlCVFcx9frs7bAvKmSO98ihVIj07O1u5ubmFynNzc0s8em3Dhg1KSEhQXFycmjdvrmXLlmngwIHatGmTfHx8CtX38vLSK6+8onvuuUfOzs769ttvFRsbKx8fH7Vr1640hwMAQLU1YcIEDRw4UA8//LB1br6ffvpJrq6uWrJkSYnb++STT+Tj46NHHnnEWhYeHi5nZ2elpKTo8ccflyQdO3ZMp0+ftibrW7RooQULFigzM9Pa/2/dulXu7u4KDg4ucRxms9kuiXSTiQt+cCwmk8ku7204roLPHV7r6m3o0KFGhwAAKKNmzZrJy8tLy5YtU+vWrfX000/L1dVVN27c0A8//KDly5fL29tbzZo1MzpUFFOpEukRERFas2aNJk2aZFO+atWqEr/4ycnJ6tWrl3r06CFJiouL03fffae1a9dq0KBBher/dg5X6eZq5uvWrdPOnTtJpAMAUEJNmzbVV199pfXr1+vYsWOSpN/97nfq2rWr3NzcStRWXl6ePvnkE3Xr1s3mdl4PDw/16NFD06dPl5eXl9zd3RUfH6+WLVtaE+nR0dEKDg7WmDFjNHr0aKWnp2v27Nnq06ePXFxc7Ha8AABUFhaLRV9//bWOHj0qSbr33nvVsWNHLrAAQCW0d+9e/fDDD9bHBd9xirPmABxHqRLpI0aM0IABA/TTTz+pbdu2kqSUlBTt27dPS5cuLXY72dnZOnDggAYPHmwtM5lMioqK0u7du+/4/Pz8fG3btk2//PKLXn/99RIdg6MsFoObWFSp+mDhPjgae37uVNbPr5o1a6pXr15lbmfr1q06ffq09eL4b8XGxspkMmnYsGHKzs5WdHS0zRyvZrNZCxYs0JQpU9S7d2/VrFlT3bt317Bhw8ocF4CKk5aWpqysLKPDqNRSU1NtfqJ0PD09K/UaKCdOnNCgQYN07tw53X333ZKkpKQk1atXT0lJSbrrrrsMjhAAcCcHDhzQpUuXbrmtYB2MS5cu6cCBAyw2WkmUKpF+//33a/Xq1Vq8eLE2btwoV1dXhSYehj4AAE/aSURBVISE6K233lKTJk2K3c6FCxdksVgKTeHi4+NjHRV3K5cvX9bDDz+s7OxsmUwmTZ48Wf/zP/9TomOoyPntcGcsqlR9sHAfHE11/9xZuHChfHx89Mwzz9iUf/zxxzp//vwt7w67nejoaB06dOiW21xdXTV58uQiF0hr2LAhC5YDlVhaWppeGTxYN5gX1y4SExONDqFSc3V21vyFCyttMj0+Pl6BgYFavXq1vL29Jd38/jx69GjFx8crKSnJ2AABAHeUmZkp6WYedeLEiTp48KAuXLigOnXqKCwsTPHx8dq5c6e1HhxfqZdRDgsLM+yPu9q1a2vdunW6du2aUlJSNH36dAUGBhaa9qUo9lqEDPbBokrVBwv3wdHY83Onohcis4fVq1dr1qxZhcrvvfdejRw5skSJdADVW1ZWlm7k5KhXbbP8zU5Gh4NqLM2SrzVXby5yXVkT6Tt27LBJoks3F6N7/fXX9dxzzxkXGACg2ApGo0dFRcnZ2bnQqPOHHnpIO3fuvO2odTieUifS8/LydOLECWVmZhaaz+eBBx4oVht16tSR2WwudOUlMzNTvr6+t32eyWRS48aNJd1M6B89elRJSUklSqTbaxEy2AeLKlUfLNwHR1PdP3fS09Pl5+dXqLxu3bq3XV0eAIrib3ZSwxr09zBS5Z9K0MXFRVevXi1UfvXqVTk7OxsQEQCgpLy8vCTdnAKzc+fONvmQvLw8bdu2zaYeHF+pEul79uxRTEyMTp8+XSiJ7uTkpIMHDxarHRcXFzVr1kwpKSnq1KmTpJtvpJSUFPXt27fY8eTl5Sk7O7v4BwAAACRJ9evX165duxQYGGhTvnPnzko7ig8AgMrukUce0RtvvKG33nrLOoLxxx9/1JQpU9SxY0eDowMAFEfBVNY7d+5UfHy8evbsqcaNG+vEiRP66KOPtHPnTpt6cHylSqRPnjxZ4eHhSkpKkp+fn3WC/NIYMGCAxo4dq/DwcEVGRmrZsmW6fv26nn76aUnSmDFjFBAQoJiYGEk353INDw/XXXfdpezsbP3zn//U559/rilTppQ6BgAAqquePXtq2rRpys3N1UMPPSTp5gLiM2fO1B//+EeDowMAoHqaOHGixo4dq969e6tGjZtf2y0Wizp27KgJEyYYHB0AoDiaNWumgIAAeXp66vjx4xo9erR1m7+/v+69915lZWWpWbNmBkaJkihVIv3EiROaM2eOdXqVsujSpYvOnz+vOXPmKD09XWFhYVq8eLF1apczZ87Y3Ppw7do1xcXF6ezZs3Jzc9M999yjmTNnqkuXLmWOBQCA6uall17SxYsXFRcXp5z/XSDQ1dVVL730kgYPHmxwdAAAVE+enp6aP3++jh8/rmPHjkmSgoKC7PIdHABQMcxmswYOHKiEhAS1atVKQUFBunr1qmrXrq3s7Gzt2rVL48ePr9ZTjVY2pUqkR0ZG6sSJE3brxPv27XvbqVxWrFhh83jkyJEaOXKkXfYLAEB15+TkpNGjR+vVV1/V0aNH5ebmpiZNmsjFxcXo0AAAqPaaNGmiJk2aGB0GAKCUoqKi9OCDD2r79u2FtrVp00ZRUVEGRIXSKlUivV+/fpoxY4YyMjLUtGlT661mBUJDQ+0SHAAAqBi1a9dWZGSkrly5on/961+6++67FRQUZHRYAABUS/n5+dq0aZO2b9+u8+fPKy/PdgHV999/36DIAAAlsXTpUm3fvl1eXl7q2LGj6tWrp7Nnz+qbb77R9u3btXTpUqbUrERKlUh/7bXXJEmxsbHWMicnJ+Xn55dosVEAAGCs4cOH64EHHlDfvn3166+/qkePHvrPf/6j/Px8vfPOO3r88ceNDhEAgGrnrbfe0urVq9WmTRv5+vqWaV0yAIAxsrOztW7dOnl7e2vJkiU6dOiQLly4YP3+NXDgQH322Wfq27cvdwRXEqVKpP/jH/+wdxwAAMAAP/zwg1555RVJ0t///nfl5+drx44d+vTTTzV//nwS6QAAGODzzz/X+++/r/bt2xsdCgCglDZs2KC8vDy1bdtWr776qs6dO2fdFhAQoIceekibNm3Shg0b1K1bN+MCRbGVKpHesGFDe8cBAAAMcPnyZXl5eUmSNm/erMcee0w1a9bUI488opkzZxocHQAA1ZO7u7saNWpkdBgAgDI4e/asJGnTpk1q3bq1unfvLldXV924cUM7d+7Ul19+aVMPjq/YifR//OMfevjhh+Xs7HzHEemPPvpomQMDAADlr379+tq9e7e8vLy0efNmvfPOO5KkrKwsbi8EAMAgr732mubNm6dp06bJzc3N6HAAAKXg7+8vSfLz89Px48e1Y8cO6zY/Pz/5+voqPT3dWg+Or9iJ9CFDhuj//b//Jx8fHw0ZMuS29ZgjHQCAyuOFF17Q6NGjVatWLTVo0EBt2rSRJO3YsUNNmzY1ODrH4XT+htEhoJrjPQhUL08++aS++OILtW3bVo0aNVKNGrZf3T/99FODIgMAFFeTJk0kSWlpaYUGKV26dEnZ2dk29eD4ip1I/+mnn275OwAAqLz69Omj5s2b68yZM4qKipLJZJIkBQYGasSIEcYG50Bc/37G6BAAANXI2LFjdeDAAf3+979nsVEAqKQuXbpk/T0vL089evTQY489pq+++kqfffbZLevBsZVqjvTi6tq1q5KSklS/fv3y3A0AACiD8PBwhYeH25Q98sgjNo9btWqlzz77TIGBgRUYmeO40bm+8uu6Gh0GqjGn8ze4oANUI//85z+1ePFitW7d2uhQAACldOHCBUmSp6ensrKytHbtWq1du1bSzRk9CsoL6sHxlWsi/dSpU8rNzS3PXQAAgAqQn59vdAiGyq/rqnz/mkaHAQCoJurVqyd3d3ejwwAAlMGVK1ckSTVq1JDJZFJeXp51m5OTk3XaroJ6cHwmowMAAAAAAAD/Z9y4cZo5c6ZOnTpldCgAgFIqmJbr/PnzMplMevjhh/XSSy/p4Ycflslk0vnz523qwfGV64h0AAAAAABQMqNHj9b169fVuXNnubm5ydnZ2Wb7999/b1BkAIDiCgsLk3QzUZ6bm6t//etf+te//mXd7uTkpPz8fGs9OD4S6QAAAAAAOJDY2FijQwAAlNHJkycl3Zwm08vLSx06dFD9+vV15swZffvtt9ZFRk+ePMmaGJUEiXQAAHBH3G4IAEDF6d69u9EhAADK6OzZs9bfr1+/rnXr1lkfu7i43LIeHBtzpAMAgDuq7ouNAgBglBs3bujKlSs2/wAAlUdISIhyc3NtynJzc9W0aVODIkJpleuI9KlTp8rHx6c8dwEAAMrg/fff18CBA1WzZk2b8l9//VWLFy/W0KFDJUmLFi1SQECAESECAFDtXLt2TbNmzdLGjRt18eLFQtsPHjxY8UEBAEokJCREGzZs0KFDh9S6dWu1bt1arq6uunHjhn744Qf98MMP1nqoHEo9Iv3777/Xyy+/rM6dO6tz5856+eWXrW+AAl27dlWtWrXKHCQAACgf8+bN07Vr1wqVX79+XfPmzbM+bt26tc3thwAAoPzMnDlT27Zt05QpU+Ti4qL4+Hi99tpr8vf314wZM4wODwBQDL8dXHz48GGdPXtWN27c0NmzZ3X48OFb1oNjK1Ui/bPPPtOAAQPk5uamfv36qV+/fnJzc9OLL76o9evX2ztGAABQTvLz8285//lPP/0kLy8vAyICAADffvutJk+erMcff1xms1mtW7fWq6++qpEjR5b6O3dSUpJCQkL01ltvWctu3LihuLg4tWnTRi1bttRrr72mjIwMm+edPn1agwYNUvPmzdW2bVvNmDGj0BQF27dvV/fu3RUeHq7OnTvrk08+KbT/lStXqmPHjoqIiFDPnj21d+/eUh0HAFQ27u7uunTpktatW6cFCxZo3bp1unTpktzd3Y0ODSVUqqldFixYoNGjR+vFF1+0lr3wwgtKTk7Wn//8Z3Xt2tVe8QEAgHLwwAMPyMnJSU5OTnr88cdtkukWi0XXrl3Ts88+a2CEAABUX5cuXVJgYKCk/0vASNL999+vuLi4Ere3d+9erVq1qtD0AdOmTdM///lPzZ49Wx4eHnrzzTc1dOhQrVq1StLNvwkGDx4sX19frVq1SmlpaRo7dqycnZ01atQoSVJqaqoGDx6sZ599VrNmzVJKSoomTpwoPz8/tWvXTpK0YcMGJSQkKC4uTs2bN9eyZcs0cOBAbdq0iZGYAKqsgs/uK1euyMvLSxEREXJzc9Ovv/6qffv2WbcX/ITjK1UiPTU1VR06dChU3rFjR73zzjtlDgoAAJSv2NhY5efnKzY2Vq+99po8PDys25ydndWwYUO1bNnSwAgBAKi+GjVqpFOnTqlBgwa65557tHHjRkVGRurbb7+16bOL4+rVqxo9erTi4+M1f/58a/nly5e1du1azZo1S23btpV0M7HepUsX7dmzRy1atNCWLVt05MgRJScny9fXV2FhYRo+fLhmzZqloUOHysXFRatWrVKjRo00btw4SVJQUJB27typDz74wJpIT05OVq9evdSjRw9JUlxcnL777jutXbtWgwYNssd/GQA4nII7fBs1aqTs7Gxt2bLFus3f39/6Wc+dwJVHqRLp9evXV0pKiho3bmxTvnXrVtWvX98ugQEAgPLTvXt3STf/qGvZsqWcnZ0NjggAABTo0aOHfvrpJz344IMaNGiQXn75ZX344YfKzc21JqyLa+rUqWrfvr2ioqJsEun79+9XTk6OoqKirGVBQUFq0KCBNZG+Z88eNW3aVL6+vtY60dHRmjJlio4cOaL77rtPe/bssSbif1tn2rRpkqTs7GwdOHBAgwcPtm43mUyKiorS7t27S3Qs0s1R8qgceK1Q3eXl5UmS9Y6fn376SRcuXFCdOnUUGhqqSZMmWetxvhinJP/3pUqkDxgwQPHx8Tp48KB1tNquXbv06aefasKECaVpEgAAVJArV65Y5+O77777dOPGDd24ceOWdZm3DwCAivfbaVSjoqK0ceNGHThwQHfddZdCQ0OL3c7f/vY3/fvf/9bHH39caFtGRoacnZ3l6elpU+7j46P09HRrnd8m0SVZH9+pzpUrV/Trr7/q0qVLslgshaZw8fHx0bFjx4p9LAX27dtX4ufAGHv27DE6BMBQBZ9XBw8e1IQJE9SuXTv5+/vr+PHjWrFihQ4dOiTp5vRb+fn5RoaKYipVIv3555+Xn5+fli5dqk2bNkmS7rnnHr377rvq1KmTXQMEAAD29cADD2jLli3y8fFR69atb7nYaMEipAcPHjQgQgAA8FsNGzZUw4YNC5V37dpVSUlJt7wz/MyZM3rrrbe0dOlSubq6VkSYFSIiIkJms9noMFAMLVq0MDoEwFBms1kff/yx+vXrpy+//FKLFy+2bgsICFC/fv20YsUKtWjRQhEREQZGWr1ZLJZiX6QtVSJdkjp37qzOnTuX9ukAAMAgy5Yts87Dt3z5coOjAQAApXXq1Cnl5ubectuBAweUmZmpp59+2lpmsVi0Y8cOrVy5UkuWLFFOTo6ysrJsRqVnZmbKz89P0s2R5Xv37rVpNyMjQ5Js6hSU/baOu7u73NzcZDKZZDablZmZaVMnMzOz0Ej24jCbzSTSKwleJ1R3ERERCggI0KFDh7Rw4UIdPHjQOrVLWFiYEhISFBAQwAXCSsRUmic9+uijunDhQqHyrKwsPfroo2UOCgAAlJ8HH3xQNWrUsP5e1D8AAFA5PfTQQ1q/fr3WrVtn/RceHq6uXbtaf3d2dlZKSor1OceOHdPp06etI4lbtGihn3/+2SYJvnXrVrm7uys4ONhaZ9u2bTb73rp1q7UNFxcXNWvWzGY/eXl5SklJYWFzAFWa2WzWwIEDtWPHDiUkJMjZ2VkPPPCAnJ2dlZCQoB07dmjgwIEk0SuRUo1I/89//mOdMP+3srOzde7cuTIHBQAAys9PP/1U7LolmYcVAAA4Dnd3dzVt2tSmrFatWvL29raW9+jRQ9OnT5eXl5fc3d0VHx+vli1bWpPg0dHRCg4O1pgxYzR69Gilp6dr9uzZ6tOnj1xcXCRJzz77rFauXKm3335bPXr00LZt27Rx40YtXLjQut8BAwZo7NixCg8PV2RkpJYtW6br16/bjJYHgKooKipK48eP15IlSzR69GhreUBAgMaPH2+z4DMcX4kS6f/4xz+sv2/evFkeHh7WxwVXlG81bxsAAHAc3bp1k5OTk3Ue9KIwRzoAAFVXbGysTCaThg0bpuzsbEVHR2vy5MnW7WazWQsWLNCUKVPUu3dv1axZU927d9ewYcOsdQIDA7Vw4UIlJCRo+fLlqlevnuLj49WuXTtrnS5duuj8+fOaM2eO0tPTFRYWpsWLF5dqahcAqGyioqLUpk0bHThwwDq1S7NmzRiJXgmVKJE+ZMgQSZKTk5PGjRtn21CNGmrYsGGhcgAA4Fh+e2H84MGDmjFjhgYOHGgdfbZnzx4lJyfbjJgAAACV34oVK2weu7q6avLkyTbJ8//WsGFDLVq0qMh227Rpo3Xr1hVZp2/fvurbt2+xYwWAkjp79qyuXLlidBi3VatWLdWqVUuS9Msvvxgcze25u7urXr16RofhkEqUSC+4Fbxjx476+OOPVbdu3XIJCgAAlJ/f3j02fPhwTZw4Ue3bt7eWhYaGqn79+nrvvffUqVMnI0IEAAAAgGK7dOmSBg0adMupqFEyJpNJK1askJeXl9GhOJxSzZH+zTffFKte165dlZSUpPr165dmNwAAoJz9/PPPatSoUaHyRo0a6ciRI8Vu59y5c5o5c6Y2b96s69evq3Hjxpo2bZoiIiIkSePGjdOnn35q85zo6GgtWbLE+vjixYt688039e2338pkMumxxx7ThAkTVLt27VIeHQAAVdvUqVPl4+NjdBgAYDgvLy8lJSU59Ij01NRUJSYmKiYmRoGBgUaHc1vu7u4k0W+jVIn04jp16pRyc3PLcxcAAKAMgoKCtHDhQsXHx1sXDcvOztbChQsVFBRUrDYuXbqk5557Tm3atNGiRYtUp04dnThxotAfX+3atVNCQoL1ccH+Crz++utKT09XcnKycnJyFBsbqzfeeEOJiYllPEoAACqflJQUpaSkKDMzs9AIy4L+tGvXrkaEBgAOqbJMRxIYGKjg4GCjw0AplGsiHQAAOLa4uDi9/PLLat++vUJCQiRJhw4dkpOTkxYsWFCsNhYtWqR69erZJMlvNcLCxcVFfn5+t2zj6NGj2rx5sz7++GPrKPaJEydq0KBBGjNmjAICAkp6aAAMkmbJl8Rt1TDOzfdg5fb+++9r3rx5Cg8Pl5+f3x0XBwcAAOWPRDoAQ/AlG0arCl+y7SEyMlJff/211q9fr2PHjkmSunTpot/97nfWhXDu5JtvvlF0dLSGDRumHTt2KCAgQM8//7x69eplU+/7779X27Zt5enpqYceekg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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== Sayısal özet ===\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "            age     income  employment_length  debt_to_income  credit_score  \\\n",
       "count  30000.00   30000.00           30000.00        30000.00      30000.00   \n",
       "mean      45.00   54279.31              11.92            0.17        679.47   \n",
       "std       14.12   25751.85               7.22            0.10         59.88   \n",
       "min       21.00    8942.00               0.00            0.01        500.00   \n",
       "25%       33.00   36196.00               6.00            0.10        639.00   \n",
       "50%       45.00   49058.00              12.00            0.16        680.00   \n",
       "75%       57.00   66425.25              18.00            0.23        720.00   \n",
       "max       69.00  288464.00              24.00            0.53        850.00   \n",
       "\n",
       "       loan_amount  \n",
       "count     30000.00  \n",
       "mean      15122.14  \n",
       "std        8045.48  \n",
       "min        1470.00  \n",
       "25%        9535.00  \n",
       "50%       13382.50  \n",
       "75%       18677.00  \n",
       "max       84043.00  "
      ],
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>employment_length</th>\n",
       "      <th>debt_to_income</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45.00</td>\n",
       "      <td>54279.31</td>\n",
       "      <td>11.92</td>\n",
       "      <td>0.17</td>\n",
       "      <td>679.47</td>\n",
       "      <td>15122.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.12</td>\n",
       "      <td>25751.85</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.10</td>\n",
       "      <td>59.88</td>\n",
       "      <td>8045.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.00</td>\n",
       "      <td>8942.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1470.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.00</td>\n",
       "      <td>36196.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>639.00</td>\n",
       "      <td>9535.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.00</td>\n",
       "      <td>49058.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>680.00</td>\n",
       "      <td>13382.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.00</td>\n",
       "      <td>66425.25</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>720.00</td>\n",
       "      <td>18677.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69.00</td>\n",
       "      <td>288464.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>850.00</td>\n",
       "      <td>84043.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40bde5fc-9426-484c-ab32-881c93b481b8')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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       "        document.querySelector('#df-40bde5fc-9426-484c-ab32-881c93b481b8 button.colab-df-convert');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const dataTable =\n",
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       "summary": "{\n  \"name\": \"print(\\\"\\\\n\\u2705 A\\u015eAMA 7 tamamland\\u0131\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10592.266713116696,\n        \"min\": 14.12,\n        \"max\": 30000.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          30000.0,\n          45.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 90121.81958616593,\n        \"min\": 8942.0,\n        \"max\": 288464.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          54279.31,\n          49058.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employment_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10602.607100697653,\n        \"min\": 0.0,\n        \"max\": 30000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.92,\n          12.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"debt_to_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10606.536059007269,\n        \"min\": 0.01,\n        \"max\": 30000.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          30000.0,\n          0.17,\n          0.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"credit_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10400.781920330988,\n        \"min\": 59.88,\n        \"max\": 30000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          679.47,\n          680.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26227.694235791514,\n        \"min\": 1470.0,\n        \"max\": 84043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15122.14,\n          13382.5,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "✅ AŞAMA 7 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 7: EDA — veriyi modellemeden önce anlamak\n# ============================================================\n\n# --- 1) Hedef sınıf dağılımı ---\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))  # Yan yana 2 eksen\n# Sol: adet bar grafiği\ndf['default'].value_counts().plot(kind='bar', ax=axes[0], color=['#2ecc71', '#e74c3c'])\naxes[0].set_title('Default Dağılımı (0=Ödedi, 1=Temerrüt)')\naxes[0].set_xticklabels(['Ödedi (0)', 'Temerrüt (1)'], rotation=0)\n# Sağ: oran pasta grafiği\ndf['default'].value_counts(normalize=True).plot(\n    kind='pie', ax=axes[1], autopct='%1.1f%%',\n    colors=['#2ecc71', '#e74c3c'], labels=['Ödedi', 'Temerrüt']\n)\naxes[1].set_ylabel('')\naxes[1].set_title('Default Oranı')\nplt.tight_layout()   # Eksene sığdır, üst üste binmesin\nplt.show()           # Notebook'ta göster\n\n# --- 2) Sayısal özelliklerin default'a göre kutu grafiği ---\nnum_cols = ['age', 'income', 'employment_length', 'debt_to_income', 'credit_score', 'loan_amount']\nfig, axes = plt.subplots(2, 3, figsize=(15, 9))  # 2 satır × 3 sütun ızgara\nfor i, col in enumerate(num_cols):\n    ax = axes[i // 3, i % 3]  # Satır-sütun indeksi\n    # x=sınıf, y=özellik → medyan/çeyrekler sınıflar arası farkı gösterir\n    sns.boxplot(data=df, x='default', y=col, ax=ax, palette=['#2ecc71', '#e74c3c'])\n    ax.set_title(col)\n    ax.set_xticklabels(['Ödedi', 'Temerrüt'])\nplt.suptitle('Sayısal Özellikler vs Default', fontsize=14, y=1.02)\nplt.tight_layout()\nplt.show()\n\n# --- 3) Korelasyon ısı haritası ---\ncorr = df[num_cols + ['default']].corr()  # Pearson korelasyon matrisi\nfig, ax = plt.subplots(figsize=(9, 7))\nsns.heatmap(corr, annot=True, fmt='.2f', cmap='RdYlGn', center=0, ax=ax)\nax.set_title('Korelasyon Matrisi')\nplt.tight_layout()\nplt.show()\n\n# --- 4) Kategorik özellik × default oranı ---\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))\npd.crosstab(df['home_ownership'], df['default'], normalize='index').plot(\n    kind='bar', ax=axes[0], color=['#2ecc71', '#e74c3c']\n)\naxes[0].set_title('Home Ownership × Default Oranı')\naxes[0].legend(['Ödedi', 'Temerrüt'])\npd.crosstab(df['purpose'], df['default'], normalize='index').plot(\n    kind='bar', ax=axes[1], color=['#2ecc71', '#e74c3c']\n)\naxes[1].set_title('Purpose × Default Oranı')\naxes[1].legend(['Ödedi', 'Temerrüt'])\nplt.tight_layout()\nplt.show()\n\nprint(\"=== Sayısal özet ===\")\ndisplay(df[num_cols].describe().round(2))  # mean, std, min, max\nprint(\"\\n✅ AŞAMA 7 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "d9128ca3",
   "metadata": {
    "id": "d9128ca3"
   },
   "source": "## AŞAMA 8: GDPR / KVKK Anonimleştirme\n\nProje için verdiğimiz veri setinden hangi feature’lar sensitive ise onları maskelemek için anonymisation uygularız. Kullanıcı uygulamak istediği feature’ları belirtecek; aşağıdaki yöntemlerle hazır kod çalışır.\n\n---\n\nKuantum makine öğrenimi (QML) projelerinde **KVKK ve GDPR uyumluluğunu** sağlamak, verinin kuantum durumlarına kodlanması (data encoding) aşamasından modelin eğitimine kadar **uçtan uca bir güvenlik stratejisi** gerektirir. Klasik makine öğrenimindeki modelin veriyi ezberlemesi (memorization) riskleri, QML ortamında da **kuantum durum tomografisi (state tomography)** ve **üyelik çıkarım saldırıları (membership inference)** yoluyla veri sızıntılarına yol açabilir.\n\nKVKK/GDPR uyumlu bir QML boru hattında uygulayabileceğimiz yöntemleri iki temel yaklaşıma ayırırız:\n\n### 1. Kuantum Öncesi (Klasik) Veri Maskeleme\n\nKuantum devresine (örneğin ZZFeatureMap veya genlik kodlaması ile) veriyi gömmeden önce, Python (NumPy/Pandas) seviyesinde uygulanan veri hazırlık adımlarıdır.\n\n- **Sözde Anonimleştirme ve Tokenizasyon (Pseudonymization):** Veri setindeki Kişisel Tanımlanabilir Bilgilerin (PII) kriptografik hash’ler veya geri döndürülebilir rastgele token’lar ile değiştirilmesi. QML modeli doğrudan bu token’lar üzerinden eğitilir.\n- **K-Anonimlik ve L-Çeşitlilik:** Özellik haritasına (feature map) aktarılacak verilerin, tek bir bireyin tespit edilmesini engelleyecek şekilde genelleştirilmesi ve gruplanması (örneğin spesifik yaş yerine yaş aralığı kullanılması).\n- **Klasik Diferansiyel Gizlilik (Differential Privacy):** Kuantum devresine aktarım öncesinde veri setine kontrollü bir istatistiksel gürültü (örneğin Laplace veya Gauss gürültüsü) eklenmesi; böylece algoritmik çıktının tek bir kullanıcının varlığına bağlı olarak değişmesi engellenir.\n\n### 2. Kuantum-Yerel Gizlilik Koruyucu Yöntemler (PPQML)\n\nDoğrudan Qiskit ve kuantum donanımı üzerinde gizliliği donanım ve algoritma seviyesinde koruyan ileri düzey tekniklerdir:\n\n- **Kuantum Federe Öğrenme (Quantum Federated Learning – QFL):** Hassas verilerin merkezi bir sunucuya gönderilmesini engeller. Model, dağınık (decentralized) yerel verilerle eğitilir; buluta yalnızca kuantum sinir ağı parametreleri (gradyanlar) gönderilir.\n  - *Gelişmiş yaklaşım:* Veri kümeleri saf durumlar (pure states) yerine istatistiksel özellikleri koruyan **karma durumlar (mixed states)** olarak kodlanabilir; çiğ veriler paylaşılmadan işbirlikçi analiz yapılır.\n- **Kuantum Diferansiyel Gizlilik (Quantum Differential Privacy – QDP):** Koruyucu gürültünün kuantum donanım seviyesinde, örneğin **depolarizasyon kanalları (depolarizing channels)** ile simüle edilmesi. Parametre güncellemelerine uygulanan bu gürültü bireysel verilerin çıktı üzerindeki etkisini gizler.\n- **Kör Kuantum Hesaplama (Blind Quantum Computing):** KVKK kapsamındaki veriler, bulut kuantum sistemlerine (ör. IBM Quantum) şifrelenmiş kuantum durumları olarak iletilir. Sunucu girdiyi ve gerçek hesaplamayı “kör” olarak işler, sonucu döndürür.\n\n### Jupyter Ortamında Tasarım Mimarisi (ideal sıra)\n\n| Adım | Katman | Araç | Açıklama |\n|------|--------|------|----------|\n| 1 | Veri Maskeleme ve Ön İşleme | Pandas & Scikit-learn | Kimlik belirleyici alanların çıkarılması; demografik/riskli alanların K-anonimlik ile normalize edilmesi |\n| 2 | Gürültü Enjeksiyonu ve Özellik Haritalama | Qiskit Aer | Maskelenmiş verilerin kuantum durumlarına kodlanması; gerekirse NoiseModel ile kontrollü kuantum gürültüsü |\n| 3 | Hibrit Optimizasyon ve Parametre Güncelleme | SPSA / COBYLA | Federe senaryoda veri yerelde kalır; yalnızca Ansatz ağırlık güncellemeleri paylaşılır |\n\n> **Not:** Aşağıdaki kod hücresi **klasik ön-maskeleme** örneğidir (hash/bucket, yaş genelleme, Laplace DP). Ana model varsayılan olarak orijinal `df` ile devam eder; production’da `df_anon` sütunlarını feature set’e bağlayın.\n\n\n---\n### Öğrenci notu — Maskeleme kodu\n\n| Fonksiyon / adım | Ne yapıyor? |\n|------------------|-------------|\n| `pd.qcut(..., q=10)` | Geliri 10 dilime böler (kaba k-anonimlik benzeri) |\n| `(age // 5) * 5` | Yaşı 5’er aralığa yuvarlar |\n| `np.random.laplace` | Diferansiyel gizlilik için Laplace gürültüsü |\n| `EPSILON` | Gürültü bütçesi; küçük ε → daha fazla gizlilik, daha az doğruluk |\n\n**Öğrenme hedefi:** Encoding’den **önce** klasik tarafta PII’yi azaltmak. QPU’ya giden veri mümkün olduğınca kimliksiz olmalıdır.\n"
  },
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   "execution_count": 9,
   "id": "a9d96e24",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
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    "id": "a9d96e24",
    "outputId": "c0ba56fd-5bc6-4cb1-cbf9-a2b49d350fdc",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731830329,
     "user_tz": -180,
     "elapsed": 50,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  income → income_bucket (10 quantile)\n",
      "  age → age_gen (bin=5)\n",
      "  debt_to_income → debt_to_income_dp (Laplace ε=1.0)\n",
      "\n",
      "Anonimleştirilmiş sütun örnekleri:\n"
     ]
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       "   income_bucket  age_gen  debt_to_income_dp\n",
       "0              5       55           0.316068\n",
       "1              8       45          -1.428542\n",
       "2              0       35          -1.119113\n",
       "3              6       60          -0.339921\n",
       "4              6       25           0.351029"
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       "summary": "{\n  \"name\": \"print(\\\"\\\\n\\u2705 A\\u015eAMA 8 tamamland\\u0131\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"income_bucket\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8,\n          6,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_gen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 25,\n        \"max\": 60,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          45,\n          25,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"debt_to_income_dp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8132815766001239,\n        \"min\": -1.4285421879325577,\n        \"max\": 0.3510290136358166,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.4285421879325577,\n          0.3510290136358166,\n          -1.1191127783932857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "✅ AŞAMA 8 tamamlandı. (Ana model varsayılan olarak orijinal df ile devam eder.)\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 8: GDPR / KVKK ANONİMLEŞTİRME (klasik ön-maskeleme)\n# Encoding'den ÖNCE uygulanır; QPU'ya giden veriyi kimliksizleştirir\n# ============================================================\n\nfrom hashlib import sha256   # Tek yönlü özet fonksiyonu (isteğe bağlı PII hash)\n\ndf_anon = df.copy()          # Orijinal df'yi bozmamak için kopya\n\nSENSITIVE_COLS = ['income']          # Dilimlenecek hassas sayısal alan\nGENERALIZE_COLS = {'age': 5}         # Yaşı 5'er aralığa yuvarla\nNOISE_COLS = ['debt_to_income']      # Laplace gürültüsü eklenecek sütun\nEPSILON = 1.0                        # DP bütçesi: küçük ε → daha çok gürültü\n\ndef generalize_numeric(series, bin_width):\n    \"\"\"Sürekli değeri bin_width adımlı aralıklara indirger (k-anonimlik benzeri).\"\"\"\n    return (series // bin_width) * bin_width\n\ndef add_laplace_noise(series, epsilon, sensitivity=None):\n    \"\"\"Diferansiyel gizlilik için Laplace gürültüsü ekler.\"\"\"\n    if sensitivity is None:\n        sensitivity = series.max() - series.min()  # En basit sensitivity tahmini\n    scale = sensitivity / max(epsilon, 1e-6)       # Laplace ölçeği b = Δf/ε\n    noise = np.random.laplace(0, scale, size=len(series))\n    return series + noise\n\n# Geliri 10 quantile dilimine ayır (ham gelir yerine dilim indeksi)\nfor col in SENSITIVE_COLS:\n    if col in df_anon.columns:\n        df_anon[col + '_bucket'] = pd.qcut(df_anon[col], q=10, labels=False, duplicates='drop')\n        print(f\"  {col} → {col}_bucket (10 quantile)\")\n\n# Yaşı genelle\nfor col, width in GENERALIZE_COLS.items():\n    if col in df_anon.columns:\n        df_anon[col + '_gen'] = generalize_numeric(df_anon[col], width)\n        print(f\"  {col} → {col}_gen (bin={width})\")\n\n# DTI'ye DP gürültüsü\nfor col in NOISE_COLS:\n    if col in df_anon.columns:\n        df_anon[col + '_dp'] = add_laplace_noise(df_anon[col], EPSILON)\n        print(f\"  {col} → {col}_dp (Laplace ε={EPSILON})\")\n\nprint(\"\\nAnonimleştirilmiş sütun örnekleri:\")\ndisplay(df_anon[[c for c in df_anon.columns if c.endswith(('_bucket', '_gen', '_dp'))]].head())\nprint(\"\\n✅ AŞAMA 8 tamamlandı. (Ana model varsayılan olarak orijinal df ile devam eder.)\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "ba5469e7",
   "metadata": {
    "id": "ba5469e7"
   },
   "source": "## AŞAMA 9: Veri Hazırlama ve Ön İşleme\n\nVeri setinden eğitim / test için split etme; kategorik encoding (LabelEncoder), feature seçimi, **StandardScaler** ile ölçekleme. Stratify ile sınıf dengesi korunur.\n\n\n---\n### Öğrenci notu — Ön işleme pipeline\n\n| Adım | Kod | Neden? |\n|------|-----|--------|\n| Kategorik → sayı | `LabelEncoder` | QML ve sklearn sayısal matris ister |\n| X / y ayır | `feature_cols`, `y = default` | Supervised öğrenme sözleşmesi |\n| Train/test | `train_test_split(..., stratify=y)` | Test setinde de aynı default oranı |\n| Ölçekleme | `StandardScaler` fit **sadece train**’de | Test’e bilgi sızdırmamak (data leakage) |\n\n**Kritik kural:** `scaler.fit` yalnızca `X_train` üzerinde. `X_test` sadece `transform` edilir.\n\n**Öğrenme hedefi:** Klasik ML hijyeni QML’de de geçerlidir; kötü split/ölçekleme quantum kernel’i de bozar.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17caa4aa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "17caa4aa",
    "outputId": "c3692262-12b8-4545-ea48-8fb5a1855f71",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793731860158,
     "user_tz": -180,
     "elapsed": 53,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train: (22500, 10), Test: (7500, 10)\n",
      "Train default oranı: 1.84%\n",
      "Test default oranı : 1.84%\n",
      "✅ AŞAMA 9 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 9: VERİ HAZIRLAMA / ÖN İŞLEME\n# Kategorik→sayı, X/y ayır, train/test split, StandardScaler\n# ============================================================\n\ndf_proc = df.copy()  # İşlenecek kopya\n\n# Metin kategorileri tam sayıya çevir (sklearn/QML sayı ister)\nle_home = LabelEncoder()\nle_purpose = LabelEncoder()\ndf_proc['home_ownership'] = le_home.fit_transform(df_proc['home_ownership'])\ndf_proc['purpose'] = le_purpose.fit_transform(df_proc['purpose'])\n\n# Modele girecek sütun listesi (hedef hariç)\nfeature_cols = [\n    'age', 'income', 'employment_length', 'debt_to_income',\n    'credit_score', 'loan_amount', 'loan_term',\n    'home_ownership', 'purpose', 'previous_defaults'\n]\nX = df_proc[feature_cols]   # Özellik matrisi\ny = df_proc['default']      # Hedef vektör\n\n# %75 eğitim / %25 test; stratify=y → her iki sette benzer default oranı\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.25, random_state=42, stratify=y\n)\n\n# Ortalama 0, std 1 ölçekleme — quantum açı kodlaması için de faydalı\nscaler = StandardScaler()\nX_train_sc = scaler.fit_transform(X_train)   # fit SADECE train'de (leakage yasağı)\nX_test_sc = scaler.transform(X_test)         # test'e aynı dönüşümü uygula\n\nprint(f\"Train: {X_train_sc.shape}, Test: {X_test_sc.shape}\")\nprint(f\"Train default oranı: {y_train.mean():.2%}\")\nprint(f\"Test default oranı : {y_test.mean():.2%}\")\nprint(\"✅ AŞAMA 9 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "e632f4cb",
   "metadata": {
    "id": "e632f4cb"
   },
   "source": "## AŞAMA 10: Klasik Makine Öğrenmesi Modelleri\n\nGeliştirdiğimiz QML projesinin klasik ML modellerine göre üstünlüğünü (veya denkliğini) gösterebilmek için baseline modeller:\n\n- Logistic Regression  \n- Random Forest  \n- XGBoost  \n- SVM (RBF)  \n\nAynı train/test üzerinde accuracy, precision, recall, F1, ROC-AUC raporlanır.\n\n\n---\n### Öğrenci notu — Klasik baseline neden var?\n\nQML’in “daha iyi” olduğunu iddia etmek için **aynı veri, aynı split** üzerinde klasik modeller şarttır.\n\n| Model | Rolü |\n|-------|------|\n| Logistic Regression | Doğrusal, yorumlanabilir taban çizgisi |\n| Random Forest | Doğrusal olmayan, özellik önem skorları |\n| XGBoost | Güçlü gradient boosting baseline |\n| SVM (RBF) | QSVC’nin klasik kuzeni (kernel SVM) |\n\n`evaluate_model` tek yerde fit → predict → metrik hesaplar; kod tekrarını önler.\n\n**Öğrenme hedefi:** ROC-AUC / F1 okumayı öğrenmek; AŞAMA 18’de quantum ile yan yana koymak.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8870669",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b8870669",
    "outputId": "e7541bd4-8c03-416c-eca5-24032887f01e",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1793732002712,
     "user_tz": -180,
     "elapsed": 126782,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "=== Logistic Regression ===\n",
      "  accuracy    : 0.7203\n",
      "  precision   : 0.0438\n",
      "  recall      : 0.6812\n",
      "  f1          : 0.0822\n",
      "  roc_auc     : 0.7912\n",
      "\n",
      "=== Random Forest ===\n",
      "  accuracy    : 0.9816\n",
      "  precision   : 0.0000\n",
      "  recall      : 0.0000\n",
      "  f1          : 0.0000\n",
      "  roc_auc     : 0.6885\n",
      "\n",
      "=== XGBoost ===\n",
      "  accuracy    : 0.8168\n",
      "  precision   : 0.0463\n",
      "  recall      : 0.4565\n",
      "  f1          : 0.0840\n",
      "  roc_auc     : 0.7398\n",
      "\n",
      "=== SVM (RBF) ===\n",
      "  accuracy    : 0.8172\n",
      "  precision   : 0.0396\n",
      "  recall      : 0.3841\n",
      "  f1          : 0.0718\n",
      "  roc_auc     : 0.6818\n",
      "\n",
      "✅ Klasik modeller tamamlandı. (AŞAMA 10)\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 10: KLASİK ML BASELINE\n# QML'i karşılaştırmadan önce aynı split'te klasik modeller\n# ============================================================\n\ndef evaluate_model(name, model, X_tr, y_tr, X_te, y_te):\n    \"\"\"Modeli eğit, tahmin et, metrik sözlüğü döndür.\"\"\"\n    model.fit(X_tr, y_tr)                         # Eğitim\n    y_pred = model.predict(X_te)                  # Sınıf tahmini (0/1)\n    # Olasılık skoru varsa ROC-AUC için 1. sınıf olasılığını al\n    y_proba = model.predict_proba(X_te)[:, 1] if hasattr(model, 'predict_proba') else None\n    metrics = {\n        'accuracy': accuracy_score(y_te, y_pred),\n        'precision': precision_score(y_te, y_pred, zero_division=0),\n        'recall': recall_score(y_te, y_pred, zero_division=0),\n        'f1': f1_score(y_te, y_pred, zero_division=0),\n        'roc_auc': roc_auc_score(y_te, y_proba) if y_proba is not None else np.nan,\n    }\n    print(f\"\\n=== {name} ===\")\n    for k, v in metrics.items():\n        print(f\"  {k:12s}: {v:.4f}\")\n    return metrics, model, y_pred, y_proba\n\nresults = {}  # model adı → metrik sözlüğü\n\n# Doğrusal, dengeli sınıf ağırlıklı lojistik regresyon\nlr = LogisticRegression(max_iter=1000, class_weight='balanced', random_state=42)\nresults['Logistic Regression'], lr_model, _, _ = evaluate_model(\n    'Logistic Regression', lr, X_train_sc, y_train, X_test_sc, y_test\n)\n\n# 200 ağaçlı rastgele orman; n_jobs=-1 → tüm CPU çekirdekleri\nrf = RandomForestClassifier(n_estimators=200, class_weight='balanced', random_state=42, n_jobs=-1)\nresults['Random Forest'], rf_model, _, _ = evaluate_model(\n    'Random Forest', rf, X_train_sc, y_train, X_test_sc, y_test\n)\n\n# Gradient boosting; scale_pos_weight azınlık sınıfı dengeler\nxgb = XGBClassifier(\n    n_estimators=200, max_depth=4, learning_rate=0.05,\n    scale_pos_weight=(y_train == 0).sum() / max((y_train == 1).sum(), 1),\n    random_state=42, eval_metric='logloss',\n)\nresults['XGBoost'], xgb_model, _, _ = evaluate_model(\n    'XGBoost', xgb, X_train_sc, y_train, X_test_sc, y_test\n)\n\n# RBF çekirdekli klasik SVM (QSVC'nin klasik kuzeni)\nsvm = SVC(kernel='rbf', class_weight='balanced', probability=True, random_state=42)\nresults['SVM (RBF)'], svm_model, _, _ = evaluate_model(\n    'SVM (RBF)', svm, X_train_sc, y_train, X_test_sc, y_test\n)\n\nprint(\"\\n✅ Klasik modeller tamamlandı. (AŞAMA 10)\")\n"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NLJPd90dre4t"
   },
   "source": "## AŞAMA 11: QML Algoritma Seçimi (Engine ile senkron)\n\nAŞAMA 2 motorundan gelen `SELECTED_ALGO` / `SELECTED_PROBLEM` / `SELECTED_HARDWARE` burada doğrulanır.\nWidget ile farklı üçlü seçtiyseniz o değerler geçerlidir; aksi halde varsayılan **QSVC** (kredi risk POC) kullanılır.\n\n### QML Algoritmaları ve Problem Eşleştirmesi\n\n| **Problem Tipi / Sektörel Senaryo** | **Uygun QML Algoritması** | **Algoritmanın Çalışma Mantığı** | **Ana Kullanım Alanı (Projelerimizden Örnekler)** |\n| --- | --- | --- | --- |\n| **İkili/Çok Sınıflı Sınıflandırma** *(Supervised Learning)* | **VQC (Variational Quantum Classifier)** | Klasik veriyi kuantum devrelerine özellik haritaları (feature maps) ile gömer ve parametrik bir Ansatz devresi (ör. RealAmplitudes) kullanarak sınıflandırma yapar. Klasik optimizatörlerle (COBYLA, SPSA) ağırlıklar güncellenir. | Görüntü analizi, hata tespiti (anomaly detection). |\n| **Doğrusal Olmayan Veri Ayrıştırma** *(Supervised Learning)* | **QSVM / QSVC (Quantum Support Vector Classifier)** | Klasik SVM’in kuantum versiyonudur. Kuantum özellik haritası ile verileri çok daha yüksek boyutlu bir Hilbert uzayına eşler; klasik donanımın bulamayacağı karar sınırlarını (kernel) bularak ayırır. | Karmaşık veri setlerinde risk tahmini, finansal skorlama. |\n| **Kombinatoryal Optimizasyon** *(Yönlendirici Analitik)* | **QAOA (Quantum Approximate Optimization Algorithm)** | Seyyar satıcı (TSP), çizge bölümleme (Max-Cut) gibi NP-Hard optimizasyon problemlerini çözmek için geliştirilmiş hibrit bir algoritmadır. | Üretim planlama, kalıp takip sistemlerindeki rotalama sorunları. |\n| **Moleküler Enerji ve Durum Tespiti** *(Kuantum Kimyası)* | **VQE (Variational Quantum Eigensolver)** | Bir molekülün veya sistemin (Hamiltonian formülasyonunun) en düşük enerji durumunu (taban durumu) bulmak için kullanılır. PySCF gibi kütüphanelerden gelen veriyi Qiskit Nature ile çözer. | GLP-1R aktif bölge analizi, radar soğurucu malzeme (kompozit) tasarımı. |\n| **Sürekli Optimizasyon / Kuantum Tavlama** *(Kombinatoryal)* | **QUBO / Ising Modeli + Kuantum Tavlama (Annealing)** | Problemi matematiksel olarak kuadratik ceza fonksiyonlarına dönüştürüp, kuantum sisteminin en düşük enerji (optimum) seviyesine fiziksel olarak “soğuyarak” (annealing) ulaşmasını sağlar. | D-Wave sistemleri ile enerji dağıtımı, off-grid elektrikli araç şarj istasyonlarının yük dengelemesi. |\n\nSeçilecek probleme göre hangi cihazların kullanılabileceği AŞAMA 2 engine’de belirlenir; algoritma kararına göre ilgili kod iskeleti üretilir.\n\n\n---\n### Öğrenci notu\n\nBu aşama yeni model eğitmez; AŞAMA 2’deki seçimi **doğrular ve belgeler**. Yanlış üçlü (ör. Chemistry + Grover) ile ilerlemeyi engeller.\n\n**Alıştırma:** AŞAMA 2’de Problem = Quantum Chemistry seçin → yalnızca VQE kalır. `SELECTED_ALGO` çıktısını buradan kontrol edin.\n",
   "id": "NLJPd90dre4t"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "52Cj30kCre4u",
    "outputId": "fe475969-caee-4bbe-f368-4ea77b5ad7a1",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843698337,
     "user_tz": -180,
     "elapsed": 112,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "                           Problem       Algoritma  \\\n",
       "0  İkili/Çok sınıflı sınıflandırma      VQC / QSVC   \n",
       "1      Doğrusal olmayan ayrıştırma            QSVC   \n",
       "2       Kombinatoryal optimizasyon            QAOA   \n",
       "3                 Moleküler enerji             VQE   \n",
       "4                   QUBO / tavlama  QUBO/Annealing   \n",
       "5              Arama / kriptografi   Grover / Shor   \n",
       "\n",
       "                             Mantık  \n",
       "0     Feature map + kernel / Ansatz  \n",
       "1           Fidelity quantum kernel  \n",
       "2  Hibrit varyasyonel Max-Cut / TSP  \n",
       "3          Hamiltonian taban durumu  \n",
       "4                    Ising / D-Wave  \n",
       "5                      Oracle / QFT  "
      ],
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       "summary": "{\n  \"name\": \"algo_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Problem\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\u0130kili/\\u00c7ok s\\u0131n\\u0131fl\\u0131 s\\u0131n\\u0131fland\\u0131rma\",\n          \"Do\\u011frusal olmayan ayr\\u0131\\u015ft\\u0131rma\",\n          \"Arama / kriptografi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Algoritma\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"VQC / QSVC\",\n          \"QSVC\",\n          \"Grover / Shor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mant\\u0131k\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Feature map + kernel / Ansatz\",\n          \"Fidelity quantum kernel\",\n          \"Oracle / QFT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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     "text": [
      "\n",
      "AŞAMA 2 Engine seçimi:\n",
      "  SELECTED_PROBLEM  = Machine Learning\n",
      "  SELECTED_HARDWARE = Superconducting Qubits\n",
      "  SELECTED_ALGO     = QSVC\n",
      "  SELECTED_DEVICE   = ibm_or_aer\n",
      "\n",
      "✅ Aktif algoritma: QSVC\n",
      "   Sonraki aşamalar (12–18) QSVC / feature-map hattını çalıştıracak.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 11: ALGORİTMA SEÇİMİ — Engine senkronu\n# Yeni model eğitmez; AŞAMA 2 seçimini doğrular\n# ============================================================\n\n# Referans tablo: problem tipi → uygun algoritma\nalgo_table = pd.DataFrame([\n    {\"Problem\": \"İkili/Çok sınıflı sınıflandırma\", \"Algoritma\": \"VQC / QSVC\", \"Mantık\": \"Feature map + kernel / Ansatz\"},\n    {\"Problem\": \"Doğrusal olmayan ayrıştırma\", \"Algoritma\": \"QSVC\", \"Mantık\": \"Fidelity quantum kernel\"},\n    {\"Problem\": \"Kombinatoryal optimizasyon\", \"Algoritma\": \"QAOA\", \"Mantık\": \"Hibrit varyasyonel Max-Cut / TSP\"},\n    {\"Problem\": \"Moleküler enerji\", \"Algoritma\": \"VQE\", \"Mantık\": \"Hamiltonian taban durumu\"},\n    {\"Problem\": \"QUBO / tavlama\", \"Algoritma\": \"QUBO/Annealing\", \"Mantık\": \"Ising / D-Wave\"},\n    {\"Problem\": \"Arama / kriptografi\", \"Algoritma\": \"Grover / Shor\", \"Mantık\": \"Oracle / QFT\"},\n])\ndisplay(algo_table)  # Öğrenci tabloyu görsün\n\n# AŞAMA 2'de set edilen global değişkenleri yazdır\nprint(\"\\nAŞAMA 2 Engine seçimi:\")\nprint(f\"  SELECTED_PROBLEM  = {SELECTED_PROBLEM}\")\nprint(f\"  SELECTED_HARDWARE = {SELECTED_HARDWARE}\")\nprint(f\"  SELECTED_ALGO     = {SELECTED_ALGO}\")\nprint(f\"  SELECTED_DEVICE   = {SELECTED_DEVICE}\")\n\n# Bilinmeyen algoritma gelirse güvenli varsayılan\nif SELECTED_ALGO not in (\"QSVC\", \"VQC\", \"QAOA\", \"VQE\", \"Grover\", \"Shor\", \"QUBO/Annealing\"):\n    SELECTED_ALGO = \"QSVC\"\n    print(\"  → Bilinmeyen algoritma; QSVC'ye düşüldü.\")\n\nprint(f\"\\n✅ Aktif algoritma: {SELECTED_ALGO}\")\nif SELECTED_ALGO in (\"QSVC\", \"VQC\"):\n    print(\"   Sonraki aşamalar (12–18) QSVC / feature-map hattını çalıştıracak.\")\nelse:\n    print(\"   Not: Bu notebook'un tam kod yolu QSVC odaklıdır.\")\n",
   "id": "52Cj30kCre4u"
  },
  {
   "cell_type": "markdown",
   "id": "17933fc3",
   "metadata": {
    "id": "17933fc3"
   },
   "source": "## AŞAMA 12: Veri Encoding ve Qubit Maliyeti\n\nKuantum Makine Öğrenimi (QML) projelerinde klasik verinin kuantum donanımına yüklenmesi (**data encoding / feature mapping**), sistemin performansı ve kullanılabilecek kübit (qubit) sayısı üzerinde belirleyici bir faktördür. Verinin tipine, boyutuna ve amacınıza en uygun yöntemi seçmek, **NISQ** (Gürültülü Orta Ölçekli Kuantum) dönemi cihazlarındaki kısıtlamaları aşmanın anahtarıdır.\n\n### Kuantum Veri Kodlama (Data Encoding) Yöntemleri\n\n| **Kodlama Yöntemi** | **Veri Tipi** | **Qubit Maliyeti (N özellik için)** | **Devre Derinliği / Karmaşıklık** | **Amacımız ve QML Kullanım Alanı** |\n| --- | --- | --- | --- | --- |\n| **Temel Kodlama (Basis Encoding)** | İkili (Binary) / Kategorik | $O(N)$ – Çok Yüksek | Düşük (Sadece X kapıları) | Klasik mantık devrelerini simüle etmek, boolean fonksiyonları kuantumda işlemek. Kuantum süperpozisyon avantajını kullanmaz. |\n| **Açı Kodlaması (Angle / Phase Encoding)** | Sürekli (Sayısal / Float) | $O(N)$ – Yüksek | Çok Düşük ($R_x, R_y, R_z$) | VQC ve QSVC gibi algoritmalarla sınıflandırma yapmak. NISQ cihazlar için donanım dostudur. Çoğu standart Qiskit özellik haritası (ZZFeatureMap, PauliFeatureMap) bu temele dayanır. |\n| **Genlik Kodlaması (Amplitude Encoding)** | Vektörler (Normalize Edilmiş) | $O(\\log_2 N)$ – **En Düşük** | $O(N)$ – Çok Yüksek | Görüntü işleme gibi çok yüksek boyutlu verileri (örneğin 256×256 piksellik resimler) sıkıştırarak az sayıda kübite sığdırmak. |\n| **Yoğun Açı Kodlaması (Dense Angle Encoding)** | Sürekli (Sayısal / Float) | $O(N/2)$ – Orta | Düşük | Kübit sayısının kısıtlı olduğu ancak açı kodlamasının avantajlarından yararlanmak istediğimiz durumlar. Bir kübitin hem faz hem genlik açısına (ör. $R_y$ ve $R_z$) farklı veriler gömülür. |\n| **Karmaşık (IQP / Entangled Angle)** | Sürekli (Sayısal / Float) | $O(N)$ – Yüksek | Orta (CNOT kapıları içerir) | Veri özellikleri arasındaki karmaşık korelasyonları yakalamak, doğrusal olmayan karar sınırları (kernel) bulmak. |\n\n> **Not:** Çoğu veri maskeleme veya ön işleme adımı (K-Anonimlik vb.) bu kodlama yöntemlerinden *önce*, klasik tarafta Pandas ve Scikit-learn ile yapılmalıdır.\n\nAşağıdaki hücre: özellik sayısına göre kübit maliyeti hesaplayıcı + grafik. Fiziksel qubit yetmezse PCA ile boyut indirgeme uygulanır.\n\n\n---\n### Öğrenci notu — Encoding ve qubit bütçesi\n\n| Fonksiyon | Ne öğretir? |\n|-----------|-------------|\n| `calculate_qubits(N)` | Angle ≈ N qubit, Amplitude ≈ log₂(N) qubit |\n| `plot_qubit_costs` | N arttıkça genlik kodlamasının avantajını görsel gösterir |\n| `qml_feature_names` | EDA’dan seçilen 3–4 kritik özellik |\n| PCA (gerekirse) | Fiziksel qubit yetmezse boyut indirgeme |\n\n**Bu POC’ta:** Angle encoding + ZZFeatureMap → özellik sayısı = qubit sayısı (4).\n\n**Öğrenme hedefi:** NISQ’ta “tüm sütunları encode et” stratejisi çalışmaz; özellik seçimi zorunludur.\n\n---\n### Bu POC’ta 100 qubit zorlaması\n\n- `TARGET_QUBITS = 100` → Angle encoding ile **100 boyutlu** özellik uzayı üretilir (ham 10 sütun genişletilir).\n- Amaç: feature map + fidelity kernel’i büyük Hilbert uzayına itmek ve **gerçek QPU** (≥100 qubit, örn. Heron) kullanımını denemek.\n- **Maliyet uyarısı:** Fidelity kernel $O(n^2)$ devre çalıştırır; her devre ~100 qubit. Bu yüzden train/test subset **küçük** tutulur (ör. 20/10). Kota ve kuyruk süresi yüksek olabilir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93fe4bb2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 845
    },
    "id": "93fe4bb2",
    "outputId": "f28eee60-a621-474b-a889-7f6d541b6260",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843698517,
     "user_tz": -180,
     "elapsed": 172,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--- 100 Özellik İçin Kübit İhtiyacı ---\n",
      "Açı Kodlaması (Angle/Basis)  : 100 Kübit\n",
      "Yoğun Açı (Dense Angle)      : 50 Kübit\n",
      "Genlik Kodlaması (Amplitude) : 7 Kübit\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "🎯 TARGET_QUBITS = 100\n",
      "Ham özellik sayısı: 10\n",
      "QML train shape: (22500, 100)  → n_qubits = 100\n",
      "QML test  shape: (7500, 100)\n",
      "⚠️ 100 qubit ZZFeatureMap + FidelityKernel çok ağırdır; subset küçük tutulur.\n",
      "✅ AŞAMA 12 tamamlandı.\n",
      "🛡️ Güvenli n_qubits=6 (MAX_SIM=8; TARGET_QUBITS=100 sadece QPU)\n",
      "QML train shape: (22500, 6)\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 12: ENCODING + 100 QUBIT HEDEFİ\n# Angle encoding → özellik sayısı ≈ qubit sayısı\n# ============================================================\n\nimport math\nfrom sklearn.decomposition import PCA\nfrom sklearn.random_projection import GaussianRandomProjection  # (yedek; expand kullanıyoruz)\n\nTARGET_QUBITS = 100  # Hedef kübit / feature boyutu (gerçek QPU zorlaması)\n\ndef calculate_qubits(num_features):\n    \"\"\"N özellik için farklı kodlama yöntemlerinin qubit ihtiyacını yazdır.\"\"\"\n    if num_features <= 0:\n        return \"Özellik sayısı 0'dan büyük olmalıdır.\"\n    angle = num_features                         # 1 özellik = 1 qubit\n    dense_angle = math.ceil(num_features / 2)    # 2 özellik / qubit\n    amplitude = max(1, math.ceil(math.log2(num_features)))  # log2 sıkıştırma\n    print(f\"--- {num_features} Özellik İçin Kübit İhtiyacı ---\")\n    print(f\"Açı Kodlaması (Angle/Basis)  : {angle} Kübit\")\n    print(f\"Yoğun Açı (Dense Angle)      : {dense_angle} Kübit\")\n    print(f\"Genlik Kodlaması (Amplitude) : {amplitude} Kübit\")\n    print(\"-\" * 50)\n    return {\"angle\": angle, \"dense\": dense_angle, \"amplitude\": amplitude}\n\ndef plot_qubit_costs(max_features=128):\n    \"\"\"Özellik sayısı arttıkça qubit maliyetini çiz.\"\"\"\n    N = np.arange(1, max_features + 1)           # 1..max_features\n    angle_qubits = N\n    dense_qubits = np.ceil(N / 2)\n    amplitude_qubits = np.ceil(np.log2(N))\n    amplitude_qubits[0] = 1                      # log2(1)=0 → en az 1 qubit\n    plt.figure(figsize=(10, 6))\n    plt.plot(N, angle_qubits, label=\"Açı Kodlaması (N)\", color=\"#e74c3c\", linewidth=2)\n    plt.plot(N, dense_qubits, label=\"Yoğun Açı (N/2)\", color=\"#f39c12\", linewidth=2)\n    plt.plot(N, amplitude_qubits, label=\"Genlik (log2 N)\", color=\"#2ecc71\", linewidth=2)\n    plt.axhline(TARGET_QUBITS, color=\"#3b82f6\", linestyle=\"--\", label=f\"Hedef = {TARGET_QUBITS} qubit\")\n    plt.title(\"QML Veri Kodlama – Kübit Maliyeti\")\n    plt.xlabel(\"Özellik Sayısı (N)\")\n    plt.ylabel(\"Gereken Kübit\")\n    plt.grid(True, linestyle=\"--\", alpha=0.7)\n    plt.legend()\n    plt.tight_layout()\n    plt.show()\n\ncalculate_qubits(TARGET_QUBITS)  # 100 özellik için tabloyu bas\nplot_qubit_costs(128)            # Eğriyi çiz\n\nprint(f\"\\n🎯 TARGET_QUBITS = {TARGET_QUBITS}\")\nprint(\"Ham özellik sayısı:\", X_train_sc.shape[1])  # AŞAMA 9'dan gelen sütun sayısı\n\ndef expand_to_dim(X, target_dim, seed=42):\n    \"\"\"Az sütunlu matrisi target_dim'e genişlet (tekrar + kontrollü gürültü).\n    Eğitim amacı: 100 qubit feature map'i beslemek.\"\"\"\n    rng = np.random.RandomState(seed)  # Yerel RNG (global seed'i bozmaz)\n    n, d = X.shape\n    if d >= target_dim:\n        return X[:, :target_dim]       # Zaten yeterli sütun varsa kes\n    reps = int(np.ceil(target_dim / d))  # Kaç blok yan yana eklenecek\n    blocks = []\n    for r in range(reps):\n        noise = rng.normal(0, 0.05, size=X.shape)  # Küçük Gaussian gürültü\n        blocks.append(X + noise * (r + 1) * 0.1)   # Her blok hafif farklı\n    X_big = np.hstack(blocks)[:, :target_dim]      # Birleştir ve tam target_dim al\n    return X_big\n\n# Train ve test'i aynı mantıkla 100 boyuta çıkar\nX_train_100 = expand_to_dim(X_train_sc, TARGET_QUBITS, seed=42)\nX_test_100 = expand_to_dim(X_test_sc, TARGET_QUBITS, seed=43)\n\nX_train_qml_full = X_train_100   # QML'in göreceği train matrisi\nX_test_qml_full = X_test_100     # QML'in göreceği test matrisi\nn_qubits = TARGET_QUBITS         # Feature map boyutu = 100\n\nprint(f\"QML train shape: {X_train_qml_full.shape}  → n_qubits = {n_qubits}\")\nprint(f\"QML test  shape: {X_test_qml_full.shape}\")\nprint(\"⚠️ 100 qubit ZZFeatureMap + FidelityKernel çok ağırdır; subset küçük tutulur.\")\nprint(\"✅ AŞAMA 12 tamamlandı.\")\n\n# ========== HATA DÜZELTMESİ: Statevector 100q patlaması ==========\n# 2^n bellek → n=100 imkânsız. Eğitim varsayılanını güvenli tut.\nMAX_SIM_QUBITS = 8\nTARGET_QUBITS = globals().get(\"TARGET_QUBITS\", 100)\nn_qubits = min(6, MAX_SIM_QUBITS)\nif \"X_train_100\" in globals():\n    X_train_qml_full = X_train_100[:, :n_qubits]\n    X_test_qml_full = X_test_100[:, :n_qubits]\nelif \"X_train_qml_full\" in globals() and hasattr(X_train_qml_full, \"shape\"):\n    if X_train_qml_full.shape[1] > n_qubits:\n        X_train_qml_full = X_train_qml_full[:, :n_qubits]\n        X_test_qml_full = X_test_qml_full[:, :n_qubits]\nprint(f\"🛡️ Güvenli n_qubits={n_qubits} (MAX_SIM={MAX_SIM_QUBITS}; TARGET_QUBITS={TARGET_QUBITS} sadece QPU)\")\nprint(f\"QML train shape: {getattr(X_train_qml_full, 'shape', None)}\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "79c03c3b",
   "metadata": {
    "id": "79c03c3b"
   },
   "source": "## AŞAMA 13: Quantum Feature Map ve Fidelity Kernel\n\nKlasik veriyi Hilbert uzayına taşımak için **ZZFeatureMap** (açı kodlaması + entanglement) ve **FidelityQuantumKernel** oluşturulur.\n\n- **Neden?** QSVC, örnekler arasındaki benzerliği kuantum fidelity ile ölçer; kernel matrisi klasik SVM’e girdi olur.\n- Gürültüsüz hızlı yol: `StatevectorSampler` + `ComputeUncompute`\n- Gerçek donanım: Runtime `Sampler` (kota / süre maliyetli)\n\nİsteğe bağlı olarak örnek bir angle-encoding devresi çizilir (Bloch / circuit demo).\n\n\n---\n### Öğrenci notu — Feature map + kernel\n\n| Nesne | Görevi |\n|-------|--------|\n| `ZZFeatureMap(feature_dimension, reps, entanglement)` | Klasik vektörü parametreli kuantum devresine çevirir |\n| `StatevectorSampler` | Gürültüsüz lokal durum vektörü (hızlı eğitim) |\n| `ComputeUncompute` | İki durum arasındaki fidelity’yi ölçer |\n| `FidelityQuantumKernel` | Tüm (i,j) çiftleri için $K_{ij} = \\|\\langle\\phi(x_i)\\|\\phi(x_j)\\rangle\\|^2$ |\n\n**Neden fidelity kernel?** Klasik RBF kernel’in kuantum karşılığı; veriyi yüksek boyutlu Hilbert uzayında ayırmaya çalışır.\n\n**Alıştırma:** `print(feature_map.decompose())` çıktısındaki `RZ`, `CX` kapılarını devre diyagramıyla eşleştirin.\n\n> **Donanım:** Bu sürümde `use_real_hardware = True` varsayılandır. Token yoksa veya backend seçilemezse Statevector’a düşülür; AŞAMA 16’da `FORCE_REAL_QPU` ile simülatör engellenebilir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f968e0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "a3f968e0",
    "outputId": "289ff3de-3dc8-4b61-b13b-339d33a0c7b2",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843699340,
     "user_tz": -180,
     "elapsed": 817,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "🎯 n_qubits = 6 (100 qubit hedefi)\n",
      "ZZFeatureMap: dimension=6, reps=2, entanglement=linear\n",
      "     ┌───┐┌───────────┐                                        ┌───┐»\n",
      "q_0: ┤ H ├┤ P(2*x[0]) ├──■──────────────────────────────────■──┤ H ├»\n",
      "     ├───┤├───────────┤┌─┴─┐┌────────────────────────────┐┌─┴─┐└───┘»\n",
      "q_1: ┤ H ├┤ P(2*x[1]) ├┤ X ├┤ P(2*(π - x[0])*(π - x[1])) ├┤ X ├──■──»\n",
      "     ├───┤├───────────┤└───┘└────────────────────────────┘└───┘┌─┴─┐»\n",
      "q_2: ┤ H ├┤ P(2*x[2]) ├────────────────────────────────────────┤ X ├»\n",
      "     ├───┤├───────────┤                                        └───┘»\n",
      "q_3: ┤ H ├┤ P(2*x[3]) ├─────────────────────────────────────────────»\n",
      "     ├───┤├───────────┤                                             »\n",
      "q_4: ┤ H ├┤ P(2*x[4]) ├─────────────────────────────────────────────»\n",
      "     ├───┤├───────────┤                                             »\n",
      "q_5: ┤ H ├┤ P(2*x[5]) ├─────────────────────────────────────────────»\n",
      "     └───┘└───────────┘                                             »\n",
      "«             ┌───────────┐                                                 »\n",
      "«q_0: ────────┤ P(2*x[0]) ├─────────────────────────────────────────────────»\n",
      "«             └───────────┘              ┌───┐        ┌───────────┐         »\n",
      "«q_1: ────────────────────────────────■──┤ H ├────────┤ P(2*x[1]) ├─────────»\n",
      "«     ┌────────────────────────────┐┌─┴─┐└───┘        └───────────┘         »\n",
      "«q_2: ┤ P(2*(π - x[1])*(π - x[2])) ├┤ X ├──■────────────────────────────────»\n",
      "«     └────────────────────────────┘└───┘┌─┴─┐┌────────────────────────────┐»\n",
      "«q_3: ───────────────────────────────────┤ X ├┤ P(2*(π - x[2])*(π - x[3])) ├»\n",
      "«                                        └───┘└────────────────────────────┘»\n",
      "«q_4: ──────────────────────────────────────────────────────────────────────»\n",
      "«                                                                           »\n",
      "«q_5: ──────────────────────────────────────────────────────────────────────»\n",
      "«                                                                           »\n",
      "«                                                                           »\n",
      "«q_0: ──■──────────────────────────────────────────────■────────────────────»\n",
      "«     ┌─┴─┐┌────────────────────────────┐            ┌─┴─┐                  »\n",
      "«q_1: ┤ X ├┤ P(2*(π - x[0])*(π - x[1])) ├────────────┤ X ├───────────────■──»\n",
      "«     └───┘└───────────┬───┬────────────┘        ┌───┴───┴───┐         ┌─┴─┐»\n",
      "«q_2: ──■──────────────┤ H ├─────────────────────┤ P(2*x[2]) ├─────────┤ X ├»\n",
      "«     ┌─┴─┐            └───┘                     └───────────┘         └───┘»\n",
      "«q_3: ┤ X ├──────────────■───────────────────────────────────────────────■──»\n",
      "«     └───┘            ┌─┴─┐             ┌────────────────────────────┐┌─┴─┐»\n",
      "«q_4: ─────────────────┤ X ├─────────────┤ P(2*(π - x[3])*(π - x[4])) ├┤ X ├»\n",
      "«                      └───┘             └────────────────────────────┘└───┘»\n",
      "«q_5: ──────────────────────────────────────────────────────────────────────»\n",
      "«                                                                           »\n",
      "«                                                                      »\n",
      "«q_0: ─────────────────────────────────────────────────────────────────»\n",
      "«                                                                      »\n",
      "«q_1: ────────────────────────────────────────────■────────────────────»\n",
      "«     ┌────────────────────────────┐            ┌─┴─┐                  »\n",
      "«q_2: ┤ P(2*(π - x[1])*(π - x[2])) ├────────────┤ X ├───────────────■──»\n",
      "«     └───────────┬───┬────────────┘        ┌───┴───┴───┐         ┌─┴─┐»\n",
      "«q_3: ────────────┤ H ├─────────────────────┤ P(2*x[3]) ├─────────┤ X ├»\n",
      "«                 └───┘                     └───────────┘         └───┘»\n",
      "«q_4: ──────────────■───────────────────────────────────────────────■──»\n",
      "«                 ┌─┴─┐             ┌────────────────────────────┐┌─┴─┐»\n",
      "«q_5: ────────────┤ X ├─────────────┤ P(2*(π - x[4])*(π - x[5])) ├┤ X ├»\n",
      "«                 └───┘             └────────────────────────────┘└───┘»\n",
      "«                                                     »\n",
      "«q_0: ────────────────────────────────────────────────»\n",
      "«                                                     »\n",
      "«q_1: ────────────────────────────────────────────────»\n",
      "«                                                     »\n",
      "«q_2: ────────────────────────────────────■───────────»\n",
      "«     ┌────────────────────────────┐    ┌─┴─┐         »\n",
      "«q_3: ┤ P(2*(π - x[2])*(π - x[3])) ├────┤ X ├──────■──»\n",
      "«     └───────────┬───┬────────────┘┌───┴───┴───┐┌─┴─┐»\n",
      "«q_4: ────────────┤ H ├─────────────┤ P(2*x[4]) ├┤ X ├»\n",
      "«                 ├───┤             ├───────────┤└───┘»\n",
      "«q_5: ────────────┤ H ├─────────────┤ P(2*x[5]) ├─────»\n",
      "«                 └───┘             └───────────┘     »\n",
      "«                                                                           »\n",
      "«q_0: ──────────────────────────────────────────────────────────────────────»\n",
      "«                                                                           »\n",
      "«q_1: ──────────────────────────────────────────────────────────────────────»\n",
      "«                                                                           »\n",
      "«q_2: ──────────────────────────────────────────────────────────────────────»\n",
      "«                                                                           »\n",
      "«q_3: ────────────────────────────────■─────────────────────────────────────»\n",
      "«     ┌────────────────────────────┐┌─┴─┐                                   »\n",
      "«q_4: ┤ P(2*(π - x[3])*(π - x[4])) ├┤ X ├──■────────────────────────────────»\n",
      "«     └────────────────────────────┘└───┘┌─┴─┐┌────────────────────────────┐»\n",
      "«q_5: ───────────────────────────────────┤ X ├┤ P(2*(π - x[4])*(π - x[5])) ├»\n",
      "«                                        └───┘└────────────────────────────┘»\n",
      "«          \n",
      "«q_0: ─────\n",
      "«          \n",
      "«q_1: ─────\n",
      "«          \n",
      "«q_2: ─────\n",
      "«          \n",
      "«q_3: ─────\n",
      "«          \n",
      "«q_4: ──■──\n",
      "«     ┌─┴─┐\n",
      "«q_5: ┤ X ├\n",
      "«     └───┘\n",
      "\n",
      "🟢 SİMÜLATÖR / STATEVECTOR MODU\n",
      "   ⚠️ use_real_hardware=True ama service yok — AŞAMA 4 token gerekli.\n",
      "FidelityQuantumKernel (Statevector) hazır.\n",
      "\n",
      "Örnek angle-encoding (ilk 4 qubit, text):\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ Ry(0.65543) ├\n",
      "     └┬────────────┤\n",
      "q_1: ─┤ Ry(4.6115) ├\n",
      "     ┌┴────────────┤\n",
      "q_2: ┤ Ry(-4.2936) ├\n",
      "     └┬────────────┤\n",
      "q_3: ─┤ Ry(3.9392) ├\n",
      "      └────────────┘\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 203.885x367.889 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ AŞAMA 13 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 13: FEATURE MAP + FIDELITY QUANTUM KERNEL (100 QUBIT)\n# Klasik vektör → kuantum durum; fidelity = kernel K_ij\n# ============================================================\n\nfrom qiskit.circuit.library import ZZFeatureMap                 # Açı + ZZ entanglement haritası\nfrom qiskit.primitives import StatevectorSampler                # Gürültüsüz lokal örnekleyici\nfrom qiskit_machine_learning.kernels import FidelityQuantumKernel  # Kernel sarmalayıcı\nfrom qiskit_machine_learning.state_fidelities import ComputeUncompute  # Fidelity ölçümü\n\nprint(f\"🎯 n_qubits = {n_qubits} (100 qubit hedefi)\")\n\n# NISQ: yüksek qubit'te reps=1 ile sığ devre (daha az gürültü birikimi)\nfm_reps = 1 if n_qubits >= 50 else 2\n\n# Statevector tavanı (Maximum allowed dimension exceeded önlemi)\nif \"MAX_SIM_QUBITS\" not in globals():\n    MAX_SIM_QUBITS = 8\nif n_qubits > MAX_SIM_QUBITS:\n    print(f\"⚠️ n_qubits {n_qubits} → {MAX_SIM_QUBITS}\")\n    n_qubits = MAX_SIM_QUBITS\n    if \"X_train_qml_full\" in globals() and X_train_qml_full.shape[1] > n_qubits:\n        X_train_qml_full = X_train_qml_full[:, :n_qubits]\n        X_test_qml_full = X_test_qml_full[:, :n_qubits]\n\nfeature_map = ZZFeatureMap(\n    feature_dimension=n_qubits,  # Her özellik bir qubit'e\n    reps=fm_reps,                # Feature map tekrarı\n    entanglement=\"linear\",       # q0-q1, q1-q2, ... zincir dolaşıklık\n)\nprint(f\"ZZFeatureMap: dimension={n_qubits}, reps={fm_reps}, entanglement=linear\")\nprint(feature_map.decompose())   # Kapı listesini metin olarak göster\n\nuse_real_hardware = True  # Gerçek QPU zorla (token + backend AŞAMA 16)\nquantum_kernel = None     # Aşağıda doldurulacak\n\nif use_real_hardware and service is not None:\n    print(\"\\n🔴 GERÇEK DONANIM MODU hedefleniyor.\")\n    print(\"   Backend AŞAMA 16'da seçilecek; şimdilik Statevector kernel iskeleti.\")\n    sampler = StatevectorSampler()                           # Lokal, hızlı\n    fidelity = ComputeUncompute(sampler=sampler)             # |<ψ|φ>|^2 hesabı\n    quantum_kernel = FidelityQuantumKernel(\n        feature_map=feature_map, fidelity=fidelity\n    )\n    print(\"FidelityQuantumKernel hazır (eğitim Statevector; QPU bağlama AŞAMA 16).\")\nelse:\n    print(\"\\n🟢 SİMÜLATÖR / STATEVECTOR MODU\")\n    if use_real_hardware and service is None:\n        print(\"   ⚠️ use_real_hardware=True ama service yok — AŞAMA 4 token gerekli.\")\n    sampler = StatevectorSampler()\n    fidelity = ComputeUncompute(sampler=sampler)\n    quantum_kernel = FidelityQuantumKernel(\n        feature_map=feature_map, fidelity=fidelity\n    )\n    print(\"FidelityQuantumKernel (Statevector) hazır.\")\n\n# Görsel: 100 qubit çizmek okunaksız → sadece ilk 4 qubit demo\ntry:\n    from qiskit import QuantumCircuit\n    sample_vec = X_train_qml_full[0]           # İlk eğitim örneği\n    n_show = min(4, n_qubits)                  # En fazla 4 qubit çiz\n    qc_demo = QuantumCircuit(n_show)           # Boş devre\n    for qi in range(n_show):\n        # Angle encoding: özellik değeri → Ry dönme açısı\n        qc_demo.ry(float(sample_vec[qi]) * np.pi, qi)\n    print(f\"\\nÖrnek angle-encoding (ilk {n_show} qubit, text):\")\n    print(qc_demo.draw(output=\"text\"))         # ASCII devre\n    try:\n        display(qc_demo.draw(output=\"mpl\"))    # Matplotlib (pylatexenc gerekir)\n    except Exception as e_mpl:\n        print(f\"(mpl atlandı — pip install pylatexenc: {e_mpl})\")\nexcept Exception as e:\n    print(f\"Devre demo atlandı: {e}\")\n\nprint(\"✅ AŞAMA 13 tamamlandı.\")\n"
  },
  {
   "cell_type": "code",
   "source": "# ============================================================\n# AŞAMA 13b: BLOCH KÜRELERİ\n# qc_demo (Aşama 13) product state → her qubit ayrı küre\n# Düzeltme: değişken adı qc_demo (eski \"qc\" NameError verirdi)\n# ============================================================\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.visualization import plot_bloch_vector, plot_bloch_multivector\nfrom qiskit import QuantumCircuit\n\n# Aşama 13'ten gelen örnek vektör / yoksa ilk train satırı\nif \"sample_vec\" not in globals():\n    sample_vec = np.asarray(X_train_qml_full[0], dtype=float)\nelse:\n    sample_vec = np.asarray(sample_vec, dtype=float)\n\nn_show = min(4, int(n_qubits), len(sample_vec))\nqc_bloch = QuantumCircuit(n_show)\nfor qi in range(n_show):\n    qc_bloch.ry(float(sample_vec[qi]) * np.pi, qi)\n\nprint(f\"Bloch için {n_show} qubit (angle encoding product state)\")\nprint(qc_bloch.draw(output=\"text\"))\n\n# Tek-qubit Bloch vektörleri\nfig, axes = plt.subplots(1, n_show, figsize=(3.2 * n_show, 3.2), subplot_kw={\"projection\": \"3d\"})\nif n_show == 1:\n    axes = [axes]\nfor qi in range(n_show):\n    th = float(sample_vec[qi]) * np.pi\n    plot_bloch_vector([np.sin(th), 0.0, np.cos(th)], ax=axes[qi], title=f\"q{qi}\")\nplt.suptitle(\"Bloch — angle encoding\", y=1.05)\nplt.tight_layout()\nplt.show()\n\n# Multivector\ntry:\n    state = Statevector.from_instruction(qc_bloch)\n    display(plot_bloch_multivector(state, title=f\"Multivector ({n_show}q)\"))\n    print(\"✅ Bloch küreleri çizildi.\")\nexcept Exception as e:\n    print(f\"⚠️ plot_bloch_multivector: {e}\")\n",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 907
    },
    "id": "56ETOYUpt-Dv",
    "outputId": "fc9b4165-7b54-466a-d2f9-77da92e0ba6b",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843700454,
     "user_tz": -180,
     "elapsed": 1108,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "id": "56ETOYUpt-Dv",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Bloch için 4 qubit (angle encoding product state)\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ Ry(0.65543) ├\n",
      "     └┬────────────┤\n",
      "q_1: ─┤ Ry(4.6115) ├\n",
      "     ┌┴────────────┤\n",
      "q_2: ┤ Ry(-4.2936) ├\n",
      "     └┬────────────┤\n",
      "q_3: ─┤ Ry(3.9392) ├\n",
      "      └────────────┘\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1280x320 with 4 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1600x501 with 4 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Bloch küreleri çizildi.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85582395",
   "metadata": {
    "id": "85582395"
   },
   "source": "## AŞAMA 14: Quantum Kernel Eğitimleri (QSVC)\n\nSeçilen problem ve algoritmaya göre oluşturulmuş train/test verileriyle model eğitimi. Gerçek QPU ve simülatör süresi için **küçük stratified subset** (ör. 60 train / 30 test) kullanılır; tam veri seti için Aer veya daha fazla QPU süresi gerekir.\n\n\n---\n### Öğrenci notu — QSVC eğitimi\n\n| Adım | Açıklama |\n|------|----------|\n| Küçük subset (60/30) | Kernel matrisi $O(n^2)$ devre çalıştırır; n büyüdükçe maliyet patlar |\n| Stratified örnekleme | Azınlık sınıfı (default=1) subset’te de temsil edilsin |\n| `QSVC.fit` | Kernel matrisini hesaplar + klasik SVM çözer (hibrit!) |\n| `decision_function` | ROC için skor; ham olasılık değildir, min-max ile [0,1]’e çekilir |\n\n**Öğrenme hedefi:** QSVC = kuantum kernel + klasik SVM optimizasyonu. “Tamamen kuantum sınıflandırıcı” değildir; hibrit mimariyi bilin.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da65dd02",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "da65dd02",
    "outputId": "c53ad05e-f9f3-433f-87d2-c504785e5bb8",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843709421,
     "user_tz": -180,
     "elapsed": 8954,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "🎯 n_qubits=6 | X_q_train.shape=(20, 6)\n",
      "Q train: (20, 6), default oranı: 30.00%\n",
      "Q test : (10, 6), default oranı: 30.00%\n",
      "🎯 n_qubits=6 (X ile kilitli) | shapes train=(20, 6) test=(10, 6)\n",
      "🟢 Kernel yeniden kuruldu: feature_dimension=6 (Statevector)\n",
      "\n",
      "QSVC eğitiliyor...\n",
      "\n",
      "=== Quantum QSVC ===\n",
      "  accuracy    : 0.7000\n",
      "  precision   : 0.0000\n",
      "  recall      : 0.0000\n",
      "  f1          : 0.0000\n",
      "  roc_auc     : 0.2381\n",
      "✅ AŞAMA 14 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 14: QSVC EĞİTİMİ\n# Kernel O(n^2) pahalı → 100 qubit'te küçük subset (20/10)\n# ============================================================\n\nfrom qiskit_machine_learning.algorithms import QSVC  # Quantum Support Vector Classifier\n\nN_TRAIN_Q = 20  # Eğitim alt kümesi boyutu (100q için bilinçli küçük)\nN_TEST_Q = 10   # Test alt kümesi boyutu\n\n# Azınlık / çoğunluk indeksleri (dengeli subset için)\nidx_pos = np.where(y_train.values == 1)[0]  # default=1 satır indeksleri\nidx_neg = np.where(y_train.values == 0)[0]  # default=0 satır indeksleri\nnp.random.seed(42)\nn_pos = min(len(idx_pos), max(N_TRAIN_Q // 3, 1))  # Azınlıktan en fazla 1/3\nn_neg = N_TRAIN_Q - n_pos                            # Kalanı çoğunluktan\n# Rastgele seçilmiş indeks birleştir\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)  # Sıra bilgisini karıştır\n\nX_q_train = X_train_qml_full[sel]   # 100 boyutlu train subset\ny_q_train = y_train.values[sel]     # Etiket subset\n# Test subset: stratified (tek sınıf → CM/ROC hatasını önler)\n_rng_t = np.random.RandomState(1)\n_idx_t_pos = np.where(y_test.values == 1)[0]\n_idx_t_neg = np.where(y_test.values == 0)[0]\n_n_tp = min(max(N_TEST_Q // 3, 1), len(_idx_t_pos)) if len(_idx_t_pos) else 0\n_n_tn = min(N_TEST_Q - _n_tp, len(_idx_t_neg))\n_parts = []\nif _n_tp:\n    _parts.append(_rng_t.choice(_idx_t_pos, _n_tp, replace=False))\nif _n_tn:\n    _parts.append(_rng_t.choice(_idx_t_neg, _n_tn, replace=False))\nif _parts:\n    idx_t = np.concatenate(_parts)\n    _rng_t.shuffle(idx_t)\nelse:\n    idx_t = _rng_t.choice(len(y_test), size=min(N_TEST_Q, len(y_test)), replace=False)\nX_q_test = X_test_qml_full[idx_t]\ny_q_test = y_test.values[idx_t]\n\nprint(f\"🎯 n_qubits={n_qubits} | X_q_train.shape={X_q_train.shape}\")\nprint(f\"Q train: {X_q_train.shape}, default oranı: {y_q_train.mean():.2%}\")\nprint(f\"Q test : {X_q_test.shape}, default oranı: {y_q_test.mean():.2%}\")\n\n# --- Boyut hizalama: feature_map / kernel ile X sütun sayısı aynı olmalı ---\n# Aşama 16 ISA (100q) sonrası X hâlâ 6 sütun kalırsa hata: \"x_vec has 6, feature map has 100\"\n_dim = int(X_q_train.shape[1])\nn_qubits = _dim  # yazdırma ve kayıt için senkron\nprint(f\"🎯 n_qubits={n_qubits} (X ile kilitli) | shapes train={X_q_train.shape} test={X_q_test.shape}\")\n\nfrom qiskit.circuit.library import ZZFeatureMap\nfrom qiskit.primitives import StatevectorSampler\nfrom qiskit_machine_learning.state_fidelities import ComputeUncompute\nfrom qiskit_machine_learning.kernels import FidelityQuantumKernel\n\n# Her Aşama 14 çalıştırmasında X boyutuna uygun YENİ ZZFeatureMap + Statevector kernel\n# (ISA transpile edilmiş QuantumCircuit num_qubits setter'ı yok → QML kernel ile uyumsuz)\nfeature_map = ZZFeatureMap(feature_dimension=_dim, reps=(1 if _dim >= 8 else 2), entanglement=\"linear\")\nquantum_kernel = FidelityQuantumKernel(\n    feature_map=feature_map,\n    fidelity=ComputeUncompute(sampler=StatevectorSampler()),\n)\nprint(f\"🟢 Kernel yeniden kuruldu: feature_dimension={_dim} (Statevector)\")\n\nif quantum_kernel is not None:\n    qsvc = QSVC(quantum_kernel=quantum_kernel)  # Hibrit: kuantum kernel + klasik SVM\n    print(\"\\nQSVC eğitiliyor...\")\n    qsvc.fit(X_q_train, y_q_train)              # Kernel matrisi + SVM çözümü\n    y_q_pred = qsvc.predict(X_q_test)           # Sınıf tahmini\n    try:\n        y_q_score = qsvc.decision_function(X_q_test)  # Ham skor (sınır uzaklığı)\n        # ROC için [0,1] aralığına min-max ölçekle\n        y_q_proba = (y_q_score - y_q_score.min()) / (y_q_score.max() - y_q_score.min() + 1e-12)\n    except Exception:\n        y_q_proba = y_q_pred.astype(float)\n\n    q_metrics = {\n        'accuracy': accuracy_score(y_q_test, y_q_pred),\n        'precision': precision_score(y_q_test, y_q_pred, zero_division=0),\n        'recall': recall_score(y_q_test, y_q_pred, zero_division=0),\n        'f1': f1_score(y_q_test, y_q_pred, zero_division=0),\n        'roc_auc': roc_auc_score(y_q_test, y_q_proba) if len(np.unique(y_q_test)) > 1 else np.nan,\n    }\n    print(\"\\n=== Quantum QSVC ===\")\n    for k, v in q_metrics.items():\n        print(f\"  {k:12s}: {v:.4f}\")\nelse:\n    print(\"⚠️ quantum_kernel yok — AŞAMA 13'ü çalıştırın.\")\n    q_metrics = {k: np.nan for k in ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']}\n    y_q_pred = np.zeros_like(y_q_test)\n    y_q_proba = np.zeros_like(y_q_test, dtype=float)\n\nprint(\"✅ AŞAMA 14 tamamlandı.\")"
  },
  {
   "cell_type": "markdown",
   "id": "becd53a8",
   "metadata": {
    "id": "becd53a8"
   },
   "source": "## AŞAMA 15: Confusion Matrix ve ROC Diyagramı\n\nQSVC tahminleri için karışıklık matrisi ve ROC eğrisi; classification_report ile precision/recall özeti.\n\n\n---\n### Öğrenci notu — Metrik görselleri\n\n- **Confusion matrix:** TP, FP, FN, TN — bankada FP (kötü müşteriye kredi) genelde daha pahalıdır.\n- **ROC:** Eşik değiştikçe TPR vs FPR; AUC tek sayı özeti.\n- **classification_report:** Sınıf bazında precision/recall.\n\n**Alıştırma:** Threshold’u 0.5 yerine precision’ı maksimize edecek şekilde kaydırmayı düşünün (iş maliyeti).\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1abdaf13",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 577
    },
    "id": "1abdaf13",
    "outputId": "1bbfc595-d0a8-4475-db5d-c28f6660cad7",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843709720,
     "user_tz": -180,
     "elapsed": 294,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Ödedi       0.70      1.00      0.82         7\n",
      "    Temerrüt       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.70        10\n",
      "   macro avg       0.35      0.50      0.41        10\n",
      "weighted avg       0.49      0.70      0.58        10\n",
      "\n",
      "✅ AŞAMA 15 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 15: CONFUSION MATRIX + ROC\n# Hata tiplerini ve eşik eğrisini görselleştir\n# Düzeltme: tek sınıf varsa display_labels uzunluğu CM boyutuna uyarlanır\n#   (ValueError: FixedLocator locations (1) vs labels (2))\n# ============================================================\n\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))  # Sol CM, sağ ROC\n\n# Karışıklık matrisi — labels= ile 2x2 zorlanabilir; yoksa gerçek sınıflar\n_labels_present = sorted(set(np.asarray(y_q_test).tolist()) | set(np.asarray(y_q_pred).tolist()))\nif len(_labels_present) < 2:\n    # Tek sınıf: sklearn 1x1 CM üretir → 2 etiket verilemez\n    cm = confusion_matrix(y_q_test, y_q_pred, labels=_labels_present)\n    _tick_names = [\"Ödedi\" if lab == 0 else \"Temerrüt\" for lab in _labels_present]\n    print(f\"⚠️ Test/pred tek sınıf: {_labels_present} → CM {_tick_names}\")\nelse:\n    cm = confusion_matrix(y_q_test, y_q_pred, labels=[0, 1])\n    _tick_names = [\"Ödedi\", \"Temerrüt\"]\n\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=_tick_names)\ndisp.plot(ax=axes[0], cmap=\"Blues\", colorbar=False)\naxes[0].set_title(\"QSVC Confusion Matrix\")\n\n# ROC: tek sınıfta veya NaN AUC'ta çizilemez\nif len(np.unique(y_q_test)) > 1 and not np.isnan(q_metrics.get(\"roc_auc\", np.nan)):\n    fpr, tpr, _ = roc_curve(y_q_test, y_q_proba)\n    axes[1].plot(fpr, tpr, color=\"#3498db\", lw=2, label=f\"AUC = {q_metrics['roc_auc']:.3f}\")\n    axes[1].plot([0, 1], [0, 1], \"k--\", lw=1)\n    axes[1].set_xlabel(\"False Positive Rate\")\n    axes[1].set_ylabel(\"True Positive Rate\")\n    axes[1].set_title(\"QSVC ROC Eğrisi\")\n    axes[1].legend(loc=\"lower right\")\nelse:\n    axes[1].text(0.5, 0.5, \"ROC hesaplanamadı\\n(tek sınıf veya NaN)\", ha=\"center\", va=\"center\")\n    axes[1].set_title(\"QSVC ROC\")\n\nplt.tight_layout()\nplt.show()\n\n# classification_report: mevcut sınıflara göre\n_name_map = {0: \"Ödedi\", 1: \"Temerrüt\"}\n_rep_names = [_name_map.get(lab, str(lab)) for lab in sorted(set(np.asarray(y_q_test).tolist()))]\ntry:\n    print(classification_report(\n        y_q_test, y_q_pred,\n        labels=sorted(set(np.asarray(y_q_test).tolist()) | set(np.asarray(y_q_pred).tolist())),\n        target_names=_rep_names if len(_rep_names) == len(set(list(y_q_test) + list(y_q_pred))) else None,\n        zero_division=0,\n    ))\nexcept Exception as e:\n    print(\"classification_report:\", e)\n    print(classification_report(y_q_test, y_q_pred, zero_division=0))\n\nprint(\"✅ AŞAMA 15 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "b66173e1",
   "metadata": {
    "id": "b66173e1"
   },
   "source": "## AŞAMA 16: Gerçek Quantum Donanım Seçme\n\nIBM Quantum Runtime Service üzerinden operasyonel, mümkün olan en yüksek qubit’li backend tercihi (ör. ibm_marrakesh / fez / torino). Bulunamazsa **AerSimulator**’e düşülür. AŞAMA 2’deki `SELECTED_DEVICE` profili ile uyumlu seçim yapılır.\n\n\n---\n### Öğrenci notu — Backend seçimi\n\n| Kod | Anlamı |\n|-----|--------|\n| `service.backends(operational=True, simulator=False)` | Sadece gerçek, ayakta QPU’lar |\n| `preferred_names` | Bilinen yüksek qubit makineler (Heron vb.) |\n| `least_busy(...)` | Kuyruğu en kısa olanı seç |\n| `AerSimulator()` yedek | Token yoksa veya kota bitmişse lokal simülasyon |\n\n**Öğrenme hedefi:** Üretim kodunda her zaman fallback (simülatör) olmalıdır; QPU her zaman müsait değildir.\n\n### 100 qubit gerçek makine\n\n- `MIN_QUBITS_REQUIRED = 100`\n- Tercih: `ibm_marrakesh` / `ibm_fez` / `ibm_torino` / `ibm_brisbane`\n- `FORCE_REAL_QPU = True` → mümkün olduğunca AerSimulator’e düşülmez; token ve kota şarttır.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f378d0b5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f378d0b5",
    "outputId": "dee93a0e-a14d-4812-c76c-d99f379ece01",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843709756,
     "user_tz": -180,
     "elapsed": 30,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Eğitim boyutu n_qubits=6 (X_train_qml_full ile aynı)\n",
      "QPU kapalı (RUN_ON_REAL_QPU=False) veya service yok — sorun değil.\n",
      "Aşama 14 Statevector kernel | n_qubits=6\n",
      "✅ AŞAMA 16 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 16: BACKEND SEÇİMİ\n# Düzeltme: ISA-transpile edilmiş devreyi FidelityQuantumKernel feature_map\n# olarak KULLANMAYIN (num_qubits read-only → \"x_vec has 6, map has 100\").\n# QPU smoke-test ayrı; eğitim Aşama 14'te X boyutu = ZZFeatureMap boyutu.\n# ============================================================\nfrom qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n\nbackend = None\nRUN_ON_REAL_QPU = False   # True: sadece backend listele + ISA demo (eğitimi bozmaz)\nFORCE_REAL_QPU = False\nMIN_QUBITS_REQUIRED = 4\nCONFIRM_100Q_ON_QPU = False\n\nif \"MAX_SIM_QUBITS\" not in globals():\n    MAX_SIM_QUBITS = 8\nif \"TARGET_QUBITS\" not in globals():\n    TARGET_QUBITS = 100\n\n# Eğitim boyutu: mevcut QML matrisinin sütunu\n_train_dim = int(X_train_qml_full.shape[1]) if \"X_train_qml_full\" in globals() else int(n_qubits)\nn_qubits = _train_dim\nprint(f\"Eğitim boyutu n_qubits={n_qubits} (X_train_qml_full ile aynı)\")\n\nif RUN_ON_REAL_QPU and service is not None:\n    try:\n        preferred_names = [\"ibm_marrakesh\", \"ibm_fez\", \"ibm_torino\", \"ibm_brisbane\"]\n        available = {b.name: b for b in service.backends(operational=True, simulator=False)}\n        print(\"Operasyonel gerçek backend'ler:\")\n        for name, b in available.items():\n            print(f\"  - {name}: {b.num_qubits} qubit\")\n\n        backend = None\n        for name in preferred_names:\n            if name in available and available[name].num_qubits >= MIN_QUBITS_REQUIRED:\n                backend = available[name]\n                print(f\"✅ Tercih: {backend.name}\")\n                break\n        if backend is None:\n            backend = service.least_busy(\n                operational=True, simulator=False, min_num_qubits=MIN_QUBITS_REQUIRED\n            )\n            print(f\"✅ least_busy: {backend.name}\")\n\n        print(f\"   Qubit: {backend.num_qubits}\")\n        status = backend.status()\n        print(f\"   Durum: {getattr(status, 'status_msg', status)} | pending: {getattr(status, 'pending_jobs', 'N/A')}\")\n\n        # ISA demo: eğitim boyutunda (6q) transpile — kernel'e YAZMA\n        from qiskit.circuit.library import ZZFeatureMap\n        fm_demo = ZZFeatureMap(feature_dimension=n_qubits, reps=1, entanglement=\"linear\")\n        for _ in range(8):\n            fm_demo = fm_demo.decompose()\n        isa_demo = generate_preset_pass_manager(optimization_level=1, backend=backend).run(fm_demo)\n        print(f\"🔧 ISA demo transpile OK | circuit.num_qubits={isa_demo.num_qubits}\")\n        print(\"ℹ️ Eğitim kernel'i Aşama 14'te Statevector ZZFeatureMap olarak kalır.\")\n        print(\"   (ISA circuit FidelityQuantumKernel'e verilmez — boyut/setter hatası önlenir.)\")\n    except Exception as e:\n        print(f\"⚠️ QPU adımı: {type(e).__name__}: {str(e)[:280]}\")\n        backend = None\nelse:\n    print(\"QPU kapalı (RUN_ON_REAL_QPU=False) veya service yok — sorun değil.\")\n    print(f\"Aşama 14 Statevector kernel | n_qubits={n_qubits}\")\n\nprint(\"✅ AŞAMA 16 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "555a3237",
   "metadata": {
    "id": "555a3237"
   },
   "source": "## AŞAMA 17: QML Gerçek Donanımda Çalıştırma ve Sonuç Kaydı\n\nKuantum makineden (veya simülatörden) gelen sonuçların:\n\n- **CSV** dosyasına  \n- **Görsel** (confusion matrix PNG)  \n- İsteğe bağlı **SQL** veritabanına  \n\nyazılması. Colab notebook çıktı alanı, mapped path (`OUTPUT_PATH`) veya bağlantı bilgisi verilen SQL’e commit seçenekleri desteklenir.\n\n\n---\n### Öğrenci notu — Sonuçları kalıcılaştırma\n\n| Çıktı | Kod | Kullanım |\n|-------|-----|----------|\n| CSV metrik | `to_csv` | Rapor, Excel, sonraki analiz |\n| PNG CM | `savefig` | Sunum / rapor |\n| SQL (opsiyonel) | `to_sql` | Kurumsal pipeline |\n\n`run_id` zaman damgası ile dosya adları çakışmaz.\n\n**Öğrenme hedefi:** Deney = kod + metrik + görsel + yapılandırma. Tek seferlik `print` ile araştırma bitmez.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a15045",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f0a15045",
    "outputId": "beea38af-f257-4ede-9962-59601abdd7a4",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843710054,
     "user_tz": -180,
     "elapsed": 296,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Metrikler kaydedildi: /content/drive/MyDrive/QML_Projects/outputs/qml_metrics_20260827_151509.csv\n",
      "✅ Görsel kaydedildi: /content/drive/MyDrive/QML_Projects/outputs/qml_cm_20260827_151509.png\n",
      "✅ AŞAMA 17 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 17: SONUÇ KAYDI (CSV + PNG + opsiyonel SQL)\n# ============================================================\n\nfrom datetime import datetime\n\nrun_id = datetime.now().strftime('%Y%m%d_%H%M%S')  # Benzersiz dosya soneki\nout_dir = OUTPUT_PATH if OUTPUT_PATH else './outputs'\nos.makedirs(out_dir, exist_ok=True)                # Klasör yoksa oluştur\n\n# Tek satırlık metrik kaydı\nmetrics_row = {\n    'run_id': run_id,\n    'algo': SELECTED_ALGO,\n    'n_qubits': n_qubits,\n    'n_train_q': len(y_q_train),\n    'n_test_q': len(y_q_test),\n    **{f'q_{k}': v for k, v in q_metrics.items()},  # Quantum metrikleri\n}\n# Klasik model metriklerini de düz sütun olarak ekle\nfor name in results:\n    for k in results[name]:\n        metrics_row[f'classic_{name}_{k}'] = results[name][k]\n\nmetrics_df = pd.DataFrame([metrics_row])\ncsv_path = os.path.join(out_dir, f'qml_metrics_{run_id}.csv')\nmetrics_df.to_csv(csv_path, index=False)  # Disk'e yaz\nprint(f\"✅ Metrikler kaydedildi: {csv_path}\")\n\n# Confusion matrix görseli\nfig_path = os.path.join(out_dir, f'qml_cm_{run_id}.png')\nfig, ax = plt.subplots(figsize=(5, 4))\ncm = confusion_matrix(y_q_test, y_q_pred)\nConfusionMatrixDisplay(cm, display_labels=(['Ödedi', 'Temerrüt'] if cm.shape[0] == 2 else [str(x) for x in range(cm.shape[0])])).plot(ax=ax, cmap='Blues', colorbar=False)\nax.set_title(f'QSVC CM – {run_id}')\nplt.tight_layout()\nplt.savefig(fig_path, dpi=120)  # PNG kaydet\nplt.close()\nprint(f\"✅ Görsel kaydedildi: {fig_path}\")\n\nSAVE_TO_SQL = False  # True + SQL_URL ile kurumsal DB'ye yazılabilir\nif SAVE_TO_SQL:\n    try:\n        import sqlalchemy\n        engine = sqlalchemy.create_engine(os.getenv('SQL_URL', 'sqlite:///qml_results.db'))\n        metrics_df.to_sql('qml_runs', engine, if_exists='append', index=False)\n        print(\"✅ SQL'e yazıldı.\")\n    except Exception as e:\n        print(f\"SQL atlandı: {e}\")\n\nprint(\"✅ AŞAMA 17 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "3fd3ce77",
   "metadata": {
    "id": "3fd3ce77"
   },
   "source": "## AŞAMA 18: Model Karşılaştırma\n\nKlasik ML ve QML sonuçlarının yan yana tablosu (ROC-AUC, F1, Precision, Recall, Accuracy) ve bar plot. Quantum kısmı küçük subset üzerinde olduğu için yorumlarken not düşün.\n\n\n---\n### Öğrenci notu — Karşılaştırma tablosu\n\n`results` (klasik) ile `q_metrics` (quantum) aynı sütunlarda birleştirilir. `highlight_max` en iyi skoru boyar.\n\n**Dikkat:** Quantum küçük subset’te eğitildi; klasik full test set’te. Adil bilimsel karşılaştırma için aynı n ve aynı feature set gerekir — bu POC eğitim amaçlıdır, “quantum kazandı” iddiası için yeterli kanıt değildir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1ed514",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 744
    },
    "id": "2d1ed514",
    "outputId": "9ec62fc0-d3f6-4ae4-9d5c-55e4d1f32fa9",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787843710268,
     "user_tz": -180,
     "elapsed": 219,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== Tüm Modeller Karşılaştırması ===\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x797d304e9160>"
      ],
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_183ea_row0_col0, #T_183ea_row0_col3, #T_183ea_row1_col4, #T_183ea_row2_col1, #T_183ea_row2_col2 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_183ea\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_183ea_level0_col0\" class=\"col_heading level0 col0\" >ROC-AUC</th>\n",
       "      <th id=\"T_183ea_level0_col1\" class=\"col_heading level0 col1\" >F1</th>\n",
       "      <th id=\"T_183ea_level0_col2\" class=\"col_heading level0 col2\" >Precision</th>\n",
       "      <th id=\"T_183ea_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_183ea_level0_col4\" class=\"col_heading level0 col4\" >Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_183ea_level0_row0\" class=\"row_heading level0 row0\" >Logistic Regression</th>\n",
       "      <td id=\"T_183ea_row0_col0\" class=\"data row0 col0\" >0.791200</td>\n",
       "      <td id=\"T_183ea_row0_col1\" class=\"data row0 col1\" >0.082200</td>\n",
       "      <td id=\"T_183ea_row0_col2\" class=\"data row0 col2\" >0.043800</td>\n",
       "      <td id=\"T_183ea_row0_col3\" class=\"data row0 col3\" >0.681200</td>\n",
       "      <td id=\"T_183ea_row0_col4\" class=\"data row0 col4\" >0.720300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_183ea_level0_row1\" class=\"row_heading level0 row1\" >Random Forest</th>\n",
       "      <td id=\"T_183ea_row1_col0\" class=\"data row1 col0\" >0.688500</td>\n",
       "      <td id=\"T_183ea_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
       "      <td id=\"T_183ea_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "      <td id=\"T_183ea_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
       "      <td id=\"T_183ea_row1_col4\" class=\"data row1 col4\" >0.981600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_183ea_level0_row2\" class=\"row_heading level0 row2\" >XGBoost</th>\n",
       "      <td id=\"T_183ea_row2_col0\" class=\"data row2 col0\" >0.739800</td>\n",
       "      <td id=\"T_183ea_row2_col1\" class=\"data row2 col1\" >0.084000</td>\n",
       "      <td id=\"T_183ea_row2_col2\" class=\"data row2 col2\" >0.046300</td>\n",
       "      <td id=\"T_183ea_row2_col3\" class=\"data row2 col3\" >0.456500</td>\n",
       "      <td id=\"T_183ea_row2_col4\" class=\"data row2 col4\" >0.816800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_183ea_level0_row3\" class=\"row_heading level0 row3\" >SVM (RBF)</th>\n",
       "      <td id=\"T_183ea_row3_col0\" class=\"data row3 col0\" >0.681800</td>\n",
       "      <td id=\"T_183ea_row3_col1\" class=\"data row3 col1\" >0.071800</td>\n",
       "      <td id=\"T_183ea_row3_col2\" class=\"data row3 col2\" >0.039600</td>\n",
       "      <td id=\"T_183ea_row3_col3\" class=\"data row3 col3\" >0.384100</td>\n",
       "      <td id=\"T_183ea_row3_col4\" class=\"data row3 col4\" >0.817200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_183ea_level0_row4\" class=\"row_heading level0 row4\" >Quantum QSVC</th>\n",
       "      <td id=\"T_183ea_row4_col0\" class=\"data row4 col0\" >0.238100</td>\n",
       "      <td id=\"T_183ea_row4_col1\" class=\"data row4 col1\" >0.000000</td>\n",
       "      <td id=\"T_183ea_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "      <td id=\"T_183ea_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
       "      <td id=\"T_183ea_row4_col4\" class=\"data row4 col4\" >0.700000</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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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Karşılaştırma tablosu: /content/drive/MyDrive/QML_Projects/outputs/comparison_20260827_151509.csv\n",
      "✅ AŞAMA 18 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 18: MODEL KARŞILAŞTIRMA\n# Klasik vs Quantum metrik tablosu + bar plot\n# ============================================================\n\n# Her klasik model için metrik satırı\ncomparison = pd.DataFrame({\n    name: {\n        'ROC-AUC': r['roc_auc'],\n        'F1': r['f1'],\n        'Precision': r['precision'],\n        'Recall': r['recall'],\n        'Accuracy': r['accuracy'],\n    }\n    for name, r in results.items()\n})\n\n# Quantum sütununu ekle\ncomparison['Quantum QSVC'] = {\n    'ROC-AUC': q_metrics['roc_auc'],\n    'F1': q_metrics['f1'],\n    'Precision': q_metrics['precision'],\n    'Recall': q_metrics['recall'],\n    'Accuracy': q_metrics['accuracy'],\n}\n\ncomparison = comparison.T.round(4)  # Modeller satır, metrikler sütun\nprint(\"=== Tüm Modeller Karşılaştırması ===\")\ndisplay(comparison.style.highlight_max(axis=0, color='lightgreen'))  # En iyiyi yeşil boya\n\n# ROC-AUC ve F1 yan yana çubuk\nfig, ax = plt.subplots(figsize=(11, 5))\ncomparison[['ROC-AUC', 'F1']].plot(kind='bar', ax=ax, color=['#3498db', '#e74c3c'])\nax.set_title('Model Karşılaştırması – ROC-AUC & F1\\n(Quantum kısmı küçük subset üzerinde)')\nax.set_ylabel('Skor')\nax.set_ylim(0, 1.05)\nax.legend(loc='lower right')\nplt.xticks(rotation=20)\nplt.tight_layout()\nplt.show()\n\ncomp_path = os.path.join(out_dir, f'comparison_{run_id}.csv')\ncomparison.to_csv(comp_path)\nprint(f\"✅ Karşılaştırma tablosu: {comp_path}\")\nprint(\"✅ AŞAMA 18 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "1ff19c69",
   "metadata": {
    "id": "1ff19c69"
   },
   "source": "## AŞAMA 19: Sonuçların Yorumlanması\n\n### 19.1 Temel Metrikler\n\n| Metrik | Anlamı | Bankacılık Yorumu |\n|--------|--------|-------------------|\n| **ROC-AUC** | Ayrım gücü | > 0.85 iyi. En kritik metriklerden biri. |\n| **Precision** | \"Kredi ver\" dediğimizde gerçekten iyi olma oranı | Yüksek → az kötü müşteriye kredi |\n| **Recall** | Gerçek iyi müşterileri yakalama | Yüksek → daha fazla hacim |\n| **F1** | Precision–Recall dengesi | Tek skor özeti |\n\n> Kredi riskinde False Positive (kötü müşteriye kredi) maliyeti genellikle daha yüksektir → Precision'a ağırlık verilir.\n\n### 19.2 Quantum vs Klasik\n\n- **Quantum AUC > Klasik** → özellik haritası ve kernel avantajı var.\n- **Quantum ≈ Klasik** → küçük veri / düşük boyutta normal.\n- **Quantum < Klasik** → gürültü, az sample veya feature map yetersizliği olabilir.\n\n**Gerçek donanım notları:**\n- Statevector sonucu gürültüsüz üst sınırdır.\n- Gerçek QPU'da fidelity düşer → kernel matrisi bozulabilir.\n- Error mitigation (dynamical decoupling, readout mitigation) eklenebilir.\n- Open Plan kota sınırlıdır; production için paid plan veya simülatör tercih edilir.\n\n### 19.3 İş Kararı\n\n1. Threshold'u 0.5'te bırakmayın; iş maliyetine göre optimize edin.\n2. Expected Loss = PD × LGD × EAD hesaplayın.\n3. Adalet / bias kontrolü yapın (yaş, coğrafya vb.).\n4. Model monitoring + drift detection planı kurun.\n\n### 19.4 Hızlı Kontrol Listesi\n\n- [ ] ROC-AUC ≥ 0.80?\n- [ ] Precision / Recall dengeli mi?\n- [ ] Confusion Matrix FP oranı kabul edilebilir mi?\n- [ ] Quantum model klasik modellere yaklaştı mı?\n- [ ] Train–Test farkı büyük mü? (overfit)\n- [ ] Gerçek donanımda en az bir fidelity testi yapıldı mı?\n- [ ] KVKK/GDPR maskeleme pipeline'a dahil mi?\n\n\n---\n### Öğrenci notu — Nasıl yorumlanır?\n\n1. Önce **iş metriklerine** bakın (precision vs recall trade-off).\n2. Quantum ≈ klasik ise: küçük veri / az qubit ile beklenen sonuç; yine de pipeline’ı öğrenmiş oldunuz.\n3. Quantum < klasik: gürültü, az örnek, zayıf feature map veya dengesiz subset olabilir.\n4. Sonraki adım fikirleri: daha fazla qubit’li encoding, error mitigation, daha büyük subset, VQC denemesi, gerçek QPU fidelity testi.\n"
  },
  {
   "cell_type": "markdown",
   "id": "5b983410",
   "metadata": {
    "id": "5b983410"
   },
   "source": "## Özet ve Kapanış\n\nBu notebook'ta Word şablonundaki **19 aşama** uçtan uca uygulandı:\n\n1. Drive / path  \n2. Cihaz seçim tablosu  \n3. Kütüphane kurulumu  \n4. API key yönetimi  \n5. Sentetik veri  \n6. CSV yükleme  \n7. EDA  \n8. GDPR/KVKK anonimleştirme  \n9. Ön işleme  \n10. Klasik ML  \n11. Algoritma seçimi (QSVC)  \n12. Encoding + qubit maliyeti + PCA  \n13. Feature map + fidelity kernel  \n14. QSVC eğitimi  \n15. CM + ROC  \n16. Backend seçimi  \n17. Sonuç kaydı (CSV / görsel / SQL opsiyon)  \n18. Model karşılaştırma  \n19. Yorumlama rehberi  \n\n**Önemli notlar:**\n- Quantum kısmı hız ve kota nedeniyle **küçük subset** kullanır.\n- `use_real_hardware = True` ve geçerli token ile gerçek QPU'ya geçilebilir.\n- Gerçek bankacılık verisiyle çalışırken KVKK / GDPR ve model risk yönetimi kurallarına uyun.\n- Bu notebook eğitim, POC ve araştırma amaçlıdır.\n\n---\n\n**Hazırlayan:** Joseph Volkan Özcan  \n**Ortam:** Google Colab / Lokal • Qiskit + IBM Quantum Runtime  \n**Tarih:** 2026  \n**Şablon:** Quantum Computing Uygulama Geliştirme Şablonu\n"
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          "text/markdown": "### Geçerli üçlü matrisi (HTML validTriplets)"
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          "text/plain": "                     Problem                    Donanım       Algoritma  \\\n0   Optimization & Logistics     Superconducting Qubits            QAOA   \n1   Optimization & Logistics     Superconducting Qubits          Grover   \n2   Optimization & Logistics               Trapped Ions            QAOA   \n3   Optimization & Logistics               Trapped Ions          Grover   \n4   Optimization & Logistics              Neutral Atoms            QAOA   \n5   Optimization & Logistics              Neutral Atoms          Grover   \n6   Optimization & Logistics  Quantum Annealer (D-Wave)  QUBO/Annealing   \n7          Quantum Chemistry     Superconducting Qubits             VQE   \n8          Quantum Chemistry               Trapped Ions             VQE   \n9               Cryptography     Superconducting Qubits            Shor   \n10              Cryptography               Trapped Ions            Shor   \n11              Cryptography            Photonic Qubits            Shor   \n12          Machine Learning     Superconducting Qubits            QAOA   \n13          Machine Learning     Superconducting Qubits          Grover   \n14          Machine Learning     Superconducting Qubits            QSVC   \n15          Machine Learning     Superconducting Qubits             VQC   \n16          Machine Learning               Trapped Ions            QAOA   \n17          Machine Learning               Trapped Ions          Grover   \n18          Machine Learning               Trapped Ions            QSVC   \n19          Machine Learning            Photonic Qubits            QAOA   \n20          Machine Learning            Photonic Qubits          Grover   \n21          Machine Learning              Neutral Atoms            QAOA   \n22          Machine Learning              Neutral Atoms          Grover   \n\n       Durum  \n0             \n1             \n2             \n3             \n4             \n5             \n6             \n7             \n8             \n9             \n10            \n11            \n12  ✅ SEÇİLİ  \n13   · uygun  \n14   · uygun  \n15   · uygun  \n16            \n17            \n18            \n19            \n20            \n21            \n22            ",
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          "text/markdown": "### Seçim: **Machine Learning** → **Superconducting Qubits** → **QAOA**"
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        },
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          "text/markdown": "**Cihaz profili (`SELECTED_DEVICE`):** `ibm_or_aer`"
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          "text/markdown": "**Matematiksel model:** $|\\gamma,\\beta\\rangle = e^{-i\\beta_p H_M} e^{-i\\gamma_p H_C}\\cdots e^{-i\\beta_1 H_M} e^{-i\\gamma_1 H_C}|+\\rangle^{\\otimes n}$"
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          "text/markdown": "**Üretilen pipeline iskeleti:**"
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          "# -*- coding: utf-8 -*-\n",
          "\"\"\"\n",
          "KURUMSAL PIPELINE İSKELETİ (AŞAMA 2 Engine çıktısı)\n",
          "Problem : Machine Learning\n",
          "Donanım : Superconducting Qubits\n",
          "Algoritma: QAOA\n",
          "\"\"\"\n",
          "import numpy as np\n",
          "import pandas as pd\n",
          "from qiskit import QuantumCircuit\n",
          "from qiskit.quantum_info import SparsePauliOp\n",
          "from qiskit.circuit.library import TwoLocal, QAOAAnsatz, GroverOperator\n",
          "from qiskit.primitives import StatevectorEstimator as Estimator\n",
          "from qiskit.primitives import StatevectorSampler as Sampler\n",
          "\n",
          "# --- Veri girişi (AŞAMA 5/6 ile doldurulur) ---\n",
          "# df_main = ...\n",
          "\n",
          "# --- Kuantum çekirdek ---\n",
          "hamiltonian = SparsePauliOp.from_list([(\"ZZ\", 1.0)])\n",
          "n_qubits = 2  # AŞAMA 12'de güncellenir\n",
          "\n",
          "ansatz = QAOAAnsatz(cost_operator=hamiltonian, reps=1)\n",
          "sampler = Sampler()\n",
          "quantum_result = [0.5, 0.5]\n",
          "\n",
          "# --- Sonuç (AŞAMA 17 ile kaydedilir) ---\n",
          "# results → CSV / API / SQL\n",
          "\n"
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          "text/html": "<div style='padding:12px;background:#f0fdf4;border-left:4px solid #22c55e;border-radius:8px;margin-top:8px;'><b>✅ Engine hazır.</b> SELECTED_ALGO=<code>QAOA</code>, SELECTED_DEVICE=<code>ibm_or_aer</code> — AŞAMA 11–16 bu değerleri kullanır.</div>"
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