{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":59156,"databundleVersionId":13874099,"sourceType":"competition"},{"sourceId":13325493,"sourceType":"datasetVersion","datasetId":8447987}],"dockerImageVersionId":31154,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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"},"0c90b0ba-cf22-42f5-bfd7-77015a6b99a7.png":{"image/png":"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"}}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport cv2\nimport PIL\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Activation, Dense, Flatten, Input, Conv2D, MaxPooling2D, Dropout, BatchNormalization\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.applications.resnet import ResNet152, preprocess_input, decode_predictions, ResNet50\nfrom sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\nfrom tensorflow.keras.utils import plot_model\nfrom tensorflow.keras.preprocessing.image import img_to_array, load_img, array_to_img\nimport seaborn as sns\nimport json\nfrom pathlib import Path\nfrom collections import defaultdict, Counter\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout\n\nfrom scipy import stats\nfrom scipy.spatial.distance import cdist, euclidean\nfrom scipy.ndimage import gaussian_filter1d\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:00:58.699018Z","iopub.execute_input":"2025-10-12T14:00:58.699661Z","iopub.status.idle":"2025-10-12T14:01:17.697333Z","shell.execute_reply.started":"2025-10-12T14:00:58.699635Z","shell.execute_reply":"2025-10-12T14:01:17.696185Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/mickey2025/train.csv')\ntest_df = pd.read_csv('/kaggle/input/mickey2025/test.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:17.698631Z","iopub.execute_input":"2025-10-12T14:01:17.699200Z","iopub.status.idle":"2025-10-12T14:01:17.863153Z","shell.execute_reply.started":"2025-10-12T14:01:17.699175Z","shell.execute_reply":"2025-10-12T14:01:17.862395Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:17.865240Z","iopub.execute_input":"2025-10-12T14:01:17.865540Z","iopub.status.idle":"2025-10-12T14:01:17.913279Z","shell.execute_reply.started":"2025-10-12T14:01:17.865517Z","shell.execute_reply":"2025-10-12T14:01:17.912361Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.head(5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:17.914111Z","iopub.execute_input":"2025-10-12T14:01:17.914399Z","iopub.status.idle":"2025-10-12T14:01:17.936701Z","shell.execute_reply.started":"2025-10-12T14:01:17.914379Z","shell.execute_reply":"2025-10-12T14:01:17.935412Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.describe().T","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:17.937495Z","iopub.execute_input":"2025-10-12T14:01:17.937805Z","iopub.status.idle":"2025-10-12T14:01:17.992790Z","shell.execute_reply.started":"2025-10-12T14:01:17.937781Z","shell.execute_reply":"2025-10-12T14:01:17.991811Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:17.994074Z","iopub.execute_input":"2025-10-12T14:01:17.994412Z","iopub.status.idle":"2025-10-12T14:01:18.031678Z","shell.execute_reply.started":"2025-10-12T14:01:17.994379Z","shell.execute_reply":"2025-10-12T14:01:18.030608Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.isnull().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.032574Z","iopub.execute_input":"2025-10-12T14:01:18.032882Z","iopub.status.idle":"2025-10-12T14:01:18.053281Z","shell.execute_reply.started":"2025-10-12T14:01:18.032847Z","shell.execute_reply":"2025-10-12T14:01:18.052270Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.duplicated().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.054291Z","iopub.execute_input":"2025-10-12T14:01:18.054647Z","iopub.status.idle":"2025-10-12T14:01:18.097815Z","shell.execute_reply.started":"2025-10-12T14:01:18.054619Z","shell.execute_reply":"2025-10-12T14:01:18.096828Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(train_df.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.100791Z","iopub.execute_input":"2025-10-12T14:01:18.101040Z","iopub.status.idle":"2025-10-12T14:01:18.106372Z","shell.execute_reply.started":"2025-10-12T14:01:18.101022Z","shell.execute_reply":"2025-10-12T14:01:18.105127Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ny_train = train_df[\"lab_id\"]\nX_train = train_df.drop(labels = [\"lab_id\"], axis = 1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.107627Z","iopub.execute_input":"2025-10-12T14:01:18.107966Z","iopub.status.idle":"2025-10-12T14:01:18.127161Z","shell.execute_reply.started":"2025-10-12T14:01:18.107945Z","shell.execute_reply":"2025-10-12T14:01:18.126170Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nprint(\"x_train shape: \",X_train.shape)\nprint(\"y_train shape: \",y_train.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.128391Z","iopub.execute_input":"2025-10-12T14:01:18.128758Z","iopub.status.idle":"2025-10-12T14:01:18.146665Z","shell.execute_reply.started":"2025-10-12T14:01:18.128721Z","shell.execute_reply":"2025-10-12T14:01:18.145451Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nimport numpy as np\n\n# Örnek Kategorik Veri\ncategories = ['video_id', 'mouse1_strain', 'mouse1_color', 'mouse1_sex',\n       'mouse1_id', 'mouse1_age', 'mouse1_condition', 'mouse2_strain',\n       'mouse2_color', 'mouse2_sex', 'mouse2_id', 'mouse2_age',\n       'mouse2_condition', 'mouse3_strain', 'mouse3_color', 'mouse3_sex',\n       'mouse3_id', 'mouse3_age', 'mouse3_condition', 'mouse4_strain',\n       'mouse4_color', 'mouse4_sex', 'mouse4_id', 'mouse4_age',\n       'mouse4_condition', 'frames_per_second', 'video_duration_sec',\n       'pix_per_cm_approx', 'video_width_pix', 'video_height_pix',\n       'arena_width_cm', 'arena_height_cm', 'arena_shape', 'arena_type',\n       'body_parts_tracked', 'behaviors_labeled', 'tracking_method']\n\n# 1. LabelEncoder'ı Başlatma\nlabel_encoder = LabelEncoder()\n\n# 2. Veriye uydurma (Fit) ve dönüştürme (Transform)\n# fit_transform() metodu hem encoder'ı veriye göre eğitir hem de dönüşümü yapar.\nencoded_labels = label_encoder.fit_transform(categories)\n\n# Sonuçları yazdırma\nprint(\"Orijinal Kategoriler:\", categories)\nprint(\"Kodlanmış Etiketler:\", encoded_labels)\n\n# Kategorilerin hangi sayısal değere karşılık geldiğini görme\n# classes_ niteliği, kodlayıcının öğrendiği etiketleri içerir.\nprint(\"Kategori-Sayısal Eşleşmesi (Alfabetik Sıraya Göre):\", label_encoder.classes_)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.147775Z","iopub.execute_input":"2025-10-12T14:01:18.148896Z","iopub.status.idle":"2025-10-12T14:01:18.169863Z","shell.execute_reply.started":"2025-10-12T14:01:18.148866Z","shell.execute_reply":"2025-10-12T14:01:18.168907Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.1, random_state=2)\nprint(\"x_train shape\",X_train.shape)\nprint(\"x_test shape\",X_val.shape)\nprint(\"y_train shape\",y_train.shape)\nprint(\"y_test shape\",y_val.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.170748Z","iopub.execute_input":"2025-10-12T14:01:18.170990Z","iopub.status.idle":"2025-10-12T14:01:18.201744Z","shell.execute_reply.started":"2025-10-12T14:01:18.170971Z","shell.execute_reply":"2025-10-12T14:01:18.200736Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n# ... önceki import'lar\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom keras.optimizers import RMSprop,Adam\n\n# Hata veren satırı aşağıdaki ile değiştirin:\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator # BU SATIRI KULLANIN\n\nfrom keras.callbacks import ReduceLROnPlateau\n# ...\nfrom sklearn.metrics import confusion_matrix\nimport itertools\nimport pandas as pd\nimport numpy as np\nimport itertools\n# Hata veren satırı aşağıdaki ile değiştirin\nfrom tensorflow.keras.utils import to_categorical \nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator  # ✅ Doğru\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom tensorflow.keras.utils import to_categorical \nfrom tensorflow.keras.optimizers import RMSprop,Adam\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.203196Z","iopub.execute_input":"2025-10-12T14:01:18.204426Z","iopub.status.idle":"2025-10-12T14:01:18.222466Z","shell.execute_reply.started":"2025-10-12T14:01:18.204394Z","shell.execute_reply":"2025-10-12T14:01:18.221249Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import tensorflow as tf\n# GPU'yu devre dışı bırakır, kodun CPU'da çalışmasını sağlar\ntf.config.set_visible_devices([], 'GPU')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.223357Z","iopub.execute_input":"2025-10-12T14:01:18.223596Z","iopub.status.idle":"2025-10-12T14:01:18.244786Z","shell.execute_reply.started":"2025-10-12T14:01:18.223578Z","shell.execute_reply":"2025-10-12T14:01:18.243501Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model = Sequential()\n# Input katmanını modelin ilk katmanı olarak ekleyin\nmodel.add(Input(shape=(28, 28, 1))) \nmodel.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n#\nmodel.add(Conv2D(filters = 16, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\nmodel.add(Dropout(0.25))\n# fully connected\nmodel.add(Flatten())\nmodel.add(Dense(256, activation = \"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation = \"softmax\"))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.245829Z","iopub.execute_input":"2025-10-12T14:01:18.246137Z","iopub.status.idle":"2025-10-12T14:01:18.407636Z","shell.execute_reply.started":"2025-10-12T14:01:18.246109Z","shell.execute_reply":"2025-10-12T14:01:18.406370Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau\n\n# Daha önceki hataları önlemek için GPU'yu devre dışı bırakabilirsiniz (isteğe bağlı)\n# tf.config.set_visible_devices([], 'GPU')\n# print(\"GPU devre dışı bırakıldı, CPU kullanılıyor.\")\n\n# --- 1. Veri Hazırlığı (Basitleştirilmiş Görüntü Boyutları) ---\n# MABe yarışması verisi karmaşık olduğu için, burada varsayımsal bir görüntü boyutu kullanıyoruz.\n# Gerçek uygulamada, HDF5 dosyalarından kareleri çıkarıp uygun şekilde etiketlemeniz gerekir.\nIMG_HEIGHT = 64\nIMG_WIDTH = 64\nCHANNELS = 3  # RGB görüntüler için 3, gri tonlamalı için 1\nNUM_CLASSES = 15 # Yarışmadaki fare davranışlarının sayısı\n\n# Yüklenen Görüntülerin (X) ve Etiketlerin (Y) olduğu varsayılır.\n# Örnek: X_train.shape = (n_samples, 64, 64, 3), Y_train.shape = (n_samples, 15)\n\n# --- 2. CNN Modelini Tanımlama ---\n# LeNet/VGG benzeri basit bir Evrişimli Sinir Ağı mimarisi\n\ndef build_cnn_model():\n    model = Sequential([\n        # GİRİŞ Katmanı: Input(shape) ile başlangıç şeklini belirtme (önerilen yöntem)\n        tf.keras.Input(shape=(IMG_HEIGHT, IMG_WIDTH, CHANNELS)),\n        \n        # Evrişim Bloğu 1\n        Conv2D(32, kernel_size=(5, 5), padding='Same', activation='relu'),\n        Conv2D(32, kernel_size=(5, 5), padding='Same', activation='relu'),\n        MaxPool2D(pool_size=(2, 2), strides=(2, 2)),\n        Dropout(0.25),\n\n        # Evrişim Bloğu 2\n        Conv2D(64, kernel_size=(3, 3), padding='Same', activation='relu'),\n        Conv2D(64, kernel_size=(3, 3), padding='Same', activation='relu'),\n        MaxPool2D(pool_size=(2, 2), strides=(2, 2)),\n        Dropout(0.25),\n\n        # Tam Bağlantılı (Dense) Katmanlar\n        Flatten(),\n        Dense(256, activation='relu'),\n        Dropout(0.5),\n        \n        # ÇIKIŞ Katmanı\n        # Davranış tespiti bir sınıflandırma problemi olduğundan 'softmax' kullanılır.\n        Dense(NUM_CLASSES, activation='softmax')\n    ])\n    \n    return model\n\nmodel = build_cnn_model()\n\n# --- 3. Modeli Derleme (Compile) ---\n# Optimizasyon algoritması, kayıp fonksiyonu (loss) ve metrikler belirlenir.\noptimizer = Adam(learning_rate=0.001)\n\nmodel.compile(optimizer=optimizer, \n              loss='categorical_crossentropy', \n              metrics=['accuracy'])\n\n# Modelin özetini göster\nmodel.summary()\n\n# --- 4. Veri Artırımı (Data Augmentation) ---\n# Modelin aşırı öğrenmesini (overfitting) engellemek için ImageDataGenerator kullanılır.\ndatagen = ImageDataGenerator(\n    rotation_range=10,        # Rastgele döndürme\n    zoom_range=0.1,           # Rastgele yakınlaştırma\n    width_shift_range=0.1,    # Yatay kaydırma\n    height_shift_range=0.1,   # Dikey kaydırma\n    horizontal_flip=False,    # Yatay çevirme (fare davranışlarında ters çevirme anlamı bozabilir)\n    vertical_flip=False       # Dikey çevirme (fare davranışlarında ters çevirme anlamı bozabilir)\n)\n\n# --- 5. Geri Çağrım Fonksiyonları (Callbacks) ---\n# Öğrenme oranını (learning rate) otomatik olarak azaltmak için kullanılır.\nlearning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', \n                                            patience=3, \n                                            verbose=1, \n                                            factor=0.5, \n                                            min_lr=0.00001)\n\n# --- 6. Modeli Eğitme (Fit) ---\n# Buradaki X_train, Y_train ve X_val, Y_val değişkenlerinin tanımlanmış olması gerekir.\n# Eğer veriniz büyükse generator kullanmak zorunludur.\n\n'''\n# Eğitim Verisinin Tanımlı Olduğunu Varsayarsak:\n# history = model.fit(\n#     datagen.flow(X_train, Y_train, batch_size=64),\n#     epochs=30,\n#     validation_data=(X_val, Y_val),\n#     verbose=2,\n#     callbacks=[learning_rate_reduction]\n# )\n'''","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.408633Z","iopub.execute_input":"2025-10-12T14:01:18.408943Z","iopub.status.idle":"2025-10-12T14:01:18.563753Z","shell.execute_reply.started":"2025-10-12T14:01:18.408921Z","shell.execute_reply":"2025-10-12T14:01:18.562918Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# --- 7. Test Verisini Hazırlama (Sahte Veri) ---\n# Gerçek Kaggle yarışmasında buraya test.csv'yi yükleyip ön işlemeler uygulanır.\nN_TEST = 2000 # Varsayımsal Test Verisi Sayısı\n\n# Test Görüntüleri (X_test): (N_TEST, 64, 64, 3) boyutunda rastgele pikseller\nX_test = np.random.rand(N_TEST, IMG_HEIGHT, IMG_WIDTH, CHANNELS).astype('float32')\n\n# Not: Test verisi ID'leri de gereklidir.\ntest_ids = pd.Series(range(N_TEST))\nprint(f\"X_test boyutu: {X_test.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.564672Z","iopub.execute_input":"2025-10-12T14:01:18.564985Z","iopub.status.idle":"2025-10-12T14:01:18.904370Z","shell.execute_reply.started":"2025-10-12T14:01:18.564964Z","shell.execute_reply":"2025-10-12T14:01:18.902864Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# --- 8. Tahminleri Oluşturma ---\n# model.predict, her sınıf için olasılıkları döndürür (softmax çıkışı).\n# predictions değişkeni artık tanımlanmıştır.\nraw_predictions = model.predict(X_test)\n\n# Sınıflandırma problemi olduğu için, en yüksek olasılığa sahip sınıfı seçmeliyiz.\n# Bu, \"One-Hot Encoding\"den sınıf etiketine geçiştir.\npredictions = np.argmax(raw_predictions, axis=1)\n\nprint(\"Tahminler başarıyla oluşturuldu.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:18.905603Z","iopub.execute_input":"2025-10-12T14:01:18.906064Z","iopub.status.idle":"2025-10-12T14:01:28.058490Z","shell.execute_reply.started":"2025-10-12T14:01:18.906037Z","shell.execute_reply":"2025-10-12T14:01:28.057444Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# --- 9. Submission Dosyasını Oluşturma ve Kaydetme ---\n\n# Pandas kütüphanesini kullanarak DataFrame'i oluşturun.\n# Sütun adları, yarışmanın formatına TAM OLARAK uymalıdır.\n# Genellikle 'ID' ve 'Target' (Hedef) veya 'Class' (Sınıf) şeklindedir.\nsubmission_csv = pd.DataFrame({\n    'ID': test_ids,\n    'Class': predictions\n})\n\n# Dosyayı kaydedin. index=False, ekstra satır numaralarının eklenmesini engeller.\nsubmission_csv.to_csv('submission.csv', index=False)\n\nprint(\"\\n---------------------------------------------------------\")\nprint(\"Submission dosyası oluşturuldu: submission.csv\")\nprint(\"İlk 5 satır:\")\nprint(submission_csv.head())\nprint(\"---------------------------------------------------------\")\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:28.059421Z","iopub.execute_input":"2025-10-12T14:01:28.059770Z","iopub.status.idle":"2025-10-12T14:01:28.077637Z","shell.execute_reply.started":"2025-10-12T14:01:28.059742Z","shell.execute_reply":"2025-10-12T14:01:28.076429Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Final step to create the submission file\nsubmission_df = pd.DataFrame({\n    'ID': test_ids,\n    'Class': predictions\n})\n\n# IMPORTANT: Must be named 'submission.csv'\nsubmission_df.to_csv('submission.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-12T14:01:28.078546Z","iopub.execute_input":"2025-10-12T14:01:28.078809Z","iopub.status.idle":"2025-10-12T14:01:28.088183Z","shell.execute_reply.started":"2025-10-12T14:01:28.078789Z","shell.execute_reply":"2025-10-12T14:01:28.087144Z"}},"outputs":[],"execution_count":null}]}