{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":48750,"databundleVersionId":5157702,"sourceType":"competition"}],"isInternetEnabled":true,"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\n# for 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 session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-01-02T12:01:15.111292Z","iopub.execute_input":"2024-01-02T12:01:15.111599Z","iopub.status.idle":"2024-01-02T12:01:15.452037Z","shell.execute_reply.started":"2024-01-02T12:01:15.111574Z","shell.execute_reply":"2024-01-02T12:01:15.451145Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"!pip install torch torchvision","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:01:19.952674Z","iopub.execute_input":"2024-01-02T12:01:19.95362Z","iopub.status.idle":"2024-01-02T12:01:33.176082Z","shell.execute_reply.started":"2024-01-02T12:01:19.953587Z","shell.execute_reply":"2024-01-02T12:01:33.175063Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (2.0.0)\nRequirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (0.15.1)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch) (3.12.2)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch) (4.5.0)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch) (3.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch) (3.1.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision) (1.24.3)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision) (2.31.0)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision) (10.1.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch) (2.1.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (3.2.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (2023.11.17)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"! pip install torchxrayvision","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:01:36.341586Z","iopub.execute_input":"2024-01-02T12:01:36.342016Z","iopub.status.idle":"2024-01-02T12:01:49.242946Z","shell.execute_reply.started":"2024-01-02T12:01:36.341982Z","shell.execute_reply":"2024-01-02T12:01:49.241994Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Collecting torchxrayvision\n  Obtaining dependency information for torchxrayvision from https://files.pythonhosted.org/packages/47/92/3c0f25fa34420dafe1b3c7547a87024ad5aa196802f6e65dd7c93b046373/torchxrayvision-1.2.1-py3-none-any.whl.metadata\n  Downloading torchxrayvision-1.2.1-py3-none-any.whl.metadata (18 kB)\nRequirement already satisfied: torch>=1 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (2.0.0)\nRequirement already satisfied: torchvision>=0.5 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (0.15.1)\nRequirement already satisfied: scikit-image>=0.16 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (0.21.0)\nRequirement already satisfied: tqdm>=4 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (4.66.1)\nRequirement already satisfied: numpy>=1 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (1.24.3)\nRequirement already satisfied: pandas>=1 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (2.0.3)\nRequirement already satisfied: requests>=1 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (2.31.0)\nRequirement already satisfied: pillow>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (10.1.0)\nRequirement already satisfied: imageio in /opt/conda/lib/python3.10/site-packages (from torchxrayvision) (2.31.1)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1->torchxrayvision) (2.8.2)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1->torchxrayvision) (2023.3)\nRequirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1->torchxrayvision) (2023.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=1->torchxrayvision) (3.2.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=1->torchxrayvision) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=1->torchxrayvision) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=1->torchxrayvision) (2023.11.17)\nRequirement already satisfied: scipy>=1.8 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16->torchxrayvision) (1.11.4)\nRequirement already satisfied: networkx>=2.8 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16->torchxrayvision) (3.1)\nRequirement already satisfied: tifffile>=2022.8.12 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16->torchxrayvision) (2023.8.12)\nRequirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16->torchxrayvision) (1.4.1)\nRequirement already satisfied: packaging>=21 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16->torchxrayvision) (21.3)\nRequirement already satisfied: lazy_loader>=0.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16->torchxrayvision) (0.3)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1->torchxrayvision) (3.12.2)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1->torchxrayvision) (4.5.0)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1->torchxrayvision) (1.12)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1->torchxrayvision) (3.1.2)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=21->scikit-image>=0.16->torchxrayvision) (3.0.9)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=1->torchxrayvision) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1->torchxrayvision) (2.1.3)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1->torchxrayvision) (1.3.0)\nDownloading torchxrayvision-1.2.1-py3-none-any.whl (29.0 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.0/29.0 MB\u001b[0m \u001b[31m43.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: torchxrayvision\nSuccessfully installed torchxrayvision-1.2.1\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nimport skimage\ndevice = torch.device(\"cuda\")\n\nclass MyDataset(Dataset):\n    def __init__(self, image_ids, image_paths, transform=None):\n        self.image_ids = image_ids\n        self.image_paths = image_paths\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.image_ids)\n\n    def __getitem__(self, idx):\n        img = skimage.io.imread(self.image_paths[idx])\n        img = xrv.datasets.normalize(img, 255) # convert 8-bit image to [-1024, 1024] range\n        img = img.mean(2)[None, ...] # Make single color channel\n        if self.transform:\n            img = self.transform(img)\n            img = torch.from_numpy(img)\n        \n        img = img.to(device)  # move the image tensor to the GPU\n        return self.image_ids[idx], img\n\n# Example usage\nbatch_size = 128","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"import torchxrayvision as xrv\nimport  torch, torchvision\nimport gc\n\n# Prepare the image:\n\n#model = xrv.models.DenseNet(weights=\"densenet121-res224-all\")\n#model = xrv.models.DenseNet(weights=\"densenet121-res224-chex\")\nmodel = xrv.models.ResNet(weights=\"resnet50-res512-all\")\nmodel.to(device)\n    \ntransform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),xrv.datasets.XRayResizer(512)])\n\n\n\ndef extract_image_features(image_ids, image_paths):\n    dataset = MyDataset(image_ids, image_paths, transform=transform)\n    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)\n    \n    feature_list=[]\n    id_list=[]\n    for batch_idx, (ids, images) in enumerate(dataloader):\n        \n        #print(f'Batch {batch_idx}: IDs = {ids}, Images = {images.shape}')\n        with torch.no_grad():\n            batch_features = model.features(images)\n        feature_list.extend(batch_features.cpu().squeeze().numpy().tolist())\n        id_list.extend( ids.cpu().numpy().tolist())\n    \n    features_df = pd.DataFrame({'imageId': id_list, 'features': feature_list})\n    return features_df","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:03:33.717086Z","iopub.execute_input":"2024-01-02T12:03:33.717994Z","iopub.status.idle":"2024-01-02T12:03:38.760233Z","shell.execute_reply.started":"2024-01-02T12:03:33.717961Z","shell.execute_reply":"2024-01-02T12:03:38.759243Z"},"trusted":true},"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"},{"name":"stdout","text":"Downloading weights...\nIf this fails you can run `wget https://github.com/mlmed/torchxrayvision/releases/download/v1/pc-nih-rsna-siim-vin-resnet50-test512-e400-state.pt -O /root/.torchxrayvision/models_data/pc-nih-rsna-siim-vin-resnet50-test512-e400-state.pt`\n[██████████████████████████████████████████████████]\nSetting XRayResizer engine to cv2 could increase performance.\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"def number_to_filename(number):\n    filename = f\"{number:06d}.png\"\n    path = '/kaggle/input/spr-x-ray-age/kaggle/kaggle/train/'\n    filename = path+filename\n    return filename","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:03:44.155201Z","iopub.execute_input":"2024-01-02T12:03:44.155591Z","iopub.status.idle":"2024-01-02T12:03:44.160712Z","shell.execute_reply.started":"2024-01-02T12:03:44.15556Z","shell.execute_reply":"2024-01-02T12:03:44.159733Z"},"trusted":true},"outputs":[],"execution_count":18},{"cell_type":"code","source":"train_age_df=pd.read_csv('/kaggle/input/spr-x-ray-age/train_age.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:03:46.437206Z","iopub.execute_input":"2024-01-02T12:03:46.438194Z","iopub.status.idle":"2024-01-02T12:03:46.448091Z","shell.execute_reply.started":"2024-01-02T12:03:46.438161Z","shell.execute_reply":"2024-01-02T12:03:46.447107Z"},"trusted":true},"outputs":[],"execution_count":19},{"cell_type":"code","source":"train_age_df['filepath'] = train_age_df['imageId'].apply(number_to_filename)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:03:48.645363Z","iopub.execute_input":"2024-01-02T12:03:48.645725Z","iopub.status.idle":"2024-01-02T12:03:48.661852Z","shell.execute_reply.started":"2024-01-02T12:03:48.645699Z","shell.execute_reply":"2024-01-02T12:03:48.660865Z"},"trusted":true},"outputs":[],"execution_count":20},{"cell_type":"code","source":"train_age_df.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:02:30.273681Z","iopub.execute_input":"2024-01-02T12:02:30.274043Z","iopub.status.idle":"2024-01-02T12:02:30.2932Z","shell.execute_reply.started":"2024-01-02T12:02:30.274015Z","shell.execute_reply":"2024-01-02T12:02:30.292344Z"},"trusted":true},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"   imageId   age                                           filepath\n0        0  89.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n1        1  72.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n2        2  25.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n3        3  68.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n4        4  37.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n5        5  62.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n6        6  52.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n7        7  46.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n8        8  83.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...\n9        9  33.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>imageId</th>\n      <th>age</th>\n      <th>filepath</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>89.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>72.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>25.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>68.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>37.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>62.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>52.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>46.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>83.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>33.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"train_age_df['age'].hist()","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:02:34.714012Z","iopub.execute_input":"2024-01-02T12:02:34.714834Z","iopub.status.idle":"2024-01-02T12:02:35.043753Z","shell.execute_reply.started":"2024-01-02T12:02:34.714802Z","shell.execute_reply":"2024-01-02T12:02:35.042839Z"},"trusted":true},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"from matplotlib import pyplot as plt\nimport cv2\n\nimg = cv2.imread(train_age_df['filepath'][0],0)\nplt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\nplt.title(str(train_age_df['age'][0]))","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:02:38.233212Z","iopub.execute_input":"2024-01-02T12:02:38.234403Z","iopub.status.idle":"2024-01-02T12:02:39.032939Z","shell.execute_reply.started":"2024-01-02T12:02:38.234359Z","shell.execute_reply":"2024-01-02T12:02:39.031823Z"},"trusted":true},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, '89.0')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"img = cv2.imread(train_age_df['filepath'][0],0)\nplt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\nplt.title(str(train_age_df['age'][100]))","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:02:44.023676Z","iopub.execute_input":"2024-01-02T12:02:44.024536Z","iopub.status.idle":"2024-01-02T12:02:44.557775Z","shell.execute_reply.started":"2024-01-02T12:02:44.024499Z","shell.execute_reply":"2024-01-02T12:02:44.556861Z"},"trusted":true},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, '28.0')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"img = cv2.imread(train_age_df['filepath'][0],0)\nplt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\nplt.title(str(train_age_df['age'][500]))","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:02:47.762825Z","iopub.execute_input":"2024-01-02T12:02:47.76347Z","iopub.status.idle":"2024-01-02T12:02:48.288041Z","shell.execute_reply.started":"2024-01-02T12:02:47.763437Z","shell.execute_reply":"2024-01-02T12:02:48.287139Z"},"trusted":true},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, '83.0')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"sampled_df = train_age_df.groupby('age').apply(lambda x: x.sample(frac=0.95, replace=False)).reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:02:51.462684Z","iopub.execute_input":"2024-01-02T12:02:51.463051Z","iopub.status.idle":"2024-01-02T12:02:51.502985Z","shell.execute_reply.started":"2024-01-02T12:02:51.463026Z","shell.execute_reply":"2024-01-02T12:02:51.502224Z"},"trusted":true},"outputs":[],"execution_count":15},{"cell_type":"code","source":"features_df = extract_image_features(sampled_df['imageId'].tolist(), sampled_df['filepath'].tolist())","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:03:52.493964Z","iopub.execute_input":"2024-01-02T12:03:52.49481Z","iopub.status.idle":"2024-01-02T12:23:09.812775Z","shell.execute_reply.started":"2024-01-02T12:03:52.494773Z","shell.execute_reply":"2024-01-02T12:23:09.811826Z"},"trusted":true},"outputs":[],"execution_count":21},{"cell_type":"code","source":"features_df.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:23:22.481874Z","iopub.execute_input":"2024-01-02T12:23:22.482608Z","iopub.status.idle":"2024-01-02T12:23:22.514108Z","shell.execute_reply.started":"2024-01-02T12:23:22.482576Z","shell.execute_reply":"2024-01-02T12:23:22.513217Z"},"trusted":true},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"   imageId                                           features\n0     7940  [0.0, 0.0, 0.0, 0.0, 0.1397303342819214, 0.027...\n1     1077  [0.0, 0.0, 0.0, 0.0, 0.3019484281539917, 0.029...\n2     4817  [0.0, 0.0, 0.0, 0.0, 0.13938364386558533, 0.00...\n3      749  [0.0, 0.0, 0.0, 0.0, 0.15368078649044037, 0.03...\n4     9461  [0.0, 0.0, 0.0, 0.0, 0.09871066361665726, 0.04...\n5      109  [0.0, 0.0, 0.0, 0.0, 0.3129827082157135, 0.026...\n6     6344  [0.0, 0.0, 0.0, 0.0, 0.12113261222839355, 0.02...\n7      421  [0.0, 0.0, 0.0, 0.0, 0.11939327418804169, 0.00...\n8     4479  [0.0, 0.0, 0.0, 0.0, 0.07483917474746704, 0.04...\n9      665  [0.0, 0.0, 0.0, 0.0, 0.1303880214691162, 0.003...","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>imageId</th>\n      <th>features</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7940</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.1397303342819214, 0.027...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1077</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.3019484281539917, 0.029...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4817</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.13938364386558533, 0.00...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>749</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.15368078649044037, 0.03...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9461</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.09871066361665726, 0.04...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>109</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.3129827082157135, 0.026...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6344</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.12113261222839355, 0.02...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>421</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.11939327418804169, 0.00...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4479</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.07483917474746704, 0.04...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>665</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.1303880214691162, 0.003...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"training_df = pd.merge(sampled_df, features_df, on='imageId')","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:23:28.432154Z","iopub.execute_input":"2024-01-02T12:23:28.432529Z","iopub.status.idle":"2024-01-02T12:23:28.450421Z","shell.execute_reply.started":"2024-01-02T12:23:28.432498Z","shell.execute_reply":"2024-01-02T12:23:28.449552Z"},"trusted":true},"outputs":[],"execution_count":23},{"cell_type":"code","source":"training_df.head(10)","metadata":{"trusted":true},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"   imageId   age                                           filepath  \\\n0     7940  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n1     1077  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n2     4817  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n3      749  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n4     9461  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n5      109  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n6     6344  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n7      421  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n8     4479  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n9      665  18.0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...   \n\n                                            features  \n0  [0.0, 0.0, 0.0, 0.0, 0.1397303342819214, 0.027...  \n1  [0.0, 0.0, 0.0, 0.0, 0.3019484281539917, 0.029...  \n2  [0.0, 0.0, 0.0, 0.0, 0.13938364386558533, 0.00...  \n3  [0.0, 0.0, 0.0, 0.0, 0.15368078649044037, 0.03...  \n4  [0.0, 0.0, 0.0, 0.0, 0.09871066361665726, 0.04...  \n5  [0.0, 0.0, 0.0, 0.0, 0.3129827082157135, 0.026...  \n6  [0.0, 0.0, 0.0, 0.0, 0.12113261222839355, 0.02...  \n7  [0.0, 0.0, 0.0, 0.0, 0.11939327418804169, 0.00...  \n8  [0.0, 0.0, 0.0, 0.0, 0.07483917474746704, 0.04...  \n9  [0.0, 0.0, 0.0, 0.0, 0.1303880214691162, 0.003...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>imageId</th>\n      <th>age</th>\n      <th>filepath</th>\n      <th>features</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7940</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.1397303342819214, 0.027...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1077</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.3019484281539917, 0.029...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4817</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.13938364386558533, 0.00...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>749</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.15368078649044037, 0.03...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9461</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.09871066361665726, 0.04...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>109</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.3129827082157135, 0.026...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6344</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.12113261222839355, 0.02...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>421</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.11939327418804169, 0.00...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4479</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.07483917474746704, 0.04...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>665</td>\n      <td>18.0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/trai...</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.1303880214691162, 0.003...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"split_columns = training_df['features'].apply(pd.Series)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:23:38.384727Z","iopub.execute_input":"2024-01-02T12:23:38.385388Z","iopub.status.idle":"2024-01-02T12:23:43.035019Z","shell.execute_reply.started":"2024-01-02T12:23:38.385356Z","shell.execute_reply":"2024-01-02T12:23:43.034088Z"},"trusted":true},"outputs":[],"execution_count":25},{"cell_type":"code","source":"training_df = pd.concat([training_df.drop('features', axis=1), split_columns], axis=1)","metadata":{"trusted":true},"outputs":[],"execution_count":26},{"cell_type":"code","source":"mod_training_df = training_df.drop(columns=['filepath','imageId'])\n","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:23:49.663462Z","iopub.execute_input":"2024-01-02T12:23:49.6638Z","iopub.status.idle":"2024-01-02T12:23:49.718668Z","shell.execute_reply.started":"2024-01-02T12:23:49.663775Z","shell.execute_reply":"2024-01-02T12:23:49.717835Z"},"trusted":true},"outputs":[],"execution_count":27},{"cell_type":"code","source":"mod_training_df.head(10)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:23:52.393133Z","iopub.execute_input":"2024-01-02T12:23:52.393782Z","iopub.status.idle":"2024-01-02T12:23:52.432844Z","shell.execute_reply.started":"2024-01-02T12:23:52.393748Z","shell.execute_reply":"2024-01-02T12:23:52.431894Z"},"trusted":true},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"    age    0    1    2    3         4         5    6    7    8  ...  2038  \\\n0  18.0  0.0  0.0  0.0  0.0  0.139730  0.027012  0.0  0.0  0.0  ...   0.0   \n1  18.0  0.0  0.0  0.0  0.0  0.301948  0.029741  0.0  0.0  0.0  ...   0.0   \n2  18.0  0.0  0.0  0.0  0.0  0.139384  0.002586  0.0  0.0  0.0  ...   0.0   \n3  18.0  0.0  0.0  0.0  0.0  0.153681  0.030726  0.0  0.0  0.0  ...   0.0   \n4  18.0  0.0  0.0  0.0  0.0  0.098711  0.045995  0.0  0.0  0.0  ...   0.0   \n5  18.0  0.0  0.0  0.0  0.0  0.312983  0.026096  0.0  0.0  0.0  ...   0.0   \n6  18.0  0.0  0.0  0.0  0.0  0.121133  0.023504  0.0  0.0  0.0  ...   0.0   \n7  18.0  0.0  0.0  0.0  0.0  0.119393  0.009738  0.0  0.0  0.0  ...   0.0   \n8  18.0  0.0  0.0  0.0  0.0  0.074839  0.049912  0.0  0.0  0.0  ...   0.0   \n9  18.0  0.0  0.0  0.0  0.0  0.130388  0.003067  0.0  0.0  0.0  ...   0.0   \n\n   2039      2040  2041  2042  2043  2044  2045  2046  2047  \n0   0.0  0.047093   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n1   0.0  0.085905   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n2   0.0  0.039918   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n3   0.0  0.096257   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n4   0.0  0.069238   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n5   0.0  0.086985   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n6   0.0  0.082676   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n7   0.0  0.073162   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n8   0.0  0.051228   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n9   0.0  0.047164   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n\n[10 rows x 2049 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>...</th>\n      <th>2038</th>\n      <th>2039</th>\n      <th>2040</th>\n      <th>2041</th>\n      <th>2042</th>\n      <th>2043</th>\n      <th>2044</th>\n      <th>2045</th>\n      <th>2046</th>\n      <th>2047</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.139730</td>\n      <td>0.027012</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047093</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.301948</td>\n      <td>0.029741</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.085905</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.139384</td>\n      <td>0.002586</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.039918</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.153681</td>\n      <td>0.030726</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.096257</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.098711</td>\n      <td>0.045995</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.069238</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.312983</td>\n      <td>0.026096</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.086985</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.121133</td>\n      <td>0.023504</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.082676</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.119393</td>\n      <td>0.009738</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.073162</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.074839</td>\n      <td>0.049912</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.051228</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.130388</td>\n      <td>0.003067</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047164</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 2049 columns</p>\n</div>"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"#from sklearn.linear_model import ElasticNet\nfrom sklearn.ensemble import GradientBoostingRegressor\n#from sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn import decomposition","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:24:52.012936Z","iopub.execute_input":"2024-01-02T12:24:52.013322Z","iopub.status.idle":"2024-01-02T12:24:52.364272Z","shell.execute_reply.started":"2024-01-02T12:24:52.013281Z","shell.execute_reply":"2024-01-02T12:24:52.363361Z"},"trusted":true},"outputs":[],"execution_count":30},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestRegressor\npipeline = Pipeline([\n    \n    ('standard_scaler', StandardScaler()),\n    ('regressor', RandomForestRegressor())\n])","metadata":{"execution":{"iopub.status.busy":"2024-01-02T13:17:45.005201Z","iopub.execute_input":"2024-01-02T13:17:45.00564Z","iopub.status.idle":"2024-01-02T13:17:45.010503Z","shell.execute_reply.started":"2024-01-02T13:17:45.005607Z","shell.execute_reply":"2024-01-02T13:17:45.009551Z"},"trusted":true},"outputs":[],"execution_count":47},{"cell_type":"code","source":"features = mod_training_df.drop('age',axis=1)\ntarget = mod_training_df['age']\nX_train, X_test, y_train, y_test = train_test_split(features, target,stratify=target, test_size=0.2, random_state=0)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T13:17:48.817613Z","iopub.execute_input":"2024-01-02T13:17:48.818006Z","iopub.status.idle":"2024-01-02T13:17:48.970115Z","shell.execute_reply.started":"2024-01-02T13:17:48.817975Z","shell.execute_reply":"2024-01-02T13:17:48.969108Z"},"trusted":true},"outputs":[],"execution_count":48},{"cell_type":"code","source":"X_train","metadata":{"execution":{"iopub.status.busy":"2024-01-02T13:14:31.653164Z","iopub.execute_input":"2024-01-02T13:14:31.653907Z","iopub.status.idle":"2024-01-02T13:14:31.693091Z","shell.execute_reply.started":"2024-01-02T13:14:31.653873Z","shell.execute_reply":"2024-01-02T13:14:31.692192Z"},"trusted":true},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"      0     1     2     3         4         5     6     7     8     9     ...  \\\n2681   0.0   0.0   0.0   0.0  0.396676  0.024855   0.0   0.0   0.0   0.0  ...   \n7110   0.0   0.0   0.0   0.0  0.145956  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<tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>2038</th>\n      <th>2039</th>\n      <th>2040</th>\n      <th>2041</th>\n      <th>2042</th>\n      <th>2043</th>\n      <th>2044</th>\n      <th>2045</th>\n      <th>2046</th>\n      <th>2047</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2681</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.396676</td>\n      <td>0.024855</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.175744</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7110</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.145956</td>\n      <td>0.028299</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.180074</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8058</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.261291</td>\n      <td>0.013521</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.063784</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1026</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.222497</td>\n      <td>0.020094</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.099316</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6592</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.200329</td>\n      <td>0.028408</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.208851</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8640</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.159527</td>\n      <td>0.005314</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.069485</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7622</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.282935</td>\n      <td>0.003525</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.051322</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2187</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.122466</td>\n      <td>0.021013</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.101112</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9198</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.159825</td>\n      <td>0.018170</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.213532</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1488</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.246999</td>\n      <td>0.024741</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.080347</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>8133 rows × 2048 columns</p>\n</div>"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"X_test","metadata":{"execution":{"iopub.status.busy":"2024-01-02T13:14:34.882685Z","iopub.execute_input":"2024-01-02T13:14:34.883014Z","iopub.status.idle":"2024-01-02T13:14:34.922218Z","shell.execute_reply.started":"2024-01-02T13:14:34.882988Z","shell.execute_reply":"2024-01-02T13:14:34.921415Z"},"trusted":true},"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"      0     1     2     3         4         5     6     7     8     9     ...  \\\n920    0.0   0.0   0.0   0.0  0.111504  0.012895   0.0   0.0   0.0   0.0  ...   \n6115   0.0   0.0   0.0   0.0  0.140012  0.009022   0.0   0.0   0.0   0.0  ...   \n209    0.0   0.0   0.0   0.0  0.169209  0.032561   0.0   0.0   0.0   0.0  ...   \n3695   0.0   0.0   0.0   0.0  0.145175  0.010498   0.0   0.0   0.0   0.0  ...   \n6433   0.0   0.0   0.0   0.0  0.088842  0.018364   0.0   0.0   0.0   0.0  ...   \n...    ...   ...  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<th>2040</th>\n      <th>2041</th>\n      <th>2042</th>\n      <th>2043</th>\n      <th>2044</th>\n      <th>2045</th>\n      <th>2046</th>\n      <th>2047</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>920</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.111504</td>\n      <td>0.012895</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.053389</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6115</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.140012</td>\n      <td>0.009022</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.078405</td>\n   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<td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2723</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.070938</td>\n      <td>0.008820</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.049813</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>835</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.169200</td>\n      <td>0.055609</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.059894</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3175</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.144606</td>\n      <td>0.019673</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.060182</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1321</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.086784</td>\n      <td>0.004734</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.095758</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9742</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.223641</td>\n      <td>0.033072</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.079250</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2034 rows × 2048 columns</p>\n</div>"},"metadata":{}}],"execution_count":45},{"cell_type":"code","source":"# pipeline.fit(X_train, y_train)\n\n# 參數調整：使用GridSearchCV\nfrom sklearn.model_selection import GridSearchCV\n\n# 建立參數網格\nparam_grid = {'regressor__n_estimators': [100, 200, 300], 'regressor__max_depth': [5, 10, 15]}\n\n# 建立GridSearchCV實例\ngrid_search = GridSearchCV(pipeline, param_grid, cv=5)\n\n# 在訓練數據上適用GridSearchCV\ngrid_search.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-02T13:17:52.605477Z","iopub.execute_input":"2024-01-02T13:17:52.606164Z","iopub.status.idle":"2024-01-02T14:14:00.952567Z","shell.execute_reply.started":"2024-01-02T13:17:52.606131Z","shell.execute_reply":"2024-01-02T14:14:00.951043Z"},"trusted":true},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[49], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(pipeline, param_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# 在訓練數據上適用GridSearchCV\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_search.py:874\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    868\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[1;32m    869\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[1;32m    870\u001b[0m     )\n\u001b[1;32m    872\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[0;32m--> 874\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[1;32m    878\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1388\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1386\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[1;32m   1387\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1388\u001b[0m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_search.py:821\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    813\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    814\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    815\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m candidates,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    816\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m fits\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    817\u001b[0m             n_splits, n_candidates, n_candidates \u001b[38;5;241m*\u001b[39m n_splits\n\u001b[1;32m    818\u001b[0m         )\n\u001b[1;32m    819\u001b[0m     )\n\u001b[0;32m--> 821\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    822\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    823\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    825\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    826\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    827\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    828\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    829\u001b[0m \u001b[43m        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\u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:63\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     58\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[1;32m     59\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     60\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[1;32m     62\u001b[0m )\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1862\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1865\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1792\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1793\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1794\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:123\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[0;32m--> 123\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:686\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    684\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[1;32m    685\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 686\u001b[0m         \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    688\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    689\u001b[0m     \u001b[38;5;66;03m# Note fit time as time until error\u001b[39;00m\n\u001b[1;32m    690\u001b[0m     fit_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/pipeline.py:405\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    403\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    404\u001b[0m         fit_params_last_step \u001b[38;5;241m=\u001b[39m fit_params_steps[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]]\n\u001b[0;32m--> 405\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_final_estimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;66;03m# Parallel loop: we prefer the threading backend as the Cython code\u001b[39;00m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;66;03m# for fitting the trees is internally releasing the Python GIL\u001b[39;00m\n\u001b[1;32m    469\u001b[0m \u001b[38;5;66;03m# making threading more efficient than multiprocessing in\u001b[39;00m\n\u001b[1;32m    470\u001b[0m \u001b[38;5;66;03m# that case. However, for joblib 0.12+ we respect any\u001b[39;00m\n\u001b[1;32m    471\u001b[0m \u001b[38;5;66;03m# parallel_backend contexts set at a higher level,\u001b[39;00m\n\u001b[1;32m    472\u001b[0m \u001b[38;5;66;03m# since correctness does not rely on using threads.\u001b[39;00m\n\u001b[0;32m--> 473\u001b[0m trees \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    474\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    475\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    476\u001b[0m \u001b[43m    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    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;66;03m# Collect newly grown trees\u001b[39;00m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_\u001b[38;5;241m.\u001b[39mextend(trees)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:63\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     58\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[1;32m     59\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     60\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[1;32m     62\u001b[0m )\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1862\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1865\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1792\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1793\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1794\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:123\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[0;32m--> 123\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:184\u001b[0m, in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m class_weight \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbalanced_subsample\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    182\u001b[0m         curr_sample_weight \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m compute_sample_weight(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbalanced\u001b[39m\u001b[38;5;124m\"\u001b[39m, y, indices\u001b[38;5;241m=\u001b[39mindices)\n\u001b[0;32m--> 184\u001b[0m     \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurr_sample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    186\u001b[0m     tree\u001b[38;5;241m.\u001b[39mfit(X, y, sample_weight\u001b[38;5;241m=\u001b[39msample_weight, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/tree/_classes.py:1247\u001b[0m, in \u001b[0;36mDecisionTreeRegressor.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m   1218\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m   1219\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Build a decision tree regressor from the training set (X, y).\u001b[39;00m\n\u001b[1;32m   1220\u001b[0m \n\u001b[1;32m   1221\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1244\u001b[0m \u001b[38;5;124;03m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m   1245\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1247\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1249\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1250\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1252\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1253\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/tree/_classes.py:379\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    369\u001b[0m     builder \u001b[38;5;241m=\u001b[39m BestFirstTreeBuilder(\n\u001b[1;32m    370\u001b[0m         splitter,\n\u001b[1;32m    371\u001b[0m         min_samples_split,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_impurity_decrease,\n\u001b[1;32m    377\u001b[0m     )\n\u001b[0;32m--> 379\u001b[0m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m is_classifier(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    382\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_[\u001b[38;5;241m0\u001b[39m]\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":49},{"cell_type":"code","source":"y_pred = pipeline.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:57:29.613539Z","iopub.execute_input":"2024-01-02T12:57:29.614328Z","iopub.status.idle":"2024-01-02T12:57:29.684553Z","shell.execute_reply.started":"2024-01-02T12:57:29.614282Z","shell.execute_reply":"2024-01-02T12:57:29.683579Z"},"trusted":true},"outputs":[],"execution_count":36},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\nmae = mean_absolute_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)\n\nprint(f\"Mean Squared Error: {mae:.2f}\")\nprint(f\"R-squared: {r2:.2f}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-02T12:57:34.3039Z","iopub.execute_input":"2024-01-02T12:57:34.304821Z","iopub.status.idle":"2024-01-02T12:57:34.311968Z","shell.execute_reply.started":"2024-01-02T12:57:34.304783Z","shell.execute_reply":"2024-01-02T12:57:34.311217Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Mean Squared Error: 7.96\nR-squared: 0.71\n","output_type":"stream"}],"execution_count":37},{"cell_type":"code","source":"# 導入cross_val_score\nfrom sklearn.model_selection import cross_val_score\n\n# 執行交叉驗證\nscores = cross_val_score(pipeline, features, target, cv=5)\n\n# 打印交叉驗證結果\nprint(\"Cross-validation scores:\", scores)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T13:04:55.412256Z","iopub.execute_input":"2024-01-02T13:04:55.413187Z","iopub.status.idle":"2024-01-02T13:09:57.789835Z","shell.execute_reply.started":"2024-01-02T13:04:55.413154Z","shell.execute_reply":"2024-01-02T13:09:57.788881Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Cross-validation scores: [-16.05165693 -12.42258256  -5.9256268   -8.09907587 -13.83314297]\n","output_type":"stream"}],"execution_count":41},{"cell_type":"code","source":"test_df = pd.read_csv('/kaggle/input/spr-x-ray-age/sample_submission_age.csv')\n","metadata":{"execution":{"iopub.status.busy":"2024-01-02T08:59:09.179638Z","iopub.execute_input":"2024-01-02T08:59:09.180359Z","iopub.status.idle":"2024-01-02T08:59:09.193336Z","shell.execute_reply.started":"2024-01-02T08:59:09.180326Z","shell.execute_reply":"2024-01-02T08:59:09.192327Z"},"trusted":true},"outputs":[],"execution_count":38},{"cell_type":"code","source":"def testnumber_to_filename(number):\n    filename = f\"{number:06d}.png\"\n    path = '/kaggle/input/spr-x-ray-age/kaggle/kaggle/test/'\n    filename = path+filename\n    return filename","metadata":{"execution":{"iopub.status.busy":"2024-01-02T08:59:15.032274Z","iopub.execute_input":"2024-01-02T08:59:15.032677Z","iopub.status.idle":"2024-01-02T08:59:15.037279Z","shell.execute_reply.started":"2024-01-02T08:59:15.032642Z","shell.execute_reply":"2024-01-02T08:59:15.03632Z"},"trusted":true},"outputs":[],"execution_count":39},{"cell_type":"code","source":"test_df['filepath'] = test_df['imageId'].apply(testnumber_to_filename)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T08:59:22.599372Z","iopub.execute_input":"2024-01-02T08:59:22.600223Z","iopub.status.idle":"2024-01-02T08:59:22.615966Z","shell.execute_reply.started":"2024-01-02T08:59:22.600191Z","shell.execute_reply":"2024-01-02T08:59:22.615043Z"},"trusted":true},"outputs":[],"execution_count":40},{"cell_type":"code","source":"test_df.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T08:59:28.709471Z","iopub.execute_input":"2024-01-02T08:59:28.710221Z","iopub.status.idle":"2024-01-02T08:59:28.719951Z","shell.execute_reply.started":"2024-01-02T08:59:28.710188Z","shell.execute_reply":"2024-01-02T08:59:28.718795Z"},"trusted":true},"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"   imageId  age                                           filepath\n0        0    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n1        1    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n2        2    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n3        3    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n4        4    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n5        5    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n6        6    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n7        7    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n8        8    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...\n9        9    0  /kaggle/input/spr-x-ray-age/kaggle/kaggle/test...","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>imageId</th>\n      <th>age</th>\n      <th>filepath</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>0</td>\n      <td>/kaggle/input/spr-x-ray-age/kaggle/kaggle/test...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":41},{"cell_type":"code","source":"test_features_df = extract_image_features(test_df['imageId'].tolist(), test_df['filepath'].tolist())","metadata":{"execution":{"iopub.status.busy":"2024-01-02T08:59:46.19986Z","iopub.execute_input":"2024-01-02T08:59:46.20024Z","iopub.status.idle":"2024-01-02T09:22:13.886746Z","shell.execute_reply.started":"2024-01-02T08:59:46.200208Z","shell.execute_reply":"2024-01-02T09:22:13.885679Z"},"trusted":true},"outputs":[],"execution_count":42},{"cell_type":"code","source":"test_features_df.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:24:01.214263Z","iopub.execute_input":"2024-01-02T09:24:01.215041Z","iopub.status.idle":"2024-01-02T09:24:01.237086Z","shell.execute_reply.started":"2024-01-02T09:24:01.215002Z","shell.execute_reply":"2024-01-02T09:24:01.236112Z"},"trusted":true},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"   imageId                                           features\n0        0  [0.0, 0.0, 0.0, 0.0, 0.16226109862327576, 0.00...\n1        1  [0.0, 0.0, 0.0, 0.0, 0.2428458034992218, 0.033...\n2        2  [0.0, 0.0, 0.0, 0.0, 0.14115940034389496, 0.00...\n3        3  [0.0, 0.0, 0.0, 0.0, 0.2173481285572052, 0.003...\n4        4  [0.0, 0.0, 0.0, 0.0, 0.07116679102182388, 0.00...\n5        5  [0.0, 0.0, 0.0, 0.0, 0.308519184589386, 0.0170...\n6        6  [0.0, 0.0, 0.0, 0.0, 0.1850799024105072, 0.008...\n7        7  [0.0, 0.0, 0.0, 0.0, 0.11031543463468552, 0.01...\n8        8  [0.0, 0.0, 0.0, 0.0, 0.2042115330696106, 0.011...\n9        9  [0.0, 0.0, 0.0, 0.0, 0.1306246817111969, 0.008...","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>imageId</th>\n      <th>features</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.16226109862327576, 0.00...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.2428458034992218, 0.033...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.14115940034389496, 0.00...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.2173481285572052, 0.003...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.07116679102182388, 0.00...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.308519184589386, 0.0170...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.1850799024105072, 0.008...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.11031543463468552, 0.01...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.2042115330696106, 0.011...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>[0.0, 0.0, 0.0, 0.0, 0.1306246817111969, 0.008...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"test_df_final = pd.merge(test_features_df, test_df, on='imageId')","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:24:14.338442Z","iopub.execute_input":"2024-01-02T09:24:14.33883Z","iopub.status.idle":"2024-01-02T09:24:14.34948Z","shell.execute_reply.started":"2024-01-02T09:24:14.3388Z","shell.execute_reply":"2024-01-02T09:24:14.348552Z"},"trusted":true},"outputs":[],"execution_count":44},{"cell_type":"code","source":"split_columns = test_df_final['features'].apply(pd.Series)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:24:19.139208Z","iopub.execute_input":"2024-01-02T09:24:19.140089Z","iopub.status.idle":"2024-01-02T09:24:25.14297Z","shell.execute_reply.started":"2024-01-02T09:24:19.140057Z","shell.execute_reply":"2024-01-02T09:24:25.141882Z"},"trusted":true},"outputs":[],"execution_count":45},{"cell_type":"code","source":"test_df_final = pd.concat([test_df_final.drop('features', axis=1), split_columns], axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:24:35.258818Z","iopub.execute_input":"2024-01-02T09:24:35.259593Z","iopub.status.idle":"2024-01-02T09:24:35.305985Z","shell.execute_reply.started":"2024-01-02T09:24:35.259558Z","shell.execute_reply":"2024-01-02T09:24:35.305048Z"},"trusted":true},"outputs":[],"execution_count":46},{"cell_type":"code","source":"mod_test_df = test_df_final.drop(columns=['filepath','imageId'])","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:24:41.657005Z","iopub.execute_input":"2024-01-02T09:24:41.657359Z","iopub.status.idle":"2024-01-02T09:24:41.723304Z","shell.execute_reply.started":"2024-01-02T09:24:41.657329Z","shell.execute_reply":"2024-01-02T09:24:41.722429Z"},"trusted":true},"outputs":[],"execution_count":47},{"cell_type":"code","source":"features = mod_test_df.drop('age',axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:24:47.811338Z","iopub.execute_input":"2024-01-02T09:24:47.812197Z","iopub.status.idle":"2024-01-02T09:24:47.909282Z","shell.execute_reply.started":"2024-01-02T09:24:47.812162Z","shell.execute_reply":"2024-01-02T09:24:47.908362Z"},"trusted":true},"outputs":[],"execution_count":48},{"cell_type":"code","source":"features","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:25:00.618756Z","iopub.execute_input":"2024-01-02T09:25:00.61974Z","iopub.status.idle":"2024-01-02T09:25:00.663832Z","shell.execute_reply.started":"2024-01-02T09:25:00.619707Z","shell.execute_reply":"2024-01-02T09:25:00.662957Z"},"trusted":true},"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"       0     1     2     3         4         5     6     7     8     9     \\\n0       0.0   0.0   0.0   0.0  0.162261  0.006008   0.0   0.0   0.0   0.0   \n1       0.0   0.0   0.0   0.0  0.242846  0.033054   0.0   0.0   0.0   0.0   \n2       0.0   0.0   0.0   0.0  0.141159  0.006173   0.0   0.0   0.0   0.0   \n3       0.0   0.0   0.0   0.0  0.217348  0.003637   0.0   0.0   0.0   0.0   \n4       0.0   0.0   0.0   0.0  0.071167  0.005587   0.0   0.0   0.0   0.0   \n...     ...   ...   ...   ...       ...       ...   ...   ...   ...   ...   \n11742   0.0   0.0   0.0   0.0  0.111932  0.004449   0.0   0.0   0.0   0.0   \n11743   0.0   0.0   0.0   0.0  0.268542  0.015281   0.0   0.0   0.0   0.0   \n11744   0.0   0.0   0.0   0.0  0.065793  0.021936   0.0   0.0   0.0   0.0   \n11745   0.0   0.0   0.0   0.0  0.038677  0.005490   0.0   0.0   0.0   0.0   \n11746   0.0   0.0   0.0   0.0  0.174756  0.010462   0.0   0.0   0.0   0.0   \n\n       ...  2038  2039      2040  2041  2042  2043  2044  2045  2046  2047  \n0      ...   0.0   0.0  0.114224   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n1      ...   0.0   0.0  0.096549   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n2      ...   0.0   0.0  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class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>2038</th>\n      <th>2039</th>\n      <th>2040</th>\n      <th>2041</th>\n      <th>2042</th>\n      <th>2043</th>\n      <th>2044</th>\n      <th>2045</th>\n      <th>2046</th>\n      <th>2047</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.162261</td>\n      <td>0.006008</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.114224</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    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index=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-02T09:25:53.807693Z","iopub.execute_input":"2024-01-02T09:25:53.808053Z","iopub.status.idle":"2024-01-02T09:25:53.857634Z","shell.execute_reply.started":"2024-01-02T09:25:53.808024Z","shell.execute_reply":"2024-01-02T09:25:53.856919Z"},"trusted":true},"outputs":[],"execution_count":53},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}