Task3-cat-vs-dog.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3362,"databundleVersionId":31148,"sourceType":"competition"}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport cv2\nimport zipfile\nimport numpy as np\nimport pandas as pd\nfrom numpy import array\nimport tensorflow as tf\nfrom skimage.io import imread\nimport sklearn.metrics as metrics\nfrom skimage.transform import resize\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import LinearSVC, SVC\nimport sklearn.model_selection as model_selection\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.preprocessing.image import load_img\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-14T18:12:11.220382Z","iopub.execute_input":"2024-05-14T18:12:11.220981Z","iopub.status.idle":"2024-05-14T18:12:12.476385Z","shell.execute_reply.started":"2024-05-14T18:12:11.220951Z","shell.execute_reply":"2024-05-14T18:12:12.475297Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"zip_file = ['test1','train']\n\nfor zi in zip_file:\n    with zipfile.ZipFile('../input/dogs-vs-cats/{}.zip'.format(zi),'r') as z:\n        z.extractall('.')\n        print(\"{} unzipped\".format(zi))","metadata":{"execution":{"iopub.status.busy":"2024-05-14T11:48:36.109944Z","iopub.execute_input":"2024-05-14T11:48:36.110467Z","iopub.status.idle":"2024-05-14T11:48:47.127773Z","shell.execute_reply.started":"2024-05-14T11:48:36.110396Z","shell.execute_reply":"2024-05-14T11:48:47.125633Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"test1 unzipped\ntrain unzipped\n","output_type":"stream"}]},{"cell_type":"code","source":"train_dir = './train'\nfile_name = os.listdir(train_dir)\nlen(file_name)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T11:50:31.565918Z","iopub.execute_input":"2024-05-14T11:50:31.566303Z","iopub.status.idle":"2024-05-14T11:50:31.591555Z","shell.execute_reply.started":"2024-05-14T11:50:31.566274Z","shell.execute_reply":"2024-05-14T11:50:31.590260Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"25000"},"metadata":{}}]},{"cell_type":"code","source":"test_dir = './test1'\nfile_name = os.listdir(test_dir)\nlen(file_name)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T11:51:25.280268Z","iopub.execute_input":"2024-05-14T11:51:25.281579Z","iopub.status.idle":"2024-05-14T11:51:25.296942Z","shell.execute_reply.started":"2024-05-14T11:51:25.281537Z","shell.execute_reply":"2024-05-14T11:51:25.295969Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"12500"},"metadata":{}}]},{"cell_type":"code","source":"img_path = '../working/train'\nfile_names = os.listdir(img_path)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:11:25.212982Z","iopub.execute_input":"2024-05-14T12:11:25.213387Z","iopub.status.idle":"2024-05-14T12:11:25.237134Z","shell.execute_reply.started":"2024-05-14T12:11:25.213357Z","shell.execute_reply":"2024-05-14T12:11:25.236023Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"target_img = []\npath = []\ntrain_cat = []\ntrain_dog = []\n\n\nprint(type(path))","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:18:23.264971Z","iopub.execute_input":"2024-05-14T12:18:23.265346Z","iopub.status.idle":"2024-05-14T12:18:23.271100Z","shell.execute_reply.started":"2024-05-14T12:18:23.265318Z","shell.execute_reply":"2024-05-14T12:18:23.269843Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"<class 'list'>\n","output_type":"stream"}]},{"cell_type":"code","source":"for file in file_names:\n    target_img.append(file.split(\".\")[0])\n    path.append(os.path.join(img_path, file))\n    \n    if target_img[-1] == \"dog\":\n        train_dog.append(path[-1])\n    elif target_img[-1] == \"cat\": \n        train_cat.append(path[-1]) \n\ndf = pd.DataFrame()\ndf['image_path'] = path\ndf['target'] = target_img","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:18:25.649176Z","iopub.execute_input":"2024-05-14T12:18:25.649581Z","iopub.status.idle":"2024-05-14T12:18:25.758095Z","shell.execute_reply.started":"2024-05-14T12:18:25.649550Z","shell.execute_reply":"2024-05-14T12:18:25.756947Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:19:19.009151Z","iopub.execute_input":"2024-05-14T12:19:19.009566Z","iopub.status.idle":"2024-05-14T12:19:19.025315Z","shell.execute_reply.started":"2024-05-14T12:19:19.009533Z","shell.execute_reply":"2024-05-14T12:19:19.024371Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"                       image_path target\n0   ../working/train/dog.9588.jpg    dog\n1  ../working/train/cat.10406.jpg    cat\n2   ../working/train/dog.9235.jpg    dog\n3   ../working/train/dog.8004.jpg    dog\n4   ../working/train/dog.9677.jpg    dog","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>image_path</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>../working/train/dog.9588.jpg</td>\n      <td>dog</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>../working/train/cat.10406.jpg</td>\n      <td>cat</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>../working/train/dog.9235.jpg</td>\n      <td>dog</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>../working/train/dog.8004.jpg</td>\n      <td>dog</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>../working/train/dog.9677.jpg</td>\n      <td>dog</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train, test_val = train_test_split(df, test_size=0.2, stratify=df['target'], random_state=20)\n\ntest, val = train_test_split(test_val, test_size=0.5,  stratify=test_val['target'], random_state=20)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:25:35.864822Z","iopub.execute_input":"2024-05-14T12:25:35.865350Z","iopub.status.idle":"2024-05-14T12:25:35.951340Z","shell.execute_reply.started":"2024-05-14T12:25:35.865309Z","shell.execute_reply":"2024-05-14T12:25:35.949908Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"t_dist = test['target'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:27:24.611619Z","iopub.execute_input":"2024-05-14T12:27:24.612031Z","iopub.status.idle":"2024-05-14T12:27:24.624600Z","shell.execute_reply.started":"2024-05-14T12:27:24.611998Z","shell.execute_reply":"2024-05-14T12:27:24.623426Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"test_img = [test_dir+i for i in os.listdir(test_dir)]","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:29:39.584233Z","iopub.execute_input":"2024-05-14T12:29:39.584685Z","iopub.status.idle":"2024-05-14T12:29:39.601403Z","shell.execute_reply.started":"2024-05-14T12:29:39.584652Z","shell.execute_reply":"2024-05-14T12:29:39.600586Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"def preprocess_img(image_path):\n    img = imread(image_path)\n    img = resize(img, (100,100))\n    img_flat = img.flatten()\n    return img_flat","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:32:05.770495Z","iopub.execute_input":"2024-05-14T12:32:05.770915Z","iopub.status.idle":"2024-05-14T12:32:05.776905Z","shell.execute_reply.started":"2024-05-14T12:32:05.770882Z","shell.execute_reply":"2024-05-14T12:32:05.775687Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"X = []\ny = df['target']\n\nfor image_path in df['image_path']:\n    X.append(preprocess_img(image_path))\nX = np.array(X)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:33:32.157354Z","iopub.execute_input":"2024-05-14T12:33:32.157836Z","iopub.status.idle":"2024-05-14T12:40:18.793627Z","shell.execute_reply.started":"2024-05-14T12:33:32.157801Z","shell.execute_reply":"2024-05-14T12:40:18.792490Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"pca = PCA(n_components=100)\nX_pca = pca.fit_transform(X)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:42:44.483094Z","iopub.execute_input":"2024-05-14T12:42:44.483503Z","iopub.status.idle":"2024-05-14T12:44:05.323433Z","shell.execute_reply.started":"2024-05-14T12:42:44.483470Z","shell.execute_reply":"2024-05-14T12:44:05.322280Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n\nmodel = svm.SVC(kernel='rbf')\nmodel.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:46:01.768781Z","iopub.execute_input":"2024-05-14T12:46:01.769378Z","iopub.status.idle":"2024-05-14T12:46:38.565432Z","shell.execute_reply.started":"2024-05-14T12:46:01.769305Z","shell.execute_reply":"2024-05-14T12:46:38.564328Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"SVC()","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = model.predict(X_test)\n\naccuracy = metrics.accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T12:46:40.283621Z","iopub.execute_input":"2024-05-14T12:46:40.284058Z","iopub.status.idle":"2024-05-14T12:46:50.041534Z","shell.execute_reply.started":"2024-05-14T12:46:40.284025Z","shell.execute_reply":"2024-05-14T12:46:50.040268Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"Accuracy: 0.6772\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}