pet-pawpularity.ipynb

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      "/kaggle/input/petfinder-pawpularity-score/sample_submission.csv\n",
      "/kaggle/input/petfinder-pawpularity-score/train.csv\n",
      "/kaggle/input/petfinder-pawpularity-score/test.csv\n",
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      "/kaggle/input/petfinder-pawpularity-score/train/af4d97c08eb06c1664fd42dfeb0cc34e.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/68318a175159a206f71964e8eb465675.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/24b0c21868c6ebfc165b0950a166c883.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/0b3a1a15404cf275d1dde53b825cd73d.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/6c7d9ef4574b173b03abae47d977ca2a.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/3e7e3dd0d85827c5b94afd29da9e12f6.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/bca56164b077ffae5dd53dedd68959b5.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/f71196f2494c279ea4b9567acf534237.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/ede2fcf304daaacc7d11eb6e2d2849b9.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/fcb176422cd0434fc3ca292355804427.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/e21de0a84d54d9faea912f81e9b131ed.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/1fa822769e0e5e7b5bfc16fe0619af73.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/8731c67467934e0dccb391205fe5a92b.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/a3becd05cdc8eb1f6449e1ec8b2b2262.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/46c19e01c12f643b6cb97b0c3650404e.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/1fecdef9d99947d7ce6e6c73d4918397.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/b7c5a4cd4211579f5e6bca8b51fb5fd2.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/86a71a412f662212fe8dcd40fdaee8e6.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/e8e2b842e5e3fe95c88345dbf2ac99a7.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/fe8a319a5f3cdf1a13344acd8a78318d.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/567e08455f77bea4f2b61ac740fb3aba.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/53b7c4186b9d57941a2bd900e1b061c4.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/95b47b2e43f01879ba9acf662173358c.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/d22da16a635031c1ae7554c839f26c9b.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/54ef90ca5972af9ebdb54fb5fcd67552.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/c86fe58fcfb959cda493dc6578c1868c.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/44293284a7b4ac2a2e4ebfa57db4c48c.jpg\n",
      "/kaggle/input/petfinder-pawpularity-score/train/9a0e3f4811b41853ff0f9024a4081cba.jpg\n"
     ]
    }
   ],
   "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",
    "\n",
    "import numpy as np # linear algebra\n",
    "import 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",
    "\n",
    "import 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37a40e25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-30T08:14:12.311631Z",
     "iopub.status.busy": "2021-12-30T08:14:12.310793Z",
     "iopub.status.idle": "2021-12-30T08:14:19.069282Z",
     "shell.execute_reply": "2021-12-30T08:14:19.070025Z",
     "shell.execute_reply.started": "2021-12-30T07:25:15.072737Z"
    },
    "papermill": {
     "duration": 6.799442,
     "end_time": "2021-12-30T08:14:19.070246",
     "exception": false,
     "start_time": "2021-12-30T08:14:12.270804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pylab as plt\n",
    "import os\n",
    "import PIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e239ae3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-30T08:14:19.157853Z",
     "iopub.status.busy": "2021-12-30T08:14:19.157028Z",
     "iopub.status.idle": "2021-12-30T08:14:19.562989Z",
     "shell.execute_reply": "2021-12-30T08:14:19.562297Z",
     "shell.execute_reply.started": "2021-12-30T07:59:22.826002Z"
    },
    "papermill": {
     "duration": 0.457972,
     "end_time": "2021-12-30T08:14:19.563140",
     "exception": false,
     "start_time": "2021-12-30T08:14:19.105168",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:43: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "img_height = 150\n",
    "img_width = 150\n",
    "img_channels = 3\n",
    "\n",
    "def readandencode(filename, reshape_dims):\n",
    "    img = tf.io.read_file(filename)\n",
    "    img = tf.image.decode_jpeg(img, channels = img_channels)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    return tf.image.resize(img, reshape_dims)\n",
    "\n",
    "def show_images(filename):\n",
    "    img = readandencode(filename, [img_height, img_width])\n",
    "    plt.imshow(img.numpy())\n",
    "    plt.axis(\"off\")\n",
    "    \n",
    "def decodecsv(csv_row):\n",
    "    record = ['Id', 'Pawpularity']\n",
    "    filename, pawpularity = tf.io.decode_csv(csv_row, record)\n",
    "    pawpularity = tf.convert_to_tensor(np.float(pawpularity), dtype = tf.float32)\n",
    "    img = readandencode(filename, [img_height, img_width])\n",
    "    return img, pawpularity\n",
    "\n",
    "data_path = '../input/petfinder-pawpularity-score/'\n",
    "data = pd.read_csv('../input/petfinder-pawpularity-score/train.csv')\n",
    "ssplit = StratifiedShuffleSplit(n_splits = 1, test_size = 0.2)\n",
    "\n",
    "for train_index, test_index in ssplit.split(data, data['Pawpularity']):\n",
    "    training_set = data.iloc[test_index]\n",
    "    eval_set = data.iloc[test_index]\n",
    "\n",
    "training_set['Pawpularity'].hist(label = 'training_set')\n",
    "eval_set['Pawpularity'].hist(label = 'Eval_set')\n",
    "\n",
    "plt.title(\"Pawpularity score distribbution in training and evaluation\")\n",
    "plt.xlabel(\"Pawpularity_score\")\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "training_set['Id'] = training_set['Id'].apply(lambda x: '../input/petfinder-pawpularity-score/train/'+x+'.jpg')\n",
    "training_set[['Id','Pawpularity']].to_csv('/kaggle/working/training_set.csv',header=False, index = False)\n",
    "eval_set['Id'] = eval_set['Id'].apply(lambda x: '../input/petfinder-pawpularity-score/train/'+x+'.jpg')\n",
    "eval_set[['Id','Pawpularity']].to_csv('/kaggle/working/eval_set.csv',header=False, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02db64be",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "User settings:\n",
      "\n",
      "   KMP_AFFINITY=granularity=fine,verbose,compact,1,0\n",
      "   KMP_BLOCKTIME=0\n",
      "   KMP_DUPLICATE_LIB_OK=True\n",
      "   KMP_INIT_AT_FORK=FALSE\n",
      "   KMP_SETTINGS=1\n",
      "   KMP_WARNINGS=0\n",
      "\n",
      "Effective settings:\n",
      "\n",
      "   KMP_ABORT_DELAY=0\n",
      "   KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n",
      "   KMP_ALIGN_ALLOC=64\n",
      "   KMP_ALL_THREADPRIVATE=128\n",
      "   KMP_ATOMIC_MODE=2\n",
      "   KMP_BLOCKTIME=0\n",
      "   KMP_CPUINFO_FILE: value is not defined\n",
      "   KMP_DETERMINISTIC_REDUCTION=false\n",
      "   KMP_DEVICE_THREAD_LIMIT=2147483647\n",
      "   KMP_DISP_NUM_BUFFERS=7\n",
      "   KMP_DUPLICATE_LIB_OK=true\n",
      "   KMP_ENABLE_TASK_THROTTLING=true\n",
      "   KMP_FORCE_REDUCTION: value is not defined\n",
      "   KMP_FOREIGN_THREADS_THREADPRIVATE=true\n",
      "   KMP_FORKJOIN_BARRIER='2,2'\n",
      "   KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n",
      "   KMP_GTID_MODE=3\n",
      "   KMP_HANDLE_SIGNALS=false\n",
      "   KMP_HOT_TEAMS_MAX_LEVEL=1\n",
      "   KMP_HOT_TEAMS_MODE=0\n",
      "   KMP_INIT_AT_FORK=true\n",
      "   KMP_LIBRARY=throughput\n",
      "   KMP_LOCK_KIND=queuing\n",
      "   KMP_MALLOC_POOL_INCR=1M\n",
      "   KMP_NUM_LOCKS_IN_BLOCK=1\n",
      "   KMP_PLAIN_BARRIER='2,2'\n",
      "   KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n",
      "   KMP_REDUCTION_BARRIER='1,1'\n",
      "   KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n",
      "   KMP_SCHEDULE='static,balanced;guided,iterative'\n",
      "   KMP_SETTINGS=true\n",
      "   KMP_SPIN_BACKOFF_PARAMS='4096,100'\n",
      "   KMP_STACKOFFSET=64\n",
      "   KMP_STACKPAD=0\n",
      "   KMP_STACKSIZE=8M\n",
      "   KMP_STORAGE_MAP=false\n",
      "   KMP_TASKING=2\n",
      "   KMP_TASKLOOP_MIN_TASKS=0\n",
      "   KMP_TASK_STEALING_CONSTRAINT=1\n",
      "   KMP_TEAMS_THREAD_LIMIT=4\n",
      "   KMP_TOPOLOGY_METHOD=all\n",
      "   KMP_USE_YIELD=1\n",
      "   KMP_VERSION=false\n",
      "   KMP_WARNINGS=false\n",
      "   OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n",
      "   OMP_ALLOCATOR=omp_default_mem_alloc\n",
      "   OMP_CANCELLATION=false\n",
      "   OMP_DEFAULT_DEVICE=0\n",
      "   OMP_DISPLAY_AFFINITY=false\n",
      "   OMP_DISPLAY_ENV=false\n",
      "   OMP_DYNAMIC=false\n",
      "   OMP_MAX_ACTIVE_LEVELS=1\n",
      "   OMP_MAX_TASK_PRIORITY=0\n",
      "   OMP_NESTED: deprecated; max-active-levels-var=1\n",
      "   OMP_NUM_THREADS: value is not defined\n",
      "   OMP_PLACES: value is not defined\n",
      "   OMP_PROC_BIND='intel'\n",
      "   OMP_SCHEDULE='static'\n",
      "   OMP_STACKSIZE=8M\n",
      "   OMP_TARGET_OFFLOAD=DEFAULT\n",
      "   OMP_THREAD_LIMIT=2147483647\n",
      "   OMP_WAIT_POLICY=PASSIVE\n",
      "   KMP_AFFINITY='verbose,warnings,respect,granularity=fine,compact,1,0'\n",
      "\n",
      "2021-12-30 08:14:19.652551: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "\n",
    "train_dataset = tf.data.TextLineDataset(\n",
    "    './training_set.csv'\n",
    ").map(decodecsv).batch(batch_size)\n",
    "\n",
    "eval_dataset = tf.data.TextLineDataset(\n",
    "    './eval_set.csv'\n",
    ").map(decodecsv).batch(batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "954f727b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-30T08:14:19.970889Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(img_height,img_width,img_channels)),\n",
    "    tf.keras.layers.Dense(units=128, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=1,activation=None)\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss=tf.keras.losses.MeanSquaredError(),\n",
    "    metrics=[tf.keras.metrics.RootMeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2649e6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-30T08:14:21.364018Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-30 08:14:21.430451: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "62/62 [==============================] - 20s 317ms/step - loss: 1163.9691 - root_mean_squared_error: 34.1170 - val_loss: 471.0649 - val_root_mean_squared_error: 21.7040\n",
      "Epoch 2/10\n",
      "62/62 [==============================] - 14s 228ms/step - loss: 488.1487 - root_mean_squared_error: 22.0941 - val_loss: 457.7978 - val_root_mean_squared_error: 21.3962\n",
      "Epoch 3/10\n",
      "62/62 [==============================] - 14s 223ms/step - loss: 481.6016 - root_mean_squared_error: 21.9454 - val_loss: 469.3423 - val_root_mean_squared_error: 21.6643\n",
      "Epoch 4/10\n",
      "62/62 [==============================] - 14s 233ms/step - loss: 466.3906 - root_mean_squared_error: 21.5961 - val_loss: 449.8257 - val_root_mean_squared_error: 21.2091\n",
      "Epoch 5/10\n",
      "62/62 [==============================] - 14s 227ms/step - loss: 469.3947 - root_mean_squared_error: 21.6655 - val_loss: 458.9624 - val_root_mean_squared_error: 21.4234\n",
      "Epoch 6/10\n",
      "62/62 [==============================] - 14s 230ms/step - loss: 453.7443 - root_mean_squared_error: 21.3013 - val_loss: 460.5105 - val_root_mean_squared_error: 21.4595\n",
      "Epoch 7/10\n",
      "62/62 [==============================] - 14s 232ms/step - loss: 452.4969 - root_mean_squared_error: 21.2720 - val_loss: 453.0938 - val_root_mean_squared_error: 21.2860\n",
      "Epoch 8/10\n",
      "62/62 [==============================] - 14s 225ms/step - loss: 447.9465 - root_mean_squared_error: 21.1647 - val_loss: 440.2368 - val_root_mean_squared_error: 20.9818\n",
      "Epoch 9/10\n",
      "62/62 [==============================] - 14s 226ms/step - loss: 441.0211 - root_mean_squared_error: 21.0005 - val_loss: 435.0806 - val_root_mean_squared_error: 20.8586\n",
      "Epoch 10/10\n",
      "62/62 [==============================] - 14s 232ms/step - loss: 434.4902 - root_mean_squared_error: 20.8444 - val_loss: 427.9568 - val_root_mean_squared_error: 20.6871\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_dataset, validation_data=eval_dataset, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c2b9892",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-30T08:17:15.324119Z",
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     "shell.execute_reply.started": "2021-12-30T08:06:02.800726Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv('../input/petfinder-pawpularity-score/sample_submission.csv')\n",
    "sample_submission['Id'] = sample_submission['Id'].apply(lambda x : '../input/petfinder-pawpularity-score/test/'+x+'.jpg')\n",
    "sample_submission.to_csv('/kaggle/working/sample_submission.csv',index=False, header=False)\n",
    "sample_submission = tf.data.TextLineDataset(\n",
    "    './sample_submission.csv'\n",
    ").map(decodecsv).batch(batch_size)\n",
    "\n",
    "sample_predictions = model.predict(sample_submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67b87a66",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-30T08:17:16.010115Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission_output = pd.concat(\n",
    "    [pd.read_csv('../input/petfinder-pawpularity-score/sample_submission.csv').drop('Pawpularity',axis=1),\n",
    "    pd.DataFrame(sample_predictions)],\n",
    "    axis=1\n",
    ")\n",
    "submission_output.columns = [['Id','Pawpularity']]\n",
    "\n",
    "submission_output.to_csv('submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93b0e6ce",
   "metadata": {
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