12_Loading&Preprocessing_with_TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43ef49fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Most Important\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "## less Important\n",
    "from functools import partial\n",
    "import os\n",
    "from scipy import stats\n",
    "import missingno as msno\n",
    "import joblib\n",
    "import tarfile\n",
    "import shutil\n",
    "import urllib\n",
    "\n",
    "## Sklearn\n",
    "from sklearn import datasets\n",
    "## Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "## Metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "## tensorflow & Keras\n",
    "import tensorflow as tf    ## i will use tf for every thing and for keras using tf.keras\n",
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22a1d716",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0a031d",
   "metadata": {},
   "source": [
    "## Data API in TensorFlow\n",
    "`Understanding Data API`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e97ae9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "## create a Dataset from a collection\n",
    "X_new = tf.range(10)\n",
    "dummy_1 = tf.data.Dataset.from_tensor_slices(X_new)  ## only returns transformation\n",
    "\n",
    "## in order to see these values you should iterate over them (Action), or casting them to list\n",
    "for value in dummy_1:\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8136b7c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int32)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)\n",
      "tf.Tensor([8 9], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "## you can repeat then batch for specific number\n",
    "\n",
    "## will repeat 3 times, then take each 7 as a batch\n",
    "## you can drop_remainder=True  ==> to delete uncompleted batches\n",
    "dummy_2 = dummy_1.repeat(3).batch(7)\n",
    "for value in dummy_2:\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2d78b398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([6 5 8 3 0 7 9], shape=(7,), dtype=int32)\n",
      "tf.Tensor([4 2 1 5 1 9 6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([3 8 0 4 7 2 2], shape=(7,), dtype=int32)\n",
      "tf.Tensor([5 8 7 3 9 6 1], shape=(7,), dtype=int32)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4, 7, 0, 6, 8],\n",
       "       [5, 1, 9, 7, 2, 8, 1],\n",
       "       [5, 3, 9, 0, 6, 4, 0],\n",
       "       [7, 4, 3, 9, 5, 1, 6]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## you can repeat then batch for specific number\n",
    "\n",
    "## will repeat 3 times, then take each 7 as a batch\n",
    "## you can drop_remainder=True  ==> to delete uncompleted batches\n",
    "## you can also shuffle\n",
    "dummy_3 = dummy_1.shuffle(buffer_size=100, seed=123).repeat(3).batch(7, drop_remainder=True)\n",
    "for value in dummy_3:\n",
    "    print(value)\n",
    "\n",
    "print()\n",
    "\n",
    "## other Method\n",
    "outs_3 = tf.squeeze(list(dummy_3)).numpy()\n",
    "outs_3  \n",
    "## without looping, diffrent outs because of shuffle,\n",
    "## to make this not happende set ==>  , reshuffle_each_iteration=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94efc55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([ 0  2  4  6  8 10 12], shape=(7,), dtype=int32)\n",
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int32)\n",
      "tf.Tensor([16 18], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "## you can map, filter, reduce or many other transformations\n",
    "dummy_4 = dummy_2.map(lambda x: x*2)\n",
    "for value in dummy_4:\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa442b7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\moham\\AppData\\Local\\Temp/ipykernel_14988/1394358411.py:2: unbatch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.unbatch()`.\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "## you can unbatch these batches\n",
    "dummy_5 = dummy_2.apply(tf.data.experimental.unbatch())\n",
    "for value in dummy_5:\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2dd10fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 8, 9])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## you can make filter\n",
    "dummy_6 = dummy_1.filter(lambda x: x>5)\n",
    "outs_6 = tf.squeeze(list(dummy_6)).numpy()\n",
    "outs_6  ## final output without looping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad2ac1d",
   "metadata": {},
   "source": [
    "## Using California Housing Dataset \n",
    "#### `understanding Interleave, shuffle, ... and other`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f23b7e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## load housing dataset\n",
    "housing_dataset = datasets.fetch_california_housing()\n",
    "\n",
    "df_housing = pd.DataFrame(np.c_[housing_dataset['data'], housing_dataset['target']], \n",
    "                         columns=housing_dataset['feature_names']+['target'])\n",
    "\n",
    "X = df_housing.iloc[:, :-1]\n",
    "y = df_housing.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ebb9c308",
   "metadata": {},
   "outputs": [],
   "source": [
    "## split the data\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(X, y, train_size=0.20, \n",
    "                                                              random_state=42)\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, test_size=0.2,\n",
    "                                                             random_state=42)\n",
    "\n",
    "## to get only the mean and std of X_train for each feature\n",
    "X_train_mean = list(X_train.mean())\n",
    "X_train_std = list(X_train.std())\n",
    "\n",
    "## or from fitting without transformation by standardscaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_mean = scaler.mean_    ## => equal to == X_train_mean\n",
    "X_std = scaler.scale_     ## equal to == X_train_std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8c5e29",
   "metadata": {},
   "source": [
    "### Split the Dataset into multiple csv Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2352bc65",
   "metadata": {},
   "outputs": [],
   "source": [
    "## craete a function to split data for each set to multiple csv files\n",
    "def split_to_mulitple_csv(dataset, dataset_name='train', split_parts=10):\n",
    "    ''' this function tries to split the dataset with split_parts required to multiple csv files\n",
    "    Args:\n",
    "    ****\n",
    "    (dataset : 2d array) ==> required dataset to be split\n",
    "    (dataset_name : string) ==> name to be used in creating directory\n",
    "    (split_parts : int) ==> required number of splits\n",
    "    '''\n",
    "    housing_dir_path = os.path.join('dataset_for_split', 'split_housing')\n",
    "    if not os.path.isdir(housing_dir_path):\n",
    "        os.makedirs(housing_dir_path)\n",
    "    each_dir_path = os.path.join(housing_dir_path, dataset_name)\n",
    "    if not os.path.isdir(each_dir_path):\n",
    "        os.makedirs(each_dir_path)\n",
    "        \n",
    "    file_paths = []\n",
    "    \n",
    "    split_data = np.array_split(dataset, split_parts)\n",
    "    \n",
    "    for chunk in range(len(split_data)):\n",
    "        path_each = os.path.join(each_dir_path, f'{dataset_name}_{chunk}.csv')\n",
    "        file_paths.append(path_each)   ## append to filepaths\n",
    "        split_data[chunk].to_csv(path_each, index=False)  ## save to csv\n",
    "        \n",
    "    return file_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6402143",
   "metadata": {},
   "outputs": [],
   "source": [
    "## before calling the function ==> make features and target in the same dataframe\n",
    "col_names = housing_dataset['feature_names'] + ['target']\n",
    "train_data = pd.DataFrame(np.c_[X_train, y_train], columns=col_names).reset_index(drop=True)\n",
    "valid_data = pd.DataFrame(np.c_[X_valid, y_valid], columns=col_names).reset_index(drop=True)\n",
    "test_data = pd.DataFrame(np.c_[X_test, y_test], columns=col_names).reset_index(drop=True)\n",
    "\n",
    "## call the function for splitting ==> train, valid, test\n",
    "train_filePaths = split_to_mulitple_csv(train_data, dataset_name='train', split_parts=20)\n",
    "valid_filePaths = split_to_mulitple_csv(valid_data, dataset_name='valid', split_parts=10)\n",
    "test_filePaths = split_to_mulitple_csv(test_data, dataset_name='test', split_parts=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "170c5179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset_for_split\\\\split_housing\\\\train\\\\train_0.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_1.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_2.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_3.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_4.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_5.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_6.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_7.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_8.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_9.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_10.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_11.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_12.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_13.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_14.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_15.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_16.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_17.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_18.csv',\n",
       " 'dataset_for_split\\\\split_housing\\\\train\\\\train_19.csv']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_filePaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8b183e2b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5156</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.139303</td>\n",
       "      <td>1.323383</td>\n",
       "      <td>543.0</td>\n",
       "      <td>2.701493</td>\n",
       "      <td>39.79</td>\n",
       "      <td>-120.53</td>\n",
       "      <td>0.719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2604</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.551020</td>\n",
       "      <td>1.081633</td>\n",
       "      <td>531.0</td>\n",
       "      <td>3.612245</td>\n",
       "      <td>37.61</td>\n",
       "      <td>-120.76</td>\n",
       "      <td>0.879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.6094</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.290141</td>\n",
       "      <td>0.977465</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>3.273239</td>\n",
       "      <td>33.85</td>\n",
       "      <td>-118.04</td>\n",
       "      <td>2.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.2413</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.967700</td>\n",
       "      <td>1.177003</td>\n",
       "      <td>2812.0</td>\n",
       "      <td>3.633075</td>\n",
       "      <td>32.69</td>\n",
       "      <td>-117.10</td>\n",
       "      <td>0.831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.3506</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.322957</td>\n",
       "      <td>0.974060</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>2.382620</td>\n",
       "      <td>37.52</td>\n",
       "      <td>-122.29</td>\n",
       "      <td>3.881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  2.5156      18.0  6.139303   1.323383       543.0  2.701493     39.79   \n",
       "1  3.2604      30.0  5.551020   1.081633       531.0  3.612245     37.61   \n",
       "2  5.6094      24.0  6.290141   0.977465      1162.0  3.273239     33.85   \n",
       "3  1.2413      11.0  3.967700   1.177003      2812.0  3.633075     32.69   \n",
       "4  5.3506      33.0  5.322957   0.974060      1837.0  2.382620     37.52   \n",
       "\n",
       "   Longitude  target  \n",
       "0    -120.53   0.719  \n",
       "1    -120.76   0.879  \n",
       "2    -118.04   2.792  \n",
       "3    -117.10   0.831  \n",
       "4    -122.29   3.881  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(train_filePaths[0]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8328a09b",
   "metadata": {},
   "source": [
    "### Building an Input Pipeline\n",
    "#### `interleave  between train_files`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "994af89a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_5.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_16.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_11.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_1.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_18.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_13.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_9.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_6.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_10.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_17.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_0.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_15.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_2.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_12.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_7.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_19.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_4.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_8.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_14.csv'>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'dataset_for_split\\\\split_housing\\\\train\\\\train_3.csv'>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset_filePaths = tf.data.Dataset.list_files(train_filePaths, shuffle=True, seed=42)\n",
    "[*train_dataset_filePaths]  ## shuffle these 20 csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9c566af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'3.8029,35.0,5.641379310344828,1.0528735632183908,1110.0,2.5517241379310347,34.11,-118.02,4.148'\n",
      "b'2.1793,39.0,3.932098765432099,0.9845679012345679,1362.0,4.203703703703703,33.99,-118.25,1.079'\n",
      "b'4.875,40.0,5.181818181818182,1.0101010101010102,399.0,4.03030303030303,33.91,-118.1,1.676'\n",
      "b'1.9014,24.0,5.024326672458732,1.0912250217202433,1990.0,1.7289313640312771,33.7,-117.19,0.835'\n",
      "b'0.9318,35.0,2.803339517625232,1.1001855287569573,914.0,1.6957328385899815,33.78,-118.19,1.875'\n"
     ]
    }
   ],
   "source": [
    "## you can call interleave method to read from five files at a time and interleave their lines\n",
    "## skipping the first line as it is a header\n",
    "## cycle_length to select the number to read from files at a time\n",
    "## num_parallel_calls=True ==> to use parallelism instead of sequential (to read files in parallel)\n",
    "\n",
    "n_readers = 10  ## interleave will take 10 files at time\n",
    "train_dataset = train_dataset_filePaths.interleave(\n",
    "                            lambda filepath: tf.data.TextLineDataset(filepath).skip(1), \n",
    "                            cycle_length=n_readers, num_parallel_calls=n_readers)\n",
    "\n",
    "for line in train_dataset.take(5):\n",
    "    print(line.numpy())   ## need to parsed as it is byte string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb5d7ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "old shape => (3302, 9)\n",
      "new shape is => 3302\n"
     ]
    }
   ],
   "source": [
    "## check shape of train_dataset\n",
    "\n",
    "# old is\n",
    "print('old shape =>', train_data.shape)\n",
    "\n",
    "# new is\n",
    "print('new shape is =>', len(tf.squeeze(list(train_dataset))))  ## the same ok"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddbd8445",
   "metadata": {},
   "source": [
    "#### `Preprocessing the data to solve byte string problem`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "41b7a24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(line):\n",
    "    ''' this function tries to preprocess and parse the output from byte string to flaot\n",
    "    '''\n",
    "    n_inputs = len(X_mean)  ## 8 fetures\n",
    "    defs = [0.] * n_inputs + [tf.constant([], dtype=tf.float32)]  ## defalut datatypes\n",
    "    fields = tf.io.decode_csv(line, record_defaults=defs) ## Convert CSV records to tensors \n",
    "\n",
    "    features = tf.stack(fields[:-1])\n",
    "    label = tf.stack(fields[-1:])\n",
    "    \n",
    "    ## Normalize the data\n",
    "    features_norm = (features - X_train_mean) / X_train_std\n",
    "    \n",
    "    return features_norm, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ec64666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float32, numpy=\n",
       " array([-0.03542254,  0.51437455,  0.0494825 , -0.090136  , -0.26611662,\n",
       "        -0.05940131, -0.7260594 ,  0.78286725], dtype=float32)>,\n",
       " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([4.148], dtype=float32)>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_test = b'3.8029,35.0,5.641379310344828,1.0528735632183908,1110.0,2.5517241379310347,34.11,-118.02,4.148'\n",
    "preprocess(line_test)    ## seems good => that what i need"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f29cda",
   "metadata": {},
   "source": [
    "#### `Putting everything together`\n",
    "\n",
    "1. read from list files\n",
    "2. interleave between these list files\n",
    "3. preprocess the output with the previous function\n",
    "4. shuffle, repeat, batch, and prefetch data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "82ee5e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def putting_all_step_together(file_paths, n_readers=5, n_shuffle=5000, n_batch_size=32):\n",
    "    ''' this function tries to put all steps of preprocessing the data which splitted\n",
    "        to multiple csv files specially for big dataset which does not fit in memory\n",
    "        reading from list files and interleave between them, preprocess the output as the \n",
    "        ouput is formatted as byte string, then shuffle, repeat, batch and prefetch\n",
    "    Args:\n",
    "    *****\n",
    "        (filepath: string) ==> list of files containing the file paths of required dataset\n",
    "        (n_readers : int) ==> number of files, tensorflow interleave them at a time\n",
    "        (n_shuffle : int) ==> buffer size of shuffle\n",
    "        (n_batch_size : int) ==> number of batch_size\n",
    "    Returns:\n",
    "    ********\n",
    "        the final dataset\n",
    "    '''\n",
    "    data_filePaths = tf.data.Dataset.list_files(file_paths)  # reading from list files\n",
    "    dataset = data_filePaths.interleave(     ## interleave, skip the header, using n_readers\n",
    "                lambda filepath: tf.data.TextLineDataset(filepath).skip(1), \n",
    "                cycle_length=n_readers, num_parallel_calls=n_readers) \n",
    "    \n",
    "    dataset = dataset.map(preprocess, num_parallel_calls=n_readers)  ## mapping to the above function\n",
    "    dataset = dataset.shuffle(buffer_size=n_shuffle, seed=123).repeat(1)\n",
    "    dataset = dataset.batch(n_batch_size).prefetch(1)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54f5f9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## final preprocessing on dataset\n",
    "train_data_final = putting_all_step_together(train_filePaths)\n",
    "valid_data_final = putting_all_step_together(valid_filePaths)\n",
    "test_data_final = putting_all_step_together(test_filePaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b6c49e12",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<tf.Tensor: shape=(32, 8), dtype=float32, numpy=\n",
      "array([[ 7.71730185e-01,  8.31815004e-01,  5.37235618e-01,\n",
      "        -5.16370423e-02, -4.56512392e-01, -6.21708259e-02,\n",
      "         1.05110610e+00, -1.35011792e+00],\n",
      "       [ 2.12278366e+00, -1.94578886e+00, -1.11743204e-01,\n",
      "        -1.80651590e-01, -7.45305955e-01, -5.60712181e-02,\n",
      "        -9.35411751e-01,  8.77332509e-01],\n",
      "       [ 1.17703581e+00,  4.35014457e-01,  1.19843662e-01,\n",
      "        -3.66109997e-01, -4.84511763e-01,  4.77443114e-02,\n",
      "        -8.51670802e-01,  8.52474809e-01],\n",
      "       [ 5.62710941e-01, -9.93467510e-01,  1.71364024e-02,\n",
      "        -3.88409317e-01, -6.52508020e-01, -3.07168253e-03,\n",
      "        -7.26059377e-01,  1.09610140e+00],\n",
      "       [ 2.48003170e-01, -7.55387187e-01, -8.90222937e-02,\n",
      "        -8.50467235e-02, -9.33204815e-02, -8.66049826e-02,\n",
      "        -7.30712652e-01,  9.56886232e-01],\n",
      "       [-4.36117113e-01, -4.37946737e-01, -4.38922763e-01,\n",
      "        -6.58479556e-02,  2.11623025e+00, -4.38315533e-02,\n",
      "        -8.98192763e-01,  8.77332509e-01],\n",
      "       [ 1.11274242e+00, -4.37946737e-01,  3.06655914e-01,\n",
      "        -6.41502365e-02,  2.29072347e-01,  2.26610731e-02,\n",
      "        -7.53973663e-01,  7.38117337e-01],\n",
      "       [-6.82391882e-01,  5.93734682e-01, -1.11683030e-02,\n",
      "        -8.55446681e-02, -1.39719442e-01, -2.89383661e-02,\n",
      "        -1.37737739e+00,  1.25520504e+00],\n",
      "       [ 1.48445204e-01,  5.93734682e-01,  8.64585266e-02,\n",
      "        -1.48351058e-01, -1.94918215e-01, -2.77873967e-02,\n",
      "        -8.51670802e-01,  7.13255882e-01],\n",
      "       [-1.02995396e+00, -1.46962821e+00, -5.38331926e-01,\n",
      "         1.46076418e-02, -5.17214052e-02,  6.69426769e-02,\n",
      "        -8.47019315e-01,  6.48621321e-01],\n",
      "       [-8.33877265e-01, -4.37946737e-01, -2.47379486e-02,\n",
      "         2.11495206e-01,  3.94765623e-02, -8.16936642e-02,\n",
      "        -3.77139449e-01, -4.25327003e-01],\n",
      "       [ 3.08995396e-01, -1.15218771e+00,  1.08162664e-01,\n",
      "        -1.37113348e-01,  1.03065455e+00, -3.34665738e-02,\n",
      "         1.34885085e+00, -8.67834032e-01],\n",
      "       [ 1.37211704e+00,  5.14374554e-01, -1.61806624e-02,\n",
      "        -2.93113738e-01, -8.46103728e-01, -2.98038460e-02,\n",
      "        -8.37714553e-01,  6.28732920e-01],\n",
      "       [-9.14623976e-01,  1.86349642e+00, -5.59919477e-01,\n",
      "        -1.99886680e-01, -9.70900953e-01, -5.69613762e-02,\n",
      "        -7.16754675e-01,  6.68509722e-01],\n",
      "       [ 1.19573422e-01,  5.14374554e-01, -3.13916683e-01,\n",
      "        -2.36475170e-01, -4.44512665e-01,  1.64022073e-02,\n",
      "        -7.35364139e-01,  7.58005738e-01],\n",
      "       [-3.67264897e-01, -9.14107382e-01, -4.23780710e-01,\n",
      "        -2.32009247e-01, -9.08502340e-01, -5.91290882e-05,\n",
      "         8.51058543e-01, -1.21587574e+00],\n",
      "       [ 2.13262737e-01, -1.86642873e+00,  1.59701258e-01,\n",
      "        -1.58663496e-01,  4.26978207e+00,  4.53134673e-03,\n",
      "        -8.00495565e-01,  1.16570902e+00],\n",
      "       [-4.90821540e-01, -8.34747314e-01, -7.86329150e-01,\n",
      "        -2.36964852e-01, -4.46912616e-01, -1.29603982e-01,\n",
      "        -7.77234614e-01,  5.88956058e-01],\n",
      "       [-5.60677685e-02, -1.15218771e+00, -2.26435065e-02,\n",
      "         2.79834121e-01, -1.65318877e-01, -1.27646074e-01,\n",
      "        -2.23615587e-01, -6.04319036e-01],\n",
      "       [-7.13158250e-02,  3.55654359e-01, -4.80985120e-02,\n",
      "        -4.26627472e-02, -3.00515860e-01, -4.38370667e-02,\n",
      "        -5.02750814e-01,  7.18228936e-01],\n",
      "       [-6.28368735e-01,  1.86349642e+00, -2.53205121e-01,\n",
      "        -7.07956031e-02, -8.41303825e-01, -1.12164304e-01,\n",
      "         4.41658616e-01, -1.15621042e+00],\n",
      "       [-2.59637535e-01, -1.62834835e+00,  5.84736429e-02,\n",
      "         7.73095414e-02, -8.23704243e-01, -2.76801120e-02,\n",
      "         6.64967179e-01, -1.27008244e-01],\n",
      "       [-2.09544227e-01, -4.37946737e-01,  2.36342885e-02,\n",
      "        -2.78475791e-01, -3.44514877e-01,  1.62797458e-02,\n",
      "         1.49772322e+00, -8.47949386e-01],\n",
      "       [-1.10787106e+00,  1.17574021e-01, -6.12827241e-01,\n",
      "        -6.21337518e-02,  3.07470590e-01,  5.07168993e-02,\n",
      "        -7.91190863e-01,  6.98340476e-01],\n",
      "       [-1.22503507e+00,  1.96934134e-01, -3.71342391e-01,\n",
      "        -7.80095309e-02, -3.12515587e-01, -9.36420262e-02,\n",
      "         2.42352486e+00, -2.23513198e+00],\n",
      "       [-5.95304966e-01, -8.34747314e-01,  4.84315485e-01,\n",
      "         8.68995011e-01, -3.54914665e-01, -7.32634291e-02,\n",
      "         1.27906621e+00, -4.10411656e-01],\n",
      "       [-1.91728517e-01, -8.34747314e-01,  4.75046746e-02,\n",
      "        -8.71676952e-02, -4.69312102e-01, -1.70620736e-02,\n",
      "         1.89781845e+00, -1.12637973e+00],\n",
      "       [-5.28339148e-01, -1.99866414e-01, -2.89304823e-01,\n",
      "        -1.58051878e-01,  3.47469717e-01,  3.07353889e-03,\n",
      "        -8.74931753e-01,  8.32586408e-01],\n",
      "       [-4.17725116e-01, -1.07282758e+00, -1.62925929e-01,\n",
      "        -1.04939967e-01,  5.01866281e-01, -6.04005009e-02,\n",
      "         1.39537275e+00, -8.47949386e-01],\n",
      "       [ 7.08694398e-01,  3.55654359e-01,  3.22393961e-02,\n",
      "        -2.02390298e-01, -3.99713665e-01, -8.80106166e-03,\n",
      "        -6.84188962e-01,  5.09406149e-01],\n",
      "       [-1.11987054e+00, -7.55387187e-01, -1.56267490e-02,\n",
      "         3.50239314e-02, -8.70103180e-01, -3.15148495e-02,\n",
      "         2.22347736e+00, -3.80580902e-01],\n",
      "       [-1.19495821e+00,  1.62541604e+00, -3.26843083e-01,\n",
      "        -5.96805401e-02,  6.58759773e-02,  2.23283023e-02,\n",
      "         9.11538482e-01, -6.98788106e-01]], dtype=float32)>, <tf.Tensor: shape=(32, 1), dtype=float32, numpy=\n",
      "array([[2.865],\n",
      "       [2.962],\n",
      "       [2.139],\n",
      "       [1.327],\n",
      "       [1.557],\n",
      "       [1.836],\n",
      "       [2.667],\n",
      "       [1.091],\n",
      "       [2.188],\n",
      "       [2.222],\n",
      "       [1.148],\n",
      "       [1.345],\n",
      "       [2.781],\n",
      "       [2.438],\n",
      "       [2.024],\n",
      "       [1.563],\n",
      "       [1.387],\n",
      "       [2.763],\n",
      "       [3.362],\n",
      "       [1.114],\n",
      "       [2.346],\n",
      "       [0.807],\n",
      "       [0.993],\n",
      "       [1.813],\n",
      "       [0.741],\n",
      "       [0.919],\n",
      "       [1.183],\n",
      "       [1.843],\n",
      "       [1.198],\n",
      "       [2.092],\n",
      "       [0.7  ],\n",
      "       [0.809]], dtype=float32)>)\n"
     ]
    }
   ],
   "source": [
    "for value in train_data_final.take(1):\n",
    "    print(value)  ## first batch with 32 array each of 8 values for features, then 32 for target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81be16ce",
   "metadata": {},
   "source": [
    "##### `Building the Model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8cf76577",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "104/104 [==============================] - 2s 8ms/step - loss: 1.0124 - mae: 0.6530 - val_loss: 0.6837 - val_mae: 0.5824\n",
      "Epoch 2/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.5829 - mae: 0.5027 - val_loss: 0.4773 - val_mae: 0.4928\n",
      "Epoch 3/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.4862 - mae: 0.4767 - val_loss: 0.4612 - val_mae: 0.4914\n",
      "Epoch 4/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.4607 - mae: 0.4707 - val_loss: 0.4525 - val_mae: 0.4874\n",
      "Epoch 5/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.4336 - mae: 0.4638 - val_loss: 0.4528 - val_mae: 0.4893\n",
      "Epoch 6/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.4193 - mae: 0.4582 - val_loss: 0.5041 - val_mae: 0.4915\n",
      "Epoch 7/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.4096 - mae: 0.4550 - val_loss: 0.4751 - val_mae: 0.4769\n",
      "Epoch 8/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.4077 - mae: 0.4486 - val_loss: 0.4360 - val_mae: 0.4664\n",
      "Epoch 9/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.3968 - mae: 0.4451 - val_loss: 0.4589 - val_mae: 0.4835\n",
      "Epoch 10/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.3970 - mae: 0.4435 - val_loss: 0.4358 - val_mae: 0.4643\n",
      "Epoch 11/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.3912 - mae: 0.4402 - val_loss: 0.4254 - val_mae: 0.4626\n",
      "Epoch 12/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.3871 - mae: 0.4372 - val_loss: 0.4299 - val_mae: 0.4651\n",
      "Epoch 13/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.3824 - mae: 0.4352 - val_loss: 0.4349 - val_mae: 0.4823\n",
      "Epoch 14/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.3814 - mae: 0.4375 - val_loss: 0.4178 - val_mae: 0.4533\n",
      "Epoch 15/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.3799 - mae: 0.4330 - val_loss: 0.4155 - val_mae: 0.4517\n",
      "Epoch 16/20\n",
      "104/104 [==============================] - 1s 7ms/step - loss: 0.3746 - mae: 0.4301 - val_loss: 0.4271 - val_mae: 0.4622\n",
      "Epoch 17/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.3759 - mae: 0.4319 - val_loss: 0.4113 - val_mae: 0.4522\n",
      "Epoch 18/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.3700 - mae: 0.4301 - val_loss: 0.4152 - val_mae: 0.4501\n",
      "Epoch 19/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.3693 - mae: 0.4289 - val_loss: 0.4122 - val_mae: 0.4506\n",
      "Epoch 20/20\n",
      "104/104 [==============================] - 1s 6ms/step - loss: 0.3661 - mae: 0.4280 - val_loss: 0.4219 - val_mae: 0.4506\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(16, activation='relu', input_shape=X_train.shape[1:]),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "n_epochs = 20\n",
    "n_batch_size = 32\n",
    "s = n_epochs * len(X_train) // n_batch_size\n",
    "lr_exponen = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=0.01, \n",
    "                                                            decay_steps=s,\n",
    "                                                            decay_rate=0.1)\n",
    "my_optimiz = tf.keras.optimizers.RMSprop(learning_rate=lr_exponen)\n",
    "model.compile(loss='mse', optimizer=my_optimiz, metrics=['mae'])\n",
    "\n",
    "early_stopp = tf.keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True)\n",
    "\n",
    "\n",
    "history = model.fit(train_data_final, validation_data=valid_data_final, \n",
    "                    epochs=n_epochs, batch_size=n_batch_size, callbacks=[early_stopp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4a9d8ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(12, 4))\n",
    "plt.xlabel('Epochs', fontsize=14);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7bc38121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_test => 0.36749348044395447\n",
      "mae_test => 0.422836035490036\n"
     ]
    }
   ],
   "source": [
    "## evaluate this model\n",
    "mse_test, mae_test = model.evaluate(test_data_final, verbose=0)\n",
    "print('mse_test =>', mse_test)\n",
    "print('mae_test =>', mae_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0e0abd8a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [2.0282965 ],\n",
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       "       [0.8847643 ],\n",
       "       [1.1133673 ],\n",
       "       [2.3165774 ],\n",
       "       [4.0186524 ],\n",
       "       [4.2916856 ],\n",
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       "       [2.2787843 ],\n",
       "       [1.5640123 ],\n",
       "       [2.1601157 ]], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## evaluate on new dataset\n",
    "X_new = test_data_final.take(3).map(lambda x, y: x) ## take only the features\n",
    "y_preds = model.predict(X_new)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4072c2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([32, 8]), TensorShape([32, 1]))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new, y_new = list(test_data_final)[50]\n",
    "X_new.shape, y_new.shape   ## test again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1df72166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_batch50 => 0.2357958\n",
      "mae_batch50 => 0.373627\n"
     ]
    }
   ],
   "source": [
    "y_pred_vals = model.predict(X_new)\n",
    "mse_batch50 = tf.reduce_mean(tf.keras.metrics.mean_squared_error(y_new, y_pred_vals)).numpy()\n",
    "print('mse_batch50 =>', mse_batch50)\n",
    "mae_batch50 = tf.reduce_mean(tf.keras.metrics.mean_absolute_error(y_new, y_pred_vals)).numpy()\n",
    "print('mae_batch50 =>', mae_batch50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17429e23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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