wine_quality_notebook.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "id": "trv376giYxMs"
   },
   "source": [
    "# Wine Quality Notebook\n",
    "The data used in this notebook is a variation of the wine quality dataset.\n",
    "\n",
    "This dataset is public available for research. The details are described in [Cortez et al., 2009]. \n",
    "  Please include this citation if you plan to use this database:\n",
    "\n",
    "  P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "  Modeling wine preferences by data mining from physicochemical properties.\n",
    "  In Decision Support Systems>, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n",
    "\n",
    "  Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016\n",
    "                [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf\n",
    "                [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib\n",
    "\n",
    "1. Title: Wine Quality \n",
    "\n",
    "2. Sources\n",
    "   Created by: Paulo Cortez (Univ. Minho), António Cerdeira, Fernando Almeida, Telmo Matos and José Reis (CVRVV) @ 2009\n",
    "   \n",
    "3. Past Usage:\n",
    "\n",
    "  P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "  Modeling wine preferences by data mining from physicochemical properties.\n",
    "  In Decision Support Systems>, Elsevier, 47(4):547-553. ISSN: 0167-9236."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ucQa0MWwYXh6"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  from google.colab import auth\n",
    "  auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "XMpffFf1Yy8U",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-21 19:04:50.072127: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-05-21 19:04:50.072189: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-05-21 19:04:50.072226: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-05-21 19:04:50.083144: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "dIladF_TcKJD",
    "tags": []
   },
   "outputs": [],
   "source": [
    "GOOGLE_CLOUD_PROJECT = 'par-devfest-sfeir'\n",
    "GOOGLE_CLOUD_DATASET = 'wine_quality'\n",
    "RED_WINE_TABLE = 'red_wine'\n",
    "WHITE_WINE_TABLE = 'white_wine'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-LPdcLokZ9LA"
   },
   "source": [
    "Construct a BigQuery Client agent to interact with BigQuery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "ZvpbwY2mYzlu",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "client = bigquery.Client(project=GOOGLE_CLOUD_PROJECT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Zs3heOQHbOPY",
    "tags": []
   },
   "outputs": [],
   "source": [
    "sql_query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `{GOOGLE_CLOUD_PROJECT}.{GOOGLE_CLOUD_DATASET}.{WHITE_WINE_TABLE}`\n",
    "\"\"\"\n",
    "white_wine_df = client.query(sql_query).to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PjUEftYMcdlm"
   },
   "source": [
    "Split the data into training and validation datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "SZsgriu8bbpT",
    "outputId": "b4125975-0b24-48b9-be74-a9095aed0234",
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
       "      <th>citric_acid</th>\n",
       "      <th>residual_sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free_sulfur_dioxide</th>\n",
       "      <th>total_sulfur_dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>ph</th>\n",
       "      <th>sulphates</th>\n",
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       "      <th>quality</th>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "      fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "4656            6.9             0.190         0.38            1.15      0.023   \n",
       "3659            6.2             0.170         0.28            4.70      0.037   \n",
       "907             7.8             0.320         0.31            1.70      0.036   \n",
       "4352            6.6             0.300         0.74            4.60      0.041   \n",
       "3271            6.2             0.320         0.12            4.80      0.054   \n",
       "4632            7.4             0.220         0.33            2.00      0.045   \n",
       "2244            7.2             0.550         0.09            1.50      0.108   \n",
       "1924            7.2             0.280         0.38            2.00      0.052   \n",
       "3801            6.4             0.230         0.32            1.90      0.038   \n",
       "2634            5.6             0.225         0.24            9.80      0.054   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide  density    ph  sulphates  \\\n",
       "4656                 30.0                 105.0  0.99047  3.11       0.38   \n",
       "3659                 39.0                 133.0  0.99310  3.41       0.46   \n",
       "907                  46.0                 195.0  0.99300  3.03       0.48   \n",
       "4352                 36.0                 159.0  0.99460  3.21       0.45   \n",
       "3271                  6.0                  97.0  0.99424  3.16       0.50   \n",
       "4632                 31.0                 101.0  0.99310  3.42       0.55   \n",
       "2244                 16.0                 151.0  0.99380  3.07       0.57   \n",
       "1924                 23.0                 156.0  0.99120  3.13       0.52   \n",
       "3801                 40.0                 118.0  0.99074  3.32       0.53   \n",
       "2634                 59.0                 140.0  0.99545  3.17       0.39   \n",
       "\n",
       "      alcohol  quality  \n",
       "4656     11.4        0  \n",
       "3659     10.8        1  \n",
       "907      10.5        0  \n",
       "4352      9.9        0  \n",
       "3271      9.3        0  \n",
       "4632     11.4        0  \n",
       "2244      9.2        0  \n",
       "1924     11.1        0  \n",
       "3801     11.8        1  \n",
       "2634     10.2        1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train = white_wine_df.sample(frac=0.8, random_state=42)\n",
    "df_valid = white_wine_df.drop(df_train.index)\n",
    "display(df_train.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UEs6_f-ZdDL_"
   },
   "source": [
    "Normalize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "opj5K3VOca90",
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = df_train.drop('quality', axis=1)\n",
    "X_valid = df_valid.drop('quality', axis=1)\n",
    "\n",
    "X_train = (X_train - X_train.min())/(X_train.max() - X_train.min())\n",
    "X_valid = (X_valid - X_valid.min())/(X_valid.max() - X_valid.min())\n",
    "\n",
    "y_train = df_train[\"quality\"]\n",
    "y_valid = df_valid[\"quality\"]\n",
    "\n",
    "y_train = np.asarray(y_train).astype(np.int_)\n",
    "y_valid = np.asarray(y_valid).astype(np.int_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
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     "height": 407
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    "id": "y0cjMHCbeNFB",
    "outputId": "3dee65aa-8a63-43ed-8f64-fc9a25c7416b",
    "tags": []
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   "outputs": [
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       "      <td>0.321101</td>\n",
       "      <td>0.406977</td>\n",
       "      <td>0.193548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>0.417722</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.228916</td>\n",
       "      <td>0.021472</td>\n",
       "      <td>0.127596</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.339535</td>\n",
       "      <td>0.078851</td>\n",
       "      <td>0.376147</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3801</th>\n",
       "      <td>0.316456</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.192771</td>\n",
       "      <td>0.019939</td>\n",
       "      <td>0.086053</td>\n",
       "      <td>0.132404</td>\n",
       "      <td>0.251163</td>\n",
       "      <td>0.069983</td>\n",
       "      <td>0.550459</td>\n",
       "      <td>0.360465</td>\n",
       "      <td>0.612903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2634</th>\n",
       "      <td>0.215190</td>\n",
       "      <td>0.142157</td>\n",
       "      <td>0.144578</td>\n",
       "      <td>0.141104</td>\n",
       "      <td>0.133531</td>\n",
       "      <td>0.198606</td>\n",
       "      <td>0.302326</td>\n",
       "      <td>0.160787</td>\n",
       "      <td>0.412844</td>\n",
       "      <td>0.197674</td>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "4656       0.379747          0.107843     0.228916        0.008436   0.041543   \n",
       "3659       0.291139          0.088235     0.168675        0.062883   0.083086   \n",
       "907        0.493671          0.235294     0.186747        0.016871   0.080119   \n",
       "4352       0.341772          0.215686     0.445783        0.061350   0.094955   \n",
       "3271       0.291139          0.235294     0.072289        0.064417   0.133531   \n",
       "4632       0.443038          0.137255     0.198795        0.021472   0.106825   \n",
       "2244       0.417722          0.460784     0.054217        0.013804   0.293769   \n",
       "1924       0.417722          0.196078     0.228916        0.021472   0.127596   \n",
       "3801       0.316456          0.147059     0.192771        0.019939   0.086053   \n",
       "2634       0.215190          0.142157     0.144578        0.141104   0.133531   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide   density        ph  \\\n",
       "4656             0.097561              0.220930  0.064777  0.357798   \n",
       "3659             0.128920              0.286047  0.115481  0.633028   \n",
       "907              0.153310              0.430233  0.113553  0.284404   \n",
       "4352             0.118467              0.346512  0.144399  0.449541   \n",
       "3271             0.013937              0.202326  0.137459  0.403670   \n",
       "4632             0.101045              0.211628  0.115481  0.642202   \n",
       "2244             0.048780              0.327907  0.128976  0.321101   \n",
       "1924             0.073171              0.339535  0.078851  0.376147   \n",
       "3801             0.132404              0.251163  0.069983  0.550459   \n",
       "2634             0.198606              0.302326  0.160787  0.412844   \n",
       "\n",
       "      sulphates   alcohol  \n",
       "4656   0.186047  0.548387  \n",
       "3659   0.279070  0.451613  \n",
       "907    0.302326  0.403226  \n",
       "4352   0.267442  0.306452  \n",
       "3271   0.325581  0.209677  \n",
       "4632   0.383721  0.548387  \n",
       "2244   0.406977  0.193548  \n",
       "1924   0.348837  0.500000  \n",
       "3801   0.360465  0.612903  \n",
       "2634   0.197674  0.354839  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "T1QMi1hhji7A",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    layers.Dense(units=16, activation='relu', input_shape=[11]),\n",
    "    layers.Dropout(0.1),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dense(units=32, activation='relu'),\n",
    "    layers.Dropout(0.1),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dense(units=32, activation='relu'),\n",
    "    layers.Dropout(0.1),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dense(units=1, activation=tf.keras.activations.sigmoid)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "o8xfucPYjq3h",
    "tags": []
   },
   "outputs": [],
   "source": [
    "rms_prop_optimizer = tf.keras.optimizers.RMSprop(learning_rate=10e-3)\n",
    "model.compile(\n",
    "      optimizer=rms_prop_optimizer,\n",
    "      loss=tf.keras.losses.binary_crossentropy,\n",
    "      metrics=['accuracy']\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TXczSFmBj3fz",
    "outputId": "54b21f7c-082c-4620-98dd-4cbb8da06ccb",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "62/62 [==============================] - 2s 7ms/step - loss: 0.5857 - accuracy: 0.7006 - val_loss: 0.6141 - val_accuracy: 0.6857\n",
      "Epoch 2/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5379 - accuracy: 0.7279 - val_loss: 0.5763 - val_accuracy: 0.6857\n",
      "Epoch 3/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5307 - accuracy: 0.7312 - val_loss: 0.5430 - val_accuracy: 0.6857\n",
      "Epoch 4/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5308 - accuracy: 0.7338 - val_loss: 0.5410 - val_accuracy: 0.6939\n",
      "Epoch 5/200\n",
      "62/62 [==============================] - 0s 3ms/step - loss: 0.5259 - accuracy: 0.7381 - val_loss: 0.5190 - val_accuracy: 0.7602\n",
      "Epoch 6/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5156 - accuracy: 0.7414 - val_loss: 0.5050 - val_accuracy: 0.7653\n",
      "Epoch 7/200\n",
      "62/62 [==============================] - 0s 3ms/step - loss: 0.5114 - accuracy: 0.7473 - val_loss: 0.5022 - val_accuracy: 0.7520\n",
      "Epoch 8/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5052 - accuracy: 0.7494 - val_loss: 0.6581 - val_accuracy: 0.6898\n",
      "Epoch 9/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5106 - accuracy: 0.7491 - val_loss: 0.6112 - val_accuracy: 0.7020\n",
      "Epoch 10/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5111 - accuracy: 0.7499 - val_loss: 0.5333 - val_accuracy: 0.7276\n",
      "Epoch 11/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5052 - accuracy: 0.7545 - val_loss: 0.7689 - val_accuracy: 0.6020\n",
      "Epoch 12/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5069 - accuracy: 0.7501 - val_loss: 0.5689 - val_accuracy: 0.7327\n",
      "Epoch 13/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.4999 - accuracy: 0.7511 - val_loss: 0.7447 - val_accuracy: 0.6173\n",
      "Epoch 14/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5055 - accuracy: 0.7580 - val_loss: 0.6463 - val_accuracy: 0.6551\n",
      "Epoch 15/200\n",
      "62/62 [==============================] - 0s 4ms/step - loss: 0.5055 - accuracy: 0.7468 - val_loss: 0.7409 - val_accuracy: 0.5745\n",
      "Epoch 16/200\n",
      "62/62 [==============================] - 0s 3ms/step - loss: 0.4999 - accuracy: 0.7583 - val_loss: 0.5818 - val_accuracy: 0.6959\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', patience=10)\n",
    "\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_valid, y_valid),\n",
    "    batch_size=64,\n",
    "    epochs=200,\n",
    "    callbacks=[early_stopping],\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TL7TLOpnkPT9"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "environment": {
   "kernel": "python3",
   "name": "tf2-gpu.2-14.m121",
   "type": "gcloud",
   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf2-gpu.2-14:m121"
  },
  "kernelspec": {
   "display_name": "Python 3 (Local)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}