Galera_David_Xarxes_Neuronals.ipynb

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      "source": [
        "# ML STUDENT COMPETITION II: Neural Networks\n",
        "\n",
        "David Galera"
      ],
      "metadata": {
        "id": "MvVH3oOcIe9I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "123BdBlLIy6Q",
        "outputId": "baa9d1d4-1bd8-49ca-e1e4-0e4074407db2"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install scikeras"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.utils import shuffle\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers.experimental.preprocessing import Normalization\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, RandomizedSearchCV, train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, Normalizer, MaxAbsScaler, RobustScaler\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "#from scikeras.wrappers import KerasClassifier\n",
        "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier"
      ],
      "metadata": {
        "execution": {
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      "execution_count": 3,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/kaggle_nn/train.csv')\n",
        "validate = pd.read_csv('/content/drive/MyDrive/kaggle_nn/validate.csv')"
      ],
      "metadata": {
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        "train.head()"
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              "       id      f_00      f_01      f_02      f_03      f_04      f_05  \\\n",
              "0  493553  0.315471 -0.183690  0.664383 -1.186794  0.665098  0.946208   \n",
              "1  237346 -1.286392  1.780592  0.576698 -2.690658  1.321997 -0.675894   \n",
              "2   37368 -0.290303 -0.485907  0.808350 -0.156288  1.083632 -1.129914   \n",
              "3  665220  1.243590  0.035112 -1.013236  0.854267  0.019192  0.597892   \n",
              "4   41499  0.702716  0.817044 -0.064907 -1.045483  0.718374  0.164451   \n",
              "\n",
              "       f_06  f_07  f_08  ...      f_22      f_23      f_24      f_25  \\\n",
              "0  0.729857     0     4  ... -1.409039  3.645067  0.233039 -3.754846   \n",
              "1  0.371070     3     0  ...  3.087653  0.494209  3.210875 -0.666457   \n",
              "2  0.767396     3     1  ...  2.084169 -4.782668 -1.671375  2.774382   \n",
              "3 -2.020416     2     0  ... -0.701691 -2.769142 -6.534231 -0.557677   \n",
              "4 -0.936620     1     2  ...  1.154162 -1.931443  2.325042  2.143811   \n",
              "\n",
              "       f_26        f_27        f_28  f_29  f_30  target  \n",
              "0 -1.061733  BDBBCACIBB   20.308715     1     0       0  \n",
              "1  0.123854  ACBDCBCADA -449.291063     1     0       0  \n",
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              "4 -1.039599  ABBBBBCMBB  296.484562     0     2       1  \n",
              "\n",
              "[5 rows x 33 columns]"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = shuffle(train, random_state=3)\n",
        "print(train.shape)\n",
        "print(train.dtypes)\n",
        "train.drop('id',axis=1,inplace=True)\n",
        "train['target'].value_counts()"
      ],
      "metadata": {
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      "execution_count": 6,
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        {
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          "text": [
            "(810000, 33)\n",
            "id          int64\n",
            "f_00      float64\n",
            "f_01      float64\n",
            "f_02      float64\n",
            "f_03      float64\n",
            "f_04      float64\n",
            "f_05      float64\n",
            "f_06      float64\n",
            "f_07        int64\n",
            "f_08        int64\n",
            "f_09        int64\n",
            "f_10        int64\n",
            "f_11        int64\n",
            "f_12        int64\n",
            "f_13        int64\n",
            "f_14        int64\n",
            "f_15        int64\n",
            "f_16        int64\n",
            "f_17        int64\n",
            "f_18        int64\n",
            "f_19      float64\n",
            "f_20      float64\n",
            "f_21      float64\n",
            "f_22      float64\n",
            "f_23      float64\n",
            "f_24      float64\n",
            "f_25      float64\n",
            "f_26      float64\n",
            "f_27       object\n",
            "f_28      float64\n",
            "f_29        int64\n",
            "f_30        int64\n",
            "target      int64\n",
            "dtype: object\n"
          ]
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              "0    415945\n",
              "1    394055\n",
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "print('Percentatge de targets')\n",
        "train.target.value_counts(normalize=True).round(2)"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2023-02-09T12:08:35.711569Z",
          "iopub.status.idle": "2023-02-09T12:08:35.729214Z",
          "shell.execute_reply.started": "2023-02-09T12:08:35.711537Z",
          "shell.execute_reply": "2023-02-09T12:08:35.728197Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDwYpU6zIe9R",
        "outputId": "cde3eed7-5628-4fef-f2d6-9f9f8f77a8d6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentatge de targets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.51\n",
              "1    0.49\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature engineering: Manipulació i tractament de dades\n",
        "\n",
        "Aplicarem les transformacions a les dades als **conjunts d'entrenament i validació al mateix temps**, per tenir el mateix format d'entrada a la xarxa neuronal en els 2 conjunts."
      ],
      "metadata": {
        "id": "ri7N2mMHIe9R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tractament de les variables de tipus *int*:"
      ],
      "metadata": {
        "id": "UBk6miByIe9S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical = [f for f in train.columns if ((train[f].dtype == 'int64') & (f != 'target'))]\n",
        "print(categorical)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:35.730996Z",
          "iopub.execute_input": "2023-02-09T12:08:35.731402Z",
          "iopub.status.idle": "2023-02-09T12:08:35.739105Z",
          "shell.execute_reply.started": "2023-02-09T12:08:35.731362Z",
          "shell.execute_reply": "2023-02-09T12:08:35.737709Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hx3xD23_Ie9S",
        "outputId": "7be5e930-04af-41cb-8639-2a6e1aa5c89d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['f_07', 'f_08', 'f_09', 'f_10', 'f_11', 'f_12', 'f_13', 'f_14', 'f_15', 'f_16', 'f_17', 'f_18', 'f_29', 'f_30']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#categorical=['f_07','f_08','f_09','f_10','f_11','f_12','f_13','f_14','f_15','f_16','f_17','f_18','f_29','f_30']"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:35.741019Z",
          "iopub.execute_input": "2023-02-09T12:08:35.741972Z",
          "iopub.status.idle": "2023-02-09T12:08:35.750293Z",
          "shell.execute_reply.started": "2023-02-09T12:08:35.741928Z",
          "shell.execute_reply": "2023-02-09T12:08:35.749257Z"
        },
        "trusted": true,
        "id": "13-s6HqiIe9T"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categorical:\n",
        "  print(i,sorted(train[i].unique()))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:35.752052Z",
          "iopub.execute_input": "2023-02-09T12:08:35.752483Z",
          "iopub.status.idle": "2023-02-09T12:08:35.823184Z",
          "shell.execute_reply.started": "2023-02-09T12:08:35.752444Z",
          "shell.execute_reply": "2023-02-09T12:08:35.822180Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSVezZvTIe9T",
        "outputId": "b321b20c-aad7-48ea-8f22-7d0bb36983a8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f_07 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
            "f_08 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16]\n",
            "f_09 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
            "f_10 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
            "f_11 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
            "f_12 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16]\n",
            "f_13 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
            "f_14 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14]\n",
            "f_15 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
            "f_16 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
            "f_17 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
            "f_18 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
            "f_29 [0, 1]\n",
            "f_30 [0, 1, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categorical:\n",
        "  print(i,sorted(validate[i].unique()))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:35.824644Z",
          "iopub.execute_input": "2023-02-09T12:08:35.825071Z",
          "iopub.status.idle": "2023-02-09T12:08:35.841440Z",
          "shell.execute_reply.started": "2023-02-09T12:08:35.825033Z",
          "shell.execute_reply": "2023-02-09T12:08:35.840415Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UstmqZDpIe9U",
        "outputId": "c19cb58c-630b-49d4-cfda-eb418ac7d84c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f_07 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15]\n",
            "f_08 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
            "f_09 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
            "f_10 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
            "f_11 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13]\n",
            "f_12 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14]\n",
            "f_13 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
            "f_14 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
            "f_15 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
            "f_16 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
            "f_17 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]\n",
            "f_18 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
            "f_29 [0, 1]\n",
            "f_30 [0, 1, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apliquem One-Hot encoding i les transformem a array:"
      ],
      "metadata": {
        "id": "mTyf1egcIe9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainval=pd.concat([train.drop(['f_27','target'],axis=1),validate.drop(['id','f_27'],axis=1)])#Horizontal Stack dels 2 dataframes\n",
        "X_cat=X_trainval[categorical]\n",
        "X_cat=pd.get_dummies(X_cat,columns=categorical,drop_first=True).values"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:35.846007Z",
          "iopub.execute_input": "2023-02-09T12:08:35.846306Z",
          "iopub.status.idle": "2023-02-09T12:08:37.257411Z",
          "shell.execute_reply.started": "2023-02-09T12:08:35.846279Z",
          "shell.execute_reply": "2023-02-09T12:08:37.256038Z"
        },
        "trusted": true,
        "id": "neoi-ZXCIe9V"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainval.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:37.259286Z",
          "iopub.execute_input": "2023-02-09T12:08:37.259832Z",
          "iopub.status.idle": "2023-02-09T12:08:37.267738Z",
          "shell.execute_reply.started": "2023-02-09T12:08:37.259759Z",
          "shell.execute_reply": "2023-02-09T12:08:37.266507Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfE6ENSUIe9V",
        "outputId": "2a96b0c2-50d3-4af1-9a3d-8f2a0b8118e1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(900000, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_cat.shape #169 columnes"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:37.269703Z",
          "iopub.execute_input": "2023-02-09T12:08:37.270658Z",
          "iopub.status.idle": "2023-02-09T12:08:37.280315Z",
          "shell.execute_reply.started": "2023-02-09T12:08:37.270617Z",
          "shell.execute_reply": "2023-02-09T12:08:37.278639Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJxgF0zMIe9W",
        "outputId": "56c26816-07a6-4c7d-9532-8ea11827df8a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(900000, 169)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tractament de les variables tipus *float*:"
      ],
      "metadata": {
        "id": "7nRxUtSqIe9W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numeric = [f for f in train.columns if train[f].dtype == 'float64']\n",
        "print(numeric)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:37.281916Z",
          "iopub.execute_input": "2023-02-09T12:08:37.282385Z",
          "iopub.status.idle": "2023-02-09T12:08:37.292422Z",
          "shell.execute_reply.started": "2023-02-09T12:08:37.282345Z",
          "shell.execute_reply": "2023-02-09T12:08:37.291235Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwoqwF8rIe9W",
        "outputId": "d103ba0e-4373-44d7-fd75-0696ef735a87"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['f_00', 'f_01', 'f_02', 'f_03', 'f_04', 'f_05', 'f_06', 'f_19', 'f_20', 'f_21', 'f_22', 'f_23', 'f_24', 'f_25', 'f_26', 'f_28']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('seaborn')\n",
        "fig, axs = plt.subplots(4, 4, figsize=(16, 16))\n",
        "for f, ax in zip(numeric, axs.ravel()):\n",
        "    ax.hist(train[f], density=True, bins=100)\n",
        "    ax.set_title(f'Train {f}, std={train[f].std():.1f}')\n",
        "plt.suptitle('Histograms of the float features', y=0.93, fontsize=20)\n",
        "plt.show()\n",
        "for i in numeric:\n",
        "  print('var: ',i,train[i].var().round(2))\n",
        "  print('mean:',i,train[i].mean().round(2))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:37.294120Z",
          "iopub.execute_input": "2023-02-09T12:08:37.294525Z",
          "iopub.status.idle": "2023-02-09T12:08:42.256416Z",
          "shell.execute_reply.started": "2023-02-09T12:08:37.294490Z",
          "shell.execute_reply": "2023-02-09T12:08:42.255140Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "y-WnoivkIe9X",
        "outputId": "44dbd544-3c88-4c6b-f35b-5b419c7c19e3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1152 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "var:  f_00 1.0\n",
            "mean: f_00 0.0\n",
            "var:  f_01 1.0\n",
            "mean: f_01 0.0\n",
            "var:  f_02 1.0\n",
            "mean: f_02 0.0\n",
            "var:  f_03 1.0\n",
            "mean: f_03 -0.0\n",
            "var:  f_04 1.0\n",
            "mean: f_04 -0.0\n",
            "var:  f_05 1.0\n",
            "mean: f_05 0.0\n",
            "var:  f_06 1.0\n",
            "mean: f_06 -0.0\n",
            "var:  f_19 5.36\n",
            "mean: f_19 0.31\n",
            "var:  f_20 5.76\n",
            "mean: f_20 -0.18\n",
            "var:  f_21 6.17\n",
            "mean: f_21 -0.16\n",
            "var:  f_22 6.0\n",
            "mean: f_22 -0.01\n",
            "var:  f_23 6.02\n",
            "mean: f_23 -0.37\n",
            "var:  f_24 5.7\n",
            "mean: f_24 -0.34\n",
            "var:  f_25 5.84\n",
            "mean: f_25 0.18\n",
            "var:  f_26 6.13\n",
            "mean: f_26 0.36\n",
            "var:  f_28 56994.8\n",
            "mean: f_28 -0.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observen que les variables de la *f_00* a la *f_06* estàn estandaritzades, però de la **f_19 a la f_28 tenen mitjana propera a 0 i variança superior a 1.**"
      ],
      "metadata": {
        "id": "sTpflON3Ie9X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Les estandaritzem:"
      ],
      "metadata": {
        "id": "lblNc1VjIe9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_num=X_trainval[numeric].values #array\n",
        "sc = StandardScaler()#Scaler\n",
        "X_num = sc.fit_transform(X_num)\n",
        "print(X_num.shape)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:42.259180Z",
          "iopub.execute_input": "2023-02-09T12:08:42.260060Z",
          "iopub.status.idle": "2023-02-09T12:08:42.488571Z",
          "shell.execute_reply.started": "2023-02-09T12:08:42.260010Z",
          "shell.execute_reply": "2023-02-09T12:08:42.487288Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmQTfaTnIe9Y",
        "outputId": "da9f3469-c465-457d-c9fb-f889e321f76a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900000, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tractament de la variable f_27 tipus *object*:"
      ],
      "metadata": {
        "id": "7jfPPDa6Ie9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.f_27.head())\n",
        "print(train.f_27.value_counts())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:42.490395Z",
          "iopub.execute_input": "2023-02-09T12:08:42.490837Z",
          "iopub.status.idle": "2023-02-09T12:08:43.119019Z",
          "shell.execute_reply.started": "2023-02-09T12:08:42.490764Z",
          "shell.execute_reply": "2023-02-09T12:08:43.117800Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUexnCVEIe9Z",
        "outputId": "e9baac19-f040-4d4c-e1d8-ff082056f3d0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "706419    BABCEBBDDC\n",
            "507964    ACBDABBDDE\n",
            "302647    ACBDABCKEC\n",
            "243186    ABAACAGMAB\n",
            "283830    BEBCBBECEH\n",
            "Name: f_27, dtype: object\n",
            "BCBBBBCLBC    12\n",
            "BBBBBBCJBC    11\n",
            "BBBBBABLCB    10\n",
            "BBBBBBDPCB    10\n",
            "ACBBBACFCC     9\n",
            "              ..\n",
            "ABBDCABHDD     1\n",
            "AAADBBGLFA     1\n",
            "AEACABBFHC     1\n",
            "BCBCEAARBC     1\n",
            "BBADCABBFA     1\n",
            "Name: f_27, Length: 678113, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La variable està composta per seqüències de 10 lletres. La seqüencia més freqüent només es repeteix 12 vegades i el 83.7% de seqüencies són úniques."
      ],
      "metadata": {
        "id": "nB48CpF3Ie9Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analitzem per cada posició i valor de la cadena quin és el valor promig del seu target:"
      ],
      "metadata": {
        "id": "FkS8igZ6Ie9Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for pos in range(10):\n",
        "    train_group = train.groupby(train.f_27.str.get(pos))\n",
        "    print(f\"Position -> {pos}\")\n",
        "    display(pd.DataFrame({\"size\":train_group.target.size(),\"mean_target\":train_group.target.mean().round(2)}))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:43.120733Z",
          "iopub.execute_input": "2023-02-09T12:08:43.121472Z",
          "iopub.status.idle": "2023-02-09T12:08:50.634734Z",
          "shell.execute_reply.started": "2023-02-09T12:08:43.121426Z",
          "shell.execute_reply": "2023-02-09T12:08:50.633541Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KxVbhlkBIe9Z",
        "outputId": "445bb9b7-fead-48da-f449-40d2a54044f6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Position -> 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        size  mean_target\n",
              "f_27                     \n",
              "A     406235         0.54\n",
              "B     403765         0.44"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>size</th>\n",
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              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>406235</td>\n",
              "      <td>0.54</td>\n",
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              "    <tr>\n",
              "      <th>B</th>\n",
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              "      <td>0.44</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acf40706-a7eb-47b0-b561-fd1a607cc8ee')\"\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "Position -> 1\n"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        size  mean_target\n",
              "f_27                     \n",
              "A     130291         0.58\n",
              "B     220669         0.48\n",
              "C     203184         0.49\n",
              "D     134634         0.45\n",
              "E      71022         0.43\n",
              "F      31862         0.42\n",
              "G      12387         0.41\n",
              "H       4209         0.40\n",
              "I       1248         0.39\n",
              "J        364         0.40\n",
              "K        104         0.37\n",
              "L         22         0.32\n",
              "M          2         0.50\n",
              "N          2         1.00"
            ],
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              "\n",
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              "      <th>A</th>\n",
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              "      <td>0.58</td>\n",
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              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>220669</td>\n",
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              "      <th>C</th>\n",
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              "      <td>1.00</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-14c90e14-7e7a-4d71-b74d-4be9e2447a22 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-14c90e14-7e7a-4d71-b74d-4be9e2447a22');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  "
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          "text": [
            "Position -> 2\n"
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          "data": {
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              "        size  mean_target\n",
              "f_27                     \n",
              "A     268058         0.53\n",
              "B     541942         0.46"
            ],
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              "    <tr>\n",
              "      <th>f_27</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
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              "      <td>0.53</td>\n",
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              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>541942</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "text": [
            "Position -> 3\n"
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              "        size  mean_target\n",
              "f_27                     \n",
              "A     144675         0.57\n",
              "B     213376         0.48\n",
              "C     189144         0.48\n",
              "D     129174         0.45\n",
              "E      73684         0.43\n",
              "F      35523         0.44\n",
              "G      15432         0.45\n",
              "H       5811         0.47\n",
              "I       2139         0.48\n",
              "J        699         0.51\n",
              "K        227         0.58\n",
              "L         75         0.68\n",
              "M         31         0.61\n",
              "N          7         0.71\n",
              "O          3         1.00"
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              "      <td>0.43</td>\n",
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              "      <td>0.44</td>\n",
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              "      <th>I</th>\n",
              "      <td>2139</td>\n",
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              "      <th>J</th>\n",
              "      <td>699</td>\n",
              "      <td>0.51</td>\n",
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              "      <td>0.68</td>\n",
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              "      <td>0.61</td>\n",
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              "      <td>7</td>\n",
              "      <td>0.71</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "        size  mean_target\n",
              "f_27                     \n",
              "A     233774         0.48\n",
              "B     255931         0.45\n",
              "C     176872         0.49\n",
              "D      89076         0.53\n",
              "E      35020         0.57\n",
              "F      12713         0.64\n",
              "G       4509         0.72\n",
              "H       1492         0.76\n",
              "I        438         0.78\n",
              "J        136         0.82\n",
              "K         27         0.89\n",
              "L          9         1.00\n",
              "M          2         1.00\n",
              "O          1         1.00"
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "Position -> 5\n"
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              "        size  mean_target\n",
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              "        size  mean_target\n",
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              "A      91397         0.58\n",
              "B     180189         0.49\n",
              "C     198101         0.49\n",
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              "I       3311         0.49\n",
              "J       1027         0.50\n",
              "K        296         0.51\n",
              "L        111         0.52\n",
              "M         20         0.70\n",
              "N         10         0.20\n",
              "O          1         1.00"
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              "            + ' to learn more about interactive tables.';\n",
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              "       size  mean_target\n",
              "f_27                    \n",
              "A     40519         0.48\n",
              "B     41248         0.41\n",
              "C     41252         0.41\n",
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              "I     41224         0.46\n",
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              "M     41302         0.51\n",
              "N     41290         0.51\n",
              "O     40781         0.53\n",
              "P     40665         0.54\n",
              "Q     40231         0.53\n",
              "R     39239         0.55\n",
              "S     37719         0.59\n",
              "T     35318         0.64"
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              "      <td>0.44</td>\n",
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              "      <td>41194</td>\n",
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              "      <td>41224</td>\n",
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              "      <th>L</th>\n",
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              "      <th>N</th>\n",
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              "      <td>40231</td>\n",
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              "      <th>R</th>\n",
              "      <td>39239</td>\n",
              "      <td>0.55</td>\n",
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              "      <th>S</th>\n",
              "      <td>37719</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T</th>\n",
              "      <td>35318</td>\n",
              "      <td>0.64</td>\n",
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              "</table>\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "Position -> 8\n"
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              "        size  mean_target\n",
              "f_27                     \n",
              "A     143885         0.40\n",
              "B     202368         0.45\n",
              "C     192011         0.49\n",
              "D     134319         0.52\n",
              "E      76021         0.56\n",
              "F      36650         0.60\n",
              "G      15835         0.62\n",
              "H       5868         0.65\n",
              "I       2127         0.64\n",
              "J        659         0.70\n",
              "K        185         0.65\n",
              "L         49         0.76\n",
              "M         18         0.67\n",
              "N          4         0.75\n",
              "O          1         1.00"
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              "      <th>A</th>\n",
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              "      <th>F</th>\n",
              "      <td>36650</td>\n",
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              "      <td>15835</td>\n",
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              "      <td>659</td>\n",
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              "      <th>N</th>\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "Position -> 9\n"
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              "        size  mean_target\n",
              "f_27                     \n",
              "A     103427         0.36\n",
              "B     188988         0.43\n",
              "C     197182         0.49\n",
              "D     149564         0.51\n",
              "E      90183         0.57\n",
              "F      46480         0.62\n",
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              "I       3083         0.72\n",
              "J       1044         0.75\n",
              "K        345         0.75\n",
              "L         81         0.83\n",
              "M         32         0.84\n",
              "N          6         0.83\n",
              "O          1         1.00"
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aquí podem veure com la lletra que hi ha a cada posició de la cadena té relació amb el valor del target. Per tant és convenient aplicar **feature engineering** per separar cada posició de la cadena en una feature independent i aplicar One Hot encoding als caràcters per que la xarxa neuronal pugui aprendre d'aquesta informació:"
      ],
      "metadata": {
        "id": "DTQDRNDTIe9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f_27=pd.concat([train.f_27,validate.f_27])\n",
        "f_27_split = pd.DataFrame([list(i) for i in f_27.values])\n",
        "f_27_split.columns = [\"ch0\",\"ch1\",\"ch2\",\"ch3\",\"ch4\",\"ch5\",\"ch6\",\"ch7\",\"ch8\",\"ch9\"]\n",
        "print(f_27_split.shape)\n",
        "f_27_split.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:50.636529Z",
          "iopub.execute_input": "2023-02-09T12:08:50.637235Z",
          "iopub.status.idle": "2023-02-09T12:08:52.909681Z",
          "shell.execute_reply.started": "2023-02-09T12:08:50.637189Z",
          "shell.execute_reply": "2023-02-09T12:08:52.908526Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "ttcY6m3UIe9a",
        "outputId": "1318fbee-a069-4762-c51b-e4fea14374be"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900000, 10)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  ch0 ch1 ch2 ch3 ch4 ch5 ch6 ch7 ch8 ch9\n",
              "0   B   A   B   C   E   B   B   D   D   C\n",
              "1   A   C   B   D   A   B   B   D   D   E\n",
              "2   A   C   B   D   A   B   C   K   E   C\n",
              "3   A   B   A   A   C   A   G   M   A   B\n",
              "4   B   E   B   C   B   B   E   C   E   H"
            ],
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              "      <th></th>\n",
              "      <th>ch0</th>\n",
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              "      <th>ch2</th>\n",
              "      <th>ch3</th>\n",
              "      <th>ch4</th>\n",
              "      <th>ch5</th>\n",
              "      <th>ch6</th>\n",
              "      <th>ch7</th>\n",
              "      <th>ch8</th>\n",
              "      <th>ch9</th>\n",
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              "      <th>0</th>\n",
              "      <td>B</td>\n",
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              "      <td>H</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ac20adb-4762-4818-8629-68f021db208c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ac20adb-4762-4818-8629-68f021db208c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ac20adb-4762-4818-8629-68f021db208c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f_27_dummy=pd.get_dummies(f_27_split,columns=f_27_split.columns,drop_first=True).values\n",
        "print(f_27_dummy.shape)\n",
        "f_27_dummy[0,:]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:52.911211Z",
          "iopub.execute_input": "2023-02-09T12:08:52.911829Z",
          "iopub.status.idle": "2023-02-09T12:08:54.081888Z",
          "shell.execute_reply.started": "2023-02-09T12:08:52.911774Z",
          "shell.execute_reply": "2023-02-09T12:08:54.080704Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuIeTpgpIe9a",
        "outputId": "7d2ae759-23d6-4cdd-d7be-f6051c1fe290"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900000, 104)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Buscant més possibles relacions entre la cadena de caràcters i el target, comprovem si el número únic de caràcters que hi ha a la cadena està relacionat amb el valor promig del target."
      ],
      "metadata": {
        "id": "F9-sUR8vIe9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f_27_unic_ch = train.f_27.apply(lambda x : len(set(x)))\n",
        "group_train = train.groupby(f_27_unic_ch)\n",
        "display(pd.DataFrame({\"size\":group_train.target.size(),\"mean_target\":group_train.target.mean().round(2)}))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:54.083725Z",
          "iopub.execute_input": "2023-02-09T12:08:54.084478Z",
          "iopub.status.idle": "2023-02-09T12:08:54.870036Z",
          "shell.execute_reply.started": "2023-02-09T12:08:54.084436Z",
          "shell.execute_reply": "2023-02-09T12:08:54.868850Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "7EejRkyKIe9b",
        "outputId": "746f3df4-e341-408a-c505-af5fd96c75b2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        size  mean_target\n",
              "f_27                     \n",
              "1          1         0.00\n",
              "2        974         0.09\n",
              "3      28864         0.09\n",
              "4     190034         0.48\n",
              "5     359805         0.49\n",
              "6     198966         0.48\n",
              "7      30282         0.91\n",
              "8       1068         0.91\n",
              "9          6         1.00"
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>size</th>\n",
              "      <th>mean_target</th>\n",
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              "      <th>5</th>\n",
              "      <td>359805</td>\n",
              "      <td>0.49</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>198966</td>\n",
              "      <td>0.48</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>1068</td>\n",
              "      <td>0.91</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f035863-1311-42cf-9ab6-112559e0373b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f035863-1311-42cf-9ab6-112559e0373b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f035863-1311-42cf-9ab6-112559e0373b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aquí podem veure com hi ha una relació molt clara, com més caràcters únics hi ha a la cadena, més probabilitat que el target sigui 1. Afegim aquesta informació i apliquem One Hot Encoding:"
      ],
      "metadata": {
        "id": "SbGSPStbIe9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unic_ch=f_27.apply(lambda x : len(set(x)))\n",
        "print(unic_ch.shape)\n",
        "unic_ch_dummy=pd.get_dummies(unic_ch,drop_first=True).values\n",
        "print(unic_ch_dummy.shape)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:54.871881Z",
          "iopub.execute_input": "2023-02-09T12:08:54.872285Z",
          "iopub.status.idle": "2023-02-09T12:08:55.763343Z",
          "shell.execute_reply.started": "2023-02-09T12:08:54.872234Z",
          "shell.execute_reply": "2023-02-09T12:08:55.762224Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wWav42YIe9b",
        "outputId": "d87c6d10-17ce-46c2-f48f-1451a4ccc70b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900000,)\n",
            "(900000, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenem les arrays de dades amb totes les transformacions realitzades:"
      ],
      "metadata": {
        "id": "iVp055VjIe9c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_total=np.hstack((X_num, X_cat,f_27_dummy,unic_ch_dummy))\n",
        "X_total.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:55.764881Z",
          "iopub.execute_input": "2023-02-09T12:08:55.765574Z",
          "iopub.status.idle": "2023-02-09T12:08:56.301407Z",
          "shell.execute_reply.started": "2023-02-09T12:08:55.765529Z",
          "shell.execute_reply": "2023-02-09T12:08:56.300266Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDbH1I-JIe9c",
        "outputId": "dfcc5e7e-124f-41e4-df0e-7a7f36116528"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(900000, 297)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Amb totes les variables transformades, separem els conjunts d'entrenament i validació:"
      ],
      "metadata": {
        "id": "c62IqKjlIe9d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_nn=X_total[:810000,:]\n",
        "print(X_nn.shape)\n",
        "X_validate=X_total[810000:,:]\n",
        "print(X_validate.shape)\n",
        "\"Target\"\n",
        "y=train['target'].copy()\n",
        "y=np.array(y)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:56.302968Z",
          "iopub.execute_input": "2023-02-09T12:08:56.303711Z",
          "iopub.status.idle": "2023-02-09T12:08:56.315282Z",
          "shell.execute_reply.started": "2023-02-09T12:08:56.303667Z",
          "shell.execute_reply": "2023-02-09T12:08:56.313122Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEUT4kvIIe9d",
        "outputId": "1bec57ad-fa51-4fe9-c6ee-692018cea5b8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(810000, 297)\n",
            "(90000, 297)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Neural network"
      ],
      "metadata": {
        "id": "ymhORR4BIe9e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aqui he estat dissenyant i fent proves amb la xarxa neural:\n",
        "\n",
        "Començant amb 1 hidden layer de 10 neurones i augmentant neurones i capes, així com ajustant el *learning rate*, la mida del *batch* i provant diferents funcions d'activació i optimitzadors. A la capa de sortida hi ha d'haver 1 neurona amb una funció d'activació *sigmoid* ja que la classificació es binaria."
      ],
      "metadata": {
        "id": "QRylAvpVIe9e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Proves loss functions"
      ],
      "metadata": {
        "id": "EHTm8eKuSVwP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Relu es la millor\n",
        "np.random.seed(1)\n",
        "def get_model(act_function):\n",
        "    model = keras.Sequential()\n",
        "    model.add(layers.Dense(50, activation='relu', input_shape = (X_nn.shape[1],)))\n",
        "    model.add(layers.Dense(50, activation=act_function))\n",
        "    model.add(layers.Dense(50, activation=act_function))\n",
        "    model.add(layers.Dense(1, activation='sigmoid'))\n",
        "    return model\n",
        "activations = ['relu','sigmoid','tanh']\n",
        "activation_results={}\n",
        "for funct in activations:\n",
        "    model =get_model(act_function=funct)\n",
        "    opt = keras.optimizers.Adam(learning_rate=0.005)\n",
        "    model.compile(optimizer=opt, loss='binary_crossentropy',metrics=['accuracy'])\n",
        "    history = model.fit(X_nn, y, validation_split=0.1, epochs=40, batch_size=512, callbacks =EarlyStopping(monitor='val_loss',patience=5))#'val_acc'\n",
        "    activation_results[funct] = history"
      ],
      "metadata": {
        "id": "u9e9FfRhSdAL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fd34dba-0a14-4a06-b4d8-f418ff5ac21f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.2822 - accuracy: 0.8758 - val_loss: 0.1837 - val_accuracy: 0.9246\n",
            "Epoch 2/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1640 - accuracy: 0.9326 - val_loss: 0.1559 - val_accuracy: 0.9368\n",
            "Epoch 3/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1416 - accuracy: 0.9424 - val_loss: 0.1410 - val_accuracy: 0.9429\n",
            "Epoch 4/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.1296 - accuracy: 0.9479 - val_loss: 0.1284 - val_accuracy: 0.9484\n",
            "Epoch 5/40\n",
            "1424/1424 [==============================] - 14s 10ms/step - loss: 0.1215 - accuracy: 0.9510 - val_loss: 0.1248 - val_accuracy: 0.9500\n",
            "Epoch 6/40\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.1156 - accuracy: 0.9535 - val_loss: 0.1191 - val_accuracy: 0.9526\n",
            "Epoch 7/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1110 - accuracy: 0.9557 - val_loss: 0.1176 - val_accuracy: 0.9526\n",
            "Epoch 8/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1074 - accuracy: 0.9570 - val_loss: 0.1150 - val_accuracy: 0.9543\n",
            "Epoch 9/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1042 - accuracy: 0.9583 - val_loss: 0.1154 - val_accuracy: 0.9541\n",
            "Epoch 10/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.1018 - accuracy: 0.9594 - val_loss: 0.1060 - val_accuracy: 0.9580\n",
            "Epoch 11/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0999 - accuracy: 0.9602 - val_loss: 0.1064 - val_accuracy: 0.9582\n",
            "Epoch 12/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0978 - accuracy: 0.9607 - val_loss: 0.1069 - val_accuracy: 0.9577\n",
            "Epoch 13/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0961 - accuracy: 0.9618 - val_loss: 0.1043 - val_accuracy: 0.9598\n",
            "Epoch 14/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0947 - accuracy: 0.9624 - val_loss: 0.1034 - val_accuracy: 0.9591\n",
            "Epoch 15/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0932 - accuracy: 0.9631 - val_loss: 0.0999 - val_accuracy: 0.9609\n",
            "Epoch 16/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0918 - accuracy: 0.9637 - val_loss: 0.1029 - val_accuracy: 0.9601\n",
            "Epoch 17/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0912 - accuracy: 0.9638 - val_loss: 0.1010 - val_accuracy: 0.9607\n",
            "Epoch 18/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0900 - accuracy: 0.9643 - val_loss: 0.1031 - val_accuracy: 0.9597\n",
            "Epoch 19/40\n",
            "1424/1424 [==============================] - 11s 7ms/step - loss: 0.0890 - accuracy: 0.9648 - val_loss: 0.0977 - val_accuracy: 0.9615\n",
            "Epoch 20/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0883 - accuracy: 0.9651 - val_loss: 0.0991 - val_accuracy: 0.9610\n",
            "Epoch 21/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0874 - accuracy: 0.9655 - val_loss: 0.0972 - val_accuracy: 0.9623\n",
            "Epoch 22/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0866 - accuracy: 0.9658 - val_loss: 0.0982 - val_accuracy: 0.9617\n",
            "Epoch 23/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0856 - accuracy: 0.9660 - val_loss: 0.0995 - val_accuracy: 0.9611\n",
            "Epoch 24/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0855 - accuracy: 0.9662 - val_loss: 0.0969 - val_accuracy: 0.9618\n",
            "Epoch 25/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0846 - accuracy: 0.9668 - val_loss: 0.0986 - val_accuracy: 0.9618\n",
            "Epoch 26/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0841 - accuracy: 0.9668 - val_loss: 0.0989 - val_accuracy: 0.9613\n",
            "Epoch 27/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0838 - accuracy: 0.9670 - val_loss: 0.0957 - val_accuracy: 0.9628\n",
            "Epoch 28/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0835 - accuracy: 0.9670 - val_loss: 0.0959 - val_accuracy: 0.9629\n",
            "Epoch 29/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0829 - accuracy: 0.9674 - val_loss: 0.0972 - val_accuracy: 0.9621\n",
            "Epoch 30/40\n",
            "1424/1424 [==============================] - 9s 7ms/step - loss: 0.0824 - accuracy: 0.9676 - val_loss: 0.0963 - val_accuracy: 0.9619\n",
            "Epoch 31/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0818 - accuracy: 0.9677 - val_loss: 0.0947 - val_accuracy: 0.9623\n",
            "Epoch 32/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0811 - accuracy: 0.9681 - val_loss: 0.0950 - val_accuracy: 0.9630\n",
            "Epoch 33/40\n",
            "1424/1424 [==============================] - 9s 7ms/step - loss: 0.0814 - accuracy: 0.9680 - val_loss: 0.0935 - val_accuracy: 0.9634\n",
            "Epoch 34/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0806 - accuracy: 0.9682 - val_loss: 0.0947 - val_accuracy: 0.9626\n",
            "Epoch 35/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0802 - accuracy: 0.9686 - val_loss: 0.0980 - val_accuracy: 0.9620\n",
            "Epoch 36/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0802 - accuracy: 0.9685 - val_loss: 0.0948 - val_accuracy: 0.9634\n",
            "Epoch 37/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0798 - accuracy: 0.9686 - val_loss: 0.0945 - val_accuracy: 0.9632\n",
            "Epoch 38/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0792 - accuracy: 0.9689 - val_loss: 0.0958 - val_accuracy: 0.9625\n",
            "Epoch 1/40\n",
            "1424/1424 [==============================] - 10s 6ms/step - loss: 0.3101 - accuracy: 0.8640 - val_loss: 0.2154 - val_accuracy: 0.9117\n",
            "Epoch 2/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1877 - accuracy: 0.9220 - val_loss: 0.1743 - val_accuracy: 0.9286\n",
            "Epoch 3/40\n",
            "1424/1424 [==============================] - 12s 8ms/step - loss: 0.1574 - accuracy: 0.9355 - val_loss: 0.1559 - val_accuracy: 0.9364\n",
            "Epoch 4/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.1408 - accuracy: 0.9426 - val_loss: 0.1386 - val_accuracy: 0.9446\n",
            "Epoch 5/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1309 - accuracy: 0.9472 - val_loss: 0.1317 - val_accuracy: 0.9472\n",
            "Epoch 6/40\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.1237 - accuracy: 0.9504 - val_loss: 0.1290 - val_accuracy: 0.9489\n",
            "Epoch 7/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.1189 - accuracy: 0.9523 - val_loss: 0.1240 - val_accuracy: 0.9502\n",
            "Epoch 8/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1142 - accuracy: 0.9543 - val_loss: 0.1214 - val_accuracy: 0.9521\n",
            "Epoch 9/40\n",
            "1424/1424 [==============================] - 11s 7ms/step - loss: 0.1113 - accuracy: 0.9556 - val_loss: 0.1185 - val_accuracy: 0.9531\n",
            "Epoch 10/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.1087 - accuracy: 0.9566 - val_loss: 0.1134 - val_accuracy: 0.9548\n",
            "Epoch 11/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1065 - accuracy: 0.9576 - val_loss: 0.1133 - val_accuracy: 0.9545\n",
            "Epoch 12/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1042 - accuracy: 0.9586 - val_loss: 0.1145 - val_accuracy: 0.9546\n",
            "Epoch 13/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.1025 - accuracy: 0.9595 - val_loss: 0.1136 - val_accuracy: 0.9554\n",
            "Epoch 14/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1016 - accuracy: 0.9596 - val_loss: 0.1086 - val_accuracy: 0.9572\n",
            "Epoch 15/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0994 - accuracy: 0.9608 - val_loss: 0.1097 - val_accuracy: 0.9561\n",
            "Epoch 16/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0983 - accuracy: 0.9612 - val_loss: 0.1096 - val_accuracy: 0.9575\n",
            "Epoch 17/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0972 - accuracy: 0.9614 - val_loss: 0.1090 - val_accuracy: 0.9577\n",
            "Epoch 18/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0963 - accuracy: 0.9620 - val_loss: 0.1070 - val_accuracy: 0.9576\n",
            "Epoch 19/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0952 - accuracy: 0.9621 - val_loss: 0.1077 - val_accuracy: 0.9581\n",
            "Epoch 20/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0943 - accuracy: 0.9628 - val_loss: 0.1054 - val_accuracy: 0.9584\n",
            "Epoch 21/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0935 - accuracy: 0.9632 - val_loss: 0.1046 - val_accuracy: 0.9593\n",
            "Epoch 22/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0925 - accuracy: 0.9636 - val_loss: 0.1061 - val_accuracy: 0.9582\n",
            "Epoch 23/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0922 - accuracy: 0.9636 - val_loss: 0.1043 - val_accuracy: 0.9585\n",
            "Epoch 24/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0918 - accuracy: 0.9638 - val_loss: 0.1014 - val_accuracy: 0.9597\n",
            "Epoch 25/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0908 - accuracy: 0.9641 - val_loss: 0.1056 - val_accuracy: 0.9579\n",
            "Epoch 26/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0899 - accuracy: 0.9647 - val_loss: 0.1014 - val_accuracy: 0.9599\n",
            "Epoch 27/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0895 - accuracy: 0.9649 - val_loss: 0.1043 - val_accuracy: 0.9599\n",
            "Epoch 28/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0891 - accuracy: 0.9648 - val_loss: 0.1035 - val_accuracy: 0.9594\n",
            "Epoch 29/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0884 - accuracy: 0.9649 - val_loss: 0.1032 - val_accuracy: 0.9600\n",
            "Epoch 1/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.2798 - accuracy: 0.8772 - val_loss: 0.1854 - val_accuracy: 0.9226\n",
            "Epoch 2/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.1685 - accuracy: 0.9307 - val_loss: 0.1548 - val_accuracy: 0.9373\n",
            "Epoch 3/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1455 - accuracy: 0.9412 - val_loss: 0.1456 - val_accuracy: 0.9414\n",
            "Epoch 4/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.1333 - accuracy: 0.9463 - val_loss: 0.1312 - val_accuracy: 0.9476\n",
            "Epoch 5/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.1253 - accuracy: 0.9498 - val_loss: 0.1263 - val_accuracy: 0.9497\n",
            "Epoch 6/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1203 - accuracy: 0.9521 - val_loss: 0.1263 - val_accuracy: 0.9499\n",
            "Epoch 7/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.1162 - accuracy: 0.9536 - val_loss: 0.1235 - val_accuracy: 0.9503\n",
            "Epoch 8/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1131 - accuracy: 0.9551 - val_loss: 0.1229 - val_accuracy: 0.9524\n",
            "Epoch 9/40\n",
            "1424/1424 [==============================] - 9s 7ms/step - loss: 0.1105 - accuracy: 0.9562 - val_loss: 0.1157 - val_accuracy: 0.9549\n",
            "Epoch 10/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1085 - accuracy: 0.9570 - val_loss: 0.1188 - val_accuracy: 0.9531\n",
            "Epoch 11/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1071 - accuracy: 0.9578 - val_loss: 0.1146 - val_accuracy: 0.9549\n",
            "Epoch 12/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1052 - accuracy: 0.9586 - val_loss: 0.1155 - val_accuracy: 0.9549\n",
            "Epoch 13/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1041 - accuracy: 0.9590 - val_loss: 0.1138 - val_accuracy: 0.9558\n",
            "Epoch 14/40\n",
            "1424/1424 [==============================] - 11s 8ms/step - loss: 0.1033 - accuracy: 0.9595 - val_loss: 0.1122 - val_accuracy: 0.9562\n",
            "Epoch 15/40\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.1028 - accuracy: 0.9595 - val_loss: 0.1119 - val_accuracy: 0.9569\n",
            "Epoch 16/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.1015 - accuracy: 0.9600 - val_loss: 0.1111 - val_accuracy: 0.9569\n",
            "Epoch 17/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.1001 - accuracy: 0.9608 - val_loss: 0.1117 - val_accuracy: 0.9566\n",
            "Epoch 18/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0998 - accuracy: 0.9611 - val_loss: 0.1105 - val_accuracy: 0.9575\n",
            "Epoch 19/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0993 - accuracy: 0.9611 - val_loss: 0.1130 - val_accuracy: 0.9562\n",
            "Epoch 20/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0986 - accuracy: 0.9615 - val_loss: 0.1145 - val_accuracy: 0.9558\n",
            "Epoch 21/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0981 - accuracy: 0.9619 - val_loss: 0.1072 - val_accuracy: 0.9592\n",
            "Epoch 22/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0980 - accuracy: 0.9618 - val_loss: 0.1108 - val_accuracy: 0.9580\n",
            "Epoch 23/40\n",
            "1424/1424 [==============================] - 8s 6ms/step - loss: 0.0973 - accuracy: 0.9621 - val_loss: 0.1113 - val_accuracy: 0.9580\n",
            "Epoch 24/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0975 - accuracy: 0.9620 - val_loss: 0.1068 - val_accuracy: 0.9597\n",
            "Epoch 25/40\n",
            "1424/1424 [==============================] - 9s 7ms/step - loss: 0.0965 - accuracy: 0.9624 - val_loss: 0.1093 - val_accuracy: 0.9582\n",
            "Epoch 26/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0965 - accuracy: 0.9623 - val_loss: 0.1081 - val_accuracy: 0.9581\n",
            "Epoch 27/40\n",
            "1424/1424 [==============================] - 10s 7ms/step - loss: 0.0960 - accuracy: 0.9624 - val_loss: 0.1102 - val_accuracy: 0.9572\n",
            "Epoch 28/40\n",
            "1424/1424 [==============================] - 9s 6ms/step - loss: 0.0960 - accuracy: 0.9627 - val_loss: 0.1104 - val_accuracy: 0.9577\n",
            "Epoch 29/40\n",
            "1424/1424 [==============================] - 9s 7ms/step - loss: 0.0961 - accuracy: 0.9627 - val_loss: 0.1088 - val_accuracy: 0.9581\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_loss_per_funct={k:v.history['val_loss'] for k,v in activation_results.items()}"
      ],
      "metadata": {
        "id": "t8cpfce-Sez_"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('seaborn')\n",
        "val_loss_curves=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in val_loss_per_funct.items() ]))\n",
        "val_loss_curves.plot(title='Loss per activation function')"
      ],
      "metadata": {
        "id": "ct_tMCyWSgdP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "04bc957e-3a1e-4f7b-cefd-4d351fb0883f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0caee3f850>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preliminar Neural network"
      ],
      "metadata": {
        "id": "WBxc2HzXShO-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "model.add(layers.Dense(50, activation='relu',kernel_initializer='normal', input_shape = (X_nn.shape[1],)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(50, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(50, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(50, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(50, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(20, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "opt = keras.optimizers.Adam(learning_rate=0.03)\n",
        "model.compile(optimizer=opt, loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:08:56.317665Z",
          "iopub.execute_input": "2023-02-09T12:08:56.318138Z",
          "iopub.status.idle": "2023-02-09T12:09:00.518730Z",
          "shell.execute_reply.started": "2023-02-09T12:08:56.318094Z",
          "shell.execute_reply": "2023-02-09T12:09:00.517593Z"
        },
        "trusted": true,
        "id": "f1XedZv_Ie9e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with tf.device('/GPU:0'):\n",
        "    history1 = model.fit(X_nn, y, validation_split=0.1, batch_size=512, epochs=300, \\\n",
        "                        callbacks =EarlyStopping(monitor='val_loss',patience=100))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:09:00.520577Z",
          "iopub.execute_input": "2023-02-09T12:09:00.521241Z",
          "iopub.status.idle": "2023-02-09T12:09:13.828615Z",
          "shell.execute_reply.started": "2023-02-09T12:09:00.521196Z",
          "shell.execute_reply": "2023-02-09T12:09:13.825694Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BC1u64SbIe9f",
        "outputId": "4357a6b1-04d2-4763-9316-5512aea3be31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "1424/1424 [==============================] - 23s 14ms/step - loss: 0.2765 - accuracy: 0.8763 - val_loss: 0.1867 - val_accuracy: 0.9237\n",
            "Epoch 2/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.1681 - accuracy: 0.9310 - val_loss: 0.1769 - val_accuracy: 0.9306\n",
            "Epoch 3/300\n",
            "1424/1424 [==============================] - 29s 20ms/step - loss: 0.1479 - accuracy: 0.9398 - val_loss: 0.1535 - val_accuracy: 0.9370\n",
            "Epoch 4/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.1368 - accuracy: 0.9444 - val_loss: 0.1310 - val_accuracy: 0.9471\n",
            "Epoch 5/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.1292 - accuracy: 0.9479 - val_loss: 0.1260 - val_accuracy: 0.9496\n",
            "Epoch 6/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.1237 - accuracy: 0.9503 - val_loss: 0.1217 - val_accuracy: 0.9526\n",
            "Epoch 7/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.1183 - accuracy: 0.9527 - val_loss: 0.1207 - val_accuracy: 0.9511\n",
            "Epoch 8/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.1146 - accuracy: 0.9543 - val_loss: 0.1179 - val_accuracy: 0.9534\n",
            "Epoch 9/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.1122 - accuracy: 0.9552 - val_loss: 0.1100 - val_accuracy: 0.9571\n",
            "Epoch 10/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.1090 - accuracy: 0.9565 - val_loss: 0.1060 - val_accuracy: 0.9585\n",
            "Epoch 11/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.1066 - accuracy: 0.9574 - val_loss: 0.1110 - val_accuracy: 0.9561\n",
            "Epoch 12/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.1043 - accuracy: 0.9583 - val_loss: 0.1112 - val_accuracy: 0.9564\n",
            "Epoch 13/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.1025 - accuracy: 0.9592 - val_loss: 0.1134 - val_accuracy: 0.9559\n",
            "Epoch 14/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.1006 - accuracy: 0.9600 - val_loss: 0.1093 - val_accuracy: 0.9572\n",
            "Epoch 15/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0989 - accuracy: 0.9607 - val_loss: 0.0976 - val_accuracy: 0.9613\n",
            "Epoch 16/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0973 - accuracy: 0.9614 - val_loss: 0.1032 - val_accuracy: 0.9597\n",
            "Epoch 17/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0960 - accuracy: 0.9616 - val_loss: 0.0969 - val_accuracy: 0.9623\n",
            "Epoch 18/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0946 - accuracy: 0.9624 - val_loss: 0.0949 - val_accuracy: 0.9628\n",
            "Epoch 19/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0935 - accuracy: 0.9630 - val_loss: 0.1029 - val_accuracy: 0.9590\n",
            "Epoch 20/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0927 - accuracy: 0.9631 - val_loss: 0.1000 - val_accuracy: 0.9616\n",
            "Epoch 21/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0909 - accuracy: 0.9642 - val_loss: 0.0962 - val_accuracy: 0.9620\n",
            "Epoch 22/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0908 - accuracy: 0.9639 - val_loss: 0.0978 - val_accuracy: 0.9618\n",
            "Epoch 23/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0897 - accuracy: 0.9645 - val_loss: 0.0951 - val_accuracy: 0.9625\n",
            "Epoch 24/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0888 - accuracy: 0.9649 - val_loss: 0.0940 - val_accuracy: 0.9633\n",
            "Epoch 25/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0881 - accuracy: 0.9651 - val_loss: 0.0944 - val_accuracy: 0.9626\n",
            "Epoch 26/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0875 - accuracy: 0.9654 - val_loss: 0.0950 - val_accuracy: 0.9631\n",
            "Epoch 27/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0869 - accuracy: 0.9659 - val_loss: 0.0923 - val_accuracy: 0.9633\n",
            "Epoch 28/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0861 - accuracy: 0.9659 - val_loss: 0.0947 - val_accuracy: 0.9639\n",
            "Epoch 29/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0854 - accuracy: 0.9660 - val_loss: 0.0929 - val_accuracy: 0.9643\n",
            "Epoch 30/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0852 - accuracy: 0.9662 - val_loss: 0.0977 - val_accuracy: 0.9610\n",
            "Epoch 31/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0846 - accuracy: 0.9665 - val_loss: 0.0916 - val_accuracy: 0.9643\n",
            "Epoch 32/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0840 - accuracy: 0.9672 - val_loss: 0.0901 - val_accuracy: 0.9651\n",
            "Epoch 33/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0835 - accuracy: 0.9671 - val_loss: 0.0891 - val_accuracy: 0.9652\n",
            "Epoch 34/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0841 - accuracy: 0.9667 - val_loss: 0.0966 - val_accuracy: 0.9631\n",
            "Epoch 35/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0830 - accuracy: 0.9671 - val_loss: 0.0899 - val_accuracy: 0.9648\n",
            "Epoch 36/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0820 - accuracy: 0.9676 - val_loss: 0.0880 - val_accuracy: 0.9656\n",
            "Epoch 37/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0815 - accuracy: 0.9678 - val_loss: 0.0892 - val_accuracy: 0.9654\n",
            "Epoch 38/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0810 - accuracy: 0.9680 - val_loss: 0.0887 - val_accuracy: 0.9664\n",
            "Epoch 39/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0805 - accuracy: 0.9681 - val_loss: 0.0927 - val_accuracy: 0.9641\n",
            "Epoch 40/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0802 - accuracy: 0.9685 - val_loss: 0.0955 - val_accuracy: 0.9623\n",
            "Epoch 41/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0798 - accuracy: 0.9687 - val_loss: 0.0957 - val_accuracy: 0.9630\n",
            "Epoch 42/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0796 - accuracy: 0.9685 - val_loss: 0.0903 - val_accuracy: 0.9648\n",
            "Epoch 43/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0794 - accuracy: 0.9685 - val_loss: 0.0900 - val_accuracy: 0.9648\n",
            "Epoch 44/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0788 - accuracy: 0.9689 - val_loss: 0.0875 - val_accuracy: 0.9657\n",
            "Epoch 45/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0787 - accuracy: 0.9689 - val_loss: 0.0907 - val_accuracy: 0.9642\n",
            "Epoch 46/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0781 - accuracy: 0.9693 - val_loss: 0.0869 - val_accuracy: 0.9659\n",
            "Epoch 47/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0774 - accuracy: 0.9696 - val_loss: 0.0893 - val_accuracy: 0.9656\n",
            "Epoch 48/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0777 - accuracy: 0.9692 - val_loss: 0.1547 - val_accuracy: 0.9662\n",
            "Epoch 49/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0770 - accuracy: 0.9696 - val_loss: 0.0878 - val_accuracy: 0.9658\n",
            "Epoch 50/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0770 - accuracy: 0.9698 - val_loss: 0.0907 - val_accuracy: 0.9640\n",
            "Epoch 51/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0761 - accuracy: 0.9700 - val_loss: 0.0884 - val_accuracy: 0.9652\n",
            "Epoch 52/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0760 - accuracy: 0.9702 - val_loss: 0.0884 - val_accuracy: 0.9654\n",
            "Epoch 53/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0761 - accuracy: 0.9701 - val_loss: 0.0896 - val_accuracy: 0.9656\n",
            "Epoch 54/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0757 - accuracy: 0.9703 - val_loss: 0.0873 - val_accuracy: 0.9667\n",
            "Epoch 55/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0757 - accuracy: 0.9703 - val_loss: 0.0938 - val_accuracy: 0.9636\n",
            "Epoch 56/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0752 - accuracy: 0.9705 - val_loss: 0.0870 - val_accuracy: 0.9659\n",
            "Epoch 57/300\n",
            "1424/1424 [==============================] - 23s 16ms/step - loss: 0.0754 - accuracy: 0.9703 - val_loss: 0.0944 - val_accuracy: 0.9640\n",
            "Epoch 58/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0750 - accuracy: 0.9706 - val_loss: 0.0880 - val_accuracy: 0.9661\n",
            "Epoch 59/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0736 - accuracy: 0.9712 - val_loss: 0.0895 - val_accuracy: 0.9658\n",
            "Epoch 60/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0740 - accuracy: 0.9708 - val_loss: 0.0844 - val_accuracy: 0.9668\n",
            "Epoch 61/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0737 - accuracy: 0.9712 - val_loss: 0.0859 - val_accuracy: 0.9670\n",
            "Epoch 62/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0735 - accuracy: 0.9711 - val_loss: 0.0983 - val_accuracy: 0.9634\n",
            "Epoch 63/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0736 - accuracy: 0.9710 - val_loss: 0.0859 - val_accuracy: 0.9672\n",
            "Epoch 64/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0729 - accuracy: 0.9714 - val_loss: 0.0869 - val_accuracy: 0.9659\n",
            "Epoch 65/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0729 - accuracy: 0.9716 - val_loss: 0.0900 - val_accuracy: 0.9661\n",
            "Epoch 66/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0725 - accuracy: 0.9715 - val_loss: 0.0873 - val_accuracy: 0.9655\n",
            "Epoch 67/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0729 - accuracy: 0.9715 - val_loss: 0.0869 - val_accuracy: 0.9664\n",
            "Epoch 68/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0726 - accuracy: 0.9713 - val_loss: 0.0889 - val_accuracy: 0.9657\n",
            "Epoch 69/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0718 - accuracy: 0.9719 - val_loss: 0.1132 - val_accuracy: 0.9667\n",
            "Epoch 70/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0719 - accuracy: 0.9717 - val_loss: 0.0858 - val_accuracy: 0.9667\n",
            "Epoch 71/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0718 - accuracy: 0.9719 - val_loss: 0.0881 - val_accuracy: 0.9667\n",
            "Epoch 72/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0715 - accuracy: 0.9719 - val_loss: 0.0843 - val_accuracy: 0.9675\n",
            "Epoch 73/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0711 - accuracy: 0.9722 - val_loss: 0.0863 - val_accuracy: 0.9669\n",
            "Epoch 74/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0710 - accuracy: 0.9721 - val_loss: 0.0889 - val_accuracy: 0.9654\n",
            "Epoch 75/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0707 - accuracy: 0.9723 - val_loss: 0.0905 - val_accuracy: 0.9655\n",
            "Epoch 76/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0710 - accuracy: 0.9723 - val_loss: 0.0882 - val_accuracy: 0.9665\n",
            "Epoch 77/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0710 - accuracy: 0.9720 - val_loss: 0.0907 - val_accuracy: 0.9649\n",
            "Epoch 78/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0705 - accuracy: 0.9724 - val_loss: 0.0885 - val_accuracy: 0.9663\n",
            "Epoch 79/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0704 - accuracy: 0.9723 - val_loss: 0.0922 - val_accuracy: 0.9660\n",
            "Epoch 80/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0695 - accuracy: 0.9729 - val_loss: 0.0921 - val_accuracy: 0.9655\n",
            "Epoch 81/300\n",
            "1424/1424 [==============================] - 16s 12ms/step - loss: 0.0701 - accuracy: 0.9724 - val_loss: 0.0868 - val_accuracy: 0.9669\n",
            "Epoch 82/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0697 - accuracy: 0.9726 - val_loss: 0.0874 - val_accuracy: 0.9668\n",
            "Epoch 83/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0706 - accuracy: 0.9724 - val_loss: 0.1368 - val_accuracy: 0.9670\n",
            "Epoch 84/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0700 - accuracy: 0.9726 - val_loss: 0.0861 - val_accuracy: 0.9673\n",
            "Epoch 85/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0695 - accuracy: 0.9728 - val_loss: 0.0846 - val_accuracy: 0.9672\n",
            "Epoch 86/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0696 - accuracy: 0.9728 - val_loss: 0.0822 - val_accuracy: 0.9676\n",
            "Epoch 87/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0695 - accuracy: 0.9729 - val_loss: 3.4059 - val_accuracy: 0.9661\n",
            "Epoch 88/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0692 - accuracy: 0.9732 - val_loss: 0.0895 - val_accuracy: 0.9651\n",
            "Epoch 89/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0688 - accuracy: 0.9731 - val_loss: 0.0863 - val_accuracy: 0.9676\n",
            "Epoch 90/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0688 - accuracy: 0.9732 - val_loss: 0.0876 - val_accuracy: 0.9672\n",
            "Epoch 91/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0703 - accuracy: 0.9724 - val_loss: 0.0891 - val_accuracy: 0.9676\n",
            "Epoch 92/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0681 - accuracy: 0.9736 - val_loss: 0.0853 - val_accuracy: 0.9667\n",
            "Epoch 93/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0681 - accuracy: 0.9735 - val_loss: 0.1043 - val_accuracy: 0.9599\n",
            "Epoch 94/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0701 - accuracy: 0.9725 - val_loss: 0.0852 - val_accuracy: 0.9671\n",
            "Epoch 95/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0679 - accuracy: 0.9737 - val_loss: 0.1056 - val_accuracy: 0.9669\n",
            "Epoch 96/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0683 - accuracy: 0.9733 - val_loss: 0.0827 - val_accuracy: 0.9683\n",
            "Epoch 97/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0678 - accuracy: 0.9735 - val_loss: 0.0840 - val_accuracy: 0.9677\n",
            "Epoch 98/300\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 0.0679 - accuracy: 0.9736 - val_loss: 0.1920 - val_accuracy: 0.9670\n",
            "Epoch 99/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0675 - accuracy: 0.9735 - val_loss: 0.0878 - val_accuracy: 0.9661\n",
            "Epoch 100/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.0676 - accuracy: 0.9737 - val_loss: 0.0839 - val_accuracy: 0.9675\n",
            "Epoch 101/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0678 - accuracy: 0.9736 - val_loss: 0.0864 - val_accuracy: 0.9672\n",
            "Epoch 102/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0674 - accuracy: 0.9737 - val_loss: 0.0849 - val_accuracy: 0.9671\n",
            "Epoch 103/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0671 - accuracy: 0.9738 - val_loss: 0.0859 - val_accuracy: 0.9668\n",
            "Epoch 104/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0674 - accuracy: 0.9737 - val_loss: 0.0863 - val_accuracy: 0.9669\n",
            "Epoch 105/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0666 - accuracy: 0.9740 - val_loss: 0.0827 - val_accuracy: 0.9684\n",
            "Epoch 106/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0664 - accuracy: 0.9740 - val_loss: 0.0864 - val_accuracy: 0.9675\n",
            "Epoch 107/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0667 - accuracy: 0.9740 - val_loss: 0.0867 - val_accuracy: 0.9668\n",
            "Epoch 108/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0664 - accuracy: 0.9740 - val_loss: 0.0872 - val_accuracy: 0.9676\n",
            "Epoch 109/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0663 - accuracy: 0.9740 - val_loss: 0.0862 - val_accuracy: 0.9678\n",
            "Epoch 110/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0662 - accuracy: 0.9741 - val_loss: 0.0901 - val_accuracy: 0.9655\n",
            "Epoch 111/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0663 - accuracy: 0.9743 - val_loss: 0.0867 - val_accuracy: 0.9678\n",
            "Epoch 112/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0664 - accuracy: 0.9741 - val_loss: 0.0883 - val_accuracy: 0.9663\n",
            "Epoch 113/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0656 - accuracy: 0.9745 - val_loss: 0.0870 - val_accuracy: 0.9673\n",
            "Epoch 114/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0660 - accuracy: 0.9743 - val_loss: 0.0855 - val_accuracy: 0.9684\n",
            "Epoch 115/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0662 - accuracy: 0.9742 - val_loss: 0.0921 - val_accuracy: 0.9658\n",
            "Epoch 116/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0653 - accuracy: 0.9744 - val_loss: 0.0849 - val_accuracy: 0.9677\n",
            "Epoch 117/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0657 - accuracy: 0.9742 - val_loss: 0.0863 - val_accuracy: 0.9673\n",
            "Epoch 118/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0657 - accuracy: 0.9743 - val_loss: 0.0844 - val_accuracy: 0.9682\n",
            "Epoch 119/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0659 - accuracy: 0.9742 - val_loss: 0.0845 - val_accuracy: 0.9675\n",
            "Epoch 120/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0654 - accuracy: 0.9743 - val_loss: 0.0857 - val_accuracy: 0.9676\n",
            "Epoch 121/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0657 - accuracy: 0.9743 - val_loss: 0.0908 - val_accuracy: 0.9657\n",
            "Epoch 122/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0659 - accuracy: 0.9744 - val_loss: 0.0892 - val_accuracy: 0.9662\n",
            "Epoch 123/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0649 - accuracy: 0.9746 - val_loss: 0.0872 - val_accuracy: 0.9660\n",
            "Epoch 124/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0651 - accuracy: 0.9745 - val_loss: 0.0874 - val_accuracy: 0.9666\n",
            "Epoch 125/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0652 - accuracy: 0.9747 - val_loss: 0.0860 - val_accuracy: 0.9674\n",
            "Epoch 126/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0651 - accuracy: 0.9745 - val_loss: 0.0850 - val_accuracy: 0.9679\n",
            "Epoch 127/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0648 - accuracy: 0.9748 - val_loss: 0.0892 - val_accuracy: 0.9672\n",
            "Epoch 128/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0646 - accuracy: 0.9747 - val_loss: 0.0840 - val_accuracy: 0.9673\n",
            "Epoch 129/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0649 - accuracy: 0.9745 - val_loss: 0.0835 - val_accuracy: 0.9687\n",
            "Epoch 130/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0646 - accuracy: 0.9749 - val_loss: 0.0851 - val_accuracy: 0.9673\n",
            "Epoch 131/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0643 - accuracy: 0.9749 - val_loss: 0.0858 - val_accuracy: 0.9672\n",
            "Epoch 132/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0645 - accuracy: 0.9748 - val_loss: 0.0873 - val_accuracy: 0.9677\n",
            "Epoch 133/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0643 - accuracy: 0.9750 - val_loss: 0.0905 - val_accuracy: 0.9656\n",
            "Epoch 134/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0643 - accuracy: 0.9750 - val_loss: 0.0850 - val_accuracy: 0.9683\n",
            "Epoch 135/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0641 - accuracy: 0.9750 - val_loss: 0.0840 - val_accuracy: 0.9672\n",
            "Epoch 136/300\n",
            "1424/1424 [==============================] - 23s 16ms/step - loss: 0.0644 - accuracy: 0.9748 - val_loss: 0.0858 - val_accuracy: 0.9678\n",
            "Epoch 137/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.0644 - accuracy: 0.9750 - val_loss: 0.0890 - val_accuracy: 0.9648\n",
            "Epoch 138/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0639 - accuracy: 0.9751 - val_loss: 0.0883 - val_accuracy: 0.9672\n",
            "Epoch 139/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0637 - accuracy: 0.9753 - val_loss: 0.0847 - val_accuracy: 0.9673\n",
            "Epoch 140/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0639 - accuracy: 0.9751 - val_loss: 0.0857 - val_accuracy: 0.9676\n",
            "Epoch 141/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0637 - accuracy: 0.9753 - val_loss: 0.0865 - val_accuracy: 0.9674\n",
            "Epoch 142/300\n",
            "1424/1424 [==============================] - 23s 16ms/step - loss: 0.0640 - accuracy: 0.9749 - val_loss: 0.0829 - val_accuracy: 0.9681\n",
            "Epoch 143/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0641 - accuracy: 0.9750 - val_loss: 0.0888 - val_accuracy: 0.9671\n",
            "Epoch 144/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0640 - accuracy: 0.9751 - val_loss: 0.0895 - val_accuracy: 0.9664\n",
            "Epoch 145/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0637 - accuracy: 0.9752 - val_loss: 0.0864 - val_accuracy: 0.9674\n",
            "Epoch 146/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0632 - accuracy: 0.9755 - val_loss: 0.0843 - val_accuracy: 0.9679\n",
            "Epoch 147/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0634 - accuracy: 0.9753 - val_loss: 0.0879 - val_accuracy: 0.9671\n",
            "Epoch 148/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0631 - accuracy: 0.9755 - val_loss: 0.0841 - val_accuracy: 0.9682\n",
            "Epoch 149/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0633 - accuracy: 0.9753 - val_loss: 0.0880 - val_accuracy: 0.9675\n",
            "Epoch 150/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0631 - accuracy: 0.9752 - val_loss: 0.0854 - val_accuracy: 0.9679\n",
            "Epoch 151/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0633 - accuracy: 0.9754 - val_loss: 0.0897 - val_accuracy: 0.9666\n",
            "Epoch 152/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0627 - accuracy: 0.9755 - val_loss: 0.0857 - val_accuracy: 0.9679\n",
            "Epoch 153/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0630 - accuracy: 0.9754 - val_loss: 0.0851 - val_accuracy: 0.9669\n",
            "Epoch 154/300\n",
            "1424/1424 [==============================] - 19s 14ms/step - loss: 0.0633 - accuracy: 0.9755 - val_loss: 0.0879 - val_accuracy: 0.9678\n",
            "Epoch 155/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0634 - accuracy: 0.9754 - val_loss: 0.0860 - val_accuracy: 0.9671\n",
            "Epoch 156/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0626 - accuracy: 0.9755 - val_loss: 0.0890 - val_accuracy: 0.9659\n",
            "Epoch 157/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0627 - accuracy: 0.9756 - val_loss: 0.0864 - val_accuracy: 0.9678\n",
            "Epoch 158/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0625 - accuracy: 0.9758 - val_loss: 0.0866 - val_accuracy: 0.9670\n",
            "Epoch 159/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0626 - accuracy: 0.9757 - val_loss: 0.0881 - val_accuracy: 0.9674\n",
            "Epoch 160/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0627 - accuracy: 0.9755 - val_loss: 0.0882 - val_accuracy: 0.9675\n",
            "Epoch 161/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0628 - accuracy: 0.9755 - val_loss: 0.0863 - val_accuracy: 0.9668\n",
            "Epoch 162/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0622 - accuracy: 0.9757 - val_loss: 0.0873 - val_accuracy: 0.9676\n",
            "Epoch 163/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0627 - accuracy: 0.9756 - val_loss: 0.0867 - val_accuracy: 0.9678\n",
            "Epoch 164/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0621 - accuracy: 0.9757 - val_loss: 0.0827 - val_accuracy: 0.9686\n",
            "Epoch 165/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0623 - accuracy: 0.9758 - val_loss: 0.0887 - val_accuracy: 0.9675\n",
            "Epoch 166/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0618 - accuracy: 0.9760 - val_loss: 0.0882 - val_accuracy: 0.9672\n",
            "Epoch 167/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0624 - accuracy: 0.9757 - val_loss: 0.0863 - val_accuracy: 0.9675\n",
            "Epoch 168/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0629 - accuracy: 0.9754 - val_loss: 0.0863 - val_accuracy: 0.9674\n",
            "Epoch 169/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0617 - accuracy: 0.9760 - val_loss: 0.0859 - val_accuracy: 0.9682\n",
            "Epoch 170/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0619 - accuracy: 0.9759 - val_loss: 0.0865 - val_accuracy: 0.9675\n",
            "Epoch 171/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0619 - accuracy: 0.9759 - val_loss: 0.0871 - val_accuracy: 0.9680\n",
            "Epoch 172/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0616 - accuracy: 0.9760 - val_loss: 0.0845 - val_accuracy: 0.9684\n",
            "Epoch 173/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0618 - accuracy: 0.9761 - val_loss: 0.0832 - val_accuracy: 0.9685\n",
            "Epoch 174/300\n",
            "1424/1424 [==============================] - 19s 14ms/step - loss: 0.0617 - accuracy: 0.9761 - val_loss: 0.0867 - val_accuracy: 0.9682\n",
            "Epoch 175/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0618 - accuracy: 0.9759 - val_loss: 0.0883 - val_accuracy: 0.9677\n",
            "Epoch 176/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0620 - accuracy: 0.9759 - val_loss: 0.0855 - val_accuracy: 0.9676\n",
            "Epoch 177/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0619 - accuracy: 0.9758 - val_loss: 0.0855 - val_accuracy: 0.9678\n",
            "Epoch 178/300\n",
            "1424/1424 [==============================] - 18s 12ms/step - loss: 0.0615 - accuracy: 0.9760 - val_loss: 0.4560 - val_accuracy: 0.9677\n",
            "Epoch 179/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0618 - accuracy: 0.9761 - val_loss: 0.7960 - val_accuracy: 0.9651\n",
            "Epoch 180/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0619 - accuracy: 0.9758 - val_loss: 0.0866 - val_accuracy: 0.9677\n",
            "Epoch 181/300\n",
            "1424/1424 [==============================] - 18s 13ms/step - loss: 0.0616 - accuracy: 0.9760 - val_loss: 0.2200 - val_accuracy: 0.9678\n",
            "Epoch 182/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0613 - accuracy: 0.9760 - val_loss: 0.0849 - val_accuracy: 0.9680\n",
            "Epoch 183/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0610 - accuracy: 0.9762 - val_loss: 0.1999 - val_accuracy: 0.9672\n",
            "Epoch 184/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0611 - accuracy: 0.9761 - val_loss: 0.0880 - val_accuracy: 0.9677\n",
            "Epoch 185/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0611 - accuracy: 0.9763 - val_loss: 0.0900 - val_accuracy: 0.9672\n",
            "Epoch 186/300\n",
            "1424/1424 [==============================] - 17s 12ms/step - loss: 0.0608 - accuracy: 0.9764 - val_loss: 0.0876 - val_accuracy: 0.9679\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('seaborn')\n",
        "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(15,8))\n",
        "\n",
        "#Accuracy\n",
        "ax1.plot(history1.history['accuracy'], label='train')\n",
        "ax1.plot(history1.history['val_accuracy'], label='test')\n",
        "ax1.set_xlabel('epoch')\n",
        "ax1.set_ylabel('accuracy')\n",
        "ax1.set_title('Accuracy')\n",
        "ax1.legend()\n",
        "\n",
        "#Loss\n",
        "ax2.plot(history1.history['loss'], label='train')\n",
        "ax2.plot(history1.history['val_loss'], label='test')\n",
        "ax2.set_xlabel('epoch')\n",
        "ax2.set_ylabel('loss')\n",
        "ax2.set_title('Loss')\n",
        "ax2.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:09:13.830282Z",
          "iopub.status.idle": "2023-02-09T12:09:13.830854Z",
          "shell.execute_reply.started": "2023-02-09T12:09:13.830557Z",
          "shell.execute_reply": "2023-02-09T12:09:13.830583Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "0V0Jbbu0Ie9g",
        "outputId": "e6a74944-8dd8-408a-8f62-cd5669bc7a8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Després de fer proves i entrenar la xarxa neuronal durant vàries hores, el màxim **val_accuracy** que he aconseguit ha estat del **96.8%**"
      ],
      "metadata": {
        "id": "Fw4YRr5RIe9g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature engineering: part 2"
      ],
      "metadata": {
        "id": "_KvPEaEVIe9g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Amb l'objectiu de superar el 97% de **val_accuracy** tornem a revisar el dataset en busca d'informació extra que poguem introduïr a la xarxa neuronal."
      ],
      "metadata": {
        "id": "qTNQPrygIe9g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 12))\n",
        "sns.heatmap(train[numeric + ['target']].corr(), center=0, annot=True, fmt='.2f')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:10:49.986824Z",
          "iopub.execute_input": "2023-02-09T12:10:49.987939Z",
          "iopub.status.idle": "2023-02-09T12:10:52.245752Z",
          "shell.execute_reply.started": "2023-02-09T12:10:49.987878Z",
          "shell.execute_reply": "2023-02-09T12:10:52.244700Z"
        },
        "trusted": true,
        "id": "5VthTUzqIe9h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "outputId": "718b69ce-86c3-42de-dc7f-618d14805739"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A primera vista no hi ha forta correlació entre cap feature."
      ],
      "metadata": {
        "id": "zbT-shBoIe9h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grafiquem la densitat del *target* en un pla en funció de 2 *features* o combinació de features:"
      ],
      "metadata": {
        "id": "YMsNFx8AIe9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['axes.facecolor'] = 'k'\n",
        "plt.figure(figsize=(11, 5))\n",
        "import matplotlib\n",
        "cmap = matplotlib.colors.ListedColormap([\"#ffd700\", \"#0057b8\"])\n",
        "# target == 0 → yellow; target == 1 → blue\n",
        "\n",
        "ax = plt.subplot(1, 3, 1)\n",
        "ax.scatter(train['f_02'], train['f_21'], s=1,\n",
        "           c=train.target, cmap=cmap)\n",
        "ax.set_xlabel('f_02')\n",
        "ax.set_ylabel('f_21')\n",
        "ax.set_aspect('equal')\n",
        "ax0 = ax\n",
        "\n",
        "ax = plt.subplot(1, 3, 2, sharex=ax0, sharey=ax0)\n",
        "ax.scatter(train['f_05'], train['f_22'], s=1,\n",
        "           c=train.target, cmap=cmap)\n",
        "ax.set_xlabel('f_05')\n",
        "ax.set_ylabel('f_22')\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "ax = plt.subplot(1, 3, 3, sharex=ax0, sharey=ax0)\n",
        "ax.scatter(train['f_00'] + train['f_01'], train['f_26'], s=1,\n",
        "           c=train.target, cmap=cmap)\n",
        "ax.set_xlabel('f_00 + f_01')\n",
        "ax.set_ylabel('f_26')\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "plt.tight_layout(w_pad=1.0)\n",
        "#plt.savefig('projeccions.png')\n",
        "plt.show()\n",
        "plt.rcParams['axes.facecolor'] = '#0057b8' # blue"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:15:06.613716Z",
          "iopub.execute_input": "2023-02-09T12:15:06.614903Z",
          "iopub.status.idle": "2023-02-09T12:15:29.710798Z",
          "shell.execute_reply.started": "2023-02-09T12:15:06.614832Z",
          "shell.execute_reply": "2023-02-09T12:15:29.709754Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "qcPrSuVkIe9h",
        "outputId": "80e0ae85-5ee1-4425-9d91-9d82167668ae"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En groc targets 0, en blau targets 1"
      ],
      "metadata": {
        "id": "SfRztD93Ie9i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualitzant combinacions de features en 2D i especificament en aquestes 3 combinacions, es troben 3 regions ben diferenciades on la densitat de targets de la mateixa classe és uniforme. Aquestes regions estàn separades linealment per una combinació de features. \n",
        "\n",
        "Si afegim una *feature* amb informació sobre a quina de les 3 regions pertany cada instància ajudarem a la xarxa neuronal a realitzar la classificació.\n",
        "\n",
        "- Si la instància es troba a la regió de dalt a la dreta, la probabilitat que el target==1 es alta -->1.\n",
        "\n",
        "- Si la instància es troba a la regió del mig, la probabilitat que el target==1 es similar a sigui 0-->0.\n",
        "\n",
        "- Si la instància es troba a la regió de baix, a l'esquerra la probabilitat que el target==1 es baixa -->-1.\n",
        "\n",
        "Afegim informació de la regió a que pertany cada instància en els 3 espais 2D:"
      ],
      "metadata": {
        "id": "VolFxGpQIe9i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for df in [X_trainval]:\n",
        "    df['i_02_21'] = (df.f_21 + df.f_02 > 5.2).astype(int) - (df.f_21 + df.f_02 < -5.3).astype(int)\n",
        "    df['i_05_22'] = (df.f_22 + df.f_05 > 5.1).astype(int) - (df.f_22 + df.f_05 < -5.4).astype(int)\n",
        "    i_00_01_26 = df.f_00 + df.f_01 + df.f_26\n",
        "    df['i_00_01_26'] = (i_00_01_26 > 5.0).astype(int) - (i_00_01_26 < -5.0).astype(int)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:15:37.829369Z",
          "iopub.execute_input": "2023-02-09T12:15:37.829939Z",
          "iopub.status.idle": "2023-02-09T12:15:37.884446Z",
          "shell.execute_reply.started": "2023-02-09T12:15:37.829882Z",
          "shell.execute_reply": "2023-02-09T12:15:37.883317Z"
        },
        "trusted": true,
        "id": "ITxW1TzbIe9j"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_trainval.shape)\n",
        "print(X_trainval.columns)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:16:07.502659Z",
          "iopub.execute_input": "2023-02-09T12:16:07.503480Z",
          "iopub.status.idle": "2023-02-09T12:16:07.509300Z",
          "shell.execute_reply.started": "2023-02-09T12:16:07.503438Z",
          "shell.execute_reply": "2023-02-09T12:16:07.508207Z"
        },
        "trusted": true,
        "id": "9gJj-ehtIe9k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36c4d010-35bb-4c15-9d3e-99c021468e7f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900000, 33)\n",
            "Index(['f_00', 'f_01', 'f_02', 'f_03', 'f_04', 'f_05', 'f_06', 'f_07', 'f_08',\n",
            "       'f_09', 'f_10', 'f_11', 'f_12', 'f_13', 'f_14', 'f_15', 'f_16', 'f_17',\n",
            "       'f_18', 'f_19', 'f_20', 'f_21', 'f_22', 'f_23', 'f_24', 'f_25', 'f_26',\n",
            "       'f_28', 'f_29', 'f_30', 'i_02_21', 'i_05_22', 'i_00_01_26'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Afegim la nova informació:"
      ],
      "metadata": {
        "id": "sKqUybv3Ie9k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_total=np.hstack((X_num, X_cat,f_27_dummy,unic_ch_dummy,np.array(X_trainval[['i_02_21','i_05_22','i_00_01_26']])))#np.column_stack()\n",
        "print(X_total.shape)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:16:12.520862Z",
          "iopub.execute_input": "2023-02-09T12:16:12.522061Z",
          "iopub.status.idle": "2023-02-09T12:16:13.172207Z",
          "shell.execute_reply.started": "2023-02-09T12:16:12.522004Z",
          "shell.execute_reply": "2023-02-09T12:16:13.171049Z"
        },
        "trusted": true,
        "id": "BzlpLYmSIe9l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e96afdd-2869-4b01-ad2c-48ae6bfb507c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900000, 300)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_nn=X_total[:810000,:]\n",
        "print(X_nn.shape)\n",
        "X_validate=X_total[810000:,:]\n",
        "print(X_validate.shape)\n",
        "\"Target\"\n",
        "y=train['target'].copy()\n",
        "y=np.array(y)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:16:20.962715Z",
          "iopub.execute_input": "2023-02-09T12:16:20.963649Z",
          "iopub.status.idle": "2023-02-09T12:16:20.973075Z",
          "shell.execute_reply.started": "2023-02-09T12:16:20.963607Z",
          "shell.execute_reply": "2023-02-09T12:16:20.971862Z"
        },
        "trusted": true,
        "id": "ukR24Zn8Ie9l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89043657-992e-40ef-f0ee-697d2db57ec9"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(810000, 300)\n",
            "(90000, 300)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Final Neural Network"
      ],
      "metadata": {
        "id": "zmieUw57Ie9l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tornem a dissenyar i fer proves amb la xarxa neuronal:"
      ],
      "metadata": {
        "id": "tQE_XYDLIe9l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "model.add(layers.Dense(64, activation='relu',kernel_initializer='normal', input_shape = (X_nn.shape[1],)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(64, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(64, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(64, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(40, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(20, activation='relu',kernel_initializer='normal'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "opt = keras.optimizers.Adam(learning_rate=0.03)\n",
        "model.compile(optimizer=opt, loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:16:57.408163Z",
          "iopub.execute_input": "2023-02-09T12:16:57.408608Z",
          "iopub.status.idle": "2023-02-09T12:16:57.565484Z",
          "shell.execute_reply.started": "2023-02-09T12:16:57.408571Z",
          "shell.execute_reply": "2023-02-09T12:16:57.564432Z"
        },
        "trusted": true,
        "id": "s0rcOTfzIe9m"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:17:00.514079Z",
          "iopub.execute_input": "2023-02-09T12:17:00.515352Z",
          "iopub.status.idle": "2023-02-09T12:17:00.520884Z",
          "shell.execute_reply.started": "2023-02-09T12:17:00.515295Z",
          "shell.execute_reply": "2023-02-09T12:17:00.519650Z"
        },
        "trusted": true,
        "id": "NsS5-cYNIe9m"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, show_layer_names=False, show_shapes=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:17:01.293691Z",
          "iopub.execute_input": "2023-02-09T12:17:01.294339Z",
          "iopub.status.idle": "2023-02-09T12:17:01.746001Z",
          "shell.execute_reply.started": "2023-02-09T12:17:01.294299Z",
          "shell.execute_reply": "2023-02-09T12:17:01.744809Z"
        },
        "trusted": true,
        "id": "kvfUAzr5Ie9m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "66d61070-3bdf-4abe-9aef-882b2098ae11"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tf.device('/GPU:0'):\n",
        "    history2 = model.fit(X_nn, y, validation_split=0.1, batch_size=512, epochs=300, \\\n",
        "                        callbacks =EarlyStopping(monitor='val_loss',patience=5))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:09:13.857565Z",
          "iopub.status.idle": "2023-02-09T12:09:13.858602Z",
          "shell.execute_reply.started": "2023-02-09T12:09:13.858306Z",
          "shell.execute_reply": "2023-02-09T12:09:13.858337Z"
        },
        "trusted": true,
        "id": "z2XGAqGeIe9n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34092f97-e91d-4298-919d-d4a46a6fff2c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "1424/1424 [==============================] - 22s 15ms/step - loss: 0.1274 - accuracy: 0.9494 - val_loss: 0.1170 - val_accuracy: 0.9545\n",
            "Epoch 2/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.1071 - accuracy: 0.9580 - val_loss: 0.1022 - val_accuracy: 0.9617\n",
            "Epoch 3/300\n",
            "1424/1424 [==============================] - 19s 13ms/step - loss: 0.0955 - accuracy: 0.9628 - val_loss: 0.1176 - val_accuracy: 0.9566\n",
            "Epoch 4/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0892 - accuracy: 0.9653 - val_loss: 0.0949 - val_accuracy: 0.9639\n",
            "Epoch 5/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0835 - accuracy: 0.9674 - val_loss: 0.0863 - val_accuracy: 0.9669\n",
            "Epoch 6/300\n",
            "1424/1424 [==============================] - 19s 14ms/step - loss: 0.0801 - accuracy: 0.9687 - val_loss: 0.0936 - val_accuracy: 0.9646\n",
            "Epoch 7/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.0770 - accuracy: 0.9701 - val_loss: 0.0861 - val_accuracy: 0.9682\n",
            "Epoch 8/300\n",
            "1424/1424 [==============================] - 26s 18ms/step - loss: 0.0745 - accuracy: 0.9708 - val_loss: 0.0884 - val_accuracy: 0.9675\n",
            "Epoch 9/300\n",
            "1424/1424 [==============================] - 24s 17ms/step - loss: 0.0725 - accuracy: 0.9719 - val_loss: 0.0787 - val_accuracy: 0.9700\n",
            "Epoch 10/300\n",
            "1424/1424 [==============================] - 22s 15ms/step - loss: 0.0706 - accuracy: 0.9727 - val_loss: 0.0792 - val_accuracy: 0.9698\n",
            "Epoch 11/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.0692 - accuracy: 0.9732 - val_loss: 0.0750 - val_accuracy: 0.9716\n",
            "Epoch 12/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.0677 - accuracy: 0.9737 - val_loss: 0.0772 - val_accuracy: 0.9709\n",
            "Epoch 13/300\n",
            "1424/1424 [==============================] - 22s 15ms/step - loss: 0.0666 - accuracy: 0.9742 - val_loss: 0.0775 - val_accuracy: 0.9703\n",
            "Epoch 14/300\n",
            "1424/1424 [==============================] - 21s 15ms/step - loss: 0.0650 - accuracy: 0.9746 - val_loss: 0.0798 - val_accuracy: 0.9692\n",
            "Epoch 15/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0647 - accuracy: 0.9749 - val_loss: 0.0851 - val_accuracy: 0.9683\n",
            "Epoch 16/300\n",
            "1424/1424 [==============================] - 20s 14ms/step - loss: 0.0638 - accuracy: 0.9751 - val_loss: 0.0768 - val_accuracy: 0.9715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('seaborn')\n",
        "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(15,8))\n",
        "\n",
        "#Accuracy\n",
        "ax1.plot(history2.history['accuracy'], label='train')\n",
        "ax1.plot(history2.history['val_accuracy'], label='test')\n",
        "ax1.set_xlabel('epoch')\n",
        "ax1.set_ylabel('accuracy')\n",
        "ax1.set_title('Accuracy')\n",
        "ax1.legend()\n",
        "\n",
        "#Loss\n",
        "ax2.plot(history2.history['loss'], label='train')\n",
        "ax2.plot(history2.history['val_loss'], label='test')\n",
        "ax2.set_xlabel('epoch')\n",
        "ax2.set_ylabel('loss')\n",
        "ax2.set_title('Loss')\n",
        "ax2.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:09:13.830282Z",
          "iopub.status.idle": "2023-02-09T12:09:13.830854Z",
          "shell.execute_reply.started": "2023-02-09T12:09:13.830557Z",
          "shell.execute_reply": "2023-02-09T12:09:13.830583Z"
        },
        "trusted": true,
        "id": "3jQYdRjhIe9p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "f76015e4-e0ab-4f4e-f4c9-de1bb20b0227"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entrenant la xarxa, en poques èpoques superem el 97% de **val_accuracy** i arribem a valors del **97.3%**. És evident que la part de **feature engineering** juga un paper molt determinant en el rendiment dels models de ML. "
      ],
      "metadata": {
        "id": "HHJwiLQVIe9q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exportació de la solució"
      ],
      "metadata": {
        "id": "VniTrvq0Ie9q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(X_validate)\n",
        "y_class = [int(i > .5) for i in y_prob]\n",
        "solution=pd.DataFrame()\n",
        "solution['target']=y_class\n",
        "print(solution.target.value_counts())\n",
        "solution['id']=validate.id\n",
        "solution=solution.set_index('id')\n",
        "print(solution.shape)\n",
        "solution.head()\n",
        "solution.to_csv('nn.csv')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-09T12:09:13.860301Z",
          "iopub.status.idle": "2023-02-09T12:09:13.860848Z",
          "shell.execute_reply.started": "2023-02-09T12:09:13.860557Z",
          "shell.execute_reply": "2023-02-09T12:09:13.860582Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibCiyTw7Ie9q",
        "outputId": "304abf63-e1c0-447c-ca51-04f2da5d9b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2813/2813 [==============================] - 6s 2ms/step\n",
            "0    45870\n",
            "1    44130\n",
            "Name: target, dtype: int64\n",
            "(90000, 1)\n"
          ]
        }
      ]
    }
  ]
}