StandardScaler_from_scratch_Lydia.ipynb

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      "name": "StandardScaler from scratch Lydia.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOlyCxHClKZL",
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      "source": [
        "\n",
        "Créer une class StandardScaler2 avec comme méthode fit, transform, fit_transform et en attribut un dicitonnaire params_ qui contient la moyenne et l'écart_type de vos données. Les données en entrées sont uniquement de array numpy (pas des dataframes)\n",
        "me dire que vous avez terminé avant de faire le bonus.\n",
        "en bonus :\n",
        "- mettre une erreur si on essaye de transform sans avoir de fit\n",
        "- permettre au code de fonctionner, peut importe si l'entrée est une numpy array ou une dataframe\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3z67n7n8rXrA",
        "colab_type": "code",
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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",
        "import warnings\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1jaer-wlKZM",
        "colab_type": "code",
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      "source": [
        "class StandardScaler2 :\n",
        "  def __init__(self):\n",
        "    self.param_={'moyenne':'', 'ecart_type':''}\n",
        "\n",
        "  def fit(self,x):\n",
        "    if str(type(x)) == \"<class 'numpy.ndarray'>\":\n",
        "       std_all=[]\n",
        "       moy=[]\n",
        "       for i in x:\n",
        "         moyy=np.mean(i)\n",
        "         moy.append(moyy)\n",
        "         std_dev= np.std(i)\n",
        "         std_all.append(std_dev)\n",
        "    \n",
        "    elif str(type(x)) == \"<class 'pandas.core.frame.DataFrame'>\" :\n",
        "      for i in x:\n",
        "        moyy=i.mean(axis = 0)\n",
        "        moy.append(moyy)\n",
        "        std_dev=i.std(axis = 0, skipna = True)\n",
        "        std_all.append(std_dev)\n",
        "\n",
        "    else:\n",
        "      moy= sum(x)/len(x)\n",
        "      summ=0\n",
        "      for i in x:\n",
        "        diff= (i-moy)^2\n",
        "        summ+=diff\n",
        "      frac= summ/(len(x))\n",
        "      std_all= frac^(0.5)\n",
        "\n",
        "    self.param_['moyenne']=moy\n",
        "    self.param_['ecart_type']=std_all\n",
        "    return self.param_\n",
        "\n",
        "  def transform(self,x):\n",
        "    try:\n",
        "      for column in x:\n",
        "        if str(type(x)) == \"<class 'numpy.ndarray'>\":\n",
        "          return (column - self.param_['moyenne'])/self.param_['ecart_type']\n",
        "\n",
        "      else:\n",
        "        res=[]\n",
        "        for column in x:\n",
        "          for i in column:\n",
        "            res.append((i - self.param_['moyenne'])/self.param_['ecart_type'])\n",
        "        return res\n",
        "\n",
        "    except TypeError:\n",
        "      print(\"You have to fit before \")\n",
        "\n",
        "\n",
        "  def fit_transform(self,x):\n",
        "    self.fit(x)\n",
        "    return self.transform(x)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3spSU4jE69G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "300eb4c8-de59-4309-f16c-a1cbca84bcf4"
      },
      "source": [
        "sc_x = StandardScaler2()\n",
        "x_train2=sc_x.fit_transform(X_train)\n",
        "# sc_x.transform(X_test)\n",
        "print(x_train2)\n",
        "sc_x.param_()\n",
        "\n",
        "\n"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-98-321d9c4d972c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msc_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx_train2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msc_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# sc_x.transform(X_test)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msc_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-97-a04e221ff6cd>\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     52\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-97-a04e221ff6cd>\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     37\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"<class 'numpy.ndarray'>\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m           \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'moyenne'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ecart_type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (11,) (1279,) "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YFmontHrfjT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "b2381a7d-912c-4550-e53a-8ff4b5946aa5"
      },
      "source": [
        "dataset = pd.read_csv(\"vine.csv\")\n",
        "dataset.head()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUmZAauQr67m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = dataset.iloc[:, :-1]\n",
        "y = dataset.iloc[:, -1:]\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hr-AULZ1sX44",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "04522265-344b-4202-bbe3-b67e1d2361cf"
      },
      "source": [
        "## Oui il faut normaliser pour aider l'algorithme à faire ses prédictions\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "X_train = sc_x.fit_transform(X_train)\n",
        "X_test = sc_x.transform(X_test)\n",
        "X_train\n"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.55247942,  1.85910882, -1.1268776 , ...,  1.61867481,\n",
              "         0.93777314,  2.29633117],\n",
              "       [-0.36073216,  0.2461231 , -0.30831934, ..., -0.96103932,\n",
              "        -0.69996858, -0.57612339],\n",
              "       [ 2.02276235, -0.56036976,  1.99387576, ..., -1.02553217,\n",
              "         1.11324547, -0.02016444],\n",
              "       ...,\n",
              "       [-0.19048256, -0.61797639, -0.51295891, ..., -0.12263223,\n",
              "        -0.29053315, -1.13208233],\n",
              "       [-0.75798125,  0.0733032 , -1.38267706, ...,  0.52229631,\n",
              "         1.05475469,  0.9064338 ],\n",
              "       [ 4.06575764,  0.67817285,  1.12415761, ..., -2.5088678 ,\n",
              "         0.46984693,  0.62845433]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ce6vtUS5soJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmEE5LFJ1otP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e5fd8b03-b67a-4383-b3bd-0f0262a6523c"
      },
      "source": [
        "## Oui il faut normaliser pour aider l'algorithme à faire ses prédictions\n",
        "\n",
        "\n",
        "c"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o41QZA8C1tID",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "sc_x = StandardScaler2()\n",
        "sc_x.transform(X_train)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}