How_to_use_DictVectorizer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "How to use DictVectorizer.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbHP1LBA1YyP"
      },
      "source": [
        "Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fcvzb2Yi1SCy"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oaOggbQJ1kQ-"
      },
      "source": [
        "Create dictionary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRC1W8a985bZ"
      },
      "source": [
        "my_map = {\"name\": \"name\", \"from\": \"from\", \"sex\": \"sex\", \"age\": \"age\"}"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbbJAN4D1okH"
      },
      "source": [
        " dict = [{'name': 'Tracy', 'from': 'Little Rock', 'sex':'female', 'age': 60},\n",
        "         {'name': 'Mike', 'from': 'Reading', 'sex':'male', 'age': 54},\n",
        "         {'name': 'Martina', 'from': 'Berlin', 'sex':'female', 'age':62},\n",
        "         {'name': 'Gerry', 'from':'Heerlen', 'sex':'female', 'age': 80},\n",
        "         {'name': 'Paz', 'from': 'Manila', 'sex':'female', 'age': 61},\n",
        "         {'name': 'Doug', 'from': 'Aberdeen', 'sex':'male', 'age': 55},\n",
        "         {'name': 'Jeff', 'from': 'Cardiff', 'sex':'male', 'age': 57},\n",
        "         {'name': 'Cindy', 'from':'Little Rock', 'sex': 'female', 'age':60},\n",
        "         {'name': 'Keith', 'from': 'Reading', 'sex': 'male', 'age':57},\n",
        "         {'name': 'Adrian', 'from': 'Tercera', 'sex': 'male', 'age': 38},\n",
        "         {'name': 'Katherine', 'from': 'Fayeteville', 'sex': 'female', 'age': 38}]\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aV5Mndpw8J-G",
        "outputId": "143773b4-c62a-467f-db90-5b1f45c51aca"
      },
      "source": [
        "df = pd.DataFrame(dict).rename(columns=my_map)\n",
        "print(df)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         name         from     sex  age\n",
            "0       Tracy  Little Rock  female   60\n",
            "1        Mike      Reading    male   54\n",
            "2     Martina       Berlin  female   62\n",
            "3       Gerry      Heerlen  female   80\n",
            "4         Paz       Manila  female   61\n",
            "5        Doug     Aberdeen    male   55\n",
            "6        Jeff      Cardiff    male   57\n",
            "7       Cindy  Little Rock  female   60\n",
            "8       Keith      Reading    male   57\n",
            "9      Adrian      Tercera    male   38\n",
            "10  Katherine  Fayeteville  female   38\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9k30nd1W3ri5"
      },
      "source": [
        "DictVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8O60jl_y3vJo",
        "outputId": "dab25065-d16b-460f-a945-9cb224d147e8"
      },
      "source": [
        "from sklearn.feature_extraction import DictVectorizer\n",
        "\n",
        "vectors = DictVectorizer(sparse=False, sort=True).fit_transform(dict)\n",
        "vectors"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[60.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.],\n",
              "       [54.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.],\n",
              "       [62.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.],\n",
              "       [80.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],\n",
              "       [61.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.],\n",
              "       [55.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.],\n",
              "       [57.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.],\n",
              "       [60.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],\n",
              "       [57.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.],\n",
              "       [38.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.],\n",
              "       [38.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBfvBlptZZYq",
        "outputId": "730d8b4a-85b3-450e-b1d3-0054fd90bc84"
      },
      "source": [
        "vectors.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4WHsZZ6Z3qy"
      },
      "source": [
        "Define X and y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzcMRnw_cvRG"
      },
      "source": [
        "X, y = vectors[:, 1:], vectors[:, 0]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vl3qll20hxR_",
        "outputId": "e32f5fe7-20b0-4de2-97b5-2ffaa7b4eef4"
      },
      "source": [
        "X"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 1., 1., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 1., 0., 0., 0., 1.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        1., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 1., 0., 1., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
              "        0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
              "        0., 0., 0., 0., 1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6TImhI-h0te",
        "outputId": "44707a20-47bf-43a4-bebd-31c6a4240132"
      },
      "source": [
        "y"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([60., 54., 62., 80., 61., 55., 57., 60., 57., 38., 38.])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VIIOvkZeorv"
      },
      "source": [
        "Analyse y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "O9L8LWHheq3F",
        "outputId": "b82271ab-2cfe-46f6-e51f-60b0f5a98eb7"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.displot(y)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fcc8b289550>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tVfUzwE3ANSsdlGR7koUkC4uLi2s6oCSNZcwA3wssfUW7adj2/1XVA1X16LB6FfCSlZ6oqnZU1XxVzc/NzY0yrCSttTED/EXglCQnJzkOuBjYufSAJCctWT0fuGfEeSRpqoz2LoiqOpDkUuAzwDrgA1V1V5IrgIWq2gm8Ocn5wAHgQeB1Y80jSdNmtAADVNUNwA3Ltl2+ZPky4LIxZ5CkadX9l3CSNLMMsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUZNcBJtiX5apI9Sd6+wv6nJLlu2L8rydYx55GkaTJagJOsA/4AeDnwAuBVSV6w7LA3AN+uqucB7wLeOdY8kjRtxnwFfDqwp6q+VlXfAz4KXLDsmAuAa4bl64Fzk2TEmSRpaqSqxnni5EJgW1VdMqy/GvhnVXXpkmPuHI7ZN6z/5XDM/cueazuwfVh9PvDVUYb+UScC9x/xqGOb58BzcJDn4fGfg/uratvyjeuf+Dzjq6odwI61/r5JFqpqfq2/7zTxHHgODvI8HP1zMOYliHuBzUvWNw3bVjwmyXrgmcADI84kSVNjzAB/ETglyclJjgMuBnYuO2Yn8Nph+ULg5hrrmogkTZnRLkFU1YEklwKfAdYBH6iqu5JcASxU1U7gauDDSfYADzKJ9DRZ88seU8hz4Dk4yPNwlM/BaH8JJ0k6PO+Ek6QmBliSmhjgJZKsS/KlJJ8a1k8ebpHeM9wyfVz3jGNL8vUkdyTZnWRh2PasJDcl+d/Djz/RPeeYkpyQ5PokX0lyT5KXztI5SPL84df/4OPhJG+dpXMAkOTfJbkryZ1Jrk1y/NFuggH+YW8B7lmy/k7gXcOt0t9mcuv0LPiFqjp1yfsd3w78SVWdAvzJsH4s+0/AjVX1T4AXMfk9MTPnoKq+Ovz6nwq8BPgu8Alm6Bwk2Qi8GZivqhcyeSPBxRzlJhjgQZJNwC8DVw3rAc5hcos0TG6Z/hc907Vbesv4MX0ekjwT+Hkm79Chqr5XVd9hhs7BMucCf1lV32D2zsF64KnDPQo/DtzHUW6CAf6BdwO/BfzDsP5s4DtVdWBY3wds7BhsjRXwP5LcOtwCDvCcqrpvWP5r4Dk9o62Jk4FF4D8Pl6OuSvI0ZuscLHUxcO2wPDPnoKruBX4X+Csm4X0IuJWj3AQDDCQ5D9hfVbd2zzIFfq6qXszkU+zelOTnl+4cbpQ5lt+7uB54MfC+qjoN+FuW/VF7Bs4BAMP1zfOBP1y+71g/B8P17QuY/A/5HwNPA37ksxyeKAM8cRZwfpKvM/nUtnOYXAc8YfjjB6x8K/UxZ/g/P1W1n8l1v9OBbyU5CWD4cX/fhKPbB+yrql3D+vVMgjxL5+CglwO3VdW3hvVZOgf/HPg/VbVYVf8X+DiTThzVJhhgoKouq6pNVbWVyR+5bq6qXwVuYXKLNExumf5vTSOuiSRPS/KMg8vALwJ38sO3jB/T56Gq/hrYm+T5w6ZzgbuZoXOwxKv4weUHmK1z8FfAGUl+fPj7oIO/D45qE7wTbpkkZwP/vqrOS/JcJq+InwV8Cfi1qnq0c74xDT/fTwyr64H/WlX/McmzgY8BW4BvAK+sqgebxhxdklOZ/GXsccDXgNczebEyS+fgaUwi9NyqemjYNmu/D94BXAQcYPLf/yVMrvketSYYYElq4iUISWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJv8PXXsR8yGN6noAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ErZN5Ap-haW"
      },
      "source": [
        "Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_RD6ecJ_DRR",
        "outputId": "8101faaf-3d58-486f-f2c3-043ea0dbdb76"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split( X, y, test_size=0.33, shuffle=True, random_state=1)\n",
        "X_train.shape, y_train.shape, X_val.shape, y_val.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7, 22), (7,), (4, 22), (4,))"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHrdU-Dfh_1S"
      },
      "source": [
        "Select model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFwWBaWgBgJ2",
        "outputId": "79d1c54d-395f-41f7-c84f-3e3d3f98907a"
      },
      "source": [
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "model = MLPRegressor(hidden_layer_sizes=500, solver='lbfgs', ).fit(X_train, y_train)\n",
        "print(model.score(X_train,y_train))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.999999998888164\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBU05K4Y_2fj"
      },
      "source": [
        "Predict val"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QcCztW9_40c",
        "outputId": "5fce0ef5-5b8d-48b9-b4b4-d15a6aa34aea"
      },
      "source": [
        "y_pred = model.predict(X_val)\n",
        "print(model.score(X_val, y_val))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-1.6784402546372248\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wEmvx4V2DTU7",
        "outputId": "9962c38d-4584-4d06-8b3d-a4128e138ffa"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 21.492451635797153\n",
            "Mean Squared Error: 595.1159440772084\n",
            "Root Mean Squared Error: 24.39499834140614\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Q0ZIWewTAMw3",
        "outputId": "9edc61ab-6d34-4659-d46a-93a1b80ac616"
      },
      "source": [
        "df1 = pd.DataFrame({'Actual': y_val, 'Predicted':y_pred})\n",
        "df1"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>62.0</td>\n",
              "      <td>41.924918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.0</td>\n",
              "      <td>40.451273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61.0</td>\n",
              "      <td>42.069518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38.0</td>\n",
              "      <td>45.415515</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0    62.0  41.924918\n",
              "1    80.0  40.451273\n",
              "2    61.0  42.069518\n",
              "3    38.0  45.415515"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "db24TgRqBQb1",
        "outputId": "80761199-b751-4a17-e34c-5374a6822c06"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.scatter(y_val, y_pred, c='crimson')\n",
        "plt.yscale('log')\n",
        "plt.xscale('log')\n",
        "\n",
        "p1 = max(max(y_pred), max(y_val))\n",
        "p2 = min(min(y_pred), min(y_val))\n",
        "plt.plot([p1, p2], [p1, p2], 'b-')\n",
        "plt.xlabel('Actual Values', fontsize=15)\n",
        "plt.ylabel('Predictions', fontsize=15)\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}