scikit_learn_practice.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "scikit-learn-practice.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "60kyy0Fta9zV"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pylab as plt\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "QEsu8QMIbeXj",
        "outputId": "30fd30fc-1e58-4bde-ff7d-588a16599707"
      },
      "source": [
        "X, y = load_boston(return_X_y=True)\n",
        "\n",
        "mod = KNeighborsRegressor()\n",
        "mod.fit(X, y)\n",
        "pred=mod.predict(X)\n",
        "\n",
        "plt.scatter(pred,y)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6ed6f51310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymzaquBfdjtQ"
      },
      "source": [
        "## Let's put everything into a pipeline."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "HrJHyLJ9bhOM",
        "outputId": "4e6eab00-0899-4733-9eb6-3be22b13e71e"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.datasets import load_boston\n",
        "from sklearn.pipeline import Pipeline\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "X, y = load_boston(return_X_y=True)\n",
        "\n",
        "pipe = Pipeline([\n",
        "    (\"scale\", StandardScaler()),\n",
        "    (\"model\", KNeighborsRegressor())\n",
        "])\n",
        "pred = pipe.fit(X, y).predict(X)\n",
        "plt.scatter(pred, y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6ed6f39d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2oPCJ35XdcbG"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjkpmisyc_f-"
      },
      "source": [
        ""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n43DgO3-dszj"
      },
      "source": [
        "\n",
        "## Let's also apply some settings!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "lgZHZhdUdt8M",
        "outputId": "071abb99-665c-48b5-e5a4-1bab3df0dc3f"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.datasets import load_boston\n",
        "from sklearn.pipeline import Pipeline\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "X, y = load_boston(return_X_y=True)\n",
        "\n",
        "# If n_neighbors = 1, we're totally cheating with the chart below.\n",
        "pipe = Pipeline([\n",
        "    (\"scale\", StandardScaler()),\n",
        "    (\"model\", KNeighborsRegressor(n_neighbors=1))\n",
        "])\n",
        "pred = pipe.fit(X, y).predict(X)\n",
        "plt.scatter(pred, y)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6ed6a256d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QB1ved6d1em"
      },
      "source": [
        "## Let's now use a gridsearch!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQ0YhAXrd2od",
        "outputId": "97556dfe-8f56-49a8-9475-b4286572ffce"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import pandas as pd\n",
        "\n",
        "mod = GridSearchCV(estimator=pipe,\n",
        "                 param_grid={\n",
        "                   'model__n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "                 },\n",
        "                 cv=3)\n",
        "mod.fit(X, y)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, error_score=nan,\n",
              "             estimator=Pipeline(memory=None,\n",
              "                                steps=[('scale',\n",
              "                                        StandardScaler(copy=True,\n",
              "                                                       with_mean=True,\n",
              "                                                       with_std=True)),\n",
              "                                       ('model',\n",
              "                                        KNeighborsRegressor(algorithm='auto',\n",
              "                                                            leaf_size=30,\n",
              "                                                            metric='minkowski',\n",
              "                                                            metric_params=None,\n",
              "                                                            n_jobs=None,\n",
              "                                                            n_neighbors=1, p=2,\n",
              "                                                            weights='uniform'))],\n",
              "                                verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'model__n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "a7F7QMaqd-pd",
        "outputId": "8af234e1-cf93-4ed8-e8c9-6689d9d2e188"
      },
      "source": [
        "pd.DataFrame(mod.cv_results_)"
      ],
      "execution_count": 6,
      "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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_model__n_neighbors</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.002540</td>\n",
              "      <td>0.000705</td>\n",
              "      <td>0.003442</td>\n",
              "      <td>0.001328</td>\n",
              "      <td>1</td>\n",
              "      <td>{'model__n_neighbors': 1}</td>\n",
              "      <td>0.226933</td>\n",
              "      <td>0.432998</td>\n",
              "      <td>0.127635</td>\n",
              "      <td>0.262522</td>\n",
              "      <td>0.127179</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.001454</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>0.002516</td>\n",
              "      <td>0.000346</td>\n",
              "      <td>2</td>\n",
              "      <td>{'model__n_neighbors': 2}</td>\n",
              "      <td>0.358216</td>\n",
              "      <td>0.409229</td>\n",
              "      <td>0.172294</td>\n",
              "      <td>0.313246</td>\n",
              "      <td>0.101821</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001355</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.002173</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>3</td>\n",
              "      <td>{'model__n_neighbors': 3}</td>\n",
              "      <td>0.413515</td>\n",
              "      <td>0.476651</td>\n",
              "      <td>0.318534</td>\n",
              "      <td>0.402900</td>\n",
              "      <td>0.064986</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.001252</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.002120</td>\n",
              "      <td>0.000119</td>\n",
              "      <td>4</td>\n",
              "      <td>{'model__n_neighbors': 4}</td>\n",
              "      <td>0.475349</td>\n",
              "      <td>0.402495</td>\n",
              "      <td>0.273014</td>\n",
              "      <td>0.383619</td>\n",
              "      <td>0.083675</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.001361</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.002424</td>\n",
              "      <td>0.000229</td>\n",
              "      <td>5</td>\n",
              "      <td>{'model__n_neighbors': 5}</td>\n",
              "      <td>0.512318</td>\n",
              "      <td>0.347951</td>\n",
              "      <td>0.262590</td>\n",
              "      <td>0.374286</td>\n",
              "      <td>0.103638</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.001301</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.002349</td>\n",
              "      <td>0.000118</td>\n",
              "      <td>6</td>\n",
              "      <td>{'model__n_neighbors': 6}</td>\n",
              "      <td>0.533611</td>\n",
              "      <td>0.389504</td>\n",
              "      <td>0.248482</td>\n",
              "      <td>0.390532</td>\n",
              "      <td>0.116406</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.001827</td>\n",
              "      <td>0.000764</td>\n",
              "      <td>0.003355</td>\n",
              "      <td>0.000981</td>\n",
              "      <td>7</td>\n",
              "      <td>{'model__n_neighbors': 7}</td>\n",
              "      <td>0.544782</td>\n",
              "      <td>0.385199</td>\n",
              "      <td>0.243668</td>\n",
              "      <td>0.391216</td>\n",
              "      <td>0.123003</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.001230</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.002382</td>\n",
              "      <td>0.000224</td>\n",
              "      <td>8</td>\n",
              "      <td>{'model__n_neighbors': 8}</td>\n",
              "      <td>0.589644</td>\n",
              "      <td>0.394650</td>\n",
              "      <td>0.209714</td>\n",
              "      <td>0.398003</td>\n",
              "      <td>0.155124</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.001199</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.002324</td>\n",
              "      <td>0.000173</td>\n",
              "      <td>9</td>\n",
              "      <td>{'model__n_neighbors': 9}</td>\n",
              "      <td>0.590352</td>\n",
              "      <td>0.407556</td>\n",
              "      <td>0.185253</td>\n",
              "      <td>0.394387</td>\n",
              "      <td>0.165643</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.001358</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>0.002930</td>\n",
              "      <td>0.000537</td>\n",
              "      <td>10</td>\n",
              "      <td>{'model__n_neighbors': 10}</td>\n",
              "      <td>0.616510</td>\n",
              "      <td>0.395077</td>\n",
              "      <td>0.164023</td>\n",
              "      <td>0.391870</td>\n",
              "      <td>0.184741</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
              "0       0.002540      0.000705  ...        0.127179               10\n",
              "1       0.001454      0.000143  ...        0.101821                9\n",
              "2       0.001355      0.000042  ...        0.064986                1\n",
              "3       0.001252      0.000021  ...        0.083675                7\n",
              "4       0.001361      0.000138  ...        0.103638                8\n",
              "5       0.001301      0.000064  ...        0.116406                6\n",
              "6       0.001827      0.000764  ...        0.123003                5\n",
              "7       0.001230      0.000037  ...        0.155124                2\n",
              "8       0.001199      0.000005  ...        0.165643                3\n",
              "9       0.001358      0.000090  ...        0.184741                4\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSGDq6Y7eHj8"
      },
      "source": [
        "## Finally, lets remember that we should look at the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMEtAjgieI-O",
        "outputId": "5d30683f-e0ad-4cac-c289-c19beb5f7b3e"
      },
      "source": [
        "print(load_boston()['DESCR'])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _boston_dataset:\n",
            "\n",
            "Boston house prices dataset\n",
            "---------------------------\n",
            "\n",
            "**Data Set Characteristics:**  \n",
            "\n",
            "    :Number of Instances: 506 \n",
            "\n",
            "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
            "\n",
            "    :Attribute Information (in order):\n",
            "        - CRIM     per capita crime rate by town\n",
            "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
            "        - INDUS    proportion of non-retail business acres per town\n",
            "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
            "        - NOX      nitric oxides concentration (parts per 10 million)\n",
            "        - RM       average number of rooms per dwelling\n",
            "        - AGE      proportion of owner-occupied units built prior to 1940\n",
            "        - DIS      weighted distances to five Boston employment centres\n",
            "        - RAD      index of accessibility to radial highways\n",
            "        - TAX      full-value property-tax rate per $10,000\n",
            "        - PTRATIO  pupil-teacher ratio by town\n",
            "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
            "        - LSTAT    % lower status of the population\n",
            "        - MEDV     Median value of owner-occupied homes in $1000's\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
            "\n",
            "This is a copy of UCI ML housing dataset.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
            "\n",
            "\n",
            "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
            "\n",
            "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
            "prices and the demand for clean air', J. Environ. Economics & Management,\n",
            "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
            "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
            "pages 244-261 of the latter.\n",
            "\n",
            "The Boston house-price data has been used in many machine learning papers that address regression\n",
            "problems.   \n",
            "     \n",
            ".. topic:: References\n",
            "\n",
            "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
            "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j53AtRzneLrW"
      },
      "source": [
        ""
      ],
      "execution_count": 7,
      "outputs": []
    }
  ]
}