lasson.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lasson.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "Y-uAcf3N9B6x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn import linear_model\n",
        "from sklearn import datasets\n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('seaborn-white')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DJj_hxCy9iac",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6c2cca17-fa32-4c08-d2d8-c8c846b433d3"
      },
      "cell_type": "code",
      "source": [
        "diabetes = datasets.load_diabetes()\n",
        "X = diabetes.data\n",
        "y = diabetes.target.reshape(-1,1)\n",
        "\n",
        "print(X.shape)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(442, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kAWkUmt4-GwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ae101476-1bf2-40e9-e621-8d1bc478ab0a"
      },
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame(X, columns=diabetes.feature_names)\n",
        "data.head()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>bp</th>\n",
              "      <th>s1</th>\n",
              "      <th>s2</th>\n",
              "      <th>s3</th>\n",
              "      <th>s4</th>\n",
              "      <th>s5</th>\n",
              "      <th>s6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.038076</td>\n",
              "      <td>0.050680</td>\n",
              "      <td>0.061696</td>\n",
              "      <td>0.021872</td>\n",
              "      <td>-0.044223</td>\n",
              "      <td>-0.034821</td>\n",
              "      <td>-0.043401</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>0.019908</td>\n",
              "      <td>-0.017646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.001882</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.051474</td>\n",
              "      <td>-0.026328</td>\n",
              "      <td>-0.008449</td>\n",
              "      <td>-0.019163</td>\n",
              "      <td>0.074412</td>\n",
              "      <td>-0.039493</td>\n",
              "      <td>-0.068330</td>\n",
              "      <td>-0.092204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.085299</td>\n",
              "      <td>0.050680</td>\n",
              "      <td>0.044451</td>\n",
              "      <td>-0.005671</td>\n",
              "      <td>-0.045599</td>\n",
              "      <td>-0.034194</td>\n",
              "      <td>-0.032356</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>0.002864</td>\n",
              "      <td>-0.025930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.089063</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.011595</td>\n",
              "      <td>-0.036656</td>\n",
              "      <td>0.012191</td>\n",
              "      <td>0.024991</td>\n",
              "      <td>-0.036038</td>\n",
              "      <td>0.034309</td>\n",
              "      <td>0.022692</td>\n",
              "      <td>-0.009362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.005383</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.036385</td>\n",
              "      <td>0.021872</td>\n",
              "      <td>0.003935</td>\n",
              "      <td>0.015596</td>\n",
              "      <td>0.008142</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>-0.031991</td>\n",
              "      <td>-0.046641</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        age       sex       bmi        bp        s1        s2        s3  \\\n",
              "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
              "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
              "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
              "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
              "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
              "\n",
              "         s4        s5        s6  \n",
              "0 -0.002592  0.019908 -0.017646  \n",
              "1 -0.039493 -0.068330 -0.092204  \n",
              "2 -0.002592  0.002864 -0.025930  \n",
              "3  0.034309  0.022692 -0.009362  \n",
              "4 -0.002592 -0.031991 -0.046641  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "id": "7rghB3Wz-ork",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "a230243d-3f02-4936-e2e7-a50252601fb6"
      },
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 442 entries, 0 to 441\n",
            "Data columns (total 10 columns):\n",
            "age    442 non-null float64\n",
            "sex    442 non-null float64\n",
            "bmi    442 non-null float64\n",
            "bp     442 non-null float64\n",
            "s1     442 non-null float64\n",
            "s2     442 non-null float64\n",
            "s3     442 non-null float64\n",
            "s4     442 non-null float64\n",
            "s5     442 non-null float64\n",
            "s6     442 non-null float64\n",
            "dtypes: float64(10)\n",
            "memory usage: 34.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "61mq0LBi-vrI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "1aac029e-fd3d-4169-b741-1268a368dc2d"
      },
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "\n",
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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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>bp</th>\n",
              "      <th>s1</th>\n",
              "      <th>s2</th>\n",
              "      <th>s3</th>\n",
              "      <th>s4</th>\n",
              "      <th>s5</th>\n",
              "      <th>s6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-3.634285e-16</td>\n",
              "      <td>1.308343e-16</td>\n",
              "      <td>-8.045349e-16</td>\n",
              "      <td>1.281655e-16</td>\n",
              "      <td>-8.835316e-17</td>\n",
              "      <td>1.327024e-16</td>\n",
              "      <td>-4.574646e-16</td>\n",
              "      <td>3.777301e-16</td>\n",
              "      <td>-3.830854e-16</td>\n",
              "      <td>-3.412882e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.072256e-01</td>\n",
              "      <td>-4.464164e-02</td>\n",
              "      <td>-9.027530e-02</td>\n",
              "      <td>-1.123996e-01</td>\n",
              "      <td>-1.267807e-01</td>\n",
              "      <td>-1.156131e-01</td>\n",
              "      <td>-1.023071e-01</td>\n",
              "      <td>-7.639450e-02</td>\n",
              "      <td>-1.260974e-01</td>\n",
              "      <td>-1.377672e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-3.729927e-02</td>\n",
              "      <td>-4.464164e-02</td>\n",
              "      <td>-3.422907e-02</td>\n",
              "      <td>-3.665645e-02</td>\n",
              "      <td>-3.424784e-02</td>\n",
              "      <td>-3.035840e-02</td>\n",
              "      <td>-3.511716e-02</td>\n",
              "      <td>-3.949338e-02</td>\n",
              "      <td>-3.324879e-02</td>\n",
              "      <td>-3.317903e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.383060e-03</td>\n",
              "      <td>-4.464164e-02</td>\n",
              "      <td>-7.283766e-03</td>\n",
              "      <td>-5.670611e-03</td>\n",
              "      <td>-4.320866e-03</td>\n",
              "      <td>-3.819065e-03</td>\n",
              "      <td>-6.584468e-03</td>\n",
              "      <td>-2.592262e-03</td>\n",
              "      <td>-1.947634e-03</td>\n",
              "      <td>-1.077698e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.807591e-02</td>\n",
              "      <td>5.068012e-02</td>\n",
              "      <td>3.124802e-02</td>\n",
              "      <td>3.564384e-02</td>\n",
              "      <td>2.835801e-02</td>\n",
              "      <td>2.984439e-02</td>\n",
              "      <td>2.931150e-02</td>\n",
              "      <td>3.430886e-02</td>\n",
              "      <td>3.243323e-02</td>\n",
              "      <td>2.791705e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.107267e-01</td>\n",
              "      <td>5.068012e-02</td>\n",
              "      <td>1.705552e-01</td>\n",
              "      <td>1.320442e-01</td>\n",
              "      <td>1.539137e-01</td>\n",
              "      <td>1.987880e-01</td>\n",
              "      <td>1.811791e-01</td>\n",
              "      <td>1.852344e-01</td>\n",
              "      <td>1.335990e-01</td>\n",
              "      <td>1.356118e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age           sex           bmi            bp            s1  \\\n",
              "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
              "mean  -3.634285e-16  1.308343e-16 -8.045349e-16  1.281655e-16 -8.835316e-17   \n",
              "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
              "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01   \n",
              "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02   \n",
              "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03   \n",
              "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564384e-02  2.835801e-02   \n",
              "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320442e-01  1.539137e-01   \n",
              "\n",
              "                 s2            s3            s4            s5            s6  \n",
              "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
              "mean   1.327024e-16 -4.574646e-16  3.777301e-16 -3.830854e-16 -3.412882e-16  \n",
              "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
              "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01  \n",
              "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02  \n",
              "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03  \n",
              "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243323e-02  2.791705e-02  \n",
              "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335990e-01  1.356118e-01  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "metadata": {
        "id": "Hg_hoAtaDkYV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "平均に注目、０なので切片はなし"
      ]
    },
    {
      "metadata": {
        "id": "vb9VIrBN-3Nd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def soft_threshold(rho, lamda):\n",
        "    if rho < - lamda:\n",
        "        return (rho + lamda)\n",
        "    elif rho >  lamda:\n",
        "        return (rho - lamda)\n",
        "    else: \n",
        "        return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_D02bx0v9nFZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def coordinate_descent(theta, X, y, lamda = .01, iter=100):\n",
        "    m, n = X.shape\n",
        "    \n",
        "    for i in range(iter): \n",
        "        for j in range(n):\n",
        "            X_j = X[:, j].reshape(-1, 1)\n",
        "            \n",
        "            # わざとy_predの形で計算してる\n",
        "            y_pred = X @ theta\n",
        "            \n",
        "            # 内積を取るようにしてる\n",
        "            rho = X_j.T @ (y - y_pred  + theta[j] * X_j)\n",
        "            \n",
        "            theta[j] = soft_threshold(rho, lamda)/np.sum(X_j**2)   \n",
        "    return theta.flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "N310oPXGEJs1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "参考のリンク先では最後の除算\n",
        "- /np.sum(X_j**2) \n",
        "\n",
        "が抜けていたので加えた"
      ]
    },
    {
      "metadata": {
        "id": "wIakCmbZAYyM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "initial_theta = np.ones((X.shape[1], 1))\n",
        "theta_list = list()\n",
        "lamda = np.logspace(0, 4, 300)/10\n",
        "\n",
        "for l in lamda:\n",
        "    theta = coordinate_descent(initial_theta, X, y, lamda = l, iter=100)\n",
        "    theta_list.append(theta)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "klrRYEJoDL7D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "86d0441c-d0e9-4cc8-e8be-8c85292c0d2b"
      },
      "cell_type": "code",
      "source": [
        "np.stack(theta_list).shape"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "id": "cgBdffKxDE_R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "outputId": "9f651aff-2b95-44a9-e305-12dbd820277c"
      },
      "cell_type": "code",
      "source": [
        "theta_lasso = np.stack(theta_list).T\n",
        "\n",
        "n, _ = theta_lasso.shape\n",
        "plt.figure(figsize = (12, 8))\n",
        "\n",
        "for i in range(n):\n",
        "    plt.plot(lamda, theta_lasso[i], label = diabetes.feature_names[i])\n",
        "\n",
        "plt.xscale('log')\n",
        "plt.xlabel('Log($\\\\lambda$)')\n",
        "plt.ylabel('Coefficients')\n",
        "plt.title('Lasso Paths - Numpy implementation')\n",
        "plt.legend()\n",
        "plt.axis('tight')"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.06309573444801936,\n",
              " 1584.8931924611109,\n",
              " -849.8147108556045,\n",
              " 820.6104516733084)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dzQUXXIDBYGDmzJlce+21KIrCDTfcgE1unSWEOE6MTicFM39H+phz2Pbnv7L3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\nRNtCSojmvlYOd9VS1VnL4a4aDnfVUdfTyNrqjQCoJJkceybFCflMSci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JJkxmsSmW4PTR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\nw9GTlgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "lcggYS-RGLYU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " # sklearnと勝負\n",
        " - https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py\n",
        " - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html\n",
        " "
      ]
    },
    {
      "metadata": {
        "id": "x_N31l1aEsxj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "outputId": "d0934ad9-c04f-4bf8-ed16-6a2dffea0169"
      },
      "cell_type": "code",
      "source": [
        "# the smaller it is the longer is the path\n",
        "eps = 5e-6\n",
        "\n",
        "alphas_lasso, coefs_lasso, _ = linear_model.lasso_path(X, y, eps, fit_intercept=False)\n",
        "\n",
        "# neg_log_alphas_lasso = -np.log10(alphas_lasso)\n",
        "# 公式だと上のものを使ってたんだが、使うと一変したので使わない\n",
        "plt.figure(figsize = (12, 8))\n",
        "\n",
        "\n",
        "for i in range(n):\n",
        "  # coefs_lasso[0]にしないとエラーが出る\n",
        "  plt.plot(alphas_lasso, coefs_lasso[0][i], label = diabetes.feature_names[i])\n",
        "\n",
        "    \n",
        "plt.xscale('log')\n",
        "plt.xlabel('Log($\\\\lambda$)')\n",
        "plt.ylabel('coefficients')\n",
        "plt.title('Lasso paths - Sklearn')\n",
        "plt.legend()\n",
        "plt.axis('tight')"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5.83393507793082e-06,\n",
              " 3.9545273822368454,\n",
              " -868.5336329731205,\n",
              " 828.1219316244385)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iHHKOxHhFAZT2Iv66ncZ0Oa50zNPeV+k8Z6exXcapXedWlU7vLzZHYi1w+zyK\ngqIq8SUF6kH7iqbF6lqsrsTPjCqaltg4YP/gTUXR4gmwpft+h5pHtWigHjAunkzLumYwIhGifj/R\n1lZ0fxvR1lairf542UqkpYVoSwvRlni9uYVIczN6W9sXzq3a7VhSUrB4UrCkpKC5XFhc8eUqDgeq\n04miKB1JfyQaX27TdflN+7H2LwtmJL7sJhLB8LclvnDQ/sXwy1KUeMLtTGwWlxPN5ep4D+11twuL\n243mdmNxu7GkxErN5UK1WnsmHiGOM0muvyTvKVOY9tJfTqwzUSfSGYReiFXOqAgheopqtWJLT8eW\nnn5U44xIhEhzM5GmZqLNzYR9TUSafEQafYQbfUR8PsI+H+GGRtr27O12HmtGBs6CfBz5+TgL4ltR\nEY78vNg68CPUsS4+fmY/FDuzb4TD6KFQrB4/s6+HghjBEHowiB6M10NB9EAQIxhEDwTQA0H0YAC9\nrY1wXV0seT9KitUa/zLhQosn5KrNFvuyqKooascXQtWiHbC0JvbXEs1ujyXsKZ5Y6UmJ1T0psbnk\n94HoAZJc9wA5WyOEEOLLUK1W7JmZ2DMzv7CvEQ4Tqm8gXF9PqK4+VtbWEqysIlBRSfOWrTR/uqXL\nGEXTcBTk4youxlVchLO4GPegYpyFhYdce60oSmLpDi5Xj73PdqauE21rQ2+LJdyxehtRvx/d7yfq\nb0v8BSDa6o8l6G0B9EBsTNjnwwgGezQm1WbD4vFgTfVg8Xji9VRs3gzsWZnYvF5smZnYMjOxuJw9\n+tqif5HkWgghhDiBqDYbzvw8nPl5hzxuRCIEq6oJVFQSrKigrayMwP4y2vbHyvpOfRWrFVdJCe7S\nQbhLSxOl5Tgk1J0pmobV48Hq8RzzHIkLaXWj04W98bJ9GUz78pdI7M4zeiiM7o8l7JGWlo5lO/Hl\nOpGWFoJV1ei79xz2tTWXi5RhQ0k/aRLpkybiLh0sZ71FgiTXQgghRD+iWq24iotwFRd1aTdNk3BD\nQzzR3o9/z178u/fQtm8f/p07OzoqCs7CQlKGD8MzfBgpI4bjHjyoz615VjQN7Tjd8cSIRGLr4pub\nCDc0xv46EP9rQbi+nmBNLU0bN9G0cRN7n34Ga3o66ZMmkH7SJDJOmoQ1Le24xCVODJJcCyGEEAOA\noiiJpSfpkyYm2o1olEB5Bf7de/Dv3o1/5y5ad+yktqyM2neXxcZaLLhLS0kdO5rU0aNIHTMaa2pq\nkt7J8adardi8Gdi8GbgHDz5kn7CvCd/6DfjWrce3fgO1y96jdtl7oKqkjhqJ97SpeKdO7fYvDKL/\nUsw+eb+zo1dWVsacOXNYunQWRqq6AAAgAElEQVQpRUVFXzxACCGEEIdk6jqB8gpad+ygZdsOWrdv\nx797T+xe4XHOoiJSx4widewY0idMwObNSGLEyWWaJm179tK4bj0NH6+m5bPPEzc6cA0qwXvqVDKn\nnYq7tFSWj/QTh8s7JbkWQgghxBfSQyFaPt9G85attGz9jObPPu9yUaGrpJi0iRNJnzSBtLFj0JwD\n96K/sM9H4+o11K/6GN/6jZiRCADOokKyz5hJ1hkz5Yz2CU6SayGEEEL0KFPXad21m+bNn+Jbv4Hm\nLVsTt9hTLBY8o0aScfJkvFNPwVVUmORok0cPBGhct566Dz6kcfUnic8oZcTwWKI98/SjvnWjSD5J\nroUQQghxXBnhMM2ffU7Tho341m+gdeeuxNIIR0E+3qmn4J06hdRRowbso9ejbW00rPyY2uXv4du4\nCQwDRdPwTp1C7lfPJn3SRLm97wlCkuvjyBeMsGxfLVHDABQ6Pcet08PeYk9r63os1vegfpDo274u\nq6Ot4wl18WfAtT/kLVEHUDvX28cpoMbnVLqUseNqfB41MaeSGKsooMb7q53a1fZ25cD2jmMdZde6\nEEKI/i3sa6Lxk09o+HgNvvUbEktILJ4UvKeeSvbM00kbP27AJtphn4+69z+kZum7+HfvBsCek03u\n2WeRM2c29kxvkiMUhyPJ9XH0YVk9f9rY/dOyxMFiSXrnpFtBU0BTO+/HknFNjdU1RUkcb69rioJF\n7di3JNrUxL6l0zGLqsbLeD0+3qqpWONt1k77VlXFqsXGyBcCIYQ4dkY4TNOmzTR8vJr6VauJNDYC\nYE1LI/P0aWTNOJ3U0aMG5Flb0zRp3bGT6rf/Te17H8S+hKgq3lNOpmDu+aSOHSMXQfZBklwfR6Zp\nUtYSIGKYnZ6AbmLGCsxYEWsz24+2/6Xs8P2MTn1Ms+txMzGP2WU+IzE2Pnf8mBGf1DA7z9cxl2Ga\nXdra+7W3G/HXMuJ9DLPTMdOMbZ36JNoS9UO1mejxfT1xzEQ3Yvu6aWIYZkfd7Hh/vc0ST7ZtmopN\nU7Al6l03u6Zit6jYNC1Wj+87NC1WWmLtDouGM74vibsQYiAxDYPmrVupe/9D6j5cQbS5GQBbZibZ\nZ55B7lmzcRYUJDnK5Ii2Bah7/32q3vo3/p27AEgZNpSCC+aSdfq0AXuWvy+S5Fr0Gx0JeDzpNkyi\n7eUBbVHDRDcMovFjUcNItEd1k6hpENHj7YZJxDCJxOth3SBixI7HSoNwfD9sGIT12BbtgWz/wGTb\nadFwWTs2p8WSqLutFtztpS3WV5JzIcSJytR1mjZtpvb9D6hfsQrd7wcgdewYcs+aQ+bp09Ds9iRH\nmRzNWz+j4tXXqF/5MZgm9pxs8r9xHrlnnyWPX+8DJLkW4jgxTJOIbhCKJ9vtZTBqENZ1QvG2UNQg\nGNUJ6gahqE4wahDUdQJRnWDEIBDVE9vRJOwK4LJqpNgseGyWjtIaq6faraTaLaTarKTZLbhtFknG\nhRB9khEOU79yFdX/XkrTxk1A7DHjWTNnkDPrK3hGjhiQy0YClZVUvPZPapa+ixEKobldFHzjPArm\nno8lxZ3s8AYsSa6FOIFE9Fiy3RbRaYuXgUgUf0THH9FpS9Sj+MM6rZEoreHY9kX/M6sKeGwW0uxW\n0h1W0u22WOmwkm63kuGw4XXacFnlT49CiOQJVlVRvfRdapa+Q7i+AQCb14v3tKlkTjuNtLFjBtwS\niUhzC1VvvkXlP/9FpKkZze2mcN5c8r9xnpzJTgJJroUYAAzTpC2i0xqO0hLfmkMRmsNRWkIRmkNR\nmsLxMhgmfJgz5A6LijeeaHsdNjKdNrJcNrJddrKcNlJsFrnARghx3Jm6jm/9Buo+XEHDxx8TbWkF\nwJKaSuapU/GMHI5qs6PabKh2G6rVGqs7HFhcTjSXC83h6FeJuB4IUPn6m5T/fQnRllYsHg+FF84j\n/+vnoDkcyQ5vwJDkWgjRhWmaBKI6vmAEXyiCLxihMRjBFwxTHwjTEAzTGIjQFtUPOd6uqWS77GS7\nbOS6HeS67YnSI4m3EOI4MKJRmjd/Sv2KldSv/JiIz3fEY1WHA4vbhSUlBUduLvbcXBx58S03F3tO\n9gm3tjva1kblP/5F+auvofvbsKalUfztS8j76tn96stEXyXJtRDimAQiOg3xhLu2LURdW5i6QChR\nD+nGQWOcFo08t538FAcFHicF8dLrsErSLYToEaau0/L5NoJV1Rjh8EGbHgyhB9rQ2wJE29rQ41uk\nqRk9EDjknPbsLBwFBTgLC3C2l4WF2LOz+vRa72irn/JXX6PitX9iBIO4BpVQetWVpE8Yn+zQ+jVJ\nroUQPc40TVrCUar8Iar9Qao7lTX+EPoBP1ocFpWCFCfFqU5KUl0Upzop9DixaX33l5YQon8xTZNo\nSyvB6mqCVdWE4mWgspJgRSXhhoaDxqgOB66SYtyDBuEaVIJrUAnuwYOwpqYm4R10L9zYyN5nn6dm\n6btgmnhPO5XS730HR15eskPrlw6Xd1qSFJMQ4gSnKEr8biRWRnhTuhyLGia1bUEqWoKUtwapaAlQ\n2Rpkb5OfXT5/op+qQJ7bQUmqi8HpLoakuynyOLFKwi2EOA4URcGa6sGa6sEzfNhBx/VAgEBlJYGy\nCgIVFQTKy2nbuw//zl20btvepa8jP4+0ceNIGz+O1HFjk/5ERVtGBsOvv5b8c89h1x+fomHlKhrX\nfELBBedTfPFFaE656LG3yJlrIUSviegGFa1B9jW3sb85wP542Xl5iUVVKPY4KU13MyTdzTBvCplO\nWxKjFkIMdEYkQqC8IpZo792Lf/ceWj77HL2tLdHHUVBA2vixpE+cQPrECVhSUg4z4/FlmiZ173/I\nnj/9mXB9Pa5BJYy5cxH2zMykxdTfyLIQIUSfZZgmNf4Qu3x+dvv87Pa1UdbSht7pJ1Om08Zwbwoj\n4luOyy7rt4UQSWXqOq27dtO0aTPNmz+lecvWjvXcqopn5AgyJp9ExsmTcZcOTsq6bT0UYs9Tf6Lq\nzbexZWUx9s7bcJWU9Hoc/ZEk10KIE0pYN9jX3MauRj/bG1vZ3tCKP9Jx55I0u4WRmR7GZqUyJiuV\ndIc1idEKIUQ82d6xE9/6DTR+so6W7dvBiP1VzpqeTsbJk8mcdirpkyaiWnvvZ5ZpmpS/8nf2PvMc\nmtvN6NtuIW3smF57/f5KkmshxAnNME0qW4Nsa4gl2tsaWmgKRRPHCz0OxmalMjYrleHeFFmzLYRI\nukhLC771G/GtXUvj2vWJWwdqTicZUyaTOW0aGZMn9dpa6Jp3lrHjt4+DqjLyxhvInHZar7xufyXJ\ntRCiXzFNk4rWIJ/WNvNpXTPbG1qJxB+KY9dUxmanclJuGuNz0nBb5bptIURymYZBy7btsXt0r1hJ\nqLoGANVmI33ySeSceQYZU04+7me0G9eu47MHf44RCjHkB98n/7xzj+vr9WeSXAsh+rWwbrCjoZXN\ndc1sqG6ipi0ExO5GMsLrYVJ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R6CIjqNywCXdra4Ai7B4BSa43b97Mv//9b157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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "POnzQr3GN45B",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "- $\\lambda$がなんで対数で撮られているのかがわからないが同じような結果になった"
      ]
    },
    {
      "metadata": {
        "id": "LWVJ-4Z6M06i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5c324e13-abd3-410d-f0a4-3bed152237dc"
      },
      "cell_type": "code",
      "source": [
        "clf_lasso= linear_model.Lasso(alpha=1.0, fit_intercept=False)\n",
        "clf_lasso.fit(X, y)\n",
        "\n",
        "print(clf_lasso.intercept_) \n",
        "print(clf_lasso.coef_) "
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n",
            "[  0.          -0.         367.70185207   6.30190419   0.\n",
            "   0.          -0.           0.         307.6057       0.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pymrVebqQNc4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}