movie_k_최근접_이웃_회귀.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VDkl1AvPAKJQ"
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      "outputs": [],
      "source": [
        "# from google.colab import files\n",
        "# train_movie = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/movies_train.csv')"
      ],
      "metadata": {
        "id": "ZKMmC9rFAMl-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. k-최근접 이웃 분류 : 스탭 수와 배우 수를 주면 관객수가 높은지 낮은지 분류\n",
        "2. k-최근접 이웃 회귀 : 스탭 수와 배우 수를 주면 관객수를 예측하는 모델 HERE!"
      ],
      "metadata": {
        "id": "jPj6CyhasBYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# df_numpy = df.to_numpy()\n",
        "# print(df_numpy)\n",
        "df"
      ],
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        "outputId": "27e24e09-317a-47e4-ff11-3cee8ad38d77"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         title   distributor genre release_time  time screening_rat director  \\\n",
              "0       개들의 전쟁      롯데엔터테인먼트    액션   2012-11-22    96      청소년 관람불가      조병옥   \n",
              "1         내부자들        (주)쇼박스   느와르   2015-11-19   130      청소년 관람불가      우민호   \n",
              "2    은밀하게 위대하게        (주)쇼박스    액션   2013-06-05   123       15세 관람가      장철수   \n",
              "3     나는 공무원이다        (주)NEW   코미디   2012-07-12   101        전체 관람가      구자홍   \n",
              "4         불량남녀  쇼박스(주)미디어플렉스   코미디   2010-11-04   108       15세 관람가      신근호   \n",
              "..         ...           ...   ...          ...   ...           ...      ...   \n",
              "595         해무        (주)NEW   드라마   2014-08-13   111      청소년 관람불가      심성보   \n",
              "596       파파로티        (주)쇼박스   드라마   2013-03-14   127       15세 관람가      윤종찬   \n",
              "597      살인의 강     (주)마운틴픽쳐스    공포   2010-09-30    99      청소년 관람불가      김대현   \n",
              "598     악의 연대기     CJ 엔터테인먼트   느와르   2015-05-14   102       15세 관람가      백운학   \n",
              "599        베를린     CJ 엔터테인먼트    액션   2013-01-30   120       15세 관람가      류승완   \n",
              "\n",
              "     dir_prev_bfnum  dir_prev_num  num_staff  num_actor  box_off_num  \n",
              "0               NaN             0         91          2        23398  \n",
              "1        1161602.50             2        387          3      7072501  \n",
              "2         220775.25             4        343          4      6959083  \n",
              "3          23894.00             2         20          6       217866  \n",
              "4              1.00             1        251          2       483387  \n",
              "..              ...           ...        ...        ...          ...  \n",
              "595         3833.00             1        510          7      1475091  \n",
              "596       496061.00             1        286          6      1716438  \n",
              "597             NaN             0        123          4         2475  \n",
              "598             NaN             0        431          4      2192525  \n",
              "599             NaN             0        363          5      7166532  \n",
              "\n",
              "[600 rows x 12 columns]"
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>distributor</th>\n",
              "      <th>genre</th>\n",
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              "      <th>0</th>\n",
              "      <td>개들의 전쟁</td>\n",
              "      <td>롯데엔터테인먼트</td>\n",
              "      <td>액션</td>\n",
              "      <td>2012-11-22</td>\n",
              "      <td>96</td>\n",
              "      <td>청소년 관람불가</td>\n",
              "      <td>조병옥</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>91</td>\n",
              "      <td>2</td>\n",
              "      <td>23398</td>\n",
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              "      <th>1</th>\n",
              "      <td>내부자들</td>\n",
              "      <td>(주)쇼박스</td>\n",
              "      <td>느와르</td>\n",
              "      <td>2015-11-19</td>\n",
              "      <td>130</td>\n",
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              "      <td>1161602.50</td>\n",
              "      <td>2</td>\n",
              "      <td>387</td>\n",
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              "      <td>7072501</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>은밀하게 위대하게</td>\n",
              "      <td>(주)쇼박스</td>\n",
              "      <td>액션</td>\n",
              "      <td>2013-06-05</td>\n",
              "      <td>123</td>\n",
              "      <td>15세 관람가</td>\n",
              "      <td>장철수</td>\n",
              "      <td>220775.25</td>\n",
              "      <td>4</td>\n",
              "      <td>343</td>\n",
              "      <td>4</td>\n",
              "      <td>6959083</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>나는 공무원이다</td>\n",
              "      <td>(주)NEW</td>\n",
              "      <td>코미디</td>\n",
              "      <td>2012-07-12</td>\n",
              "      <td>101</td>\n",
              "      <td>전체 관람가</td>\n",
              "      <td>구자홍</td>\n",
              "      <td>23894.00</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>6</td>\n",
              "      <td>217866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>불량남녀</td>\n",
              "      <td>쇼박스(주)미디어플렉스</td>\n",
              "      <td>코미디</td>\n",
              "      <td>2010-11-04</td>\n",
              "      <td>108</td>\n",
              "      <td>15세 관람가</td>\n",
              "      <td>신근호</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>251</td>\n",
              "      <td>2</td>\n",
              "      <td>483387</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>해무</td>\n",
              "      <td>(주)NEW</td>\n",
              "      <td>드라마</td>\n",
              "      <td>2014-08-13</td>\n",
              "      <td>111</td>\n",
              "      <td>청소년 관람불가</td>\n",
              "      <td>심성보</td>\n",
              "      <td>3833.00</td>\n",
              "      <td>1</td>\n",
              "      <td>510</td>\n",
              "      <td>7</td>\n",
              "      <td>1475091</td>\n",
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              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>파파로티</td>\n",
              "      <td>(주)쇼박스</td>\n",
              "      <td>드라마</td>\n",
              "      <td>2013-03-14</td>\n",
              "      <td>127</td>\n",
              "      <td>15세 관람가</td>\n",
              "      <td>윤종찬</td>\n",
              "      <td>496061.00</td>\n",
              "      <td>1</td>\n",
              "      <td>286</td>\n",
              "      <td>6</td>\n",
              "      <td>1716438</td>\n",
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              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>살인의 강</td>\n",
              "      <td>(주)마운틴픽쳐스</td>\n",
              "      <td>공포</td>\n",
              "      <td>2010-09-30</td>\n",
              "      <td>99</td>\n",
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              "      <td>2475</td>\n",
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              "      <th>598</th>\n",
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              "      <td>CJ 엔터테인먼트</td>\n",
              "      <td>느와르</td>\n",
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              "      <td>액션</td>\n",
              "      <td>2013-01-30</td>\n",
              "      <td>120</td>\n",
              "      <td>15세 관람가</td>\n",
              "      <td>류승완</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>5</td>\n",
              "      <td>7166532</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#staff, actor, box_off_num(관객 수) numpy array 만들기\n",
        "import numpy as np\n",
        "\n",
        "#일반리스트\n",
        "staff_list = df.num_staff[:].tolist()\n",
        "actor_list = df.num_actor[:].tolist()\n",
        "box_list = df.box_off_num[:].tolist() #타깃 데이터\n",
        "\n",
        "#nunmpy list\n",
        "staff_numpy = np.array(staff_list)\n",
        "actor_numpy = np.array(actor_list)\n",
        "box_numpy = np.array(box_list) #타깃 데이터"
      ],
      "metadata": {
        "id": "QalgB49WEo4R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#/2차원 배열로 만들기\n",
        "movie_full = np.column_stack((staff_numpy, actor_numpy)) #staff와 actor를 묶어서 2차원 배열로!\n",
        "\n",
        "# movie_full"
      ],
      "metadata": {
        "id": "6eDmWzHNFd1E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "#훈련세트 테스트세트 나누기\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    movie_full, box_numpy,  random_state=600)\n",
        "#[random_state] 어떻게 지정?\n",
        "\n"
      ],
      "metadata": {
        "id": "9GXoBWSOxDU2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scale 처리 <- actor에 배해 staff의 범위가 크므로, 스케일 처리 필요\n",
        "mean = np.mean(train_input, axis=0)\n",
        "std = np.std(train_input, axis=0)\n",
        "\n",
        "tmp_train_scaled = (train_input - mean) / std\n",
        "tmp_test_scaled = (test_input - mean) / std"
      ],
      "metadata": {
        "id": "uKLVfb_4Wect"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 사이킷런의 변환기 -과소적합 예방\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly = PolynomialFeatures(degree =3, include_bias = False)\n",
        "poly.fit(train_input)\n",
        "train_poly = poly.transform(tmp_train_scaled)\n",
        "test_poly = poly.transform(tmp_test_scaled)\n",
        "print(train_poly.shape)"
      ],
      "metadata": {
        "id": "NA2OlgK39mUZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41f1d7d2-632b-4784-e25d-6d262a7e6404"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(450, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#어떻게 변환되었는가\n",
        "poly.get_feature_names_out()\n",
        "# test_poly"
      ],
      "metadata": {
        "id": "ULjPI6O7__EP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a71338fd-ee69-438f-ca50-93151f9f45d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['x0', 'x1', 'x0^2', 'x0 x1', 'x1^2', 'x0^3', 'x0^2 x1', 'x0 x1^2',\n",
              "       'x1^3'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#다중 회귀 모델 훈련하기 [그냥 공부]\n",
        "# from sklearn.linear_model import LinearRegression\n",
        "# lr = LinearRegression()\n",
        "# lr.fit(train_poly, train_target)\n",
        "\n",
        "#훈련 점수 : 0.34428203344854635\n",
        "# print(lr.score(train_poly, train_target))\n",
        "\n",
        "#테스트 점수 : 0.26357899630450077\n",
        "# print(lr.score(test_poly, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhvPlwkrPaPQ",
        "outputId": "5bfa66d2-9f62-4cd5-aaeb-7b85336e16d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3646462892530694\n",
            "0.2433627096602291\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준화 전처리 : (x-mean )/ std\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "ss = StandardScaler()\n",
        "\n",
        "train_scaled = ss.fit_transform(train_poly)\n",
        "test_scaled = ss.transform(test_poly)"
      ],
      "metadata": {
        "id": "EfFEnXPWQCZ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "# 1. 릿지 회귀 _ alpha 선택없이 학습\n",
        "ridge = Ridge()\n",
        "ridge.fit(train_scaled, train_target)\n",
        "\n",
        "#훈련 점수 : 0.3640279610580628\n",
        "# print(ridge.score(train_scaled, train_target))\n",
        "#테스트 점수 : 0.24605177196396066\n",
        "# print(ridge.score(test_scaled, test_target))\n",
        "\n",
        "#___________________________________________________________\n",
        "\n",
        "# 2. 릿지 회귀 _ alpha 선택해보기(alpha는 사실 필수다)\n",
        "\n",
        "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
        "train_score_alpha = []\n",
        "test_score_alpha = []\n",
        "\n",
        "for alpha in alpha_list:\n",
        "  ridge_alpha = Ridge(alpha=alpha)\n",
        "  ridge_alpha.fit(train_scaled, train_target)\n",
        "  train_score_alpha.append(ridge_alpha.score(train_scaled, train_target))\n",
        "  test_score_alpha.append(ridge_alpha.score(test_scaled, test_target))\n",
        "\n",
        "print(train_score_alpha)  #[0.3646462875770823, 0.3646461237023575, 0.36463158091288883, 0.3640279610580628, 0.3597969159013872, 0.34973062370821417, 0.2922929956349388]\n",
        "print(test_score_alpha)  #[0.2433670618786603, 0.24340589650637068, 0.24376459993307964, 0.24605177196396066, 0.25698578969726216, 0.29943445923566225, 0.35531107961451147]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbom5EmxRTzz",
        "outputId": "c009ac66-fd04-48d9-bddc-c2cf7749c15f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.36464628923372655, 0.3646462873214108, 0.36464609866375763, 0.36462948295155306, 0.3638726337958642, 0.3521173597642657, 0.26422464048201755]\n",
            "[0.24336376696353212, 0.24337327901208816, 0.24346803938721706, 0.24438341600165292, 0.2518670216326906, 0.29072470056849553, 0.34209708647206516]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XNONTK7L74jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# alpha를 선택하기 위한 그래프 그리기 \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(np.log10(alpha_list), train_score_alpha)\n",
        "plt.plot(np.log10(alpha_list), test_score_alpha)\n",
        "plt.xlabel('alpha_power')\n",
        "plt.ylabel('R squared')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# [결론]  alpha는 10^(2.5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "AbeNFDSJRxrz",
        "outputId": "63369398-35ca-499b-97fd-4fb8fe4325ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#alpha 확정으로 최종 훈련 재진행\n",
        "\n",
        "alpha_fin = 10**(2.5)\n",
        "# print(alpha_fin)\n",
        "ridge_fin = Ridge(alpha = alpha_fin)\n",
        "ridge_fin.fit(train_scaled, train_target)\n",
        "\n",
        "print(ridge_fin.score(train_scaled, train_target)) #0.33532884692901643\n",
        "print(ridge_fin.score(test_scaled, test_target)) #0.341857425977547\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vymtj3XaVN5o",
        "outputId": "03432547-ae77-47f2-e782-83238ea6c985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.32782390341996415\n",
            "0.3347642104434154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # doctest: +SKIP\n",
        "import numpy as np\n",
        "\n",
        "# https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html?highlight=boston#sklearn.datasets.load_boston\n",
        "\n",
        "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
        "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
        "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
        "# target = raw_df.values[1::2, 2]\n",
        "# target\n",
        "\n",
        "# data\n",
        "raw_df\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "50l4KmoV75ZV",
        "outputId": "1855148d-938b-48f2-a2c1-c20c010333b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0ce70b8-abc9-4659-9865-6b73b9c30ac1')\"\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-c0ce70b8-abc9-4659-9865-6b73b9c30ac1 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-c0ce70b8-abc9-4659-9865-6b73b9c30ac1');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    }
  ]
}