Machine_Learning_3_qadam_Pipelines.ipynb

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        "<a href=\"https://colab.research.google.com/github/AbdullahTheAnalyst/Data-Science-and-AI-mohir_dev-/blob/Google-Colab-and-Jupyter/Machine_Learning_3_qadam_Pipelines.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "2A18eruof5TZ"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn # scikit-learn.org library"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "URL = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n",
        "df = pd.read_csv(URL)\n",
        "df.head()"
      ],
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
            ],
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              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
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              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
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              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.003531723502581,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571117,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111637,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2181.615251582787,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5926,\n        \"samples\": [\n          699.0,\n          1544.0,\n          3966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 421.3850700740322,\n        \"min\": 1.0,\n        \"max\": 6445.0,\n        \"num_unique_values\": 1923,\n        \"samples\": [\n          1538.0,\n          1298.0,\n          1578.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653375,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382.3297528316099,\n        \"min\": 1.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1815,\n        \"samples\": [\n          21.0,\n          750.0,\n          1447.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452732,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115395.6158744132,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          194300.0,\n          379000.0,\n          230100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, test_set = train_test_split(df, test_size = 0.2, random_state = 42)\n",
        "\n",
        "housing = train_set.drop(\"median_house_value\", axis = 1)\n",
        "housing_labels = train_set[\"median_house_value\"].copy()\n",
        "\n",
        "housing_number = housing.drop(\"ocean_proximity\", axis = 1)"
      ],
      "metadata": {
        "id": "7RA2KoPbl1eW"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sklearn orqali biz o'zimiz transformerlar yaratishimiz va avtomatlashtirishimiz mumkin\n",
        "# quyidagi transform orqali biz, avvalroq hisoblagan har bir xona, o'rtacha nechi kishi yashaydi shularni hisoblab beruvchi transform yaratamiz\n",
        "# yuqoridagi va bu transformerlar shablon sifatida boshqa loyihalarda qo'llasa bo'ladi\n",
        "\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "# bizga kerakli ustunlarning indexlari orqali o'zimizga ma'lu, o'zgaruvchiga saqlab olindi\n",
        "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
        "\n",
        "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
        "  def __init__(self, add_bedrooms_per_room = True):\n",
        "    self.add_bedrooms_per_room = add_bedrooms_per_room\n",
        "  def fit(self, X, y = None):\n",
        "    return self # bu funksiyamiz faqat transformer, estimator emas\n",
        "  def transform(self, X):\n",
        "    rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
        "    population_per_household = X[:, population_ix] / X[:, households_ix]\n",
        "    if self.add_bedrooms_per_room: # add_bedrooms_per_room ustuni ixtiyoriy bo'ladi\n",
        "        bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
        "        return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
        "    else:\n",
        "      return np.c_[X, rooms_per_household, population_per_household]"
      ],
      "metadata": {
        "id": "XKEQskqSmRw-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# o'tgan dars orqali df ichidagi dataset Machine Learning uchun tayyor hisoblanadi\n",
        "# lekin yuqoridagi kodlar juda chalkash hisoblanadi\n",
        "# shuning uchun yana ham avtomatlash uchun bizga sklearn ichidagi Pipeline yordam beradi, ya'ni ishimizni birdan bitta amal orqali bajarib beradi\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "\n",
        "numeric_pipeline = Pipeline([\n",
        "    (\"imputer\", SimpleImputer(strategy = \"median\")),\n",
        "    (\"attribute_adder\", CombinedAttributesAdder(add_bedrooms_per_room = True)),\n",
        "    (\"std_scaler\", StandardScaler())\n",
        "])\n",
        "\n",
        "numeric_pipeline.fit_transform(housing_number)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQbDF5KEgADi",
        "outputId": "d1fcdc9c-fa82-49e5-852d-75a4fafd5f33"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.27258656, -1.3728112 ,  0.34849025, ..., -0.17491646,\n",
              "         0.05137609, -0.2117846 ],\n",
              "       [ 0.70916212, -0.87669601,  1.61811813, ..., -0.40283542,\n",
              "        -0.11736222,  0.34218528],\n",
              "       [-0.44760309, -0.46014647, -1.95271028, ...,  0.08821601,\n",
              "        -0.03227969, -0.66165785],\n",
              "       ...,\n",
              "       [ 0.59946887, -0.75500738,  0.58654547, ..., -0.60675918,\n",
              "         0.02030568,  0.99951387],\n",
              "       [-1.18553953,  0.90651045, -1.07984112, ...,  0.40217517,\n",
              "         0.00707608, -0.79086209],\n",
              "       [-1.41489815,  0.99543676,  1.85617335, ..., -0.85144571,\n",
              "        -0.08535429,  1.69520292]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mavzu boshida biz jarayonlarni avtomatlashtirish haqida gapirdik. Buning uchun scikit-learn da maxsus **pipeline** tushunchasi bor. Pipeline ingliz tilidan gaz (neft) quvuri deb tarjima qilinadi. Gaz A nuqtadan B nuqtaga yetkazib berilishida bir nechta oraliq ishlov berish stansiyalaridan o'tadi.\n",
        "\n",
        "Bizning ma'lumotlar ham shunday, boshlang'ich nuqtasidan bevosita MLga yetib kelunga qadar bir nechta jarayonolardan o'tdi. Yuqorida biz har bir jarayonni qo'lda yozib chiqdik, pipeline yordamida esa biz barcha qadamlarni birlashtirib - pipeline (yoki konveyer) hosil qilishimiz mumkin.\n",
        "\n",
        "Pipeline so'zini konveyer deb tarjima qilishimga sabab, ma'lumotlarimiz huddi konveyerdan o'tgani kabi turli bosqichlarda turli o'zgarishlardan o'tayapti.\n",
        "\n",
        "Biz konveyerni 2 qismga bo'lamiz:\n",
        "- Sonli ustunlarga ishlov berish\n",
        "- Matnli ustunlarga ishlov berish"
      ],
      "metadata": {
        "id": "dFLjL4HF0pTz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sonli ustunlarga ishlov beruvchi konveyer tayyor, matni ustunlarchi?\n",
        "\n",
        "Buning uchun maxsus `ColumnTransformer` obyektiga murojaat qilamiz, bu ham pipeline bir ko'rinishi. `ColumnTransformer` ichiga biz yuqorida yasalgan `num_ipeline` ham qo'shib yuboramiz."
      ],
      "metadata": {
        "id": "dYbkmag30wtz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "num_attribs = list(housing_number)\n",
        "cat_attribs = ['ocean_proximity']\n",
        "\n",
        "full_pipeline = ColumnTransformer([\n",
        "    ('num', numeric_pipeline, num_attribs),\n",
        "    ('cat', OneHotEncoder(), cat_attribs)\n",
        "])"
      ],
      "metadata": {
        "id": "Njoca79ZnSBt"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mana yakuniy, to'liq konveyer tayyor bo'ldi (`full_pipeline`).\n",
        "\n",
        "Konveyerni ishga tushirish uchun `.fit_transform()` metodini chaqrisih kifoya."
      ],
      "metadata": {
        "id": "Q1Nwbwv31Au5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_prepared = full_pipeline.fit_transform(housing)"
      ],
      "metadata": {
        "id": "qAzE2vVI01XJ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing_prepared[0:5,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_7MeWtF1Kl-",
        "outputId": "b6e3c313-541a-41b4-bf30-3a9551150a43"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.27258656, -1.3728112 ,  0.34849025,  0.22256942,  0.21122752,\n",
              "         0.76827628,  0.32290591, -0.326196  , -0.17491646,  0.05137609,\n",
              "        -0.2117846 ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
              "         1.        ],\n",
              "       [ 0.70916212, -0.87669601,  1.61811813,  0.34029326,  0.59309419,\n",
              "        -0.09890135,  0.6720272 , -0.03584338, -0.40283542, -0.11736222,\n",
              "         0.34218528,  0.        ,  0.        ,  0.        ,  0.        ,\n",
              "         1.        ],\n",
              "       [-0.44760309, -0.46014647, -1.95271028, -0.34259695, -0.49522582,\n",
              "        -0.44981806, -0.43046109,  0.14470145,  0.08821601, -0.03227969,\n",
              "        -0.66165785,  0.        ,  0.        ,  0.        ,  0.        ,\n",
              "         1.        ],\n",
              "       [ 1.23269811, -1.38217186,  0.58654547, -0.56148971, -0.40930582,\n",
              "        -0.00743434, -0.38058662, -1.01786438, -0.60001532,  0.07750687,\n",
              "         0.78303162,  0.        ,  0.        ,  0.        ,  0.        ,\n",
              "         1.        ],\n",
              "       [-0.10855122,  0.5320839 ,  1.14200767, -0.11956547, -0.25655915,\n",
              "        -0.48587717, -0.31496232, -0.17148831,  0.3490073 , -0.06883176,\n",
              "        -0.55036364,  0.        ,  1.        ,  0.        ,  0.        ,\n",
              "         0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MA'LUMOTLAR MACHINE LEARNIG UCHUN TAYYOR**"
      ],
      "metadata": {
        "id": "5myag12j1Mab"
      }
    }
  ]
}