SKLearn_Model_Transfer_Learning_for_Heat_Transfer.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn import metrics\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV"
      ]
    },
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        "df=pd.read_csv('/content/drive/MyDrive/Self Study/data.csv')\n",
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      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "\n",
        "rfr = RandomForestRegressor()"
      ],
      "metadata": {
        "id": "e5zmWOvxiAXy"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Trees in random forest\n",
        "n_estimators = [int(x) for x in np.linspace(start=100, stop=1200, num=12)]\n",
        "\n",
        "# Features to consider at every split\n",
        "max_features = ['auto', 'sqrt']\n",
        "\n",
        "# Max number of levels in tree\n",
        "max_depth = [int(x) for x in np.linspace(5, 30, num=6)]\n",
        "# max_depth.append(None)\n",
        "\n",
        "# Min number of samples required to split a node\n",
        "min_samples_split = [2, 5, 10, 15, 100]\n",
        "\n",
        "# Min number of samples required at each leaf node\n",
        "min_samples_leaf = [1, 2, 5, 10]"
      ],
      "metadata": {
        "id": "qmOKbXBQiGfS"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the random grid\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf}\n"
      ],
      "metadata": {
        "id": "-0hJGbq7iNQk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random = RandomizedSearchCV(estimator=rfr, \n",
        "                               param_distributions=random_grid, \n",
        "                               scoring='neg_mean_squared_error', \n",
        "                               n_iter=10, cv=5, verbose=2, \n",
        "                               random_state=42, n_jobs=1)"
      ],
      "metadata": {
        "id": "l7vkheipiVJt"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCujNAv0io6V",
        "outputId": "96782dda-321e-45b3-f0aa-c50e27a43fb2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.9s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.0s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.8s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.8s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.8s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.0s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.7s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.0s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.9s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.7s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.7s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.3s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.9s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
              "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
              "                                        'min_samples_split': [2, 5, 10, 15,\n",
              "                                                              100],\n",
              "                                        'n_estimators': [100, 200, 300, 400,\n",
              "                                                         500, 600, 700, 800,\n",
              "                                                         900, 1000, 1100,\n",
              "                                                         1200]},\n",
              "                   random_state=42, scoring='neg_mean_squared_error',\n",
              "                   verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf_random.predict(X_test)"
      ],
      "metadata": {
        "id": "MgsEtFo1iq2p"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MAE: ', metrics.mean_absolute_error(y_test, y_pred))\n",
        "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))\n",
        "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbv2xZgni2EW",
        "outputId": "c86524bb-93d9-4c7c-de06-d2136b5f4f7c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE:  0.18401315393735126\n",
            "MSE:  0.2383977480716475\n",
            "RMSE:  0.4882599185594159\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = metrics.r2_score(y_test, y_pred) * 100\n",
        "print('Score:', score, '%' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYNT9uKKi5Md",
        "outputId": "c7a7ee56-e13e-4090-d034-dde0f9245d14"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 99.84410290766975 %\n"
          ]
        }
      ]
    }
  ]
}