DistanceLineProtectionZone_RandomForest.ipynb

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    "colab": {
      "name": "DifferentialProtectionCurve-RandomForest.ipynb",
      "provenance": [],
      "collapsed_sections": []
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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      "cell_type": "code",
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      "metadata": {
        "id": "guDScOVYqjLb"
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      "source": [
        "#Import scikit-learn dataset library\n",
        "from sklearn import datasets\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
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        "colab": {
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        "outputId": "88c65348-a189-4aed-a610-f4ccd865e754"
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            "Saving DistanceDataset.csv to DistanceDataset.csv\n"
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        "#Load dataset\n",
        "datasets = pd.read_csv('DistanceDataset.csv', sep=',')\n",
        "X = datasets.iloc[:, [0,1]].values\n",
        "Y = datasets.iloc[:, 2].values\n",
        "datasets.head()"
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              "      R    X  Tnominal\n",
              "0 -30.0 -1.0       0.0\n",
              "1 -28.0 -1.0       0.0\n",
              "2 -26.0 -1.0       0.0\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-20542dea-caec-4975-b1ff-2dcec1243e80 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-20542dea-caec-4975-b1ff-2dcec1243e80');\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "9uD4H3ZUrrFO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_Train)\n",
        "\n",
        "X_train1 = scaler.transform(X_Train)\n",
        "X_train1 = X_train1.astype(np.float32)\n",
        "\n",
        "X_test1 = scaler.transform(X_Test)\n",
        "X_test1 = X_test1.astype(np.float32)"
      ],
      "metadata": {
        "id": "gCFfunHdruBC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n = 0\n",
        "for item in Y_Train:\n",
        "  if item == 1.03:\n",
        "    Y_Train[n] = 1\n",
        "  elif item == 0:\n",
        "    Y_Train[n] = 0\n",
        "  elif item == 0.23:\n",
        "    Y_Train[n] = 2\n",
        "  elif item == 0.43:\n",
        "    Y_Train[n] = 3\n",
        "  else:\n",
        "    Y_Train[n] = 4\n",
        "  n += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZ4zF5p6tcNc",
        "outputId": "a440053a-3761-43bd-9399-df9ba47d3fb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 2., 4., 0., 0., 0., 0., 0., 2., 2., 0., 1., 0., 3., 0., 0., 2.,\n",
              "       0., 2., 0., 4., 2., 3., 0., 2., 0., 1., 0., 1., 0., 1., 0., 1., 3.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 2., 3., 3., 0., 0., 1., 0., 0., 0., 0.,\n",
              "       2., 0., 0., 4., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 2., 0., 0.,\n",
              "       2., 0., 0., 0., 0., 3., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       3., 1., 0., 1., 0., 0., 0., 0., 0., 0., 3., 0., 0., 0., 2., 2., 0.,\n",
              "       2., 0., 0., 0., 3., 0., 0., 2., 3., 2., 0., 1., 0., 0., 1., 0., 0.,\n",
              "       2., 0., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 4., 2., 4., 0., 0.,\n",
              "       0., 2., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 2., 0.,\n",
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              "       0., 1., 0., 0., 0., 0., 0., 0., 3., 0., 0., 3., 2., 0., 0., 0., 0.,\n",
              "       3., 0., 0., 0., 0., 0., 0., 0., 0., 0., 3., 3., 0., 0., 1., 0., 0.,\n",
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              "       0., 3., 0., 0., 4., 0., 2., 4., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 2., 0., 1., 0., 0., 0., 1., 0., 0., 0.,\n",
              "       0., 0., 1., 2., 1., 4., 1., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0.,\n",
              "       0., 1., 2., 3., 0., 0., 0., 0., 0., 0., 0., 0., 4., 0., 0., 1., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 1., 1., 2., 0., 0., 2., 0., 2., 4., 4.,\n",
              "       2., 0., 0., 3., 0., 0., 3., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
              "       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       3., 0., 1., 0., 0., 2., 2., 0., 1., 0., 1., 1., 0., 0., 0., 0., 1.,\n",
              "       1., 0., 0., 0., 2., 1., 0., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0.,\n",
              "       0., 0., 0., 2., 0., 0., 3., 0., 0., 0., 0., 0., 0., 3., 3., 0., 0.,\n",
              "       0., 0., 4., 0., 2., 0., 0., 0., 3., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
              "       3., 0., 3., 1., 0., 0., 1., 0., 1., 2., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = 0\n",
        "for item in Y_Test:\n",
        "  if item == 1.03:\n",
        "    Y_Test[n] = 1\n",
        "  elif item == 0:\n",
        "    Y_Test[n] = 0\n",
        "  elif item == 0.23:\n",
        "    Y_Test[n] = 2\n",
        "  elif item == 0.43:\n",
        "    Y_Test[n] = 3\n",
        "  else:\n",
        "    Y_Test[n] = 4\n",
        "  n += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkVRk4P-tkUy",
        "outputId": "89fe58b4-06f3-41d4-c948-746b672f4115"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 1., 0., 0., 0., 2., 0., 4., 3., 0., 0., 0., 0., 0., 3., 3.,\n",
              "       0., 2., 0., 0., 4., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 2., 0.,\n",
              "       0., 0., 0., 0., 2., 0., 0., 3., 0., 0., 0., 2., 0., 0., 0., 0., 0.,\n",
              "       0., 1., 2., 0., 2., 0., 0., 4., 0., 0., 1., 3., 0., 3., 2., 2., 0.,\n",
              "       0., 0., 1., 2., 0., 3., 3., 2., 0., 1., 4., 0., 0., 0., 0., 2., 0.,\n",
              "       1., 0., 4., 0., 4., 0., 0., 0., 1., 0., 2., 1., 1., 3., 0., 0., 3.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 3., 0., 0., 0., 0., 0., 0., 3., 0., 0.,\n",
              "       0., 0., 1., 0., 3., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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              "       0., 0., 1., 0., 0., 0., 0., 0., 1., 2.])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a Gaussian Classifier\n",
        "model_RandomForestClassifier=RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
        "model_RandomForestClassifier.fit(X_train1,Y_Train)\n",
        "\n",
        "# prediction on test set\n",
        "y_pred=model_RandomForestClassifier.predict(X_test1)\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(Y_Test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCvMarjwsPBy",
        "outputId": "7dbecc0f-d9e2-4ee9-85fc-d7847bc2555f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9693251533742331\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_nearest(array, value):\n",
        "    array = np.asarray(array)\n",
        "    idx = (np.abs(array - value)).argmin()\n",
        "    return array[idx], idx"
      ],
      "metadata": {
        "id": "619xbjzavRR2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_sample = scaler.transform([[-12, -1]])\n",
        "x_sample = scaler.transform([[14, -1]])\n",
        "x_sample = scaler.transform([[10, -5]])\n",
        "x_sample = scaler.transform([[6, -2.5]])\n",
        "\n",
        "x_sample = x_sample.astype(np.float32)\n",
        "\n",
        "predict = model_RandomForestClassifier.predict(x_sample)\n",
        "predict_prob = model_RandomForestClassifier.predict_proba(x_sample)\n",
        "\n",
        "print(predict)\n",
        "print(predict_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGOj8hMqvLdM",
        "outputId": "bffd6052-b54d-4ba2-fca8-34e612029213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.]\n",
            "[[0.01 0.36 0.52 0.11 0.  ]]\n"
          ]
        }
      ]
    }
  ]
}