SOM.ipynb

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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: minisom in /usr/local/lib/python3.8/dist-packages (2.3.0)\n"
          ]
        }
      ],
      "source": [
        "%pip install minisom"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "1281a14a",
      "metadata": {
        "id": "1281a14a"
      },
      "outputs": [],
      "source": [
        "# importing important modules\n",
        "\n",
        "from minisom import MiniSom\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split, learning_curve\n",
        "from sklearn.metrics import classification_report, accuracy_score, precision_recall_fscore_support, roc_auc_score, roc_curve, plot_confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "2f9c55ff",
      "metadata": {
        "id": "2f9c55ff"
      },
      "outputs": [],
      "source": [
        "# importing the cleaned dataset\n",
        "df = pd.read_csv('newDataset.csv')"
      ]
    },
    {
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      "execution_count": 4,
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# the index has become a column as a reult of the upsampling done in the dataset notebook\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "2718aab3",
      "metadata": {
        "id": "2718aab3"
      },
      "outputs": [],
      "source": [
        "# dropping it\n",
        "df = df.drop('Unnamed: 0', axis=1)"
      ]
    },
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              "    loc  v(g)  ev(g)  iv(g)      n       v     l      d      i         e  ...  \\\n",
              "0   4.0   1.0    1.0    1.0    6.0   15.51  0.40   2.50   6.20     38.77  ...   \n",
              "1  13.0   3.0    1.0    1.0   29.0  123.19  0.08  11.92  10.34   1468.01  ...   \n",
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              "4  33.0  10.0    1.0    7.0  142.0  739.74  0.03  30.59  24.18  22627.41  ...   \n",
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              "      <td>11.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>65.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>739.74</td>\n",
              "      <td>0.03</td>\n",
              "      <td>30.59</td>\n",
              "      <td>24.18</td>\n",
              "      <td>22627.41</td>\n",
              "      <td>...</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e99ab161-df12-40dc-af17-7a4ea9a9d586')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e99ab161-df12-40dc-af17-7a4ea9a9d586 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-e99ab161-df12-40dc-af17-7a4ea9a9d586');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# the final dataset\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "49a0abf7",
      "metadata": {
        "id": "49a0abf7"
      },
      "outputs": [],
      "source": [
        "# splitting the data into features and target\n",
        "x = df.iloc[:,:-1]\n",
        "y=df['defects']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "b2a572c8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2a572c8",
        "outputId": "65c861bc-3816-4745-b8ca-a665463ff334"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set has 10530 samples.\n",
            "Testing set has 3510 samples.\n"
          ]
        }
      ],
      "source": [
        "# checking the number of samples in each set\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y)\n",
        "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
        "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "d2184369",
      "metadata": {
        "id": "d2184369"
      },
      "outputs": [],
      "source": [
        "# splitting the data into training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y)\n",
        "\n",
        "#normalizing the data\n",
        "\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "\n",
        "#y_train = sc.transform(X_test)\n",
        "#y_test = sc.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "0b6c3f63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "0b6c3f63",
        "outputId": "1679c532-bff6-4d37-9b34-badb6cc4355f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\" som = MiniSom(100, 100, X_train.shape[1], sigma=3., learning_rate=.7, neighborhood_function='gaussian', random_seed=10)\\n\\nmax_iter = 1000\\nq_error = []\\nt_error = []\\n\\nfor i in range(max_iter):\\n    rand_i = np.random.randint(len(X_train))\\n    som.update(X_train[rand_i], som.winner(X_train[rand_i]), i, max_iter)\\n    q_error.append(som.quantization_error(X_train))\\n    t_error.append(som.topographic_error(X_train))\\n\\nplt.plot(np.arange(max_iter), q_error, label='quantization error')\\nplt.plot(np.arange(max_iter), t_error, label='topographic error')\\nplt.ylabel('error')\\nplt.xlabel('iteration index')\\nplt.legend()\\nplt.show() \""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# creating a base model with 100x100 map and triangle neighborhood function and testing its quantizationa and topographic errors\n",
        "''' som = MiniSom(100, 100, X_train.shape[1], sigma=3., learning_rate=.7, neighborhood_function='gaussian', random_seed=10)\n",
        "\n",
        "max_iter = 1000\n",
        "q_error = []\n",
        "t_error = []\n",
        "\n",
        "for i in range(max_iter):\n",
        "    rand_i = np.random.randint(len(X_train))\n",
        "    som.update(X_train[rand_i], som.winner(X_train[rand_i]), i, max_iter)\n",
        "    q_error.append(som.quantization_error(X_train))\n",
        "    t_error.append(som.topographic_error(X_train))\n",
        "\n",
        "plt.plot(np.arange(max_iter), q_error, label='quantization error')\n",
        "plt.plot(np.arange(max_iter), t_error, label='topographic error')\n",
        "plt.ylabel('error')\n",
        "plt.xlabel('iteration index')\n",
        "plt.legend()\n",
        "plt.show() '''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "8c923f83",
      "metadata": {
        "id": "8c923f83"
      },
      "outputs": [],
      "source": [
        "# function to evaluate the map classifier using different metrics\n",
        "def evaluate(som, y_test, y_pred):\n",
        "\n",
        "    acc = accuracy_score(y_test, y_pred)\n",
        "    perf = precision_recall_fscore_support(y_test, y_pred, average = 'macro')\n",
        "    print(\"Accuracy: \", acc)\n",
        "    print(f\"Precision : {perf[0]:.4f} Recall : {perf[1]:.4f} Fscore : {perf[2]:.4f}\")\n",
        "\n",
        "    auc = roc_auc_score(y_pred, y_test)\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "    plt.plot(fpr, tpr, 'g-',label ='LR AUC:%.3f'%auc)\n",
        "    plt.legend(loc=4)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "493c422d",
      "metadata": {
        "id": "493c422d"
      },
      "outputs": [],
      "source": [
        "# since this in supervised classification not unsupervised, this function chooses the class of \n",
        "# the BMU to be the final class of each instance\n",
        "def classify(som, data):\n",
        "    \"\"\"Classifies each sample in data in one of the classes definited\n",
        "    using the method labels_map.\n",
        "    Returns a list of the same length of data where the i-th element\n",
        "    is the class assigned to data[i].\n",
        "    \"\"\"\n",
        "    winmap = som.labels_map(X_train, y_train)\n",
        "    default_class = np.sum(list(winmap.values())).most_common()[0][0]\n",
        "    result = []\n",
        "    for d in data:\n",
        "        win_position = som.winner(d)\n",
        "        if win_position in winmap:\n",
        "            result.append(winmap[win_position].most_common()[0][0])\n",
        "        else:\n",
        "            result.append(default_class)\n",
        "    return result\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "05b8bc40",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "05b8bc40",
        "outputId": "fec6e12f-8a14-4a4b-d5cf-f08270fc23be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " [ 10000 / 10000 ] 100% - 0:00:00 left \n",
            " quantization error: 0.27300579698582494\n",
            "Accuracy:  0.7700854700854701\n",
            "Precision : 0.7732 Recall : 0.7704 Fscore : 0.7696\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "somClassifier = MiniSom(100,100, 21, sigma=3, learning_rate=0.7, neighborhood_function='triangle', random_seed=10)\n",
        "somClassifier.pca_weights_init(X_train)\n",
        "somClassifier.train_random(X_train, 10000, verbose=True)\n",
        "\n",
        "y_pred = classify(somClassifier, X_test)\n",
        "evaluate(somClassifier, y_test, y_pred)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "4f9c67c6",
      "metadata": {
        "id": "4f9c67c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03b57392-b50f-45ee-c121-295323619cf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantization Error for the classifier with traingle neiborhood is  0.27300579698582494\n",
            "Topographic Error for the classifier with traingle neiborhood is  0.2617283950617284\n"
          ]
        }
      ],
      "source": [
        "print('Quantization Error for the classifier with traingle neiborhood is ', somClassifier.quantization_error(X_train))\n",
        "print('Topographic Error for the classifier with traingle neiborhood is ', somClassifier.topographic_error(X_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "bd71b13f",
      "metadata": {
        "id": "bd71b13f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "1b8a0828-74b0-47a1-e31d-a1e57778234b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " [ 10000 / 10000 ] 100% - 0:00:00 left \n",
            " quantization error: 0.46609752167209745\n",
            "Accuracy:  0.7643874643874644\n",
            "Precision : 0.7749 Recall : 0.7649 Fscore : 0.7623\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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7uWv6XZzR8gzu6HaH13FEJADoWi5BavSXo1m/az1zbp5DnQi9jSKiEXpQWpu/ltFfjqb/Gf3pkdTD6zgiEiBU0IPQ0JlDibRInur5lNdRRCSA6G/1IDN99XTe//59/nTZn0honOB1HBEJIBqhB5GDhQcZPG0waXFpDDlviNdxRCTAaIQeRJ6d/yz/zfsv0341jXp16nkdR0QCjEboQSJ3dy6Pzn2Ufh360fvUSq99JiJhTgU9SNzzyT0UFhcyttdYr6OISIBSQQ8Cn6/7nInfTWT4BcNJjk32Oo6IBCgV9ABXWFxI5rRM2jVpx33n3+d1HBEJYJoUDXB/WfgXvtv6He9d9x71o+p7HUdEAphG6AFsy94tPDjnQa5IvYJfnPYLr+OISIBTQQ9g9396P/sL9jOu9zjMzOs4IhLgVNAD1IKcBby25DWGnDeEDs07eB1HRIKACnoAKiou4o6pd9Ampg0PXPSA13FEJEhoUjQAvfr1qyzatIi3/vctYurFeB1HRIKERugBJm9/Hvd/ej8Xtr2QAWcM8DqOiAQRFfQA8+DsB8k/kM8LV76giVAROSEq6AFkyeYlvLjoRe7oegdntTrL6zgiEmRU0AOEc47MqZk0q9+MUZeM8jqOiAQhTYoGiLe+fYsvN37J3/r+jabRTb2OIyJBSCP0ALD74G7u+eQeusV345b0W7yOIyJBSiP0ADDq81Fs2buFKf2nEGH6HSsiJ0fVw2PLty3nuQXPMbDzQLrGd/U6jogEMRV0DznnGDxtMI3qNuKJy57wOo6IBDkdcvHQeyve49O1n/JCnxdo0bCF13FEJMhphO6RfQX7GDJjCGe3OpvfZfzO6zgiEgI0QvfIn774Ext3b+St/32LOhF6G0Sk+jRC98DqvNU8+Z8n+dWZv+LCdhd6HUdEQoQKugeGzBhC3ci6PNnzSa+jiEgI8amgm1lvM1tpZqvNbPhx2l1jZs7MMvwXMbR8vOpjPlr1ESMvHkmbmDZexxGREFJlQTezSGA80AfoCAwws46VtIsB7gQW+DtkqDhQeIA7p9/Jac1PY/C5g72OIyIhxpcRejdgtXMu2zl3CJgE9Kuk3aPAaOCAH/OFlDHzxrAmfw3jeo+jbmRdr+OISIjxpaDHAxvLbeeU7itjZucAic65j4/3RGZ2u5llmVnWtm3bTjhsMNuwawOPzX2Ma06/hp6pPb2OIyIhqNqTomYWAYwB7q6qrXPuZedchnMuo0WL8FpIM2zmMACeueIZj5OISKjypaDnAonlthNK9x0WA5wBfGZm64DzgCmaGP3Jp9mf8s7yd7j/gvtp17Sd13FEJET5UtAXAmlmlmxmdYH+wJTDDzrndjnnmjvnkpxzScB8oK9zLqtGEgeZgqICBk0bREpsCvecf4/XcUQkhFW5RNE5V2hmmcAMIBJ41Tm3zMxGAVnOuSnHf4bw9sJXL7Bi+wqm9J9CdJ1or+OISAgz55wnL5yRkeGyskJ7EL9572baP9+eC9tdyEcDPtJNn0Wk2sxskXOu0kPaWilag+6bdR8Hiw7ybK9nVcxFpMapoNeQLzd8yRvfvMHd3e8mrVma13FEJAyooNeAouIiMqdlktA4gREXjvA6joiECV23tQa8vOhllmxewtvXvk3Dug29jiMiYUIjdD/bvm87I2aP4JKkS/hlx196HUdEwogKup89MPsBdh/czfN9ntdEqIjUKhV0P1r0wyJeXvQyg88dTKeWnbyOIyJhRgXdT4pdMZnTMmnZsCUjLx7pdRwRCUOaFPWTN755g/k583m93+s0iW7idRwRCUMaofvBzgM7uW/WfXRP6M5NZ9/kdRwRCVMaofvBw589zLYftzHtV9OIMP2OFBFvqPpU03dbv+OFr17gd11+xzmtz/E6joiEMRX0anDOMWjaIJpEN+GxSx/zOo6IhDkdcqmGycsm89m6z/jrVX+lWYNmXscRkTCnEfpJ2ntoL3fPvJvOp3TmtnNu8zqOiIhG6Cfr8bmPk7snl8m/nExkRKTXcURENEI/Gat2rOKZec9w89k387PEn3kdR0QEUEE/Yc457px+J/Wj6vPny//sdRwRkTI65HKCPlz1IdNXT2dsr7Gc0ugUr+OIiJTRCP0E7C/Yz13T76Jji47c0fUOr+OIiFSgEfoJeOo/T7F251o+/fWnREVGeR1HRKQCjdB9tG7nOv707z9xXafruDT5Uq/jiIgcRQXdR0NnDCXCIni659NeRxERqZQKug9mrpnJv77/Fw9c+ACJTRK9jiMiUikV9CocKjrE4GmDOTXuVIZ2H+p1HBGRY9KkaBWem/8cK3esZOoNU6lXp57XcUREjkkj9OPI3Z3LqLmj+Hn7n9MnrY/XcUREjksF/TjunXUvBUUFjO011usoIiJVUkE/hrnr5/KPb//BveffS2pcqtdxRESqpIJeicLiQgZNG0TbJm0ZfsFwr+OIiPjEp4JuZr3NbKWZrTazoyqcmQ01s+VmttTMPjWzdv6PWntezHqRpVuWMrbXWBpENfA6joiIT6os6GYWCYwH+gAdgQFm1vGIZl8DGc65s4B/Ak/6O2ht2frjVh6c8yA9U3py9WlXex1HRMRnvozQuwGrnXPZzrlDwCSgX/kGzrk5zrl9pZvzgQT/xqw9f/z0j+w9tJdxfcZhZl7HERHxmS8FPR7YWG47p3TfsQwEplX2gJndbmZZZpa1bds231PWkq9yv+JvX/+Nu869i9Oan+Z1HBGRE+LXSVEzuxHIAJ6q7HHn3MvOuQznXEaLFi38+dLVVuyKyZyaSetGrXnw4ge9jiMicsJ8WSmaC5S/gElC6b4KzOxyYARwsXPuoH/i1Z7Xvn6NhT8s5M2r36RxvcZexxEROWG+jNAXAmlmlmxmdYH+wJTyDcysM/AS0Nc5t9X/MWtW/v58hn86nAvaXsANZ97gdRwRkZNSZUF3zhUCmcAMYAUw2Tm3zMxGmVnf0mZPAY2Ad8xsiZlNOcbTBaSH5jxE3v48XujzgiZCRSRo+XRxLufcVGDqEfseKvf55X7OVWu+2fwNf8n6C3/I+ANnn3K213FERE5aWK8Udc6ROS2TuPpxjLpklNdxRESqJawvn/uPb//Bvzf8m//7+f8RVz/O6zgiItUStiP0PQf3cM8n99C1TVd+2/m3XscREam2sB2hPzr3UTbt3cT7/d8nwsL295qIhJCwrGTfb/+esfPHMrDzQLrFd/M6joiIX4RdQXfOMWjaIBpGNeSJy57wOo6IiN+E3SGXf33/L2Zlz2Jc73G0bNjS6zgiIn4TViP0fQX7GDJjCGe2PJM/dP2D13FERPwqrEboo/89mg27NvD5LZ9TJyKsui4iYSBsRujZ+dmM/nI0N5x5Axe1u8jrOCIifhc2BX3IjCFERUbxVM9Kr+wrIhL0wuK4w9T/TmXKyik8efmTtIlp43UcEZEaEfIj9IOFB7lz+p10aNaBO8+70+s4IiI1JuRH6GPmjWF13mpm3DiDupF1vY4jIlJjQnqEnrM7h8e+eIyrT7uaK1Kv8DqOiEiNCumCPmzmMIpdMWN6jfE6iohIjQvZgj5n7RzeXvY2919wP0lNk7yOIyJS40KyoBcUFTBo2iCSmyZzz8/u8TqOiEitCMlJ0fELx7Ns2zLev/596kfV9zqOiEitCLkR+ua9mxn52Uh6n9qbvh36Vv0FIiIhIuQK+vBZw9lfsJ/nej+HmXkdR0Sk1oTUIZd5G+cx4ZsJDD9/OO2btfc6jkhAKygoICcnhwMHDngdRSoRHR1NQkICUVFRPn9NyBT0ouIiMqdlEh8Tz4iLRngdRyTg5eTkEBMTQ1JSkv6aDTDOOXbs2EFOTg7Jyck+f13IHHJ5ZfErLN60mGeueIZGdRt5HUck4B04cIBmzZqpmAcgM6NZs2Yn/NdTSBT0Hft28MfZf6RHUg+u63Sd13FEgoaKeeA6mfcmJAr6A7MfYNeBXYzrPU7foCIStoK+oC/etJiXFr1EZrdMzmx1ptdxROQENGp09OHRhx9+mPj4eNLT0+nYsSMTJ0487nOkp6fTv3//Cvt69OhBVlZW2fa6des444wzyra/+uorLrroIjp06EDnzp259dZb2bdv31HPPWHCBNLS0khLS2PChAmVvv71119Peno66enpJCUlkZ6eDsBbb71Vtj89PZ2IiAiWLFlSlq9Dhw5lj23duvW4ffSZc86Tjy5durjqKiouct1f6e5aPtXS5e/Pr/bziYST5cuXex3BNWzY8Kh9I0eOdE899ZRzzrlVq1a5mJgYd+jQoUq/fvny5e6MM85wbdq0cXv37i3bf/HFF7uFCxeWba9du9Z16tTJOefc5s2bXdu2bd1//vOfssffeecdt3nz5grPvWPHDpecnOx27Njh8vLyXHJyssvLyztuf4YOHeoeeeSRo/YvXbrUpaSkHDPfsVT2HgFZ7hh1NajPcnlz6ZvMy5nHa/1eo2l0U6/jiAStu6bfxZLNS/z6nOmnpPNs72er9RxpaWk0aNCA/Px8WrZsedTjEydO5KabbmLFihV88MEH3HDDDVU+5/jx47n55pvp3r172b5rr732qHYzZsygZ8+exMXFAdCzZ0+mT5/OgAEDKn1e5xyTJ09m9uzZleY88q+ImhC0h1x2HdjFvZ/cy3kJ5/Hrs3/tdRwRqQGLFy8mLS2t0mIO8Pbbb9O/f38GDBhQ5aGZw7777ju6dOlS6WNZWVnceuutAOTm5pKYmFj2WEJCArm5ucd83i+++IJWrVqRlpZWac4jfxH85je/IT09nUcffZSSgXf1Be0I/ZHPH2Hrj1v5+IaPibCg/b0kEhCqO5L2t7Fjx/Laa6+xatUqPvzww0rbZGVl0bx5c9q2bUt8fDy//e1vycvLIy4urtKTI3w5YSIjI4NXXnnlpDJPnDix0tH7ggULaNCgQYVj+G+99Rbx8fHs2bOHa665hr///e/8+tfVH5j6VAnNrLeZrTSz1WY2vJLH65nZ26WPLzCzpGonO45lW5cxbsE4bjvnNrq0qfw3rYgEryFDhrBs2TLeffddBg4cWOn52BMnTuT7778nKSmJ1NRUdu/ezbvvvgtAs2bNyM/PL2ubl5dH8+bNAejUqROLFi2qMkN8fDwbN24s287JySE+Pr7StoWFhbz33ntcf/31Rz02adKkowr94eeJiYnhhhtu4Kuvvqoyjy+qLOhmFgmMB/oAHYEBZtbxiGYDgXzn3KnAWGC0X9JVwjnHoGmDaFyvMY9f9nhNvYyIBIC+ffuSkZFx1BkmxcXFTJ48mW+//ZZ169axbt06Pvjgg7LDLj169ODNN98sO5QxYcIELrnkEgAyMzOZMGECCxYsKHu+9957jy1btlR4jV69ejFz5kzy8/PJz89n5syZ9OrVq9Kcs2bN4rTTTiMhIaHSnOWPnxcWFrJ9+3ag5PILH330UYXRe3X4MkLvBqx2zmU75w4Bk4B+R7TpBxz+H/8ncJnV0Anh/1z+T+asm8Pjlz5O8wbNa+IlRKSW7Nu3j4SEhLKPMWOOvrvYQw89xJgxYyguLi7b98UXXxAfH0+bNm3K9l100UUsX76cTZs2cfvttxMTE8PZZ5/N2Wefzd69exk2bBgArVq1YtKkSQwbNowOHTpw+umnM2PGDGJiYiocQ4+Li+PBBx+ka9eudO3alYceeqhsgvTWW2+tcFpkZaNwgLlz55KYmEhKSkrZvoMHD9KrVy/OOuss0tPTiY+P57bbbqvm/2QJq+pgvJldC/R2zt1aun0TcK5zLrNcm+9K2+SUbq8pbbP9iOe6HbgdoG3btl3Wr19/woGn/XcaLy16iXeve5fIiMgT/noRKbFixQpOP/10r2PIcVT2HpnZIudcRmXta3U20Tn3snMuwzmX0aJFi5N6jj5pfXi///sq5iIiR/CloOcCieW2E0r3VdrGzOoATYAd/ggoIiK+8aWgLwTSzCzZzOoC/YEpR7SZAtxc+vm1wGznrxMrRaTG6Mc0cJ3Me1NlQXfOFQKZwAxgBTDZObfMzEaZ2eF7vP0NaGZmq4GhwFGnNopIYImOjmbHjh0q6gHIlV4PPTo6+oS+rspJ0ZqSkZHhys8Si0jt0h2LAtux7lh0vEnRoF0pKiLVExUVdUJ3w6LGlZIAAAOVSURBVJHApzXzIiIhQgVdRCREqKCLiIQIzyZFzWwbcOJLRUs0B7ZX2Sq0qM/hQX0OD9XpczvnXKUrMz0r6NVhZlnHmuUNVepzeFCfw0NN9VmHXEREQoQKuohIiAjWgv6y1wE8oD6HB/U5PNRIn4PyGLqIiBwtWEfoIiJyBBV0EZEQEdAFPdBuTl0bfOjzUDNbbmZLzexTM2vnRU5/qqrP5dpdY2bOzIL+FDdf+mxm15W+18vM7B+1ndHffPjebmtmc8zs69Lv7yu9yOkvZvaqmW0tvaNbZY+bmY0r/f9YambnVPtFnXMB+QFEAmuAFKAu8A3Q8Yg2/w94sfTz/sDbXueuhT5fAjQo/fwP4dDn0nYxwFxgPpDhde5aeJ/TgK+B2NLtll7nroU+vwz8ofTzjsA6r3NXs88XAecA3x3j8SuBaYAB5wELqvuagTxCD6ibU9eSKvvsnJvjnNtXujmfkjtIBTNf3meAR4HRQChc69WXPt8GjHfO5QM457bWckZ/86XPDmhc+nkT4IdazOd3zrm5QN5xmvQD3nAl5gNNzax1dV4zkAt6PLCx3HZO6b5K27iSG3HsAprVSrqa4UufyxtIyW/4YFZln0v/FE10zn1cm8FqkC/vc3ugvZl9aWbzzax3raWrGb70+WHgRjPLAaYCg2onmmdO9Oe9SroeepAysxuBDOBir7PUJDOLAMYAt3gcpbbVoeSwSw9K/gqba2ZnOud2epqqZg0AXnfOPWNm3YG/m9kZzrlir4MFi0AeoYfjzal96TNmdjkwAujrnDtYS9lqSlV9jgHOAD4zs3WUHGucEuQTo768zznAFOdcgXNuLbCKkgIfrHzp80BgMoBzbh4QTclFrEKVTz/vJyKQC3o43py6yj6bWWfgJUqKebAfV4Uq+uyc2+Wca+6cS3LOJVEyb9DXORfM9y/05Xv7fUpG55hZc0oOwWTXZkg/86XPG4DLAMzsdEoK+rZaTVm7pgC/Lj3b5Txgl3NuU7We0euZ4Cpmia+kZGSyBhhRum8UJT/QUPKGvwOsBr4CUrzOXAt9ngVsAZaUfkzxOnNN9/mItp8R5Ge5+Pg+GyWHmpYD3wL9vc5cC33uCHxJyRkwS4ArvM5czf5OBDYBBZT8xTUQ+D3w+3Lv8fjS/49v/fF9raX/IiIhIpAPuYiIyAlQQRcRCREq6CIiIUIFXUQkRKigi4iECBV0EZEQoYIuIhIi/j/8xK4ZoPJ+sQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "somClassifier2 = MiniSom(100, 100, 21, sigma=3, learning_rate=0.7, neighborhood_function='gaussian', random_seed=10)\n",
        "somClassifier2.pca_weights_init(X_train)\n",
        "somClassifier2.train_random(X_train, 10000, verbose=True)\n",
        "\n",
        "y_pred = classify(somClassifier2, X_test)\n",
        "evaluate(somClassifier2, y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "3c1e5a2d",
      "metadata": {
        "id": "3c1e5a2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5383a124-d238-4903-e929-0f5b1efc2e7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantization Error for the classifier with gaussian neiborhood is:  0.46609752167209745\n",
            "Topographic Error or the classifier with gaussian neiborhood is:  0.050047483380816715\n"
          ]
        }
      ],
      "source": [
        "print('Quantization Error for the classifier with gaussian neiborhood is: ', somClassifier2.quantization_error(X_train))\n",
        "print('Topographic Error or the classifier with gaussian neiborhood is: ', somClassifier2.topographic_error(X_train))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}