Group_14_presentation_of_the_test_results.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "6115996b",
      "metadata": {
        "id": "6115996b"
      },
      "source": [
        "#INTRODUCTION TO DATA SCIENCE\n",
        "#Group 14 -- Project phase 2\n",
        "\n",
        "## Presentation of the test results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07fedfbf",
      "metadata": {
        "id": "07fedfbf"
      },
      "source": [
        "## Team members\n",
        "\n",
        "|Name| Student ID|\n",
        "|:---|:-----------:|\n",
        "|Pham, Quoc Huy| 2299356|\n",
        "|Hussain, Zakiuddin| 2338350|\n",
        "|Lee, Daeul Haven| 2308018|\n",
        "|Preetham| 2288949|\n",
        "|Jayanth| 2288552|\n",
        "|Srikavya| 2311351|"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## I. Best model from step 1 and step 3"
      ],
      "metadata": {
        "id": "4Ti9ysBSOrsK"
      },
      "id": "4Ti9ysBSOrsK"
    },
    {
      "cell_type": "markdown",
      "id": "a7ad7bfc",
      "metadata": {
        "id": "a7ad7bfc"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3bc71a35",
      "metadata": {
        "id": "3bc71a35"
      },
      "source": [
        "Random Forest is an ensemble learning algorithm used for classification and regression tasks. It constructs a collection of decision trees, each trained on a random subset of the data and features. By aggregating the predictions of these trees, Random Forest enhances accuracy, generalization, and resilience to overfitting.\n",
        "\n",
        "**Building a Failure Prediction Model:** <br>\n",
        "We aim to employ the Random Forest algorithm for a supervised classification problem, specifically failure prediction labeled as 0 (no failure) and 1 (failure) in the status column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "805a50bd",
      "metadata": {
        "id": "805a50bd"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import ParameterGrid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "52304780",
      "metadata": {
        "id": "52304780",
        "outputId": "7e660aeb-5dc0-43fe-9b0b-cda44c08377e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set shape: (41611, 15) (41611,)\n",
            "Test set shape: (17834, 15) (17834,)\n",
            "     TP2    TP3     H1  DV_pressure  Reservoirs  Oil_temperature  \\\n",
            "0 -0.018  8.248  8.238       -0.024       8.248            49.45   \n",
            "1 -0.018  8.248  8.238       -0.024       8.248            49.45   \n",
            "2 -0.018  8.248  8.238       -0.024       8.248            49.45   \n",
            "3 -0.018  8.248  8.238       -0.024       8.248            49.45   \n",
            "4 -0.018  8.248  8.238       -0.024       8.248            49.45   \n",
            "\n",
            "   Motor_current  COMP  DV_eletric  Towers  MPG  LPS  Pressure_switch  \\\n",
            "0           0.04   1.0         0.0     1.0  1.0  0.0              1.0   \n",
            "1           0.04   1.0         0.0     1.0  1.0  0.0              1.0   \n",
            "2           0.04   1.0         0.0     1.0  1.0  0.0              1.0   \n",
            "3           0.04   0.0         0.0     0.0  0.0  0.0              0.0   \n",
            "4           0.04   1.0         0.0     1.0  1.0  0.0              1.0   \n",
            "\n",
            "   Oil_level  Caudal_impulses  status  \n",
            "0        1.0              1.0       1  \n",
            "1        1.0              1.0       1  \n",
            "2        1.0              1.0       1  \n",
            "3        0.0              0.0       1  \n",
            "4        1.0              1.0       1  \n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(\"Group_14_Clean_Data.csv\")\n",
        "\n",
        "df = df.drop(columns=['Unnamed: 0', 'timestamp'])\n",
        "label_column = 'status'\n",
        "X = df.drop(label_column, axis=1)\n",
        "y = df[label_column]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Print the shapes of the resulting sets\n",
        "print(\"Train set shape:\", X_train.shape, y_train.shape)\n",
        "print(\"Test set shape:\", X_test.shape, y_test.shape)\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c821b53f",
      "metadata": {
        "id": "c821b53f"
      },
      "source": [
        "**Hyperparameters of Random Forest:** <br>\n",
        "Random Forest comes with several hyperparameters that influence its performance. Here are key ones:\n",
        "\n",
        "- Number of Trees (n_estimators): The total number of decision trees in the forest. Increasing this value generally improves performance until a certain point but can also increase computational cost.\n",
        "- Maximum Depth of Trees (max_depth): The maximum depth of each decision tree. Deeper trees can capture more complex relationships but may lead to overfitting.\n",
        "- Minimum Samples Split (min_samples_split): The minimum number of samples required to split an internal node. Higher values prevent small splits that might capture noise but can result in less expressive trees.\n",
        "- Minimum Samples Leaf (min_samples_leaf): The minimum number of samples required to be in a leaf node. Similar to min_samples_split, it controls overfitting at the leaf level.\n",
        "- Maximum Features (max_features): The maximum number of features considered for splitting a node. It introduces randomness and can enhance the diversity of trees.\n",
        "\n",
        "Adjusting these hyperparameters allows fine-tuning the Random Forest model to achieve optimal performance for the specific failure prediction task. Cross-validation and grid search are common techniques for finding the best combination of hyperparameter values.\n",
        "\n",
        "We will assess the model's performance and identify the optimal hyperparameters using the accuracy score from cross-validation and the out-of-bag error. We opt for randomized search due to its efficiency—training the model with grid search is time-consuming.\n",
        "- **Randomized Search:**\n",
        "Randomized Search is a time-efficient hyperparameter tuning technique in machine learning. It randomly samples a fixed number of hyperparameter combinations from the specified space, making it particularly useful for exploring large parameter spaces.\n",
        "\n",
        "- **Cross Validation:**\n",
        "Cross Validation is a robust evaluation method that partitions the dataset into subsets, trains the model on some, and evaluates on the rest. It provides a more reliable estimate of a model's generalization performance compared to a single train-test split.\n",
        "\n",
        "- **Out-of-Bag Error:**\n",
        "Out-of-Bag (OOB) Error is a metric used in ensemble learning, like Random Forests. It estimates a model's generalization error by evaluating its performance on data points not used in the training of a specific tree. OOB Error is particularly convenient as it doesn't require a separate validation set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b78268ed",
      "metadata": {
        "scrolled": true,
        "id": "b78268ed",
        "outputId": "27477aa4-f211-4d75-a02e-ceb6c91d6397"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Hyperparameters:\n",
            "{'n_estimators': 200, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 10, 'max_depth': 20}\n",
            "Random Forest Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      9031\n",
            "           1       1.00      1.00      1.00      8803\n",
            "\n",
            "    accuracy                           1.00     17834\n",
            "   macro avg       1.00      1.00      1.00     17834\n",
            "weighted avg       1.00      1.00      1.00     17834\n",
            "\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Randomized Search\n",
        "model = RandomForestClassifier(random_state=42)\n",
        "param_dist = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': [5, 10, 15]\n",
        "}\n",
        "\n",
        "# Perform Randomized Search with Cross-Validation\n",
        "random_search = RandomizedSearchCV(model, param_distributions=param_dist, n_iter=10, cv=5, scoring='accuracy', random_state=42)\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "# Extract cross-validated scores and hyperparameters\n",
        "cv_scores = random_search.cv_results_['mean_test_score']\n",
        "hyperparameters = random_search.cv_results_['params']\n",
        "\n",
        "# Get the best parameters\n",
        "best_params = random_search.best_params_\n",
        "\n",
        "# Train the model with the best parameters\n",
        "best_model = random_search.best_estimator_\n",
        "best_model.fit(X_train, y_train)\n",
        "\n",
        "# Print the best hyperparameters\n",
        "print('Best Hyperparameters:')\n",
        "print(best_params)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "y_pred_forest = best_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred_forest)\n",
        "\n",
        "# Print results\n",
        "print(\"Random Forest Classifier:\")\n",
        "print(classification_report(y_test, y_pred_forest))\n",
        "\n",
        "# Display confusion matrix\n",
        "conf_matrix_forest = confusion_matrix(y_test, y_pred_forest)\n",
        "disp_forest = ConfusionMatrixDisplay(conf_matrix_forest, display_labels=best_model.classes_)\n",
        "disp_forest.plot(cmap='Blues', values_format='d')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f8060b53",
      "metadata": {
        "scrolled": true,
        "id": "f8060b53",
        "outputId": "d710353d-d15c-414b-e662-3e970d6bb101"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hyperparameters at Each Iteration:\n",
            "Iteration 1: {'n_estimators': 50, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 5, 'max_depth': 10} - Mean Accuracy: 99.45%\n",
            "Iteration 2: {'n_estimators': 50, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 5, 'max_depth': 10} - Mean Accuracy: 99.45%\n",
            "Iteration 3: {'n_estimators': 200, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 15, 'max_depth': 20} - Mean Accuracy: 99.62%\n",
            "Iteration 4: {'n_estimators': 100, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 15, 'max_depth': 30} - Mean Accuracy: 99.62%\n",
            "Iteration 5: {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 15, 'max_depth': 30} - Mean Accuracy: 99.61%\n",
            "Iteration 6: {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 5, 'max_depth': 30} - Mean Accuracy: 99.62%\n",
            "Iteration 7: {'n_estimators': 200, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 10, 'max_depth': 30} - Mean Accuracy: 99.61%\n",
            "Iteration 8: {'n_estimators': 50, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 15, 'max_depth': 20} - Mean Accuracy: 99.60%\n",
            "Iteration 9: {'n_estimators': 50, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 5, 'max_depth': 10} - Mean Accuracy: 99.45%\n",
            "Iteration 10: {'n_estimators': 200, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 10, 'max_depth': 20} - Mean Accuracy: 99.63%\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Print hyperparameters outside the plot\n",
        "print(\"Hyperparameters at Each Iteration:\")\n",
        "for i, params in enumerate(hyperparameters):\n",
        "    print(f\"Iteration {i + 1}: {params} - Mean Accuracy: {cv_scores[i]:.2%}\")\n",
        "\n",
        "# Create a plot for cross-validated scores\n",
        "plt.figure(figsize=(8, 5))\n",
        "\n",
        "# Plot the cross-validated scores\n",
        "plt.scatter(range(len(cv_scores)), cv_scores, c='blue', label='Cross-validated Scores')\n",
        "\n",
        "# Highlight the best score\n",
        "best_index = random_search.best_index_\n",
        "plt.scatter(best_index, cv_scores[best_index], c='red', marker='*', s=200, label='Best Score')\n",
        "\n",
        "# Customize the plot\n",
        "plt.title('Cross-Validated Scores During Randomized Search')\n",
        "plt.xlabel('Iteration Number')\n",
        "plt.ylabel('Mean Accuracy')\n",
        "\n",
        "# Set x-ticks as iteration numbers\n",
        "plt.xticks(range(len(cv_scores)))\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48108b03",
      "metadata": {
        "id": "48108b03",
        "outputId": "aa922ed3-d78e-4df1-96ab-231cad175e9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Hyperparameters:\n",
            "{'n_estimators': 100, 'max_depth': 20, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 10}\n",
            "OOB Score: 99.67%\n",
            "Random Forest Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      9031\n",
            "           1       1.00      1.00      1.00      8803\n",
            "\n",
            "    accuracy                           1.00     17834\n",
            "   macro avg       1.00      1.00      1.00     17834\n",
            "weighted avg       1.00      1.00      1.00     17834\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Out-Of-Bag Error\n",
        "param_dist = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': [5, 10, 15]\n",
        "}\n",
        "\n",
        "# Initialize a list to store results\n",
        "results = []\n",
        "oob_scores = []  # List to store OOB scores during grid search\n",
        "\n",
        "# Perform randomized grid search with 10 iterations\n",
        "for _ in range(10):\n",
        "    params = {key: np.random.choice(values) for key, values in param_dist.items()}\n",
        "    rf_model = RandomForestClassifier(oob_score=True, random_state=42, **params)\n",
        "    rf_model.fit(X_train, y_train)\n",
        "    oob_score = rf_model.oob_score_\n",
        "\n",
        "    results.append({'params': params, 'oob_score': oob_score})\n",
        "    oob_scores.append(oob_score)\n",
        "\n",
        "# Find the best hyperparameters based on the highest OOB score\n",
        "best_params = max(results, key=lambda x: x['oob_score'])['params']\n",
        "\n",
        "# Print the best hyperparameters and OOB score\n",
        "print('Best Hyperparameters:')\n",
        "print(best_params)\n",
        "print(f'OOB Score: {max(results, key=lambda x: x[\"oob_score\"])[\"oob_score\"]:.2%}')\n",
        "\n",
        "# Train the final model with the best hyperparameters on the entire training set\n",
        "best_model = RandomForestClassifier(oob_score=True, random_state=42, **best_params)\n",
        "best_model.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate and print the results on the test set\n",
        "y_pred_forest = best_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred_forest)\n",
        "\n",
        "print(\"Random Forest Classifier:\")\n",
        "print(classification_report(y_test, y_pred_forest))\n",
        "\n",
        "# Display confusion matrix\n",
        "conf_matrix_forest = confusion_matrix(y_test, y_pred_forest)\n",
        "disp_forest = ConfusionMatrixDisplay(conf_matrix_forest, display_labels=best_model.classes_)\n",
        "disp_forest.plot(cmap='Blues', values_format='d')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "515c8cb1",
      "metadata": {
        "id": "515c8cb1",
        "outputId": "f3228238-ce31-4dc2-fe73-1cb3d420b511"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hyperparameters at Each Iteration:\n",
            "Iteration 1: {'n_estimators': 100, 'max_depth': 20, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 10} - OOB Score: 99.67%\n",
            "Iteration 2: {'n_estimators': 50, 'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 5} - OOB Score: 99.46%\n",
            "Iteration 3: {'n_estimators': 100, 'max_depth': 30, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 15} - OOB Score: 99.64%\n",
            "Iteration 4: {'n_estimators': 50, 'max_depth': 20, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 10} - OOB Score: 99.64%\n",
            "Iteration 5: {'n_estimators': 200, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 15} - OOB Score: 99.59%\n",
            "Iteration 6: {'n_estimators': 50, 'max_depth': 10, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 10} - OOB Score: 99.60%\n",
            "Iteration 7: {'n_estimators': 100, 'max_depth': 20, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 10} - OOB Score: 99.67%\n",
            "Iteration 8: {'n_estimators': 100, 'max_depth': 30, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 15} - OOB Score: 99.64%\n",
            "Iteration 9: {'n_estimators': 200, 'max_depth': 20, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 15} - OOB Score: 99.65%\n",
            "Iteration 10: {'n_estimators': 100, 'max_depth': 20, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 10} - OOB Score: 99.65%\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Print hyperparameters at each iteration\n",
        "print(\"Hyperparameters at Each Iteration:\")\n",
        "for i, params in enumerate(results):\n",
        "    print(f\"Iteration {i + 1}: {params['params']} - OOB Score: {params['oob_score']:.2%}\")\n",
        "\n",
        "# Create a plot for OOB scores during grid search\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Plot the OOB scores\n",
        "plt.plot(oob_scores, marker='o', linestyle=' ', color='b', label='OOB Scores')\n",
        "\n",
        "# Highlight the best score\n",
        "best_index = np.argmax(oob_scores)\n",
        "plt.scatter(best_index, oob_scores[best_index], c='red', marker='*', s=200, label='Best Score')\n",
        "\n",
        "# Customize the plot\n",
        "plt.title('Out-of-Bag (OOB) Scores During Randomized Grid Search')\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('OOB Score')\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a3ba872",
      "metadata": {
        "id": "9a3ba872"
      },
      "source": [
        "- Best Hyperparameters using cross-validation: <br>\n",
        "Iteration 10: {'n_estimators': 200, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 10, 'max_depth': 20} - Mean Accuracy: 99.63%\n",
        "\n",
        "- Best Hyperparameters using out-of-bag error: <br>\n",
        "{'n_estimators': 100, 'max_depth': 20, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 10}\n",
        "OOB Score: 99.67%\n",
        "\n",
        "While the optimal hyperparameters differ between out-of-bag error and cross-validation, a common trend emerges: higher 'n_estimators' and 'max_depth,' along with smaller 'min_samples_split' and 'min_samples_leaf,' and larger 'max_features,' tend to result in better accuracy. Both models, each using its optimal hyperparameters, demonstrated high accuracy on test set, reaching 100%."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## II. Bidirectional Feature Elimination"
      ],
      "metadata": {
        "id": "sK3fJjGzObqW"
      },
      "id": "sK3fJjGzObqW"
    },
    {
      "cell_type": "markdown",
      "id": "dca556bb",
      "metadata": {
        "id": "dca556bb"
      },
      "source": [
        "### 1. Import library and read the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be4acfc1",
      "metadata": {
        "id": "be4acfc1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import SequentialFeatureSelector\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96ce06cf",
      "metadata": {
        "id": "96ce06cf"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('Group_14_Clean_Data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5bc85dfa",
      "metadata": {
        "id": "5bc85dfa"
      },
      "outputs": [],
      "source": [
        "X = data.iloc[:, 2:-1]\n",
        "y = data.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "283c7988",
      "metadata": {
        "id": "283c7988"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2302ffe9",
      "metadata": {
        "id": "2302ffe9"
      },
      "source": [
        "From question 1 and 3 we have our RandomForest as the best classifier. The result is shown as below:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3be39f98",
      "metadata": {
        "id": "3be39f98"
      },
      "source": [
        "Best Hyperparameters:\n",
        "* 'max_depth': 20\n",
        "* 'min_samples_leaf': 1\n",
        "* 'min_samples_split': 2\n",
        "* 'n_estimators': 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a3bea365",
      "metadata": {
        "id": "a3bea365"
      },
      "outputs": [],
      "source": [
        "best_params = {'max_depth': 20, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
        "rf = RandomForestClassifier(oob_score=True, random_state=42, **best_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5445141d",
      "metadata": {
        "id": "5445141d"
      },
      "source": [
        "|          |precision  |  recall | f1-score  | support|\n",
        "|-----------|----------|----------|----------|--------|\n",
        "|          0  |     1.00 |  1.00   |   1.00 |     4480|\n",
        "|          1  |    1.00  |  1.00   |   1.00 |     4437|\n",
        "|  accuracy   |          |         |   1.00 |     8917|\n",
        "| macro avg   |    1.00  |    1.00 |   1.00 |     8917|\n",
        "| weighted avg|    1.00  |    1.00 |   1.00 |     8917|"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b0107cf7",
      "metadata": {
        "id": "b0107cf7"
      },
      "source": [
        "### 2. Perform prediction with original data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8b1d039",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "d8b1d039",
        "outputId": "f6a02a70-d146-493b-c293-6ae0c87ff629"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, n_estimators=50, oob_score=True,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=20, n_estimators=50, oob_score=True,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(max_depth=20, n_estimators=50, oob_score=True,\n",
              "                       random_state=42)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f02c46c2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "f02c46c2",
        "outputId": "7696f0d4-76b4-447a-9aa4-5f2774e27e10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Classifier with original data:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      6026\n",
            "           1       1.00      1.00      1.00      5863\n",
            "\n",
            "    accuracy                           1.00     11889\n",
            "   macro avg       1.00      1.00      1.00     11889\n",
            "weighted avg       1.00      1.00      1.00     11889\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y_pred_forest = rf.predict(X_test)\n",
        "\n",
        "print(\"Random Forest Classifier with original data:\")\n",
        "print(classification_report(y_test, y_pred_forest))\n",
        "conf_matrix_forest = confusion_matrix(y_test, y_pred_forest)\n",
        "disp_forest = ConfusionMatrixDisplay(conf_matrix_forest, display_labels=rf.classes_)\n",
        "disp_forest.plot(cmap='Blues', values_format='d')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82ba677c",
      "metadata": {
        "id": "82ba677c"
      },
      "source": [
        "### 3. Perform Bidirectional Features Elimination"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03ad5307",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03ad5307",
        "outputId": "f711e87b-266f-4660-bfb6-7e5bc003d1c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "List of selected features:\n",
            " TP2, H1, DV_pressure, Reservoirs, Oil_temperature, DV_eletric, LPS\n"
          ]
        }
      ],
      "source": [
        "num_features_to_select = 7\n",
        "\n",
        "num_features = len(X_train.columns.tolist())\n",
        "looped_X_train = X_train.copy()\n",
        "\n",
        "while num_features> num_features_to_select:\n",
        "  n1 = num_features-1\n",
        "  n2 = num_features-2\n",
        "  sfe = SequentialFeatureSelector(estimator=rf, n_features_to_select=n1, direction = 'forward', n_jobs = -1)\n",
        "  sbe = SequentialFeatureSelector(estimator=rf, n_features_to_select=n2, direction = 'backward', n_jobs= -1)\n",
        "  #Eliminating Features with Forward pass\n",
        "  sfe.fit(looped_X_train, y_train)\n",
        "  sfe_features = looped_X_train.columns[sfe.support_].tolist()\n",
        "  looped_X_train = looped_X_train[sfe_features]\n",
        "  #Eliminating Features with Backward pass\n",
        "  sbe.fit(looped_X_train, y_train)\n",
        "  sbe_features = looped_X_train.columns[sbe.support_].tolist()\n",
        "  looped_X_train = looped_X_train[sbe_features]\n",
        "\n",
        "  num_features = len(looped_X_train.columns.tolist())\n",
        "\n",
        "selected_features = looped_X_train.columns\n",
        "print(f\"List of selected features:\\n {', '.join(selected_features)}\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8783da5f",
      "metadata": {
        "id": "8783da5f"
      },
      "source": [
        "### 4. Perform prediction with new set of parameter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f50b4f1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "f50b4f1c",
        "outputId": "289d0aa0-c468-47f7-d78a-7cbdb6fe4da2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, n_estimators=50, oob_score=True,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=20, n_estimators=50, oob_score=True,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(max_depth=20, n_estimators=50, oob_score=True,\n",
              "                       random_state=42)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Fit Random Forest model with selected features\n",
        "rf.fit(looped_X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ee754d4",
      "metadata": {
        "id": "0ee754d4"
      },
      "outputs": [],
      "source": [
        "selected_features = looped_X_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0d3762a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "c0d3762a",
        "outputId": "ca0bbb52-dab3-4371-a1ef-b3a581298609"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      6026\n",
            "           1       1.00      1.00      1.00      5863\n",
            "\n",
            "    accuracy                           1.00     11889\n",
            "   macro avg       1.00      1.00      1.00     11889\n",
            "weighted avg       1.00      1.00      1.00     11889\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#EValuate the result of new features\n",
        "y_pred_forest = rf.predict(X_test[selected_features])\n",
        "accuracy = accuracy_score(y_test, y_pred_forest)\n",
        "\n",
        "print(\"Random Forest Classifier:\")\n",
        "print(classification_report(y_test, y_pred_forest))\n",
        "conf_matrix_forest = confusion_matrix(y_test, y_pred_forest)\n",
        "disp_forest = ConfusionMatrixDisplay(conf_matrix_forest, display_labels=rf.classes_)\n",
        "disp_forest.plot(cmap='Blues', values_format='d')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3723d049",
      "metadata": {
        "id": "3723d049"
      },
      "source": [
        "### 5. Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "65662b77",
      "metadata": {
        "id": "65662b77"
      },
      "source": [
        "From the classification report, we observed that the number of false negatives and false positives has decreased:\n",
        "\n",
        "* Class 0: 22–>19\n",
        "\n",
        "* Class 1: 24->21\n",
        "\n",
        "This shows that the bi-directional feature elimination has not only reduced the dimension of our feature space but also increased the model's performance and accuracy. This suggests that certain features were not contributing significantly to the classification task, leading to more efficient and accurate predictions.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## III. All models from step 5"
      ],
      "metadata": {
        "id": "9CfSoMcjOyOP"
      },
      "id": "9CfSoMcjOyOP"
    },
    {
      "cell_type": "markdown",
      "id": "4spPL4FuDnP_",
      "metadata": {
        "id": "4spPL4FuDnP_"
      },
      "source": [
        "### 1. Import the Library and read the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ab83994",
      "metadata": {
        "id": "3ab83994"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a08bc9c",
      "metadata": {
        "id": "5a08bc9c"
      },
      "outputs": [],
      "source": [
        "selected_features = [\"TP2\", \"H1\", \"DV_pressure\", \"Reservoirs\", \"Oil_temperature\", \"Motor_current\", \"Oil_level\", 'status']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0091382c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0091382c",
        "outputId": "3d6f8763-f080-4079-f0be-247d15462036"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-7657d74c-f10e-4053-85b6-a69ae0192445\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>TP2</th>\n",
              "      <th>TP3</th>\n",
              "      <th>H1</th>\n",
              "      <th>DV_pressure</th>\n",
              "      <th>Reservoirs</th>\n",
              "      <th>Oil_temperature</th>\n",
              "      <th>Motor_current</th>\n",
              "      <th>COMP</th>\n",
              "      <th>DV_eletric</th>\n",
              "      <th>Towers</th>\n",
              "      <th>MPG</th>\n",
              "      <th>LPS</th>\n",
              "      <th>Pressure_switch</th>\n",
              "      <th>Oil_level</th>\n",
              "      <th>Caudal_impulses</th>\n",
              "      <th>status</th>\n",
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              "      <td>8.238</td>\n",
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              "      <td>2020-04-18 00:00:13</td>\n",
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              "      <td>8.238</td>\n",
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              "      <td>8.248</td>\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7657d74c-f10e-4053-85b6-a69ae0192445');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-48ba44f6-ee4d-44f1-9498-35a97c53fa0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Unnamed: 0            timestamp    TP2    TP3     H1  DV_pressure  \\\n",
              "0      562564  2020-04-18 00:00:01 -0.018  8.248  8.238       -0.024   \n",
              "1      562565  2020-04-18 00:00:13 -0.018  8.248  8.238       -0.024   \n",
              "2      562566  2020-04-18 00:00:24 -0.018  8.248  8.238       -0.024   \n",
              "3      562567  2020-04-18 00:00:36 -0.018  8.248  8.238       -0.024   \n",
              "4      562568  2020-04-18 00:00:49 -0.018  8.248  8.238       -0.024   \n",
              "\n",
              "   Reservoirs  Oil_temperature  Motor_current  COMP  DV_eletric  Towers  MPG  \\\n",
              "0       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "1       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "2       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "3       8.248            49.45           0.04   0.0         0.0     0.0  0.0   \n",
              "4       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "\n",
              "   LPS  Pressure_switch  Oil_level  Caudal_impulses  status  \n",
              "0  0.0              1.0        1.0              1.0       1  \n",
              "1  0.0              1.0        1.0              1.0       1  \n",
              "2  0.0              1.0        1.0              1.0       1  \n",
              "3  0.0              0.0        0.0              0.0       1  \n",
              "4  0.0              1.0        1.0              1.0       1  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv(\"Group_14_Clean_Data.csv\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73b2dde5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "73b2dde5",
        "outputId": "014a1814-ec62-4c4c-a800-ae92fcf891bb",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-182661ca-67b1-4a27-b7d1-e8350570a675 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Unnamed: 0            timestamp     TP2    TP3     H1  DV_pressure  \\\n",
              "32984     1484156  2020-08-27 12:38:48  -0.010  8.212  8.198       -0.016   \n",
              "56604      416777  2020-03-27 15:17:44  10.376  9.996 -0.006       -0.014   \n",
              "52785     1512857  2020-08-31 10:27:03  -0.014  9.382  9.368       -0.018   \n",
              "54798      588344  2020-04-21 03:57:12  -0.014  8.844  8.832       -0.024   \n",
              "30977      231046  2020-03-03 06:38:59   9.760  9.378 -0.012       -0.026   \n",
              "\n",
              "       Reservoirs  Oil_temperature  Motor_current  COMP  DV_eletric  Towers  \\\n",
              "32984       8.214           64.100         0.0400   1.0         0.0     1.0   \n",
              "56604       9.992           71.875         6.0925   0.0         1.0     0.0   \n",
              "52785       9.382           66.950         0.0450   1.0         0.0     1.0   \n",
              "54798       8.846           55.500         0.0425   1.0         0.0     1.0   \n",
              "30977       9.372           73.450         5.9900   0.0         1.0     1.0   \n",
              "\n",
              "       MPG  LPS  Pressure_switch  Oil_level  Caudal_impulses  status  \n",
              "32984  1.0  0.0              1.0        1.0              1.0       0  \n",
              "56604  0.0  0.0              1.0        1.0              1.0       0  \n",
              "52785  1.0  0.0              1.0        1.0              1.0       0  \n",
              "54798  1.0  0.0              1.0        1.0              1.0       0  \n",
              "30977  0.0  0.0              1.0        1.0              0.0       0  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Choose only features selected from Question 2\n",
        "#Shuffle the data\n",
        "data = data.sample(frac = 1)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c38a6d5f",
      "metadata": {
        "id": "c38a6d5f"
      },
      "source": [
        "### 2. XGBOOST\n",
        "\n",
        "- XGBoost, a Gradient Boosted decision tree implementation, excels in Kaggle Competitions.\n",
        "- It sequentially creates decision trees, assigning weights to variables that are adjusted based on prediction errors.\n",
        "- This ensemble method, with optimizations like the Approximate Greedy Algorithm and Cash-Aware Access, proves effective for regression, classification, ranking, and user-defined prediction problems.\n",
        "\n",
        "reference: <BR>\n",
        "https://xgboost.readthedocs.io/en/stable/python/python_intro.html <BR>\n",
        "https://www.geeksforgeeks.org/xgboost/ <BR>\n",
        "https://www.geeksforgeeks.org/ml-xgboost-extreme-gradient-boosting/ <BR>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5d4ec6c",
      "metadata": {
        "id": "f5d4ec6c",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "#!pip install xgboost\n",
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c8744021",
      "metadata": {
        "id": "c8744021"
      },
      "outputs": [],
      "source": [
        "X = data[selected_features]\n",
        "y = data['status']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dOshu3bGGKpH",
      "metadata": {
        "id": "dOshu3bGGKpH"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be2tVGsNGMgf",
      "metadata": {
        "id": "be2tVGsNGMgf"
      },
      "outputs": [],
      "source": [
        "class XGBoost():\n",
        "  def __init__(self):\n",
        "    self._model = xgb.XGBClassifier()\n",
        "    self._encoder = OneHotEncoder(categories='auto')\n",
        "\n",
        "  def fit(self, x, y ):\n",
        "    self._model.fit(x,y)\n",
        "\n",
        "  def predict(self, x):\n",
        "    y_pred = self._model.predict(x)\n",
        "    return self._encoder.fit_transform(y_pred[:, np.newaxis]).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfc8122f",
      "metadata": {
        "id": "dfc8122f"
      },
      "outputs": [],
      "source": [
        "# Fitting XGBoost to the training data\n",
        "model = XGBoost()\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "j086nYWeG_GM",
      "metadata": {
        "id": "j086nYWeG_GM"
      },
      "outputs": [],
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1EgigeZsG-Ti",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "1EgigeZsG-Ti",
        "outputId": "1fb158f7-cc36-45b2-9747-5e8152961297"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      5948\n",
            "           1       1.00      1.00      1.00      5941\n",
            "\n",
            "    accuracy                           1.00     11889\n",
            "   macro avg       1.00      1.00      1.00     11889\n",
            "weighted avg       1.00      1.00      1.00     11889\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y_pred = np.argmax(y_pred, axis = 1)\n",
        "# Making the Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "disp = ConfusionMatrixDisplay(cm, display_labels=['0', '1'])\n",
        "disp.plot(cmap='Blues', values_format='d')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "88f140f9",
      "metadata": {
        "id": "88f140f9"
      },
      "source": [
        "### 3. Extreme Learning Machine"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "687898ed",
      "metadata": {
        "id": "687898ed"
      },
      "source": [
        "* Extreme Learning Machine is a variation of Feed forward neural network with just 1 hidden layer.\n",
        "* Extreme Machine Learning does not use iterative method such as gradient descent to tuning the weights, instead it use linear algebra to solve for the optimal solution.\n",
        "* Extreme Machine Learning is believed to be able to approximate any abitrary function given sufficient number of hidden units and data to learn."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5426ae0",
      "metadata": {
        "id": "f5426ae0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from timeit import timeit as time\n",
        "import cupy as cp\n",
        "from  cupy.linalg import pinv as pinv2\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0137efb5",
      "metadata": {
        "id": "0137efb5"
      },
      "outputs": [],
      "source": [
        "selected_features = [\"TP2\", \"H1\", \"DV_pressure\", \"Reservoirs\", \"Oil_temperature\", \"Motor_current\", \"Oil_level\", 'status']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "939dfae0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "939dfae0",
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            "text/plain": [
              "   Unnamed: 0            timestamp    TP2    TP3     H1  DV_pressure  \\\n",
              "0      562564  2020-04-18 00:00:01 -0.018  8.248  8.238       -0.024   \n",
              "1      562565  2020-04-18 00:00:13 -0.018  8.248  8.238       -0.024   \n",
              "2      562566  2020-04-18 00:00:24 -0.018  8.248  8.238       -0.024   \n",
              "3      562567  2020-04-18 00:00:36 -0.018  8.248  8.238       -0.024   \n",
              "4      562568  2020-04-18 00:00:49 -0.018  8.248  8.238       -0.024   \n",
              "\n",
              "   Reservoirs  Oil_temperature  Motor_current  COMP  DV_eletric  Towers  MPG  \\\n",
              "0       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "1       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "2       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "3       8.248            49.45           0.04   0.0         0.0     0.0  0.0   \n",
              "4       8.248            49.45           0.04   1.0         0.0     1.0  1.0   \n",
              "\n",
              "   LPS  Pressure_switch  Oil_level  Caudal_impulses  status  \n",
              "0  0.0              1.0        1.0              1.0       1  \n",
              "1  0.0              1.0        1.0              1.0       1  \n",
              "2  0.0              1.0        1.0              1.0       1  \n",
              "3  0.0              0.0        0.0              0.0       1  \n",
              "4  0.0              1.0        1.0              1.0       1  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv(\"Group_14_Clean_Data.csv\")\n",
        "data.head()"
      ]
    },
    {
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      "execution_count": null,
      "id": "affef0c6",
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          "height": 206
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        "id": "affef0c6",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10756</th>\n",
              "      <td>842839</td>\n",
              "      <td>2020-05-30 05:16:58</td>\n",
              "      <td>7.274</td>\n",
              "      <td>8.362</td>\n",
              "      <td>-0.010</td>\n",
              "      <td>1.660</td>\n",
              "      <td>8.364</td>\n",
              "      <td>76.275</td>\n",
              "      <td>5.2675</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27418</th>\n",
              "      <td>907333</td>\n",
              "      <td>2020-06-07 12:09:00</td>\n",
              "      <td>7.994</td>\n",
              "      <td>7.928</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>1.892</td>\n",
              "      <td>7.930</td>\n",
              "      <td>75.725</td>\n",
              "      <td>5.4150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcc67654-61c4-4a3b-b719-d631027d8796')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fcc67654-61c4-4a3b-b719-d631027d8796 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-fcc67654-61c4-4a3b-b719-d631027d8796');\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",
              "\n",
              "\n",
              "<div id=\"df-7627d232-969b-4d82-b87d-798f2ca5d740\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7627d232-969b-4d82-b87d-798f2ca5d740')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7627d232-969b-4d82-b87d-798f2ca5d740 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Unnamed: 0            timestamp    TP2    TP3     H1  DV_pressure  \\\n",
              "5197       567761  2020-04-18 14:27:31  9.040  8.858 -0.006        2.024   \n",
              "14297      894090  2020-06-05 19:01:58  8.296  8.108 -0.004        2.200   \n",
              "4083       566647  2020-04-18 11:23:28  8.992  8.798 -0.006        2.014   \n",
              "10756      842839  2020-05-30 05:16:58  7.274  8.362 -0.010        1.660   \n",
              "27418      907333  2020-06-07 12:09:00  7.994  7.928 -0.002        1.892   \n",
              "\n",
              "       Reservoirs  Oil_temperature  Motor_current  COMP  DV_eletric  Towers  \\\n",
              "5197        8.858           75.500         5.7275   0.0         1.0     1.0   \n",
              "14297       8.108           75.925         5.5075   0.0         1.0     1.0   \n",
              "4083        8.800           75.525         5.7075   0.0         1.0     1.0   \n",
              "10756       8.364           76.275         5.2675   0.0         1.0     1.0   \n",
              "27418       7.930           75.725         5.4150   0.0         1.0     1.0   \n",
              "\n",
              "       MPG  LPS  Pressure_switch  Oil_level  Caudal_impulses  status  \n",
              "5197   0.0  0.0              1.0        1.0              1.0       1  \n",
              "14297  0.0  0.0              1.0        1.0              1.0       1  \n",
              "4083   0.0  0.0              1.0        1.0              1.0       1  \n",
              "10756  0.0  0.0              1.0        1.0              1.0       1  \n",
              "27418  0.0  0.0              1.0        1.0              1.0       1  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Choose only features selected from Question 2\n",
        "#Shuffle the data\n",
        "data = data.sample(frac = 1)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91bfe838",
      "metadata": {
        "id": "91bfe838"
      },
      "outputs": [],
      "source": [
        "X = data[selected_features]\n",
        "y = data['status']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "53c73158",
      "metadata": {
        "id": "53c73158"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8f9302d",
      "metadata": {
        "id": "b8f9302d"
      },
      "outputs": [],
      "source": [
        "#this Extreme Machine Learning class is implemented specificly to work on GPU\n",
        "#this will not run on a CPU machine\n",
        "\n",
        "class ExtremeLearningMachine():\n",
        "  def __init__(self, hidden_size = 10, threshold = 0.5):\n",
        "    self._hidden_size = hidden_size\n",
        "    self._input_size  = None\n",
        "    self._w = None\n",
        "    self._b = None\n",
        "    self._beta = None\n",
        "    self._threshold = 0.5\n",
        "    self._encoder = OneHotEncoder(categories='auto')\n",
        "    self._scaler  = StandardScaler()\n",
        "\n",
        "  def encode(self, y):\n",
        "    return self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
        "\n",
        "  def scale(self,x):\n",
        "    return self._scaler.transform(x)\n",
        "\n",
        "  def _h(self,x):\n",
        "    return self._tanh(cp.dot(x, self._w) + self._b )\n",
        "\n",
        "  @property\n",
        "  def hidden_size(self):\n",
        "    return self._hidden_size\n",
        "\n",
        "  @staticmethod\n",
        "  def _tanh(x):\n",
        "    return cp.tanh(x)\n",
        "\n",
        "  def fit(self, x, y):\n",
        "    x = self._scaler.fit_transform(x)\n",
        "    y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
        "\n",
        "    x_gpu = cp.asarray(x)\n",
        "    y_gpu = cp.asarray(y)\n",
        "\n",
        "    self._input_size = x.shape[1]\n",
        "    self._w = cp.random.normal( size = [self._input_size, self._hidden_size])\n",
        "    self._b = cp.random.normal(size = [self._hidden_size])\n",
        "\n",
        "    H = self._h(x_gpu)\n",
        "    self._beta = cp.dot(pinv2(H), y_gpu)\n",
        "\n",
        "    del x_gpu\n",
        "    del y_gpu\n",
        "    cp._default_memory_pool.free_all_blocks()\n",
        "\n",
        "\n",
        "  def predict(self, x):\n",
        "    x = self.scale(x)\n",
        "    x_gpu = cp.asarray(x)\n",
        "    out_gpu = cp.dot(self._h(x_gpu), self._beta)\n",
        "    out = cp.asnumpy(out_gpu)\n",
        "    del out_gpu\n",
        "    cp._default_memory_pool.free_all_blocks()\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e4fa333",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e4fa333",
        "outputId": "2b963a4e-ef60-424e-f636-a2a3c0de6232"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n"
          ]
        }
      ],
      "source": [
        "num_hidden = [5,10,20,30,40,50,60,70]\n",
        "scores = []\n",
        "\n",
        "for h in num_hidden:\n",
        "  elm = ExtremeLearningMachine(h)\n",
        "  elm.fit(X_train, y_train)\n",
        "  y_pred = elm.predict(X_test)\n",
        "  y_pred = np.argmax(y_pred, axis = 1)\n",
        "  scores.append(accuracy_score(y_test, y_pred))\n",
        "  #Clear GPU memmory\n",
        "  del elm\n",
        "  cp._default_memory_pool.free_all_blocks()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e39c81e",
      "metadata": {
        "id": "1e39c81e"
      },
      "outputs": [],
      "source": [
        "best_hidden_size_indx = np.argmax(scores)\n",
        "best_hidden_size = num_hidden[best_hidden_size_indx]\n",
        "best_score       = scores[best_hidden_size_indx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02348fcb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "02348fcb",
        "outputId": "18a0fe28-0d9a-4b66-9a67-cd318afac213"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(num_hidden, scores)\n",
        "plt.scatter(best_hidden_size, best_score, s = 250, marker = \"*\", color = 'r' , label = 'Best Accuracy')\n",
        "plt.xlabel('Number of Hidden Units')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy of ELM in relative with number of hidden units' )\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a25f80c2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a25f80c2",
        "outputId": "4ee4c757-b562-4bb8-86f2-3b15d1a601a9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n"
          ]
        }
      ],
      "source": [
        "elm = ExtremeLearningMachine(best_hidden_size)\n",
        "elm.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88f3890d",
      "metadata": {
        "id": "88f3890d"
      },
      "outputs": [],
      "source": [
        "y_pred = elm.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e9e6c9f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "8e9e6c9f",
        "outputId": "2e0fa05f-21ea-4537-c75c-6d900464f30a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best result achieved with 40 hidden units\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      5967\n",
            "           1       1.00      1.00      1.00      5922\n",
            "\n",
            "    accuracy                           1.00     11889\n",
            "   macro avg       1.00      1.00      1.00     11889\n",
            "weighted avg       1.00      1.00      1.00     11889\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y_pred = np.argmax(y_pred, axis = 1)\n",
        "\n",
        "print(f'Best result achieved with {elm.hidden_size} hidden units')\n",
        "print(classification_report(y_test, y_pred))\n",
        "c_mat  = confusion_matrix(y_test, y_pred)\n",
        "disp = ConfusionMatrixDisplay(c_mat, display_labels=['0', '1'])\n",
        "disp.plot(cmap='Blues', values_format='d')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4UKStyKVEC7A",
      "metadata": {
        "id": "4UKStyKVEC7A"
      },
      "source": [
        "### 4. Neural Network with 2 hidden layers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f4a16b1",
      "metadata": {
        "id": "6f4a16b1"
      },
      "source": [
        "**About the Model :**\n",
        "The neural network model created for this metro dataset consists of two hidden layers, each with 64 neurons, designed to capture complex patterns and relationships within the data. By utilizing a deep learning approach, the model aims to accurately predict the 'status' variable, effectively handling the non-linear and intricate dependencies likely present in the diverse range of features from the metro operational metrics."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "76dfa590",
      "metadata": {
        "id": "76dfa590"
      },
      "source": [
        "**Why TensorFlow and Keras?**\n",
        "\n",
        "Handling Large and Complex Datasets: since metro dataset is large or complex, TensorFlow and Keras can efficiently handle such datasets. Deep learning models, particularly those built with these frameworks, are known for their ability to process and extract patterns from large volumes of data.\n",
        "\n",
        "Feature Learning: Deep learning models have the capability to automatically learn and extract features from raw data. This can be particularly useful since dataset contains complex patterns or relationships that are not easily captured with traditional machine learning models.\n",
        "\n",
        "Non-linear Relationships: Neural networks, which can be easily built using TensorFlow and Keras, are adept at capturing non-linear relationships in the data. The metro dataset has intricate dependencies between variables, a neural network might model these relationships more effectively than a simpler linear model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c164385e",
      "metadata": {
        "id": "c164385e"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import classification_report, ConfusionMatrixDisplay, accuracy_score, confusion_matrix, roc_curve, auc\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ctdyqgnDFVQu",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ctdyqgnDFVQu",
        "outputId": "b0978e2d-38d3-4952-d000-8a00c873acd6"
      },
      "outputs": [
        {
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              "      <td>903144</td>\n",
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              "      <td>2020-06-06 15:12:59</td>\n",
              "      <td>6.064</td>\n",
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              "      <td>-0.006</td>\n",
              "      <td>1.444</td>\n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            ],
            "text/plain": [
              "       Unnamed: 0            timestamp    TP2    TP3     H1  DV_pressure  \\\n",
              "23262      903144  2020-06-06 23:44:43  8.194  7.974 -0.010        2.128   \n",
              "41633      736676  2020-05-14 12:09:29 -0.012  9.574  9.560       -0.022   \n",
              "14025      893818  2020-06-05 18:17:01  5.582  8.156 -0.006        1.280   \n",
              "21627      901420  2020-06-06 15:12:59  6.064  8.090 -0.006        1.444   \n",
              "35681     1514037  2020-08-31 13:41:59  9.026  8.578 -0.018       -0.016   \n",
              "\n",
              "       Reservoirs  Oil_temperature  Motor_current  COMP  DV_eletric  Towers  \\\n",
              "23262       7.976           75.300         5.6025   0.0         1.0     1.0   \n",
              "41633       9.574           58.400         3.4900   1.0         0.0     1.0   \n",
              "14025       8.158           75.675         5.0925   0.0         1.0     0.0   \n",
              "21627       8.090           75.825         5.1375   0.0         1.0     1.0   \n",
              "35681       8.576           62.475         5.9575   0.0         1.0     0.0   \n",
              "\n",
              "       MPG  LPS  Pressure_switch  Oil_level  Caudal_impulses  status  \n",
              "23262  0.0  0.0              1.0        1.0              1.0       1  \n",
              "41633  1.0  0.0              1.0        1.0              1.0       0  \n",
              "14025  0.0  0.0              0.0        1.0              1.0       1  \n",
              "21627  0.0  0.0              1.0        1.0              1.0       1  \n",
              "35681  0.0  0.0              1.0        1.0              1.0       0  "
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Read the dataset\n",
        "data = pd.read_csv('Group_14_Clean_Data.csv')\n",
        "\n",
        "# Choose only features selected from Question 2\n",
        "#Shuffle the data\n",
        "data = data.sample(frac = 1)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "uLHP575gFfwM",
      "metadata": {
        "id": "uLHP575gFfwM"
      },
      "outputs": [],
      "source": [
        "# Preprocessing the data\n",
        "selected_features = [\"TP2\", \"H1\", \"DV_pressure\", \"Reservoirs\", \"Oil_temperature\", \"Motor_current\", \"Oil_level\", 'status']\n",
        "X = data[selected_features]\n",
        "y = data['status']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eUil8hqUF_LN",
      "metadata": {
        "id": "eUil8hqUF_LN"
      },
      "outputs": [],
      "source": [
        "# Create the neural network model\n",
        "\n",
        "class NeuralNet(tf.keras.Model):\n",
        "  def __init__(self, *args, **kwargs):\n",
        "    super().__init__(**kwargs)\n",
        "    self._model = Sequential(name = \"Model\")\n",
        "    self._model.add(Dense(32, input_dim=X_train.shape[1], activation='tanh'))\n",
        "    self._model.add(Dense(32, activation='tanh'))  # First hidden layer\n",
        "    self._model.add(Dense(32, activation='tanh'))  # Second hidden layer\n",
        "    self._model.add(Dense(2, activation='softmax'))  # Output layer\n",
        "\n",
        "    early_stopping = EarlyStopping(monitor='val_accuracy', patience=3, restore_best_weights=True)\n",
        "    lr_scheduler = ReduceLROnPlateau(monitor='val_accuracy', factor=0.6, patience=0, verbose=1, min_lr=1e-6)\n",
        "\n",
        "    self._callbacks = [early_stopping, lr_scheduler]\n",
        "    # Compile the model\n",
        "    self._model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
        "\n",
        "    self._scaler = StandardScaler()\n",
        "    self._encoder= OneHotEncoder(categories='auto')\n",
        "\n",
        "  def encode(self, y):\n",
        "    return self._encoder.transform(y[:, np.newaxis]).toarray()\n",
        "\n",
        "  def fit(self, x, y, **kwargs):\n",
        "\n",
        "    x = self._scaler.fit_transform(x)\n",
        "    y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n",
        "\n",
        "    x = tf.convert_to_tensor(x)\n",
        "    y = tf.convert_to_tensor(y)\n",
        "    self._model.fit(x, y , callbacks = self._callbacks,  **kwargs)\n",
        "\n",
        "  def predict(self, x):\n",
        "    x = self._scaler.transform(x)\n",
        "    x = tf.convert_to_tensor(x)\n",
        "    y_pred = self._model.predict(x)\n",
        "    return y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "X0yLxRWr-2gb",
      "metadata": {
        "id": "X0yLxRWr-2gb"
      },
      "outputs": [],
      "source": [
        "model = NeuralNet()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "WObNHCyR6gId",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WObNHCyR6gId",
        "outputId": "294ca281-d369-4507-b456-3ee4e53fa2f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-139-2c0b25ae3745>:28: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1189/1189 [==============================] - 8s 5ms/step - loss: 0.0122 - accuracy: 0.9982 - val_loss: 2.5327e-04 - val_accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 2/5\n",
            "1186/1189 [============================>.] - ETA: 0s - loss: 1.3997e-04 - accuracy: 1.0000\n",
            "Epoch 2: ReduceLROnPlateau reducing learning rate to 0.0006000000284984708.\n",
            "1189/1189 [==============================] - 5s 4ms/step - loss: 1.3981e-04 - accuracy: 1.0000 - val_loss: 7.3704e-05 - val_accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 3/5\n",
            "1180/1189 [============================>.] - ETA: 0s - loss: 5.6537e-05 - accuracy: 1.0000\n",
            "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.0003600000170990825.\n",
            "1189/1189 [==============================] - 7s 6ms/step - loss: 5.6459e-05 - accuracy: 1.0000 - val_loss: 4.0886e-05 - val_accuracy: 1.0000 - lr: 6.0000e-04\n",
            "Epoch 4/5\n",
            "1186/1189 [============================>.] - ETA: 0s - loss: 3.4330e-05 - accuracy: 1.0000\n",
            "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.00021600000327453016.\n",
            "1189/1189 [==============================] - 5s 4ms/step - loss: 3.4323e-05 - accuracy: 1.0000 - val_loss: 2.7443e-05 - val_accuracy: 1.0000 - lr: 3.6000e-04\n"
          ]
        }
      ],
      "source": [
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=5, validation_split=0.2, verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "vJR-GK3EIZTf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJR-GK3EIZTf",
        "outputId": "052be1a4-ac8d-4087-d8d3-76c7caa361d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "372/372 [==============================] - 1s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "y_pred = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0A32Fle9GwXn",
      "metadata": {
        "id": "0A32Fle9GwXn"
      },
      "outputs": [],
      "source": [
        "y_pred = tf.math.argmax(y_pred, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "oQ76hnvqEuI1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "oQ76hnvqEuI1",
        "outputId": "d20c3fbc-574f-4f2a-c107-28a7f581728d"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display the confusion matrix\n",
        "disp = ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "T2P2plX-Ev1L",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2P2plX-Ev1L",
        "outputId": "b9bd13fc-95a0-4a3d-d1a2-4d249b89c502"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      5907\n",
            "           1       1.00      1.00      1.00      5982\n",
            "\n",
            "    accuracy                           1.00     11889\n",
            "   macro avg       1.00      1.00      1.00     11889\n",
            "weighted avg       1.00      1.00      1.00     11889\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Accuracy score\n",
        "print(accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Classification report\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WGnxzBdpJKRa",
      "metadata": {
        "id": "WGnxzBdpJKRa"
      },
      "source": [
        "### 5. Ensemble Method"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "JiPJh9f-I3KR",
      "metadata": {
        "id": "JiPJh9f-I3KR"
      },
      "source": [
        "From the performance of all algorithms, we decided to choose 3 algorithm that have highest scores and relatively short training time:\n",
        "\n",
        "* XGBoost\n",
        "* Extreme Learning Machine\n",
        "* Neural network (2 hidden layers)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ZFSO0Vy5JTEX",
      "metadata": {
        "id": "ZFSO0Vy5JTEX"
      },
      "source": [
        "Since each model will output a tensor of shape [None, 2] represent the probability of the sample belong to class 0 and class 1 respectively, our ensemble method simply calculate the average of 3 predicted probability."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "w3EeHaEJBqkg",
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "w3EeHaEJBqkg",
        "outputId": "2dbcf8fd-46f2-48dc-feba-3ac61ac6ba24"
      },
      "outputs": [
        {
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            "text/plain": [
              "       Unnamed: 0            timestamp    TP2     TP3      H1  DV_pressure  \\\n",
              "57257      517540  2020-04-11 09:23:09 -0.022  10.060  10.056       -0.024   \n",
              "43132      297575  2020-03-12 08:42:28  9.758   9.554  -0.008        0.786   \n",
              "7988       570552  2020-04-18 22:08:34  9.110   8.956  -0.010        2.026   \n",
              "32841      289916  2020-03-11 02:00:23 -0.012   9.222   9.212       -0.022   \n",
              "24629      904544  2020-06-07 04:28:11  8.134   7.912  -0.006        1.928   \n",
              "\n",
              "       Reservoirs  Oil_temperature  Motor_current  COMP  DV_eletric  Towers  \\\n",
              "57257      10.058           65.950         3.8475   1.0         0.0     1.0   \n",
              "43132       9.558           75.925         5.8825   0.0         1.0     1.0   \n",
              "7988        8.954           73.725         5.8200   0.0         1.0     0.0   \n",
              "32841       9.218           61.975         0.0400   1.0         0.0     1.0   \n",
              "24629       7.912           75.200         5.4975   0.0         1.0     1.0   \n",
              "\n",
              "       MPG  LPS  Pressure_switch  Oil_level  Caudal_impulses  status  \n",
              "57257  1.0  0.0              1.0        1.0              1.0       0  \n",
              "43132  0.0  0.0              1.0        1.0              1.0       0  \n",
              "7988   0.0  0.0              1.0        1.0              1.0       1  \n",
              "32841  1.0  0.0              1.0        1.0              0.0       0  \n",
              "24629  0.0  0.0              1.0        1.0              1.0       1  "
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Read the dataset\n",
        "data = pd.read_csv('Group_14_Clean_Data.csv')\n",
        "\n",
        "# Choose only features selected from Question 2\n",
        "# Shuffle the data\n",
        "data = data.sample(frac = 1)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "SgM_Gc0rBnEI",
      "metadata": {
        "id": "SgM_Gc0rBnEI"
      },
      "outputs": [],
      "source": [
        "# Preprocessing the data\n",
        "selected_features = [\"TP2\", \"H1\", \"DV_pressure\", \"Reservoirs\", \"Oil_temperature\", \"Motor_current\", \"Oil_level\", 'status']\n",
        "X = data[selected_features]\n",
        "y = data['status']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "G4wU6-XUDGFx",
      "metadata": {
        "id": "G4wU6-XUDGFx"
      },
      "outputs": [],
      "source": [
        "class EnsembleModel():\n",
        "\n",
        "  def __init__(self, clfs = [], *args, **kwargs):\n",
        "    self._clfs = clfs\n",
        "\n",
        "  def fit(self, x, y):\n",
        "    for c in self._clfs:\n",
        "      c.fit(x,y)\n",
        "\n",
        "  def predict(self, x):\n",
        "    y_pred = self._clfs[0].predict(x)\n",
        "    for c in self._clfs[1:]:\n",
        "      y_pred += c.predict(x)\n",
        "    return y_pred/len(self._clfs)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2U_wkg-xJO9b",
      "metadata": {
        "id": "2U_wkg-xJO9b"
      },
      "outputs": [],
      "source": [
        "clf1 = NeuralNet()\n",
        "clf2 = ExtremeLearningMachine(40)\n",
        "clf3 = XGBoost()\n",
        "\n",
        "ensembled = EnsembleModel(clfs = [clf1, clf2, clf3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qbfUW-pvCcbi",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbfUW-pvCcbi",
        "outputId": "c9bcca2b-9746-4c2d-e3f1-224d3a46398a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-139-2c0b25ae3745>:28: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1479/1487 [============================>.] - ETA: 0s - loss: 0.0128 - accuracy: 0.9978"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Learning rate reduction is conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy,lr\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
            "1487/1487 [==============================] - 8s 5ms/step - loss: 0.0128 - accuracy: 0.9978 - lr: 0.0010\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-61-5b2890666025>:34: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  y = self._encoder.fit_transform(y[:, np.newaxis]).toarray()\n"
          ]
        }
      ],
      "source": [
        "ensembled.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "vAU2xTDXCnMe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAU2xTDXCnMe",
        "outputId": "d1fe0f5b-07a1-4b5d-f14a-c83bf673302e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "372/372 [==============================] - 1s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = ensembled.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "RgBPYrq9FOpA",
      "metadata": {
        "id": "RgBPYrq9FOpA"
      },
      "outputs": [],
      "source": [
        "y_pred = np.argmax(y_pred, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "UxjQ4EE-Hh1A",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "UxjQ4EE-Hh1A",
        "outputId": "ad832862-4390-432c-cf2e-02acf57fc1af"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display the confusion matrix\n",
        "disp = ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5aMuH0eQHkl5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aMuH0eQHkl5",
        "outputId": "ef872cb1-f9aa-4b72-fda7-39d7c536b039"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Ensemble method 1.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      5948\n",
            "           1       1.00      1.00      1.00      5941\n",
            "\n",
            "    accuracy                           1.00     11889\n",
            "   macro avg       1.00      1.00      1.00     11889\n",
            "weighted avg       1.00      1.00      1.00     11889\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Accuracy score\n",
        "print(f\"Accuracy of Ensemble method {accuracy_score(y_test, y_pred)}\")\n",
        "\n",
        "# Classification report\n",
        "print(classification_report(y_test, y_pred))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "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": "3.9.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}