Reloading Saved Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8af58159",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraires\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, roc_auc_score,roc_curve\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV # Hyperparameter\n",
    "from imblearn.over_sampling import SMOTE # handle class imbalance\n",
    "import pickle # to save the model\n",
    "\n",
    "# Miscellaneous libraries\n",
    "pd.set_option('display.max_columns',200)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a541bc36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "# Load the Breast cancer Dataset\n",
    "data = load_breast_cancer()\n",
    "# Description about dataset\n",
    "print(data['DESCR'])\n",
    "\n",
    "df = pd.DataFrame(data.data, columns = data.feature_names)\n",
    "df['target'] = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "430763d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preparation\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52a4e0b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((455, 30), (455,), (114, 30), (114,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the dataset into train and test sets\n",
    "X_train, X_test, y_train, y_test =  train_test_split(X,y, test_size=0.2, random_state=42)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6a91a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train) # fit and transform on the train dataset\n",
    "X_test_scaled = scaler.transform(X_test) # transforming test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3cb259e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the saved model\n",
    "with open('best_rf_clf.pkl','rb') as f:\n",
    "    loaded_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8304d2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the predictions on the test model\n",
    "y_test_loaded_model = loaded_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce384d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9626596790042582"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_test_loaded_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "facfd2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "191176d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score :  0.9626596790042582\n"
     ]
    }
   ],
   "source": [
    "# Calculate the ROC AUC Score\n",
    "y_pred_proba = loaded_model.predict_proba(X_test_scaled)[:,1]\n",
    "roc_auc = roc_auc_score(y_test,y_test_loaded_model)\n",
    "print(\"ROC AUC Score : \", roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "137aa203",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC AUC Curve\n",
    "fpr, tpr, tresholds = roc_curve(y_test, y_pred_proba)\n",
    "plt.plot(fpr, tpr, color=\"blue\",label=f'ROC Curve (AUC = {roc_auc:.4f})')\n",
    "plt.plot([0,1],[0,1],color='red',linestyle ='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver operating Characteristics(ROC) Curve\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6456f2ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}