01_ML0104EN-Clas-SVM-cancer__M.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n",
    "\n",
    "\n",
    "#  SVM (Support Vector Machines)\n",
    "\n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "    \n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "* Use scikit-learn to Support Vector Machine to classify\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.\n",
    "\n",
    "SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#load_dataset\">Load the Cancer data</a></li>\n",
    "        <li><a href=\"#modeling\">Modeling</a></li>\n",
    "        <li><a href=\"#evaluation\">Evaluation</a></li>\n",
    "        <li><a href=\"#practice\">Practice</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.11/site-packages (1.5.2)\n",
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      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.11/site-packages (3.9.2)\n",
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      "Requirement already satisfied: numpy in /opt/conda/lib/python3.11/site-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n",
    "!pip install matplotlib\n",
    "!pip install pandas \n",
    "!pip install numpy \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"load_dataset\">Load the Cancer data</h2>\n",
    "The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepository.html]. The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n",
    "\n",
    "|Field name|Description|\n",
    "|--- |--- |\n",
    "|ID|Clump thickness|\n",
    "|Clump|Clump thickness|\n",
    "|UnifSize|Uniformity of cell size|\n",
    "|UnifShape|Uniformity of cell shape|\n",
    "|MargAdh|Marginal adhesion|\n",
    "|SingEpiSize|Single epithelial cell size|\n",
    "|BareNuc|Bare nuclei|\n",
    "|BlandChrom|Bland chromatin|\n",
    "|NormNucl|Normal nucleoli|\n",
    "|Mit|Mitoses|\n",
    "|Class|Benign or malignant|\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "For the purposes of this example, we're using a dataset that has a relatively small number of predictors in each record. To download the data, we will use `!wget` to download it from IBM Object Storage.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-11-28 06:11:08--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 19975 (20K) [text/csv]\n",
      "Saving to: ‘cell_samples.csv’\n",
      "\n",
      "cell_samples.csv    100%[===================>]  19.51K  --.-KB/s    in 0s      \n",
      "\n",
      "2024-11-28 06:11:08 (58.4 MB/s) - ‘cell_samples.csv’ saved [19975/19975]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Click here and press Shift+Enter\n",
    "!wget -O cell_samples.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data From CSV File  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Clump</th>\n",
       "      <th>UnifSize</th>\n",
       "      <th>UnifShape</th>\n",
       "      <th>MargAdh</th>\n",
       "      <th>SingEpiSize</th>\n",
       "      <th>BareNuc</th>\n",
       "      <th>BlandChrom</th>\n",
       "      <th>NormNucl</th>\n",
       "      <th>Mit</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "0  1000025      5         1          1        1            2       1   \n",
       "1  1002945      5         4          4        5            7      10   \n",
       "2  1015425      3         1          1        1            2       2   \n",
       "3  1016277      6         8          8        1            3       4   \n",
       "4  1017023      4         1          1        3            2       1   \n",
       "\n",
       "   BlandChrom  NormNucl  Mit  Class  \n",
       "0           3         1    1      2  \n",
       "1           3         2    1      2  \n",
       "2           3         1    1      2  \n",
       "3           3         7    1      2  \n",
       "4           3         1    1      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
    "cell_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ID field contains the patient identifiers. The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign.\n",
    "\n",
    "The Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4).\n",
    "\n",
    "Let's look at the distribution of the classes based on Clump thickness and Uniformity of cell size:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\n",
    "cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing and selection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first look at columns data types:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              int64\n",
       "Clump           int64\n",
       "UnifSize        int64\n",
       "UnifShape       int64\n",
       "MargAdh         int64\n",
       "SingEpiSize     int64\n",
       "BareNuc        object\n",
       "BlandChrom      int64\n",
       "NormNucl        int64\n",
       "Mit             int64\n",
       "Class           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the __BareNuc__ column includes some values that are not numerical. We can drop those rows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             int64\n",
       "Clump          int64\n",
       "UnifSize       int64\n",
       "UnifShape      int64\n",
       "MargAdh        int64\n",
       "SingEpiSize    int64\n",
       "BareNuc        int64\n",
       "BlandChrom     int64\n",
       "NormNucl       int64\n",
       "Mit            int64\n",
       "Class          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\n",
    "cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\n",
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  1,  1,  1,  2,  1,  3,  1,  1],\n",
       "       [ 5,  4,  4,  5,  7, 10,  3,  2,  1],\n",
       "       [ 3,  1,  1,  1,  2,  2,  3,  1,  1],\n",
       "       [ 6,  8,  8,  1,  3,  4,  3,  7,  1],\n",
       "       [ 4,  1,  1,  3,  2,  1,  3,  1,  1]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
    "X = np.asarray(feature_df)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want the model to predict the value of Class (that is, benign (=2) or malignant (=4)).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.asarray(cell_df['Class'])\n",
    "y [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split our dataset into train and test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (546, 9) (546,)\n",
      "Test set: (137, 9) (137,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"modeling\">Modeling (SVM with Scikit-learn)</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SVM algorithm offers a choice of kernel functions for performing its processing. Basically, mapping data into a higher dimensional space is called kernelling. The mathematical function used for the transformation is known as the kernel function, and can be of different types, such as:\n",
    "\n",
    "    1.Linear\n",
    "    2.Polynomial\n",
    "    3.Radial basis function (RBF)\n",
    "    4.Sigmoid\n",
    "Each of these functions has its characteristics, its pros and cons, and its equation, but as there's no easy way of knowing which function performs best with any given dataset. We usually choose different functions in turn and compare the results. Let's just use the default, RBF (Radial Basis Function) for this lab.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After being fitted, the model can then be used to predict new values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2, 4, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf.predict(X_test)\n",
    "yhat [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"evaluation\">Evaluation</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       1.00      0.94      0.97        90\n",
      "           4       0.90      1.00      0.95        47\n",
      "\n",
      "    accuracy                           0.96       137\n",
      "   macro avg       0.95      0.97      0.96       137\n",
      "weighted avg       0.97      0.96      0.96       137\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[85  5]\n",
      " [ 0 47]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also easily use the __f1_score__ from sklearn library:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9639038982104676"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, yhat, average='weighted') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the jaccard index for accuracy:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "jaccard_score(y_test, yhat,pos_label=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"practice\">Practice</h2>\n",
    "Can you rebuild the model, but this time with a __linear__ kernel? You can use __kernel='linear'__ option, when you define the svm. How the accuracy changes with the new kernel function?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg F1-score: 0.9639\n",
      "Jaccard score: 0.9444\n"
     ]
    }
   ],
   "source": [
    "# write your code here\n",
    "\n",
    "clf2 = svm.SVC(kernel='linear')\n",
    "clf2.fit(X_train, y_train) \n",
    "yhat2 = clf2.predict(X_test)\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_score(y_test, yhat2,pos_label=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "clf2 = svm.SVC(kernel='linear')\n",
    "clf2.fit(X_train, y_train) \n",
    "yhat2 = clf2.predict(X_test)\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_score(y_test, yhat2,pos_label=2))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "\n",
    "## Author\n",
    "\n",
    "Saeed Aghabozorgi\n",
    "\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "<!--\n",
    "## Change Log\n",
    "\n",
    "\n",
    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
    "|---|---|---|---|\n",
    "| 2021-01-21  | 2.2  | Lakshmi  |  Updated sklearn library |\n",
    "| 2020-11-03  | 2.1  | Lakshmi  |  Updated URL of csv |\n",
    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
    "|   |   |   |   |\n",
    "|   |   |   |   |\n",
    "--!>\n",
    "\n",
    "\n"
   ]
  }
 ],
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