09_machine_learning/01_supervised_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12032af8-547f-486b-baab-d9f3f46cf957",
   "metadata": {},
   "source": [
    "# Supervised machine learning\n",
    "Supervised machine learning is a technique for configuring (learning) parameters of a computational model based on annotated data. In this example, we provide sparsely annotated data, which means we only annotate some of the given data points.\n",
    "\n",
    "See also\n",
    "* [Supervised learning (Wikipedia)](https://en.wikipedia.org/wiki/Supervised_learning)\n",
    "* [Supervised learning in scikit-learn](https://scikit-learn.org/stable/supervised_learning.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "825d6705-cf79-4b52-acc6-da93d6e2d96c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import jaccard_score, accuracy_score, precision_score, recall_score\n",
    "\n",
    "# local import; this library is located in the same folder as the notebook\n",
    "from data_generator import generate_biomodal_2d_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5292a204-9e34-47dd-8fac-f7db68cd4bc6",
   "metadata": {},
   "source": [
    "Our starting point for demonstrating supervised machine learning is a a pair of measurements in a bimodal distribution. In the following data set objects with a larger area are typically also more elongated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e3707f7-77be-4a26-811d-3e6bdd47018e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'elongation')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = generate_biomodal_2d_data()\n",
    "\n",
    "plt.scatter(data[:, 0], data[:, 1], c='#DDDDDD')\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('elongation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad993994-7a52-4d58-8873-6c095debe66e",
   "metadata": {},
   "source": [
    "To get a more detailed insight into the data, we print out the first entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d78f58d-7c5b-452a-b545-8b3e791f9c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>elongation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.950088</td>\n",
       "      <td>2.848643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.955912</td>\n",
       "      <td>3.390093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.469852</td>\n",
       "      <td>5.575289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.544467</td>\n",
       "      <td>3.017479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.465662</td>\n",
       "      <td>1.463756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.156507</td>\n",
       "      <td>3.232181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.978705</td>\n",
       "      <td>6.676372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.001683</td>\n",
       "      <td>5.047063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.457139</td>\n",
       "      <td>3.416050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.672295</td>\n",
       "      <td>3.407462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.413702</td>\n",
       "      <td>7.598608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.896781</td>\n",
       "      <td>3.410599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.305432</td>\n",
       "      <td>2.850365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.640594</td>\n",
       "      <td>8.602249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.523277</td>\n",
       "      <td>2.828454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.636970</td>\n",
       "      <td>10.277392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.223721</td>\n",
       "      <td>6.531755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.146032</td>\n",
       "      <td>8.404857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.407157</td>\n",
       "      <td>6.260869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.543343</td>\n",
       "      <td>8.472226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        area  elongation\n",
       "0   3.950088    2.848643\n",
       "1   4.955912    3.390093\n",
       "2   7.469852    5.575289\n",
       "3   2.544467    3.017479\n",
       "4   3.465662    1.463756\n",
       "5   3.156507    3.232181\n",
       "6   9.978705    6.676372\n",
       "7   6.001683    5.047063\n",
       "8   2.457139    3.416050\n",
       "9   3.672295    3.407462\n",
       "10  9.413702    7.598608\n",
       "11  2.896781    3.410599\n",
       "12  2.305432    2.850365\n",
       "13  4.640594    8.602249\n",
       "14  3.523277    2.828454\n",
       "15  7.636970   10.277392\n",
       "16  7.223721    6.531755\n",
       "17  7.146032    8.404857\n",
       "18  7.407157    6.260869\n",
       "19  6.543343    8.472226"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_to_annotate = data[:20]\n",
    "\n",
    "pd.DataFrame(data_to_annotate, columns=[\"area\", \"elongation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d40342e-61c9-417f-9f58-a46d3d707941",
   "metadata": {},
   "source": [
    "## Annotating data\n",
    "As mentioned above, supervised machine learning algorithms need some form of annotation, also called _ground truth_. We create a list of annotations where `1` represents small objects and `2` represents large and elongated objects.\n",
    "\n",
    "Note: We are here annotating the first 20 data points, which is a quite small number. In real projects, larger amounts of annotation data might be necessary to train well-performing classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c00f573a-4e99-4031-acef-13a0bb56f4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_annotation = [1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27e566a-70da-4a6e-a3ef-c83e903c8868",
   "metadata": {},
   "source": [
    "Now, we visualize the measurements again and draw the annotated measurements on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c66f554-231b-40ab-a9b2-d5bdc815c0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a6f76ab670>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:, 0], data[:, 1], c='#DDDDDD')\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('elongation')\n",
    "\n",
    "colors = ['orange', 'blue']\n",
    "annotated_colors = [colors[i-1] for i in manual_annotation]\n",
    "\n",
    "plt.scatter(data_to_annotate[:, 0], data_to_annotate[:, 1], c=annotated_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f82812-4bac-4984-b90e-a646718a6adb",
   "metadata": {},
   "source": [
    "## Separating test and validation data\n",
    "Before we train our classifier, we need to split the annotated data into two subsets. Goal is to enable unbiased validation. We train on the first half of the annotated data points and measure the quality on the second half. [Read more](https://scikit-learn.org/stable/common_pitfalls.html#data-leakage)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2fcb456d-90c9-4cc2-90c6-9c69a43e72bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data_to_annotate[:10]\n",
    "validation_data = data_to_annotate[10:]\n",
    "\n",
    "train_annotation = manual_annotation[:10]\n",
    "validation_annotation = manual_annotation[10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c17985-c857-4170-84ce-66e96f8e4971",
   "metadata": {},
   "source": [
    "## Classifier training\n",
    "With the selected data to annotate and the manual annotation, we can train a [Random Forest Classifier](https://en.wikipedia.org/wiki/Random_forest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec60e60e-bc9e-4e46-84a3-90366cf45c99",
   "metadata": {},
   "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()</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()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier()\n",
    "classifier.fit(train_data, train_annotation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec31464b-c5c8-49fa-80a9-d5721b50a136",
   "metadata": {},
   "source": [
    "## Validation\n",
    "We can now apply the classifier to the validation data and measure how many of these data points have been analyzed correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01002155-5344-446d-a6bb-79fdf6aba05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Annotated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predicted  Annotated\n",
       "0          2          2\n",
       "1          1          1\n",
       "2          1          1\n",
       "3          1          2\n",
       "4          1          1\n",
       "5          2          2\n",
       "6          2          2\n",
       "7          2          2\n",
       "8          2          2\n",
       "9          2          2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = classifier.predict(validation_data)\n",
    "\n",
    "# Show results next to annotation in a table\n",
    "result_annotation_comparison_table = {\n",
    "    \"Predicted\": result,\n",
    "    \"Annotated\": validation_annotation\n",
    "}\n",
    "pd.DataFrame(result_annotation_comparison_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e98ab03-d427-4c2c-99ef-f65f8abcac16",
   "metadata": {},
   "source": [
    "To get some standardized measures of the quality of the results of our classifier, we use [scikit-learn's metrics](https://scikit-learn.org/stable/modules/model_evaluation.html). An overview about the techniques are also available on [Wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall) and mean in the context here:\n",
    "* [Accurcay](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score): What portion of predictions were correct?\n",
    "* [Precision](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score): What portion of predicted `1`s were annotated as `1`?\n",
    "* [Recall (sensitivity)](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score): What portion of predicted `2`s have been annotated as `2`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7abc020-e5ce-42b8-b8be-e6eec1c231b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(validation_annotation, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ddca7690-99a7-4e58-b639-116a756cefb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(validation_annotation, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac81820a-1e15-4228-8709-6bbb3225799f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(validation_annotation, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425d2693-80d7-431c-a6ca-3094048b1df7",
   "metadata": {},
   "source": [
    "If you want to understand more detailed how the enties are counted and the quality scores are computed, the [multilabel confusion matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html#sklearn.metrics.multilabel_confusion_matrix) may be worth a look."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32230e9-6f91-4248-b47a-688fbfe29bf9",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "After training and validation of the classifier, we can reuse it to process other data sets. \n",
    "It is uncommon to classify test- and validation data, as those should be used for making the classifier only. We here apply the classifier to the remaining data points, which have not been annotated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa01dc06-1a8a-4f79-a6c9-de7c54087f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1,\n",
       "       2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2,\n",
       "       2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1,\n",
       "       1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2,\n",
       "       1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2,\n",
       "       1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1,\n",
       "       1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1,\n",
       "       1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2,\n",
       "       1, 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1,\n",
       "       2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2,\n",
       "       2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1,\n",
       "       1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2,\n",
       "       1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remaining_data = data[20:]\n",
    "\n",
    "prediction = classifier.predict(remaining_data)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d4abcf-124f-4182-a705-e2884bb407d0",
   "metadata": {},
   "source": [
    "Here we now visualize the whole data set with class colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f35dce87-89e0-4fec-ba16-020188f6bdb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'elongation')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_colors = [colors[i-1] for i in prediction]\n",
    "\n",
    "plt.scatter(remaining_data[:, 0], remaining_data[:, 1], c=predicted_colors)\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('elongation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3988aaa0-90be-4101-a334-d262071bd8ca",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Train a [Support Vector Machine](https://scikit-learn.org/stable/modules/svm.html) and visualize its prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccbd88e3-34e8-4aee-9fcd-e6d72e7c60fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "classifier = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a0e2948-ecf7-4b74-8137-a46523b2ef50",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}