3-IntroMaskinlaering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "critical-decline",
   "metadata": {},
   "source": [
    "# Supervised, unsupervised and reinforcement learning\n",
    "This notebook will cover some examples of the concepts about supervised and unsupervised learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "false-survivor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All imports needed for this project\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recovered-ocean",
   "metadata": {},
   "source": [
    "## Supervised learning example\n",
    "Here we import a wine dataset used for supervised classification. The dataset is quite small, only 178 different wines. Our job is to classify an instance of a wine as one out of three different wine types/classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "duplicate-bailey",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import the dataset and call it by the name \"wine\"\n",
    "wine = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "accompanied-democrat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "         1.065e+03],\n",
       "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "         1.050e+03],\n",
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       "         1.185e+03],\n",
       "        ...,\n",
       "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "         8.350e+02],\n",
       "        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "         8.400e+02],\n",
       "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "         5.600e+02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
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       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),\n",
       " 'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n",
       " 'feature_names': ['alcohol',\n",
       "  'malic_acid',\n",
       "  'ash',\n",
       "  'alcalinity_of_ash',\n",
       "  'magnesium',\n",
       "  'total_phenols',\n",
       "  'flavanoids',\n",
       "  'nonflavanoid_phenols',\n",
       "  'proanthocyanins',\n",
       "  'color_intensity',\n",
       "  'hue',\n",
       "  'od280/od315_of_diluted_wines',\n",
       "  'proline']}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is what it looks like! A lot of information, but we just need some of it\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "helpful-compression",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is nice to create a Pandas Dataframe from the data\n",
    "# The data we need is from the wine-dictionary on the keys \"data\" (the features) and \"target\" (the labels)\n",
    "# np.c_ is a numpy function to concatenate (merge) the two arrays together\n",
    "# To get columns names, we get the \"feature_names\" and \"target\" from the wine-variable\n",
    "\n",
    "df = pd.DataFrame(data=np.c_[wine['data'], wine['target']],\n",
    "                     columns= wine['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "material-uzbekistan",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  target  \n",
       "0                            3.92   1065.0     0.0  \n",
       "1                            3.40   1050.0     0.0  \n",
       "2                            3.17   1185.0     0.0  \n",
       "3                            3.45   1480.0     0.0  \n",
       "4                            2.93    735.0     0.0  \n",
       "..                            ...      ...     ...  \n",
       "173                          1.74    740.0     2.0  \n",
       "174                          1.56    750.0     2.0  \n",
       "175                          1.56    835.0     2.0  \n",
       "176                          1.62    840.0     2.0  \n",
       "177                          1.60    560.0     2.0  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is an overview of the dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "rough-option",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
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       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "      <td>0.938202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "      <td>0.775035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
       "\n",
       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
       "count     178.000000  178.000000            178.000000       178.000000   \n",
       "mean        2.295112    2.029270              0.361854         1.590899   \n",
       "std         0.625851    0.998859              0.124453         0.572359   \n",
       "min         0.980000    0.340000              0.130000         0.410000   \n",
       "25%         1.742500    1.205000              0.270000         1.250000   \n",
       "50%         2.355000    2.135000              0.340000         1.555000   \n",
       "75%         2.800000    2.875000              0.437500         1.950000   \n",
       "max         3.880000    5.080000              0.660000         3.580000   \n",
       "\n",
       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
       "count       178.000000  178.000000                    178.000000   178.000000   \n",
       "mean          5.058090    0.957449                      2.611685   746.893258   \n",
       "std           2.318286    0.228572                      0.709990   314.907474   \n",
       "min           1.280000    0.480000                      1.270000   278.000000   \n",
       "25%           3.220000    0.782500                      1.937500   500.500000   \n",
       "50%           4.690000    0.965000                      2.780000   673.500000   \n",
       "75%           6.200000    1.120000                      3.170000   985.000000   \n",
       "max          13.000000    1.710000                      4.000000  1680.000000   \n",
       "\n",
       "           target  \n",
       "count  178.000000  \n",
       "mean     0.938202  \n",
       "std      0.775035  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And some statistaaaackss (\"Check the statistacccs\" -Big Shaq, Man's Not Hot)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fourth-power",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We split the dataset into X (features) and y (labels)\n",
    "X, y = df[wine['feature_names']], df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "genetic-liabilities",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to train on 80% of the data, and test on 20% of the data\n",
    "# The split is done in a stratified manner, meaning that the distribution of labels is intact after the split\n",
    "# This means that if the dataset contains 50% of wine1, 10% of wine2 and 40% of wine3, \n",
    "# then these percentages will be kept after the split\n",
    "# Why? Because we could end up with a X_test and y_test that only contains wine1 and wine3, and that is not good\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "collaborative-technique",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142 142 36 36\n"
     ]
    }
   ],
   "source": [
    "# Just to check that the split is successfull\n",
    "print(len(X_train), len(y_train), len(X_test), len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ecological-edinburgh",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import a decision tree from sklearn\n",
    "dt_model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# The .fit function trains the model on the training data\n",
    "dt_model.fit(X_train, y_train)\n",
    "\n",
    "# The .predict function is used on the already trained model (from previous step), and is now used to predict the data\n",
    "# of the test-data (but only X_test, which is the features. The labels in y_test is a secret for now)\n",
    "# The output of this (\"predictions\") is an array with the labels predicted by the model\n",
    "predictions = dt_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "severe-leisure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 2.0, 1.0, 2.0, 0.0, 2.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 2.0, 1.0, 2.0, 0.0, 2.0, 1.0, 2.0, 2.0, 2.0]\n",
      "[0.0, 2.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 2.0, 1.0, 2.0, 0.0, 2.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 2.0, 1.0, 2.0, 0.0, 2.0, 1.0, 2.0, 2.0, 2.0]\n"
     ]
    }
   ],
   "source": [
    "# This is just to print so you can see the content of your predictions and the y_test (which is the correct answers). \n",
    "# See any differences?\n",
    "print(predictions.tolist())\n",
    "print(y_test.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "laden-disease",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We check the performance of our predictions by using accuracy score (imported from sklearn)\n",
    "# Quite good score\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arbitrary-scott",
   "metadata": {},
   "source": [
    "## Unsupervised learning\n",
    "For this task we will create some datapoints (blobs) in clusters. We can define number of datapoints, clusters and density ourself. Then we will use K-means clustering to cluster the datapoints.\n",
    "\n",
    "Explore how changing the number of centers when creating the dataset and the number of clusters in the K-means model will change the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "failing-photographer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create some data\n",
    "# Here we create 5000 datapoints and we have 3 centers\n",
    "# We get X, but dont need the y (which will tell us what cluster the datapoint \"really\" belonged to)\n",
    "# This y (labels) is often not known when applied in industri etc.\n",
    "\n",
    "X, _ = make_blobs(n_samples=5000, centers=3, cluster_std=0.60, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "artificial-cyprus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.9938053 ,  0.5257808 ],\n",
       "       [-1.89657262,  2.3925938 ],\n",
       "       [-1.74817656,  3.36582105],\n",
       "       ...,\n",
       "       [-0.19318278,  3.10249179],\n",
       "       [-2.30404673,  3.0518701 ],\n",
       "       [-2.40559954,  3.5250349 ]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data is just some coordinates in a 2D array\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ongoing-address",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import K-means and initialize with 3 clusters\n",
    "kmeans = KMeans(n_clusters=3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "boring-polish",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the labels of clusters by using .fit_predict function\n",
    "label = kmeans.fit_predict(X)\n",
    " \n",
    "# Getting unique labels by using np.unique from numpy\n",
    "u_labels = np.unique(label)\n",
    "\n",
    "# Getting the Centroids\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "simplified-niagara",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the results by using matplotlib\n",
    "\n",
    "# Plot the blobs/datapoints\n",
    "for i in u_labels:\n",
    "    plt.scatter(X[label == i , 0] , X[label == i , 1] , label = i)\n",
    "\n",
    "# Plot the centers of the clusters\n",
    "plt.scatter(centroids[:,0] , centroids[:,1] , s = 80, color = 'k')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "15MOAI",
   "language": "python",
   "name": "15moai"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}