exercises-uci-iris-dataset-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises - UCI Iris Dataset - Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercises with UCI Iris Dataset.\n",
    "\n",
    "Original page [https://archive.ics.uci.edu/ml/datasets/iris](https://archive.ics.uci.edu/ml/datasets/iris)\n",
    "\n",
    "New beta version UCI website [https://archive-beta.ics.uci.edu/ml/datasets/iris](https://archive-beta.ics.uci.edu/ml/datasets/iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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   "outputs": [],
   "source": [
    "import requests # web requests\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib as plt # data visualization\n",
    "import seaborn as sb # data visualization\n",
    "import graphviz # grahp visualization\n",
    "from sklearn.model_selection import StratifiedShuffleSplit # dataset subsetting \n",
    "from sklearn.preprocessing import StandardScaler  \n",
    "from sklearn.preprocessing import LabelEncoder # mange categorical data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import tree as skl_tree # Decision tree models\n",
    "from sklearn import metrics # results evaluation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing and exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data import - (One shoot execution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the original website.\n",
    "\n",
    "Next steps are \"one shoot execution\", you should execute it only the first time, once did it you can go directly to *Starting points* that youll'find along the code.\n",
    "\n",
    "First we want to make a web request to download the dataset description and save it locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Dataset description url\n",
    "description_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.names'\n",
    "\n",
    "requestDatasetDescription = requests.get(description_url, allow_redirects=True)\n",
    "if requestDatasetDescription.status_code != 200:\n",
    "    print(f\"Request status: {requestDatasetDescription.status_code}\")\n",
    "else:\n",
    "    open('Datasets/Iris/Py_IrisDatasetDescription.txt', 'wb').write(requestDatasetDescription.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the dataset description\n",
    "DatasetDescription = open('Datasets/Iris/Py_IrisDatasetDescription.txt', 'r') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Title: Iris Plants Database\n",
      "\tUpdated Sept 21 by C.Blake - Added discrepency information\n",
      "\n",
      "2. Sources:\n",
      "     (a) Creator: R.A. Fisher\n",
      "     (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "     (c) Date: July, 1988\n",
      "\n",
      "3. Past Usage:\n",
      "   - Publications: too many to mention!!!  Here are a few.\n",
      "   1. Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "      Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions\n",
      "      to Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "      (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   3. Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "      Structure and Classification Rule for Recognition in Partially Exposed\n",
      "      Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "      Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "      -- Results:\n",
      "         -- very low misclassification rates (0% for the setosa class)\n",
      "   4. Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE \n",
      "      Transactions on Information Theory, May 1972, 431-433.\n",
      "      -- Results:\n",
      "         -- very low misclassification rates again\n",
      "   5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II\n",
      "      conceptual clustering system finds 3 classes in the data.\n",
      "\n",
      "4. Relevant Information:\n",
      "   --- This is perhaps the best known database to be found in the pattern\n",
      "       recognition literature.  Fisher's paper is a classic in the field\n",
      "       and is referenced frequently to this day.  (See Duda & Hart, for\n",
      "       example.)  The data set contains 3 classes of 50 instances each,\n",
      "       where each class refers to a type of iris plant.  One class is\n",
      "       linearly separable from the other 2; the latter are NOT linearly\n",
      "       separable from each other.\n",
      "   --- Predicted attribute: class of iris plant.\n",
      "   --- This is an exceedingly simple domain.\n",
      "   --- This data differs from the data presented in Fishers article\n",
      "\t(identified by Steve Chadwick,  spchadwick@espeedaz.net )\n",
      "\tThe 35th sample should be: 4.9,3.1,1.5,0.2,\"Iris-setosa\"\n",
      "\twhere the error is in the fourth feature.\n",
      "\tThe 38th sample: 4.9,3.6,1.4,0.1,\"Iris-setosa\"\n",
      "\twhere the errors are in the second and third features.  \n",
      "\n",
      "5. Number of Instances: 150 (50 in each of three classes)\n",
      "\n",
      "6. Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "\n",
      "7. Attribute Information:\n",
      "   1. sepal length in cm\n",
      "   2. sepal width in cm\n",
      "   3. petal length in cm\n",
      "   4. petal width in cm\n",
      "   5. class: \n",
      "      -- Iris Setosa\n",
      "      -- Iris Versicolour\n",
      "      -- Iris Virginica\n",
      "\n",
      "8. Missing Attribute Values: None\n",
      "\n",
      "Summary Statistics:\n",
      "\t         Min  Max   Mean    SD   Class Correlation\n",
      "   sepal length: 4.3  7.9   5.84  0.83    0.7826   \n",
      "    sepal width: 2.0  4.4   3.05  0.43   -0.4194\n",
      "   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)\n",
      "    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)\n",
      "\n",
      "9. Class Distribution: 33.3% for each of 3 classes.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(DatasetDescription.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the dataset directly with *pandas* and save it in a comfortable python format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "IrisDF = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)\n",
    "\n",
    "IrisDF.to_pickle(\"Datasets/Iris/IrisDF.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting point - read data\n",
    "\n",
    "Once downloaded the dataset the first time, you can restart from here reading the saved pandas DF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-03-03T09:38:08.581195Z",
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     "shell.execute_reply": "2022-03-03T09:38:08.601392Z",
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IrisDF = pd.read_pickle(\"Datasets/Iris/IrisDF.pkl\") \n",
    "type(IrisDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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      "text/plain": [
       "     0    1    2    3            4\n",
       "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
       "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "4  5.0  3.6  1.4  0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(IrisDF.shape)\n",
    "IrisDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                0           1           2           3\n",
      "count  150.000000  150.000000  150.000000  150.000000\n",
      "mean     5.843333    3.054000    3.758667    1.198667\n",
      "std      0.828066    0.433594    1.764420    0.763161\n",
      "min      4.300000    2.000000    1.000000    0.100000\n",
      "25%      5.100000    2.800000    1.600000    0.300000\n",
      "50%      5.800000    3.000000    4.350000    1.300000\n",
      "75%      6.400000    3.300000    5.100000    1.800000\n",
      "max      7.900000    4.400000    6.900000    2.500000\n"
     ]
    }
   ],
   "source": [
    "print(IrisDF.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add colunmns description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
      "count    150.000000   150.000000    150.000000   150.000000\n",
      "mean       5.843333     3.054000      3.758667     1.198667\n",
      "std        0.828066     0.433594      1.764420     0.763161\n",
      "min        4.300000     2.000000      1.000000     0.100000\n",
      "25%        5.100000     2.800000      1.600000     0.300000\n",
      "50%        5.800000     3.000000      4.350000     1.300000\n",
      "75%        6.400000     3.300000      5.100000     1.800000\n",
      "max        7.900000     4.400000      6.900000     2.500000\n"
     ]
    }
   ],
   "source": [
    "IrisDF.columns = ['Sepal_Length', 'Sepal_Width','Petal_Length', 'Petal_Width', 'Species' ]\n",
    "print(IrisDF.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "categorical data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species\n",
      "Iris-setosa        50\n",
      "Iris-versicolor    50\n",
      "Iris-virginica     50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(IrisDF.groupby('Species').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a graphical global exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:08.672899Z",
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     "shell.execute_reply.started": "2022-03-03T09:38:08.672853Z"
    }
   },
   "outputs": [],
   "source": [
    "colors_palette = {'Iris-setosa': '#377eb8', \\\n",
    "                  'Iris-versicolor': '#4eae4b',\\\n",
    "                  'Iris-virginica': '#e41a1c'}\n",
    "\n",
    "colors = [colors_palette[c] for c in IrisDF.loc[ : , 'Species']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:08.682917Z",
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='Sepal_Width', ylabel='Sepal_Width'>,\n",
       "        <AxesSubplot:xlabel='Petal_Length', ylabel='Sepal_Width'>,\n",
       "        <AxesSubplot:xlabel='Petal_Width', ylabel='Sepal_Width'>],\n",
       "       [<AxesSubplot:xlabel='Sepal_Width', ylabel='Petal_Length'>,\n",
       "        <AxesSubplot:xlabel='Petal_Length', ylabel='Petal_Length'>,\n",
       "        <AxesSubplot:xlabel='Petal_Width', ylabel='Petal_Length'>],\n",
       "       [<AxesSubplot:xlabel='Sepal_Width', ylabel='Petal_Width'>,\n",
       "        <AxesSubplot:xlabel='Petal_Length', ylabel='Petal_Width'>,\n",
       "        <AxesSubplot:xlabel='Petal_Width', ylabel='Petal_Width'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(IrisDF.iloc[: , 1: ], color=colors, figsize=(10,10))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try using seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:09.996741Z",
     "iopub.status.busy": "2022-03-03T09:38:09.996216Z",
     "iopub.status.idle": "2022-03-03T09:38:15.068295Z",
     "shell.execute_reply": "2022-03-03T09:38:15.067437Z",
     "shell.execute_reply.started": "2022-03-03T09:38:09.996707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f7dbf9eb5b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 643.25x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(IrisDF.iloc[: , 1: ], hue=\"Species\" )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T10:22:21.272919Z",
     "iopub.status.busy": "2022-02-27T10:22:21.272361Z",
     "iopub.status.idle": "2022-02-27T10:22:22.443421Z",
     "shell.execute_reply": "2022-02-27T10:22:22.441985Z",
     "shell.execute_reply.started": "2022-02-27T10:22:21.272882Z"
    }
   },
   "source": [
    "## Training and Test subsetting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create traing and test subset with variable Species propotrional to the original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:15.069819Z",
     "iopub.status.busy": "2022-03-03T09:38:15.069570Z",
     "iopub.status.idle": "2022-03-03T09:38:15.255140Z",
     "shell.execute_reply": "2022-03-03T09:38:15.254177Z",
     "shell.execute_reply.started": "2022-03-03T09:38:15.069786Z"
    }
   },
   "outputs": [],
   "source": [
    "split_3_7 = StratifiedShuffleSplit(n_splits=1, test_size=0.7, random_state=22)\n",
    "\n",
    "for train_index, test_index in split_3_7.split(IrisDF, IrisDF['Species']):\n",
    "    proportional_train = IrisDF.loc[train_index]\n",
    "    proportional_test = IrisDF.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:15.260324Z",
     "iopub.status.busy": "2022-03-03T09:38:15.258126Z",
     "iopub.status.idle": "2022-03-03T09:38:15.269358Z",
     "shell.execute_reply": "2022-03-03T09:38:15.268345Z",
     "shell.execute_reply.started": "2022-03-03T09:38:15.260281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     0.333333\n",
       "Iris-versicolor    0.333333\n",
       "Iris-setosa        0.333333\n",
       "Name: Species, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportional_train['Species'].value_counts()/len(proportional_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:15.270869Z",
     "iopub.status.busy": "2022-03-03T09:38:15.270601Z",
     "iopub.status.idle": "2022-03-03T09:38:15.286070Z",
     "shell.execute_reply": "2022-03-03T09:38:15.285313Z",
     "shell.execute_reply.started": "2022-03-03T09:38:15.270838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     0.333333\n",
       "Iris-setosa        0.333333\n",
       "Iris-versicolor    0.333333\n",
       "Name: Species, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportional_test['Species'].value_counts()/len(proportional_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:15.288087Z",
     "iopub.status.busy": "2022-03-03T09:38:15.287258Z",
     "iopub.status.idle": "2022-03-03T09:38:15.312409Z",
     "shell.execute_reply": "2022-03-03T09:38:15.311834Z",
     "shell.execute_reply.started": "2022-03-03T09:38:15.287999Z"
    }
   },
   "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>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.877778</td>\n",
       "      <td>2.991111</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>1.182222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.931980</td>\n",
       "      <td>0.456181</td>\n",
       "      <td>1.869565</td>\n",
       "      <td>0.772331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
       "count     45.000000    45.000000     45.000000    45.000000\n",
       "mean       5.877778     2.991111      3.820000     1.182222\n",
       "std        0.931980     0.456181      1.869565     0.772331\n",
       "min        4.300000     2.200000      1.000000     0.100000\n",
       "25%        5.000000     2.700000      1.500000     0.200000\n",
       "50%        5.800000     3.000000      4.500000     1.300000\n",
       "75%        6.500000     3.200000      5.100000     1.800000\n",
       "max        7.700000     4.200000      6.900000     2.500000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportional_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:15.313991Z",
     "iopub.status.busy": "2022-03-03T09:38:15.313581Z",
     "iopub.status.idle": "2022-03-03T09:38:15.336964Z",
     "shell.execute_reply": "2022-03-03T09:38:15.336161Z",
     "shell.execute_reply.started": "2022-03-03T09:38:15.313961Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.828571</td>\n",
       "      <td>3.080952</td>\n",
       "      <td>3.732381</td>\n",
       "      <td>1.205714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.783694</td>\n",
       "      <td>0.422924</td>\n",
       "      <td>1.726017</td>\n",
       "      <td>0.762817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
       "count    105.000000   105.000000    105.000000   105.000000\n",
       "mean       5.828571     3.080952      3.732381     1.205714\n",
       "std        0.783694     0.422924      1.726017     0.762817\n",
       "min        4.400000     2.000000      1.200000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.300000     1.300000\n",
       "75%        6.400000     3.400000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.700000     2.500000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportional_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing - Standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of ML models suppose that the data are distributed as a Gaussian with zero mean and unit variance. So we need to standardize the data to achive better results.\n",
    "\n",
    "*StandardScaler* *Scikit Learn* package makes a simple naive rescaling that could be a good choice in this case. It simply remove the mean and divide for the standard deviation each feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digression on standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different opinions on when and how apply standardization. We can find 3 main opinions:\n",
    "\n",
    "\n",
    "- Standardize the entire dataset before splitting\n",
    "- Standardize the dataset after splitting using the mean ad standard deviation of the training subset also for the test subset\n",
    "- Standardize the dataset after splitting using the mean ad standard deviation of the training subset only for the training subset and the mean ad standard deviation of the test subset only for the test subset\n",
    "\n",
    "\n",
    "I've an unpopular opinon: there are no a priori a real good or bad choice, except for some particular situations.\n",
    "\n",
    "Anyway I have a preference for the third choice.\n",
    "\n",
    "So let's try to explain the reasons for the third choice wich probably most of the people consider a bad choice.\n",
    "\n",
    "In most of the cases the statistic behind the model is demostrated supposing tha the data are distributed as a Gaussian with zero mean and unit variance, so make the conditions for this supposition both in the training and in the testing of the model shouldn't be considered a bad idea. We are testing the model in the conditions on wich the teorical bases have been constructed. \n",
    "\n",
    "Clearly we must not forget that the \"real world dataset\" will tend to be a Gaussian with zero mean and unit variance but our dataset will ever bee to small to be exactly that.\n",
    "Aniway we have no reasons to consider the addition of a non random noise (firsth and second options) a better choice. \n",
    "\n",
    "I'm planning to make some empirical test in future to have a feedback on my opinion, for the moment let's apply also the second choice wich is the most common."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_prop_train = proportional_train\n",
    "scaled_prop_test_T = proportional_test\n",
    "scaled_prop_test_FT = proportional_test\n",
    "\n",
    "scaler1 = StandardScaler()\n",
    "scaler2 = StandardScaler()\n",
    "\n",
    "\n",
    "scaled_prop_train.iloc[:, 0:4] = scaler1.fit_transform(scaled_prop_train.iloc[:, 0:4])\n",
    "scaled_prop_test_T.iloc[:, 0:4] = scaler1.transform(scaled_prop_test_T.iloc[:, 0:4])\n",
    "scaled_prop_test_FT.iloc[:, 0:4] = scaler2.fit_transform(scaled_prop_test_FT.iloc[:, 0:4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.595008e-17</td>\n",
       "      <td>3.172066e-18</td>\n",
       "      <td>4.229421e-18</td>\n",
       "      <td>-3.595008e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.004796e+00</td>\n",
       "      <td>1.004796e+00</td>\n",
       "      <td>1.004796e+00</td>\n",
       "      <td>1.004796e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.831612e+00</td>\n",
       "      <td>-2.568163e+00</td>\n",
       "      <td>-1.474219e+00</td>\n",
       "      <td>-1.456466e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.341223e-01</td>\n",
       "      <td>-6.674960e-01</td>\n",
       "      <td>-1.241360e+00</td>\n",
       "      <td>-1.193023e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.663225e-02</td>\n",
       "      <td>-1.923294e-01</td>\n",
       "      <td>3.304380e-01</td>\n",
       "      <td>1.241948e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.326450e-01</td>\n",
       "      <td>7.580039e-01</td>\n",
       "      <td>7.961559e-01</td>\n",
       "      <td>7.828035e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.655838e+00</td>\n",
       "      <td>3.133837e+00</td>\n",
       "      <td>1.727592e+00</td>\n",
       "      <td>1.704856e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal_Length   Sepal_Width  Petal_Length   Petal_Width\n",
       "count  1.050000e+02  1.050000e+02  1.050000e+02  1.050000e+02\n",
       "mean   3.595008e-17  3.172066e-18  4.229421e-18 -3.595008e-17\n",
       "std    1.004796e+00  1.004796e+00  1.004796e+00  1.004796e+00\n",
       "min   -1.831612e+00 -2.568163e+00 -1.474219e+00 -1.456466e+00\n",
       "25%   -9.341223e-01 -6.674960e-01 -1.241360e+00 -1.193023e+00\n",
       "50%   -3.663225e-02 -1.923294e-01  3.304380e-01  1.241948e-01\n",
       "75%    7.326450e-01  7.580039e-01  7.961559e-01  7.828035e-01\n",
       "max    2.655838e+00  3.133837e+00  1.727592e+00  1.704856e+00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_prop_test_T.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.595008e-17</td>\n",
       "      <td>3.172066e-18</td>\n",
       "      <td>4.229421e-18</td>\n",
       "      <td>-3.595008e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.004796e+00</td>\n",
       "      <td>1.004796e+00</td>\n",
       "      <td>1.004796e+00</td>\n",
       "      <td>1.004796e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.831612e+00</td>\n",
       "      <td>-2.568163e+00</td>\n",
       "      <td>-1.474219e+00</td>\n",
       "      <td>-1.456466e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.341223e-01</td>\n",
       "      <td>-6.674960e-01</td>\n",
       "      <td>-1.241360e+00</td>\n",
       "      <td>-1.193023e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.663225e-02</td>\n",
       "      <td>-1.923294e-01</td>\n",
       "      <td>3.304380e-01</td>\n",
       "      <td>1.241948e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.326450e-01</td>\n",
       "      <td>7.580039e-01</td>\n",
       "      <td>7.961559e-01</td>\n",
       "      <td>7.828035e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.655838e+00</td>\n",
       "      <td>3.133837e+00</td>\n",
       "      <td>1.727592e+00</td>\n",
       "      <td>1.704856e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal_Length   Sepal_Width  Petal_Length   Petal_Width\n",
       "count  1.050000e+02  1.050000e+02  1.050000e+02  1.050000e+02\n",
       "mean   3.595008e-17  3.172066e-18  4.229421e-18 -3.595008e-17\n",
       "std    1.004796e+00  1.004796e+00  1.004796e+00  1.004796e+00\n",
       "min   -1.831612e+00 -2.568163e+00 -1.474219e+00 -1.456466e+00\n",
       "25%   -9.341223e-01 -6.674960e-01 -1.241360e+00 -1.193023e+00\n",
       "50%   -3.663225e-02 -1.923294e-01  3.304380e-01  1.241948e-01\n",
       "75%    7.326450e-01  7.580039e-01  7.961559e-01  7.828035e-01\n",
       "max    2.655838e+00  3.133837e+00  1.727592e+00  1.704856e+00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_prop_test_FT.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-4.798631e-16</td>\n",
       "      <td>1.148772e-16</td>\n",
       "      <td>3.219647e-16</td>\n",
       "      <td>-4.096260e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.011300e+00</td>\n",
       "      <td>1.011300e+00</td>\n",
       "      <td>1.011300e+00</td>\n",
       "      <td>1.011300e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.712062e+00</td>\n",
       "      <td>-1.753801e+00</td>\n",
       "      <td>-1.525417e+00</td>\n",
       "      <td>-1.417075e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.524850e-01</td>\n",
       "      <td>-6.453593e-01</td>\n",
       "      <td>-1.254953e+00</td>\n",
       "      <td>-1.286134e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-8.439740e-02</td>\n",
       "      <td>1.970563e-02</td>\n",
       "      <td>3.678310e-01</td>\n",
       "      <td>1.542197e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.751792e-01</td>\n",
       "      <td>4.630823e-01</td>\n",
       "      <td>6.923877e-01</td>\n",
       "      <td>8.089260e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.977311e+00</td>\n",
       "      <td>2.679966e+00</td>\n",
       "      <td>1.666058e+00</td>\n",
       "      <td>1.725515e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal_Length   Sepal_Width  Petal_Length   Petal_Width\n",
       "count  4.500000e+01  4.500000e+01  4.500000e+01  4.500000e+01\n",
       "mean  -4.798631e-16  1.148772e-16  3.219647e-16 -4.096260e-16\n",
       "std    1.011300e+00  1.011300e+00  1.011300e+00  1.011300e+00\n",
       "min   -1.712062e+00 -1.753801e+00 -1.525417e+00 -1.417075e+00\n",
       "25%   -9.524850e-01 -6.453593e-01 -1.254953e+00 -1.286134e+00\n",
       "50%   -8.439740e-02  1.970563e-02  3.678310e-01  1.542197e-01\n",
       "75%    6.751792e-01  4.630823e-01  6.923877e-01  8.089260e-01\n",
       "max    1.977311e+00  2.679966e+00  1.666058e+00  1.725515e+00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_prop_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.87777778 2.99111111 3.82       1.18222222]\n",
      "[0.84928395 0.20347654 3.4176     0.58323951]\n",
      "[-0.05339427  0.19916761 -0.04739559  0.03076081]\n",
      "[0.71628167 0.87067037 0.86340151 0.9881846 ]\n"
     ]
    }
   ],
   "source": [
    "print(scaler1.mean_)\n",
    "print(scaler1.var_)\n",
    "print(scaler2.mean_)\n",
    "print(scaler2.var_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial logistic regression (softmax regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use scikit-learn package. \n",
    "\n",
    "We need to specify that has to be multinomial classification and we need to specify a solver wich allows softmax regression.\n",
    "\n",
    "In this case we have few feature and apparently all important so we can reduce the impact of the regularization term. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T09:38:15.424093Z",
     "iopub.status.busy": "2022-03-03T09:38:15.423659Z",
     "iopub.status.idle": "2022-03-03T09:38:15.612794Z",
     "shell.execute_reply": "2022-03-03T09:38:15.611591Z",
     "shell.execute_reply.started": "2022-03-03T09:38:15.424045Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the scikit-learn implementation of Softmax Regression with lbfgs solver and 100 as C parameter on test dataset standardize on traning data is 0.9809523809523809\n",
      "The accuracy of the scikit-learn implementation of Softmax Regression with lbfgs solver and 100 as C parameter on test dataset standardize on test data is 0.9809523809523809\n"
     ]
    }
   ],
   "source": [
    "# C parameter is the invers of the parameter applied to the regularization term \n",
    "softmax_regressionM_Slbfgs_C10 = LogisticRegression(multi_class='multinomial',  solver='lbfgs', C=100)\n",
    "\n",
    "#encode categorical data\n",
    "labels = scaled_prop_train.iloc[:,4].to_numpy()\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoded_labels = label_encoder.fit_transform(labels)\n",
    "\n",
    "# training\n",
    "softmax_regressionM_Slbfgs_C10.fit(scaled_prop_train.iloc[:,0:4], label_encoded_labels)\n",
    "\n",
    "# prediction\n",
    "softmax_regressionM_Slbfgs_C10_predictionT = softmax_regressionM_Slbfgs_C10.predict(scaled_prop_test_T.iloc[:,0:4])\n",
    "softmax_regressionM_Slbfgs_C10_predictionFT = softmax_regressionM_Slbfgs_C10.predict(scaled_prop_test_FT.iloc[:,0:4])\n",
    "\n",
    "test_labels = scaled_prop_test_T.iloc[:,4].to_numpy()\n",
    "test_labels_encoded = label_encoder.transform(test_labels)\n",
    "\n",
    "# results evaluation\n",
    "print(f\"The accuracy of the scikit-learn implementation of Softmax Regression with lbfgs solver and 100 as C parameter on test dataset standardize on traning data is \\\n",
    "{metrics.accuracy_score(softmax_regressionM_Slbfgs_C10_predictionT, test_labels_encoded)}\" )\n",
    "print(f\"The accuracy of the scikit-learn implementation of Softmax Regression with lbfgs solver and 100 as C parameter on test dataset standardize on test data is \\\n",
    "{metrics.accuracy_score(softmax_regressionM_Slbfgs_C10_predictionFT, test_labels_encoded)}\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree (Herarchical model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use scikit-learn package. \n",
    "Take in consideration that scikit-learn DecisionTreeClassifier is based on CART algorithm (Classification And Regression Trees).\n",
    "Let's try it with defatul values, wich means split down to the deepest possibile split using gini index (node \"purity\" check).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the scikit-learn implementation of Decision Tree with default parameters is 0.9619047619047619\n"
     ]
    }
   ],
   "source": [
    "\n",
    "default_tree = skl_tree.DecisionTreeClassifier()\n",
    "\n",
    "# There is no need of standardization for Decision trees so let's use non standardized subsets.\n",
    "labels = proportional_train.iloc[:,4].to_numpy()\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoded_labels = label_encoder.fit_transform(labels)\n",
    "\n",
    "default_tree.fit(proportional_train.iloc[:, 0:4], label_encoded_labels)\n",
    "default_tree_prediction = default_tree.predict(proportional_test.iloc[:, 0:4])\n",
    "\n",
    "test_labels = proportional_test.iloc[:,4].to_numpy()\n",
    "test_labels_encoded = label_encoder.transform(test_labels)\n",
    "\n",
    "\n",
    "print (f\"The accuracy of the scikit-learn implementation of Decision Tree with default parameters is \\\n",
    "{metrics.accuracy_score(default_tree_prediction, test_labels_encoded)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "still good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also have a graphical visualization of the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "default_tree_graph = skl_tree.export_graphviz(default_tree, \\\n",
    "                                                  feature_names = IrisDF.columns[0:4], \\\n",
    "                                                  class_names = label_encoder.inverse_transform([0,1,2]), \\\n",
    "                                                  filled = True)\n",
    "\n",
    "image_graph = graphviz.Source(default_tree_graph)\n",
    "image_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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